Safety production supervision optimization method and system based on historical data

Abstract

The invention discloses a historical data-based safety production supervision optimization method and system. The method comprises the steps of obtaining a historical inspection record and an equipment historical work record corresponding to an energy production area; based on a text analysis algorithm, according to the historical inspection record, determining a plurality of concerned sub-regions in the energy production region; according to working data corresponding to the concerned sub-regions in the historical working record of the equipment, predicting region danger corresponding to each concerned sub-region; and determining a new inspection plan corresponding to the energy production area according to the area danger corresponding to each concerned sub-area and the historical inspection record. Therefore, the method can achieve the precise inspection planning based on regional risk prediction, improves the safety management and inspection efficiency of the energy production region, and reduces the potential equipment fault risk.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for optimizing production safety supervision based on historical data. Background Art

[0002] With the increasing demand for safety management in energy production areas, energy companies are placing increasing emphasis on reducing the risk of equipment failure through inspection optimization. Existing technologies typically collect inspection records and equipment operating data from energy production areas, use manual analysis or simple statistical methods to determine key inspection areas, and formulate inspection plans based on fixed cycles or experience to ensure production safety. Existing solutions lack in-depth text analysis of historical records and dynamic prediction of regional risk levels, making it difficult to accurately identify high-risk sub-areas. Commonly used, unified inspection schedules are unable to adapt to the actual risk differences in different regions, resulting in low inspection efficiency and the easy neglect of potential equipment failure hazards, which limits the safety management and operational stability of energy production areas. Clearly, existing technologies have flaws that need to be addressed urgently. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for optimizing production safety supervision based on historical data, which can realize accurate inspection planning based on regional risk prediction, improve the safety management and inspection efficiency of energy production areas, and reduce the risk of potential equipment failure.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for optimizing production safety supervision based on historical data, the method comprising: Obtain historical inspection records and equipment historical work records corresponding to energy production areas; Based on a text analysis algorithm, determining a plurality of sub-areas of interest in the energy production area according to the historical inspection records; Predicting the regional hazard corresponding to each of the sub-areas of interest based on the operating data corresponding to the sub-areas of interest in the historical operating records of the equipment; A new inspection plan corresponding to the energy production area is determined based on the regional hazards corresponding to each of the sub-areas of concern and the historical inspection records.

[0005] As an optional embodiment, in the first aspect of the present invention, the text analysis algorithm is used to determine, based on the historical inspection records, multiple sub-areas of interest in the energy production area, including: Based on text matching rules, matching a plurality of inspection record data related to the energy production area from the historical inspection records; For each inspection record data, predict the sub-area, problem severity parameter and problem type corresponding to the inspection record data; It is determined whether the problem degree parameter is greater than a first parameter threshold; if so, the sub-region and the problem type corresponding to the inspection record data are determined as the focus sub-region and the corresponding regional problem type.

[0006] As an optional embodiment, in the first aspect of the present invention, the predicting of the sub-area, problem severity parameter, and problem type corresponding to the inspection record data includes: The inspection record data is input into a trained inspection problem prediction neural network to obtain the sub-area, problem severity parameter and problem type corresponding to the inspection record data; the inspection problem prediction neural network is trained by a training data set including multiple training inspection records and corresponding area annotations, problem severity annotations and problem type annotations.

[0007] As an optional embodiment, in the first aspect of the present invention, the problem type is a failure of different components or functional failures of different energy production equipment; the energy production equipment is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine or a biomass furnace.

[0008] As an optional embodiment, in the first aspect of the present invention, predicting the regional hazard corresponding to each sub-area of ​​interest based on the operating data corresponding to the sub-area of ​​interest in the historical operating records of the equipment includes: For each of the focus sub-areas, determining all of the problem types and corresponding problem severity parameters corresponding to the focus sub-area; Determining the energy production equipment corresponding to the problem type for which the problem degree parameter is greater than a second parameter threshold as a problem equipment; Determining device operating data corresponding to each problematic device in the device historical operating record; Based on the device operating data corresponding to each of the problematic devices, the regional risk corresponding to the sub-region of interest is predicted.

[0009] As an optional embodiment, in the first aspect of the present invention, predicting the regional risk corresponding to the sub-region of interest based on the device operating data corresponding to each problematic device includes: Inputting the device operating data corresponding to each of the problem devices into a trained device anomaly prediction neural network to obtain device anomaly parameters corresponding to each of the problem devices; the device anomaly prediction neural network is trained using a training data set including a plurality of training device operating data and corresponding device anomaly annotations; Calculating the product of the problem severity parameter corresponding to each problematic device and the device abnormality parameter to obtain an abnormality characterization parameter corresponding to each problematic device; The weighted sum of the abnormal characterization parameters corresponding to all the problem devices is calculated to obtain the regional risk corresponding to the sub-region of interest.

[0010] As an optional embodiment, in the first aspect of the present invention, determining a new inspection plan corresponding to the energy production area based on the regional hazard corresponding to each of the sub-areas of interest and the historical inspection records includes: Determining the historical inspection frequency corresponding to each of the sub-areas of interest based on the historical inspection records; Determine the objective function and constraint conditions of the inspection plan based on the historical inspection frequency and the regional hazards; Based on the dynamic programming algorithm, according to the objective function and the constraint conditions, a new inspection plan corresponding to the energy production area is calculated.

[0011] As an optional embodiment, in the first aspect of the present invention, the objective function includes minimizing the total inspection frequency corresponding to all the sub-areas in the inspection plan and minimizing the inspection time corresponding to the inspection plan; the inspection time is obtained by inputting the inspection plan generated by each algorithm planning calculation into a trained inspection time prediction neural network for prediction; the constraint conditions include: The difference between the inspection frequency corresponding to each of the sub-areas of interest in the inspection plan and the historical inspection frequency is less than a difference threshold; The inspection frequency corresponding to each sub-area of ​​concern in the inspection plan is proportional to the danger of the corresponding area; In addition, the frequency difference corresponding to each of the focus sub-areas in the inspection plan is proportional to the neglect degree parameter corresponding to the focus sub-area; the neglect degree parameter is the difference between the historical inspection frequency corresponding to the focus sub-area and the reference inspection frequency; the reference inspection frequency is calculated based on the preset mathematical correspondence between the hazard and frequency and the regional hazard corresponding to the focus sub-area.

[0012] A second aspect of an embodiment of the present invention discloses a safety production supervision optimization system based on historical data, the system comprising: The acquisition module is used to obtain the historical inspection records and equipment historical work records corresponding to the energy production area; A first determination module is configured to determine a plurality of sub-areas of interest in the energy production area based on the historical inspection records based on a text analysis algorithm; a prediction module, configured to predict the regional hazard corresponding to each of the sub-areas of interest based on the operating data corresponding to the sub-areas of interest in the historical operating records of the equipment; The second determination module is used to determine a new inspection plan corresponding to the energy production area according to the regional hazard corresponding to each of the concerned sub-areas and the historical inspection records.

[0013] As an optional embodiment, in the second aspect of the present invention, the first determination module determines the specific manner of the multiple sub-areas of interest in the energy production area based on the historical inspection records based on a text analysis algorithm, including: Based on text matching rules, matching a plurality of inspection record data related to the energy production area from the historical inspection records; For each inspection record data, predict the sub-area, problem severity parameter and problem type corresponding to the inspection record data; It is determined whether the problem degree parameter is greater than a first parameter threshold; if so, the sub-region and the problem type corresponding to the inspection record data are determined as the focus sub-region and the corresponding regional problem type.

[0014] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the first determination module predicts the sub-area, problem severity parameter, and problem type corresponding to the inspection record data includes: The inspection record data is input into a trained inspection problem prediction neural network to obtain the sub-area, problem severity parameter and problem type corresponding to the inspection record data; the inspection problem prediction neural network is trained by a training data set including multiple training inspection records and corresponding area annotations, problem severity annotations and problem type annotations.

[0015] As an optional embodiment, in the second aspect of the present invention, the problem type is a failure of different components or functional failures of different energy production equipment; the energy production equipment is a solar panel, a wind turbine, a nuclear reactor, a water turbine, a geothermal pump, a coal-fired furnace, a gas turbine or a biomass furnace.

[0016] As an optional embodiment, in the second aspect of the present invention, the prediction module predicts the specific manner of the regional hazard corresponding to each sub-area of ​​interest based on the operating data corresponding to the sub-area of ​​interest in the historical operating record of the equipment, including: For each of the focus sub-areas, determining all of the problem types and corresponding problem severity parameters corresponding to the focus sub-area; Determining the energy production equipment corresponding to the problem type for which the problem degree parameter is greater than a second parameter threshold as a problem equipment; Determining device operating data corresponding to each problematic device in the device historical operating record; Based on the device operating data corresponding to each of the problematic devices, the regional risk corresponding to the sub-region of interest is predicted.

[0017] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the prediction module predicts the regional risk corresponding to the sub-region of interest based on the device operating data corresponding to each of the problem devices includes: Inputting the device operating data corresponding to each of the problem devices into a trained device anomaly prediction neural network to obtain device anomaly parameters corresponding to each of the problem devices; the device anomaly prediction neural network is trained using a training data set including a plurality of training device operating data and corresponding device anomaly annotations; Calculating the product of the problem severity parameter corresponding to each problematic device and the device abnormality parameter to obtain an abnormality characterization parameter corresponding to each problematic device; The weighted sum of the abnormal characterization parameters corresponding to all the problem devices is calculated to obtain the regional risk corresponding to the sub-region of interest.

[0018] As an optional embodiment, in the second aspect of the present invention, the second determination module determines a specific manner of a new inspection plan corresponding to the energy production area based on the regional hazard corresponding to each of the sub-areas of interest and the historical inspection records, including: Determining the historical inspection frequency corresponding to each of the sub-areas of interest based on the historical inspection records; Determine the objective function and constraint conditions of the inspection plan based on the historical inspection frequency and the regional hazards; Based on the dynamic programming algorithm, according to the objective function and the constraint conditions, a new inspection plan corresponding to the energy production area is calculated.

[0019] As an optional embodiment, in the second aspect of the present invention, the objective function includes minimizing the total inspection frequency corresponding to all the sub-areas in the inspection plan and minimizing the inspection time corresponding to the inspection plan; the inspection time is obtained by inputting the inspection plan generated by each algorithm planning calculation into a trained inspection time prediction neural network for prediction; the constraint conditions include: The difference between the inspection frequency corresponding to each of the sub-areas of interest in the inspection plan and the historical inspection frequency is less than a difference threshold; The inspection frequency corresponding to each sub-area of ​​concern in the inspection plan is proportional to the danger of the corresponding area; In addition, the frequency difference corresponding to each of the focus sub-areas in the inspection plan is proportional to the neglect degree parameter corresponding to the focus sub-area; the neglect degree parameter is the difference between the historical inspection frequency corresponding to the focus sub-area and the reference inspection frequency; the reference inspection frequency is calculated based on the preset mathematical correspondence between the hazard and frequency and the regional hazard corresponding to the focus sub-area.

[0020] A third aspect of the present invention discloses another safety production supervision optimization system based on historical data, the system comprising: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the safety production supervision optimization method based on historical data disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the safety production supervision optimization method based on historical data disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention obtains historical inspection records and historical equipment work records of energy production areas and uses text analysis algorithms to identify multiple sub-areas of concern. It predicts the dangers of each sub-area in combination with corresponding work data, and formulates new inspection plans based on the dangers and inspection records of each sub-area. This can achieve accurate inspection planning based on regional risk prediction, improve safety management and inspection efficiency in energy production areas, and reduce the risk of potential equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a method for optimizing production safety supervision based on historical data disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a structural diagram of a production safety supervision optimization system based on historical data disclosed in an embodiment of the present invention.

[0026] Figure 3This is a structural diagram of another historical data-based production safety supervision optimization system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0029] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] This invention discloses a method and system for optimizing production safety supervision based on historical data. By acquiring historical inspection records and equipment operation records for energy production areas and using text analysis algorithms to identify multiple sub-areas of concern, the system then predicts the hazards of each sub-area based on the corresponding operation data. New inspection plans are then formulated based on the hazards and inspection records for each sub-area. This enables precise inspection planning based on regional risk prediction, improves safety management and inspection efficiency in energy production areas, and reduces the risk of potential equipment failures. These are described in detail below.

[0031] Example 1 See also Figure 1 , Figure 1 This is a flow chart of a method for optimizing production safety supervision based on historical data disclosed in an embodiment of the present invention. Figure 1The safety production supervision optimization method based on historical data described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the safety production supervision optimization method based on historical data may include the following operations: 101. Obtain historical inspection records and equipment historical work records corresponding to the energy production area.

[0032] 102. Based on text analysis algorithms and historical inspection records, multiple sub-areas of concern in the energy production area are identified. 103. Predict the regional hazard corresponding to each sub-area of ​​concern based on the working data corresponding to the sub-area of ​​concern in the historical working records of the equipment. 104. Determine a new inspection plan for the energy production area based on the regional hazards corresponding to each sub-area of ​​concern and historical inspection records.

[0033] It can be seen that the above-mentioned embodiment of the invention obtains the historical inspection records and historical work records of the equipment in the energy production area and uses the text analysis algorithm to identify multiple sub-areas of concern, combines the corresponding work data to predict the dangers of each sub-area, and formulates a new inspection plan based on the dangers and inspection records of each sub-area, thereby realizing accurate inspection planning based on regional risk prediction, improving the safety management and inspection efficiency of the energy production area, and reducing the risk of potential equipment failure.

[0034] As an optional embodiment, in the above steps, based on the text analysis algorithm and according to the historical inspection records, multiple sub-areas of interest in the energy production area are determined, including: Based on text matching rules, multiple inspection record data related to energy production areas are matched from historical inspection records; For each inspection record data, predict the sub-area, problem severity parameter and problem type corresponding to the inspection record data; It is determined whether the problem degree parameter is greater than a first parameter threshold. If so, the sub-region and the problem type corresponding to the inspection record data are determined as the focus sub-region and the corresponding regional problem type.

[0035] It can be seen that through the above optional embodiments, by extracting inspection record data related to the energy production area from historical inspection records based on text matching rules, the sub-area, problem degree parameter and problem type corresponding to each record are predicted, and the sub-area and problem type with problem degree parameters exceeding the first threshold are screened out as the sub-area of ​​interest and regional problem type, thereby realizing accurate sub-area screening based on the severity of inspection problems, and improving the accuracy of risk identification in energy production areas and the efficiency of inspection resource allocation.

[0036] As an optional embodiment, in the above step, predicting the sub-area, problem severity parameter, and problem type corresponding to the inspection record data includes: The inspection record data is input into the trained inspection problem prediction neural network to obtain the sub-area, problem severity parameter and problem type corresponding to the inspection record data; the inspection problem prediction neural network is trained by a training data set including multiple training inspection records and corresponding area annotations, problem severity annotations and problem type annotations.

[0037] It can be seen that through the above optional embodiments, accurate sub-region risk identification is achieved through neural network-based prediction, so as to facilitate the subsequent implementation of accurate inspection planning based on regional risk prediction, improve the safety management and inspection efficiency of energy production areas, and reduce the risk of potential equipment failure.

[0038] As an optional embodiment, in the above steps, the problem type is different component failure or functional failure of different energy production equipment; the energy production equipment is solar panels, wind turbines, nuclear reactors, water turbines, geothermal pumps, coal-fired furnaces, gas turbines or biomass furnaces.

[0039] It can be seen that through the above optional embodiments, the content of the problem type is limited, and the problem characteristics of the inspection records are comprehensively characterized to assist in realizing accurate inspection planning based on regional risk prediction, improve the safety management and inspection efficiency of energy production areas, and reduce the risk of potential equipment failure.

[0040] As an optional embodiment, in the above step, predicting the regional hazard corresponding to each sub-area of ​​interest based on the operating data corresponding to the sub-area of ​​interest in the historical operating records of the equipment includes: For each sub-area of ​​interest, determining all problem types and corresponding problem severity parameters corresponding to the sub-area of ​​interest; Determine the energy production equipment corresponding to the problem type whose problem degree parameter is greater than the second parameter threshold as the problem equipment; Determine the equipment working data corresponding to each problematic equipment in the equipment historical working records; Based on the equipment working data corresponding to each problematic equipment, the regional risk corresponding to the sub-area of ​​concern is predicted.

[0041] It can be seen that through the above optional embodiments, by identifying the problem type and problem severity parameters of each sub-area of ​​concern and screening out energy production equipment corresponding to the problem type with a problem severity exceeding the second threshold as problem equipment, its working data is extracted from the equipment's historical working records to predict the regional risk of the sub-area of ​​concern, thereby achieving accurate risk prediction based on the severity of equipment problems and working data, improving the safety management and targeted inspection of energy production areas, and reducing the risk of equipment failure.

[0042] As an optional embodiment, in the above step, predicting the regional risk corresponding to the sub-region of interest based on the device operating data corresponding to each problematic device includes: Inputting the device operating data corresponding to each problematic device into a trained device anomaly prediction neural network to obtain device anomaly parameters corresponding to each problematic device; optionally, the device anomaly prediction neural network is trained using a training data set comprising a plurality of training device operating data and corresponding device anomaly annotations; Calculate the product of the problem severity parameter and the device abnormality parameter corresponding to each problem device to obtain the abnormality characterization parameter corresponding to each problem device; The weighted sum of the abnormal characterization parameters corresponding to all problematic devices is calculated to obtain the regional risk corresponding to the sub-region of concern.

[0043] It can be seen that through the above optional embodiments, the equipment working data of the problem equipment in each sub-area of ​​concern is input into the trained equipment anomaly prediction neural network to obtain the equipment anomaly parameters, and the anomaly characterization parameters are calculated in combination with the problem degree parameters and weighted summation is performed to determine the regional risk of the sub-area, thereby realizing accurate risk assessment based on equipment anomalies and working data, improving the pertinence and safety of the inspection plan of the energy production area, and reducing the risk of equipment failures and production accidents.

[0044] As an optional embodiment, in the above steps, determining a new inspection plan corresponding to the energy production area based on the regional hazards corresponding to each sub-area of ​​concern and historical inspection records includes: Determine the historical inspection frequency corresponding to each sub-area of ​​concern based on historical inspection records; Determine the objective function and constraints of inspection planning based on historical inspection frequency and regional hazards; Based on the dynamic programming algorithm, a new inspection plan corresponding to the energy production area is calculated according to the objective function and constraints.

[0045] It can be seen that through the above optional embodiments, the inspection frequency of each sub-area of ​​concern is determined by analyzing historical inspection records and the objective function and restriction conditions of the inspection plan are constructed in combination with the regional danger level. The new inspection plan for the energy production area is obtained based on the dynamic programming algorithm, thereby realizing optimized inspection scheduling based on risk and historical frequency, improving the efficiency and resource utilization of safety management in the energy production area, and reducing the risk of potential equipment failure.

[0046] As an optional embodiment, in the above steps, the objective function includes minimizing the total inspection frequency corresponding to all sub-areas in the inspection plan and minimizing the inspection time corresponding to the inspection plan; the inspection time is obtained by inputting the inspection plan generated by each algorithm planning calculation into the trained inspection time prediction neural network for prediction; the constraint conditions include: The difference between the inspection frequency of each sub-area of ​​interest in the inspection plan and the historical inspection frequency is less than the difference threshold; The inspection frequency of each sub-area of ​​concern in the inspection plan is proportional to the danger of the corresponding area; In addition, the frequency difference corresponding to each sub-area of ​​concern in the inspection plan is proportional to the neglect degree parameter corresponding to the sub-area of ​​concern; optionally, the neglect degree parameter is the difference between the historical inspection frequency corresponding to the sub-area of ​​concern and the reference inspection frequency; the reference inspection frequency is calculated based on the preset mathematical correspondence between the hazard and frequency and the regional hazard corresponding to the sub-area of ​​concern.

[0047] It can be seen that through the above optional embodiments, the details of the objective function and the restriction conditions are defined, so as to analyze the historical inspection records to determine the inspection frequency of each sub-area of ​​concern and construct the objective function in combination with the regional hazards to achieve the minimization of the total inspection frequency and inspection time. At the same time, the conditions such as the difference between the inspection frequency and the historical frequency is less than the threshold, the frequency is proportional to the regional hazard, and the frequency difference is proportional to the neglect degree parameter are restricted, so as to calculate the new inspection plan based on the dynamic programming algorithm, thereby realizing optimized inspection scheduling based on risk and historical data, improving the safety management efficiency and resource utilization of the energy production area, and significantly reducing the risk of equipment failure.

[0048] Example 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a safety production supervision optimization system based on historical data disclosed in an embodiment of the present invention. Figure 2 The safety production supervision optimization system based on historical data described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the safety production supervision optimization system based on historical data may include: The acquisition module 201 is used to acquire historical inspection records and equipment historical operation records corresponding to the energy production area.

[0049] The first determination module 202 is configured to determine a plurality of sub-areas of interest in the energy production area based on a text analysis algorithm and according to historical inspection records. The prediction module 203 is configured to predict the regional hazard corresponding to each sub-region of interest based on the operating data corresponding to the sub-region of interest in the historical operating records of the equipment. The second determining module 204 is configured to determine a new inspection plan corresponding to the energy production area according to the regional hazards corresponding to each sub-area of ​​concern and historical inspection records.

[0050] It can be seen that the above-mentioned embodiment of the invention obtains the historical inspection records and historical work records of the equipment in the energy production area and uses the text analysis algorithm to identify multiple sub-areas of concern, combines the corresponding work data to predict the dangers of each sub-area, and formulates a new inspection plan based on the dangers and inspection records of each sub-area, thereby realizing accurate inspection planning based on regional risk prediction, improving the safety management and inspection efficiency of the energy production area, and reducing the risk of potential equipment failure.

[0051] As an optional embodiment, the first determination module determines the specific manner of multiple sub-areas of interest in the energy production area based on a text analysis algorithm and historical inspection records, including: Based on text matching rules, multiple inspection record data related to energy production areas are matched from historical inspection records; For each inspection record data, predict the sub-area, problem severity parameter and problem type corresponding to the inspection record data; It is determined whether the problem degree parameter is greater than a first parameter threshold. If so, the sub-region and the problem type corresponding to the inspection record data are determined as the focus sub-region and the corresponding regional problem type.

[0052] It can be seen that through the above optional embodiments, by extracting inspection record data related to the energy production area from historical inspection records based on text matching rules, the sub-area, problem degree parameter and problem type corresponding to each record are predicted, and the sub-area and problem type with problem degree parameters exceeding the first threshold are screened out as the sub-area of ​​interest and regional problem type, thereby realizing accurate sub-area screening based on the severity of inspection problems, and improving the accuracy of risk identification in energy production areas and the efficiency of inspection resource allocation.

[0053] As an optional embodiment, the specific manner in which the first determination module predicts the sub-area, problem severity parameter, and problem type corresponding to the inspection record data includes: The inspection record data is input into the trained inspection problem prediction neural network to obtain the sub-area, problem severity parameter and problem type corresponding to the inspection record data; the inspection problem prediction neural network is trained by a training data set including multiple training inspection records and corresponding area annotations, problem severity annotations and problem type annotations.

[0054] It can be seen that through the above optional embodiments, accurate sub-region risk identification is achieved through neural network-based prediction, so as to facilitate the subsequent implementation of accurate inspection planning based on regional risk prediction, improve the safety management and inspection efficiency of energy production areas, and reduce the risk of potential equipment failure.

[0055] As an optional embodiment, the problem type is different component failures or functional failures of different energy production equipment; the energy production equipment is solar panels, wind turbines, nuclear reactors, water turbines, geothermal pumps, coal-fired furnaces, gas turbines or biomass furnaces.

[0056] It can be seen that through the above optional embodiments, the content of the problem type is limited, and the problem characteristics of the inspection records are comprehensively characterized to assist in realizing accurate inspection planning based on regional risk prediction, improve the safety management and inspection efficiency of energy production areas, and reduce the risk of potential equipment failure.

[0057] As an optional embodiment, the prediction module predicts the specific manner of regional danger corresponding to each sub-area of ​​interest based on the operating data corresponding to the sub-area of ​​interest in the historical operating records of the equipment, including: For each sub-area of ​​interest, determining all problem types and corresponding problem severity parameters corresponding to the sub-area of ​​interest; Determine the energy production equipment corresponding to the problem type whose problem degree parameter is greater than the second parameter threshold as the problem equipment; Determine the equipment working data corresponding to each problematic equipment in the equipment historical working records; Based on the equipment working data corresponding to each problematic equipment, the regional risk corresponding to the sub-area of ​​concern is predicted.

[0058] It can be seen that through the above optional embodiments, by identifying the problem type and problem severity parameters of each sub-area of ​​concern and screening out energy production equipment corresponding to the problem type with a problem severity exceeding the second threshold as problem equipment, its working data is extracted from the equipment's historical working records to predict the regional risk of the sub-area of ​​concern, thereby achieving accurate risk prediction based on the severity of equipment problems and working data, improving the safety management and targeted inspection of energy production areas, and reducing the risk of equipment failure.

[0059] As an optional embodiment, the prediction module predicts the regional risk corresponding to the sub-region of interest based on the device operating data corresponding to each problematic device in a specific manner, including: Inputting the device operating data corresponding to each problematic device into a trained device anomaly prediction neural network to obtain device anomaly parameters corresponding to each problematic device; optionally, the device anomaly prediction neural network is trained using a training data set comprising a plurality of training device operating data and corresponding device anomaly annotations; Calculate the product of the problem severity parameter and the device abnormality parameter corresponding to each problem device to obtain the abnormality characterization parameter corresponding to each problem device; The weighted sum of the abnormal characterization parameters corresponding to all problematic devices is calculated to obtain the regional risk corresponding to the sub-region of concern.

[0060] It can be seen that through the above optional embodiments, the equipment working data of the problem equipment in each sub-area of ​​concern is input into the trained equipment anomaly prediction neural network to obtain the equipment anomaly parameters, and the anomaly characterization parameters are calculated in combination with the problem degree parameters and weighted summation is performed to determine the regional risk of the sub-area, thereby realizing accurate risk assessment based on equipment anomalies and working data, improving the pertinence and safety of the inspection plan of the energy production area, and reducing the risk of equipment failures and production accidents.

[0061] As an optional embodiment, the second determination module determines a specific method of a new inspection plan corresponding to the energy production area based on the regional hazards corresponding to each sub-area of ​​interest and historical inspection records, including: Determine the historical inspection frequency corresponding to each sub-area of ​​concern based on historical inspection records; Determine the objective function and constraints of inspection planning based on historical inspection frequency and regional hazards; Based on the dynamic programming algorithm, a new inspection plan corresponding to the energy production area is calculated according to the objective function and constraints.

[0062] It can be seen that through the above optional embodiments, the inspection frequency of each sub-area of ​​concern is determined by analyzing historical inspection records and the objective function and restriction conditions of the inspection plan are constructed in combination with the regional danger level. The new inspection plan for the energy production area is obtained based on the dynamic programming algorithm, thereby realizing optimized inspection scheduling based on risk and historical frequency, improving the efficiency and resource utilization of safety management in the energy production area, and reducing the risk of potential equipment failure.

[0063] As an optional embodiment, the objective function includes minimizing the total inspection frequency corresponding to all sub-areas in the inspection plan and minimizing the inspection time corresponding to the inspection plan; the inspection time is predicted by inputting the inspection plan generated by each algorithm planning calculation into a trained inspection time prediction neural network; the constraints include: The difference between the inspection frequency of each sub-area of ​​interest in the inspection plan and the historical inspection frequency is less than the difference threshold; The inspection frequency of each sub-area of ​​concern in the inspection plan is proportional to the danger of the corresponding area; In addition, the frequency difference corresponding to each sub-area of ​​concern in the inspection plan is proportional to the neglect degree parameter corresponding to the sub-area of ​​concern; optionally, the neglect degree parameter is the difference between the historical inspection frequency corresponding to the sub-area of ​​concern and the reference inspection frequency; the reference inspection frequency is calculated based on the preset mathematical correspondence between the hazard and frequency and the regional hazard corresponding to the sub-area of ​​concern.

[0064] It can be seen that through the above optional embodiments, the details of the objective function and the restriction conditions are defined, so as to analyze the historical inspection records to determine the inspection frequency of each sub-area of ​​concern and construct the objective function in combination with the regional hazards to achieve the minimization of the total inspection frequency and inspection time. At the same time, the conditions such as the difference between the inspection frequency and the historical frequency is less than the threshold, the frequency is proportional to the regional hazard, and the frequency difference is proportional to the neglect degree parameter are restricted, so as to calculate the new inspection plan based on the dynamic programming algorithm, thereby realizing optimized inspection scheduling based on risk and historical data, improving the safety management efficiency and resource utilization of the energy production area, and significantly reducing the risk of equipment failure.

[0065] Example 3 See also Figure 3 , Figure 3 This is another production safety supervision optimization system based on historical data disclosed in an embodiment of the present invention. Figure 3 The described safety production supervision optimization system based on historical data is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the safety production supervision optimization system based on historical data may include: A memory 301 storing executable program code; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the method for optimizing production safety supervision based on historical data described in the first embodiment.

[0066] Example 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for optimizing production safety supervision based on historical data described in the first embodiment.

[0067] Example 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the safety production supervision optimization method based on historical data described in Example 1.

[0068] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0077] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0079] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0081] Finally, it should be noted that the method and system for optimizing production safety supervision based on historical data disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A safety production supervision optimization method based on historical data, characterized in that: The method comprises: Obtain historical inspection records and equipment historical work records corresponding to energy production areas; Based on a text analysis algorithm, determining a plurality of sub-areas of interest in the energy production area according to the historical inspection records; Predicting the regional hazard corresponding to each of the sub-areas of interest based on the operating data corresponding to the sub-areas of interest in the historical operating records of the equipment; A new inspection plan corresponding to the energy production area is determined based on the regional hazards corresponding to each of the sub-areas of concern and the historical inspection records.

2. The method for optimizing production safety supervision based on historical data according to claim 1, characterized in that: The text analysis algorithm is used to determine, based on the historical inspection records, a plurality of sub-areas of interest in the energy production area, including: Based on text matching rules, matching a plurality of inspection record data related to the energy production area from the historical inspection records; For each inspection record data, predict the sub-area, problem severity parameter and problem type corresponding to the inspection record data; It is determined whether the problem degree parameter is greater than a first parameter threshold; if so, the sub-region and the problem type corresponding to the inspection record data are determined as the focus sub-region and the corresponding regional problem type.

3. The method for optimizing production safety supervision based on historical data according to claim 2, characterized in that: The prediction of the sub-area, problem severity parameter, and problem type corresponding to the inspection record data includes: The inspection record data is input into a trained inspection problem prediction neural network to obtain the sub-area, problem severity parameter and problem type corresponding to the inspection record data; the inspection problem prediction neural network is trained by a training data set including multiple training inspection records and corresponding area annotations, problem severity annotations and problem type annotations.

4. The method for optimizing production safety supervision based on historical data according to claim 3 is characterized in that: The problem types are failures of different components or functional failures of different energy production equipment; the energy production equipment is solar panels, wind turbines, nuclear reactors, water turbines, geothermal pumps, coal-fired furnaces, gas turbines or biomass furnaces.

5. The method for optimizing production safety supervision based on historical data according to claim 2, characterized in that: The predicting of the regional hazard corresponding to each sub-area of ​​interest based on the operating data corresponding to the sub-area of ​​interest in the historical operating record of the equipment includes: For each of the focus sub-areas, determining all of the problem types and corresponding problem severity parameters corresponding to the focus sub-area; Determining the energy production equipment corresponding to the problem type for which the problem degree parameter is greater than a second parameter threshold as a problem equipment; Determining device operating data corresponding to each problematic device in the device historical operating record; Based on the device operating data corresponding to each of the problematic devices, the regional risk corresponding to the sub-region of interest is predicted.

6. The method for optimizing production safety supervision based on historical data according to claim 5 is characterized in that: The predicting of the regional risk corresponding to the sub-region of interest based on the device operating data corresponding to each problematic device includes: Inputting the device operating data corresponding to each of the problem devices into a trained device anomaly prediction neural network to obtain device anomaly parameters corresponding to each of the problem devices; the device anomaly prediction neural network is trained using a training data set including a plurality of training device operating data and corresponding device anomaly annotations; Calculating the product of the problem severity parameter corresponding to each of the problem devices and the device abnormality parameter to obtain the abnormality characterization parameter corresponding to each of the problem devices; The weighted sum of the abnormal characterization parameters corresponding to all the problem devices is calculated to obtain the regional risk corresponding to the sub-region of interest.

7. The method for optimizing production safety supervision based on historical data according to claim 1, characterized in that: Determining a new inspection plan corresponding to the energy production area based on the regional hazards corresponding to each of the sub-areas of interest and the historical inspection records includes: Determining the historical inspection frequency corresponding to each of the sub-areas of interest based on the historical inspection records; Determine the objective function and constraint conditions of the inspection plan based on the historical inspection frequency and the regional hazards; Based on the dynamic programming algorithm, according to the objective function and the constraint conditions, a new inspection plan corresponding to the energy production area is calculated.

8. The method for optimizing production safety supervision based on historical data according to claim 7, characterized in that: The objective function includes minimizing the total inspection frequency corresponding to all the sub-areas in the inspection plan and minimizing the inspection time corresponding to the inspection plan; The inspection time is obtained by inputting the inspection plan generated by each algorithm planning calculation into the trained inspection time prediction neural network for prediction; The restrictions include: The difference between the inspection frequency corresponding to each of the sub-areas of interest in the inspection plan and the historical inspection frequency is less than a difference threshold; The inspection frequency corresponding to each sub-area of ​​concern in the inspection plan is proportional to the danger of the corresponding area; In addition, the frequency difference corresponding to each of the focus sub-areas in the inspection plan is proportional to the neglect degree parameter corresponding to the focus sub-area; the neglect degree parameter is the difference between the historical inspection frequency corresponding to the focus sub-area and the reference inspection frequency; the reference inspection frequency is calculated based on the preset mathematical correspondence between the hazard and frequency and the regional hazard corresponding to the focus sub-area.

9. A safety production supervision optimization system based on historical data, characterized in that: The system comprises: The acquisition module is used to obtain the historical inspection records and equipment historical work records corresponding to the energy production area; A first determination module is configured to determine a plurality of sub-areas of interest in the energy production area based on the historical inspection records based on a text analysis algorithm; a prediction module, configured to predict the regional hazard corresponding to each of the sub-areas of interest based on the operating data corresponding to the sub-areas of interest in the historical operating records of the equipment; The second determination module is used to determine a new inspection plan corresponding to the energy production area according to the regional hazard corresponding to each of the concerned sub-areas and the historical inspection records.

10. A safety production supervision optimization system based on historical data, characterized in that: The system comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the safety production supervision optimization method based on historical data as described in any one of claims 1 to 8.

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