Distribution method, device and equipment of power grid patrol task and medium

Abstract

The invention discloses a power grid patrol task distribution method and device, equipment and a medium, and the method comprises the steps: obtaining the equipment position, equipment information and real-time operation data of power grid equipment from a patrol task when the patrol task is received; determining a safety risk level value of the power grid equipment at the current moment according to the equipment information and the real-time operation data; generating safety risk reminding information according to a matching result of the safety risk level value and a preset safety risk level table; and distributing the patrol task to a target patrol terminal according to the equipment position and the personnel information of each patrol terminal, and pushing safety risk reminding information. Therefore, patrol personnel with corresponding skills are matched in a power grid equipment safety risk calculation mode, task allocation is effectively and accurately carried out, and patrol efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of task allocation, and in particular, to a method, device, equipment, and medium for allocating power grid inspection tasks. Background Art

[0002] In modern society, power supply is a key factor in ensuring the stable operation of society and economic development. As an important infrastructure for power transmission and distribution, the safe and stable operation of the power grid is directly related to the national economy and people's livelihood. Therefore, in the field of power grid operation and maintenance management, ensuring the safe operation of power grid equipment and being able to quickly respond to potential risks has become a crucial task.

[0003] With the continuous expansion of the power grid scale and the increasing complexity of the structure, many problems have gradually emerged in traditional power grid safety supervision methods. Especially in the link of allocating inspection tasks for safety supervision personnel, some areas still rely on manual scheduling and paper work orders. This traditional mode seems powerless in dealing with emergencies and complex situations.

[0004] In actual operation, the traditional manual scheduling method lacks an intelligent task allocation mechanism, and the use of paper work orders restricts the real-time transmission and sharing of information, resulting in inaccurate and ineffective task allocation between power grid areas with different risks and reducing the inspection efficiency. Summary of the Invention

[0005] The present invention provides a method, device, equipment, and medium for allocating power grid inspection tasks, which solves the technical problems that in actual operation, the traditional manual scheduling method lacks an intelligent task allocation mechanism, and the use of paper work orders restricts the real-time transmission and sharing of information, resulting in inaccurate and ineffective task allocation between power grid areas with different risks and reducing the inspection efficiency.

[0006] A method for allocating power grid inspection tasks provided by the first aspect of the present invention includes:

[0007] When receiving an inspection task, obtain the equipment location, equipment information, and real-time operation data of the power grid equipment from the inspection task;

[0008] According to the equipment information and the real-time operation data, determine the safety risk level value of the power grid equipment at the current moment;

[0009] Generate a safety risk reminder message according to the matching result of the safety risk level value and a preset safety risk rating table;

[0010] According to the equipment location and the personnel information of each inspection terminal, allocate the inspection task to the target inspection terminal and push the safety risk reminder message.

[0011] Optionally, the method further includes:

[0012] In response to a request for creating a risk level table, obtain historical status-related data corresponding to the power grid equipment;

[0013] Extract safety risk features from the historical status-related data, and create a safety level division objective function using the safety risk features;

[0014] Perform genetic iterative optimization according to the safety level division objective function, determine multiple safety risk division thresholds, and construct a safety risk level table.

[0015] Optionally, the performing genetic iterative optimization according to the safety level division objective function, determining multiple safety risk division thresholds, and constructing a safety risk level table includes:

[0016] Randomly create a group of level division thresholds;

[0017] Calculate the fitness value corresponding to each level division threshold in the group of level division thresholds according to the safety level division objective function;

[0018] Select the level division thresholds with fitness values greater than a preset fitness threshold as the first level division thresholds;

[0019] Randomly select the first level division thresholds to perform pairwise crossover operations, generate second level division thresholds and perform random mutations to obtain new level division thresholds;

[0020] Jump to execute the step of calculating the fitness value corresponding to each level division threshold in the group of level division thresholds according to the safety level division objective function;

[0021] When the number of jumps reaches a preset number of iterations, select the level division threshold with the highest fitness value at the current moment as the safety risk division threshold, and construct a safety risk level table.

[0022] Optionally, the safety level division objective function is:

[0023]

[0024] Wherein, is the actual voltage of the power grid equipment; is the actual current of the power grid equipment; is the actual temperature of the power grid equipment; is the actual power factor of the power grid equipment; is the normal voltage reference value of the power grid equipment; is the normal current reference value of the power grid equipment; is the normal temperature reference value of the power grid equipment; is the normal power factor reference value of the grid equipment; is the frequency of the grid equipment failure; is the severity of the grid equipment failure; is the average repair time of the grid equipment failure; 、 and are the weight coefficients of the historical failure data risk score; is the influence degree of the weather condition on the grid equipment; is the influence degree of the ambient temperature on the grid equipment; is the influence degree of the ambient humidity on the grid equipment; 、 and are the weight coefficients of the environmental factor risk score; is the criticality degree of the grid equipment in the power grid; d is the weight coefficient of the grid equipment importance risk score; 、 、 and are the weight coefficients of each part.

[0025] Optionally, the device information includes historical failure data, environmental factors, and device importance; determining the safety risk level value of the grid equipment at the current moment according to the device information and the real-time operation data includes:

[0026] Substituting the historical failure data, the environmental factors, the device importance, and the real-time operation data into the risk level calculation formula to determine the safety risk level value of the grid equipment at the current moment;

[0027] The risk level calculation formula is:

[0028]

[0029] where is the current safety risk level value; 、 、 and are the weight coefficients; is the weight coefficient of the i-th operation parameter; is the real-time value of the i-th operation parameter; is the normal value of the i-th operation parameter; is the total number of operation parameters; is the weight coefficient of the j-th historical failure data index; is the value of the j-th historical failure data index; m is the total number of historical failure data indexes; is the weight coefficient of the k-th environmental factor index; The value of the k-th environmental factor indicator; l is the total number of environmental factor indicators; The weight coefficient of the p-th power grid equipment importance indicator; The value of the p-th power grid equipment importance indicator; q is the total number of power grid equipment importance indicators.

[0030] Optionally, generating a security risk reminder message according to the matching result of the security risk level value and a preset security risk level table includes:

[0031] Matching the security risk level value to a preset security risk level table to determine the target security risk level of the power grid equipment at the current moment;

[0032] Generating a security risk reminder message according to the information content of the interval to which the target security risk level belongs in the security risk level table.

[0033] Optionally, the personnel information includes the terminal location, the current task volume, and the function information; allocating the inspection task to a target inspection terminal according to the equipment location and the personnel information of each inspection terminal, and pushing the security risk reminder message includes:

[0034] Calculating the estimated time for each terminal location to reach the equipment location;

[0035] Selecting at least one inspection terminal whose function information matches the fault type of the power grid equipment and whose estimated time is less than a preset time threshold as a pending terminal;

[0036] Selecting a pending terminal with a current task volume less than a preset task volume threshold as the target inspection terminal;

[0037] Allocating the inspection task to the target inspection terminal and pushing the security risk reminder message.

[0038] The second aspect of the present invention provides a device for allocating power grid inspection tasks, including:

[0039] A data acquisition module, configured to obtain the equipment location, equipment information, and real-time operation data of the power grid equipment from the inspection task when receiving the inspection task;

[0040] A risk level calculation module, configured to determine the security risk level value of the power grid equipment at the current moment according to the equipment information and the real-time operation data;

[0041] A security risk reminder generation module, configured to generate a security risk reminder message according to the matching result of the security risk level value and a preset security risk level table;

[0042] A task allocation module, configured to allocate the inspection task to a target inspection terminal according to the device location and the personnel information of each inspection terminal, and push the safety risk reminder information.

[0043] A third aspect of the present invention provides an electronic device, including a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor is caused to execute the steps of the power grid inspection task allocation method according to any one of the first aspects of the present invention.

[0044] A fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the power grid inspection task allocation method according to any one of the first aspects of the present invention is implemented.

[0045] From the above technical solutions, it can be seen that the present invention has the following advantages:

[0046] When receiving an inspection task, obtain the device location, device information, and real-time operation data of the power grid device from the inspection task; determine the safety risk level value of the power grid device at the current moment according to the device information and real-time operation data; generate a safety risk reminder information according to the matching result of the safety risk level value and a preset safety risk rating table; allocate the inspection task to a target inspection terminal according to the device location and the personnel information of each inspection terminal, and push the safety risk reminder information. Thus, by calculating the safety risk of the power grid device, the inspection personnel with corresponding skills are matched, and the task allocation is effectively and accurately carried out, effectively improving the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a flowchart of the steps of a power grid inspection task allocation method provided by an embodiment of the present invention;

[0049] Figure 2 It is a structural block diagram of a power grid inspection task allocation device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] An embodiment of the present invention provides a method, device, equipment, and medium for allocating power grid inspection tasks, which are used to solve the technical problems that in actual operation, the traditional manual scheduling method lacks an intelligent task allocation mechanism, and the use of paper work orders limits the real-time transmission and sharing of information, resulting in inaccurate and ineffective task allocation between power grid areas with different risks and reduced inspection efficiency.

[0051] In the embodiment of the present invention, by comprehensively considering the operating status of power grid equipment (including detailed data such as voltage, current, temperature, and power factor), historical fault data, environmental factors, and equipment importance, this method can provide a refined and multi-dimensional safety risk assessment, which helps to more accurately identify potential risks and vulnerable points in the power grid. By using the real-time operating data of power grid equipment, combined with historical fault data and environmental factors, the accuracy of decision-making is improved, which helps to prevent potential faults. By obtaining device information and operating data in real time, and calculating the safety risk level in real time, this method can timely detect the abnormal status of power grid equipment and give early warnings, greatly enhancing the preventive nature of power grid safety supervision and helping to reduce the occurrence of sudden faults. The automatically generated safety risk classification reminder information, which is pushed to the safety inspection personnel in real time, significantly reduces the delay of information transmission, improves the response speed and processing efficiency. Generating work orders according to the safety risk level can help the safety supervision department allocate resources more reasonably, focus attention and resources on high-risk areas, thereby improving the safety and efficiency of the overall power grid operation.

[0052] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Please refer to Figure 1 , Figure 1 which is a flowchart of the steps of a method for allocating power grid inspection tasks provided by an embodiment of the present invention.

[0054] A method for allocating power grid inspection tasks provided by the present invention includes:

[0055] Step 101, when receiving an inspection task, obtain the device location, device information, and real-time operating data of the power grid equipment from the inspection task;

[0056] An inspection task refers to the work arrangement assigned to relevant staff to ensure the normal operation of power grid equipment, promptly detect and handle potential safety hazards. It includes, but is not limited to, the equipment location, equipment information, and real-time operation data of the power grid equipment to be inspected. Equipment information includes historical fault data, environmental factors, and equipment importance, etc.

[0057] In an embodiment of the present invention, the inspection task can be generated by responding to the operation status monitoring of power grid equipment or issued by the management personnel from the upstream management system. After the device receives the inspection task, it parses it to obtain the equipment location, equipment information, and real-time operation data of the corresponding power grid equipment, which serves as the data basis for subsequent inspection task allocation.

[0058] Step 102: Determine the safety risk level value of the power grid equipment at the current moment according to the equipment information and real-time operation data;

[0059] In this embodiment, after obtaining the equipment information and real-time operation data, by comprehensively considering the historical fault data, environmental factors, equipment importance, etc. included in the equipment information, and combining the real-time operation data, calculate the safety risk level value of the power grid equipment at the current moment.

[0060] It should be noted that the safety risk level value refers to the probability value of potential risks in real-time operation of the power grid equipment under the influence of current environmental factors and historical faults.

[0061] In an example of the present invention, the equipment information includes historical fault data, environmental factors, and equipment importance; Step 102 may include the following sub-steps:

[0062] Substitute the historical fault data, environmental factors, equipment importance, and real-time operation data into the risk level calculation formula to determine the safety risk level value of the power grid equipment at the current moment;

[0063] The risk level calculation formula is:

[0064]

[0065] Wherein, The current safety risk level value; 、 、 And Are weight coefficients; The weight coefficient of the i-th operation parameter; The real-time value of the i-th operation parameter; The normal value of the i-th operation parameter; The total number of operation parameters; The weight coefficient of the j-th historical fault data index; The value of the j-th historical fault data index; m is the total number of historical fault data indexes; The weight coefficient of the k-th environmental factor index; The value of the k-th environmental factor index; l is the total number of environmental factor indexes; The weight coefficient of the p-th power grid equipment importance index; The value of the p-th power grid equipment importance index; q is the total number of power grid equipment importance indexes.

[0066] In the embodiment of the present invention, multiple aspects such as the operating parameters of the equipment, historical fault data, environmental factors, and equipment importance are comprehensively considered, so as to more comprehensively and accurately reflect the current safety risk level of the equipment. By quantifying each historical fault data, environmental factor, equipment importance, and real-time operating data and substituting them into the formula for calculation, a safety risk level value is calculated, thereby providing a scientific basis for risk management decisions.

[0067] Step 103, generate a safety risk reminder message according to the matching result of the safety risk level value and the preset safety risk level table;

[0068] In this embodiment, to implement the warning operation of the inspection terminal carried by the technical personnel, the safety risk level value is matched with the preset safety risk level table to determine the interval to which the safety risk level value belongs, so as to determine the corresponding target safety risk level, and a safety risk reminder message is generated according to the information content associated with each interval. Through timely safety risk classification reminders, relevant personnel can be prompted to take necessary preventive measures, thereby reducing the probability of equipment accidents. Clear reminder messages help relevant personnel quickly determine the urgency and severity of the problem, and then quickly make decisions and activate the corresponding emergency plans. According to different safety risk levels, enterprises can more reasonably allocate maintenance and management resources, prioritize the treatment of high-risk equipment, and ensure the efficient use of resources.

[0069] In an example of the present invention, step 103 may include the following sub-steps:

[0070] Match the safety risk level value to the preset safety risk level table to determine the target safety risk level of the power grid equipment at the current moment;

[0071] Generate a safety risk reminder message according to the information content of the interval to which the target safety risk level belongs in the safety risk level table.

[0072] In this embodiment, after calculating the safety risk level value, it is mapped to the safety risk level table for matching. Specifically, the safety risk level table can be divided into several intervals, and each interval corresponds to a safety risk level (such as low risk, medium risk, high risk, etc.); according to the interval where the safety risk level value is located, the target safety risk level of the power grid equipment at the current moment is determined.

[0073] For each interval in the safety risk level table, different reminder information templates can be set respectively. The information content of the template can include the information points that should be concerned and the recommended countermeasures at the corresponding level. After determining the target safety risk level, by selecting the corresponding template and filling in specific equipment information, risk level description, recommended countermeasures, etc., so as to facilitate the subsequent release of the generated safety risk classification reminder information to relevant personnel through appropriate channels (such as internal communication systems, emails, text messages, etc.), and then timely understand and respond to the safety risk status of the equipment.

[0074] In addition, before generating the safety risk reminder information, the thresholds of each interval can be refined, and further comparison is made between the target safety risk level and multiple risk thresholds within the interval. This comparison process is to further confirm the current safety risk level of the equipment and may trigger subsequent response measures at different levels. Through comparison, the specific safety risk level to which the equipment currently belongs is clarified.

[0075] Step 104: According to the equipment location and the personnel information of each inspection terminal, allocate the inspection task to the target inspection terminal and push the safety risk reminder information.

[0076] In an example of the present invention, the personnel information includes the terminal location, the current task volume, and the function information; Step 104 may include the following sub-steps:

[0077] Calculate the estimated time for each terminal location to reach the equipment location;

[0078] Select at least one inspection terminal whose function information matches the fault type of the power grid equipment and whose estimated time is less than the preset time threshold as the pending terminal;

[0079] Select the pending terminal with the current task volume less than the preset task volume threshold as the target inspection terminal;

[0080] Allocate the inspection task to the target inspection terminal and push the safety risk reminder information.

[0081] In this embodiment, the current terminal positions of all inspection terminals can be obtained in real time through GPS or mobile network positioning technology. At the same time, by querying the database or system records, the professional skills, qualification certificates, and past experience in handling similar tasks of the safety supervision personnel to whom each inspection terminal belongs are understood as functional information. Check the current task queue of each safety supervision personnel, evaluate their workload and remaining available time, and obtain the current task volume. For each inspection terminal and the current inspection task, use a map API (such as Google Maps API, Baidu Maps API, etc.) to calculate the estimated arrival time from the current terminal position of the inspection terminal to the equipment position, and store the estimated time for each safety supervision personnel to reach each task in a data structure, such as a two-dimensional array or dictionary. According to the professional skills required for the inspection task, select a subset of personnel with corresponding functional information from all safety supervision personnel, and create an empty task assignment list or dictionary to store the final assignment results.

[0082] For each inspection task to be assigned, first sort the selected subset according to the professional skill matching degree of the safety supervision personnel (the most matching first). Then, among the safety supervision personnel with the same matching degree, sort them according to their estimated time to reach the task location (the shortest estimated time first); when assigning tasks, check the current task volume of each safety supervision personnel to avoid overloading; select the first safety supervision personnel in the sorted list (that is, the person with matching professional skills, the shortest estimated arrival time, and the current task volume not overloaded), assign the task to him, and update the task assignment list and the task volume record of this safety supervision personnel. The device generates an assignment result and notifies the target inspection terminal of the assigned safety supervision personnel together with the safety risk reminder information. Through the confirmation of the safety supervision personnel to accept the task and give feedback in the device, so that the device can update the task status and personnel status.

[0083] In a specific implementation, after obtaining the safety risk reminder information, each inspection task can also be attached with a clear safety risk level (such as high, medium, low) and possible urgency identification. First, sort all inspection tasks to be assigned from high to low according to the safety risk level. Within the same risk level, perform a secondary sorting according to the urgency of the task (such as whether to respond immediately, the scheduled response time, etc.). The result is an ordered list of inspection tasks, with high-risk and urgent tasks at the top of the list. Select safety supervision personnel who meet the professional requirements. The safety supervision personnel database contains information such as the professional skills, qualification certificates, and past experience of each safety supervision personnel; for each inspection task, the system selects a subset of safety supervision personnel with corresponding professional skills according to its equipment type, fault type, etc. The subset contains a sufficient number of safety supervision personnel to ensure enough room for selection to optimize the assignment.

[0084] By regularly or real-time monitoring the security risk level of the device, potential security hazards can be discovered in a timely manner, so as to take corresponding preventive maintenance measures, reduce the occurrence probability of device failures, and improve the service life and reliability of the device. Understanding the security risk level of the device helps the enterprise to reasonably allocate maintenance resources, focus on monitoring and maintaining high-risk devices, and achieve optimal allocation of resources. For this purpose, the security risk level table can be constructed through the following steps S11 - S13.

[0085] In an example of the present invention, the method further includes the following steps S11 - S13:

[0086] S11. Respond to the risk level table creation request and obtain the historical status related data corresponding to the power grid device;

[0087] S12. Extract security risk features from the historical status related data, and create a security level division objective function using the security risk features;

[0088] In this embodiment, the historical status related data can be collected from different sources (such as power grid monitoring systems, historical databases, environmental monitoring systems, etc.). By integrating the collected multi-dimensional data into a unified data set; checking whether there are missing values in the data set, and filling or processing these missing values using methods such as interpolation and deletion according to the actual situation; detecting outliers in the data set using statistical methods (such as Z-score), and deciding whether to correct, delete or retain these outliers according to the actual situation; in order to eliminate the dimensional difference between different features, standardize or normalize the data. Count the number of times a device fails within a period of time. The number of failures can be grouped and counted according to factors such as device type and operating environment to calculate the failure frequency, that is, the number of failures per unit time. For each failure, record the time interval from the occurrence of the failure to the repair of the failure, and calculate the average repair time of all failures, which can reflect the maintenance efficiency and the complexity of the device. Define the standard of "severe environment" according to environmental factor data (such as temperature, humidity, weather conditions, etc.), count the number of device failures or failure rates in the severe environment, and calculate the average trouble-free operation time of the device in the severe environment, which is used as an index of stability; calculate the Pearson correlation coefficient between the extracted features and the device security risk, and determine the key feature set closely related to the device security risk according to the Pearson correlation coefficient. The value range of the Pearson correlation coefficient is [-1, 1]. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and the larger the absolute value, the stronger the correlation. According to the calculated correlation coefficient, determine a threshold (such as |r| > 0.5) for screening out the features closely related to the device security risk; according to the set threshold, screen out the features closely related to the device security risk, form a security risk feature set with these features, and construct a security level division objective function based on this.

[0089] S13. Perform genetic iterative optimization on the objective function classified by safety level, determine multiple safety risk classification thresholds, and construct a safety risk level table.

[0090] Further, S13 may include the following sub-steps:

[0091] Randomly create a group of classification thresholds;

[0092] Calculate the fitness value corresponding to each classification threshold in the group of classification thresholds according to the objective function classified by safety level;

[0093] Select the classification thresholds with fitness values greater than the preset fitness threshold as the first classification thresholds;

[0094] Randomly select two first classification thresholds for pairwise crossover operation to generate second classification thresholds and perform random mutation to obtain new classification thresholds;

[0095] Jump to execute the step of calculating the fitness value corresponding to each classification threshold in the group of classification thresholds according to the objective function classified by safety level;

[0096] When the number of jumps reaches the preset number of iterations, select the classification threshold with the highest fitness value at the current moment as the safety risk classification threshold and construct a safety risk level table.

[0097] In the embodiment of the present invention, by randomly generating a group of initial solutions, each solution represents a way of classifying safety risk levels. These solutions can be the thresholds of safety risk scores, used to divide devices into different risk levels, that is, randomly create a group of classification thresholds; for each initial solution, use the objective function classified by safety level to calculate its fitness value. The fitness value reflects the safety risk distribution of devices within each risk level under this classification method. A good classification method should make the devices within the same risk level have similar safety risk levels.

[0098] Select a part of the individuals with fitness values greater than the preset fitness threshold (i.e., the first classification thresholds) to enter the next generation; randomly select two first classification thresholds for pairwise crossover operation to generate new individuals, that is, the second classification thresholds. Perform random mutation on the newly generated individuals to obtain new classification thresholds to increase the diversity of the population.

[0099] Among them, the crossover operation can be implemented by single-point crossover, multi-point crossover or uniform crossover, aiming to combine the advantages of two parent individuals to generate better offspring individuals. The mutation operation can be implemented by changing certain gene values ​​in the individual (i.e., the threshold of the safety risk score); repeat the above selection, crossover and mutation operations until the preset number of iterations is reached; finally, the level division threshold with the highest fitness value at the current moment is selected as the safety risk division threshold. After obtaining multiple safety risk division thresholds, the safety risk level table is constructed with this group of level division threshold groups.

[0100] Among them, the security level classification objective function is:

[0101]

[0102] in, is the actual voltage of the power grid equipment; is the actual current of the power grid equipment; is the actual temperature of the power grid equipment; is the actual power factor of the power grid equipment; It is the normal voltage reference value of the power grid equipment; It is the normal current reference value of the power grid equipment; It is the normal temperature reference value of the power grid equipment; It is the normal power factor reference value of the power grid equipment; is the frequency of power grid equipment failure; is the fault severity of the power grid equipment; The mean repair time for power grid equipment failures; , and is the weight coefficient of the risk score of historical failure data; The degree of impact of weather conditions on power grid equipment; The degree of influence of ambient temperature on power grid equipment; The degree of influence of environmental humidity on power grid equipment; , and is the weight coefficient of environmental factor risk score; is the criticality of the power grid equipment in the power grid; d is the weight coefficient of the importance risk score of the power grid equipment; , , and is the weight coefficient of each part.

[0103] In an embodiment of the present invention, first, the actual operating parameters of the device, including voltage, current, temperature, and power factor, are compared with the normal reference values of the device. By calculating the square of the difference between the actual value and the normal value and multiplying it by the corresponding weight coefficient, the risk of the device in terms of operating parameters can be evaluated. This part, based on the basic operating condition of the device, can reflect whether there are abnormal conditions such as overload and overheating of the device. According to the historical failure data of the device, including failure frequency, failure severity, and mean time to repair, multiplying these data by the corresponding weight coefficient can evaluate the risk of the device in terms of historical failures. This part, based on the historical performance of the device, can reflect the reliability and repair efficiency of the device. According to the influence of environmental factors on the device, including weather conditions, ambient temperature, and ambient humidity, multiplying these factors by the corresponding weight coefficient can evaluate the risk of the device in terms of environmental factors. This part, based on the influence of the external environment on the operation of the device, can reflect the adaptability and stability of the device in different environments.

[0104] According to the criticality of the device in the power grid, multiplying by the corresponding weight coefficient can evaluate the risk of the device in terms of criticality. This part, based on the importance and influence scope of the device in the power grid, can reflect the impact of the device failure on the overall operation of the power grid. By multiplying the results of the above four parts by the corresponding weight coefficients and summing them up, the final risk assessment result is obtained. Based on multiple aspects of the device, including operating parameters, historical failure data, environmental factors, and device criticality, the safety risk of the device can be comprehensively evaluated; the weight coefficients can be adjusted according to the actual situation to meet the requirements of different devices and different scenarios; by quantifying each factor and calculating the scores, the safety risk levels of different devices can be intuitively compared.

[0105] In a preferred embodiment of the present invention, the calculation formula for the severity of device failure is:

[0106] , where represents the frequency of the i-th failure; represents the severity level of the i-th failure; n is the total number of failures;

[0107] , where represents rainfall; W represents wind speed; L represents the frequency of lightning activity; , and are the weight coefficients of rainfall, wind speed, and lightning activity respectively; the calculation formula for the degree of influence of weather conditions on the device is:

[0108] , where represents ambient temperature; represents the optimal operating temperature of the device; Represents the highest temperature that the device can tolerate; the calculation formula for the degree of influence of environmental humidity on the device is:

[0109] , where Represents the environmental humidity; Represents the optimal operating humidity of the device; Represents the highest humidity that the device can tolerate; the calculation formula for the criticality degree of the device in the power grid is:

[0110] , where Represents the load capacity borne by the device; Represents the number of customers supplied by the device; Represents the power transmitted by the device; 、 and Are the weight coefficients of the load capacity, the number of customers, and the transmitted power respectively.

[0111] In the embodiments of the present invention, the calculation of the severity of the device failure can quantify the severity of the device failure, facilitating the comparison and evaluation of the severity of failures of different devices or the same device at different time periods. The calculation of the degree of influence of weather conditions on the device can quantify the influence of environmental temperature on the device performance, providing a basis for the heat dissipation design and operation and maintenance of the device.

[0112] In an embodiment of the present invention, when a patrol task is received, the device location, device information, and real-time operation data of the power grid equipment are obtained from the patrol task; according to the device information and real-time operation data, the safety risk level value of the power grid equipment at the current moment is determined; according to the matching result of the safety risk level value and the preset safety risk rating table, a safety risk reminder message is generated; according to the device location and the personnel information of each patrol terminal, the patrol task is assigned to the target patrol terminal, and the safety risk reminder message is pushed. Thus, by comprehensively considering the operation status, historical fault data, environmental factors, and device importance of the power grid equipment, this method can more comprehensively and accurately evaluate the safety risk of the equipment, avoiding the evaluation deviation that may be caused by incomplete information in the traditional method. It can obtain the detailed information and operation data of the equipment in real time, so as to calculate and update the safety risk level of the equipment in real time. This helps to timely discover and handle potential safety hazards, improving the stability and safety of the power grid system. By automatically generating safety risk classification reminder messages and pushing them to the safety supervision patrol personnel in real time, this method greatly reduces the time for manual processing and information transmission, improving work efficiency. At the same time, this also reduces the possibility of human errors, further enhancing the reliability of safety supervision. According to the safety risk level of the equipment, the safety supervision patrol personnel can more reasonably allocate resources and attention, giving priority to dealing with high-risk equipment, so as to achieve the optimal allocation and efficient utilization of resources. This method introduces an intelligent risk assessment and early warning mechanism, making the power grid safety supervision system more intelligent and automated, improving the response speed and accuracy of the system.

[0113] Please refer to Figure 2 , Figure 2 which shows the structural block diagram of a power grid patrol task allocation device in an embodiment of the present invention.

[0114] An embodiment of the present invention provides a power grid patrol task allocation device, including:

[0115] A data acquisition module 201, configured to obtain the device location, device information, and real-time operation data of the power grid equipment from the patrol task when a patrol task is received;

[0116] A risk level calculation module 202, configured to determine the safety risk level value of the power grid equipment at the current moment according to the device information and real-time operation data;

[0117] A safety risk reminder generation module 203, configured to generate a safety risk reminder message according to the matching result of the safety risk level value and the preset safety risk rating table;

[0118] A task allocation module 204, configured to assign the patrol task to the target patrol terminal according to the device location and the personnel information of each patrol terminal, and push the safety risk reminder message.

[0119] Optionally, the device further includes:

[0120] A historical data acquisition module, configured to respond to a risk level table creation request and acquire historical status-related data corresponding to grid devices;

[0121] A target function construction module, configured to extract security risk features from the historical status-related data and create a security level division target function using the security risk features;

[0122] A security risk level table determination module, configured to perform genetic iterative optimization according to the security level division target function, determine multiple security risk division thresholds, and construct a security risk level table.

[0123] Optionally, the security risk level table determination module is specifically configured to:

[0124] Randomly create a group of level division thresholds;

[0125] Calculate the fitness value corresponding to each level division threshold in the group of level division thresholds according to the security level division target function;

[0126] Select the level division thresholds with fitness values greater than a preset fitness threshold as the first level division thresholds;

[0127] Randomly select two of the first level division thresholds for pairwise crossover operations, generate second level division thresholds and perform random mutations to obtain new level division thresholds;

[0128] Jump to execute the step of calculating the fitness value corresponding to each level division threshold in the group of level division thresholds according to the security level division target function;

[0129] When the number of jumps reaches a preset number of iterations, select the level division threshold with the highest fitness value at the current moment as the security risk division threshold, and construct a security risk level table.

[0130] Optionally, the security level division target function is:

[0131]

[0132] Wherein, is the actual voltage of the grid device; is the actual current of the grid device; is the actual temperature of the grid device; is the actual power factor of the grid device; is the normal voltage reference value of the grid device; is the normal current reference value of the grid device; is the normal temperature reference value of the grid device; is the normal power factor reference value of the grid device; is the frequency of grid equipment failures; is the severity of grid equipment failures; is the average repair time of grid equipment failures; 、 and are the weight coefficients of the historical failure data risk score; is the degree of influence of weather conditions on grid equipment; is the degree of influence of environmental temperature on grid equipment; is the degree of influence of environmental humidity on grid equipment; 、 and are the weight coefficients of the environmental factor risk score; is the criticality of grid equipment in the power grid; d is the weight coefficient of the grid equipment importance risk score; 、 、 and are the weight coefficients of each part.

[0133] Optionally, the device information includes historical failure data, environmental factors, and device importance; the risk level calculation module 202 is specifically configured to:

[0134] Substitute the historical failure data, environmental factors, device importance, and real-time operation data into the risk level calculation formula to determine the safety risk level value of the grid equipment at the current moment;

[0135] The risk level calculation formula is:

[0136]

[0137] where, is the current safety risk level value; 、 、 and are the weight coefficients; is the weight coefficient of the i-th operating parameter; is the real-time value of the i-th operating parameter; is the normal value of the i-th operating parameter; is the total number of operating parameters; is the weight coefficient of the j-th historical failure data index; is the value of the j-th historical failure data index; m is the total number of historical failure data indexes; is the weight coefficient of the k-th environmental factor index; is the value of the k-th environmental factor index; l is the total number of environmental factor indexes; is the weight coefficient of the p-th grid equipment importance index; The value of the importance index of the p-th power grid device; q is the total number of importance indexes of power grid devices.

[0138] Optionally, the security risk reminder generation module 203 is specifically configured to:

[0139] Match the security risk level value to a preset security risk level table to determine the target security risk level of the power grid device at the current moment;

[0140] Generate a security risk reminder message according to the information content of the interval to which the target security risk level belongs in the security risk level table.

[0141] Optionally, the personnel information includes the terminal location, the current task volume, and the function information; the task assignment module 204 is specifically configured to:

[0142] Calculate the estimated time for each terminal location to reach the device location;

[0143] Select at least one inspection terminal whose function information matches the fault type of the power grid device and whose estimated time is less than the preset time threshold as the pending terminal;

[0144] Select the pending terminal with the current task volume less than the preset task volume threshold as the target inspection terminal;

[0145] Assign the inspection task to the target inspection terminal and push the security risk reminder message.

[0146] An embodiment of the present invention provides an electronic device, including a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the power grid inspection task allocation method according to any embodiment of the present invention.

[0147] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, it implements the power grid inspection task allocation method according to any embodiment of the present invention.

[0148] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0149] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical or other forms.

[0150] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0152] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical discs that can store program codes.

[0153] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A method for allocating power grid inspection tasks, characterized in that: include: When receiving an inspection task, obtaining device location, device information and real-time operation data of the power grid device from the inspection task; Determine the security risk level value of the power grid equipment at the current moment according to the equipment information and the real-time operation data; Generate security risk warning information according to the matching result of the security risk level value and the preset security risk level table; According to the equipment location and the personnel information of each inspection terminal, the inspection task is assigned to the target inspection terminal, and the safety risk reminder information is pushed.

2. The method according to claim 1, characterized in that The method further comprises: In response to a request to create a risk level table, obtaining historical status related data corresponding to the power grid equipment; Extracting security risk features from the historical state related data, and using the security risk features to create a security level classification objective function; Genetic iterative optimization is performed according to the security level classification objective function, multiple security risk classification thresholds are determined, and a security risk level table is constructed.

3. The method according to claim 2, characterized in that The genetic iterative optimization is performed according to the security level classification objective function to determine multiple security risk classification thresholds and construct a security risk level table, including: Randomly create groups of grading thresholds; Calculate the fitness value corresponding to each level division threshold in the level division threshold group according to the security level division objective function; Selecting a level division threshold whose fitness value is greater than a preset fitness threshold as a first level division threshold; Randomly select the first level division thresholds to perform a pairwise crossover operation, generate a second level division threshold and perform a random mutation to obtain a new level division threshold; Jump to the step of calculating the fitness value corresponding to each level division threshold in the level division threshold group according to the security level division objective function; When the number of jumps reaches the preset number of iterations, the level division threshold with the highest fitness value at the current moment is selected as the security risk division threshold to construct a security risk level table.

4. The method according to claim 2, characterized in that: The security level classification objective function is: ； in, is the actual voltage of the power grid equipment; is the actual current of the power grid equipment; is the actual temperature of the power grid equipment; is the actual power factor of the power grid equipment; It is the normal voltage reference value of the power grid equipment; It is the normal current reference value of the power grid equipment; It is the normal temperature reference value of the power grid equipment; It is the normal power factor reference value of the power grid equipment; is the frequency of power grid equipment failure; is the fault severity of the power grid equipment; The mean repair time for power grid equipment failures; , and is the weight coefficient of the risk score of historical failure data; The degree of impact of weather conditions on power grid equipment; The degree of influence of ambient temperature on power grid equipment; The degree of influence of environmental humidity on power grid equipment; , and is the weight coefficient of environmental factor risk score; is the criticality of the power grid equipment in the power grid; d is the weight coefficient of the importance risk score of the power grid equipment; , , and is the weight coefficient of each part.

5. The method according to claim 1, characterized in that The device information includes historical fault data, environmental factors and device importance; and determining the safety risk level value of the power grid device at the current moment based on the device information and the real-time operation data includes: Substituting the historical fault data, the environmental factors, the importance of the equipment and the real-time operation data into the risk level calculation formula to determine the safety risk level value of the power grid equipment at the current moment; The risk level calculation formula is: ； in, Current security risk level value; , , and is the weight coefficient; The weight coefficient of the i-th operating parameter; The real-time value of the i-th operating parameter; The normal value of the i-th operating parameter; The total number of run parameters; The weight coefficient of the jth historical fault data indicator; The value of the jth historical fault data indicator; m is the total number of historical fault data indicators; The weight coefficient of the kth environmental factor indicator; The value of the kth environmental factor indicator; l is the total number of environmental factor indicators; The weight coefficient of the importance index of the pth power grid equipment; The value of the pth grid equipment importance index; q is the total number of grid equipment importance indexes.

6. The method according to claim 1, characterized in that The generating of security risk reminder information according to the matching result of the security risk level value and the preset security risk level table includes: According to the security risk level value, the preset security risk level table is matched to determine the target security risk level of the power grid equipment at the current moment; Generate security risk reminder information according to the information content of the interval to which the target security risk level belongs in the security risk level table.

7. The method according to claim 1, characterized in that The personnel information includes terminal location, current task volume and functional information; the inspection task is assigned to the target inspection terminal according to the device location and the personnel information of each inspection terminal, and the safety risk reminder information is pushed, including: Calculating the estimated time for each of the terminal locations to arrive at the device location; Selecting at least one inspection terminal whose functional information matches the fault type of the power grid equipment and whose estimated time is less than a preset time threshold as a pending terminal; Select pending terminals whose current task volume is less than the preset task volume threshold as target inspection terminals; The inspection task is assigned to the target inspection terminal and the safety risk reminder information is pushed.

8. A device for allocating power grid inspection tasks, characterized in that: include: A data acquisition module, for acquiring, when receiving an inspection task, the device location, device information and real-time operation data of the power grid device from the inspection task; A risk level calculation module, used to determine the safety risk level value of the power grid equipment at the current moment according to the equipment information and the real-time operation data; A security risk reminder generating module, used to generate security risk reminder information according to the matching result of the security risk level value and the preset security risk level table; The task allocation module is used to allocate the inspection task to the target inspection terminal according to the equipment location and the personnel information of each inspection terminal, and push the security risk reminder information.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for allocating power grid inspection tasks as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for allocating power grid inspection tasks as described in any one of claims 1 to 7 is implemented.

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