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Background: Turkey has been one of the earliest participants of the international climate policy process, since the Ministerial Conference on Atmospheric Pollution and Climate Change held in 1989. The country has prepared strategy documents, actions plans, sectoral policies and projects to detect and adapt climate change effects. However, any of this has not turned into a main plan to support climate change mitigation on an international scale. The purpose of this paper is to identify local climate change mitigation strategies of Sakarya city, Turkey by strengths, weaknesses, opportunities and threats (SWOT) analysis. For this purpose, relevant information were gathered from Covenant of Mayors, greenhouse gas inventories of the city, National Energy Efficiency Action Plan, online workshop with 44 local stakeholders from private sector and local universities held on 13.10.2020. Results: Transportation sector action plans proposed within SWOT analysis promise to decrease local transportation-based emissions by 27% compared to 2017 and by 26% compared to 2030 business as usual (BAU) Scenario. Buildings sector action plans proposed within SWOT analysis promises to decrease local building-sector based emissions by 20% compared to 2017 and by 33% compared to 2030 BAU Scenario. All mitigation strategies proposed within SWOT analysis promises to decrease local total emissions by 24% compared to 2017 and by 28% compared to 2030 BAU Scenario.Conclusion: The results emphasize the importance of the cross-link between local adaptation and mitigation in terms of energy demand and energy-based emissions on national and international scale. This paper proposes that developing complete greenhouse gas inventories by accurate data within current technologies like the Internet of Things (IoT) and formulating climate change targets accordingly is the key to achieve the adaptation of local mitigation plans. In conclusion, it is emphasized that efforts to monitor, to report and to develop local mitigation legislations will determine the success rate of national sustainable development goals.

Digital sensors use biotechnology and information processing technology to strengthen the processing of relevant visual and auditory information, which is helpful to ensure that the receiver can obtain more accurate information, so as to improve the learning effect and reduce the impact on the environment. This paper designs an experiment to explore the role of digital sensors in language audio-visual teaching, which provides a reference for the application of digital sensors in the future. The impulse response function in sensor technology is introduced. The speech time domain envelope and time-varying mouth area of the sensor device are calculated. The auditory attention transfer detection based on line of sight rotation estimation is carried out through the auditory attention decoding fusion technology and the sensor auditory attention conversion detection method. At the same time, the characteristic of sensor heog signal is analyzed. The results show that the algorithm proposed in this paper has good results.

In order to explore the impact of different decision-making methods on the profits of various entities in the supply chain of the community e-commerce platform, this paper adopts the method of the Stackelberg game. For the community e-commerce platform supply chain composed of suppliers, community e-commerce platforms, and grid station service providers, considering the degree of supplier value cocreation efforts, this paper studies the optimal decisions under centralized decision-making, supplier-led decentralized decision-making, and community e-commerce platform-led decentralized decision-making, respectively. The results show that the supply chain obtains the highest profit in centralized decision-making; under decentralized decision-making, the dominant party will get higher profits; and the supplier value cocreation sensitivity coefficient is positively correlated with sales price, value cocreation effort level, and total supply chain value. The results are helpful to improve the competitiveness of the community e-commerce platform supply chain in the market and are of great significance to the long-term development of the community e-commerce industry.

From the time dimension, this paper analyzes the characteristics of the scale, industrial structure, employment flexibility, and comprehensive employment effects of regional tourism employment based on the three-level criteria of the scope of tourism employment based on the regional Internet of Things. From the spatial dimension, we take the regional city as the basic research unit, adopt multiple indicators, and conduct a comprehensive evaluation of the regional development of regional tourism employment through horizontal and vertical comparisons. This paper uses multiple linear regression analysis to establish the relationship between the development level of the county tourism economy and the influencing factors; in order of magnitude of influence, they are tourism resource endowment, location traffic conditions, and economic development. Using a combination of a single indicator and multiple indicators, the county tourism economy is evaluated and analyzed for differences in time and space. We select the total tourism revenue as an indicator and use methods such as range, standard deviation, coefficient of variation, and coefficient to analyze the time difference of the tourism economy in each county. We adopt the Granger causality test and other methods to analyze the factors affecting tourism employment in the area and the growth mode. Through the establishment of a structural model of the tourism employment growth dynamics system, causality test and other methods are adopted to analyze the regional tourism employment influencing factors and growth methods, and the results show that the regional tourism employment growth mode is an investment-driven tourism employment growth mode.

In order to improve the humanization and convenience of online art design, this paper applies digital simulation technology to the art online design system and establishes a set of sequential multi-free-form surface design methods. Based on the obtained front free-form surfaces, this paper establishes the relationship between the discrete points on the subsequent free-form surfaces and their spatial solid angles and, through extremely complex theoretical deduction, finally obtains the subsequent free-form surfaces. In addition, by combining the two free-form surfaces to enter the 3D modeling software, we can obtain an optical lens with multiple free-form surfaces to improve the digital simulation effect. Finally, this paper uses the intelligent system constructed in this paper to conduct multiple sets of simulation experiments to evaluate the digital effect and artistic design effect of the system constructed in this paper. From the experimental research, it can be known that the art online design system based on digital simulation technology constructed in this paper basically meets the expected goals of the system constructed in this paper.

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

Portfolio theory mainly studies how to optimize the allocation of assets under the premise of maximizing expected returns and minimizing investment risks. In view of the instability of the financial market, a diversified investment portfolio can help control the loss of the investment portfolio. In addition to paying attention to the safety and return of asset allocation, we cannot ignore the liquidity of assets, that is, their liquidity. Adding high-liquidity products to asset allocation, such as equity investment, can better control the financial cash flow in response to emergencies. One of the ways to make assets flow is to securitize assets and sell them to the market. In order to revitalize the stock assets, good investment efficiency is a necessary choice for financial investment. Various financial products and their derivatives continue to enter people’s vision. There are many financial products in reality, and optimizing the investment portfolio can bring high economic benefits. The purpose of this paper is to study the application of optimization algorithms in financial portfolio problems. (1) Monetary policy remains prudent and neutral. It is not easy to expect flooding, but flexibility is required in complex situations. (2) Financial resources are tilted towards innovation and transformation and capital markets, which is beneficial to the development of capital markets in the medium and long term. (3) Unblocking the transmission mechanism is conducive to lenient credit and tapping the wrong killing opportunities in private enterprise debt. (4) Banks and other financial institutions have moderate pressure to give benefits to entities, but in the long run, the interests of the two are consistent. (5) Finance risk prevention will continue, orderly breaking the rigid exchange and reshaping the financial structure and ecology. (6) The pace of opening up of the financial industry has accelerated, and the bond market investor structure has improved. In this paper, we establish different optimization schemes to compare and study the portfolio problem and then use MATLAB to solve the modeling and programming problem, calculate the highest return rate and the lowest risk value before and after optimization, and then make a comparative analysis to get a better optimization scheme. The results show that the genetic algorithm model is superior to the quadratic programming method in terms of risk control. The minimum risk of portfolio optimization through genetic algorithm has been reduced by about 40%, and the maximum return has increased by about 25%. The comprehensive optimization effect is better than the quadratic planning method and ultimately can obtain higher economic benefits. It can be seen that the optimization algorithm is of great significance for the comparative study of financial portfolio problems.

Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow system is built to reconstruct these images. The results show that, compared with D-AMP and ReconNet algorithms, the SCDA algorithm has the advantages of higher reconstruction accuracy and lower time complexity and space complexity. Therefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images.

With the deepening of diversified and professional business philosophy in large enterprises, enterprises play more and more abundant social functions in society. Sense of mission and honor play an important role in the healthy development of enterprises. With the in-depth integration of enterprise management and computer technology, some human resource management problems related to corporate social responsibility often appear in this process. From the traditional human resource management paradigm to people-oriented social responsibility human resource management, “strengthening effectiveness” has become the main research direction of enterprise management in the electronic age. On this basis, through the discrete modeling method of a large amount of data, this paper creatively puts forward the coupling correlation between corporate social responsibility and enterprise human resource management based on grey correlation algorithm. Compared with the management mode based on trapezoidal data analysis and cluster center adopted in the current mainstream enterprise human resource management research, the innovation of this algorithm is to analyze the dynamic data of corporate social responsibility and enterprise human resources and establish a coupling model related to corporate social responsibility. It can not only realize the dynamic tracking of human resource data, but also make full use of the relevant characteristic information of the coupling relationship between enterprise human resource management and corporate social responsibility. According to the dynamic big data such as employee welfare, employee compensation, employee training, employee overtime, and timeliness of transaction processing, this paper analyzes the social problems such as corporate social responsibility evaluation index, employee turnover rate, and enterprise income, so as to provide theoretical support for enterprise business strategy.

With the accelerated pace of urbanization, green buildings and green smart buildings gradually come into people’s vision and are highly valued by all sectors of society on the premise of meeting sustainable development strategy. Firstly, this paper selects 7 first-level index factors and 20 second-level index factors to establish the green smart building evaluation system. Secondly, this paper uses the analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) method to determine the weight of each secondary index. Finally, the feasibility of the evaluation system is verified by case analysis, and some suggestions on green smart building are put forward.

When the rationality and importance of cultural education in colleges and universities have been confirmed, the key question is how to better realize cultural education. This requires an in-depth understanding and grasp of the cultural education work of colleges and universities, analyzing the internal logic contained in this education system and then discussing its practical rationale. This paper proposes a multiobjective Support Vector Machine (SVM) image recognition method for college cultural and educational behaviors based on three decision-making theories. Aimed at the problem of classification errors caused by image segmentation in image recognition of cultural and educational behaviors in colleges and universities, it is found that the reason that causes the same target to be divided into different subblocks is that the ordinary SVM classifier has only two choices of “yes” and “not” in classification. It is impossible to set the conditions for the classification of cultural and educational behavior images to satisfy all the existing conditions. In order to solve the above problems, this paper combines the three decision-making ideas with the image recognition of university cultural education behaviors and designs a multiobjective SVM image recognition method of university cultural education behaviors based on the three decision-making theories. We establish a higher education service quality structure model and use LISREL to explore the causal relationship among students’ perceived service quality, university image, student’s perceived service value, student trust, student satisfaction, and student behavior intentions in the field of higher education. The empirical results show that student satisfaction has a significant direct and positive effect on student behavior. Service quality has a significant direct and positive impact on perceived value and student trust. The perceived risk of higher education services determines the image of colleges and universities, and student trust plays an important role. The image of colleges and universities acts on student satisfaction through perceived value and student trust. The image of colleges and universities has a direct impact on students’ behavioral inclination, and it acts on students’ behavioral inclination through student trust and student satisfaction. Among the influences on behavior tendency, the direct positive effect of student trust is the largest, followed by the image of colleges and universities and the quality of service.

The existing research paradigm has caused the research of educational theory to be criticized for lack of logic and scientificity. With the gradual deepening and refinement of the development of smart education platforms, the educational process can be quantified, and the research paradigm of educational science has moved from a sampling model to a full-sample model, and it has become a real empirical science. This paper combines the Internet of Things technology to construct a hybrid intelligent learning system and applies it to digital education in a diverse society to construct a functional structure of the extraction system. Moreover, starting from the actual situation of intelligent education, this paper combines a simulation system to implement an IoT-assisted hybrid intelligent learning architecture and evaluates the teaching effect of the system. Through experimental research, it can be known that the IoT-assisted hybrid intelligent learning framework based on social diversified digital education has certain effects and has a certain reference effect for the development of subsequent intelligent education.

The Internet of Things takes data as the center, and its core is data storage and management. With the emergence and rapid development of wireless communication technology, with the huge number of terminals in human society, massive data will be generated. Undoubtedly, data storage and management technology will attract much attention. In view of this, this paper proposes a data storage scheme based on the Internet of Things. This paper introduces the Internet of Things technology, designs it from the perspective of the massive data storage system of the Internet of Things, realizes the intelligent processing of data storage, and provides security guarantee for information services. By combing the business process management of doctors, nurses and patients, this paper constructs a medical record encryption management system, makes a comparative analysis before and after the system goes online, and carries out simulation experiments. The simulation results show that (1) the cost of paper is significantly reduced, and the related forms of medical records are more unified and standard, (2) medical record inquiry and reading are more convenient and controllable, and (3) the safety of medical records is well guaranteed. Except that the relevant doctors and nurses of patients can view the relevant medical records, and others have no authority to query and access them. Therefore, the encrypted medical record storage system based on Internet of Things technology can effectively solve the collection, statistics, and integration of patient treatment information, which can be summarized into a unified, shared, and interconnected electronic medical record management system to realize the collection of patient treatment information in the whole process.

In this paper, an in-depth study and analysis of attribute modelling and knowledge acquisition of massive images are conducted using image recognition. For the complexity of association relationships between attributes of incomplete data, a single-output subnetwork modelling method for incomplete data is proposed to build a neural network model with each missing attribute as output alone and other attributes as input in turn, and the network structure can deeply portray the association relationships between each attribute and other attributes. To address the problem of incomplete model inputs due to the presence of missing values, we propose to treat and describe the missing values as system-level variables and realize the alternate update of network parameters and dynamic filling of missing values through iterative learning among subnets. The method can effectively utilize the information of all the present attribute values in incomplete data, and the obtained subnetwork population model is a fit to the attribute association relationships implied by all the present attribute values in incomplete data. The strengths and weaknesses of existing image semantic modelling algorithms are analysed. To reduce the workload of manually labelling data, this paper proposes the use of a streaming learning algorithm to automatically pass image-level semantic labels to pixel regions of an image, where the algorithm does not need to rely on external detectors and a priori knowledge of the dataset. Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. The efficient vectorized mapping scheme is designed for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc. in the deep convolutional neural network model so that the utilization of MAC components in the core loop can reach 100%.

In recent years, because the complex system discrete model has brought good results in the application of many research directions such as human management analysis, control system prediction, and animal group prediction, how to combine the complex system discrete model with the prediction of enterprise brand communication effect has become a current research hotspot. Based on this, this paper studies the application of the discrete model based on the complex system in sports club to enterprise brand communication and designs a model of the impact of professional sports club on enterprise brand communication based on the SIR model and PSO algorithm. Firstly, this paper analyzes the research status of the impact on enterprise brand communication at home and abroad and optimizes and improves the impact of professional sports clubs on enterprise brand communication and the shortcomings of big data analysis technology in the current research. The complex system discrete model can accurately analyze the enterprise brand communication and conduct big data mining analysis with known professional sports clubs to achieve accurate calculation. The results show that in the process of analyzing the impact of professional sports clubs and enterprise brand communication, the communication mode design of professional sports clubs based on SIR-PSO algorithm can not only effectively deal with various complex discrete data evaluated by club users but also analyze and evaluate the impact of professional sports clubs on enterprise brand communication.

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.

The groundwater in Gaobeidian city is used for drinking, irrigation, industrial production, and other purposes. With the rapid development of the economy and urbanization, groundwater quality has been seriously affected. The main purposes of this paper are to evaluate the groundwater quality in the study area on the basis of understanding the hydrochemical characteristics of the study area and assess the possible health risks of groundwater to children and adults. In this paper, the entropy weight method was used to determine the weight of each evaluation parameter, and on this basis, groundwater quality evaluation was carried out, and the spatial distribution map of groundwater quality was drawn according to the evaluation results. The results show that the weight values of the five parameters of NO2–, Fe, As, Cr6+, and NO2–N are more than 0.1 among the total of fifteen parameters, and the concentration of these five parameters can be considered as the main influencing parameters of groundwater quality. The calculation results of the entropy weighted water quality index (EWQI) show that all the groundwater quality in the study area is class 1 water, which is Excellent Water. However, the EWQI value is the highest in the southwest of the study area, showing a trend of deterioration of groundwater water quality. Since all groundwater samples were evaluated as “excellent water,” it was speculated that the natural environment had more influence on groundwater chemical characteristics than human factors. The study found that 7.407% and 55.556% of the water samples posed a noncarcinogenic health risk to adults and children, respectively. The main responsible parameters for noncarcinogenic risk are F−, NO2−, NO3−, and Cr6+. The carcinogenic risk for adults ranged from 0 to 6.91E-04, with a mean of 1.00E-04. The carcinogenic risk for children ranged from 0 to 1.03E-03, with a mean of 1.55E-04. These toxic elements are mainly from industries. Therefore, the deterioration of groundwater quality can be prevented by strengthening the sewage management of various industries.

Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB-D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB-D data is available for training so as to attain the complementary information. Fortunately, a kind of method via multimodal training and unimodal testing has been proposed. However, unimodal feature representation and cross-modality transfer still need to be further improved. To this end, this paper proposes a new 3D-Ghost and Spatial Attention Inflated 3D ConvNet (3DGSAI) to extract high-quality features for each modality. The baseline of 3DGSAI network is Inflated 3D ConvNet (I3D), and two main improvements are proposed. One is 3D-Ghost module, and the other is the spatial attention mechanism. The 3D-Ghost module can extract richer features for hand gesture representation, and the spatial attention mechanism makes the network pay more attention to hand region. This paper also proposes an adaptive parameter for positive knowledge transfer, which ensures that the transfer always occurs from the strong modality network to the weak one. Extensive experiments on SKIG, VIVA, and NVGesture datasets demonstrate that our method is competitive with the state of the art. Especially, the performance of our method reaches 97.87% on the SKIG dataset using only RGB, which is the current best result.

This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy C -means clustering algorithms show a downward trend. However, with the same number of neighbors, the filtering matching algorithm is greater than FCA regarding the mean values of MAE and MSE. Meanwhile, on the electronic music performance system of AI, the digital module is designed and the sound data are imaged on the oscilloscope, and the collaboration of electronic music online education and wireless network is completed. The following conclusion is drawn: modularizing the creative mode of intelligent electronic music has achieved higher computational efficiency. Through the oscilloscope, the sound feature is converted into the image structure, and the corresponding sound and image mode is formed, which realizes the purpose of online electronic music intelligent matching and optimizes the effect of online education. In the AI environment, the matching degree of verification electronic music curriculum resources is better than traditional matching algorithms, and the accuracy is higher.

Sports wearable monitoring equipment is an intelligent device that collects many physiological signals of the human body through multiple sensors. It has a very active role in promoting data testing in the field of sports. This article is aimed at studying the impact of sports wearable detection devices based on vision sensors on the sports industry and at proposing effective strategies for the development of sports wearable devices in the sports industry. This paper proposes an azimuth integration algorithm based on wearable sensor data. This goal establishes a new feature based on azimuth angle information for a reliable human behavior recognition system based on acceleration data. Based on summarizing and comparing the advantages and disadvantages of existing azimuth conversion algorithms, this paper develops an azimuth code conversion algorithm based on the combination of additional processing and Kalman processing to explore the impact of wearable devices on the sports industry. The experimental results of this article show that in the current sports industry, more than 19.74 million sports wearable testing devices have been put into use normally, which also means that the industry is about to enter a significant stage of development.

Liquid storage tanks are lifeline structures and strategically very important. Heavy damages or even collapse of these facilities subjected to strong earthquakes may cause disastrous consequences. In this paper, the seismic response of a multistage series liquid storage tank was simulated by a finite element method and verified by a scaled-down experiment. The structural flexibility of the tank and the liquid-structure coupling characteristics between the liquid and tank wall were considered in the research. A multimass-block and spring model was employed to be equivalent to the longitudinal vibration of the liquid in the storage tank. The relationships between the connection springs and the elements of the stiffness matrix were explicitly deduced. The seismic response analysis of a four-stage series liquid tank was carried out, and the acceleration response, the stress response of the tank, and the vertical vibration of the liquid were obtained. The experimental results are in good agreement with the simulation results, which verifies the effectiveness of the modeling method in this paper.

Background. Organic development is one of the evolutionary development models for cities, and it can facilitate cities to continually adapt to their surroundings and therefore adjust their development. Former research is mostly focused on cities and environmental changes, and research on the organic development of cities in various conditions is rather scarce. Purpose. By creating an evaluation index system for organic cities, this paper has established a multifactor evaluation model and improved the evaluation methodologies for organic cities. Methods. The authors have come up with a method that combines PCA and CNN, established an optimized model to calculate the organic cities, verified the accuracy of the evaluation index system for organic cities, and finally concluded that urban organic development is mostly associated with the aspects of the natural environment, economic basis, land use, and population scale of the city. Case Study. In addition, the examples of Bishan District, Chongqing, and Jiangyin, Jiangsu Province, are simulated and compared, and an accurate prediction for the organic development of small cities in China is given. Conclusion. The evaluation index system for organic cities devised by this paper can be applied effectively to evaluate the organic city of cities, and the neural network prediction model for organic cities designed by this paper can provide references for further research on organic cities.

With the rapid development of economy and society and the continuous optimization of industrial structure, the demand for high skilled talents is increasing. Education plays an irreplaceable role in China’s economic growth. Therefore, it is of great significance to study the impact of talent supply of vocational education on economic growth from the perspective of human capital and comprehensively consider the spatial interaction of economic growth. Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. This paper expounds the content of intelligent image recognition technology, constructs an image recognition system based on neural network, and studies the relationship between human resource utilization efficiency and regional economy under intelligent image recognition technology based on the empirical analysis of intelligent image recognition technology. Finally, it makes an empirical study on the spatial effect in the image recognition system and expounds the relationship between feature space and economic growth. It verifies the relationship between educational human capital and green economic growth. The results show that the intelligent image recognition technology has a good effect in the research of spatial effect.

This paper is the first in the two-part series quantitatively modelling human grasp functionality and understanding the way human grasp objects. The aim is to investigate the thumb movement behavior influenced by object shapes, sizes, and relative positions. Ten subjects were requested to grasp six objects ( 3 shapes × 2 sizes ) in 27 different relative positions ( 3 X deviation × 3 Y deviation × 3 Z deviation ). Thumb postures were investigated to each specific joint. The relative position ( X , Y , and Z deviation) significantly affects thumb opposition rotation (Rot) and flexion (interphalangeal (IP) and metacarpo-phalangeal (MCP)), while the object property (object shape and size) significantly affects thumb abduction/adduction (ABD) motion. Based on the F value, the Y deviation has the primary effects on thumb motion. When the Y deviation changing from proximal to distal, thumb opposition rotation (Rot) and flexion (IP and MCP joint) angles were increased and decreased, respectively. For principal component analysis (PCA) results, thumb grasp behavior can be accurately reconstructed by first two principal components (PCs) which variance explanation ratio reached 93.8% and described by the inverse and homodromous coordination movement between thumb opposition and IP flexion. This paper provides a more comprehensive understanding of thumb grasp behavior. The postural synergies can reproduce the anthropomorphic motion, reduce the robot hardware, and control dimensionality. All of these provide a more accurate and general basis for the design and control of the bionic thumb and novel wearable assistant robot, thumb function assessment, and rehabilitation.

Enterprise economy refers to the comprehensive situation reflected in the gross product, production scale, total production and efficiency, technological content, marketing means, and so on; under certain social conditions, enterprises use resources obtained by law to engage in economic activities. Under the guidance of consciousness or culture, enterprises use “legally obtained resources” to promote economic development. Enterprise economy is affected by manpower, capital, management, operation, policy, and other aspects. In the context of the rapid development of big data in the current era, this paper proposes a prediction model of enterprise economic activity behavior based on neural network and ARIMA by investigating a variety of artificial intelligence models and verifies its feasibility. Commodity circulation enterprises have a more urgent demand for the development of business audit due to their operation characteristics. Therefore, this paper takes commodity circulation enterprises as representatives and predicts business audit in the big data environment based on the model proposed in this paper.

Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.

In order to improve the effect of urban landscape design, this paper combines big data technology with digital technology. For scenes and solutions containing SDS paths, a processing method similar to photon graphs is used and added to the calculation of two-way optical path tracking. In the processing scene, this paper uses the two-way optical path tracking method to perform specular reflection or refraction from the subpath starting from the light source and then store information such as the light energy of the points on the diffuse reflection surface or the directional reflection surface. Moreover, this paper combines the actual needs of urban landscape design to construct an urban landscape design system based on big data technology and digital technology. Finally, this paper designs experiments to carry out urban landscape simulation and design effect evaluation. From the test results, it can be seen that the system designed in this paper basically meets the needs of urban landscape planning and design.

As the essential content of intelligent animal husbandry, identifying each livestock is the only way to achieve modern and refined scientific husbandry. This paper proposes a sheep face recognition method based on European spatial metrics and realizes noncontact sheep identity recognition by training the network using sheep face image samples in the natural environment. The SheepBase data set was first proposed in this process, which contains 6559 images of Inner Mongolia fine-wool sheep and Sunite sheep. To enhance the diversity of the data, the sheep face images were data-enhanced. Secondly, to solve the problems of more redundant information in the sheep face image and the poor posture and angle of the sheep face, we propose the sheep face detection and correction (SheepFaceRepair) method. This method aims to detect the sheep face area in the image to be recognized and align the sheep face area. On this basis, we offer an open sheep facial recognition network (SheepFaceNet) based on the European spatial metric. This method incorporates the biological identity information features of the sheep face to achieve sheep identity. We also tested the effectiveness of this method in the SheepBase data set. The experimental results show that the method proposed in this paper is much higher than the other methods, and the precision of recognition reaches 89.12%. In addition, we found that integrating the biometrics of the sheep face can effectively improve the network’s recognition capacity.

Logistics distribution is the terminal link that connects the manufacturer and product user and determines the efficiency of the manufacturer’s service. Therefore, the disruption risk of the joint system is an essential factor affecting the product user experience. In this paper, while considering the product user’s supply disruption risk preference (PUSDRP), a biobjective integer nonlinear programming (INLP) model with subjective cost-utility is proposed to solve the manufacturer’s combined location routing inventory problem (CLRIP). According to the user’s time satisfaction requirement, a routing change selection framework (RCSF) is designed based on the bounded rational behavior of the user. Additionally, the Lagrange Relaxation and Modified Genetic Algorithm (LR-MGA) is proposed. The LR method relaxes the model, and the MGA finds a compromise solution. The experimental results show that the biobjective cost-utility model proposed in this paper is effective and efficient. The RCSF based on user behavior is superior to the traditional expected utility theory model. The compromise solution provides a better solution for the manufacturer order allocation delivery combinatorial optimization problem. The compromise solution not only reduces the manufacturer’s total operating cost but also improves the user's subjective utility. To improve the stability of cooperation between manufacturers and users, the behavior decision-making method urges manufacturers to consider product users’ supply disruption risk preferences (PUSDRPs) in attempting to optimize economic benefits for the long term. This paper uses behavior decision-making methods to expand the ideas of the CLRIP joint system.

The purpose of this article is to use the Internet of Things related technology to analyze the characteristics of multisource and easy-to-purchase data for the different types of planning data and different levels of cognitive needs of participants in the entire urban planning process. This paper uses the ontology idea to reconstruct the relationship between multisource and heterogeneous planning data including Internet of Things data, planning documents, and planning drawings, to design the data semantic relationship of the ontology model elements, define the relationship between the data types, and implement the ontology-based method. The semantic expression algorithm in the planning field facilitates the exchange of various planning participants’ understanding of the planning scheme, at the same time, according to the classification of multisource heterogeneous data features, logical reasoning of ontology relationships, filtering redundant information, and multisource heterogeneous planning data visualization. Finally, the information of the same nature collected by the sensor nodes of the Internet of Things is batched, and the calculated fusion information is closer to the true value through a series of weighting formulas. Experiments prove that the feature analysis method proposed in this paper can maintain a loss of 0.02% and achieve an accuracy rate of 79.1% when the overall characteristics of digital city planning are reduced by 67%, which effectively proves the multisource heterogeneous data feature analysis for digital city planning importance.

B-cells that induce antigen-specific immune responses in vivo produce large numbers of antigen-specific antibodies by recognizing subregions (epitopes) of antigenic proteins, in which they can inhibit the function of antigen protein. Epitope region prediction facilitates the design and development of vaccines that induce the production of antigen-specific antibodies. There are many diseases which are difficult to treat without vaccines. And the COVID-19 has destroyed many people’s lives. Therefore, making vaccines to COVID-19 is very important. Making vaccines needs a large number of experiments to get labeled targets. However, obtaining tremendous labeled data from experiments is a challenge for humans. Big data analysis has proposed some solutions to deal with this challenge. Big data technology has developed very fast and has been applied in many areas. In the bioinformatics area, big data analysis solves a large number of problems, particularly in the area of active learning. Active learning is a method of building more predictive models with less labeled data. Active learning establishes models with less data by asking the oracle (human) for the most valuable samples to train models. Hence, active learning’s application in making vaccines is meaningful that the scientists do not need to do tremendous experiments. This paper proposed a more robust active learning method based on uncertainty sampling and K-nearest density and applies it to the vaccine manufacture. This paper evaluates the new algorithm with accuracy and robustness. In order to evaluate the robustness of active learners, a new robustness index is designed in this paper. And this paper compares the new algorithm with a pool-based active learning algorithm, density-weighted active learning algorithm, and traditional machine learning algorithm. Finally, the new algorithm is applied to epitope prediction of B-cell data, which is significant to making vaccines.

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine-oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed-loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed-loop system is helpful to further improve the accuracy of machine English oral pronunciation.

Diabetic retinopathy (DR) is a diabetic complication affecting the eyes, which is the main cause of blindness in young and middle-aged people. In order to speed up the diagnosis of DR, a mass of deep learning methods have been used for the detection of this disease, but they failed to attain excellent results due to unbalanced training data, i.e., the lack of DR fundus images. To address the problem of data imbalance, this paper proposes a method dubbed retinal fundus images generative adversarial networks (RF-GANs), which is based on generative adversarial network, to synthesize retinal fundus images. RF-GANs is composed of two generation models, RF-GAN1 and RF-GAN2. Firstly, RF-GAN1 is employed to translate retinal fundus images from source domain (the domain of semantic segmentation datasets) to target domain (the domain of EyePACS dataset connected to Kaggle (EyePACS)). Then, we train the semantic segmentation models with the translated images, and employ the trained models to extract the structural and lesion masks (hereafter, we refer to it as Masks) of EyePACS. Finally, we employ RF-GAN2 to synthesize retinal fundus images using the Masks and DR grading labels. This paper verifies the effectiveness of the method: RF-GAN1 can narrow down the domain gap between different datasets to improve the performance of the segmentation models. RF-GAN2 can synthesize realistic retinal fundus images. Adopting the synthesized images for data augmentation, the accuracy and quadratic weighted kappa of the state-of-the-art DR grading model on the testing set of EyePACS increase by 1.53% and 1.70%, respectively.

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

In order to improve the effect of intelligent terminal container management, this paper improves the Internet of Things and big data technology, analyzes the RFID middleware architecture based on the actual needs of container handling management, and proposes a new method of RFID middleware load balancing. Moreover, this paper combines the Internet of Things technology and big data technology to analyze the terminal container loading and unloading process and build a corresponding intelligent system. After constructing a terminal container handling intelligent management system based on the Internet of Things and big data technology, the performance of the system is verified, and multiple sets of simulation data are used to conduct research. The experimental research results show that the terminal container handling management system based on the Internet of Things and big data constructed in this paper basically meets the actual needs of use.

The development of information technology has led to the rapid development of modern book packaging design. Book packaging design is different from painting. It is a kind of design process that combines artistry and practicality and has double characteristics. With the continuous progress of society, people’s requirements for book packaging design have become higher and higher, and modern book packaging design has become an important topic in the field of art design. To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. Specifically, it includes the principles and specific implementation steps for AdaBoost classification algorithm and SVR regression algorithm, as well as evaluation indexes of AdaBoost classification and SVR regression analysis. Realization of physical books reflects artistry, creativity, professionalism, popularity, and vitality of books in the packaging and design of books in the electronic information era. The stimulation effect of this paper starts from packaging design, inspection mechanism, and brand psychology to get the superior design in modern book packaging design.

Many papers, in which the submerged arc discharge (SAD) method in nanoparticle synthesis was used, reported similar operating parameters, but different electrode erosion rate values, different yields and purities of the obtained nanostructures, and a different sort of contaminants present in the synthesis. Analyzing these articles, we found insufficient attention to ensure the arc power stability, which is a key factor guaranteeing the product homogeneity and quality. This paper presents an analysis of different control strategies, remarks their advantages and drawbacks, and proposes the most appropriate technique to be used in SAD. The most appropriate technique is proposed from the SAD stabilization method analysis.

In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.

Economic development has gradually made tourism one of the most common options for people’s leisure and entertainment. The fast-paced life and heavy pressure in the city have made the relaxed and comfortable rural tourism sought after by many people. In our country, many villages have rich tourist attractions and unique cultural heritage, but they have not been fully developed due to information obstruction. Now, the advancement of Internet technology has made e-commerce, an emerging trade method, more closely related to rural tourism. Based on the background of big data, this paper has launched a research on the SWOT analysis of the development of rural tourism e-commerce system. Starting from the advantages, disadvantages, opportunities, and threats of the tourism e-commerce system, this paper discusses the necessity of combining rural tourism and e-commerce platforms in the Internet era, combining various internal and external conditions. From the perspective of the e-commerce system, all kinds of special features in the countryside can be transformed into product selling points on the platform to attract tourists, and the promotion of tourism revenue through the platform can further promote the continuous improvement of the rural tourism industry and form a virtuous circle. According to our data from the China Tourism Administration and related tourism statistics, the number of rural tourists in my country has increased from 630 million in 2011 to 3.13 billion in 2019, and the increase in rural tourism revenue has also reached 4.3 times. In order to prevent the rural backward marketing strategy from hindering the development of rural tourism, the introduction of a tourism e-commerce platform is a very necessary choice.

High growth of the population and developed industries, on the one hand, increased the demanded energy considerably. Growth of the fossil fuel-based power generation, on the other hand, has high environmental effects. Accordingly, renewable energy sources are mainly interested in the recent years as clean resources. These sources also can be economical cases in long term. Nevertheless, their intermittent feature is a main drawback. Integration of these resources with the rest of the grid can increase their advantages and tackle their main drawbacks. In the present work, a novel programming method is proposed for energy management and optimization of operational expenses over a grid-connected microgrid (MG). The considered MG contains various renewable resources. Some uncertainties should be considered in the integration of renewable resources with the grid, which can affect the system performance. Main uncertain parameters are studied in this paper such as predicted values for green power sources, maximum capacity for fuel cells (FCs), energy storage sources (ESSs), maximum capacity for transmission line, and also hourly loads, achieving a higher efficiency. This paper utilized fuzzy idea for presentation of mentioned uncertain parameters in the MG. Moreover, this work employed a 3-stage optimization algorithm for obtaining the best programming for the MG by considering the uncertain parameters. This paper also used a practical test system to validate the proposed method through minimizing the total cost. Additionally, a sensitivity analysis is performed to study the effects of oscillations of all components in the MG and their associated uncertainties over the obtained results.

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

Today, with the development of the times and the advancement of science and technology, informatization has gradually become an important standard for measuring the state of national development in the course of national development. The artificial intelligence multimedia technology has been developed under the background of informationization and has gradually become an important means to achieve national development and regional economic progress. The impact often depends not only on the economic attributes of the industry but also on the economic attributes of urban planning. Countries and regions with high levels of regional integration and industrial economic development have begun to inject economic knowledge and methods into regional integration and industrial economic processes, which are used to solve or alleviate problems and contradictions in the process of urban and rural construction and development. It can be seen that industrial economy and regional integration are not only a spatial phenomenon but also an economic phenomenon. Based on the needs of the development of urban planning discipline theory and the era of national industrial economic integration, this paper puts forward the research methods and ideas of this paper and combines current multimedia backgrounds and their development trends to analyze key innovations and research frameworks, incorporating the main theoretical research elements into the concept of “combination” of traditional Chinese philosophy, aiming at the main problems and contradictions in the process of China’s industrial economic development, respectively, the cultural economy and industrial economy, and the integration of public economy and environmental economy, land economy, and housing economy, a new urbanization development strategy of population economy and regional economic competition.

The decisions and actions of operators in the operation of transport infrastructure play a crucial role in the sustainability of the project but are influenced by factors such as the strength of government regulation and the frequency of use by users. The influence of social recognition and acceptance by the authorities on the decisions of the various parties involved in a project is becoming increasingly significant. To address this issue, this paper attempts to apply prospect theory to characterize the impact of changes in recognition on the decisions of project managers and the government from the perspective of recognition and to construct a tripartite evolutionary game model for the government, the operator, and the users, taking into account the combined effect of multiple factors, to explore the evolutionary law of the operator’s strategy choice. Evolutionary game theory, in which each person is considered irrational and behavior is changeable, is more realistic. The addition of prospect theory allows the model to more realistically reflect the decisions of each participant in the game process when faced with risk. The results of the study show that there is an optimal level of external regulation to maximize the benefits for all three parties in the game, strong government regulation does not necessarily improve service quality, operators tend to provide low-quality services in the game process and incentives should be increased, and that attempts should be made to provide users with a variety of transport infrastructure options to ensure that users’ interests are maximized. The paper further analyzes the indicators established by sensitive factors using BP neural networks on the basis of the analysis of transportation infrastructure operation and impact sensitive factors using evolutionary games and finds that the conclusions obtained by deep learning algorithms are more consistent with those obtained using evolutionary games, achieving cross-validation of the results. The reliability of the result is improved, and it is demonstrated that deep learning algorithms can be introduced as a supplement in the process of future analysis of transportation infrastructure operations. Finally, management suggestions are made in light of the actual situation.

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

Aiming at not only economic benefits but also social benefits in university-enterprise cooperation, this paper considers both technological innovation and social responsibility in university-enterprise cooperation. By using differential game theory, this paper provides three models of Nash noncooperative game, Stackelberg game, and cooperative game and discusses the impact of these three models on university-enterprise cooperation. Our results show that the Stackelberg game model can improve the innovation level of the university based on innovation cost-sharing and does not change the social responsibility level of the university without sharing the social cost. Among the three game models, the cooperative game model can bring the greatest innovation level and social responsibility level to the university-enterprise cooperation and make the whole system realize Pareto optimal. Our research can provide a theoretical basis and practical guidance for university-enterprise cooperation.

With the rise of network strategies, various businesses using the Internet as a platform have been vigorously developed, among which the scale of e-commerce transactions has increased on a large scale. In order to deeply explore the role and advantages of data fusion and multiobjective optimization technology in consumer online reviews, this paper uses the new and old evaluation model comparison method, algorithm design method, and multiobject research method to collect samples, analyze the technical model, and streamline the algorithm. And it will create an analysis algorithm model that can improve and optimize the consumer’s current online reviews. First, we choose the electricity supplier on the platform of a total of four mobile phones grabbed 32,145 comments. Based on this research on the number of online comment fields of consumers, the results show that 78% of the comments are less than 55 words, indicating that most of the online comments left by consumers are short comments; at the same time, a small number of consumers have left detailed comments. Description, the longest of is reached 612 words. On this basis, further study the efficiency and function analysis of the algorithm proposed in this paper, and we can see that DCDG-MOMA is used in 14-7 and 28-7 use cases as 1 and 2, respectively, which is the least, and at 40-7 and 50-7, the time used is 15 and 20 which is close to PBI, but it is also much less time than the MOMAD algorithm. This further shows that the algorithm really plays an effective role in the actual decision-making process. It has basically realized a more efficient algorithm for consumer online reviews under the background of applying data fusion and multiobjective optimization technology.

The noise pollution in tourist street view images is caused by various reasons. A major challenge that researchers have been facing is to find a way to effectively remove noise. Although in the past few decades people have proposed many methods of denoising tourist street scene images, the research on denoising technology of tourist street scene images is still not outdated. There is no doubt that it has become a basic and important research topic in the field of digital image processing. The evolutionary diffusion method based on partial differential equations is helpful to improve the quality of noisy tourist street scene images. This method can process tourist street scene images according to people’s expected diffusion behavior. The adaptive total variation model proposed in this paper is improved on the basis of the total variation model and the Gaussian thermal diffusion model. We analyze the classic variational PDE-based denoising model and get a unified variational PDE energy functional model. We also give a detailed analysis of the diffusion performance of the total variational model and then propose an adaptive total variational diffusion model. By improving the diffusion coefficient and introducing a curvature operator that can distinguish details such as edges, it can effectively denoise the tourist street scene image, and it also has a good effect on avoiding the step effect. Through the improvement of the ROF model, the loyalty term and regular term of the model are parameterized, the adaptive total variation denoising model of this paper is established, and a detailed analysis is carried out. The experimental results show that compared with some traditional denoising models, the model in this paper can effectively suppress the step effect in the denoising process, while protecting the texture details of the edge area of the tourist street scene image. In addition, the model in this paper is superior to traditional denoising models in terms of denoising performance and texture structure protection.