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Abstract It may be difficult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information. In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network, the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied. Based on the risk transmission path, the distribution automation security situational awareness factors are analyzed, and the main factors affecting the distribution automation security situation are divided into two dimensions: internal source and external source, and eight main awareness factors; Different types of sensors are set in the main areas of security situational awareness factors to collect data of different awareness factors. Using ant colony algorithm to optimize DS evidence fusion method, data with different perception factors are fused, and data fusion results with different perception factors are obtained. The distribution automation security situational awareness model is constructed, and the security situational awareness results are obtained based on the data fusion results of the awareness factors. If the results are higher than the set threshold, the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located. The experimental results show that the multi-source data fusion effect of this method is good, and it can accurately perceive the security status of different nodes of the experimental object at different time nodes.

It may be difficult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information. In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network, the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied. Based on the risk transmission path, the distribution automation security situational awareness factors are analyzed, and the main factors affecting the distribution automation security situation are divided into two dimensions: internal source and external source, and eight main awareness factors; Different types of sensors are set in the main areas of security situational awareness factors to collect data of different awareness factors. Using ant colony algorithm to optimize DS evidence fusion method, data with different perception factors are fused, and data fusion results with different perception factors are obtained. The distribution automation security situational awareness model is constructed, and the security situational awareness results are obtained based on the data fusion results of the awareness factors. If the results are higher than the set threshold, the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located. The experimental results show that the multi-source data fusion effect of this method is good, and it can accurately perceive the security status of different nodes of the experimental object at different time nodes. [ABSTRACT FROM AUTHOR]

The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 10-hectare, 20-hectare, 30-hectare, and 50-hectare experimental cornfields. Results indicate that the proposed MSIF-based CYP Random Forest model outperforms control models such as support vector machine (SVM) and Long Short-Term Memory (LSTM), achieving the highest prediction accuracy of 89.30%, surpassing SVM and LSTM by approximately 13.44%. Meanwhile, as the experimental field size increases, the proposed model demonstrates higher prediction accuracy, reaching a maximum of 98.71%. This study is anticipated to offer early warnings of potential factors affecting crop yields and to further advocate for the adoption of MSIF-based CYP. These findings hold significant research implications for personnel involved in Agricultural and Forestry Economic Management within the context of developing agricultural economy. [ABSTRACT FROM AUTHOR]

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of 'sky', 'air', and 'ground' , as well as integrated collaborative monitoring method of 'sky-air-ground'. The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the 'sky-air-ground' is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 10-hectare, 20-hectare, 30-hectare, and 50-hectare experimental cornfields. Results indicate that the proposed MSIF-based CYP Random Forest model outperforms control models such as support vector machine (SVM) and Long Short-Term Memory (LSTM), achieving the highest prediction accuracy of 89.30%, surpassing SVM and LSTM by approximately 13.44%. Meanwhile, as the experimental field size increases, the proposed model demonstrates higher prediction accuracy, reaching a maximum of 98.71%. This study is anticipated to offer early warnings of potential factors affecting crop yields and to further advocate for the adoption of MSIF-based CYP. These findings hold significant research implications for personnel involved in Agricultural and Forestry Economic Management within the context of developing agricultural economy.

The detection of external fire in coal mines and the reliable identification of initial fire are of great significance for improving the level of coal mine fire detection. It is also an important direction of intelligent mine construction in the future. In order to improve the speed, precision and real-time of coal mine external fire detection, a coal mine external fire detection method based on edge intelligence is proposed. The feature scale of the backbone network of the YOLOv5s model is improved. The model can fully learn the shallow features and improve the small target detection performance. At the same time, an adaptive attention module is added on the basis of the original feature pyramid network (FPN) to improve the detection precision of the model. There are problems of image detection error and missed detection caused by poor light conditions, more dust and camera shooting angle in the underground mine. In order to solve the above problems, the YOLOv5s-as model is constructed by using multi-sensor auxiliary detection and weighting fusion identification of video detection information and multi-sensor detection information through dynamic weighting algorithm. The YOLOv5s-as model is transplanted to the intelligent edge processor, and lightweight processing is carried out to realize the deployment of edge intelligent devices. The experimental results show that the reasoning time of the YOLOv5s-as model is slightly longer than that of the YOLOv5s-a model without sensor information fusion reasoning, but mean value of average precision when the intersection over union is 0.5 (mAP@0.5) is increased by 7.24%. Compared with the YOLOv5s model before transplantation, the mAP@0.5 of the YOLOv5s-as model transplanted to the intelligent edge processor and subjected to lightweight processing increased by 15.04%. For small target fire sources, SSD 300, SSD 512 and YOLOv5s models cannot identify them. The YOLOv5s-a and YOLOv5s-as models can detect small target fire sources with good adaptability. When using the edge processing method, the response period of YOLOv5s as model is 238 ms, which is 38.66% shorter than the centralized processing method.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exploration and development of offshore oil and gas has greatly alleviated the tension of international energy consumption, however, the complex geological and engineering conditions of offshore drilling lead to complex downhole accidents such as overflow and kick when the wellbore pressure regulation accuracy is poor in offshore drilling. Well killing technology is the primary well control means after oil and gas well overflow and blowout. And the accuracy, real-time and intelligence of wellbore pressure calculation will directly affect the construction period and even the success or failure of well killing. The existing wellbore multiphase flow model still has uncertain parameters depending on the empirical equation determined based on laboratory experiments, and generally lacks real-time interaction with the physical parameters of the drilled formation and the physical parameters of the intrusive fluid under wellbore conditions, which makes the multiphase flow model have the defect of poor pertinence to the formation characteristics and wellbore fluid information at the drilling well formation. In this paper, the differences between the simulation data of multiphase flow model under overflow and circulating well killing conditions and the trend of real-time data at drilling platform, seabed position and bottomhole monitoring points are analyzed. The key parameters of multiphase flow model relying on Laboratory experiment are corrected by real-time inversion. Based on the original universality, the annulus multiphase flow model further highlights the pertinence to the formation characteristics and intrusive fluid characteristics of the drilled section, which is of important guiding significance for the safe and effective conduct of offshore drilling. • An intelligent well killing control method driven by the coupling of multiple flow simulation and real-time data is proposed. • The improved genetic algorithm is used to optimize, analyze and modify the key parameters in multiphase flow model. • Combined with the drilling gas kick data of a well in the South China Sea, the rationality of this method is verified. • This work present new methods for improving the accuracy of wellbore pressure calculation by combining multi-source data. [ABSTRACT FROM AUTHOR]