Portable intelligent power line repair metering method, system, equipment and medium

Abstract

The invention discloses a portable intelligent power first-aid repair metering method, system, equipment and medium, and belongs to the technical field of power distribution network maintenance, and the method comprises the steps: receiving and integrating the position and type information of a fault point and the real-time state data of each first-aid repair device, dynamically predicting the first-aid repair time of each device based on the historical efficiency and the real-time efficiency, and calculating the first-aid repair time of each device; the total scheduling time is calculated in combination with the path distance and the distribution speed; and through intelligent comparison, predicting a matching relationship between the first-aid repair time and the total scheduling time, selecting a device with the optimal comprehensive scheduling time efficiency from the devices meeting the time efficiency conditions as a supply source, generating a distribution instruction and issuing the distribution instruction to distribution personnel. According to the method, the historical efficiency data, the real-time working condition information and the multi-device synergistic effect are integrated, the first-aid repair time is predicted, and the optimal device is dynamically selected, so that the timeliness of first-aid repair response and the scientificity of resource scheduling are improved, and the whole-process intelligence from first-aid repair operation to equipment maintenance is realized while the continuous and reliable operation of first-aid repair equipment is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network maintenance, in particular to a portable intelligent power repair measurement method, system, device and medium. BACKGROUND

[0002] The traditional power repair method usually starts from fault alarm, the alarm information comes from the dispatching system, user complaint or inspection discovery, the repair management center first locates the fault and makes a preliminary judgment according to the fault alarm, determines the nearest available resource point through the geographic location, and determines the repair priority by relying on the experience of dispatch personnel, then dispatches the repair personnel to the scene for fault diagnosis, after confirming the fault type, the repair personnel assesses the corresponding type of repair turnaround box and repair materials required, and then carries out equipment replacement or maintenance operation after material allocation, the whole process involves multiple links.

[0003] For the above technical solution, the existing method usually needs frequent communication and coordination, which leads to a longer response time of the repair task, and the scheduling decision of the repair personnel and the repair turnaround box depends more on experience judgment, which makes the resource scheduling efficiency lower, thereby reducing the repair efficiency of the power distribution network. SUMMARY

[0004] In view of the above problems, the present application provides a portable intelligent power repair measurement method, system, device and medium.

[0005] Therefore, the technical problem solved by the present application is that the existing method usually needs frequent communication and coordination, which leads to a longer response time of the repair task, and the scheduling decision of the repair personnel and the repair turnaround box depends more on experience judgment, which makes the resource scheduling efficiency lower, thereby reducing the repair efficiency of the power distribution network.

[0006] To solve the above technical problems, the present application provides the following technical solution: a portable intelligent power repair measurement method, comprising, Receiving the position data and fault type data of the fault point, obtaining the path distance of the i-th repair device from the fault point, obtaining the actual number of repair devices and the distribution speed of the distribution personnel.

[0007] Based on the historical efficiency data and real-time efficiency data of the repair device, the predicted repair time of the repair device is calculated.

[0008] The total scheduling time of the repair device is calculated.

[0009] According to the matching relationship between the predicted repair time and the total scheduling time, from the repair devices that meet the conditions, the device with the optimal comprehensive scheduling time is selected as the supply source, a distribution order signal is generated and sent to the task scheduling module.

[0010] The task scheduling module receives the delivery order signal sent by the cloud metering module, and sends the delivery order signal and the location data of the fault point to the delivery personnel.

[0011] As a preferred scheme of the portable intelligent power repair measurement method, the predicted repair time of the repair device comprises, The predicted repair time of the i-th repair device is calculated based on the short-term average repair efficiency of the i-th repair device in a specified time period, the historical cooperative efficiency of all participating devices, the repair efficiency correction coefficient, and the corresponding conventional repair time.

[0012] The repair efficiency correction coefficient is adaptively adjusted based on the cooperative relationship between the actual number of repair devices and the minimum cooperative number.

[0013] As a preferred scheme of the portable intelligent power repair measurement method, the dispatching total time of the repair device comprises, The repair task is decomposed and synchronously issued to the microcontroller and the PLC controller, the PLC activates the actuator control flow, monitors and records the actual departure start and completion time of the repair device, and generates the departure time.

[0014] The dispatching total time of the i-th repair device from the task assignment to the predicted arrival at the fault point is calculated based on the departure time, the path distance of the i-th repair device from the fault point, and the delivery speed of the delivery personnel.

[0015] As a preferred scheme of the portable intelligent power repair measurement method, the task scheduling module receives the delivery order signal sent by the cloud metering module, and sends the delivery order signal and the location data of the fault point to the delivery personnel comprises, For each to-be-assigned fault point, a task urgency index is defined.

[0016] An asynchronous scheduling strategy is introduced, and the ready task queue and the to-be-selected device queue form a bidirectional interaction.

[0017] As a preferred scheme of the portable intelligent power repair measurement method, based on the GPS coordinates of the current position of the repair device, the target fault point coordinates, the current path distance, and the device speed, a plurality of types of data are periodically received and fused within a time t, and a comprehensive delay index S is calculated.

[0018] Based on the current and historical traffic flow data, the prediction probability of the current path congestion is predicted by calling the Bayesian optimization and sliding window clustering.

[0019] The weather risk weight is assigned by fuzzy logic combined with real-time alarm levels, and the risk weight of the weather warning is real-time corrected.

[0020] The actual path extension distance caused by traffic and weather conditions is simulated by collecting data from multiple paths using a GPS navigation module, and the difference between the minimum time cost of each path and the current path is selected.

[0021] When the comprehensive delay index S exceeds the dynamic threshold If the delivery speed is slow, it will be immediately determined as a slow delivery speed, triggering automatic route replanning.

[0022] The beneficial effects of this preferred technical solution are as follows: by using Bayesian optimization prediction of traffic congestion probability and fuzzy logic dynamic correction of weather risk, potential delay risks can be identified proactively; by using the comprehensive delay index S calculated by integrating multi-source data, a multi-dimensional quantitative evaluation of delivery timeliness is achieved; when the S value exceeds the dynamic threshold set based on historical performance, this invention can automatically and timely trigger route replanning, thereby forming a closed-loop optimization mechanism of monitoring-evaluation-early warning-response in the delivery process.

[0023] As a preferred embodiment of the portable intelligent power emergency repair metering method described in this invention, after the emergency repair of the fault point is completed and the emergency repair device is retrieved, the health status of various performance parameters of the emergency repair device is tested.

[0024] Based on the health status detection results, a corresponding feedback signal is generated and sent to the management terminal.

[0025] The beneficial effects of this preferred technical solution are that by automatically performing health checks on the equipment after the completion of an emergency repair task, closed-loop management and proactive preventative maintenance of the equipment's operating status are achieved. This invention can accurately assess the performance degradation and wear of the equipment during service, automatically generate graded maintenance recommendations based on the test results, and provide real-time feedback to the management end, enabling managers to promptly grasp the equipment's health status and arrange targeted maintenance plans.

[0026] In a preferred embodiment of the portable intelligent power emergency repair metering method described in this invention, the health status detection of various performance parameters of the emergency repair device includes: Obtain the test value of the nth performance parameter. The standard value of the nth performance parameter is The total number of performance parameters is L, and the service life of the i-th emergency repair device is The service life of the i-th emergency repair device is The health status of the i-th repair device is calculated as follows: Where A is the first diagnostic weight, B is the second diagnostic weight, and A+B=1.

[0027] The process of generating corresponding feedback signals based on health status detection results includes... when When the device generates a good status feedback signal, Generate preventative maintenance feedback signals in a timely manner, when An immediate maintenance feedback signal is generated, and the feedback signal and the health status of the i-th emergency repair device are sent to the management terminal.

[0028] The beneficial effect of this preferred technical solution is that it achieves accurate and objective assessment of the status of emergency repair equipment through a quantitative model of performance parameter deviation and service life attenuation. This invention weighted and integrated the overall deviation between real-time performance detection values ​​and standard values, as well as the relative loss between service life and design life, thereby comprehensively reflecting the overall status of the equipment in both performance reliability and lifespan consumption dimensions. By setting clear grading thresholds, this invention can automatically convert continuous health values ​​into discrete, executable maintenance decision instructions (excellent, preventative maintenance, immediate maintenance), and synchronously send the specific values ​​and signals to the management terminal.

[0029] This invention provides a portable intelligent power emergency repair metering system.

[0030] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a portable intelligent power emergency repair metering system, comprising: a communication module, a cloud data acquisition module, a data analysis module, a task processing module, a cloud metering module, a task scheduling module, a dynamic path optimization module, a self-testing module, and a feedback module.

[0031] The communication module is used to receive the location data and fault type data of the fault point, obtain the path distance from the i-th repair device to the fault point, and the delivery speed of the delivery personnel.

[0032] The cloud data acquisition module acquires the average long-term emergency repair efficiency of the emergency repair device in historical data, and acquires the short-term emergency repair efficiency of the i-th emergency repair device within a specified time period.

[0033] The data analysis module calculates the predicted repair time for the i-th repair device.

[0034] The task processing module calculates the total scheduling time for the i-th emergency repair device.

[0035] The cloud metering module compares the total scheduling time of each device with the predicted repair time, filters out devices that meet the timeliness requirements, and selects the device with the shortest total scheduling time as the final dispatched supply source, generating a delivery order.

[0036] The task scheduling module receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel.

[0037] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the portable intelligent power emergency repair metering method.

[0038] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the portable intelligent power emergency repair metering method.

[0039] The beneficial effects of this invention are as follows: This invention can comprehensively analyze historical efficiency data, real-time operating condition information, and the synergistic effect of multiple devices to predict emergency repair time and dynamically select the optimal device, thereby improving the timeliness of emergency repair response and the scientific nature of resource scheduling. Simultaneously, this invention integrates an automatic device health detection and graded maintenance early warning mechanism, ensuring the continuous and reliable operation of emergency repair equipment while achieving full automation and intelligence from emergency repair operations to equipment maintenance. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The above is a flowchart of a portable intelligent power emergency repair metering method provided in one embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of a portable intelligent power emergency repair metering system provided in one embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of a portable intelligent power emergency repair metering system turnover box provided in one embodiment of the present invention.

[0044] Figure 3 The modules are: 1-Communication module, 2-Cloud data acquisition module, 3-Data analysis module, 4-Task processing module, 5-Cloud metering module, and 6-Task scheduling module. Detailed Implementation

[0045] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0046] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a portable intelligent power emergency repair metering method, comprising: S1. Receive the location data and fault type data of the fault point, obtain the path distance from the i-th repair device to the fault point, obtain the actual number of repair devices and the delivery speed of the delivery personnel.

[0047] S2. Based on the historical and real-time performance data of the emergency repair device, calculate the predicted emergency repair time of the device.

[0048] S3. Calculate the total scheduling time for the emergency repair equipment.

[0049] S4. Based on the matching relationship between the predicted emergency repair time and the total scheduling time, select the device with the best overall scheduling efficiency from the eligible emergency repair devices as the supply source, generate a delivery order signal and send it to the task scheduling module.

[0050] S5. The task scheduling module receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel.

[0051] This invention constructs a fully intelligent emergency repair scheduling system, achieving closed-loop optimized management from fault reporting to resource allocation. First, it integrates real-time fault data and emergency repair resource status. Then, based on historical performance data and real-time performance indicators, it intelligently predicts the repair time for each device and accurately calculates the total scheduling time by combining path distance and delivery speed. By dynamically matching and optimizing the predicted repair time with the total scheduling time, it automatically identifies the repair device with the best overall timeliness as the supply source and generates delivery instructions. This invention effectively solves the problems of reliance on manual experience, delayed response, and inaccurate resource matching in traditional emergency repair scheduling, achieving scientific allocation and rapid response of emergency repair resources, and improving the efficiency and reliability of power emergency repairs.

[0052] Example 2, an embodiment of the present invention, provides a portable intelligent power emergency repair metering method based on the previous embodiment, comprising: Furthermore, in S1, the path distance from the i-th repair device to the fault point is obtained as follows: The delivery speed of the delivery personnel is The number of emergency repair devices at the fault point is N.

[0053] In this embodiment of the application, the historical performance data and real-time performance data of the emergency repair devices in S2, that is, the average long-term emergency repair efficiency of I emergency repair devices in the historical data, are: Get the short-term repair efficiency of the i-th emergency repair device within a specified time period as Specifically, this includes retrieving historical emergency repair efficiency parameters from the cloud data acquisition module to obtain the short-term average emergency repair efficiency of the i-th emergency repair device within a specified time period. Historical collaborative efficiency with all participating devices Meanwhile, the microprocessor unit analyzes the fault type of the current emergency repair task in real time and matches it with the corresponding routine emergency repair time. Combining on-site hardware testing data, the edge computing device uses an innovative prediction model to calculate the estimated repair time for the i-th emergency repair device.

[0054] In one alternative implementation, the historical performance data of the emergency repair device can be the success rate and average repair time of the device in handling similar faults in the past, and the performance degradation curve under different weather conditions and operating environments can be taken into account. The real-time performance data further integrates the current battery power of the device, the real-time working status signals of core components (such as insulation detection module and communication module), and the average operating proficiency score of the operator in the most recent tasks.

[0055] In another optional implementation, the historical performance data of the emergency repair device is expanded into a dynamic performance map based on the device's full lifecycle operation log. This map not only includes efficiency indicators but also records the device's maintenance records, component replacement cycles, and their impact weights on subsequent efficiency. The real-time performance data is enhanced into multi-dimensional physical parameters such as device vibration, temperature rise, and noise collected in real time by IoT sensors. These parameters are then converted into real-time health indices and performance correction coefficients using online learning algorithms, and input into the prediction model in conjunction with historical data.

[0056] Furthermore, the calculation of the predicted repair time for the emergency repair device in S2 includes steps A1-A2: A1. Based on the short-term average repair efficiency of the i-th emergency repair device within a specified time period, the historical collaborative efficiency of all participating devices, the repair efficiency correction coefficient, and the corresponding conventional repair time, calculate the estimated repair time of the i-th emergency repair device.

[0057] Specifically, data analysis module 3 first retrieves historical emergency repair efficiency parameters from cloud data acquisition module 2, and obtains the short-term average emergency repair efficiency of the i-th emergency repair device within a specified time period. Historical collaborative efficiency with all participating devices Simultaneously, the microprocessor unit analyzes the fault type of the current emergency repair task in real time and matches it with the corresponding routine emergency repair time. Based on on-site hardware testing data, the edge computing device uses an innovative predictive model to calculate the estimated repair time for the i-th emergency repair device. .

[0058] The estimated repair time is expressed as follows: in, and The first and second weighting coefficients are respectively (and The weights are dynamically adjusted based on the adaptive learning module. Highlighting the average level of team collaboration Focus on reflecting the current short-term performance of the device.

[0059] A2. The emergency repair efficiency correction coefficient is adaptively adjusted based on the coordination relationship between the actual number of emergency repair devices and the minimum number of coordination devices.

[0060] In this embodiment of the application, the cooperative relationship in A2 is the emergency repair efficiency correction coefficient. The piecewise nonlinear adaptive setting specifically includes, when the actual number of emergency repair devices N is not less than the minimum number of coordinated units... hour, This means the team effect has met the minimum requirements; if ,but Through the emergency repair efficiency coordination coefficient K Excessive nonlinear penalty devices may lead to resource dispersion and reduced management bandwidth, and the marginal benefits of dynamic adjustment of collective emergency repair should be considered.

[0061] In one alternative implementation, the collaborative relationship can be a dynamic allocation based on the Lorenz curve and Gini coefficient. Specifically, a theoretically optimal team size is first evaluated based on task complexity. When the actual number of participating devices N deviates When, correction factor Calculate as follows: =1-h*|N- | / Where h is the penalty coefficient. It aims to penalize team size overload or underload relative to the theoretical optimum, emphasizing the fairness and efficiency boundaries of resource allocation.

[0062] In another alternative implementation, the cooperative relationship can be a continuous adaptive logic based on fuzzy logic and real-time environment awareness. Specifically, a single, fixed minimum number of cooperative relationships is no longer set. Instead, team size sufficiency is defined as a fuzzy variable (e.g., "insufficient," "average," "sufficient"), whose membership function is jointly determined by the urgency of the task, the complexity of the geographical environment of the failure point (e.g., mountainous or urban areas), and the total available manpower. (Correction coefficient) As output variables, they are obtained through real-time inference and defuzzification calculations using a set of preset fuzzy rules (e.g., IF sufficiency IS "insufficient" AND task urgency IS "high", THENC_adj IS "significantly increased").

[0063] Throughout the computational chain, the FPGA acceleration card enables hardware-level parallel processing of operations such as averaging and correction coefficient calculations for batch historical data and real-time queue data, and piecewise function lookup tables, greatly improving the overall throughput and rapid feedback capabilities of the module. Finally, the processing results, along with the input parameters, algorithm weights, and collaborative correction data, are uploaded to the main control system, providing accurate and dynamic predictions of repair time for subsequent path optimization, task scheduling, and management decisions.

[0064] Furthermore, the total scheduling time for the emergency repair device in S3 includes steps B1-B2: B1. Decompose the emergency repair task and simultaneously distribute it to the microcontroller and PLC controller. The PLC activates the actuator control process, monitors and records the actual start and end times of the emergency repair device's dispatch, and generates the dispatch time. .

[0065] B2. Based on the actual detected outbound time The actual road distance from the i-th repair device to the fault point to the target. And the delivery speed of the delivery personnel Calculate the total scheduling time from the issuance of the task to the expected arrival time of the i-th repair device at the fault point.

[0066] The total scheduling time is expressed as follows: in, Let i be the total dispatch time for each repair device from the issuance of the task to its estimated arrival at the fault point. This refers to the actual outbound time detected by the device.

[0067] In this embodiment of the application, the matching relationship between the predicted repair time and the total scheduling time in S4 is statistically significant. The total scheduling time corresponding to the emergency repair device is recorded as a set Z. Specifically, the emergency repair device with the shortest total scheduling time is selected from set Z as the supply source, a delivery order signal is generated and sent to the task scheduling module, the task scheduling module receives the delivery order signal sent by the cloud metering module, and sends the delivery order signal and the location data of the fault point to the delivery personnel.

[0068] In one alternative implementation, the matching relationship between the predicted repair time and the total scheduling time can be a comprehensive optimization scheme based on a weighted average of "time margin" and device health. Specifically, all schemes that meet the criteria are first selected. The emergency repair equipment forms a preliminary selection set. Within this set, a time margin is introduced. =- and device real-time health status As a comprehensive evaluation dimension, it is assessed through a weighted scoring function. =a*(1 / )+b* +c* (Where a, b, and c are adjustable weighting coefficients) Calculate the score for each initial selection device, and finally select the device with the highest score as the supply source.

[0069] In another alternative implementation, the matching relationship between the predicted repair time and the total scheduling time can be a two-stage matching scheme that incorporates task-device adaptability. Specifically, the first stage performs a hard screening, retaining only those that meet the requirements. In the second stage, the fit of each candidate device i with the current fault type j is calculated. The adaptability is modeled based on the success rate and efficiency of the device in handling similar faults in historical data. Finally, a trade-off function is used... =(1 / )* + * Calculate the overall utility value and select the device with the highest utility value as the supply source.

[0070] Furthermore, the task scheduling module in S5 receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel, including steps C1-C2: C1. Define a task urgency index for each fault point to be assigned.

[0071] Specifically, this includes assigning each fault point to be assigned. Define task urgency indicators Taking into account the severity of the fault Population density in geographical coverage area Historical response delay With current device health )(in, (This refers to the pre-assigned delivery device number), and its calculation formula is as follows: in, Weights that are automatically corrected by the system's online learning; A higher value indicates a wider range of impact from the fault. Prioritizing densely populated urban areas Reflecting the potential risks of delays, This is used to prioritize units with higher device health.

[0072] C2. An asynchronous scheduling strategy is introduced, and the ready task queue and the candidate device queue form a two-way interaction.

[0073] In this application's implementation, the asynchronous scheduling strategy, namely the dual-queue elastic scheduling mechanism, specifically includes bidirectional interaction between the ready task queue and the candidate device queue, employing the following innovative scheduling decision function: in, It is the score for assigning the i-th repair device to handle the j-th fault point. This is the actual estimated delivery time derived from upstream path optimization. , To assign weights, To ensure optimal dynamic matching between different devices and tasks, the communication quality between the current device and the dispatch center is assessed (e.g., Wi-Fi / 4G signal strength and real-time online status indicators, with automatic penalty for weak signals). All dispatch results are pushed in JSON task order format. The dispatch server sends the final delivery order and fault location's geographical location to the designated mobile terminal via wireless AP, ensuring one order per person and zero-delay distribution. The mobile terminal supports dual backup of Wi-Fi and 4G, guaranteeing reliable delivery of tasks in different network scenarios, and transmits check-in and task feedback in real time, forming a closed-loop management data link.

[0074] In one optional implementation, the asynchronous scheduling strategy can be a dynamic preemptive scheduling mechanism based on multi-level priorities. Specifically, neither the task queue nor the device queue is limited to a single queue. Instead, it is divided into multiple priority sub-queues (high, medium, and low) based on task urgency indicators and the overall status of the devices. The high-priority task queue is periodically scanned, and tasks are dynamically allocated in real time from the currently idle or preemptible device queues based on overall matching degree. When a higher-priority task arrives, lower-priority tasks that have been allocated but not yet started can be interrupted and rescheduled, thereby maximizing the response to emergency tasks.

[0075] In another alternative implementation, the asynchronous scheduling strategy can be a negotiation scheduling mechanism based on distributed collaboration and auctions. Specifically, the task scheduling module does not directly perform centralized assignment, but instead publishes the pending fault tasks as tenders to a virtual task market. Each available repair device, based on its own status, location, capabilities, and cost assessment, bids for tasks of interest, with the bids including its promised estimated delivery time and execution confidence coefficient. The scheduling center selects the winning device based on a comprehensive evaluation function, or conducts multiple rounds of negotiation to achieve the optimal match.

[0076] Furthermore, the specific hardware implementation of the dynamic path optimization module includes, but is not limited to, an edge computing gateway, a traffic data interface unit, and a GPS navigation module. The edge computing gateway is connected to the industrial router via Ethernet, the traffic data interface unit is connected to the edge computing gateway via Ethernet, and the GPS navigation module is connected to the edge computing gateway via a serial port.

[0077] The detailed implementation process of the dynamic path optimization module is as follows: The module first connects the edge computing gateway to the industrial router via an industrial Ethernet network, enabling high-speed access to an external traffic management platform. The traffic data interface unit collects data including current road traffic conditions, historical congestion probabilities, special events (such as road construction and accidents), and real-time weather warnings. The acquired data is transmitted to the edge computing gateway via control commands and data streams. The gateway runs the Adaptive Multi-factor Dynamic Re-planning Algorithm (AMDRPA) of this invention.

[0078] The algorithm uses the GPS coordinates of the current location of the repair equipment. Target fault point coordinates ( ), current path distance Based on the device speed v, multiple types of data are periodically received and fused at each time slice t, and a comprehensive delay index S is introduced, which is defined as: in, Due to the actual route extension caused by the latest traffic and weather conditions, This represents the predicted probability of congestion occurring on the current path in the next cycle. To correspond to the risk weights of weather warnings, The weights are dynamically adjusted by the system's adaptive learning module (the initial value can be set to 1 / 3, and then adaptively adjusted according to the differences in repair efficiency).

[0079] The dynamic path optimization module first uses current and historical traffic flow data to call Bayesian optimization and sliding window clustering pairs. For forecasting, meteorological risk weights are assigned using fuzzy logic combined with real-time alarm level assignments, and are corrected in real time. ; The GPS navigation module is used to collect and simulate data from multiple optional paths, and the difference between the minimum time cost of each path and the current path is selected. When the comprehensive delay index S exceeds the dynamic threshold (Based on the system's historical average repair time) and short-term efficiency If the overall settings are considered, it will be immediately determined as "slow delivery speed" and trigger automatic route replanning. The route replanning process adopts a batch-by-batch backtracking method: the original route is divided into k segments (each segment starts at a key intersection or congestion warning point), and several feasible paths are dynamically selected for each segment and iterated progressively for the above three factors. In each iteration, the following incremental cost function is used for iteration.

[0080] in, To adjust the coefficients, we can determine whether to exit the backtracking path in the next step, thereby improving the flexibility and dynamic robustness of optimization.

[0081] All data processing and decision-making are completed at the edge computing gateway, and it interacts with communication module 1 in real time via Ethernet to update the actual path distance from the i-th repair device to the fault point. The final replanning results and adjustment strategies will be sent to the delivery personnel's terminals via wireless network and automatically uploaded to the system cloud for storage, dynamically visualizing the entire path evolution for subsequent adaptive learning and historical efficiency optimization.

[0082] Furthermore, the adaptive learning module obtains the actual repair time of the i-th repair device as follows: The actual scheduling time is The prediction error is calculated as follows: The error threshold is The error learning rate is ε. If the error is reasonable, then let , And update α and β in the data analysis module 3, if If the error is deemed unreasonable, an early warning signal is generated and sent to the management terminal.

[0083] Furthermore, after the self-testing module completes the emergency repair and recovery of the repair device, it performs health checks on various performance parameters of the repair device.

[0084] The specific hardware implementation of the self-test module includes, but is not limited to, a sensor array, a data acquisition card, and an embedded diagnostic controller. The sensor array is connected to the data acquisition card via analog input or digital I / O. The data acquisition card is connected to the embedded diagnostic controller via USB or Ethernet. The embedded diagnostic controller sends health data to the feedback module via UART or Ethernet.

[0085] The feedback module generates corresponding feedback signals based on the health detection results and sends these signals to the management terminal.

[0086] The specific hardware implementation of the feedback module includes, but is not limited to, the HMI (Human Machine Interface), an alarm, and a management software platform. The HMI is connected to the self-test module via RS485 or Ethernet to receive health data. The alarm is connected to the HMI via digital output or directly to the diagnostic controller. The management software platform communicates with the HMI via the OPC UA protocol and sends feedback signals to the management terminal.

[0087] Furthermore, health checks are performed on various performance parameters of the emergency repair equipment, including step D1- D1. Obtain the detection value of the nth performance parameter. The standard value of the nth performance parameter is The total number of performance parameters is N, and the service life of the i-th emergency repair device is . The service life of the i-th emergency repair device is .

[0088] D2. Calculate the health status of the i-th emergency repair device. Where A is the first diagnostic weight, B is the second diagnostic weight, and A+B=1.

[0089] Furthermore, the feedback module generates corresponding feedback signals based on the health status detection results, including: Generate corresponding feedback signals based on health status diagnosis rules. ,when When the device generates a good status feedback signal, Generate preventative maintenance feedback signals in a timely manner, when An immediate maintenance feedback signal is generated, and the feedback signal and the health status of the i-th emergency repair device are sent to the management terminal.

[0090] Example 3, referring to Figure 2 and Figure 3This embodiment of the present invention provides a portable intelligent power emergency repair metering system, including: a communication module, a cloud data acquisition module, a data analysis module, a task processing module, a cloud metering module, a task scheduling module, a dynamic path optimization module, a self-testing module, and a feedback module.

[0091] The communication module is used to receive the location data and fault type data of the fault point, obtain the path distance from the i-th repair device to the fault point, and the delivery speed of the delivery personnel.

[0092] The specific hardware implementation of communication module 1 includes, but is not limited to, a GPS receiver, wireless communication module 1, and an industrial router. The GPS positioning unit and wireless communication unit are connected to the main controller, such as an industrial computer, via a USB interface or a UART serial port. The industrial router is connected to other modules via an Ethernet switch, providing local area network and wide area network access.

[0093] The cloud data acquisition module obtains the average long-term emergency repair efficiency of the emergency repair device in historical data, and obtains the short-term emergency repair efficiency of the i-th emergency repair device within a specified time period.

[0094] The specific hardware implementation of cloud data acquisition module 2 includes, but is not limited to, a cloud server, a database server, and a data collector. The cloud server is connected to an industrial router via the Internet, the database server interacts with the cloud server as a backend, and the data collector is connected to the emergency repair device via Modbus TCP or OPC UA protocol, and uploads the collected data to the cloud server.

[0095] The data analysis module calculates the predicted repair time for the i-th emergency repair device.

[0096] The specific hardware implementation of the data analysis module 3 includes, but is not limited to, edge computing devices, microprocessor units, and FPGA accelerator cards. The edge computing devices are connected to the main controller via a PCIe interface or directly to the cloud data acquisition module 2 via Ethernet. The microprocessor unit, as an embedded system, is connected to other sensor interfaces via I2C or SPI bus. The FPGA accelerator card is embedded in the industrial computer via a PCIe slot.

[0097] The task processing module calculates the total scheduling time for the i-th emergency repair device.

[0098] The specific hardware implementation of the task processing module 4 includes, but is not limited to, a PLC controller, an industrial computer, and a microcontroller. The PLC controller is connected to the actuator of the emergency repair device, such as the outbound robot, via the PROFINET or Modbus RTU protocol. The industrial computer is connected to the data analysis module 3 via Ethernet. The microcontroller communicates with the PLC controller via the CAN bus or serial port.

[0099] Based on a multi-layered industrial control network, task processing module 4 achieves fully automated management and precise timing control of the emergency repair device scheduling process. In terms of hardware architecture, the PLC controller is at the core, maintaining a high-speed and secure real-time control connection with the on-site emergency repair device's outbound actuators (such as electric manipulators and automatic conveyor platforms) via PROFINET or Modbus RTU protocols. The PLC automatically collects and feeds back the inbound and outbound processes and on-site status signals, while simultaneously reporting outbound completion signals and relevant timestamps to upstream modules in real time. The industrial computer, as the main processing unit for task processing and data aggregation, works closely with the upstream data analysis module 3 via Ethernet to read various basic parameters and analysis results related to this scheduling in real time. The microcontroller collaborates with the PLC controller via a CAN bus or serial interface for local process control, redundancy detection, and fault self-diagnosis, accurately capturing the start and end times of each outbound action.

[0100] The cloud metering module compares the scheduling time of each device with the predicted emergency repair time, filters out the devices that meet the timeliness requirements, and selects the one with the shortest scheduling time as the final supply source to be dispatched, generating a delivery order.

[0101] The specific hardware implementation of the cloud metering module 5 includes, but is not limited to, cloud platform services, message middleware servers, and application servers. The cloud platform services are connected to the task processing module 4 via the Internet to receive scheduling time data. The message middleware servers are connected to the cloud platform via the AMQP protocol to manage delivery order signals. The application servers interact with the message middleware via REST API and send signals to the task scheduling module 6.

[0102] The cloud metering module 5 relies on a cloud-native distributed architecture throughout the entire process, fully utilizing elastic computing, real-time message management, and highly available service interfaces to achieve intelligent decision-making for the scheduling and delivery of emergency repair equipment. Firstly, the cloud platform service, acting as the core data aggregation and computing resource pool, receives batch-reported scheduling time data for each emergency repair unit from the task processing module 4 via a secure internet connection. The corresponding predicted emergency repair time Task parameter information, etc. All transmitted data is finely tagged and archived according to device number and stored in the cloud platform database in real time, supporting high-concurrency real-time read and write and backtracking analysis.

[0103] In the core decision-making process, the cloud platform service automatically performs data filtering and aggregation on all reported emergency repair equipment information: the system algorithm extracts all equipment that meet the requirement that the scheduling time is no greater than the predicted emergency repair time from all equipment, i.e. The scheduling time of each real-time upload task is statistically analyzed and recorded as a set Z. This set is dynamically associated with each real-time upload task and refreshed in real time, maintaining data consistency and processing performance even under large-scale, high-concurrency scenarios. For the target candidate devices in set Z, the cloud metering module 5 further uses a built-in algorithm to select the device with the shortest scheduling time, using it as the supply source for this fault repair task, thus achieving optimal resource matching.

[0104] The message distribution mechanism is guaranteed by a message middleware server, which establishes a stable and efficient data channel with the cloud platform through the standardized AMQP protocol. Whenever the cloud platform completes the supplier source screening, the system automatically generates a delivery order signal containing elements such as the supplier device number, target fault location information, and estimated arrival time, and pushes the structured signal to the message middleware server. The message middleware implements unified task queue management, distributed caching, and multi-channel push strategies to ensure the timeliness and integrity of delivery order signals across subsystems, and supports task retries and anomaly tracking.

[0105] The application server acts as a "bridge" between the cloud metering module 5 and the downstream business system (i.e., the task scheduling module 6), periodically or event-triggeredly pulling messages to be pushed via REST API. Upon receiving a delivery order signal, the application server encapsulates and forwards it to the task scheduling module 6 in batches or individually according to scheduling priority, achieving seamless task allocation. The entire process is supported by strong decoupling between modules, high message availability, and standardized interfaces, enabling the cloud metering module 5 to play a central role in real-time collaboration and decision optimization in emergency repair task scheduling, resource coordination, and overall management.

[0106] The task scheduling module receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel.

[0107] like Figure 3 As shown, all modules of the portable intelligent power emergency repair metering system are installed in a portable intelligent power emergency repair metering turnover box. The turnover box contains a communication module, a cloud data acquisition module, a data analysis module, a task processing module, a cloud metering module, and a task scheduling module.

[0108] Specifically, the communication module 1 receives the location data and fault type data of the fault point. Since there are usually sensor networks distributed in the existing distribution network nodes, various fault data of the distribution network nodes can be obtained in real time. Thus, the fault type can be judged at the distribution network node or cloud management platform, enabling the communication module 1 to quantify the path distance between the fault point and the emergency repair device.

[0109] By accessing data sources from external traffic management platforms, the dynamic route optimization module can obtain real-time road condition information and weather warning data, providing multi-source data support for route evaluation. By analyzing the delivery speed of the current route, it can identify potential delays in the delivery of emergency repair boxes, thereby triggering timely route replanning decisions in response to dynamically changing road conditions.

[0110] After triggering the route replanning decision, the dynamic route optimization module sends decision information to delivery personnel, promoting the coordination between on-site operations and command and dispatch. The dynamic update mechanism of route distance enables the scheduling time calculation of emergency repair boxes to reflect the latest road conditions, enhances the adaptability to changes in the traffic environment, and improves the reliability of emergency repair box scheduling.

[0111] The cloud data acquisition module 2 acquires the average long-term emergency repair efficiency and some recent short-term emergency repair efficiencies collected from historical data, thereby providing a quantitative basis for evaluating the predictive emergency repair capabilities of different devices. The data analysis module 3 calculates the optimized predicted emergency repair time based on the harmonic mean formula, taking into account the differences between long-term baseline efficiency and short-term performance, making the prediction results more stable and improving the reliability of subsequent task scheduling.

[0112] By introducing correction coefficients, the impact of various uncertainties can be reflected. This correction method enables the prediction model to adapt to different emergency repair environments. For example, in severe weather conditions or complex fault scenarios, the predicted emergency repair time can be increased by adjusting the γ value, making the predicted emergency repair time closer to the actual operation requirements. After introducing correction coefficients, the system can exhibit better environmental adaptability and provide differentiated time predictions based on different site conditions, reducing prediction deviations caused by environmental factors, thereby improving resource scheduling efficiency and the emergency repair efficiency of the distribution network.

[0113] There is no linear relationship between the number of emergency repair containers and the repair efficiency; rather, there is a certain diminishing marginal efficiency. When the number of emergency repair containers is lower than the minimum coordination quantity, no coordination effect occurs between the containers, and the repair efficiency does not change significantly. However, when the number of emergency repair containers exceeds the minimum coordination quantity, the repair efficiency increases to a certain extent. By using a repair efficiency correction coefficient, we can simulate the coordination efficiency loss that occurs when the scale of repairs expands, quantify the degree of coordination efficiency loss, and more accurately assess the actual efficiency of large-scale repair operations, reduce prediction bias, and thus improve resource scheduling efficiency and the repair efficiency of the distribution network.

[0114] By introducing weighting coefficients and This allows for adjusting the contribution ratio of long-term efficiency to short-term efficiency in prediction, thereby enabling the system to adjust its dependence on historical and recent data based on actual conditions. When α is larger, the system relies more on long-term average efficiency, which may produce more stable prediction results. When β is larger, the system pays more attention to recent performance and may be more sensitive to changes in efficiency, thus enabling different emergency repair turnover boxes to adapt to different application scenarios.

[0115] In addition, for emergency repair containers with stable performance, the weight of long-term efficiency can be increased, while for devices with frequent efficiency changes, the weight of short-term efficiency can be increased. The weighting coefficient provides parameter space for system optimization. By adjusting the weighting coefficient value, it is beneficial to find parameter configurations that are more suitable for specific environments or specific types of containers, thereby improving prediction accuracy, reducing prediction deviation, and thus improving resource scheduling efficiency and emergency repair efficiency of the distribution network.

[0116] The task processing module 4 calculates the total scheduling time by adding the outbound time and the delivery time. Then, the cloud metering module 5 filters out a set of feasible scheduling schemes and selects the optimal scheduling scheme. The task scheduling module 6 sends the scheduling scheme to the delivery personnel, improving the rationality of emergency repair resource allocation. This allows task scheduling to make judgments and decisions based on quantified data, rather than relying solely on human experience, which significantly reduces the response time of emergency repair tasks and improves resource scheduling efficiency and the emergency repair efficiency of the power distribution network.

[0117] By comparing actual and predicted values ​​to calculate relative error, and judging the reasonableness of the error based on a preset error threshold, the emergency repair turnover box has a certain adaptive monitoring capability. It can identify the deviation between prediction and actual execution, and generate an early warning signal when abnormal error is detected. This helps managers to pay attention to abnormal situations in a timely manner, analyze the causes of deviations, improve the practicality and reliability of the emergency repair turnover box, and provide support for adjusting model parameters or improving emergency repair procedures. This is conducive to improving prediction accuracy and thus improving the emergency repair efficiency of the power distribution network.

[0118] By calculating the prediction error and dynamically adjusting the weight coefficients α and β according to the error learning rate, the emergency repair turnover box can automatically optimize its parameter configuration according to the actual situation. The error learning rate reflects the step size of parameter adjustment, thereby adjusting the balance between learning speed and stability. Through adaptive learning, it can adapt to the long-term changing trend of emergency repair efficiency, such as the efficiency improvement brought about by technological progress or personnel training.

[0119] Through continuous parameter adjustments, a weight configuration more suitable for the current conditions can be gradually found, reducing prediction errors, decreasing reliance on manual parameter tuning, and improving the reliability and long-term applicability of emergency repair containers. As operating time accumulates, prediction accuracy may show a gradual improvement trend, thereby reducing prediction deviations, improving resource scheduling efficiency, and ultimately improving the emergency repair efficiency of the distribution network.

[0120] The self-test module performs health checks on various performance parameters of the emergency repair and turnover box, and the feedback module converts the test results into feedback signals, thereby establishing an information transmission channel between the equipment status and the management end, forming a closed-loop management of equipment use and status monitoring, providing data support for maintenance decisions, and accumulating historical data on equipment operating status through regular testing and feedback, providing information sources for analyzing equipment performance change trends, helping managers understand equipment status, and providing a reference for maintenance and upgrade plans.

[0121] By comprehensively considering both performance parameter deviation and service life, the condition of the emergency repair turnover box can be evaluated from multiple perspectives. The performance parameter evaluation uses the average value of relative deviations, which can reduce the impact of large fluctuations in individual parameters on the overall evaluation and make the evaluation results more stable. Introducing service life allows the evaluation to not only focus on current performance but also comprehensively consider the equipment's historical usage.

[0122] The setting of weighting coefficients A and B allows for adjustment of the emphasis on the two dimensions according to actual needs, increasing the reliability and stability of health assessment, reducing the variability of subjective judgment, and facilitating the comparison of equipment status and priority ranking through numerical health indicators, thereby improving the reliability of emergency repair turnover box scheduling and improving resource scheduling efficiency.

[0123] The health status values ​​are divided into different intervals, and a corresponding feedback signal is assigned to each interval. This provides differentiated handling suggestions for equipment in different states. The feedback signal of excellent equipment status indicates that the emergency repair turnover box can continue to be used, reducing unnecessary maintenance interventions. The feedback signal of preventive maintenance indicates that the equipment status may need attention but has not yet reached the level of urgency, so as to carry out planned maintenance. The feedback signal of immediate maintenance indicates that the equipment with poor health status needs to be prioritized, so as to prioritize the allocation of maintenance resources. This provides managers with detailed assessment basis, thereby further decision analysis, facilitating the comparison of the status of equipment and prioritization, thereby improving the reliability of emergency repair turnover box scheduling and improving resource scheduling efficiency.

[0124] This embodiment also provides an electronic device applicable to a portable intelligent power emergency repair metering method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the portable intelligent power emergency repair metering method proposed in the above embodiment.

[0125] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a portable intelligent power emergency repair metering method as proposed in the above embodiment.

[0126] The storage medium proposed in this embodiment belongs to the same inventive concept as the portable intelligent power emergency repair metering method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0127] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A portable intelligent power emergency repair metering method, characterized in that: include, Receive the location data and fault type data of the fault point, obtain the path distance from the i-th emergency repair device to the fault point, obtain the actual number of emergency repair devices and the delivery speed of the delivery personnel; Based on the historical and real-time performance data of the emergency repair equipment, the predicted emergency repair time of the equipment is calculated. Calculate the total scheduling time for the emergency repair equipment; Based on the matching relationship between the predicted repair time and the total scheduling time, the device with the best overall scheduling efficiency is selected from the eligible repair devices as the supply source, and a delivery order signal is generated and sent to the task scheduling module. The task scheduling module receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel.

2. The portable intelligent power emergency repair metering method as described in claim 1, characterized in that: The predicted repair time of the calculation-based emergency repair device includes... Based on the short-term average repair efficiency of the i-th emergency repair device within a specified time period, the historical collaborative efficiency of all participating devices, the repair efficiency correction coefficient, and the corresponding conventional repair time, calculate the estimated repair time of the i-th emergency repair device. The emergency repair efficiency correction coefficient is adaptively adjusted based on the coordination relationship between the actual number of emergency repair devices and the minimum number of coordinated units.

3. The portable intelligent power emergency repair metering method as described in claim 2, characterized in that: The total scheduling time for the calculation and repair device includes... The emergency repair task is broken down and simultaneously sent to the microcontroller and PLC controller. The PLC activates the actuator control process, monitors and records the actual start and end times of the emergency repair device's departure from the warehouse, and generates the departure time. Based on the outbound time, the path distance between the i-th repair device and the fault point, and the delivery speed of the delivery personnel, calculate the total scheduling time from the issuance of the task to the expected arrival time of the i-th repair device at the fault point.

4. The portable intelligent power emergency repair metering method as described in claim 3, characterized in that: The task scheduling module receives the delivery order signal sent by the cloud metering module, and sends the delivery order signal and the location data of the fault point to the delivery personnel. Define a task urgency index for each fault point to be assigned; An asynchronous scheduling strategy is introduced, enabling bidirectional interaction between the ready task queue and the candidate device queue.

5. The portable intelligent power emergency repair metering method as described in claim 4, characterized in that: Based on the GPS coordinates of the current location of the repair device, the coordinates of the target fault point, the current path distance, and the device speed, multiple types of data are periodically received and fused within time t to calculate the comprehensive delay index S. Based on current and historical traffic flow data, Bayesian optimization and sliding window clustering are used to predict the probability of congestion on the current path. Meteorological risk weights are adjusted in real time by combining fuzzy logic with real-time alarm level assignment; The actual path extension distance caused by traffic and weather conditions is simulated by collecting data from multiple paths using a GPS navigation module, and the difference between the minimum time cost of each path and the current path is selected. When the comprehensive delay index S exceeds the dynamic threshold If the delivery speed is slow, it will be immediately determined as a slow delivery speed, triggering automatic route replanning.

6. The portable intelligent power emergency repair metering method as described in claim 4, characterized in that: After the emergency repair is completed and the repair equipment is retrieved, the health status of various performance parameters of the repair equipment is tested. Based on the health status detection results, a corresponding feedback signal is generated and sent to the management terminal.

7. The portable intelligent power emergency repair metering method as described in claim 4, characterized in that: The health check of various performance parameters of the emergency repair device includes... Obtain the test value of the nth performance parameter. The standard value of the nth performance parameter is The total number of performance parameters is L, and the service life of the i-th emergency repair device is The service life of the i-th emergency repair device is The health status of the i-th repair device is calculated as follows: Where A is the first diagnostic weight, B is the second diagnostic weight, and A+B=1; The process of generating corresponding feedback signals based on health status detection results includes... when When the device generates a good status feedback signal, Generate preventative maintenance feedback signals in a timely manner, when An immediate maintenance feedback signal is generated, and the feedback signal and the health status of the i-th emergency repair device are sent to the management terminal.

8. A portable intelligent power emergency repair metering system, employing the portable intelligent power emergency repair metering method as described in any one of claims 1 to 7, characterized in that, include: Communication module, cloud data acquisition module, data analysis module, task processing module, cloud metering module, task scheduling module; The communication module is used to receive location data and fault type data of the fault point, obtain the path distance from the i-th repair device to the fault point, and the delivery speed of the delivery personnel; The cloud data acquisition module acquires the average long-term emergency repair efficiency of the emergency repair device in historical data, and acquires the short-term emergency repair efficiency of the i-th emergency repair device within a specified time period. The data analysis module calculates the predicted repair time for the i-th emergency repair device. The task processing module calculates the total scheduling time for the i-th emergency repair device; The cloud metering module compares the total scheduling time of each device with the predicted repair time, filters out devices that meet the timeliness requirements, and selects the device with the shortest total scheduling time as the final dispatched supply source, generating a delivery order. The task scheduling module receives the delivery order signal sent by the cloud metering module and sends the delivery order signal and the location data of the fault point to the delivery personnel.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the portable intelligent power emergency repair metering method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the portable intelligent power emergency repair metering method according to any one of claims 1 to 7.

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