A power distribution station house diversification inspection method based on a device set

Abstract

This invention discloses a diversified inspection method for substations based on equipment sets, relating to the field of intelligent equipment control technology. Existing autonomous substation inspections mostly employ inspection robots, which are costly to configure and difficult to cover all substations. This invention includes the following steps: acquiring equipment resource information of the substation; calculating evaluation indicators for various characteristic values ​​to generate a set of equipment characteristics; obtaining weight coefficients for characteristic values ​​using the analytic hierarchy process (AHP) and entropy method; calculating the inspection index and inspection cycle; establishing the association relationship r1 between video monitoring equipment and primary equipment resources and sensors, and establishing the association relationship r2 between preset video monitoring positions and sensors corresponding to different parts of the primary equipment; determining the inspection plan; and conducting the inspection of the substation. This technical solution utilizes existing video surveillance equipment within the substation, actively completing equipment status inspections through coordinated linkage between video monitoring equipment, sensors, and primary equipment resources. It is low-cost and widely applicable.

Description

Technical Field

[0001] This invention relates to the field of intelligent device control technology, and in particular to a diversified inspection method for power distribution substations based on device sets. Background Technology

[0002] Substations play a crucial role in ensuring stable power supply to the community, making the safe operation of primary equipment within them of paramount importance. Traditional power distribution network inspections rely solely on visual inspections by personnel, requiring manual data entry. This labor-intensive approach is highly dependent on the inspectors' specialized skills. Furthermore, traditional substation inspections are prone to human error and inefficiency.

[0003] Existing autonomous station inspections mostly rely on inspection robots, which are expensive and can only be carried out by pre-setting specific routes or physical guide rails. Data transmission and analysis require additional terminal equipment, which is cumbersome, costly, and difficult to cover all stations.

[0004] With the continuous advancement of power grid automation, the types of monitoring equipment installed in substations are increasing. According to the future construction requirements of State Grid substations, the coverage rate of video monitoring equipment in future substations should reach 100%. However, many video monitoring devices currently installed in substations can only be used for video recording and real-time querying, and are not linked with various monitoring devices in the substation. The methods are limited and fail to make the most efficient use of cameras and other video monitoring equipment. Summary of the Invention

[0005] The technical problem to be solved and the technical task proposed by this invention is to improve and refine existing technical solutions, and to provide a diversified inspection method for power distribution substations based on equipment sets, so as to achieve the purpose of autonomous inspection of power distribution substations. To this end, this invention adopts the following technical solution.

[0006] A diversified inspection method for substations based on equipment sets includes the following steps:

[0007] 1) Obtain equipment resource information of the substation and generate an equipment resource set; the equipment resource information includes equipment information, sensor information, and video detection equipment information; the equipment resource set includes a primary equipment resource set, a sensor set, and a video monitoring equipment set;

[0008] 2) Based on the equipment resource set extracted in step 1), obtain historical inspection data, equipment hidden danger data, equipment defect data, and equipment failure data; obtain the rated life, service life, number of historical hidden dangers, number of historical defects, and number of historical failures for each piece of equipment;

[0009] 3) Based on the equipment resource set extracted in step 1), calculate the evaluation indicators for various feature values, and generate a set of equipment characteristics after normalization. The evaluation indicators include: evaluation indicators for equipment service life; evaluation indicators for historical hidden dangers; evaluation indicators for historical failures; evaluation indicators for equipment importance level; and evaluation indicators for equipment control level.

[0010] 4) The importance of each eigenvalue is evaluated pairwise using the analytic hierarchy process (AHP) to generate an evaluation matrix;

[0011] The largest eigenvalue and its corresponding eigenvector are obtained from the evaluation matrix. A consistency test is performed on the eigenvectors. The largest eigenvector that passes the consistency test is normalized to obtain the weight coefficients of each eigenvalue.

[0012] 5) Correct the weighting coefficients of the eigenvalues ​​using the entropy method; and calculate the inspection index and inspection cycle of the equipment to be inspected;

[0013] 6) Obtain panoramic photos of the substation room collected by the video monitoring equipment, establish the association relationship r1 between the video monitoring equipment and primary equipment resources and sensors based on the image clarity and sensor readings, and set preset positions within the monitoring system;

[0014] 7) Based on the sensor type and equipment location in the substation, establish the association relationship r2 between the video monitoring preset positions and sensors corresponding to different parts of the primary equipment;

[0015] 8) Determine the inspection plan based on the correlation r1, correlation r2 and inspection index. The inspection plan includes the autonomous inspection and photo taking cycle of each device determined by the inspection index, the preset position information of the corresponding video monitoring equipment determined by the correlation r1, the corresponding sensor information determined by the correlation r2 and the preset position information of the video monitoring equipment corresponding to the sensor.

[0016] 9) Conduct an inspection of the substation according to the inspection plan;

[0017] 901) Determine whether an equipment alarm or fault event has occurred in the substation. If so, according to the correlation r1, mobilize the corresponding video monitoring equipment to take pictures of the equipment to be inspected and upload them. At the same time, increase the number of corresponding indicators in the equipment evaluation index by 1, recalculate and generate the equipment weight coefficient and inspection index, refresh the equipment autonomous inspection cycle, and determine a new inspection plan. If not, proceed to the next step.

[0018] 902) Determine whether a sensor alarm event has occurred in the power distribution room. If so, mobilize the corresponding video monitoring equipment according to the correlation relationship r1 and r2 to take pictures of the equipment to be inspected and upload them. At the same time, start a new round of autonomous inspection based on the time node when the terminal obtains the alarm event. If not, proceed to the next step.

[0019] 903) Automatic inspection and photography will be carried out and uploaded to the monitoring system to monitor the substation.

[0020] This technical solution employs two different subjective and objective evaluation methods: the analytic hierarchy process (AHP) and the entropy method. Based on equipment lifespan, historical hazard data, historical defect quantity, historical fault data, equipment importance, and equipment control level, a weighted inspection index is calculated for the equipment to be inspected. An inspection plan is then developed based on this index. The combination of AHP and entropy method effectively reduces data errors, making the weighted index more consistent with reality and ensuring a reasonable inspection plan.

[0021] Based on the equipment to be inspected, the system establishes relationships between these equipment and video surveillance devices, as well as between them and sensors. Through these relationships, a set of equipment to be inspected and an inspection plan are created. Video surveillance devices are then rationally deployed according to the inspection plan. Intelligent inspection can be achieved without adding extra equipment; configuration is convenient, and modification costs are low. It can essentially cover all substations. This system enables comprehensive autonomous inspection of substations, automatic identification of potential hazards and defects, reduces the inspection workload of work teams, improves inspection efficiency, and ensures safety and reliability, better meeting the development needs of substations.

[0022] The inspection cycle can be adjusted as needed. For important equipment, the inspection interval can be shortened, while for less important equipment, the inspection interval can be increased. This reduces the amount of data processing while ensuring the timely detection of potential hazards and defects, improving inspection efficiency and safety. Furthermore, the inspection plan can be self-adjusted to better adapt to each substation and meet the needs of each substation.

[0023] As a preferred technical means: in step 1),

[0024] Generate a primary equipment resource set based on the resource information of all primary equipment in the substation:

[0025] S psr =(P1,P2,P3,···,P m ), where m represents the number of primary equipment resources in the substation;

[0026] Sensor information within a substation is extracted on a per-substation basis to generate a sensor set:

[0027] S ser=(S1,S2,S3,···,S n ), where n represents the number of sensors in the substation;

[0028] Extract video monitoring equipment information from each substation to generate a video monitoring equipment set.

[0029] S vcr = (V1,V2,V3,···,Vo), where o represents the number of video monitoring devices in the substation.

[0030] As a preferred technical means: in step 3)

[0031] Evaluation index for equipment service life: The higher the ratio of service life to rated life, the higher the score. The minimum score is 0, and the maximum score is 10. This indicator scores 10 points when the service life exceeds the rated life.

[0032] Evaluation index for historical hidden dangers: During operation and use, the higher the ratio of hidden dangers discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical hazards exceeds the service life, 10 points will be awarded.

[0033] Evaluation index for the number of historical defects: During operation and use, the higher the ratio of defects discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10. If the number of historical hazards exceeds the service life, 10 points will be awarded.

[0034] Evaluation index for historical failures: During operation and use, the higher the ratio of the number of failures to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical faults exceeds the service life, 10 points will be awarded.

[0035] Evaluation indicators for the importance level of equipment: The larger the area of ​​power outage caused by equipment failure, the higher the level. The highest level is 10, which means that the equipment failure will cause a complete power outage in the downstream power supply area of ​​the substation. The lowest level is 0, which means that the equipment failure will only cause non-power outage faults or momentary power outage events and will not affect the power supply stability of the power supply area.

[0036] Evaluation indicators for equipment control level: The greater the importance of the area affected by the equipment failure and power outage, the higher the level, with a maximum of 10, meaning that the equipment failure and power outage will affect hospitals, major public facilities and institutions, and a minimum of 0, meaning that the equipment failure will only cause non-power outage faults and will not affect the power supply stability of the power supply area.

[0037] Based on the acquired data, a set of device characteristics is generated after normalization:

[0038] P i = (p1, p2, p3, p4, p5, p6), where i represents the i-th device in the substation, and p represents the corresponding evaluation index.

[0039] It is simple to operate and effective, can speed up calculations, and provides intuitive and easily identifiable data.

[0040] As a preferred technical means: In step 4), the evaluation matrix is ​​generated as follows:

[0041]

[0042] In the evaluation matrix, A i Let a represent the evaluation matrix of the i-th device. ij This indicates the relative importance of the i-th and j-th feature values ​​of the device; a 7-degree rating system is used, where 1: both elements are equally important; 3: one element is slightly more important than the other; 5: one element is significantly more important than the other; 7: one element is strongly more important than the other; and 2, 4, 6: between the above two ratings.

[0043] When determining the weight vector, the largest eigenvalue and the corresponding eigenvector are obtained from the evaluation matrix described above: λ. i A i =λ i α i , where λ i α is the largest eigenvalue of the evaluation matrix. i It is the largest eigenvector corresponding to the largest eigenvalue of the evaluation matrix.

[0044] As a preferred technical means: In step 5), when correcting the weight coefficients of eigenvalues ​​using the entropy method, the proportion of the m-th expert in the weight coefficient of the n-th eigenvalue is calculated:

[0045]

[0046] Where 6 represents the order of the evaluation matrix;

[0047] Calculate entropy redundancy: k n =1-e n ;

[0048] Calculate the information weight of each indicator:

[0049] The weights α obtained by modifying the information weights determined by the entropy method are obtained by the analytic hierarchy process. i :

[0050]

[0051] Each device is scored using weighted coefficients and actual data. This score serves as the device's inspection index score; a higher score indicates that the device received more attention during the inspection process.

[0052] P value =P i α' i P value This indicates the inspection index of the equipment.

[0053] As a preferred technical means: in step 6)

[0054] If multiple video monitoring devices can clearly read the same sensor reading, arbitrarily select one of the video monitoring devices to establish a correlation relationship.

[0055] If multiple video monitoring devices can clearly capture images of the same location on the same equipment, an association can be established with any one of the video monitoring devices.

[0056] For different parts of the same device that can be clearly photographed by different monitoring devices, establish a correlation between the video monitoring devices and the different parts.

[0057] The system leverages the capabilities of video monitoring equipment and provides a degree of redundancy, facilitating the replacement of associated relationships and reducing the likelihood of inspections being impossible due to equipment damage, thereby ensuring the reliability and stability of the operation.

[0058] As a preferred technical approach: In step 9), during the monitoring of the substation, the data refreshes the equipment characteristic value data through an anomaly synchronization mechanism. When an equipment failure event occurs, the backend algorithm acquires the anomaly signal, promptly synchronizes the newly added fault information, refreshes the equipment characteristic values, and simultaneously regenerates the equipment inspection index and inspection cycle, automatically generating a new inspection plan. This achieves automatic adjustment of the inspection plan, thereby realizing automatic maintenance through inspection.

[0059] Beneficial effects:

[0060] 1. This technical solution establishes a coordinated and orderly autonomous inspection scheme for substation cameras, which breaks away from the current disorderly recording and photo-taking mode of cameras in the station. It no longer relies on operators to actively collect video or image data. This invention can actively collect relevant image or video data and establish a practical and effective inspection linkage mechanism through the established correlation relationship, providing a basis for subsequent event analysis.

[0061] 2. Determine the equipment inspection cycle based on the inspection index calculated from historical equipment data, thereby strengthening the autonomous inspection of substations and reducing network pressure on internal and external networks of the power distribution network.

[0062] 3. Breakthrough in high-cost automated inspection robots: Video surveillance equipment in power distribution substations is more widely distributed and cheaper. Coordination and linkage between each video monitoring device, sensor, and primary equipment resource can proactively complete equipment status inspections. Based on the background intelligent analysis algorithm, it is convenient for inspection personnel to confirm the equipment status.

[0063] 4. Make full use of the various monitoring devices existing in the station building, coordinate the relationship between various sensors and video monitoring devices, and strengthen the real-time inspection capability of the equipment by establishing correlation relationships and conducting inspections and taking pictures of the corresponding parts of the equipment.

[0064] 5. By using IoT technology, we have achieved panoramic data collection and autonomous inspection of primary equipment in the substation, including information data from primary equipment, video data, and sensor data. Combined with intelligent image recognition technology, we have achieved all-round autonomous inspection of the substation, automatic identification of hidden dangers and defects, reduced the inspection pressure on the shift team, improved inspection efficiency, and ensured safety and reliability, thus better meeting the development needs of the substation. Attached Figure Description

[0065] Figure 1 This is a flowchart of the present invention.

[0066] Figure 2 This is the inspection flowchart of the present invention. Detailed Implementation

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings.

[0068] like Figure 1 and Figure 2 As shown, the present invention includes the following steps:

[0069] S1: Obtain primary equipment resource information from the power grid resource business platform and extract data such as equipment lifespan. Specifically, select a substation equipped with sensors and video monitoring equipment, and obtain all primary equipment resource ledger data and various sensor data within the substation based on the power grid resource business platform; obtain information on all video monitoring equipment within the substation based on the power grid resource business platform; obtain historical inspection information of the substation and historical hidden danger data, equipment defect quantity, and equipment failure data for each piece of equipment; and extract the importance and control level of the primary equipment resources within the substation.

[0070] Extract all primary equipment resource information within the station building and generate a primary equipment resource set:

[0071] S psr =(P1,P2,P3,···,P m ), where m represents the number of primary equipment resources in the station building;

[0072] Sensor information within each station building is extracted to generate a sensor set:

[0073] S ser =(S1,S2,S3,···,S n ), where n represents the number of sensors in the station building;

[0074] Extract video monitoring equipment information within each station building to generate a video monitoring equipment set:

[0075] S vcr = (V1, V2, V3, ..., Vo), where o represents the number of video monitoring devices in the station building;

[0076] S2: Based on the equipment resource set extracted in step S1), obtain the rated life, service life, number of historical hidden dangers, number of historical defects, and number of historical failures for each piece of equipment;

[0077] S3: Based on the equipment resource set extracted in step S1), calculate the evaluation index of various feature values;

[0078] Evaluation index for equipment service life: The higher the ratio of service life to rated life, the higher the score. The minimum score is 0, and the maximum score is 10. This indicator scores 10 points when the service life exceeds the rated life.

[0079] Evaluation index for historical hidden dangers: During operation and use, the higher the ratio of hidden dangers discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical hazards exceeds the service life, 10 points will be awarded.

[0080] Evaluation index for the number of historical defects: During operation and use, the higher the ratio of defects discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10. If the number of historical hazards exceeds the service life, 10 points will be awarded.

[0081] Evaluation index for historical failures: During operation and use, the higher the ratio of the number of failures to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical faults exceeds the service life, 10 points will be awarded.

[0082] Evaluation indicators for the importance level of equipment: The larger the area of ​​power outage caused by equipment failure, the higher the level. The highest level is 10, which means that the equipment failure will cause a complete power outage in the downstream power supply area of ​​the station. The lowest level is 0, which means that the equipment failure will only cause non-power outage faults or momentary power outage events and will not affect the power supply stability of the power supply area.

[0083] Evaluation indicators for equipment control level: The greater the importance of the area affected by the equipment failure and power outage, the higher the level. The highest level is 10, which means that the equipment failure and power outage will affect the power outage of hospitals, major public facilities, institutions, etc. The lowest level is 0, which means that the equipment failure will only cause non-power outage faults and will not affect the power supply stability of the power supply area.

[0084] Based on the data obtained above, a set of device characteristics is generated after normalization processing:

[0085] P i = (p1, p2, p3, p4, p5, p6), where i represents the i-th piece of equipment in the station building, and p represents the corresponding evaluation indicators;

[0086] S4: The importance of each eigenvalue is evaluated pairwise using the analytic hierarchy process (AHP) to generate an evaluation matrix.

[0087]

[0088] In the evaluation matrix, A i Let a represent the evaluation matrix of the i-th device. ij This indicates the relative importance of the i-th and j-th feature values ​​of the device. In this embodiment, a 7-degree rating system is used: 1: Both elements are equally important; 3: One element is slightly more important than the other; 5: One element is significantly more important than the other; 7: One element is strongly more important than the other; 2, 4, 6: Between the above two rating systems.

[0089] Determine the weight vector, and based on the evaluation matrix above, find its largest eigenvalue and the corresponding eigenvector: λ. i A i =λ i α i , where λ i α is the largest eigenvalue of the evaluation matrix. i This is the largest eigenvector corresponding to the largest eigenvalue of the evaluation matrix;

[0090] Furthermore, this eigenvalue undergoes a consistency check;

[0091] After normalizing the largest eigenvector that passes the consistency test, each element becomes the weight coefficient of each eigenvalue of the device to be inspected.

[0092] S5: Use the entropy method to correct the weight coefficients of the eigenvalues ​​in step (4), and calculate the proportion of the weight coefficient of the m-th expert for the n-th eigenvalue:

[0093]

[0094] Where 6 represents the order of the evaluation matrix. In this embodiment, there are a total of 6 experts and 6 eigenvalues, so the order of the evaluation matrix in this embodiment is 6.

[0095] Calculate entropy redundancy: k n =1-e n ;

[0096] Calculate the information weight of each indicator:

[0097] Furthermore, the weights α obtained from the analytic hierarchy process are corrected using the information weights determined by the entropy method. i :

[0098]

[0099] Each piece of equipment can be scored using weighting coefficients and actual data. This score serves as the equipment's inspection index score; a higher score indicates that the equipment received more attention during the inspection process.

[0100] P value =P i α' i P value This indicates the inspection index of the equipment;

[0101] S6: Based on the proactive acquisition of panoramic photos of the station building by video monitoring equipment, select those with good image clarity, establish the correlation relationship r1 between video monitoring equipment and primary equipment resources and sensors, and set preset positions within the monitoring system;

[0102] When multiple video monitoring devices can clearly read the same sensor readings, arbitrarily select one of the video monitoring devices to establish a correlation relationship.

[0103] When multiple video monitoring devices can clearly capture images of the same part of the same equipment, arbitrarily select one of the video monitoring devices to establish a relationship.

[0104] When different monitoring devices can clearly capture images of different parts of the same equipment, establish a correlation between the different parts and the video monitoring devices.

[0105] S7: Establish a correlation r2 based on the sensor type, monitoring location and equipment location within the station building; and set video monitoring preset positions for the correlation between the equipment location and the video monitoring equipment.

[0106] For example, the water immersion sensor in the cable trench establishes a correlation with multiple video monitoring preset positions of the cable. After a water immersion alarm is triggered, the video monitoring equipment takes pictures of different positions of the cable in the cable trench.

[0107] S8: Generate an equipment inspection plan P based on the above steps.ro ={P i ,T i ,r1(V m … V n ),r2(S j ,V0…V P )}, that is, using device P i Based on this, the inspection cycle T is determined. i Preset location information, etc.; specifically, it involves determining the autonomous inspection and photography cycle of each device based on the inspection index determined in step S5. According to the association r1 established in step S6, obtain the preset position information of the corresponding video monitoring device; according to the association r2 established in step S7, obtain the corresponding sensor information and the preset position information of the video monitoring device corresponding to the sensor.

[0108] If an equipment alarm or malfunction occurs in the station building, the corresponding video surveillance equipment is mobilized according to the correlation r1 to take pictures of the equipment to be inspected and upload them. At the same time, the number of corresponding indicators in the equipment evaluation index is increased by 1, the equipment weight coefficient and inspection index are recalculated and generated, and the equipment autonomous inspection cycle is refreshed.

[0109] If a sensor alarm event occurs in the station building, the corresponding video surveillance equipment will be mobilized according to the correlation r1 and r2 to take pictures of the equipment to be inspected and upload them. At the same time, a new round of autonomous inspection will start based on the time node when the terminal obtains the alarm event.

[0110] S9: Automatically inspects and takes photos according to the inspection plan and uploads them to the monitoring system to monitor the substation;

[0111] Conduct inspection tasks in accordance with the currently generated autonomous inspection plan, take photos of the equipment to be inspected, and upload them to the platform;

[0112] Based on real-time equipment alarm or fault data within the station building, the equipment inspection index is refreshed, the inspection cycle is recalculated, and the equipment is photographed and uploaded to the video image recognition system.

[0113] Based on sensor alarm data, the system takes photos of the corresponding equipment and uploads them to the video image recognition system, thus updating the station's inspection cycle.

[0114] Based on the aforementioned snapshot images, image recognition is performed to calculate the device's health value. If the health value deviates from the normal range, an alarm message is issued.

[0115] During the inspection, the data refreshes the equipment characteristic value data through the anomaly synchronization mechanism. When an equipment failure event occurs, the backend algorithm obtains the anomaly signal, promptly synchronizes the newly added fault information, refreshes the equipment characteristic value, and at the same time regenerates the equipment inspection index and inspection cycle, and automatically generates a new inspection plan.

[0116] above Figure 1 , 2 The method for diversified inspection of substations based on equipment sets shown is a specific embodiment of the present invention, which has demonstrated the substantial features and progress of the present invention. Based on the actual needs of use, equivalent modifications in shape, structure, etc. can be made to it according to the inspiration of the present invention, all of which are within the protection scope of this solution.

Claims

1. A power distribution substation diversification patrol method based on a device set, characterized by: Including steps 1) Obtain equipment resource information of the substation and generate an equipment resource set; the equipment resource information includes equipment information, sensor information, and video detection equipment information; the equipment resource set includes a primary equipment resource set, a sensor set, and a video monitoring equipment set; 2) Based on the equipment resource set extracted in step 1), obtain historical inspection data, equipment hidden danger data, equipment defect data, and equipment failure data; obtain the rated life, service life, number of historical hidden dangers, number of historical defects, and number of historical failures for each piece of equipment; 3) Based on the equipment resource set extracted in step 1), calculate the evaluation indicators for various feature values, and generate a set of equipment characteristics after normalization. The evaluation indicators include: evaluation indicators for equipment service life; evaluation indicators for historical hidden dangers; evaluation indicators for historical failures; evaluation indicators for equipment importance level; evaluation indicators for equipment control level; and evaluation indicators for the number of historical defects. 4) The importance of each eigenvalue is evaluated pairwise using the analytic hierarchy process (AHP) to generate an evaluation matrix; The largest eigenvalue and its corresponding eigenvector are obtained from the evaluation matrix. A consistency test is performed on the eigenvectors. The largest eigenvector that passes the consistency test is normalized to obtain the weight coefficients of each eigenvalue. 5) Correct the weighting coefficients of the eigenvalues ​​using the entropy method; calculate the inspection index score for each device using the weighting coefficients and actual data: ,in This is a collection of equipment features. The corrected weighting coefficients are used; and the autonomous inspection and photography cycle for each device is determined based on the inspection index score. ; 6) Obtain panoramic photos of the substation room collected by the video monitoring equipment, establish the association relationship r1 between the video monitoring equipment and primary equipment resources and sensors based on the image clarity and sensor readings, and set preset positions within the monitoring system; 7) Based on the sensor type and equipment location within the substation, establish the association relationship r2 between the preset video monitoring positions and sensors corresponding to different parts of the primary equipment; 8) Determine the inspection plan based on the correlation r1, correlation r2 and inspection index. The inspection plan includes the autonomous inspection and photo taking cycle of each device determined by the inspection index, the preset position information of the corresponding video monitoring equipment determined by the correlation r1, the corresponding sensor information determined by the correlation r2 and the preset position information of the video monitoring equipment corresponding to the sensor. 9) Conduct an inspection of the substation according to the inspection plan; 901) Determine whether an equipment alarm or fault event has occurred in the substation. If so, according to the correlation r1, mobilize the corresponding video monitoring equipment to take pictures of the equipment to be inspected and upload them. At the same time, increase the number of corresponding indicators in the equipment evaluation index by 1, recalculate and generate the equipment weight coefficient and inspection index, refresh the equipment autonomous inspection cycle, and determine a new inspection plan. If not, proceed to the next step. 902) Determine whether a sensor alarm event has occurred in the power distribution room. If so, mobilize the corresponding video monitoring equipment to take pictures and upload them to the equipment to be inspected according to the correlation relationship r1 and r2. At the same time, start a new round of autonomous inspection based on the time node when the terminal obtains the alarm event. If not, proceed to the next step. 903) Automatic inspection and photography will be carried out and uploaded to the monitoring system to monitor the power distribution station.

2. The power distribution stationhouse multi-element inspection method based on a device set according to claim 1, characterized in that: In step 1), Generate a primary equipment resource set based on the resource information of all primary equipment in the substation: S psr =(P1,P2,P3,···,P m ), where m represents the number of primary equipment resources in the substation; Sensor information within a substation is extracted on a per-substation basis to generate a sensor set: S ser =(S1,S2,S3,···,S n ), where n represents the number of sensors in the substation; Extract video monitoring equipment information from each substation to generate a video monitoring equipment set. S vcr = (V1, V2, V3, ..., Vo), where o represents the number of video monitoring devices in the substation.

3. The method of claim 2, wherein the method further comprises: In step 3), Evaluation index for equipment service life: The higher the ratio of service life to rated life, the higher the score. The minimum score is 0, and the maximum score is 10. When the service life exceeds the rated life, this indicator will be scored 10 points. Evaluation indicators for historical hidden dangers: During operation and use, the higher the ratio of hidden dangers discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical hidden dangers exceeds the service life, 10 points will be awarded. Evaluation index for the number of historical defects: During operation and use, the higher the ratio of defects discovered to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10. If the number of historical defects exceeds the service life, 10 points will be awarded. Evaluation index for historical failures: During operation and use, the higher the ratio of the number of failures to the years of service, the higher the score, ranging from a minimum of 0 to a maximum of 10 points. If the number of historical faults exceeds the service life, 10 points will be awarded. Evaluation indicators for the importance level of equipment: The larger the area of ​​power outage caused by equipment failure, the higher the level. The highest level is 10, which means that the equipment failure will cause a complete power outage in the downstream power supply area of ​​the substation. The lowest level is 0, which means that the equipment failure will only cause non-power outage faults or momentary power outage events and will not affect the power supply stability of the power supply area. Evaluation indicators for equipment control level: The greater the importance of the area affected by the equipment failure and power outage, the higher the level, with a maximum of 10, meaning that the equipment failure and power outage will affect hospitals, major public facilities and institutions, and a minimum of 0, meaning that the equipment failure will only cause non-power outage faults and will not affect the power supply stability of the power supply area. Based on the acquired data, a set of device characteristics is generated after normalization: P i =(p1,p2,p3,p4,p5,p6), where i represents the i-th device in the substation, and p represents the corresponding evaluation index.

4. The power distribution stationhouse multi-element inspection method based on a device set according to claim 3, characterized in that: In step 4), the evaluation matrix is ​​generated as follows: In the evaluation matrix, A i Let a represent the evaluation matrix of the i-th device. ij This indicates the relative importance of the i-th and j-th feature values ​​of the device; a 7-degree evaluation method is used, where 1 means both elements are equally important. 3: One element is slightly more important than another element; 5: One element is significantly more important than another element; 7: One element is more strongly important than another; 2, 4, 6: These are somewhere in between the two evaluations mentioned above; When determining the weight vector, the largest eigenvalue and the corresponding eigenvector are obtained from the evaluation matrix described above: , where λ i α is the largest eigenvalue of the evaluation matrix. i It is the largest eigenvector corresponding to the largest eigenvalue of the evaluation matrix.

5. The method of claim 4, wherein the method further comprises: In step 5), when correcting the weight coefficients of eigenvalues ​​using the entropy method, the proportion of the weight coefficient of the m-th expert for the n-th eigenvalue is calculated: Where 6 represents the order of the evaluation matrix; Calculate entropy redundancy: ; Information weight of each index is calculated: ; The weight obtained by the analytic hierarchy process is corrected by using the information weight determined by the entropy method : (m = 1, 2, 3, 4, 5, 6; n = 1, 2, 3, 4, 5, 6) Each device is scored using weighted coefficients and actual data. This score serves as the device's inspection index score. The higher the score, the greater the level of attention the device receives during the inspection process.

6. The power distribution stationhouse multi-element inspection method based on a device set according to claim 5, characterized in that: In step 6), If multiple video monitoring devices can clearly read the same sensor reading, arbitrarily select one of the video monitoring devices to establish a correlation relationship. If multiple video monitoring devices can clearly capture images of the same location on the same equipment, an association can be established with any one of the video monitoring devices. For different parts of the same device that can be clearly photographed by different monitoring devices, establish a correlation between the video monitoring devices and the different parts.

7. The method of claim 6, wherein the method further comprises: In step 9), when monitoring the substation, the data refreshes the equipment characteristic value data through the anomaly synchronization mechanism. When an equipment failure event occurs, the backend algorithm obtains the anomaly signal, promptly synchronizes the newly added fault information, refreshes the equipment characteristic value, and regenerates the equipment inspection index and inspection cycle, and automatically generates a new inspection plan.

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