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To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality.

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results.

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm.

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths, and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposed model outperforms the other two models regarding total flexibility and trim loss ratio.

Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process.

Estimating power spectrum density (PSD) is essential in signal processing. This short paper gives a theorem to represent a smoothed PSD estimate with the Cauchy integral. It may be used for the approximation of the smoothed PSD estimate.

A sludge composite modifier (SCM) which comprises a mixture of three cementitious components was proposed for sludge drying and stabilization. Effect of SCM components on sludge moisture content was analyzed using uniform design and the optimum composition of SCM was determined by computer-aided modeling and optimization. To compare the drying effect of SCM, quicklime, and Portland cement, the effects of material content and curing time on moisture content of sludge were also studied. The results showed that the optimum ratio of modifier component was slag/cement clinker/dihydrate gypsum = 0.64/0.292/0.068 and the moisture content of SCM-stabilized sludge decreased with the increasing material content and extending curing time. Besides, the experimental results showed that optimized SCM behaved better than quicklime and Portland cement in sludge semi-drying and XRD analysis revealed that the main hydrated product of stabilization was ettringite, which played an important role in the effective drying process. Sewage sludge stabilized using SCM could be used as an effective landfill cover.

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph.

The goal of this paper is to establish a feasible three-species equilibrium model to analyze the symbiotic relationship of an automobile manufacturing community. In order to extend the Lotka–Volterra model to empirical analysis, this paper proposes an enterprise community symbiosis model based on a three-dimensional Lotka–Volterra model, as the classical two-dimensional Lotka–Volterra model has limited application scenarios. This paper takes the innovation assets of three communities of the Chinese automobile manufacturing industry as samples. The industrial community related to automobile manufacturing consists of the automobile manufacturing population, automobile parts population, and enterprise service industry population. The symbiosis system is empirically analyzed from two aspects: the balanced development of the three populations and the competitive evolution of the three populations. The stability of the model is tested by the data from information technology and the intelligent manufacturing community. In the process of dynamic simulation, the symbiotic relationship between automobile manufacturing-related populations shows a significant “skew symbiotic relationship.” This paper reconstructs the “whole population symbiosis” optimization model as skew distribution is difficult to apply to support collaborative development. The symbiosis optimization under the equilibrium state of the three populations shows that the growth of the three automobile manufacturing industry populations has the possibility of equilibrium and reciprocity. The empirical analysis fully demonstrates the feasibility of this research paradigm. The evolution analysis of the symbiotic system shows that cooperative behavior is better than competitive strategy. The research paradigm proposed in this paper can better analyze the symbiosis mechanism of the enterprise community.

The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three-stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three-stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape.

Management accounting plays a greater role in providing business decisions for enterprises. As the core technology of big data processing, data mining technology can process a large amount of complex, structured, and unstructured information very efficiently. In this paper, the nonlinear system is taken as the research object, and the obtained system data is directly used. In the framework of divide and conquer strategy, multi-model method is combined with data clustering and a local modeling algorithm to study the nonlinearity of management accounting data mining. Through online adaptive estimation of parameters, aiming at the zero dynamic problem of the non-minimum phase system, through the decoupling matrix analysis of the nonlinear system, the state of the original strategy is dynamically extended to make the estimated parameters asymptotically approach the true value. The estimated parameters are applied to the nonlinear hypothesis strategy to achieve the goal of linearization control. It is assumed that the generation strategy is applied to the mining of different related types of rules without manually setting parameters, which greatly improves the efficiency of rule discovery. Through case analysis, this paper demonstrates the feasibility of applying nonlinear hypothesis strategy of data mining technology to management accounting and puts forward the relevant application process framework. The validity and feasibility of nonlinear hypothesis strategy data mining technology in management accounting are verified.

As a hot topic in military research, small combat robots are always in a dynamic process when traveling, causing the body tube to vibrate which affects its firing accuracy. Different from the traditional two-degree-of-freedom combat robot, this paper establishes a rigid-flexible coupling dynamics model for the new three-degree-of-freedom combat robot to study the response characteristics of its body tube during travel. To accurately reflect the driving conditions of the mobile robot, a road surface unevenness model was established using the sine wave superposition method. The firing accuracy of a small combat robot can be affected by the speed and road conditions, as shown in numerous studies. The effects of arm structure, arm mounting position, and firing angle on the vibration response of the body tube are analyzed in this paper. The results show that a reference for improving the design of small combat robots can be found in a correlation between the structure, mounting position, and firing angle of the robotic arm, and the body tube’s vibration response.

Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.

Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.

With the rise of low-carbon concept and the trend of low-carbon planning, the evaluation of low-carbon planning has been paid more and more attention by the academic and planning circles. Carbon evaluation has become a hot topic after “environmental evaluation” and “green evaluation.” In order to promote the important content of energy conservation and sustainable development in the construction of low-carbon cities, this paper constructs and plans the content of low-carbon city planning and construction. On the basis of the effectiveness evaluation index system, this paper discusses the linkage between analytic hierarchy process and fuzzy comprehensive evaluation method and makes a comprehensive quantitative evaluation on the effectiveness of case planning EIA. Combining the advantages of analytic hierarchy process and fuzzy comprehensive evaluation method, the subobjectives and weights of each index are determined by the analytic hierarchy process. The multi-level fuzzy comprehensive evaluation method is used to evaluate the factors that affect the low-carbon effect of urban high-rise residential district planning. This study leads the new trend of future urban construction, and the construction of low-carbon residential areas, as an important part of promoting urban sustainable development, is the core component of future low-carbon urban planning and construction.

In order to effectively improve the quality of video image transmission, this paper proposes a method of digital multimedia video image coding. The transmission of digital multimedia video image fault-tolerant coding requires sparse decomposition of a digital multimedia video image to obtain the linear form of the image and complete the transmission of video image fault-tolerant coding. The traditional method of fault-tolerant coding is based on human visual characteristics but ignores the linear form of the digital multimedia video image, which leads to the unsatisfactory effect of coding and transmission. In this paper, a fault-tolerant coding method based on wavelet transform and vector quantization is proposed to decompose and reconstruct digital multimedia video images. The smoothness of wavelet transform can remove visual redundancy; the decomposed image is vector quantized. The mean square deviation method and the similar scalar optimal quantization method are used to select and calculate the image vector, construct the over complete database of a digital multimedia video image, and normalize it; the digital multimedia video image is thinly decomposed by asymmetric atoms, and a linear representation of the image is obtained. According to the above-given operations, we can master the distribution range and law of pixels and realize fault-tolerant coding. The experimental results show that when the number of iterations is 15, the CR index is the same, PSNR increases by 8.7%, coding is 23.7% faster and decoding is 15% faster. Conclusion. The proposed method can not only improve the speed of fault-tolerant coding but also improve the quality of video image transmission.

Higher education question and answer system is a current research hotspot in the field of natural language processing and artificial intelligence, which can return accurate answers directly and also allow users to input in natural language, avoiding the need to return large collections of valid and invalid data links for users to filter twice. In recent years, deep learning techniques have made great progress in the field of natural language processing, thus making it possible to apply it in the field of teaching and learning in junior high school. Most of the traditional quiz systems based on traditional retrieval techniques have problems such as insufficient semantic portrayal of text, inability to extract semantic features in context, and poor processing of complex utterances. In order to fill the gap and deficiencies of question and answer systems in junior high school teaching, this paper applies multimodal and adversarial network-related technologies to build a multimodal adversarial network-based question and answer system for higher education. The experimental results show that the principles of the traditional application system and the multimodal adversarial system designed in this paper are similar, but the current application system is relatively more effective.

For nonsmooth Filippov systems, the stability of the system is assumed to be proved by nonsmooth Lyapunov functions, such as piecewise smooth Lyapunov functions. This extension was based on the Filippov solution and Clarke generalized gradient. However, it is difficult to estimate the gradient of a non-smooth Lyapunov function. In some cases, the nonsmooth system can be divided into continuous and discontinuous components. If the Lebesgue measure of the discontinuous components is zero, the smooth Lyapunov function can be utilized to prove the stability of the system owing to the inner product of the gradient of the Lyapunov function of the discontinuous components being zero. In this paper, we apply the smooth Lyapunov function to prove the stability of the nonsmooth ratio-dependent predator-prey system. In contrast to the existing literature, in this paper, although the system is divided into continuous and discontinuous components, the inner product of the gradient of the Lyapunov function of the discontinuous part is not zero but negative. In the proof of stability, the negative value condition is stricter than the zero-value condition. This proof method only needs to construct a smooth Lyapunov function, which is simpler than a non-smooth Lyapunov function or other methods.

At present, China’s academic circles have made some research results on the hidden dropout of junior middle school students, but they have not formed a relatively mature theory. Based on Durkheim’s “social integration” theory, this paper studies hidden dropout from the perspective of class sociology and puts forward the influencing mechanism of “class integration” of junior middle school students’ hidden dropout behavior. By class observation, this paper obtains the hidden dropout theoretical model of the interaction between individuals and class organization.

In this paper, we consider the following indirect signal generation and logarithmic sensitivity ε n t = Δ n − χ ∇ · n ∇ ln c x ∈ Ω , t > 0 c t = Δ c − c + w x ∈ Ω , t > 0 w t = Δ w − w + n x ∈ Ω , t > 0 under homogeneous Neumann boundary conditions in a ball domain Ω ⊂ R N N ≥ 4 with smooth boundary ∂ Ω . This paper considers in the singular limit ε ⟶ 0 ; the result comes from the finite time blow-up of arbitrary large values of n in the corresponding nonlocal scalar parabolic equation case when N ≥ 4 and χ > 2 N / N − 2 .

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior.

After years of development, today’s society is a prosperous era of the knowledge economy. Different from the previous period of development by consuming natural resources, people have realized that talents with technology and knowledge have become an important factor in promoting the development of social production. That is to say, the improvement of population quality has not only an impact on its own development but also an important impact on the comprehensive strength and competitiveness of its location. Population quality is the stipulation of the population in terms of quality, also known as population quality. It includes ideological quality, cultural quality, physical quality, and so on. Data fusion is a technology for the comprehensive analysis and processing of information from multiple sources. Therefore, this paper comprehensively considers the influence of ideological and moral quality and population structure. Under the algorithm of fusion data, the impact of population quality on residents’ income is explored. This paper uses the analytic hierarchy process to weight each indicator and calculate the comprehensive score. This paper ranks the quality of population and economic development of each province, city, and autonomous region in China according to their high scores. The results of the experiments in this paper indicate a relevant analysis with the income distribution of the nation’s residents, and the correlation is −0.9493. There exists a negative relation between the overall quality of Chinese residents and the income gap.

A huge number of scientific research institutions and scholars are now researching this topic in depth, with promising results. Meanwhile, research development in dance visual frequency movement detection is rather modest due to the high complexity of dance movement and the challenges of human body self-shielding in dance performance. Aiming at the problem of the combination of motion recognition and dance video, the feature extraction, representation, and motion recognition methods based on dance video are emphatically studied. This paper studies an effective feature extraction method according to the characteristics of dance movements. Firstly, each dance movement video in the data set is separated into equal sections, and the edge characteristics of all video pictures in each segment are gathered into one image, from which the direction gradient histogram features are extracted. Secondly, a group of directional gradient histogram feature vectors is used to represent the local appearance information and shape features of the video dance moves. In view of the existing problem of heterogeneous feature fusion, this paper chooses the multi-core learning method to fuse the three kinds of features for dance movement recognition. Finally, the effectiveness of the proposed dance movement detection algorithm is tested using the Dance DB data set from the University of Cyprus and the Folk Dance data set from my laboratory. Experimental results show that the proposed algorithm can maintain a certain recognition rate for relatively complex dance movements and can still ensure a certain accuracy when the background and target are easily confused. This also confirms the efficacy of the movement recognition system used in this paper for recognizing dance movements.

Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author’s keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations.

This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments.

Multivariate statistical analysis is a new statistical method emerging in recent years. Due to the continuous update and continuous development of Internet technology, statistical analysis of data from all walks of life is required. The method of multivariate statistics was soon applied to all walks of life, bringing great convenience to people’s work and industry. The method of multivariate statistics is used to process and evaluate the data, guide the school’s physical education work, and make suggestions for various functional units. This paper focuses on the multivariate statistical analysis of principal components and factors, uses factor analysis to comprehensively evaluate college students, and uses the variance method to make statistics on the four-year physical education performance of 141 undergraduates in grades 13 and four subjects. And based on this, the effect of different majors on their academic achievement is discussed. This paper believes that in college physical education, this method should be applied to daily physical education, and students should be rationally analyzed and scientifically evaluated. This model can make a comprehensive investigation of the education work so that their education work can be improved more effectively, and sports talents in colleges and universities can get better development.

The research on the influencing factors of residents’ low-carbon consumption willingness and low-carbon consumption behavior of fresh food has certain practical guiding significance. Existing studies have analyzed the low-carbon consumption willingness, but the factors considered are not comprehensive and the degree of fit needs to be improved. Therefore, this paper starts with 37 variables from six aspects: demographic factors, psychological factors, low-carbon related knowledge, external factors, policy norms, and product factors. The binary logistic model is used to carry out regression analysis on low-carbon consumption willingness and low-carbon consumption behavior, and the fitting degree is higher and reaches about 90%. The regression results show that sense of responsibility, government tax, low-carbon product quality, and low-carbon product price have a significant impact on residents’ low-carbon consumption willingness. Whether there are fake and shoddy products in the market and whether the products are really of low carbon have a significant impact on low-carbon consumption behavior. Finally, starting from the three subjects of government, enterprises, and residents, this paper puts forward targeted suggestions to improve residents’ low-carbon consumption willingness and promote residents’ low-carbon consumption behavior, in order to promote low-carbon consumption.

Actively responding to population aging has become the new national condition of China’s aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China’s aging society. From the perspective of big data technology, this paper systematically studies the concerns of China’s aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China’s aging, which indirectly illustrates the applicability of big data analysis to the concerns of China’s aging.

Research on the price prediction of natural gas is of great significance to market participants of all kinds. In order to predict natural gas prices more reliably, this paper introduces a quadratic decomposition technology based on the combination of variational modal decomposition (VMD) and ensemble empirical modal decomposition (EEMD), which decomposes the residual term (Res) after VMD by EEMD; then, a new hybrid model called VMD-EEMD-Res.-LSTM is constructed in combination with the long short-term memory (LSTM) prediction model. The contribution of this new hybrid model is that, unlike existing application research that combines existing decomposition technology with the LSTM model, it does not ignore the important information contained in the residual after the VMD. In order to verify the predictive performance of the proposed new model, this paper uses the data of the spot price of natural gas in the United States to conduct a multistep-ahead empirical comparative analysis. The results show that the new hybrid model constructed in this paper has significant predictive advantages.

Due to the continuous progress of the era of mega data, it is more and more important to carry out accurate marketing of online video. Based on the analysis of users’ big data, this paper proposes and implements an online video precision marketing algorithm, which provides a reference for the precision marketing strategy of video websites. This paper introduces the concept and characteristics of precision marketing and analyzes the application forms of precision marketing of video websites. It also summarizes the operation system, operation mode of precision marketing, and the influence of online video advertising on enterprises and users. In order to realize the accurate marketing of big data, it needs to be carried out from three parts: data collection, data analysis, and marketing plan formulation and implementation. Through continuously collecting customer data, it gradually and clearly acquires the characteristics of customers and constantly dynamically adjusts the marketing strategy to customers, so as to accurately conduct video marketing to customers and greatly improve the marketing efficiency. In order to test the effectiveness of the marketing algorithm in this paper, we conducted an experiment. The research in this paper has important practical value and reference significance for reducing the cost of video advertising of enterprises and improving the communication effect of online video advertising.

The teaching characteristic of colleges and universities is determined by the teaching quality of teachers, but it is difficult to calculate with a linear mathematical explanation how to judge the teaching quality of teachers. In most colleges and universities, expert evaluations, supervision groups, peers listening to lectures in the classroom, and students' after-class evaluations are still used to determine the teaching ability of professors. It is undeniable that these judgment methods have certain practicality. Especially for new teachers, experts and peers can find problems in time and help new teachers quickly correct them, and they can also know how to better practice the “student-centered” teaching concept from the feedback of students after class. However, these judgment standards also have their limitations. For example, the classroom quality judgment standards for experts and peers are mostly set by some administrative departments according to social requirements, leadership requirements, and subjective cognition. Various institutions should have different weights for the same criterion, and if they all employ the same criteria, it will easily lead to imprecise conclusions. In addition, the expert group and students are also subjective and incomplete in the stage of teacher evaluation. Based on different nonquantitative factors, the evaluation standards for teachers in colleges and universities also need the scientific theoretical basis. Therefore, many scientists are considering whether to use a more intelligent and reasonable way to judge the quality of teaching. Nowadays, with the popularization of artificial intelligence (AI), the use of neural networks (NNs) is becoming more and more comprehensive. According to the characteristics of NNs, this paper uses the GA NNs algorithm to signify that this algorithm can be effectively used in the evaluation of teachers' teaching quality. In this paper, a large number of experimental results show that the use of GA NNs can be used to evaluate the quality of teaching, and the evaluation results can be obtained quantitatively in this way. For the assessment of teaching quality, quantitative results are also more powerful in teaching evaluation.

With the rapid development of science and technology, modeling technology involves more and more fields and the data objects studied are more and more abstract. Based on the Internet plus complex discrete dynamic modeling technology, this paper studies the auditing and control of computerized accounting computerization. In order to maintain the steady state of the discrete dynamic system, the lifting method boosting, least square method, gradient boost, and LS-Ensem algorithm are added in the modeling process. Then, in order to improve the data recognition ability of the system, the recognition algorithm is added. The results show that the complex discrete dynamic system composed of the above algorithms is different from the ordinary system and can be better suitable for small-scale data. It also improves the stability of the whole system and then improves the accuracy of the model. Compared with ordinary discrete systems, the complex discrete dynamic systems studied in this paper can better overcome external factors and avoid errors. The system can quickly analyze and process the data that need statistics and has good application value.

Smart new retail mode has rapidly become the hot shopping mode. In order to develop vigorously in the new retail environment, it is urgent for merchants to set up supply chain network reasonably. This paper constructs a supply chain newsvendor model consisting of 1 wholesaler and N retailers. Analyse how channel conversion rate, negotiation ability of supply chain members, number of retailers, and other factors affect the balance among supply chain members when offline demand, online demand, and cross-channel demand exist simultaneously. Combined with the case data of two fast fashion brands, this paper compares and analyses the profit changes of decentralized supply chain and centralized supply chain under the influence of multiple factors. The study shows that there is no absolute optimal supply chain model and the choice of supply chain model will change with the change of influencing factors. However, no matter in which supply chain mode, improving channel conversion rate and controlling the number of retailers are beneficial. In addition, in decentralized supply chain, balance can be achieved among supply chain members so as to achieve Pareto optimality. Wholesalers should take the initiative to promote the transformation of demand among channels, and should not be too strong in member negotiations. This paper analyses the basic characteristics of the supply chain network under the new retail, and establishes the network optimization strategy that is easy to implement, which can help enterprises to carry out the supply network layout more effectively under the smart new retail mode.

The rapid development of digital economy puts forward theoretical and practical requirements for the construction of legal model of big data transaction management (BDTM). The new problems of ambiguous data ownership, privacy leakage, chaotic digital transaction order, and low nationalization in the context of big data transactions have largely restricted the role of China’s big data transaction market and related laws and regulations and are not conducive to promoting the scientific and standardized development of China’s digital economy. In this paper, we firstly analyze the connotation, importance, and necessity of the construction of legal model of big data transaction in the era of digital economy from the basic connotation of legal model. Then, we analyze the need to adhere to the principles of systematization, security, and openness in the construction of legal model of big data transaction management and focus on four methods of contract law, market behavior, replication behavior, and dispute resolution in the construction of legal model of big data transaction management. Finally, we summarized the conclusions of this paper. The conclusions of this paper are important for promoting the transformation of digital resources into effective data elements and advancing the scientific and standardized development of China’s digital economy.

How the macro-external environment, i.e., the uncertainty of economic policies, affects the peer effect of corporate investment and whether the ability of managers, as the main decision of corporate investment, affects the peer effect of enterprise investment is worthy of further study. This paper uses Baker’s economic policy uncertainty index to obtain China’s economic policy uncertainty index and uses the DEA-Tobit two-stage model to measure the ability of managers, combined with the relevant data of China’s A-share listed companies from 2010 to 2020, to study the impact of economic policy uncertainty and managers’ ability on corporate investment peer effect. We found that China’s economic policy uncertainty increases the information acquisition cost of micro-firms and deepens the degree of information asymmetry, and firms are strongly motivated to learn or even imitate the investment decisions of enterprises in the same industry, thus intensifying the peer effect of investment. For enterprises of different nature, the economic policy uncertainty has a more significant impact on the investment peer effect of non-state-owned enterprises than state-owned enterprises. Compared with large enterprises, the impact of economic policy uncertainty on the investment cohort effect of small enterprises is more significant. Then, through group regression according to the median of managerial ability, we found that the investment peer effect is more obvious in enterprises with low management ability and the effect of economic policy uncertainty on investment peer effect is more significant in the sample with low management ability. Based on the empirical research conclusions, this paper puts forward the following suggestions: improve the corporate governance mechanism and the management ability of managers. In issuing economic policies, the government should be cautious and stable to reduce the uncertainty of economic policies.

The paper measures the income distribution effects and welfare effects of VAT itself based on Chip2013 household income and expenditure microdata using the Gini coefficient, Suits index, and Theil index from the perspective of annual income and lifetime income, respectively, and the effective VAT rate. Taking provinces as the basic research unit, we use the first-right method, LISA time path, and Spatiotemporal leap to evaluate China’s well-being level from 2010 to 2020 and analyze its Spatiotemporal dynamic characteristics. At present, most countries adopt a tax rate model with 1∼3 bands. The current VAT rate structure is “13% + 9% + 6%,” which is in line with the international development trend. In the data preprocessing module, the HTTP(S) responses of non-IoT devices are first filtered out and then the text that may contain device information is extracted from the remaining HTTP(S) responses by integrating multidimensional features, and finally, the irrelevant strings in the text are filtered out. At the end of the paper, simulation experiments are designed to verify the function and performance of the identification information extraction system for this heritage connected device, and the results verify that the model identification method in this paper can achieve better identification results compared with existing methods. The results verify that the model recognition method in this paper can achieve better recognition results compared with existing methods.

Corner detection is a common method to obtain image features, and the detection effect influences the performance of matching and tracking directly. A FAST-Harris fusion corner detection algorithm is proposed to improve the shortcomings of the Harris algorithm, such as the low detection accuracy and low positioning accuracy, and a corner detection fusion model is established. First, the detected target image is padded, and then the FAST algorithm is used with a 25% reduced contrast points to achieve fast capture roughly; in this way, a candidate corner set is obtained. Then, screening the candidate corner is set one by one by calculating the response function of the Harris with Scharr operator to achieve capture accurately. Finally, the real corners are obtained using SAD for nonmaximum suppression. The positioning error, error detection rate, robustness, and running time of corner detection are obtained by the PyCharm platform. Compared with Harris, the error detection rate and localization error of the algorithm are reduced by 16.89% and 42.04%, respectively. Compared with 8 popular corner detection algorithms, the error detection rate and localization error of the algorithm in this paper are the lowest, which are 24.60% and 1.42 pixels. The robust performance in lossy JPEG compression is the best, with 17.37% shorter running time than Harris algorithm. The method in this paper can be used in scenarios such as autonomous driving and image search services.

Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support.

The current Internet development situation regarding the analysis of sports teaching information is very necessary and can be a way to improve the effectiveness of sports teaching in the information environment. Aiming at the defects of strong subjectivity and low discrimination accuracy of the current sports video classification results, this paper proposes an effective sports video classification method based on deep learning, which can effectively evaluate the sports assisted teaching. Specifically, the key frame features are obtained by using the similarity coefficient key frame extraction algorithm, and the sports video image classification is established through the deep learning coding model. Thus, the ability of the school to rely on the scheme proposed in this paper to improve the teaching facilities, physical education curriculum teaching materials, assessment teaching materials, management, and so on. The results show that for different types of sports videos, the overall effect of the classification of the method in the paper is significantly better than that of other current sports-assisted teaching evaluation methods, which has significantly improved the effect of sports-assisted teaching evaluation.

With the advancement of technology and the arrival of the era of integrated media, big data, artificial intelligence, cloud computing, and virtual reality are gradually changing the ecology of public opinion. This will bring new challenges to the technical discovery, data analysis, and response of network disease dissemination to public opinion. Internet data barriers are still widespread. The specific requirements are to explore the application mode of blockchain technology to bring a better service experience to the people. Therefore, this paper focuses on the dissemination of disease public opinion information and risk management research in the blockchain environment. Furthermore, the application of advanced blockchain technology is used to seek breakthroughs in the field of risk management of network disease transmission and public opinion in terms of technology and data. This paper systematically sorts out the research results of domestic and foreign disease transmission network public opinion, public opinion risk management, and blockchain through comprehensive use of literature analysis, grounded research, empirical research, simulation research, and other research methods. Based on theories of the disease transmission network, public opinion, risk management, blockchain, and system dynamics, we conduct research on network public opinion and risk management of disease transmission in the block chain environment. At the theoretical level, the research in this paper can promote the risk management of disease transmission network public opinion in the blockchain environment and carry out the construction of the theoretical system to deepen the application of blockchain technology in the field of disease public opinion risk management. At the practical level, the research in this paper can guide the improvement of the effect of public opinion risk management. Then, blockchain technology is applied to manage the risk of disease public opinion. In future research, the theory of public opinion risk management will be further enriched. We will apply blockchain technology to develop the evolution of the risk of disease public opinion transmission, carry out research on public opinion management and other aspects, and at the same time, try the design and development of a blockchain public opinion system. In this way, the innovation of blockchain in the development and application of disease network public opinion research will be promoted.

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance.

This paper proposes a solar collector that utilizes supercritical CO2 as the working fluid to detect implicit water heating and boost the collector’s heating rate efficiency. Solar water heating system efficiency, cost, and environmental friendliness all depend on the working fluid used. CO2 is a possible natural refrigerant replacement. Even a little increase in temperature or pressure may have a big impact on the density of CO2 at the critical point. Because of this, solar heating can readily generate a spontaneous convection flow of supercritical carbon dioxide. The most basic collector characteristics, such as CO2 pressure and temperature, were determined by building and testing an experimental setup using a CO2-based solar collector. Due to solar radiation, liquid, gas, or supercritical CO2 pressures and temperatures change throughout the test. There was a 50% time average collector efficiency (ηcol) and a 30% heat recovery efficiency (ηRE). Solar thermal collectors based on supercritical CO2 have now been shown in this paper. Since the results show that even though the solar energy is low, the CO2 temperature, pressure, and supercritical stress remain constant, this is distinct from conventional liquid-based solar collectors.

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.

By analyzing the historical background of the times and summarizing the current situation of urban development research at home and abroad, we find that there is a lack of theoretical and practical research related to intelligent park cities. Therefore, this paper first starts with the connotation of park city theory, focuses on the improvement of planning methods, and studies the application paths of big data technology in the planning and construction of park cities. Taking Jinzhai Country as the research object, this paper further illustrates the applicability of this research method at the macrolevel. In the context of the park city, we analyze the problems of the current status of city construction in general and explore the path of “planning concept first, big data-assisted design” for innovative and intelligent city construction. According to the study, the overlook corridor control has an impact on the building height. In terms of landscape protection, overlook system simulation is the other key factor. In addition, the analytic hierarchy process is the basis for development intensity control. The results show that big data technology can assist in the landscape conservation, morphology formation, and efficient operation of Jinzhai Country Park City. Our aim is to achieve the protection and utilization of its ecological environment and natural resources and thus to comprehensively coordinate the multidimensional urban spaces and build a park city model. As an urban development model that meets the requirements of being “people-oriented, efficient, green, and aesthetic” in the new stage, park cities need to be faced by researchers in order to further realize the overall city goal and vision for the whole society to be smart. It also provides relevant design ideas and methods for the planning and construction of park cities in other similar cities or regions.

This paper contributes to the goal of finding an efficient compression solution for post-training quantization from the perspective of support region choice under the framework of low-bit uniform quantization. The decision to give preference to uniform quantization comes from the fact that support region choice is the most sensitive in the uniform quantization of nonuniform sources (e.g., Laplacian sources). Therefore, in this paper, we analyse in detail how the choice of the support region influences the performance of two-bits uniform quantization, measured with signal to quantization noise ratio (SQNR), and the accuracy of the compressed neural network (NN) model. We provide experimental and theoretical results for a few significant cases of two-bits uniform quantizer design, where we assume that Laplacian source models the distribution of weights in our fully connected NN. We opt for Laplacian distribution since it models well weights of NNs. Specifically, we analyse whether it is possible to apply the simplest uniform quantization in trained NN model weight representation with a bit rate of R = 2 bit/sample while preserving the accuracy of the model to a great extent. Also, our goal is to determine whether the choice of the key parameter of two-bits uniform quantizer (support region threshold) equally reflects on both, SQNR and accuracy. Moreover, we extend our analysis to the application of layer-wise two-bits uniform quantization in order to examine whether it is possible to achieve an additional improvement of the accuracy of our NN model for the MNIST dataset. We believe that the detailed analysis of post-training quantization described and conducted in this paper is very useful for all further research studies of this very current topic, especially due to the fact that the problem regarding post-training quantization is addressed from a particularly important perspective of choosing the support region.

In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on the realization of objectives in terms of project duration, cost, and quality. The current engineering project investment presents the characteristics of large scale, long period, and many risks, which puts forward higher requirements for the multiobjective coordinated management of the project. Therefore, the analysis and optimization of the various objectives of the engineering project is the basis for achieving multiobjective balance and coordination. The comprehensive consideration of the risks faced by the engineering project and the dynamics of the environment in which it is located can make the objective optimization more realistic. On the basis of risk identification and risk evaluation, this paper is committed to considering multiple objective factors that affect the outcome of risk decision-making to achieve the optimization of decision-making schemes and applies multiobjective genetic algorithm to the optimization of decision-making schemes, thus finding a way to optimize the decision-making scheme. This paper analyzes the research progress and status of construction project risk management decision and multiobjective evolutionary algorithm and points out the imperfections of the current research. Then it introduces the related theory of construction project risk management decision, the related terminology of multiobjective optimization problem and the method used in this paper —the principle, process, and characteristics of genetic algorithm and prepares for the following problem solving. In this paper, a mathematical model for multiobjective decision-making of engineering project risk management is established, and the multiobjective genetic algorithm is used to solve the model. Through the analysis of examples, a series of Pareto optimal solutions with good convergence and diversity are obtained, the best combination of various risk control measures is found, and three goals of risk evaluation value, management cost, and risk loss are achieved. The two models with or without risk correlation present certain differences, the model with correlation considered is more accurate and applicable than the model without correlation considered in the existing research.

Chinese Semantic Role Labeling (SRL) is the core technology of semantic understanding. In the field of Chinese information processing, where statistical machine learning is still the mainstream, the traditional labeling methods rely heavily on the parsing degree of syntax and semantics of sentences. Therefore, the labeling precision is limited and cannot meet the current needs. This paper adopts the model based on a bidirectional long short-term memory network combined with the Conditional Random Field (Bi-LSTM-CRF). In the feature processing stage, pooling technology is combined with sampling and selecting multifeature vector groups to improve the performance of the sequence labeling model. Lexical, syntactic, and other multilevel linguistic features are integrated into the training to realize in-depth improvement of the original labeling model. Through several groups of experiments, the precision of model annotation in this paper has been significantly improved combined with linguistic-assisted analysis, which proves that it can optimize the annotation performance of the model by integrating relevant linguistic features into the model based on Bi-LSTM-CRF and sampling and extracting multifeature groups; the evaluation of F increases to 82.18 percent.