A branch box remote operation and maintenance method and system based on cloud collaboration

Abstract

This invention belongs to the field of power operation and maintenance technology, and discloses a cloud-based collaborative remote operation and maintenance method and system for branch boxes. The steps include: collecting multi-source operating parameters and statistically analyzing them to obtain normal operating baseline data; comparing the baseline data with current operating data item by item to generate abnormal event records; packaging complete cross-sectional operating data before and after an anomaly trigger and uploading it to the cloud; performing waveform morphology matching based on a standard fault feature library to determine the fault type and suspected fault points; generating an initial handling action sequence based on fault information; replacing the operation object according to the real-time topology relationship of the branch box to obtain a remote control command set; performing time-series parsing of the command set to generate control messages, sending them to the corresponding controllable switching equipment; collecting remote signaling change information and telemetry change data of the equipment; and finally feeding back the relevant status data to the cloud server, completing the entire process of cloud-based collaborative remote operation and maintenance of the branch box. This invention can improve the efficiency and reliability of remote operation and maintenance of branch boxes.

Description

Technical Field

[0001] This invention belongs to the field of power operation and maintenance technology, specifically relating to a remote operation and maintenance method and system for branch boxes based on cloud collaboration. Background Technology

[0002] Branch boxes, as key equipment in power distribution networks, are widely used in the branching connections and power distribution of distribution lines. Their operating status directly affects the stability and power supply reliability of the power grid distribution system, making them one of the core control targets of power operation and maintenance. Currently, the industry's operation and maintenance of branch boxes still mainly relies on traditional on-site manual inspections combined with limited local monitoring. Equipment operating data is only stored on local terminals and subjected to simple analysis, lacking the ability for cross-device and cross-regional full-domain data interoperability and collaborative processing, and failing to achieve centralized control of multiple branch boxes and real-time full-domain perception of their operating status.

[0003] Currently, branch box fault handling mostly relies on manual inspection and operation, lacking cloud-based collaborative intelligent decision-making and remote control capabilities. On the one hand, the isolation of data makes it difficult to conduct correlation analysis and accurate judgment of faults, resulting in delayed anomaly detection, low fault location efficiency, and maintenance response speed that falls far short of the actual needs of efficient power grid operation. On the other hand, fault handling mainly depends on on-site inspection and manual operation by maintenance personnel. Fault judgment is easily limited by experience, leading to misjudgment and omissions. Moreover, the handling actions cannot be dynamically adjusted in conjunction with the real-time topology connection relationship of the power grid, resulting in low operational accuracy and execution efficiency. This not only significantly increases manpower and time costs but also creates potential safety hazards for power grid operation due to untimely fault handling, thus driving up the overall cost of power distribution operation and maintenance in the long term and bringing safety risks. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, the present invention aims to provide a cloud-based collaborative remote operation and maintenance method and system for branch boxes. By performing integrity verification of abnormal cross-section data, standardized metadata annotation, lossless compression and verification, fragmented and orderly transmission, and precise cloud reassembly, the method achieves secure, complete, and efficient cloud uploading of abnormal analysis data packets, providing standardized, reliable, and high-quality data support for accurate fault matching.

[0005] To achieve the above objectives, the technical solution adopted by this invention is a remote operation and maintenance method for branch boxes based on cloud collaboration, comprising the following steps:

[0006] S1. Collect multi-source operating parameters of the branch box under different operating conditions during the historical fault-free period, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box.

[0007] S2. Compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the abnormal event record of the branch box;

[0008] S3. Package the complete cross-sectional operation data of the collection cycle before and after the abnormal event is triggered into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server;

[0009] S4. Based on the preset standard fault feature library, perform waveform morphology matching on the abnormal analysis data packets to obtain the fault type and suspected fault point of the branch box.

[0010] S5. Based on the fault type and suspected fault point, determine the initial handling action sequence of the branch box, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box.

[0011] S6. Perform operation timing parsing on the remote control command set to obtain the control message of the branch box. Send the control message to the controllable switchgear with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switchgear. Feed back the remote signaling change information and telemetry change data to the cloud server.

[0012] Preferably, in step S1, obtaining the normal operating baseline data of the branch box includes:

[0013] By collecting electrical quantity parameters, equipment status parameters, and environmental and operating condition identification parameters of the branch box under different operating conditions during historical fault-free periods, the multi-source operating parameters of the branch box are obtained.

[0014] Data cleaning is performed on the multi-source operating parameters to remove outlier values ​​and communication interruption data segments, resulting in a continuous sequence of operating parameters for the branch box.

[0015] Based on the zero-crossing moments of voltage and current waveform data in the continuous operating parameter sequence, the synchronization of the three-phase voltage and current of the branch box is calibrated to obtain the synchronization electrical quantity set of the branch box.

[0016] Feature extraction is performed on the zero-sequence voltage and zero-sequence current waveforms in the synchronous electrical quantity set to obtain the characteristic quantities of the synchronous electrical quantity set;

[0017] Multi-dimensional statistics were performed on the characteristic quantities to obtain the cluster centers of the zero-sequence voltage and zero-sequence current waveforms and the fluctuation range of the fundamental phase angle under different operating conditions. The cluster centers and fluctuation ranges were then summarized into the normal operation benchmark data of the branch box.

[0018] Preferably, in step S2, obtaining the abnormal event record of the branch box includes:

[0019] Obtain the current running data of the branch box, synchronize and pair the current running data with the normal running baseline data to obtain the paired dataset of the branch box;

[0020] Extract the waveform area ratio of the zero-sequence voltage waveform and zero-sequence current waveform in the paired dataset, and perform a difference analysis between the waveform area ratio and the corresponding waveform morphology cluster center in the normal operation reference data to obtain the waveform distortion degree of the branch box.

[0021] The deviation of the branch box parameters is obtained by performing deviation analysis between the paired datasets and the normal operating baseline data;

[0022] By fusing waveform distortion and parameter deviation in multiple dimensions, abnormal event records of the branch box are obtained.

[0023] Preferably, in step S3, the complete cross-sectional operation data of the collection cycle before and after triggering the abnormal event recording is packaged into an abnormal analysis data package, and the abnormal analysis data package is uploaded to the cloud server, including:

[0024] Obtain the trigger timestamp corresponding to the abnormal event record, and combine it with the cross-sectional running data of the complete collection cycle of the abnormal event record to obtain the original dataset of the branch box;

[0025] Perform data integrity verification on the original dataset to obtain the complete cross-sectional data sequence of the branch box;

[0026] Metadata tags are added to the complete cross-sectional data sequence. The metadata tags include anomaly event identifier code, branch box equipment identifier, data acquisition time range, timestamp list of cross-sectional data, and data quality markers, resulting in the main body of the branch box anomaly analysis data packet.

[0027] The main body of the anomaly analysis data packet is subjected to lossless compression, and a cyclic redundancy check code is embedded in the compressed data stream to obtain the compressed data packet of the branch box;

[0028] The compressed data packet is divided into fixed-length data fragments, and fragment sequence numbers and total fragment count identifiers are added to the data fragments to obtain the fragment transmission queue of the branch box;

[0029] Data fragments are uploaded to the cloud server sequentially according to the fragment transmission queue. After the data fragments are uploaded, a data packet merging command is sent to the cloud server, so that the cloud server can reassemble the received data fragments into a complete anomaly analysis data packet based on the fragment sequence number and the total number of fragments.

[0030] Preferably, in step S4, obtaining the fault type and suspected fault point of the branch box includes:

[0031] Extract the zero-sequence voltage waveform sequence and zero-sequence current waveform sequence from the anomaly analysis data packet to obtain the waveform dataset to be matched from the anomaly analysis data packet;

[0032] Read the standard fault waveform template of the branch box from the preset standard fault feature library. The standard fault waveform template includes the standard waveform of zero-sequence voltage, the standard waveform of zero-sequence current and the location of typical fault points.

[0033] Analyze the voltage waveform similarity between the zero-sequence voltage waveform sequence and the zero-sequence voltage standard waveform, and the current waveform similarity between the zero-sequence current waveform sequence and the zero-sequence current standard waveform;

[0034] The basic waveform matching degree of the branch box is obtained by weighted fusion of voltage waveform similarity and current waveform similarity.

[0035] Extract the instantaneous zero-sequence voltage and zero-sequence current values ​​of the first abnormal cycle from the waveform dataset to be matched;

[0036] The maximum values ​​of the voltage cross-correlation coefficients between the instantaneous zero-sequence voltage value sequence and the standard zero-sequence voltage waveform, and the maximum values ​​of the current cross-correlation coefficients between the instantaneous zero-sequence current value sequence and the standard zero-sequence current waveform are calculated.

[0037] The phase difference deviation of the branch box is determined based on the phase difference between the instantaneous zero-sequence voltage value sequence and the instantaneous zero-sequence current value sequence and the typical phase difference marked in the standard fault waveform template.

[0038] The sum of squares of the instantaneous zero-sequence current values ​​is taken as the transient energy value, and the ratio of the transient energy value to the transient energy reference value in the standard fault waveform template is analyzed to obtain the energy ratio of the branch box.

[0039] The comprehensive matching index of the branch box is calculated based on the basic waveform matching degree, the maximum value of the voltage waveform cross-correlation coefficient, the maximum value of the current waveform cross-correlation coefficient, the phase difference deviation, and the energy ratio.

[0040] The standard fault waveform template with the largest comprehensive matching index is used as the fault type of the branch box, and the typical fault point location markers carried in the standard fault waveform template are identified as the suspected fault points of the branch box.

[0041] Preferably, the formula for calculating the comprehensive matching index is:

[0042] ;

[0043] Where S represents the overall matching index, This represents the maximum value of the voltage cross-correlation coefficient. M represents the maximum value of the current cross-correlation coefficient, and M represents the basic waveform matching degree. Indicates the phase difference deviation. Indicates the energy ratio. This represents the preset first adjustment coefficient. This represents the preset second adjustment coefficient.

[0044] Preferably, in step S5, the remote control command set for the branch box is obtained, including:

[0045] Based on the fault type and suspected fault location, the corresponding standard handling action template is retrieved from the handling strategy library of the branch box;

[0046] Obtain the current topology connection relationship data of the branch box from the real-time topology data of the cloud server, and replace the operation object type label of the atomic operation in the standard handling action template with the network address in the current topology connection relationship data to obtain the initial instruction sequence of the branch box;

[0047] Based on the electrical connection paths in the current topology connection data, identify the topological dependencies between adjacent atomic operations in the preliminary instruction sequence, and adjust the execution order of atomic operations in the preliminary instruction sequence according to the topological dependencies to obtain the intermediate instruction sequence of the branch box;

[0048] The operation command codes and network addresses of atomic operations in the intermediate instruction sequence are encapsulated into a remote control instruction set for the branch box.

[0049] Preferably, in step S6, sending a control message to the controllable switchgear corresponding to the network address within the branch box to collect remote signaling change information and telemetry change data from the controllable switchgear, and then feeding back the remote signaling change information and telemetry change data to the cloud server, includes:

[0050] Parse the remote control instruction set to obtain the device network address, operation command code, and execution timing label of the remote control instruction set. Sort and integrate the control instructions in the remote control instruction set according to the execution timing label to obtain the executable instruction sequence of the branch box.

[0051] The operation command codes in the executable instruction sequence are encapsulated into control messages, and the instruction sequence number and target network address are appended to the control messages to obtain the message queue of the branch box;

[0052] According to the time sequence of executable instructions, the message queue is sent to the controllable switching device with the corresponding network address in sequence, and the response messages returned by the controllable switching device are monitored in real time. Remote signaling change information and telemetry change data are parsed from the response messages.

[0053] The remote signaling change information and telemetry change data are packaged into a feedback data packet, and the feedback data packet is uploaded to the designated interface of the cloud server.

[0054] Preferably, the system monitors the response messages returned by the controllable switching equipment in real time, and parses the remote signaling change information and telemetry change data from the response messages, including:

[0055] Continuously receive the raw data stream returned by the controllable switching device, perform frame synchronization verification on the raw data stream, and obtain the response message from the controllable switching device;

[0056] Parse the message header of the response message to obtain the device network address and message type identifier of the controllable switchgear, and separate the remote signaling change information field and the telemetry change data field from the message payload of the response message based on the message type identifier;

[0057] Perform state transition analysis on the remote signaling change information field to obtain the remote signaling change information of the response message;

[0058] Perform interpolation analysis on the telemetry change data fields to obtain the telemetry change data of the response message.

[0059] A cloud-based collaborative remote operation and maintenance system for branch boxes, used to implement the above method, includes:

[0060] The data acquisition module is used to collect multi-source operating parameters of the branch box under different operating conditions during historical fault-free periods, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box.

[0061] The anomaly recording module is used to compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the anomaly event records of the branch box;

[0062] The data upload module is used to package the complete cross-sectional operation data of the collection cycle before and after the triggering of the abnormal event record into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server;

[0063] The fault matching module is used to perform waveform morphology matching on abnormal analysis data packets based on a preset standard fault feature library to obtain the fault type and suspected fault point of the branch box.

[0064] The instruction generation module is used to determine the initial handling action sequence of the branch box based on the fault type and suspected fault point, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box.

[0065] The data feedback module is used to parse the operation timing of the remote control command set, obtain the control message of the branch box, and send the control message to the controllable switch equipment with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switch equipment, and then feed the remote signaling change information and telemetry change data back to the cloud server.

[0066] The beneficial effects of this invention are as follows:

[0067] This invention enables accurate collection of branch box operation data, anomaly identification, and fault matching through cloud collaboration. It establishes normal operation benchmarks based on multi-source parameter statistical analysis and combines waveform morphology matching and comprehensive index calculation to quickly locate fault types and suspected fault points, significantly improving the identification accuracy and diagnostic efficiency of remote maintenance of branch boxes.

[0068] This invention generates a suitable remote control instruction set through real-time topology relationships, completes instruction timing parsing and message distribution, realizes precise control of controllable switching equipment and real-time feedback of status data, forms a closed-loop operation and maintenance process, effectively simplifies remote operation and maintenance of branch boxes, improves operation and maintenance response speed and execution reliability, and ensures stable and efficient operation of branch boxes. Attached Figure Description

[0069] Figure 1 This is a flowchart of the method of the present invention;

[0070] Figure 2 This is the waveform distortion variation curve in Example 1;

[0071] Figure 3 This is the curve showing the change in parameter deviation in Example 1;

[0072] Figure 4 It is a comparison curve between the fusion score and the preset threshold in Example 1;

[0073] Figure 5 This is a functional block diagram of the system of the present invention. Detailed Implementation

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0075] Example 1: As Figure 1 As shown, a remote operation and maintenance method for branch boxes based on cloud collaboration includes the following steps:

[0076] S1. Collect multi-source operating parameters of the branch box under different operating conditions during the historical fault-free period, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box.

[0077] S2. Compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the abnormal event record of the branch box;

[0078] S3. Package the complete cross-sectional operation data of the collection cycle before and after the abnormal event is triggered into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server;

[0079] S4. Based on the preset standard fault feature library, perform waveform morphology matching on the abnormal analysis data packets to obtain the fault type and suspected fault point of the branch box.

[0080] S5. Based on the fault type and suspected fault point, determine the initial handling action sequence of the branch box, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box.

[0081] S6. Perform operation timing parsing on the remote control command set to obtain the control message of the branch box. Send the control message to the controllable switchgear with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switchgear. Feed back the remote signaling change information and telemetry change data to the cloud server.

[0082] In S1, the normal operating baseline data of the branch box is obtained, including:

[0083] By collecting electrical quantity parameters, equipment status parameters, and environmental and operating condition identification parameters of the branch box under different operating conditions during historical fault-free periods, the multi-source operating parameters of the branch box are obtained.

[0084] Data cleaning is performed on the multi-source operating parameters to remove outlier values ​​and communication interruption data segments, resulting in a continuous sequence of operating parameters for the branch box.

[0085] Based on the zero-crossing moments of voltage and current waveform data in the continuous operating parameter sequence, the synchronization of the three-phase voltage and current of the branch box is calibrated to obtain the synchronization electrical quantity set of the branch box.

[0086] Feature extraction is performed on the zero-sequence voltage and zero-sequence current waveforms in the synchronous electrical quantity set to obtain the characteristic quantities of the synchronous electrical quantity set;

[0087] Multi-dimensional statistics were performed on the characteristic quantities to obtain the cluster centers of the zero-sequence voltage and zero-sequence current waveforms and the fluctuation range of the fundamental phase angle under different operating conditions. The cluster centers and fluctuation ranges were then summarized into the normal operation benchmark data of the branch box.

[0088] The electrical parameters, equipment status parameters, and environmental and operating condition identification parameters of the branch box under different operating conditions during historical fault-free periods are collected and integrated to form the multi-source operating parameters of the branch box.

[0089] Each data point in the multi-source operating parameters is checked one by one. Abnormal values ​​generated during the acquisition process are identified and removed. At the same time, data segments that could not be transmitted normally during the communication interruption period are deleted, and continuous and valid data content is retained to form a continuous operating parameter sequence for the branch box.

[0090] The zero-crossing moments of voltage and current waveforms in the continuous operating parameter sequence are located. Using these zero-crossing moments as a unified reference, the three-phase voltage and three-phase current of the branch box are aligned in time. After completing the synchronization adjustment, the synchronization electrical quantity set of the branch box is obtained.

[0091] The inherent shape and variation characteristics of zero-sequence voltage and zero-sequence current waveforms are extracted from the synchronous electrical quantity set, and all the extracted contents are integrated into the characteristic quantities of the synchronous electrical quantity set.

[0092] Multi-dimensional data statistics were conducted on the characteristic quantities to determine the cluster centers of the zero-sequence voltage waveform and the zero-sequence current waveform under different operating conditions, as well as the fluctuation range of the fundamental phase angle. After summarizing and integrating the above cluster centers and fluctuation ranges, the normal operation benchmark data of the branch box was formed.

[0093] By collecting parameters from multiple sources and performing precise data cleaning, the continuity and effectiveness of operational data are ensured. By relying on the zero-crossing moment of the waveform to complete the synchronous calibration of three-phase electrical quantities, the consistency of data is improved. Based on waveform feature extraction and multi-dimensional statistics, standardized normal operation benchmark data is formed, providing an accurate and reliable basis for comparison in subsequent anomaly identification and fault judgment.

[0094] In S2, the abnormal event records of the branch box are obtained, including:

[0095] Obtain the current running data of the branch box, synchronize and pair the current running data with the normal running baseline data to obtain the paired dataset of the branch box;

[0096] Extract the waveform area ratio of the zero-sequence voltage waveform and zero-sequence current waveform in the paired dataset, and perform a difference analysis between the waveform area ratio and the corresponding waveform morphology cluster center in the normal operation reference data to obtain the waveform distortion degree of the branch box.

[0097] The deviation of the branch box parameters is obtained by performing deviation analysis between the paired datasets and the normal operating baseline data;

[0098] By fusing waveform distortion and parameter deviation in multiple dimensions, abnormal event records of the branch box are obtained.

[0099] Obtain the current running data of the branch box, and combine the current running data with the normal operation baseline data one by one according to the same time node and the same data type to form a pair of data sets of the branch box.

[0100] From the paired datasets, the preceding and following segments of the zero-sequence voltage waveform are extracted respectively. The area of ​​the region enclosed by the waveform is calculated, and the ratio of the two areas is obtained to obtain the waveform area ratio of the preceding and following segments of the zero-sequence voltage waveform. At the same time, the waveform area ratio of the preceding and following segments of the zero-sequence current waveform is calculated in the same way. The waveform area ratios of the two waveforms are compared item by item with the corresponding waveform morphology cluster centers in the normal operation reference data to calculate the degree of difference between the two and form the waveform distortion degree of the branch box.

[0101] Each data item in the paired dataset is compared with the corresponding item in the normal operating baseline data to determine the degree of deviation of the current data relative to the baseline data. By comparing each item and statistically analyzing the degree of deviation, the parameter deviation of the branch box is obtained.

[0102] The waveform distortion and parameter deviation are integrated item by item according to the same data dimension. Based on the integration results, it is determined whether there is an abnormal state in the branch box. The judgment process and judgment results are recorded in full to form the abnormal event record of the branch box.

[0103] By establishing a unified comparison basis through synchronous pairing, the waveform distortion degree is accurately quantified by relying on the difference analysis of waveform area ratio and cluster center. Combined with parameter deviation, the operational status deviation is fully reflected. Through multi-dimensional fusion judgment, accurate and complete abnormal event records are obtained, providing a reliable basis for subsequent fault analysis.

[0104] Figure 2 The waveform distortion curve for branch box operation data is used to quantify the morphological differences between the voltage / current waveform and the reference template. Within the 0–1.2 second range, the distortion remains at an extremely low level (less than 0.02), indicating a high degree of conformity between the waveform and the template, and normal equipment operation. A sharp peak (D≈0.17) appears around 1.2 seconds, reflecting instantaneous waveform distortion, corresponding to the fault trigger moment. After the peak, the distortion quickly falls back and remains in a slightly disturbed state, reflecting the transient characteristics of the distortion event. Waveform distortion provides a direct indicator for the initial triggering of abnormal events.

[0105] Figure 3 The parameter deviation curves for the branch box operating parameters are used to quantify the amplitude deviation between multi-source operating parameters and the normal operating baseline. Within the 0-1.2 second range, the parameter deviation remains stable at a low level (less than 2), indicating that the parameter fluctuations are within the normal range. After 1.2 seconds, the parameter deviation continuously climbs and gradually stabilizes at around 20, indicating that after the fault occurred, the operating parameters continuously deviated from the baseline value, reflecting the sustained impact of the anomaly on the equipment's operating status. The parameter deviation characterizes the severity and duration of the anomaly from an amplitude perspective.

[0106] The fusion score of comprehensive anomalies is obtained by weighted fusion of waveform distortion degree D and parameter deviation degree. As a specific implementation method of multi-dimensional fusion judgment, it can accurately judge abnormal events by comparing with preset thresholds, and provide quantitative basis for subsequent fault type matching and remote operation and maintenance command generation.

[0107] Figure 4 The curve comparing the fusion score with a preset threshold (red dashed line, value 0.72) shows that within the 0-1.2 second range, the fusion score is significantly lower than the preset threshold, and the device is judged to be operating normally. After a momentary spike around 1.2 seconds, the score rapidly rises and stabilizes in the 0.4-0.5 range. Although it does not exceed the preset threshold, it is significantly higher than the normal level. Combined with the increase in distortion peak and deviation, it can be comprehensively judged as an abnormal event. The fusion score realizes the fusion decision of multi-dimensional features, providing a reliable quantitative basis for fault type matching and remote operation and maintenance command generation.

[0108] In S3, the complete cross-sectional operation data of the collection cycle before and after triggering the abnormal event record is packaged into an abnormal analysis data package, and the abnormal analysis data package is uploaded to the cloud server, including:

[0109] Obtain the trigger timestamp corresponding to the abnormal event record, and combine it with the cross-sectional running data of the complete collection cycle of the abnormal event record to obtain the original dataset of the branch box;

[0110] Perform data integrity verification on the original dataset to obtain the complete cross-sectional data sequence of the branch box;

[0111] Metadata tags are added to the complete cross-sectional data sequence. The metadata tags include anomaly event identifier code, branch box equipment identifier, data acquisition time range, timestamp list of cross-sectional data, and data quality markers, resulting in the main body of the branch box anomaly analysis data packet.

[0112] The main body of the anomaly analysis data packet is subjected to lossless compression, and a cyclic redundancy check code is embedded in the compressed data stream to obtain the compressed data packet of the branch box;

[0113] The compressed data packet is divided into fixed-length data fragments, and fragment sequence numbers and total fragment count identifiers are added to the data fragments to obtain the fragment transmission queue of the branch box;

[0114] Data fragments are uploaded to the cloud server sequentially according to the fragment transmission queue. After the data fragments are uploaded, a data packet merging command is sent to the cloud server, so that the cloud server can reassemble the received data fragments into a complete anomaly analysis data packet based on the fragment sequence number and the total number of fragments.

[0115] Obtain the trigger timestamp corresponding to the abnormal event record. Using the trigger timestamp as the center, extract all cross-section operation data within the complete collection period corresponding to the abnormal event record. Integrate the trigger timestamp information with the cross-section operation data to form the original dataset of the branch box.

[0116] Each cross-sectional running data in the original dataset is checked one by one to see if there are any missing, erroneous or incomplete contents. Incomplete data is removed and complete data that can be recovered is added. After verification, the complete cross-sectional data sequence of the branch box is obtained.

[0117] Add corresponding identification information to each complete cross-sectional data sequence, including anomaly event identification code, branch box equipment identification, data acquisition time range, cross-sectional data timestamp list, and data quality marker. Use the complete cross-sectional data sequence with all identification information added as the main body of the branch box anomaly analysis data package.

[0118] The anomaly analysis data packet body is compressed without losing any data information. A cyclic redundancy check code for verifying data integrity is embedded at a fixed position in the compressed data stream. The compressed data is combined with the embedded check code to form the compressed data packet of the branch box.

[0119] The compressed data packet is divided into multiple independent data fragments according to a preset fixed length. Each data fragment is labeled with a unique fragment number, and the total number of fragments is also labeled. The data fragments are arranged in order of their fragment numbers to form a fragment transmission queue for the branch box.

[0120] According to the order set in the fragment transmission queue, each data fragment is sent to the cloud server in sequence. After all data fragments have been uploaded, an instruction for data packet merging is sent to the cloud server. After receiving the instruction, the cloud server sorts and splices the received data fragments according to the fragment sequence number and the total number of fragments to restore the complete anomaly analysis data packet.

[0121] By accurately extracting complete cross-sectional data through trigger timestamps and ensuring data quality through integrity verification, adding standardized metadata tags enhances the identifiability and traceability of data packets. Lossless compression and cyclic redundancy check codes ensure data transmission security and integrity. Data fragmentation and orderly uploading adapt to network transmission conditions and ensure accurate reassembly of data packets in the cloud, providing a standardized and complete data carrier for cloud-based fault analysis.

[0122] In S4, the fault type and suspected fault point of the branch box are obtained, including:

[0123] Extract the zero-sequence voltage waveform sequence and zero-sequence current waveform sequence from the anomaly analysis data packet to obtain the waveform dataset to be matched from the anomaly analysis data packet;

[0124] Read the standard fault waveform template of the branch box from the preset standard fault feature library. The standard fault waveform template includes the standard waveform of zero-sequence voltage, the standard waveform of zero-sequence current and the location of typical fault points.

[0125] Analyze the voltage waveform similarity between the zero-sequence voltage waveform sequence and the zero-sequence voltage standard waveform, and the current waveform similarity between the zero-sequence current waveform sequence and the zero-sequence current standard waveform;

[0126] The basic waveform matching degree of the branch box is obtained by weighted fusion of voltage waveform similarity and current waveform similarity.

[0127] Extract the instantaneous zero-sequence voltage and zero-sequence current values ​​of the first abnormal cycle from the waveform dataset to be matched;

[0128] The maximum values ​​of the voltage cross-correlation coefficients between the instantaneous zero-sequence voltage value sequence and the standard zero-sequence voltage waveform, and the maximum values ​​of the current cross-correlation coefficients between the instantaneous zero-sequence current value sequence and the standard zero-sequence current waveform are calculated.

[0129] The phase difference deviation of the branch box is determined based on the phase difference between the instantaneous zero-sequence voltage value sequence and the instantaneous zero-sequence current value sequence and the typical phase difference marked in the standard fault waveform template.

[0130] The sum of squares of the instantaneous zero-sequence current values ​​is taken as the transient energy value, and the ratio of the transient energy value to the transient energy reference value in the standard fault waveform template is analyzed to obtain the energy ratio of the branch box.

[0131] The comprehensive matching index of the branch box is calculated based on the basic waveform matching degree, the maximum value of the voltage waveform cross-correlation coefficient, the maximum value of the current waveform cross-correlation coefficient, the phase difference deviation, and the energy ratio.

[0132] The standard fault waveform template with the largest comprehensive matching index is used as the fault type of the branch box, and the typical fault point location markers carried in the standard fault waveform template are identified as the suspected fault points of the branch box.

[0133] The formula for calculating the overall matching index is:

[0134] ;

[0135] Where S represents the overall matching index, This represents the maximum value of the voltage cross-correlation coefficient. M represents the maximum value of the current cross-correlation coefficient, and M represents the basic waveform matching degree. Indicates the phase difference deviation. Indicates the energy ratio. This represents the preset first adjustment coefficient. This represents the preset second adjustment coefficient. , All are preset fixed coefficients.

[0136] The zero-sequence voltage waveform sequence and the zero-sequence current waveform sequence are completely extracted from the anomaly analysis data packet. The two types of waveform sequences are combined to form the waveform dataset to be matched in the anomaly analysis data packet.

[0137] The standard fault waveform template corresponding to the branch box is retrieved from the pre-set standard fault feature library. The retrieved template fully includes the standard waveform of zero-sequence voltage, the standard waveform of zero-sequence current, and the location markings of typical fault points.

[0138] The zero-sequence voltage waveform sequence in the waveform dataset to be matched is compared point by point with the zero-sequence voltage standard waveform in the standard fault waveform template to determine the similarity between the two voltage waveforms. At the same time, the zero-sequence current waveform sequence is compared point by point with the zero-sequence current standard waveform to determine the similarity between the two current waveforms.

[0139] The voltage waveform similarity and current waveform similarity are calculated by merging them according to a fixed weight, and the merged result is determined as the basic waveform matching degree of the branch box.

[0140] Locate and extract the instantaneous zero-sequence voltage and zero-sequence current values ​​within the first complete cycle in which an anomaly occurs from the waveform dataset to be matched.

[0141] The instantaneous value sequence of zero-sequence voltage is correlated point by point with the standard waveform of zero-sequence voltage, and the maximum value of the voltage cross-correlation coefficient between the two is obtained. At the same time, the instantaneous value sequence of zero-sequence current is correlated point by point with the standard waveform of zero-sequence current, and the maximum value of the current cross-correlation coefficient between the two is obtained.

[0142] Calculate the actual phase difference between the instantaneous zero-sequence voltage value sequence and the instantaneous zero-sequence current value sequence. Compare the actual phase difference with the typical phase difference marked in the standard fault waveform template, calculate the degree of deviation between the two, and obtain the phase difference deviation of the branch box.

[0143] The energy ratio of the branch box is obtained by squaring each value in the instantaneous value sequence of zero-sequence current and summing the results. The sum is then used as the transient energy value. This transient energy value is compared with the transient energy reference value recorded in the standard fault waveform template.

[0144] The comprehensive matching index of the branch box is obtained by comprehensively calculating the basic waveform matching degree, the maximum value of the voltage waveform cross-correlation coefficient, the maximum value of the current waveform cross-correlation coefficient, the phase difference deviation, and the energy ratio according to a fixed rule.

[0145] The maximum voltage cross-correlation coefficient is obtained by cross-correlation calculation between the zero-sequence voltage instantaneous value sequence of the first abnormal cycle extracted from the anomaly analysis data packet and the zero-sequence voltage standard waveform in the standard fault waveform template.

[0146] The maximum value of the current cross-correlation coefficient is obtained by cross-correlation calculation between the zero-sequence current instantaneous value sequence of the first abnormal cycle in the anomaly analysis data package and the zero-sequence current standard waveform in the standard fault waveform template.

[0147] The basic waveform matching degree is obtained by performing a weighted fusion calculation on the voltage waveform similarity and current waveform similarity between the anomaly analysis data package and the standard fault waveform template.

[0148] Phase difference deviation is obtained by comparing the phase difference between the zero-sequence voltage instantaneous value sequence and the zero-sequence current instantaneous value sequence in the anomaly analysis data packet with the typical phase difference marked in the standard fault waveform template.

[0149] The energy ratio is obtained by performing a ratio analysis between the sum of squares of the instantaneous zero-sequence current values ​​in the anomaly analysis data package as the transient energy value and the transient energy reference value in the standard fault waveform template.

[0150] This formula is used to calculate the comprehensive matching index of the branch box by fusing the maximum voltage cross-correlation coefficient, the maximum current cross-correlation coefficient, the basic waveform matching degree, the phase difference deviation, the energy ratio, the first adjustment coefficient, and the second adjustment coefficient. The comprehensive matching index is used to objectively quantify the overall fit between the abnormal waveform and the standard fault waveform template, and serves as the core basis for determining the fault type and suspected fault point of the branch box.

[0151] When the average of the maximum values ​​of the voltage cross-correlation coefficient and the current cross-correlation coefficient increases, the comprehensive matching index shows a positive upward change; when the value of the basic waveform matching degree increases, the comprehensive matching index shows a positive upward change; when the value of the phase difference deviation increases, the comprehensive matching index shows a negative downward change; when the value of the energy ratio increases, the comprehensive matching index shows a positive upward change; the natural exponential function takes the square of the phase difference deviation as the independent variable, multiplies it by the first adjustment coefficient, and takes a negative value, so the calculation result decreases as the phase difference deviation increases; It approaches 1 as the energy ratio increases, and the corresponding value continues to rise.

[0152] The comprehensive matching index corresponding to all standard fault waveform templates is compared. The standard fault waveform template with the largest comprehensive matching index is selected. The fault type corresponding to this template is taken as the fault type of the branch box. At the same time, the typical fault point location marker carried by this template is identified as the suspected fault point of the branch box.

[0153] Accurate fault type identification is achieved through multi-dimensional waveform feature extraction and standardized template matching. The comprehensive matching index is calculated by combining multiple indicators such as cross-correlation coefficient, phase difference and transient energy, which improves the accuracy of fault judgment and can quickly locate suspected fault points, providing a clear basis for subsequent remote handling.

[0154] In S5, the remote control command set of the branch box is obtained, including:

[0155] Based on the fault type and suspected fault location, the corresponding standard handling action template is retrieved from the handling strategy library of the branch box;

[0156] Obtain the current topology connection relationship data of the branch box from the real-time topology data of the cloud server, and replace the operation object type label of the atomic operation in the standard handling action template with the network address in the current topology connection relationship data to obtain the initial instruction sequence of the branch box;

[0157] Based on the electrical connection paths in the current topology connection data, identify the topological dependencies between adjacent atomic operations in the preliminary instruction sequence, and adjust the execution order of atomic operations in the preliminary instruction sequence according to the topological dependencies to obtain the intermediate instruction sequence of the branch box;

[0158] The operation command codes and network addresses of atomic operations in the intermediate instruction sequence are encapsulated into a remote control instruction set for the branch box.

[0159] Based on the identified fault type and suspected fault location, the corresponding standard handling action template is retrieved from the pre-established branch box handling strategy library.

[0160] The topology connection data of the current branch box and associated devices is obtained from the real-time topology data stored on the cloud server. The operation object type label of each atomic operation in the standard handling action template is replaced with the actual device network address in the current topology connection data. After the replacement is completed, the preliminary instruction sequence of the branch box is formed.

[0161] Based on the electrical connection paths in the current topology connection data, the topology dependencies between adjacent atomic operations in the initial instruction sequence are identified one by one. The execution order of each atomic operation in the initial instruction sequence is adjusted according to the constraints of the topology dependencies, and the intermediate instruction sequence of the branch box is obtained after adjustment.

[0162] The operation command code of each atomic operation in the intermediate instruction sequence is combined and encapsulated with the corresponding actual device network address to form the remote control instruction set of the branch box.

[0163] By accurately matching standard handling action templates with fault types and suspected fault points, and by replacing operation objects and adjusting execution order in real-time topology relationships, the instructions are ensured to be consistent with the field wiring and actual device addresses. The encapsulated remote control instruction set can be directly used for execution on remotely controllable devices, improving the accuracy and efficiency of remote operation and maintenance.

[0164] In S6, control messages are sent to the controllable switching equipment at the corresponding network address within the branch box to collect remote signaling change information and telemetry change data from the controllable switching equipment. This remote signaling change information and telemetry change data are then fed back to the cloud server, including:

[0165] Parse the remote control instruction set to obtain the device network address, operation command code, and execution timing label of the remote control instruction set. Sort and integrate the control instructions in the remote control instruction set according to the execution timing label to obtain the executable instruction sequence of the branch box.

[0166] The operation command codes in the executable instruction sequence are encapsulated into control messages, and the instruction sequence number and target network address are appended to the control messages to obtain the message queue of the branch box;

[0167] Following the chronological order of the executable instruction sequence, the message queue is sequentially sent to the controllable switching devices corresponding to the network addresses. The response messages returned by the controllable switching devices are monitored in real time, and remote signaling change information and telemetry change data are parsed from the response messages, including:

[0168] Continuously receive the raw data stream returned by the controllable switching device, perform frame synchronization verification on the raw data stream, and obtain the response message from the controllable switching device;

[0169] Parse the message header of the response message to obtain the device network address and message type identifier of the controllable switchgear, and separate the remote signaling change information field and the telemetry change data field from the message payload of the response message based on the message type identifier;

[0170] Perform state transition analysis on the remote signaling change information field to obtain the remote signaling change information of the response message;

[0171] Perform interpolation analysis on the telemetry change data fields to obtain the telemetry change data of the response message;

[0172] The remote signaling change information and telemetry change data are packaged into a feedback data packet, and the feedback data packet is uploaded to the designated interface of the cloud server.

[0173] The remote control instruction set is disassembled and analyzed to extract the device network address, operation command code, and execution timing label contained in the instruction. The control instructions are sorted and integrated according to the order marked by the execution timing label to form the executable instruction sequence of the branch box.

[0174] Each operation command code in the executable instruction sequence is encapsulated into a control message according to the communication format. A unique instruction sequence number and a corresponding target network address are added to each control message. All control messages are arranged in execution order to form the message queue of the branch box.

[0175] According to the time sequence set by the executable instruction sequence, the control messages in the message queue are sent one by one to the controllable switching device at the corresponding network address. The system continuously receives the information fed back by the controllable switching device and listens for the returned response messages. Remote signaling change information and telemetry change data are extracted and separated from the response messages.

[0176] The extracted remote signaling change information and telemetry change data are integrated and packaged in a unified format to form a feedback data packet. The feedback data packet is then sent to a dedicated interface set up on the cloud server to complete the data upload.

[0177] It continuously receives raw data streams sent by controllable switching devices, performs frame synchronization verification on the raw data streams, checks the start and end positions of data frames, ensures that the received data stream format is correct, and obtains a standardized and complete response message from the controllable switching devices.

[0178] Parse the header of the response message to obtain the device network address and message type identifier of the controllable switchgear. Based on the message type identifier, distinguish the remote signaling change information field and the telemetry change data field from the message payload of the response message.

[0179] By comparing and analyzing the status data before and after the remote signaling change information field, the content of the equipment status change is determined, and the remote signaling change information corresponding to the response message is obtained.

[0180] By comparing and analyzing the numerical data in the telemetry change data field before and after, the magnitude and content of the data change are determined, and the telemetry change data corresponding to the response message is obtained.

[0181] By using time-series parsing and orderly encapsulation to form a standardized and sendable control message queue, response messages are monitored and parsed in real time to accurately obtain remote signaling change information and telemetry change data. After packaging and uploading, a complete closed-loop feedback of remote operation results is achieved, ensuring that the cloud has real-time control over the branch box's operating status and execution results.

[0182] Example 2: As Figure 5As shown, a cloud-based collaborative remote operation and maintenance system for branch boxes, used to implement the method in Embodiment 1, includes a data acquisition module, an anomaly recording module, a data uploading module, a fault matching module, an instruction generation module, and a data feedback module. A module, also known as a unit, refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0183] The functions of each module are as follows:

[0184] The data acquisition module is used to collect multi-source operating parameters of the branch box under different operating conditions during historical fault-free periods, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box.

[0185] The anomaly recording module is used to compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the anomaly event records of the branch box;

[0186] The data upload module is used to package the complete cross-sectional operation data of the collection cycle before and after the triggering of the abnormal event record into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server;

[0187] The fault matching module is used to perform waveform morphology matching on abnormal analysis data packets based on a preset standard fault feature library to obtain the fault type and suspected fault point of the branch box.

[0188] The instruction generation module is used to determine the initial handling action sequence of the branch box based on the fault type and suspected fault point, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box.

[0189] The data feedback module is used to parse the operation timing of the remote control command set, obtain the control message of the branch box, and send the control message to the controllable switch equipment with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switch equipment, and then feed the remote signaling change information and telemetry change data back to the cloud server.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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.

Claims

1. A method for remote operation and maintenance of branch boxes based on cloud collaboration, characterized by the following steps: include: S1. Collect multi-source operating parameters of the branch box under different operating conditions during the historical fault-free period, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box. S2. Compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the abnormal event record of the branch box; S3. Package the complete cross-sectional operation data of the collection cycle before and after the abnormal event is triggered into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server; S4. Based on a preset standard fault feature library, perform waveform morphology matching on the anomaly analysis data packets to obtain the fault type and suspected fault points of the branch box, including: Extract the zero-sequence voltage waveform sequence and zero-sequence current waveform sequence from the anomaly analysis data packet to obtain the waveform dataset to be matched from the anomaly analysis data packet; Read the standard fault waveform template of the branch box from the preset standard fault feature library. The standard fault waveform template includes the standard waveform of zero-sequence voltage, the standard waveform of zero-sequence current and the location of typical fault points. Analyze the voltage waveform similarity between the zero-sequence voltage waveform sequence and the zero-sequence voltage standard waveform, and the current waveform similarity between the zero-sequence current waveform sequence and the zero-sequence current standard waveform; The basic waveform matching degree of the branch box is obtained by weighted fusion of voltage waveform similarity and current waveform similarity. Extract the instantaneous zero-sequence voltage and zero-sequence current values ​​of the first abnormal cycle from the waveform dataset to be matched; The maximum values ​​of the voltage cross-correlation coefficients between the instantaneous zero-sequence voltage value sequence and the standard zero-sequence voltage waveform, and the maximum values ​​of the current cross-correlation coefficients between the instantaneous zero-sequence current value sequence and the standard zero-sequence current waveform are calculated. The phase difference deviation of the branch box is determined based on the phase difference between the instantaneous zero-sequence voltage value sequence and the instantaneous zero-sequence current value sequence and the typical phase difference marked in the standard fault waveform template. The sum of squares of the instantaneous zero-sequence current values ​​is used as the transient energy value. The ratio of the transient energy value to the transient energy reference value in the standard fault waveform template is analyzed to obtain the energy ratio of the branch box. The comprehensive matching index of the branch box is calculated based on the basic waveform matching degree, the maximum value of the voltage waveform cross-correlation coefficient, the maximum value of the current waveform cross-correlation coefficient, the phase difference deviation, and the energy ratio. The standard fault waveform template with the largest comprehensive matching index is taken as the fault type of the branch box, and the typical fault point location markers carried in the standard fault waveform template are identified as the suspected fault points of the branch box. S5. Based on the fault type and suspected fault point, determine the initial handling action sequence of the branch box, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box. S6. Perform operation timing parsing on the remote control command set to obtain the control message of the branch box. Send the control message to the controllable switchgear with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switchgear. Feed back the remote signaling change information and telemetry change data to the cloud server.

2. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, In S1, the normal operating baseline data of the branch box is obtained, including: By collecting electrical quantity parameters, equipment status parameters, and environmental and operating condition identification parameters of the branch box under different operating conditions during historical fault-free periods, the multi-source operating parameters of the branch box are obtained. Data cleaning is performed on the multi-source operating parameters to remove outlier values ​​and communication interruption data segments, resulting in a continuous sequence of operating parameters for the branch box. Based on the zero-crossing moments of voltage and current waveform data in the continuous operating parameter sequence, the synchronization of the three-phase voltage and current of the branch box is calibrated to obtain the synchronization electrical quantity set of the branch box. Feature extraction is performed on the zero-sequence voltage and zero-sequence current waveforms in the synchronous electrical quantity set to obtain the characteristic quantities of the synchronous electrical quantity set; Multi-dimensional statistics were performed on the characteristic quantities to obtain the cluster centers of the zero-sequence voltage and zero-sequence current waveforms and the fluctuation range of the fundamental phase angle under different operating conditions. The cluster centers and fluctuation ranges were then summarized into the normal operation benchmark data of the branch box.

3. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, In S2, the abnormal event records of the branch box are obtained, including: Obtain the current running data of the branch box, synchronize and pair the current running data with the normal running baseline data to obtain the paired dataset of the branch box; Extract the waveform area ratio of the zero-sequence voltage waveform and zero-sequence current waveform in the paired dataset, and perform a difference analysis between the waveform area ratio and the corresponding waveform morphology cluster center in the normal operation reference data to obtain the waveform distortion degree of the branch box. The deviation of the branch box parameters is obtained by performing deviation analysis between the paired datasets and the normal operating baseline data; By fusing waveform distortion and parameter deviation in multiple dimensions, abnormal event records of the branch box are obtained.

4. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, In step S3, the complete cross-sectional operation data of the collection cycle before and after triggering the abnormal event recording is packaged into an abnormal analysis data package, and the abnormal analysis data package is uploaded to the cloud server, including: Obtain the trigger timestamp corresponding to the abnormal event record, and combine it with the cross-sectional running data of the complete collection cycle of the abnormal event record to obtain the original dataset of the branch box; Perform data integrity verification on the original dataset to obtain the complete cross-sectional data sequence of the branch box; Metadata tags are added to the complete cross-sectional data sequence. The metadata tags include anomaly event identifier code, branch box equipment identifier, data acquisition time range, timestamp list of cross-sectional data, and data quality markers, resulting in the main body of the branch box anomaly analysis data packet. The main body of the anomaly analysis data packet is subjected to lossless compression, and a cyclic redundancy check code is embedded in the compressed data stream to obtain the compressed data packet of the branch box; The compressed data packet is divided into fixed-length data fragments, and fragment sequence numbers and total fragment count identifiers are added to the data fragments to obtain the fragment transmission queue of the branch box; Data fragments are uploaded to the cloud server sequentially according to the fragment transmission queue. After the data fragments are uploaded, a data packet merging command is sent to the cloud server, so that the cloud server can reassemble the received data fragments into a complete anomaly analysis data packet based on the fragment sequence number and the total number of fragments.

5. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, The formula for calculating the overall matching index is: ； Where S represents the overall matching index, This represents the maximum value of the voltage cross-correlation coefficient. M represents the maximum value of the current cross-correlation coefficient, and M represents the basic waveform matching degree. Indicates the phase difference deviation. Indicates the energy ratio. This represents the preset first adjustment coefficient. This represents the preset second adjustment coefficient.

6. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, In S5, the remote control command set for the branch box is obtained, including: Based on the fault type and suspected fault location, the corresponding standard handling action template is retrieved from the handling strategy library of the branch box; Obtain the current topology connection relationship data of the branch box from the real-time topology data of the cloud server, and replace the operation object type label of the atomic operation in the standard handling action template with the network address in the current topology connection relationship data to obtain the initial instruction sequence of the branch box; Based on the electrical connection paths in the current topology connection data, identify the topological dependencies between adjacent atomic operations in the preliminary instruction sequence, and adjust the execution order of atomic operations in the preliminary instruction sequence according to the topological dependencies to obtain the intermediate instruction sequence of the branch box; The operation command codes and network addresses of atomic operations in the intermediate instruction sequence are encapsulated into a remote control instruction set for the branch box.

7. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 1, characterized in that, In step S6, a control message is sent to the controllable switchgear corresponding to the network address within the branch box to collect remote signaling change information and telemetry change data from the controllable switchgear, and then the remote signaling change information and telemetry change data are fed back to the cloud server, including: Parse the remote control instruction set to obtain the device network address, operation command code, and execution timing label of the remote control instruction set. Sort and integrate the control instructions in the remote control instruction set according to the execution timing label to obtain the executable instruction sequence of the branch box. The operation command codes in the executable instruction sequence are encapsulated into control messages, and the instruction sequence number and target network address are appended to the control messages to obtain the message queue of the branch box; According to the time sequence of executable instructions, the message queue is sent to the controllable switching device with the corresponding network address in sequence, and the response messages returned by the controllable switching device are monitored in real time. Remote signaling change information and telemetry change data are parsed from the response messages. The remote signaling change information and telemetry change data are packaged into a feedback data packet, and the feedback data packet is uploaded to the designated interface of the cloud server.

8. The remote operation and maintenance method for branch boxes based on cloud collaboration as described in claim 7, characterized in that, Real-time monitoring of response messages returned by controllable switching equipment; parsing remote signaling change information and telemetry change data from the response messages, including: Continuously receive the raw data stream returned by the controllable switching device, perform frame synchronization verification on the raw data stream, and obtain the response message from the controllable switching device; Parse the message header of the response message to obtain the device network address and message type identifier of the controllable switchgear, and separate the remote signaling change information field and the telemetry change data field from the message payload of the response message based on the message type identifier; Perform state transition analysis on the remote signaling change information field to obtain the remote signaling change information of the response message; Perform interpolation analysis on the telemetry change data fields to obtain the telemetry change data of the response message.

9. A cloud-based collaborative remote operation and maintenance system for branch boxes, used to implement the cloud-based collaborative remote operation and maintenance method for branch boxes as described in any one of claims 1 to 8, characterized in that, Includes the following modules connected in sequence: The data acquisition module is used to collect multi-source operating parameters of the branch box under different operating conditions during historical fault-free periods, perform statistical analysis on the multi-source operating parameters, and obtain the normal operating baseline data of the branch box. The anomaly recording module is used to compare the normal operating baseline data with the current operating data of the branch box item by item to obtain the anomaly event records of the branch box; The data upload module is used to package the complete cross-sectional operation data of the collection cycle before and after the triggering of the abnormal event record into an abnormal analysis data package, and upload the abnormal analysis data package to the cloud server; The fault matching module is used to perform waveform morphology matching on abnormal analysis data packets based on a preset standard fault feature library to obtain the fault type and suspected fault point of the branch box. The instruction generation module is used to determine the initial handling action sequence of the branch box based on the fault type and suspected fault point, and replace the operation object in the initial handling action sequence according to the real-time topology connection relationship between the branch boxes to obtain the remote control instruction set of the branch box. The data feedback module is used to parse the operation timing of the remote control command set, obtain the control message of the branch box, and send the control message to the controllable switch equipment with the corresponding network address in the branch box to collect the remote signaling change information and telemetry change data of the controllable switch equipment, and then feed the remote signaling change information and telemetry change data back to the cloud server.

Images