Power cabinet inspection personnel scheduling platform

Abstract

The invention relates to the technical field of electric power cabinet operation and maintenance, and provides an electric power cabinet inspection personnel scheduling platform. Comprising a power cabinet operation state risk analysis module, a power cabinet operation state judgment module, a power cabinet inspection task determination module and the like. According to the method, risk parameters of the power cabinet are monitored and analyzed to generate risk indexes of all dimensions; generating a power cabinet operation state risk coefficient and various types of risk signals according to the risk index of each dimension; screening out power cabinets which are not well operated based on the power cabinet operation state risk coefficients; based on each type of risk signal, determining an inspection task corresponding to the power cabinet in poor operation; based on the inspection path reference predicted arrival time of the optimal inspection personnel and the first and second predicted arrival time correction factors, screening the corresponding optimal inspection path; on-demand inspection and accurate scheduling of the optimal inspection personnel and the optimal inspection path are realized, the inspection efficiency is improved, and the power supply stability is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power cabinet operation and maintenance technology, specifically a power cabinet inspection personnel dispatch platform. Background Technology

[0002] With the acceleration of urbanization and the intelligent upgrading of power systems, the number of power distribution cabinets, as core nodes of urban power distribution networks, is growing exponentially, covering various scenarios such as urban main roads, residential communities, and industrial parks. The stable operation of power distribution cabinets is directly related to the safety of electricity use for residents and the continuity of industrial production. A failure to operate these cabinets can lead to regional power outages, equipment damage, and even safety accidents. Therefore, extremely high demands are placed on the timeliness, accuracy, and efficiency of their inspection and maintenance. However, current power distribution cabinet inspection personnel scheduling technology remains fragmented, static, and rudimentary, unable to adapt to the maintenance needs of complex scenarios, and suffers from the following key shortcomings: Existing platforms generate scheduling tasks based solely on the static classification of power cabinets, such as key area cabinets and ordinary area cabinets, without considering real-time health status factors such as voltage fluctuations, cabinet physical condition, and insulation performance. Furthermore, the allocation of power cabinet inspection tasks often employs a "one-size-fits-all" approach, failing to develop differentiated inspection plans based on the specific risk types of power cabinets. This leads to wasted inspection resources and low efficiency: on the one hand, traditional inspection tasks are mostly routine checks, such as visual inspection and parameter recording. Regardless of whether the power cabinet has different risks such as cabinet corrosion, voltage fluctuations, or insulation gas leaks, the same inspection flow is executed. For example, for power cabinets with only unclear cabinet markings, personnel with high-voltage testing qualifications are still assigned to perform a full set of electrical tests, resulting in a waste of highly skilled inspection personnel resources. For power cabinets with the risk of arc protection failure, no special insulation gas testing tasks are arranged, resulting in the risk not being eradicated. On the other hand, the existing technology only issues general instructions to inspection personnel to inspect power cabinets in a certain area, without clearly informing them of the key risk points that need to be investigated, such as checking the cabinet door sealing and testing the SF6 gas concentration. This causes inspection personnel to spend a lot of time on a comprehensive investigation, resulting in low efficiency in the investigation of key risk points and high average inspection time per cabinet.

[0003] The current dispatching of inspection personnel relies heavily on the single logic of proximity or experience-based assignment, without establishing a multi-dimensional dispatching parameter system. This results in poor personnel-task compatibility and low dispatching efficiency. Furthermore, inspection route planning is mostly based on the straight-line distance between the start and end points or a pre-set fixed route, without incorporating dynamic environmental factors such as real-time traffic and weather. This leads to large deviations in estimated arrival times and frequent inspection delays. In summary, existing power distribution cabinet inspection personnel dispatching technologies suffer from multiple pain points, including inaccurate risk assessment, imprecise task matching, suboptimal personnel dispatching, and unreliable route planning. These shortcomings fail to meet the current operational and maintenance needs of urban power distribution cabinets for precise early warning, efficient dispatching, and rapid response. To address these issues, there is an urgent need for an integrated dispatching platform that can combine multi-dimensional risk assessment, differentiated task matching, precise personnel dispatching, dynamic route planning, and real-time feedback. This platform would improve the efficiency and quality of power distribution cabinet inspections and ensure the stable operation of the power distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide a power cabinet inspection personnel dispatching platform to solve the problems mentioned in the background.

[0005] The objective of this invention can be achieved through the following technical solution: a power cabinet inspection personnel dispatch platform, comprising: The power cabinet operation status risk analysis module is used to acquire power cabinets in the target urban area, number the power cabinets, obtain the risk parameters of each power cabinet in the target urban area during the current monitoring period, and generate the operation status risk coefficient of each power cabinet during the current monitoring period. Among them, the risk parameters include cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas. The power cabinet operation status judgment module is used to determine whether the power cabinet is operating normally based on the power cabinet operation status risk coefficient, generate power cabinet operation status signals, and count the power cabinets with poor operation status; among them, the power cabinet operation status signals include power cabinet operation status good signals and power cabinet operation status poor signals; The power cabinet inspection task determination module is used to determine the inspection tasks corresponding to each power cabinet with poor operating conditions based on the cabinet health risk index, electrical condition risk index, temperature condition risk index and arc protection risk index of the insulating gas. The inspection personnel information collection module is used to obtain the inspection personnel of each power cabinet with poor operating status within a preset area, and to obtain the scheduling parameters of each inspection personnel corresponding to each power cabinet with poor operating status; among which, the scheduling parameters include skill matching degree, skill level value, distance value and load adaptability; The optimal inspection personnel analysis module is used to determine the optimal inspection personnel for each power cabinet with poor operating status based on the scheduling parameters of each inspection personnel. The optimal inspection path determination module is used to plan the inspection paths of the optimal inspection personnel corresponding to each power cabinet with poor operating status based on the location of the optimal inspection personnel corresponding to each power cabinet with poor operating status, and analyze the optimal inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status. The display feedback terminal is used to send the inspection task instructions and the optimal inspection arrival path for each power cabinet with poor operating status to the mobile terminal of the corresponding optimal inspection personnel. The optimal inspection personnel can quickly arrive at the location of the power cabinet with poor operating status according to the received inspection task instructions and optimal inspection arrival path to carry out the corresponding inspection.

[0006] The beneficial effects of this invention are: This invention monitors and analyzes multiple risk parameters of electrical cabinets in a target urban area, including physical structure, electrical performance, and safety protection, to avoid misjudgments of risk caused by single-dimensional monitoring. By establishing a standardized model, it generates multi-dimensional risk assessment results and generates a risk coefficient for the operating status of electrical cabinets based on these results. This identifies electrical cabinets with good and poor operating status, providing clear problem targets for subsequent inspections and avoiding ineffective inspections of normal cabinets and omissions of cabinets with multiple risks.

[0007] This invention achieves on-demand inspection by accurately matching risk signals generated from power cabinets with poor operating conditions with tasks, thereby improving the efficiency of inspection tasks and the effectiveness of risk mitigation. It avoids the problems of resource redundancy and omissions in key risk screening caused by vague instructions in traditional full-process inspections.

[0008] This invention establishes a dispatch evaluation model for inspection personnel based on the skill compatibility, skill level, distance, and load adaptability of each inspection personnel for power cabinets with poor operating conditions. It calculates the dispatch matching degree for each inspection personnel for power cabinets with poor operating conditions and selects the optimal inspection personnel. This achieves optimal matching between inspection personnel and inspection tasks, avoiding the skill deficiency problem caused by prioritizing distance alone. For example, personnel who are far away but have perfectly matched skills are far more efficient at handling complex inspection tasks than personnel who are nearby but lack skills. Simultaneously, it avoids assigning inspection tasks to inspection personnel already under heavy workload, achieving dynamic balance of personnel workload and reducing inspection oversights caused by personnel fatigue.

[0009] This invention generates inspection routes and their corresponding baseline estimated arrival times based on the location of power cabinets in poor operating condition and the optimal inspection personnel. It calculates the traffic congestion index for each inspection route using traffic flow data and matches it with a first estimated arrival time correction factor. It also calculates the meteorological state index for each inspection route using meteorological data and matches it with a second estimated arrival time correction factor. This quantifies the impact of traffic flow and meteorological conditions on the baseline estimated arrival time, making the final estimated arrival time assessment more accurate and relevant to real-world scenarios. It enables real-time updates of the optimal route, ensuring timely inspection of high-risk power cabinets. This avoids the problems of traditional inspection route planning, which often relies on fixed routes or default shortest paths on maps, failing to consider the impact of real-time traffic congestion and severe weather on arrival time, and the serious consequences of power outages caused by prolonged waiting times for high-risk power cabinets. Attached Figure Description

[0010] The invention will now be further described with reference to the accompanying drawings.

[0011] Figure 1 This is a system block diagram of the present invention.

[0012] Figure 2 This is a schematic diagram illustrating the analysis logic of the arc protection risk index of the insulating gas in the power cabinet of the present invention.

[0013] Figure 3 This is a logical diagram of the optimal inspection personnel analysis module of the present invention.

[0014] Figure 4 This is a logical schematic diagram of the optimal inspection path determination module of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 As shown, the present invention is a dispatching platform for power cabinet inspection personnel, comprising: The power cabinet operation status risk analysis module is used to acquire power cabinet data for a target urban area, assign numbers to the power cabinets, obtain risk parameters for each power cabinet in the target urban area during the current monitoring period, and generate an operation status risk coefficient for each power cabinet during the current monitoring period. These risk parameters include the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index for insulating gas. Specifically: 101: The system uses intelligent high-definition cameras to acquire images of each power cabinet in the target area of ​​the city during the current monitoring period. Based on image processing technology, it identifies the number of abnormal areas on the cabinet shell, the area of ​​each abnormal area, and each cabinet label. The areas of each abnormal area are accumulated to obtain the total area of ​​the abnormal areas of the cabinet. The outlines corresponding to each cabinet label are compared with their corresponding standard outlines to obtain the overlapping outline area of ​​each cabinet label. The overlapping outline area of ​​each cabinet label is then accumulated to obtain the total overlapping outline area of ​​the cabinet labels. It should be noted that abnormal areas on the cabinet exterior include, but are not limited to, deformed areas, rusted areas, damaged areas, and areas with peeling paint; cabinet markings include, but are not limited to, brand logos, cabinet numbers and names, safety warning slogans and color codes.

[0017] The displacement of the cabinet door is detected by a displacement sensor installed on the cabinet body, and the displacement data is recorded as the marked displacement. The marked displacement of the cabinet door is compared with a set reference displacement. If the marked displacement of the cabinet door is greater than or equal to the set reference displacement, the cabinet door is determined to be in the open state, and the cabinet door is assigned a sealing condition. Conversely, if the cabinet door is open, it is determined to be closed, thus assigning the cabinet door a sealing rating. ,in ; Establish a cabinet health risk prediction model, and substitute the total area of ​​abnormal areas, the total area of ​​overlapping outlines of cabinet markings, and the cabinet door sealing of each power cabinet in the current monitoring period into the cabinet health risk prediction model to generate the cabinet health risk index of each power cabinet in the current monitoring period. The expression for the cabinet health risk prediction model is as follows: ; In the formula, This represents the cabinet health risk index of the d-th power cabinet during the current monitoring period, where d = 1, 2, ..., D, and D represents the total number of power cabinet numbers. These represent the total area of ​​the abnormal area of ​​the d-th power cabinet, the total area of ​​the overlapping outline of the cabinet markings, and the cabinet door sealing performance, respectively, during the current monitoring period. All are weighting coefficients. .

[0018] 102: Calculate the average voltage of each power cabinet in the target urban area at each monitoring time point during the current monitoring period by averaging the voltage values ​​at the incoming and outgoing terminals. The average voltage of each power cabinet at each monitoring time point during the current monitoring period is calculated to generate the average voltage of each power cabinet during the current monitoring period; The average main circuit current of each power cabinet in the target urban area is calculated at each monitoring time point during the current monitoring period to generate the average main circuit current of each power cabinet during the current monitoring period. A voltage fluctuation assessment model and a current fluctuation assessment model are established. The voltage values ​​at the incoming and outgoing terminals of each power cabinet at each monitoring time point during the current monitoring period, as well as the average voltage of each power cabinet during the current monitoring period, are substituted into the voltage fluctuation assessment model. The main circuit current of each power cabinet at each monitoring time point during the current monitoring period, as well as the average current of each power cabinet during the current monitoring period, are substituted into the current fluctuation assessment model. The voltage fluctuation value and current fluctuation value of each power cabinet during the current monitoring period are generated. The expressions for the voltage fluctuation assessment model and the current fluctuation assessment model are as follows: ; In the formula, These represent the voltage fluctuation value and current fluctuation value of the d-th power cabinet during the current monitoring period, respectively. Let represent the incoming voltage, outgoing voltage, and main circuit current of the d-th power cabinet at the r-th monitoring time point during the current monitoring period, where r = 1, 2, ..., R, and R represents the total number of monitoring time point numbers. These represent the average voltage and average main circuit current of the d-th power cabinet during the current monitoring period, respectively. The voltage fluctuation value and the current fluctuation value are summed and calculated to generate the electrical condition risk index of each power cabinet during the current monitoring period.

[0019] 103: Temperature sensors are used to acquire the temperature of each area of ​​each power cabinet at each monitoring time point during the current monitoring period. The average temperature of each area of ​​the power cabinet during the current monitoring period is calculated. Simultaneously, the temperatures of each area of ​​each power cabinet at each monitoring time point corresponding to each historical day are extracted from the system repository. The maximum and minimum temperatures of each area of ​​each power cabinet during the current monitoring period are then filtered to obtain the allowable temperature range for each area of ​​each power cabinet. , i is the number of each region, i=1,2,...,I, where I represents the total number of region numbers; It should be noted that the various areas of the power cabinet include, but are not limited to, the busbar compartment area, circuit breaker compartment area, capacitor compartment area, relay compartment area, and transformer compartment area. It should be further explained that the specific process of selecting the maximum and minimum temperature values ​​of each area of ​​each power cabinet during the current monitoring period is as follows: sort the temperature of each area of ​​each power cabinet at each monitoring time point corresponding to each historical day during the current monitoring period, obtain the maximum and minimum temperature values ​​of each area of ​​each power cabinet during each historical day, then sort the maximum temperature values ​​of each area of ​​each power cabinet during each historical day, obtain the maximum temperature values ​​of each area of ​​each power cabinet during the current monitoring period, and similarly obtain the minimum temperature values ​​of each area of ​​each power cabinet during the current monitoring period.

[0020] A temperature status risk assessment model is established. The average temperature of each area of ​​each power cabinet during the current monitoring period and the allowable temperature range of each area of ​​each power cabinet are substituted into the temperature status risk assessment model to generate the temperature status risk index of each power cabinet during the current monitoring period. The expression for the temperature state risk assessment model is as follows: ; In the formula, It represents the average temperature of the i-th area of ​​the d-th power cabinet during the current monitoring period.

[0021] 104: Please refer to Figure 2 As shown, the arc light monitoring device detects the arc light signals of each power cabinet in the target urban area during the current monitoring period, obtaining the output waveform characteristics and output light intensity of the arc light signals of each power cabinet in the current monitoring period. The output waveform characteristics include pulse shape, pulse duration and pulse frequency, where s represents the number of each region, s=1,2,...,S, and S represents the total number of region numbers; Extract the reference waveform diagram of the arc light signal from the power cabinet and the standard threshold of the arc light signal output intensity stored in the system's repository. Calculate the matching degree between the output waveform characteristics of the arc light signal in each area of ​​each power cabinet during the current monitoring period and the waveform characteristics of the reference waveform diagram of the arc light signal. ; It should be noted that the specific calculation process for the matching degree between the output waveform characteristics of the arc light signal in each area of ​​each power cabinet during the current monitoring period and the waveform characteristics of the reference waveform diagram of the arc light signal is as follows: The reference pulse shape, reference pulse duration, and reference pulse frequency are extracted from the reference waveform diagram of the arc light signal of the power cabinet. The pulse shape of the arc light signal in each area of ​​each power cabinet during the current monitoring period is compared with the reference pulse shape to obtain the pulse shape overlap length of the arc light signal in each area of ​​each power cabinet during the current monitoring period. This overlap length is then calculated as a ratio to the pulse length of the arc light signal in each area of ​​each power cabinet during the current monitoring period to obtain the matching degree of the arc light signal in each area of ​​each power cabinet during the current monitoring period. The similarity of pulse shape of the arc light signal in the domain is calculated by obtaining the absolute difference between the pulse frequency of the arc light signal in each area of ​​each power cabinet during the current monitoring period and the reference pulse frequency, and then calculating the pulse frequency similarity by comparing the absolute difference with the pulse frequency of the arc light signal in each area of ​​each power cabinet during the current monitoring period. Similarly, the similarity of pulse duration of the arc light signal in each area of ​​each power cabinet during the current monitoring period is obtained by analyzing the pulse duration similarity. The sum of the pulse shape similarity, pulse duration similarity and pulse frequency similarity is used as the matching degree. Based on this, the matching degree between the output waveform characteristics of the arc light signal in each area of ​​each power cabinet during the current monitoring period and the waveform characteristics of the reference waveform diagram of the arc light signal is obtained.

[0022] If the conditions are met If an arc leakage is detected in a certain area of ​​a power cabinet during the current monitoring period, it is determined that the arc protection of the insulating gas of the power cabinet is ineffective during the current monitoring period, and an arc protection risk index of the insulating gas of the power cabinet is assigned to it. If the conditions are met The gas monitoring device continues to monitor the gas in each area of ​​each power cabinet during the current monitoring period, obtaining gas data for each area of ​​each power cabinet during the current monitoring period, including gas type and concentration; it should be noted that... The logical symbols are represented as AND and NOT, respectively, and the preset waveform feature matching degree is represented as . Based on the insulating gas type and its dispersed gas type range of the power cabinet stored in the system repository, the gases in each area of ​​each power cabinet during the current monitoring period that fall within the insulating gas dispersed gas type range are marked as dispersed gases, and the gases other than insulating gases and dispersed gases are marked as interfering gases. Thus, the insulating gas, dispersed gas and interfering gas of each area of ​​each power cabinet during the current monitoring period are obtained. If a power cabinet has dispersed gas in a certain area during the current monitoring period, the arc protection of the insulating gas of the power cabinet is deemed invalid, and an arc protection risk index of the insulating gas of the power cabinet is assigned. If no dispersed gas is found in a certain area of ​​a power distribution cabinet during the current monitoring period, then the insulation gas concentration of that power distribution cabinet in each area during the current monitoring period is obtained. and the concentration of each interfering gas Let n represent the number of each interfering gas, n=1,2,...,N, where N represents the total number of interfering gas numbers. Analyze the effective protection quality coefficient of the insulating gas in this power cabinet. And compare it with the preset effective protection quality coefficient threshold of insulating gas. Compare the results; if the conditions are met... If the arc protection of the insulating gas in the power cabinet is deemed effective, then the arc protection risk index of the insulating gas in the power cabinet is assigned as follows: If the conditions are met If the arc protection of the insulating gas in the power cabinet is deemed ineffective, then the arc protection risk index of the insulating gas in the power cabinet is assigned as follows: ,in Based on this, an arc protection risk index for the insulating gas of each power cabinet is generated during the current monitoring period.

[0023] It should be noted that the specific steps for analyzing the effective protective quality coefficient of the insulating gas in the power cabinet are as follows: extracting the rated permissible concentration of the insulating gas in the power cabinet stored in the system's storage tank. Based on the rated permissible concentrations of various types of interfering gases, and according to the categories of interfering gases in each area of ​​the power cabinet during the current monitoring period, obtain the rated permissible concentrations of each interfering gas in each area of ​​the power cabinet during the current monitoring period. According to the formula The effective insulation gas protection quality coefficient of the power cabinet during the current monitoring period was calculated. , where e represents the natural constant.

[0024] 105: The cabinet health risk index, electrical condition risk index, temperature condition risk index and arc protection risk index of insulating gas are weighted and summed to generate the operating status risk coefficient of each power cabinet during the current monitoring period.

[0025] The power cabinet operation status judgment module is used to determine whether the power cabinet is operating normally based on the power cabinet operation status risk coefficient, generate power cabinet operation status signals, and statistically analyze the power cabinets with poor operation status. The power cabinet operation status signals include signals indicating good operation and signals indicating poor operation. Specifically: A preset threshold for the operating status risk coefficient of the power cabinet is established, and the operating status risk coefficient of the power cabinet is compared with the threshold. It should be noted that the threshold for the operating status risk coefficient of the power cabinet refers to the highest value of the operating status risk coefficient of the power cabinet under standard conditions. If the risk coefficient of the power cabinet operation status is less than or equal to the threshold of the power cabinet operation status risk coefficient, it means that the lower the risk coefficient of the power cabinet operation status, the better the operation status of the power cabinet, and a signal of good health status of the power cabinet is generated; when a signal of good operation status of the power cabinet is received, the operation status of the power cabinet continues to be monitored. If the risk coefficient of the power cabinet's operating status is greater than the threshold of the power cabinet's operating status risk coefficient, it means that the higher the risk coefficient of the power cabinet's operating status, the worse the operating status of the power cabinet, and a poor operating status signal of the power cabinet is generated; when a poor operating status signal of the power cabinet is received, the power cabinet is marked, and each power cabinet with a poor operating status is statistically obtained.

[0026] The power cabinet inspection task determination module is used to determine the inspection tasks corresponding to each power cabinet with poor operating conditions based on its cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of the insulating gas; specifically: The system acquires the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas for each power cabinet with poor operating conditions, and compares them with preset thresholds for the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas, respectively. If the cabinet health risk index of a power cabinet with poor operating status exceeds the preset cabinet health risk index threshold, a cabinet health risk signal will be generated. If the electrical condition risk index of a power cabinet with poor operating status exceeds the preset electrical condition risk index threshold, an electrical condition risk signal will be generated. If the temperature status risk index of a power cabinet with poor operating status exceeds the preset temperature status risk index threshold, a temperature status risk signal will be generated. If the arc protection risk index of the insulating gas of a power cabinet with poor operating condition is greater than the preset arc protection risk index threshold of the insulating gas, an arc protection risk signal of the insulating gas will be generated. Set up inspection tasks corresponding to each risk signal, match the risk signals generated by each power cabinet with the inspection tasks corresponding to each risk signal, obtain the inspection tasks for each power cabinet with poor operating status, and generate inspection task instructions for each power cabinet with poor operating status.

[0027] It should be noted that the inspection tasks for each power cabinet with poor operating status can be one or more.

[0028] The inspection personnel information collection module is used to obtain the inspection personnel of each power cabinet with poor operating status within a preset area, and to obtain the scheduling parameters of each inspection personnel corresponding to each power cabinet with poor operating status; among which, the scheduling parameters include skill matching degree, skill level value, distance value and load adaptability; It should be noted that the preset area range refers to the circular area range constructed with the power cabinet in poor operating condition as the center and a preset radius.

[0029] Specifically: Obtain the validity period of the professional certificates of each inspection personnel corresponding to each power cabinet with poor operating status. If the professional certificate is valid, assign a qualification validity coefficient of 1; otherwise, assign a qualification validity coefficient of 0. Calculate the qualification validity coefficient for each inspection personnel corresponding to each power cabinet with poor operating status, denoted as [missing information]. f represents the number of each power cabinet with poor operating status, f=1,2,...,F, F represents the total number of power cabinets with poor operating status, and k represents the number of each inspection personnel, k=1,2,...,K, K represents the total number of inspection personnel. Set a set of task requirement skill tags for each inspection task. Match the inspection task corresponding to each power cabinet with poor operating status with the set of task requirement skill tags for each inspection task to obtain the set of task requirement skill tags for each power cabinet with poor operating status, denoted as . Simultaneously, obtain the skill tag set of each inspection personnel corresponding to each power cabinet with poor operating status, and record it as follows: According to the formula The skill compatibility of each inspection personnel corresponding to each power cabinet with poor operating status was calculated. Obtain the inspection logs of each inspector within a set historical period, and extract the number of inspections, duration of each inspection, and inspection tasks of each inspector within the set historical period. Match each inspection task with the preset reference inspection duration range corresponding to each inspection task to obtain the reference inspection duration range of each inspection task. The inspection duration of each inspector within a set historical time period is compared with its corresponding reference inspection duration range. If the inspection duration is less than the lower limit of the corresponding reference inspection duration range, the inspection is determined to be an efficient inspection. The number of efficient inspections is obtained, and the ratio of the number of efficient inspections to the total number of inspections is calculated to obtain the percentage of efficient inspections. The actual inspection duration of each efficient inspection is obtained, and the average time value of efficient inspections is calculated. The number of efficient inspections, the percentage of efficient inspections, and the average time value of efficient inspections are input into the graphics processor. The graphics processor converts them into values ​​according to a certain ratio and inputs them into a line graph to obtain three corresponding points. The three points are connected sequentially with line segments to obtain a line. The two endpoints of the line are perpendicular to the X-axis, so that the line and the two perpendicular lines form a closed image with the X-axis. The area of ​​the closed image is identified as the skill level value. Based on this, the skill level value of each inspector corresponding to each power cabinet with poor operating status is obtained. Obtain the latitude and longitude of each power switch with poor operating status. And the latitude and longitude of each inspection personnel The algorithm formula using the spherical cosine theorem The distance values ​​of each inspection personnel corresponding to each power cabinet with poor operating status were calculated, where R represents the Earth's radius and R = 6371.0 km; Obtain the current inspection task status of each inspection personnel. If the current inspection task status is "task in progress", assign an inspection task status coefficient of 1. If the current inspection task status is "idle", assign an inspection task status coefficient of 2. Generate the current inspection task status coefficient of each inspection personnel accordingly. Obtain the current workload rate of each inspection personnel. Based on the current inspection task status coefficient and workload rate of each inspection personnel, generate the load adaptability of each inspection personnel for each power cabinet with poor operating status through the load adaptability formula. It should be noted that the current workload rate of each inspector refers to the ratio of the time each inspector has completed their daily inspection tasks to the standard working hours for the day; the load fit formula is: .

[0030] The optimal inspection personnel analysis module is used to determine the optimal inspection personnel for each power cabinet with poor operating status based on the scheduling parameters of each inspection personnel. Specifically: Please see Figure 3 As shown, an inspection personnel scheduling evaluation model is established. The skill fit, skill level, distance and load adaptability of each inspection personnel corresponding to each power cabinet with poor operating status are normalized and their values ​​are substituted into the inspection personnel scheduling evaluation model to generate the scheduling matching degree of each inspection personnel corresponding to each power cabinet with poor operating status. The expression for the inspection personnel scheduling and evaluation model is:

[0031] In the formula, This represents the scheduling matching degree between the f-th power cabinet with poor operating status and the k-th inspection personnel. These represent the skill compatibility, skill level, distance, and load suitability of the k-th inspection personnel corresponding to the f-th power cabinet with poor operating status. The scheduling matching degree of each inspection personnel for each power cabinet with poor operating status is arranged in descending order, and the inspection personnel with the highest scheduling matching degree is extracted as the optimal inspection personnel. Based on this, the optimal inspection personnel for each power cabinet with poor operating status are obtained.

[0032] The optimal inspection path determination module is used to plan the inspection paths of the optimal inspection personnel corresponding to each power cabinet with poor operating status based on the location of the optimal inspection personnel corresponding to each power cabinet with poor operating status, and analyze the optimal inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status. It should be noted that, based on the location of each power cabinet with poor operating status and the location of its corresponding optimal inspection personnel, the planned routes and estimated arrival times of the optimal inspection personnel for each power cabinet with poor operating status are obtained through map service software. These routes serve as the inspection paths and baseline estimated arrival times for the optimal inspection personnel for each power cabinet with poor operating status.

[0033] Specifically: Please refer to Figure 4 As shown, traffic flow data is collected for each inspection route. The traffic flow data includes vehicle flow, pedestrian flow, average vehicle speed, and vehicle space occupancy. The traffic flow data of each inspection route is compared and analyzed with the preset standard traffic flow data to generate the traffic congestion index of each inspection route. The standard traffic flow data includes the maximum vehicle flow, the maximum pedestrian flow, the minimum average vehicle speed, and the maximum vehicle space occupancy. It should be noted that the specific process for generating the traffic congestion index for each inspection arrival route is as follows: The traffic flow, pedestrian flow, average vehicle speed, and vehicle space occupancy rate of each inspection route are calculated as ratios to the maximum traffic flow, maximum pedestrian flow, minimum average vehicle speed, and maximum vehicle space occupancy rate, respectively. This yields the traffic flow ratio, pedestrian flow ratio, speed ratio, and space occupancy rate ratio for each inspection route. These ratios are then converted into lengths according to preset proportions. An ellipse is constructed using the lengths of the traffic flow ratio and pedestrian flow ratio as the major and minor axes, respectively. The center of the ellipse is selected as the starting point, and a cone-shaped solid is constructed with the length of the space occupancy rate ratio as the height. Inside the cone, a hollow sphere is constructed with the length of the speed ratio as the radius. The volume of the cone is used as the traffic congestion index, and the traffic congestion index for each inspection route is generated accordingly.

[0034] Collect meteorological data for each inspection route, including wind speed, rainfall, snowfall, and visibility. Based on the meteorological condition data of each inspection path, a meteorological condition index is generated for each inspection path. It should be noted that the specific process for generating the meteorological state index for each inspection path is as follows: The meteorological status data of each inspection route is compared with the standard meteorological status data. If a meteorological index in the meteorological status data is greater than its corresponding standard index, the difference between the two is calculated to obtain the high index difference; otherwise, the difference is calculated to obtain the low index difference. Based on this, the high index difference and low index difference in the meteorological status data of each inspection route are statistically obtained. The average of each high index difference and low index difference is calculated to obtain the average high index difference and the average low index difference. The average high index difference and the average low index difference are input into the graphics processor, which converts them into numerical values ​​according to a certain ratio and inputs them into a line graph to obtain two corresponding points. The two points are connected sequentially by line segments to obtain a line. The two endpoints of the line are perpendicular to the X-axis, so that the line and the two perpendicular lines form a closed image with the X-axis. The area of ​​the closed image is identified as the meteorological status index, and the meteorological status index of each inspection route is generated accordingly.

[0035] The traffic congestion index of each inspection route is matched with the traffic congestion index corresponding to each preset first estimated arrival time correction factor to obtain the first estimated arrival time correction factor for each inspection route. The meteorological state index of each inspection path is matched with the meteorological state index corresponding to each preset second estimated arrival time correction factor to obtain the second estimated arrival time correction factor for each inspection path. The first and second estimated arrival time correction factors for each inspection route are summed to obtain the comprehensive estimated arrival time correction factor for each inspection route. Based on the baseline estimated arrival time of each inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status, the baseline estimated arrival time is multiplied by the estimated arrival time comprehensive correction factor to generate the final estimated arrival time of each inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status. Arrange the final estimated arrival times of each inspection path for the optimal inspection personnel corresponding to each power cabinet with poor operating status in ascending order, and extract the inspection path corresponding to the minimum final estimated arrival time as the optimal inspection path for each power cabinet with poor operating status.

[0036] The display feedback terminal is used to send the inspection task instructions and the optimal inspection arrival path for each power cabinet with poor operating status to the mobile terminal of the corresponding optimal inspection personnel. The optimal inspection personnel can quickly arrive at the location of the power cabinet with poor operating status according to the received inspection task instructions and optimal inspection arrival path to carry out the corresponding inspection.

[0037] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0038] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A dispatching platform for electrical cabinet inspection personnel, characterized in that, include: The power cabinet operation status risk analysis module is used to acquire power cabinets in the target urban area, number the power cabinets, obtain the risk parameters of each power cabinet in the target urban area during the current monitoring period, and generate the operation status risk coefficient of each power cabinet during the current monitoring period. Among them, the risk parameters include cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas. The power cabinet operation status judgment module is used to determine whether the power cabinet is operating normally based on the power cabinet operation status risk coefficient, generate power cabinet operation status signals, and count the power cabinets with poor operation status; among them, the power cabinet operation status signals include power cabinet operation status good signals and power cabinet operation status poor signals; The power cabinet inspection task determination module is used to determine the inspection tasks corresponding to each power cabinet with poor operating conditions based on the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas. The inspection personnel information collection module is used to obtain the inspection personnel of each power cabinet with poor operating status within a preset area, and to obtain the scheduling parameters of each inspection personnel corresponding to each power cabinet with poor operating status; among which, the scheduling parameters include skill matching degree, skill level value, distance value and load adaptability; The optimal inspection personnel analysis module is used to determine the optimal inspection personnel for each power cabinet with poor operating status based on the scheduling parameters of each inspection personnel. The optimal inspection path determination module is used to plan the inspection paths of the optimal inspection personnel corresponding to each power cabinet with poor operating status based on the location of the optimal inspection personnel, and analyze the optimal inspection paths of the optimal inspection personnel corresponding to each power cabinet with poor operating status.

2. The power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, Also includes: The display feedback terminal is used to send the inspection task instructions and the optimal inspection arrival path for each power cabinet with poor operating status to the mobile terminal of the corresponding optimal inspection personnel. The optimal inspection personnel can quickly arrive at the location of the power cabinet with poor operating status according to the received inspection task instructions and optimal inspection arrival path to carry out the corresponding inspection.

3. The power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The process of generating the operational status risk coefficient of each power cabinet during the current monitoring period is as follows: 101: Establish a cabinet health risk prediction model. Substitute the total area of ​​abnormal areas, the total area of ​​overlapping outlines of cabinet markings, and the sealing performance of cabinet doors of each power cabinet into the cabinet health risk prediction model during the current monitoring period to generate the cabinet health risk index of each power cabinet during the current monitoring period. 102: Establish voltage fluctuation assessment models and current fluctuation assessment models. Substitute the voltage values ​​of the incoming and outgoing terminals of each power cabinet at each monitoring time point during the current monitoring period, as well as the average voltage of each power cabinet during the current monitoring period, into the voltage fluctuation assessment model; substitute the main circuit current of each power cabinet at each monitoring time point during the current monitoring period, as well as the average current of each power cabinet during the current monitoring period, into the current fluctuation assessment model; generate the voltage fluctuation value and current fluctuation value of each power cabinet during the current monitoring period. The voltage fluctuation value and the current fluctuation value are summed and calculated to generate the electrical condition risk index of each power cabinet during the current monitoring period.

4. The power cabinet inspection personnel dispatching platform according to claim 3, characterized in that, The process of generating the operational status risk coefficient of each power cabinet during the current monitoring period also includes: 103: Establish a temperature status risk assessment model. Substitute the average temperature of each area of ​​each power cabinet during the current monitoring period and the allowable temperature range of each area of ​​each power cabinet into the temperature status risk assessment model to generate the temperature status risk index of each power cabinet during the current monitoring period.

5. A power cabinet inspection personnel dispatching platform according to claim 4, characterized in that, The process of generating the operational status risk coefficient of each power cabinet during the current monitoring period also includes: 104: Obtain the output waveform characteristics and output light intensity of the arc light signal of each area of ​​each power cabinet during the current monitoring period. The output waveform characteristics include pulse shape, pulse duration and pulse frequency. d represents the power cabinet number, d=1,2,...,D, where D represents the total number of power cabinet numbers. s represents the number of each area, s=1,2,...,S, where S represents the total number of area numbers. Extract the reference waveform of the arc light signal from the power cabinet and the standard threshold of the arc light signal output intensity. Calculate the matching degree between the output waveform characteristics of the arc light signal in each area of ​​each power cabinet during the current monitoring period and the waveform characteristics of the reference waveform diagram of the arc light signal. ; If the conditions are met If an arc leakage is detected in a certain area of ​​a power cabinet during the current monitoring period, it is determined that the arc protection of the insulating gas of the power cabinet is ineffective during the current monitoring period, and an arc protection risk index of the insulating gas of the power cabinet is assigned to it. If the conditions are met Continue to monitor the gas in each area of ​​each power cabinet during the current monitoring period using the gas monitoring device, and obtain the gas data of each area of ​​each power cabinet during the current monitoring period, including gas type and concentration; Based on the insulating gas type and its dispersed gas type range of the power cabinet, the gases in each area of ​​each power cabinet during the current monitoring period that fall within the insulating gas dispersed gas type range are marked as dispersed gases, and the gases other than insulating gases and dispersed gases are marked as interfering gases. Thus, the insulating gas, dispersed gas and interfering gas of each area of ​​each power cabinet during the current monitoring period are obtained. If a power cabinet has dispersed gas in a certain area during the current monitoring period, the arc protection of the insulating gas of the power cabinet is deemed invalid, and an arc protection risk index of the insulating gas of the power cabinet is assigned. If no dispersed gases are present in a certain area of ​​a power cabinet during the current monitoring period, then the concentrations of insulating gases and interfering gases in each area of ​​the power cabinet during the current monitoring period are obtained, and the effective protection quality coefficient of the insulating gas of the power cabinet is analyzed. And compare it with the preset effective protection quality coefficient threshold of insulating gas. Compare the results; if the conditions are met... If the arc protection of the insulating gas in the power cabinet is deemed effective, then the arc protection risk index of the insulating gas in the power cabinet is assigned as follows: If the conditions are met If the arc protection of the insulating gas in the power cabinet is deemed ineffective, then the arc protection risk index of the insulating gas in the power cabinet is assigned as follows: ,in Based on this, an arc protection risk index for the insulating gas of each power cabinet is generated during the current monitoring period; 105: The cabinet health risk index, electrical condition risk index, temperature condition risk index and arc protection risk index of insulating gas are weighted and summed to generate the operating status risk coefficient of each power cabinet during the current monitoring period.

6. The power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The method for statistically analyzing the power cabinets with suboptimal operating status is as follows: Preset a threshold for the risk coefficient of the power cabinet's operating status, and compare the risk coefficient of the power cabinet's operating status with the threshold for the risk coefficient of the power cabinet's operating status. If the risk coefficient of the power cabinet operation status is less than or equal to the threshold of the power cabinet operation status risk coefficient, it means that the lower the risk coefficient of the power cabinet operation status, the better the operation status of the power cabinet, and a signal of good health status of the power cabinet is generated; when a signal of good operation status of the power cabinet is received, the operation status of the power cabinet continues to be monitored. If the risk coefficient of the power cabinet's operating status is greater than the threshold of the power cabinet's operating status risk coefficient, it means that the higher the risk coefficient of the power cabinet's operating status, the worse the operating status of the power cabinet, and a poor operating status signal of the power cabinet is generated; when a poor operating status signal of the power cabinet is received, the power cabinet is marked, and each power cabinet with a poor operating status is statistically obtained.

7. The power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The method for determining the inspection tasks corresponding to each power cabinet with poor operating status is as follows: The system acquires the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas for each power cabinet with poor operating status. These indices are then compared with preset thresholds for the cabinet health risk index, electrical condition risk index, temperature condition risk index, and arc protection risk index of insulating gas, respectively, to generate cabinet health risk signals, electrical condition risk signals, temperature condition risk signals, and arc protection risk signals of insulating gas. Set up inspection tasks corresponding to each risk signal, match the risk signals generated by each power cabinet with the inspection tasks corresponding to each risk signal, obtain the inspection tasks for each power cabinet with poor operating status, and generate inspection task instructions for each power cabinet with poor operating status.

8. A power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The method for obtaining the scheduling parameters for each inspection personnel corresponding to each power cabinet with poor operating status is as follows: The validity coefficient of the qualifications of each inspection personnel corresponding to each power cabinet with poor operating status was obtained statistically. At the same time, a set of task requirement skill tags for each inspection task was set, and the inspection tasks corresponding to each power cabinet with poor operating status were matched with the set of task requirement skill tags for each inspection task to obtain the set of task requirement skill tags for each power cabinet with poor operating status. The set of skill tags possessed by each inspection personnel corresponding to each power cabinet with poor operating status was obtained, and the skill fit degree of each inspection personnel corresponding to each power cabinet with poor operating status was calculated. Each inspection task is matched with the preset reference inspection time range corresponding to each inspection task to obtain the reference inspection time range for each inspection task. By comparing and analyzing the inspection duration of each inspection personnel within a set historical period with the corresponding reference inspection duration range, the number of university inspections, the proportion of university inspections, and the average time value of efficient inspections are obtained. The number of inspections of universities, the proportion of university inspections, and the average time value of efficient inspections are input into the graphics processor for numerical conversion. Then, they are input into the line graph to obtain three corresponding points. The three points are connected sequentially by line segments to obtain a line. The two endpoints of the line are drawn perpendicular to the X-axis to form a closed image with the line and the two perpendicular lines. The area of ​​the closed image is identified as the skill level value. Based on this, the skill level values ​​of each inspection personnel corresponding to each power cabinet with poor operating status are obtained. Obtain the latitude and longitude of each power cabinet with poor operating status and the latitude and longitude of each inspection personnel. Calculate the distance value of each inspection personnel corresponding to each power cabinet with poor operating status using the spherical cosine theorem algorithm formula. Obtain the current inspection task status of each inspection personnel. If the current inspection task status is "task in progress", assign an inspection task status coefficient of 1. If the current inspection task status is "idle", assign an inspection task status coefficient of 2. Generate the current inspection task status coefficient of each inspection personnel accordingly. Obtain the current workload rate of each inspection personnel. Based on the current inspection task status coefficient and workload rate of each inspection personnel, generate the load adaptability of each inspection personnel for each power cabinet with poor operating status.

9. A power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The method for determining the optimal inspection personnel for each power cabinet with poor operating status is as follows: Establish an inspection personnel scheduling evaluation model. Normalize the skill fit, skill level, distance and load adaptability of each inspection personnel for each power cabinet with poor operating status, take the values ​​and substitute them into the inspection personnel scheduling evaluation model to generate the scheduling matching degree of each inspection personnel for each power cabinet with poor operating status. The scheduling matching degree of each inspection personnel for each power cabinet with poor operating status is arranged in descending order, and the inspection personnel with the highest scheduling matching degree is extracted as the optimal inspection personnel. Based on this, the optimal inspection personnel for each power cabinet with poor operating status are obtained.

10. A power cabinet inspection personnel dispatching platform according to claim 1, characterized in that, The optimal inspection path for the best inspection personnel corresponding to each power cabinet with poor operating status is as follows: Traffic flow data for each inspection route is obtained and compared with preset standard traffic flow data to generate a traffic congestion index for each inspection route. Based on the meteorological condition data of each inspection path, a meteorological condition index for each inspection path is generated. The traffic congestion index of each inspection route is matched with the traffic congestion index corresponding to each preset first estimated arrival time correction factor to obtain the first estimated arrival time correction factor for each inspection route. The meteorological state index of each inspection path is matched with the meteorological state index corresponding to each preset second estimated arrival time correction factor to obtain the second estimated arrival time correction factor for each inspection path. The first and second estimated arrival time correction factors for each inspection route are summed to obtain the comprehensive estimated arrival time correction factor for each inspection route. Based on the baseline estimated arrival time of each inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status, the baseline estimated arrival time is multiplied by the estimated arrival time comprehensive correction factor to generate the final estimated arrival time of each inspection path of the optimal inspection personnel corresponding to each power cabinet with poor operating status. Arrange the final estimated arrival times of each inspection path for the optimal inspection personnel corresponding to each power cabinet with poor operating status in ascending order, and extract the inspection path corresponding to the minimum final estimated arrival time as the optimal inspection path for each power cabinet with poor operating status.

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