A smart cable management system and method for high-voltage direct current converter stations

Abstract

This invention relates to the field of electrical measurement technology, specifically to an intelligent cable management system and method for high-voltage direct current converter stations. The system includes: a data analysis module, a path evaluation module, a current shunting module, an anomaly detection module, and a load mapping module. In this invention, based on the collection of node temperature, current, and current-carrying capacity data, the system jointly calculates the heat capacity utilization rate and redundancy rate to improve the accuracy of margin quantification. Based on the margin, it dynamically compares path endpoint deviations and combines current-carrying capacity to sort path states, solving the problem of uneven heat capacity distribution. It generates shunting commands based on path current-carrying ratio and redundancy rate to improve load balance. It identifies abnormal nodes through sudden changes in temperature rise rate and current change rate, and remaps the load based on redundancy rate and shunting commands. This enables closed-loop control of heat capacity evaluation, path sorting, shunting control, and anomaly reconstruction, enhancing thermal safety management and response sensitivity.

Description

Technical Field

[0001] This invention relates to the field of electrical measurement technology, and in particular to an intelligent cable management system and method for high-voltage direct current converter stations. Background Technology

[0002] The field of electrical measurement technology encompasses methods and devices for detecting, analyzing, and monitoring electrical quantities. It primarily aims to accurately acquire and control electrical parameters such as voltage, current, power, energy, resistance, inductance, and capacitance. This technology is centered on sensing technology, signal acquisition circuits, and data conversion and processing circuits, and is widely used in power systems, electrical equipment safety monitoring, fault diagnosis, and power quality analysis. In high-voltage direct current (HVDC) transmission systems, electrical measurement not only undertakes real-time status sensing but also needs to ensure the stability and reliability of measurement data under high voltage, high current, and strong interference environments. Typically, a complete measurement link is formed by combining photoelectric conversion devices, voltage dividers, current transformers, and embedded acquisition chips, supplemented by isolation designs, anti-interference circuits, and digital signal processing techniques to improve the system's anti-interference capability and measurement accuracy.

[0003] One of the intelligent cable management systems and methods for high-voltage direct current (HVDC) converter stations refers to a device and process for collecting and centrally managing the status information of multiple HVDC cables within the converter station. This system mainly involves real-time acquisition of cable temperature, current, voltage, and insulation status. It monitors the temperature rise of the cable sheath and partial discharge signals by setting up fiber optic grating sensors. Simultaneously, it acquires cable current data using series sampling resistors and performs data acquisition through analog-to-digital conversion circuits. The data is then sent to a centralized control unit for unified encoding and identification management via a transmission interface compatible with the communication protocol. This method achieves synchronous reporting and archiving of cable information by setting cable numbering rules, signal acquisition timing, and data packaging structure, forming an intelligent cable management mechanism with location traceability and information synchronization.

[0004] Current technologies in cable measurement and management primarily focus on the real-time acquisition and synchronous reporting of physical quantities such as voltage, current, and temperature. While they can achieve status monitoring, they lack in-depth modeling of heat capacity and load relationships between paths, making it impossible to formulate quantitative and dynamically responsive scheduling strategies. In scenarios where multiple cables operate in the same path, structural imbalances in heat load distribution are highly likely, with some nodes remaining under high heat loads for extended periods without effective current diversion, increasing the risk of localized overheating and insulation aging. Furthermore, the archiving mechanism based on fixed numbering rules and synchronous acquisition structures only records status and lacks the ability to evaluate and respond to path status. When faced with sudden temperature rises, it can only passively trigger alarms, lacking proactive handling mechanisms, thus affecting the system's real-time adaptability. For example, when a sudden increase in load on a path causes abnormal temperature rise at a node, current technologies often only record alarms and upload data, lacking the ability to divert and reconfigure current at the control level. This results in response lag and even irreversible damage such as thermal breakdown, severely limiting its practicality in high-density, high-load DC converter station scenarios. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide an intelligent cable management system and method for high-voltage direct current converter stations. The technical solution is as follows:

[0006] On the one hand, an intelligent cable management system for high-voltage direct current converter stations is provided, the system comprising:

[0007] The data analysis module collects cable node temperature, load current and current carrying capacity data, inputs them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, generates node thermal capacity margin and transmits it to the path evaluation module.

[0008] The path evaluation module, based on the thermal capacity margin of the node, compares the thermal capacity redundancy rate deviation of the path endpoints, combines the current carrying capacity data, judges the status of the path segments and sorts them through a fuzzy comprehensive evaluation model, generates primary and backup paths and transmits them to the current shunting module.

[0009] The current shunting module calculates the ratio of the main path current to the backup path current based on the main and backup paths, and shunts the main path current in combination with the backup path thermal redundancy rate, generating a shunting current command and transmitting it to the anomaly detection module.

[0010] The anomaly detection module calculates the continuous periodic cable temperature rise rate based on the shunt current command and extracts the node temperature rise rate mutation value. It also judges the error and filters nodes by combining the load current change rate, generates potential conflict nodes, and transmits them to the load mapping module.

[0011] The load mapping module, based on the potential conflict nodes, combines the full path redundancy rate value with the current splitting command to remap the load and redistribute the current, generating cable management data.

[0012] As a further embodiment of the present invention, the node thermal capacity margin includes thermal capacity utilization rate, thermal capacity redundancy rate, and principal component score; the primary and backup paths include primary path number, backup path set, and path sorting result; the current shunting command includes primary path current ratio, backup path current shunting rate, and thermal capacity adjustment factor; the potential conflict nodes include temperature rise rate mutation value, current change error, and abnormal node number; and the cable management data specifically includes current redistribution, node load mapping relationship, and full path redundancy configuration.

[0013] As a further aspect of the present invention, the data analysis module includes:

[0014] The data acquisition submodule collects temperature, load current and current current carrying capacity data of the cable nodes of the high voltage DC converter station, binds the location information of the cable nodes with the node temperature, load current and current current carrying capacity data with a number and performs comparison calculation to generate a node operation parameter group;

[0015] The index extraction submodule extracts principal component terms from temperature data, load current data, and current carrying capacity data based on the node's operating parameter values ​​using principal component analysis. It also calculates the cumulative contribution rate of the principal components and combines them with the original indices to perform combined calculations on node characteristic indices, generating node characteristic index values.

[0016] The margin calculation submodule calls the node characteristic index value, converts the current heat capacity usage into the current ratio of the node's load current to the current carrying capacity data, and then performs cross-calculation with the node temperature principal component to establish the correlation interval between the node's heat capacity utilization rate and heat capacity redundancy rate, generating the node's heat capacity margin.

[0017] As a further aspect of the present invention, the path evaluation module includes:

[0018] The thermal redundancy module calculates the thermal redundancy rate between the start and end points of the path segment based on the thermal redundancy margin data of the nodes at the path endpoints, and generates the thermal redundancy rate deviation by calculating the difference between the thermal redundancy rates of the two nodes.

[0019] The current carrying analysis submodule calls the thermal redundancy rate deviation and the corresponding current carrying capacity data. Based on the load change range of the path segment, the thermal redundancy rate deviation value is used as the input fuzzy factor. The fuzzy comprehensive evaluation method is used to calculate the tension index of the path segment under the current current carrying capacity range and obtain the tension coefficient of the path segment.

[0020] The path filtering submodule sorts all path segments in ascending order of their stress coefficients, selects the path segment with the optimal stress coefficient to construct the primary path, and sequentially selects the path segments with the next optimal stress coefficients to construct backup paths, thus generating the primary and backup paths.

[0021] As a further aspect of the present invention, the current shunt module includes:

[0022] The path ratio calculation submodule extracts real-time current data from the main path and current carrying data from the backup path based on the main and backup paths, calculates the ratio coefficient between the current value of the main path and the current carrying capacity of the backup path, performs path correspondence verification in conjunction with the extracted path number information, and generates the main and backup path current ratio.

[0023] The redundancy analysis submodule calls the primary and backup path current ratio, combines the thermal capacity data and load change data of the backup path, constructs the redundancy thermal capacity comparison parameter based on the transient change rate between thermal capacity and load, calculates the redundancy rate of the current backup path, and filters the data by combining the path number to obtain the thermal capacity redundancy ratio.

[0024] The current shunting command generation submodule calculates the optimal current shunting ratio between the main path and the backup path under constraints based on the main and backup path current ratio and the thermal redundancy ratio. Then, it combines the current shunting control section number information with the control logic table to generate the current shunting command.

[0025] As a further aspect of the present invention, the anomaly detection module includes:

[0026] The temperature rise rate extraction submodule obtains the node shunt current command value and the current cable temperature within a continuous period based on the shunt current command and performs time matching, calculates the temperature rise rate of the node under the continuous period, and extracts the temperature rise rate change through differential method to generate the node temperature rise rate change.

[0027] The error discrimination calculation submodule calculates the error between the node temperature rise rate change and the corresponding current command change rate based on the node temperature rise rate change and the corresponding current command change rate using the mutation judgment method. It selects the node numbers whose errors exceed the current fluctuation error threshold and generates a set of node numbers with errors exceeding the limit.

[0028] The risk node marking submodule, based on the error exceeding the limit node number set, calls the original node numbering order to mark the exceeding node numbers, and generates the corresponding risk joint number information by combining the current period data, thus generating potential conflict nodes.

[0029] As a further aspect of the present invention, the current fluctuation error threshold is set by power system equipment specifications or operation and maintenance experience.

[0030] As a further aspect of the present invention, the load mapping module includes:

[0031] The number association submodule obtains the node path number and branch number in the cable topology based on the potential conflict nodes and performs path identification. It extracts the path identification number and the total number of path nodes of the risk joint in the whole path, and generates a path identification number set by combining the operating status parameters of the path where the joint is located.

[0032] The heat capacity comparison submodule obtains the branch current command value and the full path redundancy rate in the current cycle of the corresponding path according to the path identification number set, calculates the path heat capacity utilization rate according to the normalization ratio, allocates the current in combination with the allocation mapping relationship between the current command and the heat capacity utilization rate, and generates a normalized current command distribution value.

[0033] The thermal diffusion assessment submodule, based on the normalized current command distribution value, obtains the difference between the thermal capacity utilization rate of the path node in the current cycle and the record of the previous cycle, calls the segmented time interval sequence, analyzes the rate of change of thermal capacity utilization rate over a continuous period, and summarizes the data according to the node order to obtain cable management data.

[0034] As a further aspect of the present invention, the operating status parameters are physical operating information obtained through a smart terminal or a remote terminal unit (RTU), including whether the switch is closed, the direction of the current, and the voltage level.

[0035] On the other hand, a smart cable management method for a high-voltage direct current converter station is provided. This method is applied to a smart cable management system for a high-voltage direct current converter station and includes:

[0036] S1: Collect cable node temperature, load current and current carrying capacity data, input them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, and generate node thermal capacity margin.

[0037] S2: Based on the thermal capacity margin of the node, compare the thermal capacity redundancy rate deviation of the path endpoints, combine the current carrying capacity data, use the fuzzy comprehensive evaluation model to determine the status of the path segments and sort them to generate primary and backup paths.

[0038] S3: Based on the primary and backup paths, calculate the ratio of the primary path current to the backup path current carrying capacity, and combine the backup path thermal redundancy rate to shunt the primary path current and generate a shunt current command.

[0039] S4: Calculate the cable temperature rise rate for consecutive cycles based on the shunt current command and extract the node temperature rise rate mutation value. Combine the load current change rate to determine the error and screen the nodes to generate potential conflict nodes.

[0040] S5: Based on the potential conflict nodes, combine the full path redundancy rate value with the current splitting command to perform load remapping and redistribute current, and generate cable management data.

[0041] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0042] By collecting multi-dimensional data on temperature, load current, and current carrying capacity of cable nodes, principal component analysis is used to jointly calculate the node thermal capacity utilization rate and redundancy rate, making the quantification of thermal capacity margin more precise and improving the operability of thermal capacity indicators in cable condition assessment. Based on this margin data, a fuzzy comprehensive evaluation method is introduced by dynamically comparing the redundancy rate deviation at the path endpoints and combining it with current carrying capacity data to perform fine-grained sorting of path segment conditions, realizing multi-parameter coupling and uncertainty accommodation in path assessment, and effectively avoiding the problem of uneven thermal capacity distribution under static path selection. Furthermore, the thermal capacity redundancy rate of the backup path is linked to the current carrying capacity ratio of the main path and the backup path to dynamically generate shunt current commands. While fully releasing the current carrying capacity of the backup path, the thermal load distribution of the main path is optimized, improving the overall load-bearing balance of cable resources. Based on the shunt results, the temperature rise rate mutation of continuous cycles is extracted and fused with the load current change rate to construct a precise node anomaly identification mechanism, which can quickly locate potential conflict nodes and shorten the response time to fault hazards. Based on the identification results, the system introduces full-path redundancy rate and historical current shunting commands for load remapping, dynamically adjusting the current distribution path to enable real-time closed-loop adjustment of load configuration between cables. The overall processing logic realizes continuous closed-loop control from thermal capacity mining, path analysis, load adjustment to fault identification and mapping reconstruction, promoting cable management from traditional state perception to proactive scheduling and intelligent adaptation, and significantly improving the system's thermal safety margin management efficiency, load balancing accuracy, and anomaly response sensitivity. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a system flowchart of the present invention;

[0045] Figure 2 This is a system block diagram of the present invention;

[0046] Figure 3 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0048] In embodiments of the present invention, words such as "exemplarily" and "comprising" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0049] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0050] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0051] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0052] This invention provides an intelligent cable management system for high-voltage direct current converter stations. Please refer to [link to relevant documentation]. Figures 1 to 2 This invention provides a technical solution: an intelligent cable management system for high-voltage direct current converter stations includes:

[0053] The data analysis module collects cable node temperature, load current and current carrying capacity data, inputs them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, generates node thermal capacity margin and transmits it to the path evaluation module.

[0054] The path evaluation module compares the thermal capacity margin of nodes with the thermal capacity redundancy rate deviation of path endpoints, combines current carrying capacity data, uses a fuzzy comprehensive evaluation model to determine the status of path segments and sort them, generates primary and backup paths and transmits them to the current shunting module.

[0055] The current shunting module calculates the ratio of the current in the main path to the current carrying capacity of the backup path based on the main and backup paths, and shunts the current in the main path in combination with the thermal redundancy rate of the backup path, generating a shunting current command and transmitting it to the anomaly detection module.

[0056] The anomaly detection module calculates the continuous periodic cable temperature rise rate based on the shunt current command and extracts the node temperature rise rate mutation value. It also judges the error and filters nodes by combining the load current change rate, generates potential conflict nodes, and passes them to the load mapping module.

[0057] The load mapping module, based on potential conflict nodes, combines the full path redundancy rate value with the current splitting command to remap the load and redistribute the current, generating cable management data.

[0058] The node thermal margin includes thermal capacity utilization rate, thermal capacity redundancy rate, and principal component score. The primary and backup paths include primary path number, backup path set, and path sorting result. The current shunting command includes primary path current ratio, backup path current shunting, and thermal capacity adjustment factor. Potential conflict nodes include temperature rise rate mutation value, current change error, and abnormal node number. The cable management data specifically includes current redistribution, node load mapping relationship, and full path redundancy configuration.

[0059] Please see Figure 2 The data analysis module includes:

[0060] By collecting temperature, load current and current current carrying capacity data at the cable nodes of the high voltage DC converter station, the location information of the cable nodes is bound with the node temperature, load current and current current carrying capacity data and a number is assigned and compared to generate a node operation parameter group.

[0061] Temperature sensors (model PT100, range -50~150℃) and Hall current sensors (range 0-5000A) are deployed at the cable nodes of the high-voltage DC converter station. The sensors collect data at a sampling frequency of 10Hz. The monitoring point numbering rule is: area code-equipment number-node number, including ZA-3B-12 representing the 12th node of equipment number 3 in area A. During the acquisition period (07-15, 14:00:00 to 14:00:30), the temperature data sequence [68.2, 69.5, 71.3, 70.8, 72.1]℃ (standard deviation 2.3℃) and the load current sequence [2432, 2457]℃ are obtained. [2489, 2463, 2478]A, current carrying capacity design value 3000A. The three sets of data are bound to the node coordinates (23.45°N, 115.67°E) to generate the parameter group ZA-3B-12-TI-LC-EC=([68.2, 69.5, 71.3, 70.8, 72.1], [2432, 2457, 2489, 2463, 2478], 3000). When comparing the calculation, the average current carrying capacity ratio is 0.817 (2478 / 3000), and the temperature fluctuation coefficient is 0.023 (2.3 / 100). The generated operating parameter group includes a 6-dimensional feature vector.

[0062] Table 1. Examples of Node Operating Parameters

[0063] Node number Average temperature (°C) Average current (A) Design current carrying capacity (A) Current carrying capacity ratio Temperature standard deviation (°C) Temperature fluctuation coefficient ZA-3B-12 70.3 2478 3000 0.826 2.3 0.023

[0064] As shown in Table 1, the parameter group records the node operating characteristics through structured storage. The temperature standard deviation is calculated using the sample standard deviation formula, taking the data window of the five most recent sampling points. The current carrying ratio is calculated using the ratio of the average current within the sliding window to the design current carrying capacity.

[0065] The index extraction submodule extracts principal component terms from temperature data, load current data, and current carrying capacity data based on node operating parameter values ​​using principal component analysis. It also calculates the cumulative contribution rate of the principal components and combines them with the original indices to perform combined calculations on node characteristic indices, generating node characteristic index values.

[0066] The ZA-3B-12 node parameter set was standardized. The temperature data, after z-score standardization, yielded [-0.91, -0.35, 0.43, 0.22, 0.78], and the current data, normalized to the [0, 1] interval, yielded [0.486, 0.491, 0.498, 0.493, 0.496]. Representing the Principal component eigenvalues ​​were obtained by covariance analysis using PCA, and the first three components were selected. , , , Representing the maximum eigenvalue, the covariance is calculated to be the temperature-current covariance coefficient ω = 0.87. Representing the baseline dimension of covariance, it is set as the maximum estimated covariance value of standardized data. The moving average of the current is calculated as the arithmetic mean of the most recent 5 sampling points. This represents the design current carrying capacity, determined according to the IEC 60287 standard. The unit of measurement is the rated current carrying capacity of the equipment. Represents the standard deviation of temperature, calculated , This represents the Bessel correction factor, which corrects for statistical biases in small sample sizes. Representing the The actual measured temperature value sampled, such as the temperature value collected at node 14:00:03. , The arithmetic mean of the temperature data series is calculated as follows: Taking the data of node ZA-3B-12 in Table 1 as an example: , Representing the temperature baseline, take 20% of the equipment's allowable temperature rise threshold, and substitute it into the formula to calculate the cumulative contribution rate of the third principal component: The results showed that the contribution rate of the third principal component reached 42.3%. When the contribution rate exceeded the 40% threshold, the principal component was activated, and the feature index vector [0.87, 0.826, 0.423] was generated.

[0067] The margin calculation submodule calls the node characteristic index value, converts the current heat capacity usage into the current ratio of the node's load current to the current carrying capacity data, and then performs cross calculation with the node temperature principal component to establish the correlation interval between the node heat capacity utilization rate and the heat capacity redundancy rate, generating the node heat capacity margin.

[0068] Using the characteristic index [0.87, 0.826, 0.423], the real-time heat capacity utilization rate UTR is calculated as 0.826 × 0.87 = 0.718, and the heat capacity redundancy rate SRR is calculated as (1 - 0.718) × 0.423 = 0.119. When establishing the correlation interval, UTR is divided into: safe (<0.7), warning (0.7-0.8), and dangerous (>0.8). The current value of 0.718 falls into the warning interval, generating a heat capacity margin parameter group including warning level code 02. It is recommended that the load adjustment range ΔI = 3000 × (0.8 - 0.718) = 246A. The calculation process uses matrix multiplication. The temperature principal component weight coefficient is taken as 0.87 and linearly combined with the current carrying capacity ratio of 0.826. The output margin assessment result must meet the 20% redundancy requirement specified in the IEC60287 standard.

[0069] Please see Figure 2 The path evaluation module includes:

[0070] The thermal redundancy submodule calculates the thermal redundancy rate between the start and end points of a path segment based on the thermal redundancy margin data of the path endpoint nodes. It generates the thermal redundancy rate deviation by calculating the difference between the thermal redundancy rates of the two endpoint nodes.

[0071] The thermal redundancy rate of the starting node ZA-3B-12 in path segment P12 is 0.119, and the redundancy rate of the ending node ZC-5D-08 is 0.087. The deviation Δξ is calculated as 0.119 - 0.087 = 0.032. A deviation threshold of 0.03 is set. An alarm is triggered when Δξ > 0.03. Redundancy rate data is acquired every 5 minutes during the acquisition period (07-15, 14:00-14:30). The starting data sequence is [0.119, 0.123, 0.118, 0.121, 0.11]. 7], the endpoint sequence [0.087, 0.085, 0.088, 0.083, 0.086], calculate the sliding window mean difference Δξavg=0.120-0.086=0.034, trigger the path segment status mark when the threshold is exceeded for 3 consecutive cycles, the data acquisition uses RS485 bus transmission, the Modbus protocol parses the 16-bit integer data of register address 0x3100-0x310F, and converts it to floating point using the formula: redundancy rate = original value / 32767×100%.

[0072] Table 2 Node Thermal Redundancy Rate Data Table

[0073] Node number Starting redundancy rate Endpoint redundancy rate Deviation value P12 0.119 0.087 0.032 P15 0.132 0.095 0.037

[0074] As shown in Table 2, the deviation value is calculated using double-precision floating-point arithmetic, and the data storage precision retains three decimal places. When the deviation value continuously exceeds the threshold, the data verification mechanism is activated. The verification method is to take the 10 most recent sampling points and perform a t-test (confidence level 95%).

[0075] The current carrying analysis submodule calls the thermal redundancy rate deviation and the corresponding current carrying capacity data. Based on the load change range of the path segment, the thermal redundancy rate deviation value is used as the input fuzzy factor. The fuzzy comprehensive evaluation method is used to calculate the tension state index of the path segment under the current current carrying capacity range and obtain the tension state coefficient of the path segment.

[0076] Molecular part, Representative to The weighted summation of the evaluation dimensions. These are the original weight coefficients, after normalization. After processing, the weights are assigned as [0.3, 0.4, 0.3]. The thermal redundancy rate deviation for path segment P12 is derived from the deviation values ​​for path segment P12 in Table 2. The square root of the ratio of current carrying capacity is used for calculation. The current carrying capacity is a moving average. The current carrying capacity data for the most recent 5 minutes is [2870, 2875, 2872, 2876, 2872]A, with a mean of 2873A. The denominator is the reference parameter for the rated current carrying capacity of the line. : Dimensional conversion of current gradient , representing the current gradient dimension conversion factor, according to IEC 60287 standard. , calculate: , Representing the instantaneous change gradient of the current-carrying capacity, it is calculated using the current difference between adjacent sampling points. Temperature normalization processing This refers to the critical temperature of XLPE insulation. Substitute the temperature normalization reference parameter into the formula. The result of 0.718 exceeded the threshold of 0.7, triggering a level 2 warning.

[0077] The path filtering submodule sorts all path segments in ascending order of their stress coefficients, selects the path segment with the best stress coefficient to build the primary path, and then selects the path segments with the second best stress coefficients to build backup paths, thus generating the primary and backup paths.

[0078] The stress coefficients of all 5 path segments [0.718, 0.653, 0.592, 0.702, 0.635] are obtained and sorted in ascending order to obtain [0.592, 0.635, 0.653, 0.702, 0.718]. P09 (0.592) is selected as the primary path and P14 (0.635) as the first backup. The sorting algorithm adopts quicksort, with a time complexity of O(nlogn). During implementation, a path priority queue is established, and the sorting results are updated every 5 minutes. When the primary path coefficient is >0.7 for 2 consecutive minutes, the backup path is automatically switched. The data storage adopts a circular buffer structure with a buffer depth of 60 cycles (5 hours of data). The path switching command is transmitted through GOOSE messages with a transmission delay of <4ms, which meets the real-time control requirements of the power grid.

[0079] Please see Figure 2 The current shunt module includes:

[0080] The path ratio calculation submodule extracts real-time current data from the primary path and current carrying data from the backup path based on the primary and backup paths, calculates the ratio coefficient between the current value of the primary path and the current carrying capacity of the backup path, and performs path correspondence verification in combination with the extracted path number information to generate the primary and backup path current ratio.

[0081] The real-time current of the main path P09 is 2873A, and the design current carrying capacity of the backup path P14 is 3200A. Calculate the proportional gain. When verifying the path number, the topological connection relationship between P09 and P14 is matched to verify that there is a parallel connection between them between nodes ZA-3B-12 and ZC-5D-08. The data verification uses CRC-16 checksum comparison. The original data frames are 0x3A7B and 0x5C2D, and the checksum 0x8E9F is generated. After the verification is successful, the current ratio parameter group P09-P14-CR=0.898 is generated. The data update cycle is 1 second, and it is stored in IEEE754 double-precision floating-point format. When the proportional coefficient is >0.9 for 5 consecutive cycles, the path verification command is triggered. The verification method is to take the most recent 10 current sampling points and perform a moving average calculation. The sampling point data is [2873, 2865, 2881, 2877, 2869, 2872, 2883, 2875, 2868, 2876]A, with an average value of 2874.9A. After verification, the proportional coefficient is corrected to 0.898.

[0082] Table 3. Current Parameters for Main and Backup Paths

[0083] Path number Real-time current (A) Design capacity (A) Current ratio P09 2873 3200 0.898 P14 2956 3200 0.924

[0084] As shown in Table 3, the current ratio calculation uses the instantaneous ratio of real-time data to the design value. When the main path ratio exceeds the 0.95 threshold, the path switching evaluation is initiated. The threshold setting refers to the IEC60865 short-circuit current thermal stability standard.

[0085] The redundancy analysis submodule calls the primary and backup path current ratio, combines the thermal capacity data and load change data of the backup path, constructs the redundancy thermal capacity comparison parameters based on the transient change rate between thermal capacity and load, calculates the redundancy rate of the current backup path, and filters the data by combining the path number to obtain the thermal capacity redundancy ratio.

[0086] The thermal capacity data redundancy rate of the backup path P14 is 0.135, and the load change rate is... Construct transient rate of change parameters The calculation uses 10 load data points [2873, 2881, 2892, 2905, 2917, 2930, 2942, 2951, 2956, 2960]A within a sliding window, with a rate of change sequence [0.027, 0.037, 0.043, 0.040, 0.043, 0.040, 0.030, 0.017, 0.013]A / s and a variance of 0.00012. Data is retained when γ>0.1. After filtering, the effective redundancy rate is 0.128. When associating data, the matching degree between the path number and the device ID in the thermal capacity database is verified. A hash table retrieval algorithm is used to retrieve the key value P14-Hash=0x9A3F corresponding to the storage address 0x7E00. The 16-bit integer data in registers 0x7E00-0x7E0F is read and converted into floating-point numbers.

[0087] The current shunting instruction generation submodule calculates the optimal current shunting ratio between the main path and the backup path under constraints based on the current ratio of the main path and the thermal redundancy ratio, and then combines the current shunting control section number information with the control logic table to generate the current shunting instruction.

[0088] With a current ratio of 0.898 and a redundancy rate of 0.128, set the weighting coefficient. (Current ratio weighting) (Redundancy ratio weight) Calculate the split ratio The control logic table entry CTRL-09 (current shunting ratio range 0.85-0.90) is matched, generating instruction code 0xD2, which is sent to circuit breaker CB-09A via GOOSE message. The instruction parameters include target current 2873×0.888=2552A, timestamp 2025-07-15T, 14:05:23.456Z. The instruction verification adopts dual-channel XOR verification. The original instruction 0xD2 is XORed with the mask 0x7E to obtain the check code 0xAC. The complete instruction frame is [0xAA, 0xD2, 0xAC, 0x55]. When the current shunting ratio exceeds 0.95, level 3 protection is triggered, the main path is immediately cut off and the backup path is switched. The control logic table update cycle is 5 minutes, and the LRU cache eviction mechanism is used to maintain the 20 most recently used control entries.

[0089] Please see Figure 2 The anomaly detection module includes:

[0090] The temperature rise rate extraction submodule obtains the node shunt current command value and the current cable temperature within a continuous period based on the shunt current command and performs time matching, calculates the temperature rise rate of the node under the continuous period, and extracts the temperature rise rate change through differential method to generate the node temperature rise rate change.

[0091] Obtain the shunt current command value [2552, 2548, 2555, 2561, 2557]A of node ZA-3B-12 from 14:05:00 to 14:05:30, and simultaneously collect temperature data [72.1, 72.5, 73.3, 74.0, 73.7]℃. Calculate the temperature rise rate ΔT / Δt per unit time, with a time interval of 300 seconds. The calculated rate sequence is [(72.5-72.1) / 300=0.0013℃ / s]. Simultaneously calculate (73.3-72.5) / 300=0.0027℃ / s, (74.0-73.3) / 300=0.0023℃ / s, (73.7-74.0) / 300=0.0023℃ / s, (73.7-74.0) / 300=0.0027℃ / s, (74.0-73.3 ...7℃ / s, [0 = -0.0010℃ / s], the rate of change is calculated using the second-order difference method, [(0.0027 - 0.0013) / 300 = 0.0000047, (0.0023 - 0.0027) / 300 = -0.0000013, (-0.0010 - 0.0023) / 300 = -0.000011], and combined to generate the temperature rise rate change parameter group ZA-3B-12-DT = [0.0000047, -0.0000013, -0.000011]. The data storage adopts a ring buffer structure with a buffer depth of 60 cycles. When the rate of change for 3 consecutive cycles is > 0.000005, data verification is triggered.

[0092] Table 4 Example of temperature rise rate calculation

[0093] Timestamp Temperature (°C) Temperature rise rate (°C / s) 14:05:00 72.1 - 14:05:30 72.5 0.0013 14:06:00 73.3 0.0027

[0094] As shown in Table 4, the temperature rise rate is calculated by dividing the difference between adjacent sampling points by the time interval. When a temperature sensor malfunction causes a negative rate, abnormal data is automatically removed. The removal condition is that the rate < -0.005℃ / s lasts for 2 cycles.

[0095] The error discrimination calculation submodule calculates the error between the node temperature rise rate and the corresponding current command change rate based on the node temperature rise rate change and the corresponding current command change rate using the mutation judgment method. It selects the node numbers whose errors exceed the current fluctuation error threshold and generates a set of node numbers with errors exceeding the limit.

[0096] The average temperature rise rate at node ZA-3B-12 is taken as 0.0000047. Simultaneously, the corresponding current change rates ΔI / Δt are calculated as follows: ΔI / Δt = [(2548-2552) / 300 = -0.013 A / s, (2555-2548) / 300 = 0.023 A / s, (2561-2555) / 300 = 0.020 A / s, (2557-2561) / 300 = -0.013 A / s]. The calculation error ε = |ΔT / Δt - k×ΔI / Δt|, k = 0.0002, where k is the thermal resistance coefficient. Represents the rate of temperature rise. Representing the rate of change of current, the error sequence is: [|0.0013-0.0002×(-0.013)|=0.0013026, |0.0027-0.0002×0.023|=0.0026954, |0.0023-0.0002×0.020|=0.002696, |-0.0010-0.0002×(-0.013)|=0.0 [009974] Set the error threshold δ=0.0025, filter out nodes that exceed the limit, and mark them when the error is >0.0025. In this example, the errors of the 2nd and 3rd cycles are 0.0026954 and 0.002696 respectively, which exceed the limit. Generate the set of nodes that exceed the limit {ZA-3B-12}. Manchester encoding is used for data verification. The original data frame 0xB5A7 can be stored in the database only after it is successfully matched with the check code 0x6D3C.

[0097] The risk node marking submodule, based on the error exceeding the limit node number set, calls the original node number order to mark the exceeding node number, and generates the corresponding risk joint number information by combining the current period data, thus generating potential conflict nodes.

[0098] Read the set of nodes exceeding the limit {ZA-3B-12}, sort them by original topology, number them 32, and mark them as R-32. Associate them with the current period data 07-15-14:05:00-14:06:00 to generate risk record R-32-202507151405. The data storage adopts a B+ tree index structure, with the key value being [timestamp, node number]. When the same node is marked for 3 consecutive periods, it is upgraded to a high-risk node, triggering a red alert. The alert information is uploaded to the monitoring system via the IEC 61850 MMS protocol. The message includes the risk level code 0xE1, the suggested remedial measure code 0x73 (reduce the load by 10%), and simultaneously update bit 5 of the node status register 0x2100-0x210F to 1, indicating the existence of potential hot conflict risk.

[0099] Please see Figure 2 The load mapping module includes:

[0100] The number association submodule obtains the node path number and branch number in the cable topology based on potential conflict nodes and performs path identification. It extracts the path identification number and the total number of path nodes of the risk joint in the whole path, and generates a path identification number set by combining the operating status parameters of the path where the joint is located.

[0101] Obtain the topology path P09 of risk node R-32. The path branch structure is ZA-3B-12→ZC-5D-08→ZE-7F-15, with a total of 15 nodes. Extract the path identifier number P09-15 and associate it with the operating status parameters including current 2873A, temperature 73.7℃, and redundancy rate 0.128. Generate the path identification number set {P09-15}. During data verification, match the path number hash value 0x9E4A with the topology database record. Use a Bloom filter for retrieval with a false positive rate of 0.1%. When the number of path nodes is greater than 20, segmented retrieval is enabled with a segment size of 5 nodes. The current path segment is [ZA-3B-12, ZC-5D-08, ZE-7F-15]. After successful verification, update bit 3 of the path status register 0x4100 to 1, indicating that path P09-15 has a risk association.

[0102] Table 5 Path Recognition Parameters

[0103] Path number Total number of nodes Current current (A) Redundancy P09-15 15 2873 0.128

[0104] As shown in Table 5, the path identification number set includes key parameters of the topology. When the redundancy rate is less than 0.15, the path optimization evaluation is triggered, and the evaluation period is 30 seconds.

[0105] The heat capacity comparison submodule obtains the branch current command value and the full path redundancy rate in the current cycle of the corresponding path based on the path identification number set, calculates the path heat capacity utilization rate according to the normalization ratio, allocates the current based on the allocation mapping relationship between the current command and the heat capacity utilization rate, and generates a normalized current command distribution value.

[0106] Obtain the branch current command value [2552, 2548, 2555]A for path P09-15, with a total path redundancy rate of 0.128, and calculate the heat capacity utilization rate. ,in This is the actual current. To design the maximum value, For redundancy rate, we get Distribute current according to normalized ratio and assign weights. Generate instruction distribution values Data storage adopts IEEE 754 double precision format. When the utilization rate is >0.8, a load reduction command is triggered, and the load reduction is 20% of the excess.

[0107] The thermal diffusion assessment submodule, based on the normalized current command distribution value, obtains the difference between the thermal capacity utilization rate of the path node in the current cycle and the record of the previous cycle, calls the segmented time interval sequence, analyzes the rate of change of thermal capacity utilization rate in continuous time, and summarizes it in node order to obtain cable management data.

[0108] Given the current cycle's heat capacity utilization rate of 0.796 and the previous cycle's record of 0.782, with a time interval of 300 seconds, calculate the rate of change of heat capacity. , This represents a five-minute time interval. Representing heat capacity utilization, data is summarized in node order. The change rate of node ZA-3B-12 is 0.0000467, the change rate of ZC-5D-08 is 0.0000382, and the change rate of ZE-7F-15 is 0.0000415, generating a cable management dataset {0.0000467, 0.0000382, 0.0000415}. Data compression uses the LZ77 algorithm with a compression ratio of 60%. When the change rate of any node is >0.00005, it is marked as a heat diffusion hotspot, triggering the real-time monitoring data stream sampling rate to be increased to 10Hz. The raw data is stored in the time series database after CRC-32 verification, with a timestamp alignment accuracy of ±1ms.

[0109] Please see Figure 3 A smart cable management method for a high-voltage direct current converter station, comprising the following steps: (The method is used to implement the aforementioned smart cable management system for a high-voltage direct current converter station.)

[0110] S1: Collect cable node temperature, load current and current carrying capacity data, input them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, and generate node thermal capacity margin.

[0111] S2: Based on the thermal capacity margin of the node, compare the thermal capacity redundancy rate deviation of the path endpoints, combine the current carrying capacity data, and use the fuzzy comprehensive evaluation model to judge the status of the path segments and sort them to generate primary and backup paths.

[0112] S3: Based on the primary and backup paths, calculate the ratio of the primary path current to the backup path current carrying capacity, and combine the backup path thermal redundancy rate to shunt the primary path current and generate a shunt current command.

[0113] S4: Calculate the cable temperature rise rate for continuous cycles based on the shunt current command and extract the node temperature rise rate mutation value. Combine the load current change rate to identify the error and screen the nodes to generate potential conflict nodes.

[0114] S5: Based on potential conflict nodes, combine the full path redundancy rate value and the current shunting command to perform load remapping and redistribute current, generating cable management data.

[0115] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist: A and / or B, which can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship; please refer to the context for a more accurate understanding.

[0116] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. "At least one of a, b, or c" can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0117] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0119] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. This includes the fact that the apparatus embodiments described above are merely illustrative, and that the division of units is only a logical functional division; in actual implementation, there may be other division methods, including the possibility that multiple units or components can be combined or integrated into another device, or that some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed can be through some interface; the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent cable management system for high-voltage direct current converter stations, characterized in that, The system includes: The data analysis module collects cable node temperature, load current and current carrying capacity data, inputs them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, generates node thermal capacity margin and transmits it to the path evaluation module. The path evaluation module, based on the thermal capacity margin of the node, compares the thermal capacity redundancy rate deviation of the path endpoints, combines the current carrying capacity data, judges the status of the path segments and sorts them through a fuzzy comprehensive evaluation model, generates primary and backup paths and transmits them to the current shunting module. The current shunting module calculates the ratio of the main path current to the backup path current based on the main and backup paths, and shunts the main path current in combination with the backup path thermal redundancy rate, generating a shunting current command and transmitting it to the anomaly detection module. The anomaly detection module calculates the continuous periodic cable temperature rise rate based on the shunt current command and extracts the node temperature rise rate mutation value. It also judges the error and filters nodes by combining the load current change rate, generates potential conflict nodes, and transmits them to the load mapping module. The load mapping module, based on the potential conflict nodes, combines the full path redundancy rate value with the current splitting command to remap the load and redistribute the current, generating cable management data.

2. The intelligent cable management system for high-voltage DC converter stations according to claim 1, characterized in that, The node thermal capacity margin includes thermal capacity utilization rate, thermal capacity redundancy rate, and principal component score; the primary and backup paths include primary path number, backup path set, and path sorting result; the current shunting command includes primary path current ratio, backup path current shunting, and thermal capacity adjustment factor; the potential conflict nodes include temperature rise rate mutation value, current change error, and abnormal node number; and the cable management data specifically includes current redistribution, node load mapping relationship, and full path redundancy configuration.

3. The intelligent cable management system for high-voltage DC converter stations according to claim 1, characterized in that, The data analysis module includes: The data acquisition submodule collects temperature, load current and current current carrying capacity data of the cable nodes of the high voltage DC converter station, binds the location information of the cable nodes with the node temperature, load current and current current carrying capacity data with a number and performs comparison calculation to generate a node operation parameter group; The index extraction submodule extracts principal component terms from temperature data, load current data, and current carrying capacity data based on the node's operating parameter values ​​using principal component analysis. It also calculates the cumulative contribution rate of the principal components and combines them with the original indices to perform combined calculations on node characteristic indices, generating node characteristic index values. The margin calculation submodule calls the node characteristic index value, converts the current heat capacity usage into the current ratio of the node's load current to the current carrying capacity data, and then performs cross-calculation with the node temperature principal component to establish the correlation interval between the node's heat capacity utilization rate and heat capacity redundancy rate, generating the node's heat capacity margin.

4. The intelligent cable management system for high-voltage DC converter stations according to claim 1, characterized in that, The path evaluation module includes: The thermal redundancy submodule calculates the thermal redundancy rate between the start and end points of the path segment based on the thermal redundancy margin data of the nodes at the path endpoints, and generates the thermal redundancy rate deviation by calculating the difference between the thermal redundancy rates of the two nodes. The current carrying analysis submodule calls the thermal redundancy rate deviation and the corresponding current carrying capacity data. Based on the load change range of the path segment, the thermal redundancy rate deviation value is used as the input fuzzy factor. The fuzzy comprehensive evaluation method is used to calculate the tension index of the path segment under the current current carrying capacity range and obtain the tension coefficient of the path segment. The path filtering submodule sorts all path segments in ascending order of their stress coefficients, selects the path segment with the optimal stress coefficient to construct the primary path, and sequentially selects the path segments with the next optimal stress coefficients to construct backup paths, thus generating the primary and backup paths.

5. The intelligent cable management system for high-voltage direct current converter stations according to claim 1, characterized in that, The current shunt module includes: The path ratio calculation submodule extracts real-time current data from the main path and current carrying data from the backup path based on the main and backup paths, calculates the ratio coefficient between the current value of the main path and the current carrying capacity of the backup path, performs path correspondence verification in conjunction with the extracted path number information, and generates the main and backup path current ratio. The redundancy analysis submodule calls the primary and backup path current ratio, combines the thermal capacity data and load change data of the backup path, constructs the redundancy thermal capacity comparison parameter based on the transient change rate between thermal capacity and load, calculates the redundancy rate of the current backup path, and filters the data by combining the path number to obtain the thermal capacity redundancy ratio. The current shunting command generation submodule calculates the optimal current shunting ratio between the main path and the backup path under constraints based on the main and backup path current ratio and the thermal redundancy ratio. Then, it combines the current shunting control section number information with the control logic table to generate the current shunting command.

6. The intelligent cable management system for high-voltage direct current converter stations according to claim 1, characterized in that, The anomaly detection module includes: The temperature rise rate extraction submodule obtains the node shunt current command value and the current cable temperature within a continuous period based on the shunt current command and performs time matching, calculates the temperature rise rate of the node under the continuous period, and extracts the temperature rise rate change through differential method to generate the node temperature rise rate change. The error discrimination calculation submodule calculates the error between the node temperature rise rate change and the corresponding current command change rate based on the node temperature rise rate change and the corresponding current command change rate using the mutation judgment method. It selects the node numbers whose errors exceed the current fluctuation error threshold and generates a set of error-exceeding node numbers. The risk node marking submodule, based on the error exceeding the limit node number set, calls the original node numbering order to mark the exceeding node numbers, and generates the corresponding risk joint number information by combining the current period data, thus generating potential conflict nodes.

7. The intelligent cable management system for high-voltage direct current converter stations according to claim 6, characterized in that, The current fluctuation error threshold is set based on the power system equipment specifications or operation and maintenance experience.

8. The intelligent cable management system for high-voltage DC converter stations according to claim 1, characterized in that, The load mapping module includes: The number association submodule obtains the node path number and branch number in the cable topology based on the potential conflict nodes and performs path identification. It extracts the path identification number and the total number of path nodes of the risk joint in the whole path, and generates a path identification number set by combining the operating status parameters of the path where the joint is located. The heat capacity comparison submodule obtains the branch current command value and the full path redundancy rate in the current cycle of the corresponding path according to the path identification number set, calculates the path heat capacity utilization rate according to the normalization ratio, allocates the current in combination with the allocation mapping relationship between the current command and the heat capacity utilization rate, and generates a normalized current command distribution value. The thermal diffusion assessment submodule, based on the normalized current command distribution value, obtains the difference between the thermal capacity utilization rate of the path node in the current cycle and the record of the previous cycle, calls the segmented time interval sequence, analyzes the rate of change of thermal capacity utilization rate over a continuous period, and summarizes the data according to the node order to obtain cable management data.

9. The intelligent cable management system for high-voltage direct current converter stations according to claim 8, characterized in that, The operating status parameters are physical operating information obtained through a smart terminal or remote terminal unit (RTU), including whether the switch is closed, the direction of the current, and the voltage level.

10. A smart cable management method for high-voltage direct current converter stations, characterized in that, The method is used to implement the intelligent cable management system for high-voltage DC converter stations according to any one of claims 1-9, the method comprising: S1: Collect cable node temperature, load current and current carrying capacity data, input them into principal component analysis to perform joint calculation of node thermal capacity utilization rate and thermal capacity redundancy rate, and generate node thermal capacity margin. S2: Based on the thermal capacity margin of the node, compare the thermal capacity redundancy rate deviation of the path endpoints, combine the current carrying capacity data, use the fuzzy comprehensive evaluation model to determine the status of the path segments and sort them to generate primary and backup paths. S3: Based on the main and backup paths, calculate the ratio of the main path current to the backup path current carrying capacity, and combine the backup path thermal redundancy rate to shunt the main path current and generate a shunt current command. S4: Calculate the cable temperature rise rate for consecutive cycles based on the shunt current command and extract the node temperature rise rate mutation value. Combine the load current change rate to determine the error and screen the nodes to generate potential conflict nodes. S5: Based on the potential conflict nodes, combine the full path redundancy rate value with the current splitting command to perform load remapping and redistribute current, and generate cable management data.

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