An on-site operation full-process online control method and system

Abstract

The present invention relates to the technical field of on-site power management and control, and discloses a full-process online management and control method and system for on-site operations. The method includes the following steps: collecting real-time channel parameters, calculating channel quality coefficients, performing weighted calculation on the data differences between the edge and the cloud, allocating real-time bandwidth according to channel quality and data differences, calculating real-time synchronization delay and starting a hierarchical caching mechanism, optimizing the transmission packet structure for the maintenance task data to be preferentially transmitted by using differential coding, synchronously obtaining the real-time coordinates on the edge side and the reference coordinates on the cloud, and calculating the coordinate deviation in the plane rectangular coordinate system. The present invention constructs a cross-layer perception system of channel-data-positioning, still maintains a control instruction closed-loop at the 300ms level under weak network conditions, compresses the meter-level error of the geographical fence to the sub-meter level through compensation algorithms and multi-source verification, and establishes a triangular error correction mechanism for model drift-conflict events-channel degradation, greatly improving the system's mean time between failures.

Description

Technical Field

[0001] The present invention relates to the field of on-site control of electric power, and more specifically, to an online control method and system for the entire process of on-site operations. Background Art

[0002] Currently, the on-site operation control system of electric power still takes manual inspection as the core. In mountainous operation scenarios where the 5G signal is unstable between edge computing nodes and the cloud, heterogeneous data synchronization conflicts are faced, resulting in digital twin model drift, positioning error of the electronic fence exceeding the warning value, and the false alarm rate of the three-level warning rising to 17.3%. There is an urgent need for a dynamic collaborative optimization mechanism to solve the problems of real-time performance, accuracy, and stability contradictions. Summary of the Invention

[0003] The present invention provides an online control method and system for the entire process of on-site operations to solve the technical problems in the related art.

[0004] The present invention provides an online control method for the entire process of on-site operations, including the following steps:

[0005] S100, Dynamic Bandwidth Allocation and Data Difference Prediction: Collect real-time channel parameters, calculate the channel quality coefficient, perform weighted calculation on the data difference between the edge and the cloud, allocate real-time bandwidth according to the channel quality and data difference, calculate the real-time synchronization delay and start the hierarchical caching mechanism, and optimize the transmission packet structure for the maintenance task data to be preferentially transmitted by using differential coding;

[0006] S200, Dual-source Acquisition and Compensation of Positioning Data: Synchronously obtain the real-time coordinates on the edge side and the reference coordinates in the cloud, calculate the coordinate deviation in the plane rectangular coordinate system, extract the historical trajectory data for cubic spline interpolation compensation, calculate the compensated offset and trigger the multi-source verification strategy, and feedback the final offset to the bandwidth allocation process;

[0007] S300, Incremental Learning and Calibration of the Twin Model: Verify the integrity of the synchronized data and extract the key indicators of the equipment health status, calculate the deviation between the predicted life and the actual life of the twin model, load the lightweight model for incremental learning on the edge side, adopt the gradient compression and backpropagation mechanism, update the global model in the cloud and perform calibration efficiency verification;

[0008] S400, Dynamic Adjustment and Alarm of the Electronic Fence: Use high-precision positioning equipment for real-time calibration, dynamically adjust the boundary of the electronic fence, monitor the relationship between the personnel position and the electronic fence and set hierarchical alarms, calculate the false alarm rate and start multi-source data verification, and reduce the false alarm rate by adjusting system parameters;

[0009] S500, Conflict Event Analysis and Optimization: Extract key parameters to construct a spatio-temporal correlation dataset, calculate the bandwidth utilization efficiency index and the impact of channel quality attenuation, generate an optimization report and output recommended rules, adjust the QoS level according to the bandwidth compression ratio, reconstruct the hierarchical cache threshold and conduct closed-loop verification.

[0010] Further, the real-time coordinates on the edge side come from a Beidou / GPS dual-mode positioning terminal, and the reference coordinates in the cloud come from the GIS coordinates of the iPMS3.0 hidden danger database;

[0011] ;

[0012] ;

[0013] where represents the real-time coordinates on the edge side, represents the reference coordinates in the cloud, where and are the abscissa and ordinate of the edge-side coordinates respectively, and are the abscissa and ordinate of the cloud coordinates respectively.

[0014] Further, calculating the compensated offset and triggering the multi-source verification strategy includes the following:

[0015] Calculating the compensated offset:

[0016] ;

[0017] where and are the compensated abscissa and ordinate respectively, and are the abscissa and ordinate of the cloud reference coordinates respectively, represents the compensated offset. If , represents the offset threshold, triggering the multi-source verification strategy:

[0018] Fusing the RFID positioning data of safety helmets for correction;

[0019] Adopting weighted average correction:

[0020] ;

[0021] ;

[0022] where represents the weighted weight coefficient, , and Represent the abscissa and ordinate of the RFID positioning coordinates.

[0023] Furthermore, the verification of the integrity of the synchronization data and the extraction of the key indicators of the device health status include the following:

[0024] Verify the integrity of the synchronization data in S100, requiring ;

[0025] ;

[0026] where is the edge - side data, is the cloud - side data, is the data difference amount, represents the integrity of the synchronization data;

[0027] Extract the key indicators of the device health status: insulation resistance 、partial discharge amount and vibration spectrum ;

[0028] Establish a data quality score:

[0029] ;

[0030] ;

[0031] where represents the data quality score, represents the minimum insulation resistance, represents the maximum discharge amount, represents the maximum vibration spectrum value, 、 、 and respectively represent the data integrity weight, insulation resistance weight, discharge amount weight and vibration spectrum weight.

[0032] Furthermore, the calculation of the deviation between the predicted life and the actual life of the twin model and the loading of the lightweight model for edge - side incremental learning include the following:

[0033] Calculate the deviation between the predicted life and the actual life of the twin model:

[0034] ;

[0035] where represents the predicted life, represents the actual life, represents the life deviation. When , trigger incremental learning, where represents the life deviation threshold. ;

[0036] Establish drift trend monitoring:

[0037] ;

[0038] where is the monitoring window size, represents the drift trend, represents the current timestamp;

[0039] Edge-side incremental learning:

[0040] Load a lightweight model , and construct a training set:

[0041] ;

[0042] where is the edge-side data, is the actual lifespan, is the historical data, is the number of samples;

[0043] Local training loss function:

[0044] ;

[0045] where is the model function, is the model parameter, is the regularization coefficient, represents the loss function value.

[0046] Furthermore, adopt a gradient compression and backpropagation mechanism to update the cloud global model and perform calibration efficiency verification, including the following:

[0047] Calculate the parameter gradient matrix:

[0048] ;

[0049] where represents the parameter gradient matrix;

[0050] Adopt Top-K sparsification compression:

[0051] ;

[0052] where represents the compressed gradient matrix, represents the operation of taking the largest K gradient values, represents the compression rate, ;

[0053] Backhaul data volume control:

[0054] ;

[0055] where is the allocated bandwidth, is the channel quality coefficient, is the tolerance coefficient, represents the backhaul time;

[0056] Cloud global model fusion:

[0057] ;

[0058] where are the cloud model parameters, , is the learning rate, is the compressed gradient, represents the updated model parameters;

[0059] Update the device health status prediction function:

[0060] ;

[0061] where represents the updated predicted life, represents the updated model function, represents the edge-side data;

[0062] Model version control:

[0063] Record the update timestamp and performance metrics:

[0064] ;

[0065] where represents the model version record;

[0066] Calculate the calibrated deviation:

[0067] ;

[0068] where represents the calibrated prediction deviation;

[0069] Generate a calibration report: If , then trigger the dynamic feedback of S250 to record the calibration failure reason and start the manual intervention process;

[0070] Establish performance tracking metrics:

[0071] ;

[0072] where is the tracking window size, is the time interval, represents the calibration efficiency index.

[0073] Furthermore, the steps of dynamic adjustment and alarm of the electronic fence also include the following:

[0074] S410, positioning data calibration: Use high-precision positioning equipment for real-time calibration and establish a calibration accuracy evaluation;

[0075] S420, dynamically adjust the electronic fence boundary: According to the calibrated positioning data, adjust the electronic fence boundary in real time, considering the safety margin;

[0076] S430, monitor the relationship between the personnel position and the electronic fence: Monitor whether the personnel position crosses the boundary, calculate the crossing distance, and set the grading alarm threshold;

[0077] Monitor whether the personnel position crosses the boundary:

[0078] ;

[0079] where represents the crossing state, represents the calibrated positioning data, represents the dynamically adjusted electronic fence boundary;

[0080] Calculate the crossing distance:

[0081] ;

[0082] where represents the crossing distance;

[0083] Set the grading alarm threshold:

[0084] ;

[0085] where represents the alarm level;

[0086] S440, false alarm rate monitoring: Calculate the current false alarm rate and establish a false alarm analysis matrix;

[0087] Calculate the current false alarm rate:

[0088] ;

[0089] where represents the number of false alarms, represents the total number of alarms, represents the false alarm rate;

[0090] Establish a false alarm analysis matrix:

[0091] ;

[0092] where represents a time period, represents a location area, represents the number of false alarms, represents the cause classification, where represents the false alarm analysis matrix;

[0093] S450, Multi-source data verification enabled: When the false alarm rate exceeds 15%, start multi-source data verification, calculate the false alarm rate after verification, and establish a verification credibility score;

[0094] S460, Reduce the false alarm rate: Adjust the alarm system parameters to ensure that the false alarm rate after adjustment ≤ 5%, adopt an optimization strategy, and establish long-term optimization indicators.

[0095] Furthermore, in S450, when the false alarm rate exceeds 15%, start multi-source data verification, including RFID signal verification, camera image recognition, and work ticket information verification;

[0096] According to the multi-source data verification, calculate the false alarm rate after verification:

[0097] ;

[0098] where represents the number of false alarms confirmed after verification, represents the total number of alarms, represents the false alarm rate after verification;

[0099] And establish a verification credibility score:

[0100] ;

[0101] where represents the RFID signal weight coefficient, represents the video recognition weight coefficient, represents the work ticket verification weight coefficient, represents the comprehensive verification credibility score, represents the RFID signal verification accuracy rate, represents the video recognition accuracy rate, represents the work ticket verification accuracy rate.

[0102] Furthermore, in S460, the optimization strategy includes dynamically adjusting the detection threshold, updating the alarm rules, and improving the filtering algorithm;

[0103] Establish long-term optimization indicators:

[0104] ;

[0105] wherein is the evaluation period, represents the long-term optimization effect evaluation index, represents the adjusted false alarm rate, represents the evaluation time interval.

[0106] The present invention also provides an on-line control system for the whole process of on-site operation, which executes the steps in the foregoing on-line control method for the whole process of on-site operation, including:

[0107] Dynamic bandwidth intelligent allocation module: Real-time monitor channel parameters, calculate channel quality coefficients, and dynamically allocate bandwidth;

[0108] Positioning calibration and compensation module: Synchronize dual-source coordinates, calculate the offset, and execute cubic spline interpolation to generate compensation coordinates;

[0109] Digital twin self-optimization module: Verify the data integrity rate, detect model drift, and execute TensorFlow Lite incremental learning;

[0110] Electronic fence interlock control module: Dynamically adjust the fence, issue three-level alarms, and false alarm through the false alarm rate;

[0111] Conflict backtracking optimization module: Record the key parameter set, generate a heat map, and reconstruct the policy library.

[0112] The beneficial effects of the present invention are as follows:

[0113] Based on the real-time perception of channel quality and data difference weights, the present invention improves the bandwidth utilization rate, introduces threshold rules, reduces the recurrence rate of conflict events, reduces the volume of maintenance task data packets by 44%, ensures that 100% of critical instructions can be reached even in weak networks, among which the calibration response time of the digital twin model is shortened, the prediction life deviation rate is stabilized within 5%, the communication overhead is reduced, and it supports global model updates every 2 hours. Among them, the coordinate offset of the electronic fence is reduced, the over-alarm rate is zero, after fusing RFID + vision verification, the false alarm rate is reduced, and the fence boundary is dynamically corrected based on DEM elevation data, reducing the number of missed reports in complex terrain scenarios.

[0114] In summary, a cross-layer perception system of channel-data-positioning is constructed, and a control instruction closed-loop of 300ms level is still maintained under weak networks. Through the compensation algorithm and multi-source verification, the meter-level error of the geographical fence is compressed to the sub-meter level, and a triangular error correction mechanism of model drift-conflict event-channel degradation is established, and the system MTBF (mean time between failures) is increased from 76 hours to 310 hours. Description of the Drawings

[0115] Figure 1It is a flowchart of an on - line control method for the whole process of on - site operations proposed by the present invention;

[0116] Figure 2 It is a structural block diagram of an on - line control system for the whole process of on - site operations proposed by the present invention.

[0117] In the figure: 101, dynamic bandwidth intelligent allocation module; 102, positioning calibration and compensation module; 103, digital twin self - optimization module; 104, electronic fence inter - control module; 105, conflict backtracking optimization module. Specific embodiments

[0118] Now, the subject matter described herein will be discussed with reference to example embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0119] As Figure 1 shown, an on - line control method for the whole process of on - site operations includes the following steps:

[0120] S100, dynamic bandwidth allocation and data difference prediction: Collect real - time channel parameters, calculate the channel quality coefficient, perform weighted calculation on the data difference between the edge and the cloud, allocate real - time bandwidth according to the channel quality and data difference, calculate the real - time synchronization delay and start the hierarchical caching mechanism, and adopt differential coding and transmission packet structure optimization for the maintenance task data to be preferentially transmitted;

[0121] In an embodiment of the present invention, it specifically includes the following steps:

[0122] S110, dynamic evaluation of channel quality: Collect real - time channel parameters: bandwidth, signal - to - noise ratio, and packet loss rate, and calculate the channel quality coefficient;

[0123] The calculation formula of the channel quality coefficient is as follows:

[0124] ;

[0125] Where = 100Mbps, represents the theoretical maximum bandwidth, is the real - time bandwidth, is the packet loss rate, is the signal - to - noise ratio, represents the channel quality coefficient;

[0126] When When When it is <0.6, it is determined that the channel deteriorates;

[0127] S120, Key difference data quantization: Perform weighted calculation on the data differences between the edge and the cloud;

[0128] ;

[0129] Among them is the data dimension weight, is the number of synchronized data dimensions ( When ≥8, high-dimensional optimization is triggered), is the data of the i-th dimension at the edge, is the data of the i-th dimension in the cloud, represents the data difference amount;

[0130] Among them, the maintenance task =0.6, the hidden danger coordinate =0.3, the historical record =0.1;

[0131] S130, Dynamic bandwidth allocation strategy: Allocate real-time bandwidth according to the channel quality coefficient and the data difference amount;

[0132] ;

[0133] Among them is the allocated bandwidth, is the maximum bandwidth, is the tolerance coefficient, is the data difference amount, is the channel quality coefficient;

[0134] Among them The allocation priorities are as follows: maintenance task > hidden danger coordinate > other data;

[0135] Bandwidth compression ratio control:

[0136] ;

[0137] ;

[0138] Among them is the theoretical required bandwidth, represents the bandwidth compression ratio;

[0139] S140, Synchronization conflict detection and cache trigger: Calculate the real-time synchronization delay. If > , then start the hierarchical cache mechanism and cache non-critical data step by step from low to high according to the weight ;

[0140] ;

[0141] wherein is the data difference amount, is the allocated bandwidth, is the channel quality coefficient, is the tolerance coefficient, represents the synchronization data;

[0142] ;

[0143] wherein represents the buffer ratio;

[0144] S150, core data compression and transmission: The maintenance task data to be preferentially transmitted is differentially encoded, and the transmission packet structure is optimized;

[0145] ;

[0146] wherein is the edge - side task data, is the cloud - side task data, is the reference value, = 10, represents the task data after differential encoding;

[0147] Optimization of the transmission packet structure:

[0148] ;

[0149] wherein represents the transmission packet size, represents floor function, is the signal - to - noise ratio;

[0150] S200, dual - source acquisition and compensation of positioning data: Synchronously obtain the real - time coordinates on the edge side and the reference coordinates in the cloud, calculate the coordinate deviation in the plane rectangular coordinate system, extract the historical trajectory data for cubic spline interpolation compensation, calculate the offset after compensation and trigger the multi - source verification strategy, and feedback the final offset to the bandwidth allocation module;

[0151] In an embodiment of the present invention, it specifically includes the following steps:

[0152] S210, dual - source acquisition of positioning data: Synchronously obtain the real - time coordinates on the edge side and the reference coordinates in the cloud, wherein the real - time coordinates on the edge side come from a Beidou / GPS dual - mode positioning terminal (with an accuracy better than 5 meters), and the reference coordinates in the cloud come from the GIS coordinates of the iPMS3.0 hidden danger database;

[0153] wherein represents the real - time coordinates on the edge side, , Represents the cloud reference coordinates, ;

[0154] S220, Real-time offset calculation: Calculate the coordinate deviation in the plane rectangular coordinate system and solve the Euclidean distance offset;

[0155] ;

[0156] ;

[0157] Where and are the abscissa and ordinate of the edge-side coordinates respectively, and are the abscissa and ordinate of the cloud coordinates respectively, represents the coordinate deviation on the abscissa, represents the coordinate deviation on the ordinate;

[0158] The calculation formula of the Euclidean distance offset is as follows:

[0159] ;

[0160] Constraint condition: When ( = 0.5 meters), trigger the compensation mechanism;

[0161] Where represents the Euclidean distance offset, represents the offset threshold;

[0162] S230, Historical trajectory interpolation compensation: Extract the historical trajectory data within the time window, construct a cubic spline interpolation function, and generate the compensated coordinates;

[0163] The extraction formula of the historical trajectory data is as follows:

[0164] ;

[0165] ;

[0166] Where represents the time window, is the current time, is the time window length, = 5 minutes, and are the abscissa and ordinate of the k-th historical coordinate point respectively, represents the total number of historical coordinate points, represents the historical trajectory data set;

[0167] The calculation formula of the cubic spline interpolation function is as follows:

[0168] ;

[0169] ;

[0170] Where and are the interpolation coefficients of the abscissa and ordinate respectively, obtained by least squares fitting to obtain the time variable, represents the i-th power of time, represents the abscissa after interpolation, represents the ordinate after interpolation;

[0171] The coordinates after compensation are as follows:

[0172] ;

[0173] ;

[0174] Where is the coordinate after compensation, represents the abscissa of the coordinate after compensation, represents the ordinate of the coordinate after compensation;

[0175] S240, verification of compensation results: Calculate the offset after compensation and fuse the helmet RFID positioning data for correction;

[0176] Calculate the offset after compensation:

[0177] ;

[0178] Where and are the abscissa and ordinate after compensation respectively, and are the abscissa and ordinate of the cloud reference coordinate respectively, represents the offset after compensation. If , trigger the multi-source verification strategy:

[0179] Fuse the helmet RFID positioning data for correction;

[0180] Adopt weighted average correction:

[0181] ;

[0182] ;

[0183] Where represents the weighted weight coefficient, , and represent the abscissa and ordinate of the RFID positioning coordinates;

[0184] S250, dynamic error feedback: Feed the final offset back to the bandwidth allocation process in S100, update the differential data weight in S120, and record historical error data for long-term optimization;

[0185] Feed the final offset back to the bandwidth allocation process in S100:

[0186] ;

[0187] where is the final offset, is the offset threshold (0.5 meters), is the weight of the original coordinate data, = 0.3, represents the weight of the position data;

[0188] Update the differential data weight in step S120:

[0189] ;

[0190] where represents the updated differential data weight;

[0191] Record historical error data for long-term optimization:

[0192] ;

[0193] where is the length of the historical record is the recording timestamp, represents the historical error data, represents the kth final offset.

[0194] S300, twin model incremental learning and calibration: Verify the integrity of the synchronization data and extract key indicators of the device health status, calculate the deviation between the predicted life and the actual life of the twin model, load the lightweight model for edge-side incremental learning, adopt the gradient compression and backpropagation mechanism, update the cloud global model and perform calibration effectiveness verification;

[0195] In one embodiment of the present invention, it specifically includes the following steps:

[0196] S310, hidden danger data synchronization verification: Verify the integrity of the synchronization data in S100, extract key indicators of the device health status, and establish a data quality score;

[0197] Verify the integrity of the synchronization data in S100, requiring ;

[0198] ;

[0199] where is the edge - end data, is the cloud - end data, is the data difference amount, represents the integrity of the synchronization data;

[0200] Extract the key indicators of the device health status: insulation resistance (MΩ), partial discharge amount (pC) and vibration spectrum (Hz);

[0201] Establish a data quality score:

[0202] ;

[0203] ;

[0204] where represents the data quality score, represents the minimum insulation resistance, represents the maximum discharge amount, represents the maximum vibration spectrum value, , , and respectively represent the data integrity weight, insulation resistance weight, discharge amount weight and vibration spectrum weight;

[0205] S320, model drift quantization detection: Calculate the deviation between the predicted life and the actual life of the twin model, and establish a drift trend monitoring;

[0206] Calculate the deviation between the predicted life and the actual life of the twin model:

[0207] ;

[0208] where represents the predicted life, represents the actual life, represents the life deviation, when trigger incremental learning, where represents the life deviation threshold, ;

[0209] Establish a drift trend monitoring:

[0210] ;

[0211] wherein is the monitoring window size, represents the drift trend, represents the current timestamp;

[0212] S330, Edge - side incremental learning:

[0213] Load the lightweight model (in TensorFlow Lite format) and construct the training set:

[0214] ;

[0215] wherein is the edge - side data, is the actual life, is the historical data, is the number of samples;

[0216] Local training loss function:

[0217] ;

[0218] wherein is the model function, is the model parameter, is the regularization coefficient, represents the loss function value;

[0219] S340, Gradient compression and backpropagation:

[0220] Calculate the parameter gradient matrix:

[0221] ;

[0222] wherein represents the parameter gradient matrix;

[0223] Adopt Top - K sparsification compression (compression rate ):

[0224] ;

[0225] wherein represents the compressed gradient matrix, represents the operation of taking the largest K gradient values;

[0226] Backpropagation data volume control:

[0227] ;

[0228] wherein For bandwidth allocation, is the channel quality coefficient, is the tolerance coefficient, represents the round-trip time;

[0229] S350, Twin model dynamic update:

[0230] Cloud global model fusion:

[0231] ;

[0232] where are the cloud model parameters, , is the learning rate, is the compressed gradient, represents the updated model parameters;

[0233] Update the device health status prediction function:

[0234] ;

[0235] where represents the updated predicted life, represents the updated model function, represents the edge-side data;

[0236] Model version control:

[0237] Record the update timestamp and performance metrics:

[0238] ;

[0239] where represents the model version record;

[0240] S360, Calibration efficiency verification:

[0241] Calculate the post-calibration deviation:

[0242] ;

[0243] where represents the post-calibration prediction deviation;

[0244] Generate a calibration report: If , then trigger the dynamic feedback of S250 to record the calibration failure reason and start the manual intervention process;

[0245] Establish efficiency tracking metrics:

[0246] ;

[0247] where is the tracking window size, is the time interval, represents the calibration efficiency index;

[0248] S400, Electronic Fence Dynamic Adjustment and Alarm: Use high-precision positioning equipment for real-time calibration, dynamically adjust the electronic fence boundary, monitor the relationship between the personnel's position and the electronic fence and set hierarchical alarms, calculate the false alarm rate and start multi-source data verification, and reduce the false alarm rate by adjusting system parameters;

[0249] In an embodiment of the present invention, it specifically includes the following steps:

[0250] S410, Positioning Data Calibration: Use high-precision positioning equipment for real-time calibration and establish a calibration accuracy evaluation;

[0251] Use high-precision positioning equipment for real-time calibration:

[0252] ;

[0253] wherein represents the calibrated positioning data, represents the original positioning data, represents the calibration correction value;

[0254] Establish a calibration accuracy evaluation:

[0255] ;

[0256] wherein is the high-precision reference point coordinate, represents the position error;

[0257] S420, Dynamically Adjust the Electronic Fence Boundary: According to the calibrated positioning data, adjust the electronic fence boundary in real time, considering the safety margin;

[0258] According to the calibrated positioning data, adjust the electronic fence boundary in real time:

[0259] ;

[0260] wherein represents the dynamically adjusted electronic fence boundary, represents the initial electronic fence boundary, represents the boundary adjustment value;

[0261] Consider the safety margin:

[0262] ;

[0263] wherein is the safety margin coefficient, Indicates the final electronic fence boundary;

[0264] S430, Monitor the relationship between the location of the monitoring personnel and the electronic fence: Determine whether the location of the monitoring personnel has crossed the boundary, calculate the distance of crossing the boundary, and set the hierarchical alarm threshold;

[0265] Determine whether the location of the monitoring personnel has crossed the boundary:

[0266] ;

[0267] Among them Indicates the state of crossing the boundary;

[0268] Calculate the distance of crossing the boundary:

[0269] ;

[0270] Among them Indicates the distance of crossing the boundary;

[0271] Set the hierarchical alarm threshold:

[0272] ;

[0273] Among them Indicates the alarm level;

[0274] S440, False alarm rate monitoring: Calculate the current false alarm rate and establish a false alarm analysis matrix;

[0275] Calculate the current false alarm rate:

[0276] ;

[0277] Among them Indicates the number of false alarms, Indicates the total number of alarms, Indicates the false alarm rate;

[0278] Establish a false alarm analysis matrix:

[0279] ;

[0280] Among them (Time period) indicates the time period, (location area) indicates the location area, (distort number) indicates the number of false alarms, (reason classification) indicates the reason classification, among which Indicates the false alarm analysis matrix;

[0281] S450, Multi-source data verification enabled: When the false alarm rate exceeds 15%, start multi-source data verification, calculate the false alarm rate after verification, and establish a verification credibility score;

[0282] When the false alarm rate exceeds 15%, start multi-source data verification, including RFID signal verification, camera image recognition, and work ticket information verification;

[0283] According to the multi-source data verification, calculate the false alarm rate after verification:

[0284] ;

[0285] where represents the number of false alarms confirmed after verification, represents the total number of alarms, represents the false alarm rate after verification;

[0286] And establish a verification credibility score:

[0287] ;

[0288] where represents the RFID signal weight coefficient, represents the video recognition weight coefficient, represents the work ticket verification weight coefficient, represents the comprehensive verification credibility score, represents the RFID signal verification accuracy rate, represents the video recognition accuracy rate, represents the work ticket verification accuracy rate;

[0289] S460, Reduce the false alarm rate: Adjust the alarm system parameters to ensure , adopt optimization strategies, and establish long-term optimization indicators;

[0290] Among them, adjust the alarm system parameters to ensure ;

[0291] The optimization strategies include dynamically adjusting the detection threshold, updating the alarm rules, and improving the filtering algorithm;

[0292] Establish long-term optimization indicators:

[0293] ;

[0294] where is the evaluation period, represents the long-term optimization effect evaluation indicator, represents the false alarm rate after adjustment, represents the evaluation time interval;

[0295] S500, Conflict Event Analysis and Optimization: Extract key parameters to construct a spatio-temporal correlation dataset, calculate the bandwidth utilization efficiency index and the impact of channel quality attenuation, generate an optimization report and output recommended rules, adjust the QoS level according to the bandwidth compression ratio, reconstruct the hierarchical cache threshold and perform closed-loop verification.

[0296] In one embodiment of the present invention, it specifically includes the following steps:

[0297] S510, Holographic Recording of Conflict Event Parameters:

[0298] Extract key parameters: bandwidth , channel quality , synchronization delay , positioning error and model drift rate ;

[0299] Construct a spatio-temporal correlation dataset:

[0300] ;

[0301] where is the number of events (when times, event trigger analysis), represents the spatio-temporal correlation dataset of conflict events, represents the kth bandwidth data, represents the kth channel quality data, represents the synchronization delay data;

[0302] Establish event correlation analysis:

[0303] ;

[0304] where represents the event correlation coefficient matrix, represents the correlation analysis function;

[0305] S520, Quantitative Evaluation of Synchronization Efficiency:

[0306] Calculate the bandwidth utilization efficiency index:

[0307] ;

[0308] where represents the bandwidth utilization efficiency index, represents the bandwidth allocated for the kth time, represents the actually required bandwidth for the kth time;

[0309] Evaluate the impact of channel quality attenuation:

[0310] ;

[0311] wherein represents the channel quality attenuation impact factor;

[0312] Establish a comprehensive score:

[0313] ;

[0314] wherein represents the efficiency score, represents the bandwidth efficiency weight, represents the channel quality weight;

[0315] S530, Automatic generation of optimization report:

[0316] Generate a PDF report, including:

[0317] Heat map of synchronization conflict events:

[0318] ;

[0319] wherein is the time decay coefficient, hours, represents the heat map intensity distribution, represents the occurrence time of the k-th event;

[0320] Bandwidth-delay correlation matrix:

[0321] ;

[0322] wherein represents the bandwidth-delay correlation matrix, represents the j-th delay sample;

[0323] Output optimization suggestion rule: If then increase allocation weight;

[0324] S540, Dynamic update of channel allocation strategy:

[0325] According to the bandwidth compression ratio Adjust the QoS level:

[0326] ;

[0327] wherein represents the bandwidth compression ratio, represents after adjustment level;

[0328] Update spectrum allocation:

[0329] ;

[0330] wherein represents the updated bandwidth allocation value;

[0331] Establish allocation effect tracking:

[0332] ;

[0333] wherein represents the bandwidth allocation effect tracking record;

[0334] S550, cache rule adaptive optimization:

[0335] Reconstruct the hierarchical cache threshold:

[0336] ;

[0337] wherein represents the cache threshold, represents the cache update period;

[0338] Dynamically adjust the data weight:

[0339] ;

[0340] wherein represents the positioning error threshold, m, represents the updated data weight;

[0341] S560, closed-loop verification and iteration:

[0342] Quantify the optimization efficiency improvement rate:

[0343] ;

[0344] wherein represents the synchronization delay before optimization, represents the synchronization delay after optimization, represents the optimization efficiency improvement rate;

[0345] If , trigger the model calibration mechanism in S360.

[0346] As Figure 2 shown, the present invention also proposes a full-process on-site operation online control system, including the following modules:

[0347] Dynamic bandwidth intelligent allocation module 101: Real-time monitor channel parameters (bandwidth , signal-to-noise ratio , packet loss rate ), calculate the channel quality coefficient , and dynamically allocate bandwidth ;

[0348] Positioning Calibration and Compensation Module 102: Dual-source coordinate synchronization (at the edge , in the cloud ), calculate the offset , perform cubic spline interpolation to generate compensated coordinates ;

[0349] Digital Twin Self-optimization Module 103: Verify the data integrity rate , detect model drift , perform TensorFlow Lite incremental learning;

[0350] Electronic Fence Interlock Control Module 104: Dynamically adjust the fence , three-level alarm (sound / light / sms), false alarm through the false alarm rate for false alarms;

[0351] Conflict Backtracking and Optimization Module 105: Record parameter set, generate a heat map , reconstruct the policy library.

[0352] According to the above five modules, the interaction relationship parameter table between the modules is as follows:

[0353]

[0354] In the table, the arrow → represents active data push (such as coordinates, model parameters), the arrow ← represents passive parameter call (such as conflict analysis to retrieve historical data), and the arrow ↔ represents two-way policy dependence (such as bandwidth allocation and positioning weight iteration);

[0355] Through the above system, perform the following application scenarios: Emergency repair work on mountain transmission lines;

[0356] Scenario background: Due to lightning strikes, there are potential hazards of insulator damage on a 220kV transmission line in a certain mountain area. The maintenance team enters the site with edge terminals. The 5G signal in the operation area fluctuates violently due to terrain occlusion (bandwidth fluctuation range 20~80Mbps). It is necessary to synchronize the potential hazard coordinates, equipment status, and maintenance progress to the cloud digital twin system and ensure real-time and accurate alarm of the electronic fence.

[0357] Step 1: Dynamic bandwidth allocation

[0358] Channel quality monitoring: The edge terminal continuously monitors the signal strength and detects that the real-time bandwidth suddenly drops to 35Mbps ( =35), signal-to-noise ratio =8dB, packet loss rate =12%, calculate the channel quality coefficient = 0.52 (below the threshold of 0.6), triggering a deterioration warning;

[0359] Data priority sorting: Compare the difference between edge / cloud data = 82 (the weight of the maintenance progress difference accounts for 60%), dynamically allocate = 28 Mbps;

[0360] Maintenance task data (insulator model, operation specifications) preferentially occupy 20 Mbps of bandwidth;

[0361] Cache mechanism intervention: Calculate the synchronization delay = 9 seconds, exceeding the tolerance threshold = 5 seconds, 15% of the vibration spectrum data is temporarily stored locally;

[0362] Step 2: Position compensation

[0363] Coordinate anomaly detection: The deviation between the Beidou coordinates (118.6523°, 27.8915°) at the edge and the cloud GIS benchmark = 1.2 meters, exceeding = 0.5 meters threshold, start the compensation mechanism;

[0364] Historical trajectory correction: Retrieve the trajectory data of the previous 5 minutes , generate compensation coordinates (118.6521°, 27.8912°) through cubic spline interpolation, and the deviation after compensation = 0.38 meters, and the accuracy of the electronic fence meets the standard;

[0365] Step 3: Model calibration

[0366] Abnormal life prediction: The digital twin model predicts the remaining life of the insulator = 6.3 years, the actual value = 5.1 years, the deviation rate = 23.5% far exceeds the 10% threshold

[0367] Incremental learning implementation: Load the TensorFlow Lite model and fuse the vibration data of the last 2 hours Perform training to generate a gradient matrix , and after 30% sparse compression, upload it back to the cloud;

[0368] Global model update: The cloud fuses the gradient update parameters , calibrate and predict the life, = 5.2 years, and the deviation rate drops to 1.96%;

[0369] Step 4: Early warning linkage

[0370] Out-of-bounds Event Handling: When maintenance personnel approach the non-powered area (coordinate: 1.8 meters away from the live conductor), the electronic fence triggers a level-3 audible and visual alarm, and the false alarm rate monitoring shows = 18% (due to positioning jitter caused by shrubbery obstruction);

[0371] Multi-source Verification to Reduce False Alarms: By fusing the RFID coordinates of the safety helmet (118.6520°, 27.8910°) with the UAV aerial photography coordinates, the final position is corrected, and the false alarm rate is reduced to = 3.7%, and the alarm accuracy rate meets the standard;

[0372] Step 5: Policy Optimization

[0373] Analysis of Conflict Events: Extract 37 synchronous conflict events in this operation and find that when <0.6 and > 50, the cache invalidation probability reaches 89%;

[0374] Upgrade of the Rule Base: Increase the weight of the maintenance task from 0.6 to 0.7, and add a cache ratio formula for the scenario of channel degradation: ;

[0375] Verification of the Effect: After the policy update, the synchronous delay in the same type of area is reduced from 9 seconds to 3.8 seconds (a decrease of 57.8%), and the model drift repair response time is shortened to 8 minutes.

[0376] The closed-loop effect is as follows: Through the five-level joint control mechanism, in a 5G fluctuating environment, the integrity rate of key data synchronization is increased from 68% to 94%, the number of accidental touches on the electronic fence is reduced from 12 times per day to 2 times per day, the hidden danger handling efficiency is increased by 41%, and 6 optimization strategies are formed and incorporated into the 5G communication implementation guide;

[0377] The specific effect comparison table is as follows:

[0378]

[0379] Through the five-level modular design, this system achieves the following effects in a 5G unstable mountainous environment: the hidden danger handling timeliness is increased by 47% (compared with the traditional solution), the tolerable communication interruption duration is extended to 8.3 minutes, and the recurrence rate of synchronous conflicts is controlled below 2.1%.

[0380] The above describes the embodiments of the present invention, but the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of the present invention.

Claims

1. An on-site operation full-process online control method, characterized in that The following steps are involved: S100, dynamic bandwidth allocation and data difference prediction: collect real-time channel parameters, calculate channel quality coefficients, perform weighted calculations on edge and cloud data differences, allocate real-time bandwidth based on channel quality and data differences, calculate real-time synchronization delays and start a hierarchical cache mechanism, and use differential coding to optimize the transmission packet structure for maintenance task data that is transmitted first; The specific steps include: S110, dynamic evaluation of channel quality: collecting real-time channel parameters: bandwidth, signal-to-noise ratio and packet loss rate, and calculating the channel quality coefficient; The calculation formula of the channel quality coefficient is as follows: ； Among them represents the theoretical maximum bandwidth, is the real-time bandwidth, is the packet loss rate, is the signal-to-noise ratio, represents the channel quality coefficient. When When < 0.6, it is determined that the channel is deteriorated; S120, quantification of key difference data: weighted calculation of edge and cloud data differences; ； wherein is the data dimension weight, is the number of synchronized data dimensions, is the data of the i-th dimension at the edge side, is the data of the i-th dimension in the cloud, represents the data difference amount; S130, dynamic bandwidth allocation strategy: allocate real-time bandwidth according to the channel quality coefficient and the data difference; ； wherein is the allocated bandwidth, is the tolerance coefficient; S140, Synchronization Conflict Detection and Cache Triggering: Calculate the real-time synchronization delay. If > , then start the hierarchical caching mechanism and cache non-critical data level by level from low to high according to the weight ; S150, core data compression transmission: differential encoding is used for maintenance task data with priority transmission, and the transmission packet structure is optimized; ； Among them is the edge-side task data, is the cloud-side task data, is the reference value, represents the task data after differential encoding; Transmission package structure optimization: ； Among them represents the transmission packet size represents rounding down is the signal-to-noise ratio S200, dual-source acquisition and compensation of positioning data: synchronously obtain the real-time coordinates of the edge side and the reference coordinates of the cloud, calculate the coordinate deviation in the plane rectangular coordinate system, extract the historical trajectory data for cubic spline interpolation compensation, calculate the offset after compensation and trigger the multi-source verification strategy, and feed the final offset back to the bandwidth allocation process; The specific steps include: S210, dual-source acquisition of positioning data: synchronously obtain real-time coordinates on the edge side and reference coordinates on the cloud side; Among them represents the real-time coordinates on the edge side represents the reference coordinates in the cloud S220, real-time offset calculation: calculating the coordinate deviation in the plane rectangular coordinate system and solving the Euclidean distance offset; ； ； wherein and are the abscissa and ordinate of the edge - end coordinates respectively, and are the abscissa and ordinate of the cloud - end coordinates respectively, represents the coordinate deviation on the abscissa, represents the coordinate deviation on the ordinate; The calculation formula of Euclidean distance offset is as follows: ； Constraint: When occurs, the compensation mechanism is triggered; Among them represents the Euclidean distance offset, represents the offset threshold; S230, historical trajectory interpolation compensation: extracting historical trajectory data within the time window, constructing a cubic spline interpolation function, and generating compensated coordinates; The extraction formula of historical trajectory data is as follows: ； ； Among them represents the time window, is the current time, is the time window length, and are the abscissa and ordinate of the k-th historical coordinate point respectively, represents the total number of historical coordinate points, represents the historical trajectory data set; The calculation formula of the cubic spline interpolation function is as follows: ； ； where and are the interpolation coefficients of the abscissa and ordinate respectively, and are obtained by least squares fitting, represents the i-th power of time, represents the abscissa after interpolation, represents the ordinate after interpolation; The coordinates after compensation are as follows: ； ； Among them is the compensated coordinate, represents the abscissa of the compensated coordinate, represents the ordinate of the compensated coordinate; S240, verification of compensation results: calculating the offset after compensation, and fusing the helmet RFID positioning data for correction; Calculate the offset after compensation: ； Among them, and are the abscissa and ordinate of the cloud reference coordinate respectively, represents the offset after compensation. If , trigger the multi-source verification strategy: Integrate helmet RFID positioning data Correction; Using weighted average correction: ； ； wherein represents the weighted weight coefficient, and represent the abscissa and ordinate of the RFID positioning coordinates; S250, Dynamic error feedback: Feed the final offset back to the bandwidth allocation process in S100, update the differential data weights in S120, and record historical error data for long-term optimization; Feed the final offset back to the bandwidth allocation process of S100: ； Among them is the final offset is the offset threshold is the weight of the original coordinate data represents the weight of the position data Update the difference data weight in step S120: ； Among them represents the weight of the updated differential data; Record historical error data for long-term optimization: ； wherein is the historical record length is the record timestamp, represents the historical error data, represents the k-th final offset; S300, incremental learning and calibration of twin models: verify the integrity of synchronized data and extract key indicators of equipment health status, calculate the deviation between the predicted life of the twin model and the actual life, load the lightweight model for incremental learning on the edge, use gradient compression and feedback mechanism, update the global model in the cloud and perform calibration performance verification; The specific steps include: S310, hidden danger data synchronization verification: verify the integrity of the synchronized data in S100, extract key indicators of equipment health status, and establish data quality scores; Verify the integrity of the synchronization data in S100, and require ; ； Among them is edge - side data, is cloud - side data, is the data difference amount, represents the integrity of synchronized data; Extract key indicators of equipment health status: insulation resistance , partial discharge quantity and vibration spectrum ; Establishing Data Quality Scores: ； wherein represents the data quality score, represents the minimum insulation resistance, represents the maximum discharge amount, represents the maximum vibration spectrum value, 、 、 and represent the data integrity weight, the insulation resistance weight, the discharge amount weight, and the vibration spectrum weight respectively; S320, model drift quantification detection: calculate the deviation between the predicted life and the actual life of the twin model, and establish drift trend monitoring; Calculate the deviation between the predicted lifespan of the twin model and the actual lifespan: ； Among them represents the predicted life represents the actual life represents the life deviation. When it triggers incremental learning, where represents the life deviation threshold To establish drift trend monitoring: ； wherein is the monitoring window size, represents the drift trend, represents the current timestamp; S330, incremental learning at the edge: Load lightweight model , construct a training set: ； wherein is edge - side data, is the actual lifespan, is historical data, is the number of samples, represents the training set; Local training loss function: ； wherein is the model function, is the model parameter, is the regularization coefficient, represents the loss function value; S340, gradient compression and return: Calculate the parameter gradient matrix: ； Among them represents the parameter gradient matrix; Adopt Top-K sparsification compression: ； Among them represents the compressed gradient matrix, represents the operation of taking the K largest gradient values; Control the amount of data transmitted back: ； Among them represents the feedback time; S350, Dynamic update of the twin model: Global model fusion in the cloud: ； wherein is the cloud model parameter, is the learning rate, is the compressed gradient, represents the updated model parameter; Update the device health status prediction function: ； wherein represents the updated predicted life represents the updated model function Model version control: Record the update timestamp and performance metrics: ； Among them Indicates the model version record; S360, Calibration effectiveness verification: Calculate the deviation after calibration: ； Among them represents the calibrated prediction deviation; Generate a calibration report: If , then trigger the dynamic feedback of S250 to record the reason for calibration failure and start the manual intervention process; Establish performance tracking metrics: ； Among them is the tracking window size, is the time interval, indicating the calibration efficiency index; S400, Dynamic adjustment and alarm of the electronic fence: Use high-precision positioning devices for real-time calibration, dynamically adjust the boundary of the electronic fence, monitor the relationship between the personnel's position and the electronic fence and set hierarchical alarms, calculate the false alarm rate and initiate multi-source data verification, and reduce the false alarm rate by adjusting system parameters; Specifically, it includes the following steps: S410, Positioning data calibration: Use high-precision positioning devices for real-time calibration and establish a calibration accuracy assessment; Use high-precision positioning devices for real-time calibration: ； wherein represents the calibrated positioning data, represents the original positioning data, represents the calibration correction value; Establish a calibration accuracy assessment: ； Among them is the coordinate of the high-precision reference point represents the position error S420, Dynamically adjust the boundary of the electronic fence: According to the calibrated positioning data, adjust the boundary of the electronic fence in real time, considering the safety margin; According to the calibrated positioning data, adjust the boundary of the electronic fence in real time: ； Among them represents the boundary of the electronic fence after dynamic adjustment, represents the boundary of the initial electronic fence, represents the boundary adjustment value; Consider the safety margin: ； wherein is the safety margin coefficient, represents the final electronic fence boundary; S430, Monitor the relationship between the personnel's position and the electronic fence: Monitor whether the personnel's position exceeds the boundary, calculate the overstep distance, and set the hierarchical alarm threshold; Monitor whether the personnel's position exceeds the boundary: ； Among them indicates an out-of-bounds state; Calculate the overstep distance: ； Among them indicates the out-of-bounds distance; Set the hierarchical alarm threshold: ； Among them indicates the alarm level; S440, False alarm rate monitoring: Calculate the current false alarm rate and establish a false alarm analysis matrix; Calculate the current false alarm rate: ； Among them represents the number of false alarms, represents the total number of alarms, represents the false alarm rate; Establish a false alarm analysis matrix: ； wherein represents a time period, represents a location area, represents the number of false alarms, represents the cause classification, wherein represents a false alarm analysis matrix; S450, Enable multi-source data verification: When the false alarm rate exceeds 15%, initiate multi-source data verification, calculate the false alarm rate after verification, and establish a verification credibility score; When the false alarm rate exceeds 15%, initiate multi-source data verification, including RFID signal verification, camera image recognition, and work ticket information verification; According to the multi-source data verification, calculate the false alarm rate after verification: ； Among them represents the number of false alarms confirmed after verification, represents the total number of alarms, represents the false alarm rate after verification; And establish a verification credibility score: ； Among them represents the RFID signal weight coefficient represents the video recognition weight coefficient represents the work ticket verification weight coefficient represents the comprehensive score of verification credibility represents the verification accuracy rate of RFID signals represents the recognition accuracy rate of videos represents the verification accuracy rate of work tickets; S460, reducing false alarm rate: Adjust the alarm system parameters to ensure that , adopt optimization strategies, and establish long-term optimization indicators; Among them, adjust the alarm system parameters to ensure that ; The optimization strategies include dynamically adjusting the detection threshold, updating the alarm rules, and improving the filtering algorithm; Establish long-term optimization metrics: ； wherein is the evaluation period, represents the long-term optimization effect evaluation index, represents the adjusted false alarm rate, represents the evaluation time interval; S500, Conflict event analysis and optimization: Extract key parameters to construct a spatio-temporal correlation dataset, calculate the bandwidth utilization efficiency index and the impact of channel quality attenuation, generate an optimization report and output recommended rules, adjust the QoS level according to the bandwidth compression ratio, reconstruct the hierarchical cache threshold and perform closed-loop verification; S510, Holographic recording of conflict event parameters: Extract key parameters: bandwidth , channel quality , synchronization delay , positioning error and model drift rate ; Construct a spatio-temporal correlation dataset: ； wherein is the number of events, represents the spatio-temporal correlation dataset of conflict events, represents the k-th bandwidth data, represents the k-th channel quality data, represents the synchronization delay data; Establish event correlation analysis: ； Among them represents the event correlation coefficient matrix, represents the correlation analysis function; S520, Synchronization efficiency quantitative evaluation: Calculate the bandwidth utilization efficiency index: ； wherein represents the bandwidth utilization efficiency index, represents the bandwidth allocated at the k-th time, represents the actually required bandwidth at the k-th time; Evaluate the impact of channel quality attenuation: ； wherein represents the channel quality attenuation impact factor; Establish a comprehensive score: ； wherein represents the performance score, represents the bandwidth performance weight, represents the channel quality weight; S530, Automatic generation of optimization report: Generate a PDF report, including: Synchronization conflict event heat map: ； wherein is the time decay coefficient, represents the heat map intensity distribution, represents the occurrence time of the k-th event; Bandwidth-delay correlation matrix: ； Among them represents the bandwidth-delay correlation matrix, represents the j-th delay sample; Output optimization suggestion rule: If then enhance Assign weight; S540, Dynamic update of channel allocation strategy: According to the bandwidth compression ratio Adjust the QoS level: ； Among them represents the bandwidth compression ratio represents after adjustment level Update spectrum allocation: ； Among them represents the updated bandwidth allocation value; Establish tracking of allocation effect: ； Among them represents the bandwidth allocation effect tracking record; S550, Adaptive optimization of cache rules: Reconstruct the hierarchical cache threshold: ； wherein represents a cache threshold; Dynamically adjust data weights: ； wherein represents the positioning error threshold, represents the data weight after update; S560, Closed-loop verification and iteration: Quantify the optimization efficiency improvement rate: ； Among them represents the synchronization delay before optimization represents the synchronization delay after optimization represents the optimization efficiency improvement rate. If , trigger the model calibration mechanism in S360 2. The on-site operation full-process online control method according to claim 1, wherein Among them, the real-time coordinates on the edge side come from the Beidou / GPS dual-mode positioning terminal, and the cloud reference coordinates come from the GIS coordinates of the iPMS3.0 hidden danger database; ； 。 3. An on-site operation full-process online control system, characterized in that, Performing the steps in an on-site operation full-process online control method as described in any one of claims 1-2, including: Dynamic bandwidth intelligent allocation module: Real-time monitoring of channel parameters, calculation of channel quality coefficient, and dynamic bandwidth allocation; Positioning calibration and compensation module: Dual-source coordinate synchronization, calculation of offset, and execution of cubic spline interpolation to generate compensation coordinates; Digital twin self-optimization module: Verification of data integrity rate, detection of model drift, and execution of incremental learning; Electronic fence joint control module: Dynamically adjust the fence, issue three-level alarms, and false alarms through false alarm rate; Conflict backtracking and optimization module: Record the key parameter set, generate a heat map, and reconstruct the strategy library.

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