Intelligent coal sample access remote monitoring system and method

Abstract

The invention discloses an intelligent coal sample access remote monitoring system and method, and the system comprises an obtaining module which is used for setting monitoring parameters of each monitoring region, and obtaining real-time monitoring information according to the monitoring parameters; the determination module is used for presetting an attention target, performing target extraction on the real-time monitoring information of the monitoring area based on the attention target, obtaining a target monitoring factor, analyzing the target monitoring factor, and determining a feature monitoring factor of the attention target; the evaluation module is used for comparing the feature monitoring factor with a standard feature factor of the target of interest and generating an abnormal evaluation value corresponding to the target of interest according to a comparison result; the regulation and control module is used for judging whether the corresponding concerned target is abnormal or not according to the abnormal evaluation value, if yes, a regulation and control instruction is generated, accurate monitoring of the coal sample storage and taking system is achieved through the remote monitoring system, the fraud behavior risk is reduced, problems in the coal sample storage and taking process are found in time, a regulation and control strategy is adopted, and the management efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of coal sample storage and retrieval systems, and in particular to an intelligent coal sample storage and retrieval remote monitoring system and method. Background Art

[0002] The coal sampling storage and retrieval process includes a series of quality inspection operations such as sampling, sample preparation and testing when the coal is accepted into the factory, so as to determine whether the quality of the coal entering the factory meets the standards. At present, the coal sampling storage and retrieval process mostly relies on manual completion by staff, which has a great risk of fraud or potential problems such as equipment status and environmental conditions. Therefore, an intelligent coal sampling storage and retrieval remote monitoring system and method is urgently needed to achieve accurate monitoring of the coal sampling storage and retrieval process, reduce the risk of fraud, timely discover potential problems, and improve management efficiency. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides an intelligent coal sample storage and retrieval remote monitoring system and method, which improves the accuracy of real-time monitoring information in the monitoring area by establishing multiple monitoring areas and setting monitoring parameters, extracts characteristic monitoring factors of each target of interest and performs abnormality assessment, obtains abnormal assessment values ​​and configures reasonable control strategies, thereby achieving accurate monitoring of the coal sample storage and retrieval process, reducing the risk of fraud, timely discovering potential problems, and improving management efficiency.

[0004] In some embodiments of the present application, a smart coal sample storage and access remote monitoring system is provided, comprising: The acquisition module is used to establish multiple monitoring areas according to the coal sample storage and access process, set monitoring parameters for each monitoring area, and obtain real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; A determination module is used to pre-set a target of interest, extract the target from the real-time monitoring information of the monitoring area based on the target of interest, obtain the target monitoring factor, analyze the target monitoring factor of the same target of interest, and determine the characteristic monitoring factor of the target of interest based on the analysis result; An evaluation module is used to compare the characteristic monitoring factor with the standard characteristic factor of the target of interest, and generate an abnormal evaluation value corresponding to the target of interest according to the comparison result; The control module is used to determine whether the corresponding target of interest is abnormal based on the abnormal evaluation value, and if abnormal, generate a control instruction.

[0005] In some embodiments of the present application, multiple monitoring areas are established according to the coal sample storage and access process, and monitoring parameters of each monitoring area are set, including: Divide the coal sample storage and retrieval process into a plurality of coal sample storage and retrieval sub-processes, and set the operation area of ​​each coal sample storage and retrieval sub-process as a monitoring area; Obtain the historical operating parameters and process important coefficients of each coal sample storage and retrieval sub-process; Generate various fraud behaviors in the corresponding coal sample storage and access sub-process according to historical operation parameters, and generate the influence coefficient of the corresponding fraud behavior according to the occurrence frequency of each fraud behavior and the influence degree of the corresponding fraud behavior; Generate a comprehensive influence coefficient according to the influence coefficients of all fraudulent behaviors in the same coal sample storage and access sub-process, and generate a monitoring evaluation value of the current coal sample storage and access sub-process in combination with the process importance coefficient of the corresponding coal sample storage and access sub-process; The calculation formula of the monitoring evaluation value is: ; Among them, K is the monitoring evaluation value, a1 is the weight coefficient of the comprehensive influence coefficient, z1 is the first monitoring evaluation conversion coefficient, Gi is the influence coefficient of the i-th fraud behavior, f is the number of all fraud behaviors in the current coal sample storage and access sub-process, a2 is the weight coefficient of the process influence coefficient, z2 is the second monitoring evaluation conversion coefficient, and r is the process importance coefficient; The monitoring parameters of the corresponding monitoring area are set according to the monitoring evaluation value of each coal sample storage and access sub-process, and the monitoring parameters include the number of monitoring points and the monitoring time interval between adjacent monitoring time nodes.

[0006] In some embodiments of the present application, the monitoring parameters of the corresponding monitoring area are set according to the monitoring evaluation value of each coal sample storage and access sub-process, including: Presetting a first preset monitoring evaluation value interval, a second preset monitoring evaluation value interval, a third preset monitoring evaluation value interval and a fourth preset monitoring evaluation value interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the first preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the first preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the fourth preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the second preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the second preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the third preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the third preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the third preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the second preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access subprocess is in the fourth preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the fourth preset monitoring point number, and the monitoring time interval between adjacent monitoring time nodes is set to the first preset monitoring time interval.

[0007] In some embodiments of the present application, target extraction is performed on the real-time monitoring information of the monitoring area based on the target of interest to obtain the target monitoring factor, including: According to the preset operation parameters of the coal sample storage and access sub-process corresponding to each monitoring area, the focus targets of the corresponding monitoring area are set, and the focus targets include equipment, environment and operation behavior; The real-time monitoring information includes real-time monitoring video and real-time monitoring data; According to the focus target of each monitoring area, the real-time monitoring information of the corresponding monitoring area is extracted. When the focus target is equipment or environment, the monitoring data of the equipment or environment in the actual operation process in the real-time monitoring data is obtained, and the monitoring data is preprocessed and standardized to obtain multiple categories of target monitoring factors of the equipment or environment; When the target of attention is the operation behavior, image frames with the operation behavior in the real-time monitoring video are obtained, and after image preprocessing, the clarity of the image frames after image preprocessing is judged, and the image frames with clarity greater than the preset clarity threshold are set as the target monitoring factors of the operation behavior.

[0008] In some embodiments of the present application, the target monitoring factors of the same target of interest are analyzed, and the characteristic monitoring factors of the target of interest are determined according to the analysis results, including: When the target of interest is a device, target monitoring factors of multiple categories of the corresponding device are obtained; Pre-set multiple equipment evaluation indicators of the current equipment in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding equipment and equipment evaluation indicators, and set the target monitoring factor whose correlation with each equipment evaluation indicator is greater than a preset first correlation threshold as the characteristic monitoring factor of the corresponding equipment evaluation indicator; The characteristic monitoring factors of all equipment evaluation indicators are set as the characteristic monitoring factors of the corresponding equipment; When the target of interest is the environment, multiple categories of target monitoring factors corresponding to the environment are obtained; Pre-set multiple environmental evaluation indicators in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding environments and environmental evaluation indicators, and set the target monitoring factor whose correlation with each environmental evaluation indicator is greater than a preset second correlation threshold as the characteristic monitoring factor of the corresponding environmental evaluation indicator; The characteristic monitoring factors of all environmental assessment indicators are set as the characteristic monitoring factors of the corresponding environment; When the target of attention is an operation behavior, the target monitoring factor corresponding to the operation behavior is obtained; The target monitoring factors are classified according to the monitoring point sources of the target monitoring factors of the corresponding operation behaviors to obtain target monitoring factors of multiple categories, a first monitoring time sequence is generated according to the monitoring time nodes of the target monitoring factors of each category, and the target monitoring factors of all categories are sorted to obtain a target monitoring factor sequence; Generate a second monitoring time sequence according to a preset operation path of the operation behavior in the corresponding monitoring area and sort the monitoring points involved to obtain a monitoring point operation sequence; Mapping the monitoring point operation sequence with the target monitoring factor sequence to determine whether the monitoring point and the target monitoring factor at the same position in the monitoring point operation sequence and the target monitoring factor sequence correspond to each other; If so, determine whether the critical target monitoring factors of adjacent categories in the target monitoring factor sequence meet the splicing rule. If the edge area ratio of the critical target monitoring factors of the adjacent categories meets the standard edge area ratio and the edge area dispersion ratio meets the standard edge area dispersion ratio, then determine that the critical target monitoring factors of the adjacent categories meet the splicing rule, and splice the critical target monitoring factors of the adjacent categories. If the critical target monitoring factors of the adjacent categories do not meet the splicing rule, screen the target monitoring factors of the adjacent categories until the target monitoring factors of the adjacent categories meet the splicing rule, and achieve the splicing of all the target monitoring factors of the adjacent categories, and obtain a complete operation splicing image of the operation behavior of the current monitoring area, and set the complete operation splicing image as the characteristic monitoring factor of the corresponding operation behavior; If not, filter out the non-corresponding monitoring points and target monitoring factors, and replace the non-corresponding target monitoring factors with the target monitoring factors of the non-corresponding monitoring points to obtain a replaced target monitoring factor sequence, and determine whether the critical target monitoring factors of adjacent categories in the replaced target monitoring factor sequence meet the splicing rules. If so, obtain a complete operation splicing image of the operation behavior in the current monitoring area and set it as the characteristic monitoring factor of the corresponding operation behavior.

[0009] In some embodiments of the present application, the characteristic monitoring factor is compared with the standard characteristic factor of the target of interest, and an abnormal evaluation value corresponding to the target of interest is generated according to the comparison result, including: When the target of attention is equipment or environment, the characteristic monitoring factors are compared with the corresponding standard characteristic factors to obtain characteristic difference values, and the abnormal evaluation value of the equipment or environment is generated according to the characteristic difference values; When the target of interest is an operation behavior, a standard complete operation stitching image of the current operation behavior is generated according to the monitoring points and monitoring time nodes of the corresponding monitoring area, and the standard complete operation stitching image is divided into multiple standard stitching sub-images according to the operation process of the current operation behavior, and the standard stitching sub-images of all operation processes are set as the standard characteristic factors of the current operation behavior; The characteristic monitoring factors of the operating behavior in each monitoring area are divided into multiple characteristic monitoring sub-factors according to the operation process of the current operating behavior. The characteristic monitoring sub-factors are compared with the standard characteristic factors of the same operation process to obtain the degree of behavioral deviation, and the abnormal evaluation value of the current operating behavior is generated according to the degree of behavioral deviation.

[0010] In some embodiments of the present application, when the target of interest is a device, the characteristic difference value is compared with a preset first characteristic difference value threshold, a characteristic monitoring factor whose characteristic difference value is greater than the preset first characteristic difference value threshold is obtained and set as an abnormal characteristic monitoring factor, and an abnormal evaluation value of the device is generated according to the number of abnormal characteristic monitoring factors and the abnormal characteristic values ​​of the abnormal characteristic monitoring factors; ; Among them, Y1 is the abnormal evaluation value of the equipment, n1 is the number of abnormal characteristic monitoring factors of the current equipment, and m1 is the total number of characteristic monitoring factors of the current equipment. is the abnormal characteristic value of the cth abnormal characteristic monitoring factor of the current device; When the target of interest is the environment, the feature difference value is compared with a preset second feature difference value threshold, a feature monitoring factor whose feature difference value is greater than the preset second feature difference value threshold is obtained and set as an abnormal feature monitoring factor, and an abnormal assessment value of the environment is generated according to the number of abnormal feature monitoring factors and the abnormal feature values ​​of the abnormal feature monitoring factors; ; Among them, Y2 is the abnormal evaluation value of the environment, n2 is the number of abnormal characteristic monitoring factors of the current environment, and m2 is the total number of characteristic monitoring factors of the current environment. is the abnormal characteristic value of the sth abnormal characteristic monitoring factor of the current environment; When the target of attention is an operation behavior, the behavior deviation degree is compared with a preset behavior deviation degree threshold, an abnormal operation behavior whose behavior deviation degree is greater than the preset behavior deviation degree threshold is obtained, and an abnormal evaluation value of the operation behavior is generated according to the abnormal degree of the abnormal operation behavior; ; Among them, Y3 is the abnormal evaluation value of the operation behavior, n3 is the number of abnormal operation behaviors, is the abnormal degree of the vth abnormal operation behavior, is the weight coefficient of the vth abnormal operation behavior.

[0011] In some embodiments of the present application, judging whether the corresponding target of interest is abnormal according to the abnormality evaluation value includes: Pre-set abnormal assessment value thresholds; If the abnormal evaluation value is less than the abnormal evaluation value threshold, it is determined that the current target of interest has no abnormality; If the abnormality assessment value is greater than the abnormality assessment value threshold, the current focus target is determined to be abnormal.

[0012] In some embodiments of the present application, if an abnormality occurs, a control instruction is generated, including: If the target of the abnormality is a device, a first preset control strategy library in the current monitoring area is selected, the first preset control strategy library includes a number of preset abnormal feature values ​​of the device, and each preset abnormal feature value is associated with a specific preset control strategy, the abnormal feature monitoring factor and the abnormal feature value of the device are determined, the similarity between the preset abnormal feature value of the current device and the abnormal feature value of the abnormal feature monitoring factor in the first preset control strategy library is calculated, the preset control strategy of the abnormal feature value with the greatest similarity is set as the control strategy of the abnormal feature monitoring factor of the current device, and a control instruction is generated; If the abnormal target of concern is the environment, select a second preset control strategy library of the current monitoring area, the second preset control strategy library includes a number of preset abnormal feature values ​​of the environment, and each preset abnormal feature value is associated with a specific preset control strategy, determine the abnormal feature monitoring factor and the abnormal feature value of the environment, calculate the similarity between the preset abnormal feature value in the second preset control strategy library and the abnormal feature value of the abnormal feature monitoring factor, set the preset control strategy of the preset abnormal feature value with the greatest similarity as the control strategy of the abnormal feature monitoring factor of the current environment, and generate a control instruction; If the abnormal target of concern is an operation behavior, select a third preset control strategy library for the current monitoring area, the third preset control strategy library includes several preset abnormality levels of the operation behavior, and each preset abnormality level is associated with a specific preset control strategy, determine the abnormal operation behavior and the abnormality level of the operation behavior, calculate the similarity between the preset behavior abnormality level in the third preset control strategy library and the abnormality level of the abnormal operation behavior, set the preset control strategy with the preset abnormality level with the greatest similarity as the control strategy for the abnormal operation behavior of the current operation behavior, and generate a control instruction.

[0013] In some embodiments of the present application, a smart coal sample storage and remote monitoring method is also included: Establish multiple monitoring areas according to the coal sample storage and access process, set monitoring parameters for each monitoring area, and obtain real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; Preset the target of interest, extract the real-time monitoring information of the monitoring area based on the target of interest, obtain the target monitoring factor, analyze the target monitoring factor of the same target of interest, and determine the characteristic monitoring factor of the target of interest according to the analysis result; The characteristic monitoring factor is compared with the standard characteristic factor of the target of interest, and an abnormal evaluation value corresponding to the target of interest is generated according to the comparison result; According to the abnormal evaluation value, it is judged whether the corresponding target of attention is abnormal. If it is abnormal, a control instruction is generated.

[0014] Compared with the prior art, the intelligent coal sample storage and access remote monitoring system and method of the embodiment of the present application has the following beneficial effects: By establishing multiple monitoring areas and setting monitoring parameters, the accuracy of real-time monitoring information in the monitoring areas can be improved, the characteristic monitoring factors of each target of concern can be extracted and abnormal evaluation can be performed, the abnormal evaluation values ​​can be obtained and reasonable control strategies can be configured to achieve accurate monitoring of the coal sample storage and retrieval process, reduce the risk of fraud, discover potential problems in a timely manner, and improve management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of an intelligent coal sample storage and access remote monitoring system in a preferred embodiment of the present application; Figure 2 It is a flow chart of an intelligent coal sample storage and remote monitoring method in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0016] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0017] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0020] like Figure 1 As shown, a smart coal sample storage and access remote monitoring system according to a preferred embodiment of the present application includes: The acquisition module is used to establish multiple monitoring areas according to the coal sample storage and access process, set monitoring parameters for each monitoring area, and obtain real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; A determination module is used to pre-set a target of interest, extract the target from the real-time monitoring information of the monitoring area based on the target of interest, obtain the target monitoring factor, analyze the target monitoring factor of the same target of interest, and determine the characteristic monitoring factor of the target of interest based on the analysis result; An evaluation module is used to compare the characteristic monitoring factor with the standard characteristic factor of the target of interest, and generate an abnormal evaluation value corresponding to the target of interest according to the comparison result; The control module is used to determine whether the corresponding target of interest is abnormal based on the abnormal evaluation value, and if abnormal, generate a control instruction.

[0021] In some embodiments of the present application, multiple monitoring areas are established according to the coal sample storage and access process, and monitoring parameters of each monitoring area are set, including: Divide the coal sample storage and retrieval process into a plurality of coal sample storage and retrieval sub-processes, and set the operation area of ​​each coal sample storage and retrieval sub-process as a monitoring area; Obtain the historical operating parameters and process important coefficients of each coal sample storage and retrieval sub-process; Generate various fraud behaviors in the corresponding coal sample storage and access sub-process according to historical operation parameters, and generate the influence coefficient of the corresponding fraud behavior according to the occurrence frequency of each fraud behavior and the influence degree of the corresponding fraud behavior; Generate a comprehensive influence coefficient according to the influence coefficients of all fraudulent behaviors in the same coal sample storage and access sub-process, and generate a monitoring evaluation value of the current coal sample storage and access sub-process in combination with the process importance coefficient of the corresponding coal sample storage and access sub-process; The calculation formula of the monitoring evaluation value is: ; Among them, K is the monitoring evaluation value, a1 is the weight coefficient of the comprehensive influence coefficient, z1 is the first monitoring evaluation conversion coefficient, Gi is the influence coefficient of the i-th fraud behavior, f is the number of all fraud behaviors in the current coal sample storage and access sub-process, a2 is the weight coefficient of the process influence coefficient, z2 is the second monitoring evaluation conversion coefficient, and r is the process importance coefficient; The monitoring parameters of the corresponding monitoring area are set according to the monitoring evaluation value of each coal sample storage and access sub-process, and the monitoring parameters include the number of monitoring points and the monitoring time interval between adjacent monitoring time nodes.

[0022] In this embodiment, the frequency of occurrence refers to the frequency of occurrence of each fraudulent behavior in the historical operation parameters, the degree of influence refers to the impact of each fraudulent behavior on the subsequent operation quality and operation results, and the impact coefficient is obtained by converting the frequency of occurrence and the degree of influence into numerical values ​​of the same dimension and calculating them. The greater the frequency of occurrence and the greater the degree of influence, the larger the corresponding impact coefficient, and vice versa.

[0023] In this embodiment, a monitoring evaluation value is generated based on the historical operating parameters of the coal sample storage and retrieval sub-process and the process importance coefficient, and the monitoring parameters of the monitoring area are set according to the monitoring evaluation value, thereby improving the monitoring accuracy of the remote monitoring system and reducing the fraud risk of the coal sample storage and retrieval system. Potential problems in the coal sample storage and retrieval process can be discovered and optimized in a timely manner, thereby improving the management efficiency of the coal sample storage and retrieval system.

[0024] In some embodiments of the present application, the monitoring parameters of the corresponding monitoring area are set according to the monitoring evaluation value of each coal sample storage and access sub-process, including: Presetting a first preset monitoring evaluation value interval, a second preset monitoring evaluation value interval, a third preset monitoring evaluation value interval and a fourth preset monitoring evaluation value interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the first preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the first preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the fourth preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the second preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the second preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the third preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the third preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the third preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the second preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access subprocess is in the fourth preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the fourth preset monitoring point number, and the monitoring time interval between adjacent monitoring time nodes is set to the first preset monitoring time interval.

[0025] In this embodiment, the first preset monitoring evaluation value interval is less than the second preset monitoring evaluation value interval, less than the third preset monitoring evaluation value interval, less than the fourth preset monitoring evaluation value interval; the first preset monitoring point number is less than the second preset monitoring point number, less than the third preset monitoring point number, less than the fourth preset monitoring point number; the first preset monitoring time interval is less than the second preset monitoring time interval, less than the third preset monitoring time interval, less than the fourth preset monitoring time interval.

[0026] In this embodiment, when the monitoring evaluation value is larger, it means that the comprehensive impact coefficient of the fraud behavior in the coal sample storage and retrieval sub-process corresponding to the current monitoring area is larger and the process importance coefficient is larger, then the number of monitoring points that should be set should be more, and the monitoring time interval between adjacent monitoring time nodes of the monitoring points should be smaller, so as to achieve accurate monitoring of subsequent operating parameters of the corresponding monitoring area, reduce the risk of fraud, and timely discover potential problems in the coal sample storage and retrieval process.

[0027] In some embodiments of the present application, target extraction is performed on the real-time monitoring information of the monitoring area based on the target of interest to obtain the target monitoring factor, including: According to the preset operation parameters of the coal sample storage and access sub-process corresponding to each monitoring area, the focus targets of the corresponding monitoring area are set, and the focus targets include equipment, environment and operation behavior; The real-time monitoring information includes real-time monitoring video and real-time monitoring data; According to the focus target of each monitoring area, the real-time monitoring information of the corresponding monitoring area is extracted. When the focus target is equipment or environment, the monitoring data of the equipment or environment in the actual operation process in the real-time monitoring data is obtained, and the monitoring data is preprocessed and standardized to obtain multiple categories of target monitoring factors of the equipment or environment; When the target of attention is the operation behavior, image frames with the operation behavior in the real-time monitoring video are obtained, and after image preprocessing, the clarity of the image frames after image preprocessing is judged, and the image frames with clarity greater than the preset clarity threshold are set as the target monitoring factors of the operation behavior.

[0028] In this embodiment, data preprocessing includes unifying time units, removing outliers, filling missing values ​​and data cleaning processing. Standardization processing classifies monitoring data according to data categories and standardizes monitoring data of the same data category to improve the quality and credibility of monitoring data, laying the foundation for subsequent data analysis and feature data extraction. Image preprocessing includes coarse extraction of image frames using hierarchical analysis method, followed by histogram feature comparison to remove redundant frames, improving the accuracy and efficiency of target recognition, and also includes grayscale, denoising, normalization, etc., to improve the image quality of image frames, laying the foundation for subsequent image analysis and feature extraction.

[0029] In this embodiment, the focus target of each monitoring area is set in advance, and the target monitoring data or target monitoring image corresponding to the focus target is extracted from the real-time monitoring video or real-time monitoring data, and the target monitoring data and target monitoring image are set as the target monitoring factors of the equipment or environment, and the operating behavior, respectively, to ensure the validity and accuracy of the target monitoring information factors, lay the foundation for subsequent analysis and feature extraction, and improve the monitoring accuracy and efficiency during the operation of the coal sample storage and retrieval system.

[0030] In some embodiments of the present application, the target monitoring factors of the same target of interest are analyzed, and the characteristic monitoring factors of the target of interest are determined according to the analysis results, including: When the target of interest is a device, target monitoring factors of multiple categories of the corresponding device are obtained; Pre-set multiple equipment evaluation indicators of the current equipment in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding equipment and equipment evaluation indicators, and set the target monitoring factor whose correlation with each equipment evaluation indicator is greater than a preset first correlation threshold as the characteristic monitoring factor of the corresponding equipment evaluation indicator; The characteristic monitoring factors of all equipment evaluation indicators are set as the characteristic monitoring factors of the corresponding equipment; When the target of interest is the environment, multiple categories of target monitoring factors corresponding to the environment are obtained; Pre-set multiple environmental evaluation indicators in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding environments and environmental evaluation indicators, and set the target monitoring factor whose correlation with each environmental evaluation indicator is greater than a preset second correlation threshold as the characteristic monitoring factor of the corresponding environmental evaluation indicator; The characteristic monitoring factors of all environmental assessment indicators are set as the characteristic monitoring factors of the corresponding environment; When the target of attention is an operation behavior, the target monitoring factor corresponding to the operation behavior is obtained; The target monitoring factors are classified according to the monitoring point sources of the target monitoring factors of the corresponding operation behaviors to obtain target monitoring factors of multiple categories, a first monitoring time sequence is generated according to the monitoring time nodes of the target monitoring factors of each category, and the target monitoring factors of all categories are sorted to obtain a target monitoring factor sequence; Generate a second monitoring time sequence according to a preset operation path of the operation behavior in the corresponding monitoring area and sort the monitoring points involved to obtain a monitoring point operation sequence; Mapping the monitoring point operation sequence with the target monitoring factor sequence to determine whether the monitoring point and the target monitoring factor at the same position in the monitoring point operation sequence and the target monitoring factor sequence correspond to each other; If so, determine whether the critical target monitoring factors of adjacent categories in the target monitoring factor sequence meet the splicing rule. If the edge area ratio of the critical target monitoring factors of the adjacent categories meets the standard edge area ratio and the edge area dispersion ratio meets the standard edge area dispersion ratio, then determine that the critical target monitoring factors of the adjacent categories meet the splicing rule, and splice the critical target monitoring factors of the adjacent categories. If the critical target monitoring factors of the adjacent categories do not meet the splicing rule, screen the target monitoring factors of the adjacent categories until the target monitoring factors of the adjacent categories meet the splicing rule, and achieve the splicing of all the target monitoring factors of the adjacent categories, and obtain a complete operation splicing image of the operation behavior of the current monitoring area, and set the complete operation splicing image as the characteristic monitoring factor of the corresponding operation behavior; If not, filter out the non-corresponding monitoring points and target monitoring factors, and replace the non-corresponding target monitoring factors with the target monitoring factors of the non-corresponding monitoring points to obtain a replaced target monitoring factor sequence, and determine whether the critical target monitoring factors of adjacent categories in the replaced target monitoring factor sequence meet the splicing rules. If so, obtain a complete operation splicing image of the operation behavior in the current monitoring area and set it as the characteristic monitoring factor of the corresponding operation behavior.

[0031] In this embodiment, multiple categories of target monitoring factors of equipment include target monitoring data of operating status category, target monitoring data of operating efficiency category, and target monitoring data of operating energy consumption category, etc., equipment evaluation indicators include equipment operating status evaluation indicators, equipment operating efficiency evaluation indicators, equipment remaining life evaluation indicators, etc., and the equipment evaluation indicators are set in advance according to the equipment-related parameters that affect the coal sample storage and retrieval operation effect in the current monitoring area, and multiple categories of target control factors of the environment include target monitoring data of temperature category, target monitoring data of humidity category, target monitoring data of air pressure category, etc., and environmental evaluation indicators include environmental temperature evaluation indicators, environmental humidity evaluation indicators, environmental pressure evaluation indicators, etc., and the environmental evaluation indicators are set in advance according to the environmental-related parameters that affect the coal sample storage and retrieval operation effect in the current monitoring area.

[0032] In this embodiment, the critical target monitoring factor refers to the image frame of the target monitoring factor collected at the last monitoring time node of each monitoring point, the edge area refers to 1 / 3 of the image frame corresponding to each target monitoring factor, the edge area area ratio refers to the ratio of the edge area of ​​the critical target monitoring factor of the adjacent category to the total area, and the edge area dispersion ratio is compared with the edge area ratio of the critical target monitoring factors of the adjacent categories. The standard edge area area ratio is set according to the edge area area ratio of the spliced ​​target monitoring factors, and the standard edge area dispersion ratio is set according to the edge area dispersion ratio of the spliced ​​target monitoring factors.

[0033] In this embodiment, by screening out characteristic monitoring factors of the target of interest, the amount of data processing and analysis is reduced, accurate monitoring of specific equipment, environment and operating behaviors in the coal sample storage and retrieval process is achieved, the risk of fraud is reduced, and optimized regulation in the coal sample storage and retrieval process is achieved.

[0034] In some embodiments of the present application, the characteristic monitoring factor is compared with the standard characteristic factor of the target of interest, and an abnormal evaluation value corresponding to the target of interest is generated according to the comparison result, including: When the target of attention is equipment or environment, the characteristic monitoring factors are compared with the corresponding standard characteristic factors to obtain characteristic difference values, and the abnormal evaluation value of the equipment or environment is generated according to the characteristic difference values; When the target of interest is an operation behavior, a standard complete operation stitching image of the current operation behavior is generated according to the monitoring points and monitoring time nodes of the corresponding monitoring area, and the standard complete operation stitching image is divided into multiple standard stitching sub-images according to the operation process of the current operation behavior, and the standard stitching sub-images of all operation processes are set as the standard characteristic factors of the current operation behavior; The characteristic monitoring factors of the operating behavior in each monitoring area are divided into multiple characteristic monitoring sub-factors according to the operation process of the current operating behavior. The characteristic monitoring sub-factors are compared with the standard characteristic factors of the same operation process to obtain the degree of behavioral deviation, and the abnormal evaluation value of the current operating behavior is generated according to the degree of behavioral deviation.

[0035] In some embodiments of the present application, when the target of interest is a device, the characteristic difference value is compared with a preset first characteristic difference value threshold, a characteristic monitoring factor whose characteristic difference value is greater than the preset first characteristic difference value threshold is obtained and set as an abnormal characteristic monitoring factor, and an abnormal evaluation value of the device is generated according to the number of abnormal characteristic monitoring factors and the abnormal characteristic values ​​of the abnormal characteristic monitoring factors; ; Among them, Y1 is the abnormal evaluation value of the equipment, n1 is the number of abnormal characteristic monitoring factors of the current equipment, and m1 is the total number of characteristic monitoring factors of the current equipment. is the abnormal characteristic value of the cth abnormal characteristic monitoring factor of the current device; When the target of interest is the environment, the feature difference value is compared with a preset second feature difference value threshold, a feature monitoring factor whose feature difference value is greater than the preset second feature difference value threshold is obtained and set as an abnormal feature monitoring factor, and an abnormal assessment value of the environment is generated according to the number of abnormal feature monitoring factors and the abnormal feature values ​​of the abnormal feature monitoring factors; ; Among them, Y2 is the abnormal evaluation value of the environment, n2 is the number of abnormal characteristic monitoring factors of the current environment, and m2 is the total number of characteristic monitoring factors of the current environment. is the abnormal characteristic value of the sth abnormal characteristic monitoring factor of the current environment; When the target of attention is an operation behavior, the behavior deviation degree is compared with a preset behavior deviation degree threshold, an abnormal operation behavior whose behavior deviation degree is greater than the preset behavior deviation degree threshold is obtained, and an abnormal evaluation value of the operation behavior is generated according to the abnormal degree of the abnormal operation behavior; ; Among them, Y3 is the abnormal evaluation value of the operation behavior, n3 is the number of abnormal operation behaviors, is the abnormal degree of the vth abnormal operation behavior, is the weight coefficient of the vth abnormal operation behavior.

[0036] In this embodiment, the abnormal characteristic value of the abnormal characteristic monitoring factor of the equipment = the characteristic difference value - the preset first characteristic difference value threshold, the abnormal characteristic value of the abnormal characteristic monitoring factor of the environment = the characteristic difference value - the preset second characteristic difference value threshold, the abnormality degree = the behavior deviation degree - the preset behavior deviation degree threshold, and the weight coefficient of the abnormal operation behavior is set according to the importance and impact of the work process corresponding to the abnormal operation behavior.

[0037] In this embodiment, by calculating the abnormal evaluation values ​​of the equipment, environment and operating behavior respectively, the abnormal focus targets in each monitoring area are obtained, the abnormal focus targets are discovered in time, and the corresponding control strategies are configured for the abnormal focus targets, so as to realize accurate monitoring of the coal sample storage and retrieval system, truly reflect the process of the coal sample storage and retrieval process, reduce the risk of fraud, optimize the operating behavior and equipment and environmental status, and improve the management efficiency of the coal sample storage and retrieval system.

[0038] In some embodiments of the present application, judging whether the corresponding target of interest is abnormal according to the abnormality evaluation value includes: Pre-set abnormal assessment value thresholds; If the abnormal evaluation value is less than the abnormal evaluation value threshold, it is determined that the current target of interest has no abnormality; If the abnormality assessment value is greater than the abnormality assessment value threshold, the current focus target is determined to be abnormal.

[0039] In some embodiments of the present application, if an abnormality occurs, a control instruction is generated, including: If the target of the abnormality is a device, a first preset control strategy library in the current monitoring area is selected, the first preset control strategy library includes a number of preset abnormal feature values ​​of the device, and each preset abnormal feature value is associated with a specific preset control strategy, the abnormal feature monitoring factor and the abnormal feature value of the device are determined, the similarity between the preset abnormal feature value of the current device and the abnormal feature value of the abnormal feature monitoring factor in the first preset control strategy library is calculated, the preset control strategy of the abnormal feature value with the greatest similarity is set as the control strategy of the abnormal feature monitoring factor of the current device, and a control instruction is generated; If the abnormal target of concern is the environment, select a second preset control strategy library of the current monitoring area, the second preset control strategy library includes a number of preset abnormal feature values ​​of the environment, and each preset abnormal feature value is associated with a specific preset control strategy, determine the abnormal feature monitoring factor and the abnormal feature value of the environment, calculate the similarity between the preset abnormal feature value in the second preset control strategy library and the abnormal feature value of the abnormal feature monitoring factor, set the preset control strategy of the preset abnormal feature value with the greatest similarity as the control strategy of the abnormal feature monitoring factor of the current environment, and generate a control instruction; If the abnormal target of concern is an operation behavior, select a third preset control strategy library for the current monitoring area, the third preset control strategy library includes several preset abnormality levels of the operation behavior, and each preset abnormality level is associated with a specific preset control strategy, determine the abnormal operation behavior and the abnormality level of the operation behavior, calculate the similarity between the preset behavior abnormality level in the third preset control strategy library and the abnormality level of the abnormal operation behavior, set the preset control strategy with the preset abnormality level with the greatest similarity as the control strategy for the abnormal operation behavior of the current operation behavior, and generate a control instruction.

[0040] In this embodiment, the first preset control strategy library, the second preset control strategy library, and the third preset control strategy library are respectively constructed based on the historical control logs of the equipment in the corresponding monitoring area, the historical control logs of the environment, and the historical control logs of the operation behavior.

[0041] In this embodiment, abnormal evaluation values ​​are calculated for the equipment, environment, and operating behavior to determine whether an abnormality exists. If an abnormality exists, the corresponding control strategy is determined, problems in the coal sample storage and retrieval process are discovered in time and corresponding control strategies are adopted to achieve accurate monitoring of the coal sample storage and retrieval system, reduce fraud in the coal sample storage and retrieval system, and improve the management efficiency of the coal sample storage and retrieval system.

[0042] In some embodiments of the present application, a smart coal sample storage and remote monitoring method is also included: Step S201: establishing multiple monitoring areas according to the coal sample storage and access process, setting monitoring parameters for each monitoring area, and obtaining real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; Step S202: presetting a target of interest, extracting a target from the real-time monitoring information of the monitoring area based on the target of interest, obtaining a target monitoring factor, analyzing the target monitoring factor of the same target of interest, and determining a characteristic monitoring factor of the target of interest according to the analysis result; Step S203: comparing the characteristic monitoring factor with the standard characteristic factor of the target of interest, and generating an abnormality assessment value corresponding to the target of interest according to the comparison result; Step S204: determine whether the corresponding target of interest is abnormal based on the abnormal evaluation value, and if abnormal, generate a control instruction.

[0043] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent coal sample storage and access remote monitoring system, characterized in that: include: The acquisition module is used to establish multiple monitoring areas according to the coal sample storage and access process, set monitoring parameters for each monitoring area, and obtain real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; A determination module is used to pre-set a target of interest, extract the target from the real-time monitoring information of the monitoring area based on the target of interest, obtain the target monitoring factor, analyze the target monitoring factor of the same target of interest, and determine the characteristic monitoring factor of the target of interest based on the analysis result; An evaluation module is used to compare the characteristic monitoring factor with the standard characteristic factor of the target of interest, and generate an abnormal evaluation value corresponding to the target of interest based on the comparison result; The control module is used to determine whether the corresponding target of interest is abnormal based on the abnormal evaluation value, and if abnormal, generate a control instruction.

2. The intelligent coal sample storage and access remote monitoring system according to claim 1, characterized in that: Establish multiple monitoring areas according to the coal sample storage and retrieval process, and set monitoring parameters for each monitoring area, including: Divide the coal sample storage and retrieval process into a plurality of coal sample storage and retrieval sub-processes, and set the operation area of ​​each coal sample storage and retrieval sub-process as a monitoring area; Obtain the historical operating parameters and process important coefficients of each coal sample storage and retrieval sub-process; Generate various fraud behaviors in the corresponding coal sample storage and access sub-process according to historical operation parameters, and generate the influence coefficient of the corresponding fraud behavior according to the occurrence frequency of each fraud behavior and the influence degree of the corresponding fraud behavior; Generate a comprehensive influence coefficient according to the influence coefficients of all fraudulent behaviors in the same coal sample storage and access sub-process, and generate a monitoring evaluation value of the current coal sample storage and access sub-process in combination with the process importance coefficient of the corresponding coal sample storage and access sub-process; The calculation formula of the monitoring evaluation value is: ； Among them, K is the monitoring evaluation value, a1 is the weight coefficient of the comprehensive influence coefficient, z1 is the first monitoring evaluation conversion coefficient, Gi is the influence coefficient of the i-th fraud behavior, f is the number of all fraud behaviors in the current coal sample storage and access sub-process, a2 is the weight coefficient of the process influence coefficient, z2 is the second monitoring evaluation conversion coefficient, and r is the process importance coefficient; The monitoring parameters of the corresponding monitoring area are set according to the monitoring evaluation value of each coal sample storage and access sub-process, and the monitoring parameters include the number of monitoring points and the monitoring time interval between adjacent monitoring time nodes.

3. The intelligent coal sample storage and access remote monitoring system according to claim 2, characterized in that: According to the monitoring evaluation value of each coal sample storage and access sub-process, the monitoring parameters of the corresponding monitoring area are set, including: Presetting a first preset monitoring evaluation value interval, a second preset monitoring evaluation value interval, a third preset monitoring evaluation value interval and a fourth preset monitoring evaluation value interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the first preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the first preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the fourth preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the second preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the second preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the third preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access sub-process is within the third preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the third preset number of monitoring points, and the monitoring time interval between adjacent monitoring time nodes is set to the second preset monitoring time interval; When the monitoring evaluation value of the coal sample storage and access subprocess is in the fourth preset monitoring evaluation value interval, the number of monitoring points in the corresponding monitoring area is set to the fourth preset monitoring point number, and the monitoring time interval between adjacent monitoring time nodes is set to the first preset monitoring time interval.

4. The intelligent coal sample storage and access remote monitoring system according to claim 3, characterized in that: Based on the target of interest, the real-time monitoring information of the monitoring area is extracted to obtain the target monitoring factors, including: According to the preset operation parameters of the coal sample storage and access sub-process corresponding to each monitoring area, the focus targets of the corresponding monitoring area are set, and the focus targets include equipment, environment and operation behavior; The real-time monitoring information includes real-time monitoring video and real-time monitoring data; According to the focus target of each monitoring area, the real-time monitoring information of the corresponding monitoring area is extracted. When the focus target is equipment or environment, the monitoring data of the equipment or environment in the actual operation process in the real-time monitoring data is obtained, and the monitoring data is preprocessed and standardized to obtain multiple categories of target monitoring factors of the equipment or environment; When the target of attention is the operation behavior, image frames with the operation behavior in the real-time monitoring video are obtained, and after image preprocessing, the clarity of the image frames after image preprocessing is judged, and the image frames with clarity greater than the preset clarity threshold are set as the target monitoring factors of the operation behavior.

5. The intelligent coal sample storage and access remote monitoring system according to claim 4, characterized in that: Analyze the target monitoring factors of the same target and determine the characteristic monitoring factors of the target based on the analysis results, including: When the target of interest is a device, target monitoring factors of multiple categories of the corresponding device are obtained; Pre-set multiple equipment evaluation indicators of the current equipment in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding equipment and equipment evaluation indicators, and set the target monitoring factor whose correlation with each equipment evaluation indicator is greater than a preset first correlation threshold as the characteristic monitoring factor of the corresponding equipment evaluation indicator; The characteristic monitoring factors of all equipment evaluation indicators are set as the characteristic monitoring factors of the corresponding equipment; When the target of interest is the environment, multiple categories of target monitoring factors corresponding to the environment are obtained; Pre-set multiple environmental evaluation indicators in the corresponding monitoring area; Calculate the correlation between target monitoring factors of multiple categories of corresponding environments and environmental evaluation indicators, and set the target monitoring factor whose correlation with each environmental evaluation indicator is greater than a preset second correlation threshold as the characteristic monitoring factor of the corresponding environmental evaluation indicator; The characteristic monitoring factors of all environmental assessment indicators are set as the characteristic monitoring factors of the corresponding environment; When the target of attention is an operation behavior, the target monitoring factor corresponding to the operation behavior is obtained; The target monitoring factors are classified according to the monitoring point sources of the target monitoring factors of the corresponding operation behaviors to obtain target monitoring factors of multiple categories, a first monitoring time sequence is generated according to the monitoring time nodes of the target monitoring factors of each category, and the target monitoring factors of all categories are sorted to obtain a target monitoring factor sequence; Generate a second monitoring time sequence according to a preset operation path of the operation behavior in the corresponding monitoring area and sort the monitoring points involved to obtain a monitoring point operation sequence; Mapping the monitoring point operation sequence with the target monitoring factor sequence to determine whether the monitoring point and the target monitoring factor at the same position in the monitoring point operation sequence and the target monitoring factor sequence correspond to each other; If so, determine whether the critical target monitoring factors of adjacent categories in the target monitoring factor sequence meet the splicing rule. If the edge area ratio of the critical target monitoring factors of the adjacent categories meets the standard edge area ratio and the edge area dispersion ratio meets the standard edge area dispersion ratio, then determine that the critical target monitoring factors of the adjacent categories meet the splicing rule, and splice the critical target monitoring factors of the adjacent categories. If the critical target monitoring factors of the adjacent categories do not meet the splicing rule, screen the target monitoring factors of the adjacent categories until the target monitoring factors of the adjacent categories meet the splicing rule, and achieve the splicing of all the target monitoring factors of the adjacent categories, and obtain a complete operation splicing image of the operation behavior of the current monitoring area, and set the complete operation splicing image as the characteristic monitoring factor of the corresponding operation behavior; If not, filter out the non-corresponding monitoring points and target monitoring factors, and replace the non-corresponding target monitoring factors with the target monitoring factors of the non-corresponding monitoring points to obtain a replaced target monitoring factor sequence, and determine whether the critical target monitoring factors of adjacent categories in the replaced target monitoring factor sequence meet the splicing rules. If so, obtain a complete operation splicing image of the operation behavior in the current monitoring area and set it as the characteristic monitoring factor of the corresponding operation behavior.

6. The intelligent coal sample storage and access remote monitoring system according to claim 5, characterized in that: The characteristic monitoring factor is compared with the standard characteristic factor of the target of interest, and the abnormal evaluation value of the corresponding target of interest is generated according to the comparison result, including: When the target of attention is equipment or environment, the characteristic monitoring factors are compared with the corresponding standard characteristic factors to obtain characteristic difference values, and the abnormal evaluation value of the equipment or environment is generated according to the characteristic difference values; When the target of interest is an operation behavior, a standard complete operation stitching image of the current operation behavior is generated according to the monitoring points and monitoring time nodes of the corresponding monitoring area, and the standard complete operation stitching image is divided into multiple standard stitching sub-images according to the operation process of the current operation behavior, and the standard stitching sub-images of all operation processes are set as the standard characteristic factors of the current operation behavior; The characteristic monitoring factors of the operating behavior in each monitoring area are divided into multiple characteristic monitoring sub-factors according to the operation process of the current operating behavior. The characteristic monitoring sub-factors are compared with the standard characteristic factors of the same operation process to obtain the degree of behavioral deviation, and the abnormal evaluation value of the current operating behavior is generated according to the degree of behavioral deviation.

7. The intelligent coal sample storage and access remote monitoring system according to claim 6, characterized in that: When the target of interest is a device, the characteristic difference value is compared with a preset first characteristic difference value threshold, a characteristic monitoring factor whose characteristic difference value is greater than the preset first characteristic difference value threshold is obtained and set as an abnormal characteristic monitoring factor, and an abnormal evaluation value of the device is generated according to the number of abnormal characteristic monitoring factors and the abnormal characteristic values ​​of the abnormal characteristic monitoring factors; ； Among them, Y1 is the abnormal evaluation value of the equipment, n1 is the number of abnormal characteristic monitoring factors of the current equipment, and m1 is the total number of characteristic monitoring factors of the current equipment. is the abnormal characteristic value of the cth abnormal characteristic monitoring factor of the current device; When the target of interest is the environment, the feature difference value is compared with a preset second feature difference value threshold, a feature monitoring factor whose feature difference value is greater than the preset second feature difference value threshold is obtained and set as an abnormal feature monitoring factor, and an abnormal assessment value of the environment is generated according to the number of abnormal feature monitoring factors and the abnormal feature values ​​of the abnormal feature monitoring factors; ； Among them, Y2 is the abnormal evaluation value of the environment, n2 is the number of abnormal characteristic monitoring factors of the current environment, and m2 is the total number of characteristic monitoring factors of the current environment. is the abnormal characteristic value of the sth abnormal characteristic monitoring factor of the current environment; When the target of attention is an operation behavior, the behavior deviation degree is compared with a preset behavior deviation degree threshold, an abnormal operation behavior whose behavior deviation degree is greater than the preset behavior deviation degree threshold is obtained, and an abnormal evaluation value of the operation behavior is generated according to the abnormal degree of the abnormal operation behavior; ； Among them, Y3 is the abnormal evaluation value of the operation behavior, n3 is the number of abnormal operation behaviors, is the abnormal degree of the vth abnormal operation behavior, is the weight coefficient of the vth abnormal operation behavior.

8. The intelligent coal sample storage and access remote monitoring system according to claim 7, characterized in that: Determine whether the corresponding target of interest is abnormal based on the abnormal evaluation value, including: Pre-set abnormal assessment value thresholds; If the abnormal evaluation value is less than the abnormal evaluation value threshold, it is determined that the current target of interest has no abnormality; If the abnormality assessment value is greater than the abnormality assessment value threshold, the current focus target is determined to be abnormal.

9. The intelligent coal sample storage and access remote monitoring system according to claim 8, characterized in that: If abnormal, generate control instructions, including: If the target of the abnormality is a device, a first preset control strategy library in the current monitoring area is selected, the first preset control strategy library includes a number of preset abnormal feature values ​​of the device, and each preset abnormal feature value is associated with a specific preset control strategy, the abnormal feature monitoring factor and the abnormal feature value of the device are determined, the similarity between the preset abnormal feature value of the current device and the abnormal feature value of the abnormal feature monitoring factor in the first preset control strategy library is calculated, the preset control strategy of the abnormal feature value with the greatest similarity is set as the control strategy of the abnormal feature monitoring factor of the current device, and a control instruction is generated; If the abnormal target of concern is the environment, select a second preset control strategy library of the current monitoring area, the second preset control strategy library includes a number of preset abnormal feature values ​​of the environment, and each preset abnormal feature value is associated with a specific preset control strategy, determine the abnormal feature monitoring factor and the abnormal feature value of the environment, calculate the similarity between the preset abnormal feature value in the second preset control strategy library and the abnormal feature value of the abnormal feature monitoring factor, set the preset control strategy of the preset abnormal feature value with the greatest similarity as the control strategy of the abnormal feature monitoring factor of the current environment, and generate a control instruction; If the abnormal target of concern is an operation behavior, select a third preset control strategy library for the current monitoring area, the third preset control strategy library includes several preset abnormality levels of the operation behavior, and each preset abnormality level is associated with a specific preset control strategy, determine the abnormal operation behavior and the abnormality level of the operation behavior, calculate the similarity between the preset behavior abnormality level in the third preset control strategy library and the abnormality level of the abnormal operation behavior, set the preset control strategy with the preset abnormality level with the greatest similarity as the control strategy for the abnormal operation behavior of the current operation behavior, and generate a control instruction.

10. An intelligent coal sample storage and access remote monitoring method, characterized in that: include: Establish multiple monitoring areas according to the coal sample storage and access process, set monitoring parameters for each monitoring area, and obtain real-time monitoring information of the corresponding monitoring area according to the monitoring parameters; Preset the target of interest, extract the real-time monitoring information of the monitoring area based on the target of interest, obtain the target monitoring factor, analyze the target monitoring factor of the same target of interest, and determine the characteristic monitoring factor of the target of interest according to the analysis result; The characteristic monitoring factor is compared with the standard characteristic factor of the target of interest, and an abnormal evaluation value corresponding to the target of interest is generated according to the comparison result; According to the abnormal evaluation value, it is judged whether the corresponding target of attention is abnormal. If it is abnormal, a control instruction is generated.

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