Electric power overhaul operation risk identification method and system

Abstract

The invention provides an electric power overhaul operation risk identification method and system, and belongs to the technical field of risk identification, and the method specifically comprises the steps: determining the relative positions of a monitoring device and different operation regions, determining historical similar monitoring scenes under a matched operation type through combining with the distribution data of operation risk points in the different operation regions, and carrying out the calculation of the historical similar monitoring scenes; according to the method, identification deviation data of operation risks in different historical similar monitoring scenes are determined, and when it is determined that the setting state of the monitoring device does not meet requirements based on the identification deviation data, optimization setting measures of the monitoring device are determined according to operation monitoring images in different operation areas. The monitoring device is optimized by using the optimization setting measures to obtain the optimization setting result, and the operation risk of the electric power overhaul operation is identified based on the optimization setting result, so that the accuracy of the identification processing of the operation risk is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk identification, and in particular relates to a method and system for identifying risks in power maintenance operations. Background Art

[0002] In order to identify the operational risks during power maintenance operations, the invention patent application CN201910457266.1 "Maintenance Operation Risk Analysis Method, Device and Terminal Based on Neural Network" uses multiple operational feature items of this maintenance operation to obtain the operational risk probabilities corresponding to the multiple operational feature items, thereby reducing the difficulty of quantitative risk analysis of maintenance operations and improving the accuracy of risk analysis results. However, there are the following technical problems: During the power maintenance operation, differences in operation types will lead to differences in the distribution of risk areas during the power maintenance operation. Therefore, if differentiated risk identification strategies cannot be generated based on the differences in operation types, the accuracy of the identification results of the power maintenance operation risks cannot be guaranteed.

[0003] In response to the above technical problems, the present application specifically provides a method and system for identifying risks in power maintenance operations. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a method for identifying risks of power maintenance operations is provided.

[0005] A method for identifying risks in power maintenance operations, specifically comprising: S1 determines the matching conditions between the operation monitoring images in different operation areas and different operation types based on the recognition results of the operation monitoring images of the power maintenance operation, and determines the matching operation types in the operation types based on the matching conditions with the different operation types; S2 determines the relative position of the monitoring device and different operation areas based on the setting data of the monitoring device, and determines the historical similar monitoring scenes under the matching operation type in combination with the distribution data of the operation risk points in the different operation areas; S3 determines identification deviation data of operation risks in different historical similar monitoring scenarios, and when it is determined based on the identification deviation data that the setting state of the monitoring device does not meet the requirements, proceeds to the next step; S4 determines the optimization setting measures of the monitoring device according to the operation monitoring images in different operation areas, and uses the optimization setting measures to optimize the monitoring device to obtain the optimization setting results, and identifies the operation risks of the power maintenance operation based on the optimization setting results.

[0006] The beneficial effects of the present invention are: The matching job type in the job type is determined based on the matching situation with different job types, thereby realizing the determination of the current matching job type from the perspective of job monitoring images of different job areas. It also lays the foundation for further determination of the distribution data and risk data of job risk points based on the matching job type, thereby improving the reliability of the identification and processing of job risks.

[0007] Based on the identification deviation data of operational risks in different historically similar monitoring scenarios, it is determined whether the setting status of the monitoring device meets the requirements. This avoids the technical problem that the current monitoring device scheme cannot accurately identify operational risks when the identification deviation of operational risks in historically similar monitoring scenarios is relatively serious. This realizes the dynamic adjustment of the monitoring setting scheme and lays the foundation for avoiding the occurrence of operational risk accidents.

[0008] A further technical solution is that the types of operations include live maintenance operations, live testing operations, live operation operations, power-off installation operations, and power-off testing operations.

[0009] A further technical solution is that the matching condition includes image similarity coefficients with job monitoring images of different job types.

[0010] A further technical solution is that a method for determining a matching job type in the job type is: Determine the similarity of the operation equipment of different operation types in different operation areas by matching the operation monitoring images in different operation areas with different operation types; Determining the number of similar working equipment in different working areas based on similarities with working equipment in different working areas; The total number of similar operating equipment is determined according to the number of similar operating equipment in different operating areas, and the matching operating type in the operating types is determined using the total number.

[0011] A further technical solution is that the matching operation type is the operation type with the largest total number of similar operation equipment.

[0012] A further technical solution is that the method for determining the optimization setting measures of the monitoring device is: Determine the operation risk points in the operation monitoring images in the different operation areas based on the analysis results of the operation monitoring images in the different operation areas; Determine the recognition difficulty coefficients of different operation risk points according to the image sizes of different operation risk points in the operation monitoring image; Based on the identification difficulty coefficients of different operation risk points, identification difficulty risk points in the operation area are determined, and the number of identification difficulty risk points is used to determine the optimal setting measures for the monitoring device in the operation area.

[0013] A further technical solution is to use the number of the identification difficulty risk points to determine the optimal setting measures for the monitoring device in the operation area, specifically including: The number of the identification difficulty risk points is used to determine the identification difficulty operation area in the operation area, and an independent monitoring device is set for the identification difficulty operation area within a preset area.

[0014] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned method for identifying risks of power maintenance operations when running the computer program.

[0015] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0018] Figure 1 It is a flow chart of a method for identifying risks in power maintenance operations; Figure 2 is a flow chart of a method for determining a matching job type among job types; Figure 3 is a flow chart of a method for determining historical similar monitoring scenarios under matching job types; Figure 4 The present invention is a flow chart of a method for determining optimal setting measures of a monitoring device. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0020] For ease of understanding, the scheme will be described below through Example 1 and Example 2: Example 1

[0021] To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a method for identifying risks in power maintenance operations is provided, which specifically includes: S1 determines the matching conditions between the operation monitoring images in different operation areas and different operation types based on the recognition results of the operation monitoring images of the power maintenance operation, and determines the matching operation types in the operation types based on the matching conditions with the different operation types; Furthermore, the operation types include live maintenance operations, live testing operations, live operation operations, power-off installation operations, and power-off testing operations.

[0022] It can be understood that the matching condition includes image similarity coefficients with job monitoring images of different job types.

[0023] Specifically, Figure 2 As shown, the method for determining the matching job type in the job type is: Determine the similarity of the operation equipment of different operation types in different operation areas by matching the operation monitoring images in different operation areas with different operation types; Determining the number of similar working equipment in different working areas based on similarities with working equipment in different working areas; The total number of similar operating equipment is determined according to the number of similar operating equipment in different operating areas, and the matching operating type in the operating types is determined using the total number.

[0024] It should be noted that the matching operation type is the operation type with the largest total number of similar operation equipment.

[0025] Optionally, the method for determining the matching job type in the job type is: Determine the similarity between the operation equipment of different operation types in different operation areas based on the matching between the operation monitoring images in different operation areas and different operation types, and determine the number of similar operation equipment in different operation areas based on the similarity between the operation equipment in different operation areas; When there is an operation area where the number of similar operation equipment does not meet the requirement, it is determined that the operation type does not belong to the matching operation type; When there is no operating area where the number of similar operating equipment does not meet the requirements: Determine the total number of similar operating equipment according to the number of similar operating equipment in different operating areas, and when the total number does not meet the requirement, determine that the operating type does not belong to the matching operating type; When the total quantity meets the requirements: Based on the similarity of the operation types of operators in different similar operation equipment, the operation similarity weight coefficients of different similar operation equipment are determined, and when the number of similar operation equipment whose operation similarity weight coefficients do not meet the requirements does not meet the requirements, it is determined that the operation type does not belong to the matching operation type; When the number of similar operation equipment that does not meet the requirements for the operation similarity weight coefficient meets the requirements: Based on the matching situation of the work monitoring images in different work areas and different work types, the similarity of the operation types of the operators in the work monitoring images in different work areas is determined, and combined with the number of similar work equipment in different work areas, the operation similarity coefficients in different work areas are determined. Based on the operation similarity coefficients in different work areas, the comprehensive operation similarity coefficients with different work types are determined, and the comprehensive operation similarity coefficient is used to determine whether the work type belongs to a matching work type.

[0026] S2 determines the relative position of the monitoring device and different operation areas based on the setting data of the monitoring device, and determines the historical similar monitoring scenes under the matching operation type in combination with the distribution data of the operation risk points in the different operation areas; Furthermore, the distribution data of the operation risk points in the operation area is determined according to the matching operation type, and specifically determined according to the distribution data of the operation risk points of the matching operation type in different operation areas.

[0027] Specifically, Figure 3 As shown, the method for determining the historical similar monitoring scenes under the matching job type is: Determine the deviation from the relative position of the historical monitoring scene in the different operating areas based on the relative position of the monitoring device and the different operating areas; Determine a deviation operation area in the operation area by using the deviation amount of the relative position; According to the distribution data of the operation risk points in the deviation operation area, the number of the operation risk points in the deviation operation area is determined, and according to the number of the operation risk points in the deviation operation area, it is determined whether the historical monitoring scene is a historical similar monitoring scene.

[0028] Furthermore, the identified deviation area is an operating area where the deviation of the relative position is not within a preset deviation range.

[0029] It can be understood that when the number of operation risk points in the deviation operation area is greater than the preset number of risk points, it is determined that the historical monitoring scene does not belong to a historical similar monitoring scene.

[0030] In another embodiment, the method for determining the historical similar monitoring scenes under the matching operation type is: S21 determines the deviation of the relative position of the monitoring device and the different operation areas from the historical monitoring scene in the different operation areas based on the relative position of the monitoring device and the different operation areas, and determines the number of operation risk points in the different operation areas based on the distribution data of the operation risk points in the different operation areas; S22 determines the identification deviation coefficients of different operation areas based on the number of operation risk points in different operation areas and the deviation of relative positions; S23 determines a comprehensive identification deviation coefficient according to the identification deviation coefficients of different operation areas, and uses the comprehensive identification deviation coefficient to determine whether the historical monitoring scene is a historical similar monitoring scene.

[0031] Furthermore, the identification deviation coefficient of the operation area is determined according to a preset deviation coefficient corresponding to the number of the operation risk points and the deviation amount of the relative position.

[0032] Specifically, when the identification deviation coefficient is greater than a preset deviation coefficient threshold, it is determined that the historical monitoring scene does not belong to a historical similar monitoring scene.

[0033] Optionally, the above step S21 includes the following contents: S211: Determine the deviation of the relative position of the monitoring device and the different operating areas from the historical monitoring scene in the different operating areas based on the relative position of the monitoring device. When the deviation of the relative position is used to determine that there is a deviation operating area, and when the deviation of the relative position is used to determine that there is no deviation operating area, it is determined that the historical monitoring scene belongs to a historical similar monitoring scene. S212: When the number of the deviation operation areas does not meet the requirement, it is determined that the historical monitoring scene does not belong to the historical similar monitoring scene. When the number of the deviation operation areas meets the requirement, the process proceeds to step S213; S213 determines the number of operation risk points in different operation areas based on the distribution data of the operation risk points in different operation areas. When the total number of operation risk points in the deviation operation area does not meet the requirements, it is determined that the historical monitoring scene does not belong to a historical similar monitoring scene. When the total number of operation risk points in the deviation operation area meets the requirements, proceed to step S22.

[0034] Optionally, the above step S22 includes the following contents: S221 determines the identification deviation coefficients of different operation areas based on the number of operation risk points in different operation areas and the deviation of relative positions. When the average value of the identification deviation coefficients of different operation areas does not meet the requirements, it is determined that the historical monitoring scene does not belong to the historical similar monitoring scene. When the average value of the identification deviation coefficients of different operation areas meets the requirements, it proceeds to step S222; S222: when it is determined that there is an operating area whose identification deviation coefficient does not meet the requirements according to the identification deviation coefficients of different operating areas, the operating area whose identification deviation coefficient does not meet the requirements is used as the identification deviation area, and the process proceeds to step S223: when there is no operating area whose identification deviation coefficient does not meet the requirements, it is determined that the historical monitoring scene belongs to a historical similar monitoring scene; S223 obtains the number of identification deviation areas. When the number ratio of the identification deviation areas does not meet the requirement, it is determined that the historical monitoring scene does not belong to the historical similar monitoring scene. When the number ratio of the identification deviation areas meets the requirement, the process proceeds to step S224. S224 determines the area identification deviation coefficient based on the number of identification deviation areas and the proportion of the number of identification deviation areas. When the area identification deviation coefficient does not meet the requirements, it is determined that the historical monitoring scene does not belong to a historical similar monitoring scene. When the area identification deviation coefficient meets the requirements, proceed to step S23.

[0035] S3 determines identification deviation data of operation risks in different historical similar monitoring scenarios, and when it is determined based on the identification deviation data that the setting state of the monitoring device does not meet the requirements, proceeds to the next step; Further, determining that the setting state of the monitoring device does not meet the requirements specifically includes: Determine the number of identification deviations of operation risks in different historically similar monitoring scenarios using identification deviation data of operation risks in different historically similar monitoring scenarios; According to the number of identification deviations of the operation risk in different historical similar monitoring scenarios, the average number of identification deviations of the operation risk in different historical similar monitoring scenarios is calculated and used as the average number of deviations; Based on the average value of the number of deviations, it is determined whether the setting state of the monitoring device meets the requirements.

[0036] It should be noted that when the average value of the number of deviations is greater than a preset threshold value of the number of deviations, it is determined that the setting state of the monitoring device does not meet the requirements.

[0037] Optionally, determining that the setting state of the monitoring device does not meet the requirements specifically includes: Determine the number of identification deviations of operation risks in different historically similar monitoring scenarios using identification deviation data of operation risks in different historically similar monitoring scenarios; The historical similar monitoring scenarios with the number of identification deviations are used as identification deviation monitoring scenarios; Based on the number of the identified deviation monitoring scenarios, it is determined whether the setting state of the monitoring device meets the requirements.

[0038] In another embodiment, determining that the setting state of the monitoring device does not meet the requirements specifically includes: Determine the number of identification deviations of the operation risk in different historical similar monitoring scenarios based on the identification deviation data of the operation risk in different historical similar monitoring scenarios, and when there is a historical similar monitoring scenario in which the number of identification deviations does not meet the requirements, determine that the setting state of the monitoring device does not meet the requirements; When there is no historical similar monitoring scenario where the number of identification deviations does not meet the requirements: Using historical similar monitoring scenes with identification deviation times as identification deviation monitoring scenes, and when the number of the identification deviation monitoring scenes does not meet the requirement, determining that the setting state of the monitoring device does not meet the requirement; When the number of the identification deviation monitoring scenarios meets the requirement: Determine the number of risk point identification deviations at different operation risk points according to the number of identification deviations of different identification deviation monitoring scenarios, and when there is an operation risk point where the number of risk point identification deviations does not meet the requirements, determine that the setting state of the monitoring device does not meet the requirements; When there is no operational risk point where the number of risk point identification deviations does not meet the requirements: Determine the identification deviation probability of different operation risk points based on the number of identification deviations and the number of accurate identifications of different operation risk points in different historical similar monitoring scenes; when there is an operation risk point whose identification deviation probability does not meet the requirements, determine that the setting state of the monitoring device does not meet the requirements; When there are no operational risk points where the probability of identification deviation does not meet the requirements: The identification reliability coefficients in different operation areas are determined by the identification deviation probabilities of operation risk points in different operation areas, and a comprehensive identification reliability coefficient is determined based on the identification reliability coefficients in different operation areas. Whether the setting status of the monitoring device meets the requirements is determined by the comprehensive identification reliability coefficient.

[0039] S4 determines the optimization setting measures of the monitoring device according to the operation monitoring images in different operation areas, and uses the optimization setting measures to optimize the monitoring device to obtain the optimization setting results, and identifies the operation risks of the power maintenance operation based on the optimization setting results.

[0040] Specifically, Figure 4 As shown, the method for determining the optimization setting measures of the monitoring device is: Determine the operation risk points in the operation monitoring images in the different operation areas based on the analysis results of the operation monitoring images in the different operation areas; Determine the recognition difficulty coefficients of different operation risk points according to the image sizes of different operation risk points in the operation monitoring image; Based on the identification difficulty coefficients of different operation risk points, identification difficulty risk points in the operation area are determined, and the number of identification difficulty risk points is used to determine the optimal setting measures for the monitoring device in the operation area.

[0041] Furthermore, the number of the identification difficulty risk points is used to determine the optimal setting measures for the monitoring device in the operation area, specifically including: The number of the identification difficulty risk points is used to determine the identification difficulty operation area in the operation area, and an independent monitoring device is set for the identification difficulty operation area within a preset area. Example 2

[0042] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned method for identifying risks of power maintenance operations when running the computer program.

[0043] Optionally, the method for determining the power limitation strategy for the time period is: Determine the cumulative reverse power transmission time of the matched user in the time period based on the reverse current data of the matched user in the time period, and when the number of matched users whose cumulative reverse power transmission time is greater than a preset cumulative time threshold is greater than the preset number of matched users, determine the power limitation strategy for the time period using a preset strategy; When the number of matching users whose cumulative reverse power transmission duration is greater than the preset cumulative duration threshold is not greater than the preset number of matching users: Obtaining the cumulative duration of reverse power transmission of the matched user in the time period, and determining the cumulative duration sum in combination with the number of the matched users, and when the cumulative duration sum is greater than a preset duration limit value, determining to adopt a preset strategy to determine the power limitation strategy for the time period; When the cumulative duration is not greater than the preset duration limit: Determine the total reverse power transmission current of the matching users on different dates in the time period based on the reverse power transmission data of the matching users on different dates in the time period, and when the number of dates when the total reverse power transmission current is greater than the preset current threshold is greater than the preset number of dates, determine to adopt the preset strategy to determine the power limitation strategy for the time period; When the number of days when the total reverse power transmission current is greater than the preset current threshold is not greater than the preset number of days: Determine the reverse power transmission abnormality coefficients of different dates according to the total reverse power transmission current of the matching users on different dates in the time period and the number of matching users with reverse power transmission, and when the number of dates with the reverse power transmission abnormality coefficient greater than the preset abnormality coefficient is greater than the preset number of dates, determine to adopt the preset strategy to determine the power limitation strategy for the time period; When the number of days when the reverse power transmission abnormality coefficient is greater than the preset abnormality coefficient is not greater than the preset number of days: The power limit threshold of the time period is determined according to the reverse power transmission abnormality coefficient on different dates, and the power limit strategy of the time period is determined based on the power limit threshold.

[0044] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0045] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0046] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for identifying risks in power maintenance operations, characterized in that: Specifically include: Based on the recognition results of the operation monitoring images of the power maintenance operation, the matching conditions between the operation monitoring images in different operation areas and different operation types are determined, and the matching operation types among the operation types are determined based on the matching conditions with the different operation types; Based on the setting data of the monitoring device, the relative position of the monitoring device and different operation areas is determined, and in combination with the distribution data of the operation risk points in the different operation areas, the historical similar monitoring scenes under the matching operation type are determined; Determine identification deviation data of operation risks in different historical similar monitoring scenarios, and when it is determined based on the identification deviation data that the setting state of the monitoring device does not meet the requirements, proceed to the next step; Based on the operation monitoring images in different operation areas, the optimization setting measures of the monitoring device are determined, and the optimization setting measures are used to optimize the monitoring device to obtain the optimization setting results. Based on the optimization setting results, the operation risks of the power maintenance operation are identified.

2. A method for identifying risks in power maintenance operations according to claim 1, characterized in that: The types of work include live maintenance work, live testing work, live operation work, power-off installation work, and power-off testing work.

3. A method for identifying risks in power maintenance operations according to claim 1, characterized in that: The matching conditions include image similarity coefficients with job monitoring images of different job types.

4. A method for identifying risks in power maintenance operations according to claim 1, characterized in that: The method for determining the matching job type in the job type is: Determine the similarity of the operation equipment of different operation types in different operation areas by matching the operation monitoring images in different operation areas with different operation types; Determining the number of similar working equipment in different working areas based on similarities with working equipment in different working areas; The total number of similar operating equipment is determined according to the number of similar operating equipment in different operating areas, and the matching operating type in the operating types is determined using the total number.

5. A method for identifying risks in power maintenance operations according to claim 4, characterized in that: The matching operation type is the operation type having the largest total number of similar operation devices.

6. A method for identifying risks in power maintenance operations according to claim 1, characterized in that: The distribution data of the operation risk points in the operation area is determined according to the matching operation type, and specifically determined according to the distribution data of the operation risk points of the matching operation type in different operation areas.

7. A method for identifying risks in power maintenance operations according to claim 6, characterized in that: The method for determining the historical similar monitoring scenes under the matching operation type is: Determine the deviation from the relative position of the historical monitoring scene in the different operating areas based on the relative position of the monitoring device and the different operating areas; Determine a deviation operation area in the operation area by using the deviation amount of the relative position; According to the distribution data of the operation risk points in the deviation operation area, the number of the operation risk points in the deviation operation area is determined, and according to the number of the operation risk points in the deviation operation area, it is determined whether the historical monitoring scene is a historical similar monitoring scene.

8. A method for identifying risks in power maintenance operations according to claim 1, characterized in that: The method for determining the optimization setting measures of the monitoring device is: Determine the operation risk points in the operation monitoring images in the different operation areas based on the analysis results of the operation monitoring images in the different operation areas; Determine the recognition difficulty coefficients of different operation risk points according to the image sizes of different operation risk points in the operation monitoring image; Based on the identification difficulty coefficients of different operation risk points, identification difficulty risk points in the operation area are determined, and the number of identification difficulty risk points is used to determine the optimal setting measures for the monitoring device in the operation area.

9. A method for identifying risks in power maintenance operations according to claim 8, characterized in that: The optimization setting measures of the monitoring device in the operation area are determined by using the number of the identification difficulty risk points, specifically including: The number of the identification difficulty risk points is used to determine the identification difficulty operation area in the operation area, and an independent monitoring device is set for the identification difficulty operation area within a preset area.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a method for identifying risks of power maintenance operations as described in any one of claims 1-9 is executed.

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