A cable line operation and maintenance method based on human-computer collaborative driving

Abstract

The application discloses a cable line operation and maintenance method based on man-machine collaborative driving, which comprises the following steps: collecting real-time operation parameters of a cable line, importing a bridge balance fault positioning model to construct a basic database; starting a Monte Carlo simulation RAMS model to generate a fault probability distribution matrix, and calling the bridge balance fault positioning model to determine a preliminary range of a fault section; combining a man-machine collaborative interactive interface, dynamically comparing the preliminary positioning result with real-time parameters, and iteratively correcting the parameters supplemented by operation and maintenance personnel; and generating comprehensive operation and maintenance instructions according to the correction result and maintenance priority sorting. Through the above steps, the method fuses the two models and cable operation parameters, and realizes accurate fault positioning and scientific maintenance decision of the cable line through multi-step processing and man-machine collaborative correction, thereby improving operation and maintenance efficiency and reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cable line operation and maintenance, and in particular to a cable line operation and maintenance method based on human-computer collaborative driving. BACKGROUND

[0002] With the continuous expansion of the power system, as the key carrier of power transmission, the operation state of the cable line directly affects the stability and safety of the power system. In the long-term operation process, the cable line is easily affected by various factors such as load fluctuation, environmental temperature and humidity change, insulation aging and external force damage, and is prone to various faults such as insulation breakdown and conductor overheating. The traditional cable line operation and maintenance relies on manual inspection and regular detection, and has problems such as untimely data collection and lagging fault positioning, which is difficult to meet the high requirements of modern power system on operation and maintenance efficiency and reliability. Therefore, the human-computer collaborative operation and maintenance method combining advanced sensing technology, intelligent algorithm and artificial experience has become an important direction to improve the management level of the cable line. By integrating the bridge balance fault positioning model and the Monte Carlo simulation RAMS model, the accurate monitoring and efficient maintenance of the cable operation state are realized.

[0003] The prior art has two significant shortcomings in the operation and maintenance of the cable line: on the one hand, the fault positioning accuracy is insufficient, the traditional bridge balance fault positioning method only relies on the line parameters at a single moment for calculation, and does not fully combine the reliability data of long-term operation of the cable and the change of environmental parameters, resulting in large error in fault point positioning under complex working conditions, and it is difficult to accurately lock the fault section; on the other hand, the maintenance decision lacks scientificity, the existing method is mostly based on fixed period or single fault index to formulate maintenance plan, does not quantitatively analyze the reliability and availability of different maintenance schemes through Monte Carlo simulation RAMS model, and does not effectively integrate the field experience of operation and maintenance personnel, resulting in unreasonable maintenance operation time sequence, waste of resources, and inability to realize dynamic optimization based on actual operation state. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the prior art, the present application provides a cable line operation and maintenance method based on human-computer collaborative driving.

[0005] The technical solution adopted by the present application is a cable line operation and maintenance method based on human-computer collaborative driving, comprising the following steps:

[0006] S1, collecting real-time operation parameters of the cable line under different load conditions through a distributed sensing device, the parameters including cable conductor temperature, insulation layer dielectric loss factor, sheath ground current, line impedance value and environmental temperature;

[0007] S2, import the collected parameters into the initial parameter configuration module of the bridge balance fault location model, and build a model basic database containing the cable line topology structure, the length of each section of cable and the material attribute;

[0008] S3, start the Monte Carlo simulation RAMS model to perform multi-dimensional simulation operation on the reliability, availability, maintainability and safety of the cable line, and generate a fault probability distribution matrix in different operation periods;

[0009] S4, based on the fault probability distribution matrix obtained in S3, call the bridge balance fault location model to perform fault point pre-positioning operation, and determine the preliminary range of the fault section by adjusting the ratio relationship between the standard resistance and the variable resistance in the model;

[0010] S5, combined with the man-machine cooperative interface, dynamically compare the preliminary positioning result output by S4 with the real-time collected parameters, input the supplementary parameters of the on-site investigation by the operation and maintenance personnel, and iteratively correct the fault location result;

[0011] S6, according to the corrected fault location result and the maintenance priority order output by the Monte Carlo simulation RAMS model, generate comprehensive operation and maintenance instructions including fault handling scheme, spare parts demand and maintenance operation time sequence.

[0012] Further, in S3, in the simulation operation process of the Monte Carlo simulation RAMS model on the reliability of the cable line, a reliability decay model based on the cable conductor temperature and the operation time is introduced, and the model formula is: wherein, is the reliability coefficient of the cable line at the operation time and the conductor temperature , is the initial reliability coefficient of the cable, is the time decay coefficient, is the temperature influence index, is the rated operation temperature of the cable; at the same time, the model combines the dielectric loss factor of the cable insulation layer to correct the availability parameter, and the correction formula is: wherein, is the corrected availability coefficient, is the initial availability coefficient, is the dielectric loss influence coefficient.

[0013] Further, in S4, the fault point pre-positioning operation of the bridge balance fault location model adopts a double bridge comparison method to build a fault point distance calculation model, and the model formula is: wherein, is the length of the fault point distance test end, is the total length of the cable line, the line resistance from the fault point to the test terminal, respectively the adjustable calibration resistances on both sides of the bridge, a standard resistance; in the calculation process, the model automatically introduces the sheath ground current The resistance parameters are compensated, and the compensation formula is: wherein, the line resistance after compensation, the ground current influence coefficient.

[0014] Further, in S5, when the fault location result is iteratively corrected, a man-machine collaborative correction model is established, which weights and fuses the on-site survey parameters input by the operation and maintenance personnel and the output parameters of the bridge balance fault location model, and the fusion formula is: wherein, the final fault location probability, the fault location probability calculated by the model, the fault location probability determined by the operation and maintenance personnel based on the on-site parameters, respectively the weight coefficients of the model calculation and manual judgment, and satisfy ; the value of the weight coefficient is dynamically adjusted by the historical positioning error rate output by the Monte Carlo simulation RAMS model, and the adjustment formula is: wherein, the error influence factor.

[0015] Further, in S6, when the comprehensive operation instruction is generated, based on the maintenance priority ranking output by the Monte Carlo simulation RAMS model, a maintenance operation time sequence optimization model is constructed, and the model formula is: wherein, the optimal start time of the maintenance operation, the maintenance cost coefficient, the safety coefficient of the power system, the current availability coefficient of the cable line, the fault influence factor, the fault point repair difficulty coefficient; at the same time, the real-time load current of the cable line is used to correct the maintenance duration, and the correction formula is: wherein, the corrected maintenance duration, the reference maintenance duration, the load influence coefficient, the rated load current.

[0016] Further, in S2, when the model database is constructed, the correlation model of the cable line material properties and environmental parameters is introduced, and the model formula is: wherein, is the electrical conductivity of the cable conductor at ambient temperature and humidity , is the electrical conductivity at standard conditions, is the standard ambient temperature, is the temperature coefficient, is the humidity coefficient; meanwhile, the length parameters of each cable segment stored in the database need to be verified by the line impedance value , and the verification formula is: wherein, is the verification length of the nth cable segment, is the impedance value of the cable segment, is the cross-sectional area of the conductor, is the resistivity of the conductor, is the length correction coefficient.

[0017] Further, S3 comprises the following sub-steps: S31, dividing the cable line parameters in the model-based database constructed in S2 according to preset operating condition intervals, each interval corresponding to a group of parameter combinations containing conductor temperature range, load current range and ambient temperature range, and initializing the simulation scenario through traversal of the parameter combinations; S32, based on the divided parameter combinations, calling the random number generation module of the Monte Carlo simulation RAMS model to generate a fault trigger parameter sequence conforming to normal distribution, the sequence including insulation aging rate, joint contact resistance fluctuation value and external force damage probability random variables; S33, substituting the randomly generated fault trigger parameter sequence into the calculation sub-modules of reliability, availability, maintainability and safety, and obtaining the state parameters in each simulation period through iterative operation, the state parameters including fault occurrence frequency, average repair time and safety margin value; S34, statistically analyzing the state parameters of all simulation periods to construct a distribution matrix with time as the horizontal axis and fault probability as the vertical axis, each element in the matrix corresponding to the fault occurrence probability under the preset time node and parameter combination.

[0018] Further, S4 includes the following sub-steps: S41, extracting the section parameters whose fault probability exceeds the preset threshold from the fault probability distribution matrix generated in S3, taking these parameters as the input of the bridge balance fault location model, and starting the bridge configuration procedure of the model; S42, changing the ratio of the standard resistance to the variable resistance through the automatic adjustment module of the model, while monitoring the voltage difference across the bridge, when the voltage difference is less than the set balance threshold, recording the resistance ratio and the corresponding line parameters at this time; S43, based on the recorded resistance ratio and line parameters, calling the fault point distance calculation submodule, combining the total length of the cable line and the impedance values of each section, and calculating the preliminary distance value of the fault point from the test end; S44, segment matching is performed on the preliminary distance value, which is corresponded to the specific cable line section, and the preliminary positioning result containing the line section number, fault type and confidence range is output.

[0019] Further, S5 includes the following sub-steps: S51, converting the preliminary positioning result output by S4 into a visual cable line topology graph mark, displaying the location of the fault section, related operating parameters and model calculation basis in the human-computer collaborative interaction interface; S52, the operation and maintenance personnel input the supplementary parameters obtained through on-site investigation through the interaction interface, the supplementary parameters include the soil humidity near the fault point, the cable laying method and the recent construction record, these parameters are transmitted to the data fusion module in real time; S53, the data fusion module analyzes the supplementary parameters in association with the real-time collected cable operating parameters, and adjusts the environmental influence coefficient in the bridge balance fault location model to make the first correction to the preliminary positioning result; S54, comparing the first correction result with the historical fault data output by the Monte Carlo simulation RAMS model, if the deviation value exceeds the allowed range, return to S52 to reacquire the supplementary parameters, until the deviation value is within the allowed range, output the final fault location correction result.

[0020] Beneficial effects: the present application proposes a kind of cable line operation maintenance method based on man-machine collaborative driving, real-time acquisition multi-dimensional operating parameters is carried out by distributed sensing device, combine bridge balance fault location model and Monte Carlo simulation RAMS model, realize the intelligent upgrading of fault location and maintenance decision.In the fault location aspect, it is no longer dependent on single time parameter, but fusion long-term reliability data and environmental parameter change, by model iteration operation and artificial supplementary parameter correction, greatly improve positioning accuracy, accurately lock fault section, solve the problem of big positioning error of traditional method;In maintenance decision aspect, based on the fault probability distribution and maintenance priority output by Monte Carlo simulation RAMS model, combined with man-machine collaborative correction result generates dynamic operation and maintenance instruction, both quantitative analysis the reliability and availability of different schemes, also fusion the field experience of operation and maintenance personnel, avoid the blindness of fixed cycle maintenance, optimize operation time sequence and resource allocation, realize the dynamic maintenance based on actual operating state, overcome the defect that traditional decision lacks scientific nature, improve cable line operation and maintenance efficiency and reliability. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 For the method step flow chart of the present application;

[0022] Figure 2 For the method implementation unit composition diagram of the present application. DETAILED DESCRIPTION

[0023] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict, and the present application will be further described in detail with specific embodiments and the accompanying drawings.

[0024] As Figure 1 Shown, a kind of cable line operation maintenance method based on man-machine collaborative driving, comprising the following steps:

[0025] S1, by distributed sensing device, real-time operating parameters of cable line under different load conditions are collected, the parameters include cable conductor temperature, dielectric loss factor of insulating layer, sheath ground current, line impedance value and environmental temperature;

[0026] Specifically, S1 step is the data basis link of the whole maintenance method, and its core lies in comprehensively and real-timely collecting key operation parameters of the cable line under different load conditions through the distributed sensing device. The collected parameters have clear technical direction. The cable conductor temperature directly reflects the heating state of the cable, and high temperature will accelerate the aging of the insulation. The insulation layer dielectric loss factor reflects the advantages and disadvantages of the insulation performance, and the increase in the value often means that the insulation is aging or damp. The sheath grounding current can reflect the sheath insulation condition, and abnormal increase may exist in the sheath damage. The line impedance value reflects the line conduction performance, and its change can prompt the poor connection or local damage of the conductor. The environmental temperature is an important external factor affecting the cable heat dissipation and operation state. The collection of these parameters provides original data support for subsequent model operation and is the premise of realizing accurate fault location and scientific maintenance decision. The accuracy and real-timeliness thereof are directly related to the effectiveness of the whole method.

[0027] In specific implementation, the distributed sensing device is installed along the cable line in a manner of being arranged at intervals of 50 meters, covering different laying environments such as direct burial, pipe penetration and tunnel. The sensing device includes a fiber grating temperature sensor for collecting the conductor temperature, a measurement range of which is-50℃ to 150℃, an accuracy of which reaches ±0.5℃, and a sampling frequency of which is set to 1 time per minute; a dielectric loss sensor installed at the cable terminal for measuring the insulation layer dielectric loss factor, a measurement range of which is 0 to 0.1, an accuracy of which is ±0.001, and data of which is collected once every 5 minutes; a through-type current sensor sleeved on the sheath grounding wire for measuring the sheath grounding current, a range of which is 0 to 5A, an accuracy of which is ±0.01A, and which is real-timely collected and recorded; an impedance measurement module connected through the test interfaces at both ends of the cable for measuring the line impedance value, a range of which is 0 to 10Ω, an accuracy of which is ±0.01Ω, and which is measured once every hour; and an environmental temperature sensor arranged in the soil or air near the cable, a measurement range of which is-30℃ to 60℃, an accuracy of which is ±1℃, and which is collected once every 10 minutes. All the sensors are connected to a data acquisition terminal through an industrial bus, and the terminal transmits data to a central processing system through a 4G network after packaging, so that the parameters can be continuously and stably acquired in the process of changing the load from 10% rated load to 120% rated load.

[0028] S2, the collected parameters are introduced into an initial parameter configuration module of a bridge balance fault location model, and a model basic database containing the cable line topological structure, the length of each cable section and the material property is constructed;

[0029] Specifically, step S2 is a crucial step in building the foundation for model computation. Its function is to systematically organize the raw parameters collected in S1, providing structured initial data support for the bridge-balanced fault location model. After importing the parameters, the model's initial parameter configuration module categorizes the data, distinguishing between cable intrinsic properties and operational status parameters. The constructed model foundation database includes the cable line topology, clearly presenting the cable connections, branching, and routing; the length parameters of each cable segment provide a basis for distance calculations in fault location; and material properties include conductor material and insulation material type, which directly affect the cable's electrical characteristics and physical performance. The establishment of this database enables the model to perform calculations based on actual cable parameters, avoiding model errors caused by missing or inaccurate parameters, and is a vital guarantee for the reliability of subsequent fault location and simulation results.

[0030] In practice, the parameters transmitted from S1 to the central processing system are first screened, removing obviously abnormal data, such as values ​​exceeding the sensor's measurement range. Then, the screened parameters are imported into the initial parameter configuration module of the bridge balancing fault location model, where the module automatically categorizes and stores the parameters. For the cable line topology, a three-dimensional topology model is constructed in the database by importing digitized information from the line design drawings and combining it with GPS positioning data. This model clarifies the start, end, and connection points of each cable segment. For example, a cable line might be divided into three segments: the first segment runs from substation A to junction box B, the second from junction box B to junction box C, and the third from junction box C to user terminal D. The lengths of each cable segment are entered after calibration using design drawing data and actual measurement data; for example, the first segment is 800 meters long, the second 650 meters, and the third 420 meters. Regarding material properties, the conductor is recorded as copper with a cross-sectional area of ​​240 mm². The information includes details such as the insulation layer being cross-linked polyethylene and the sheath being polyvinyl chloride. The database adopts a distributed storage architecture, divided into an attribute database and an operational parameter database. The attribute database stores relatively fixed parameters such as topology, length, and material, while the operational parameter database updates dynamic parameters collected by S1 in real time. The two are linked through a unique identifier for the cable segment, ensuring that the required data can be quickly and accurately obtained when the model is called.

[0031] S3, start the Monte Carlo simulation RAMS model to perform multi-dimensional simulation calculations on the reliability, availability, maintainability and safety of the cable line, and generate a fault probability distribution matrix in different operating cycles;

[0032] Specifically, step S3 is the core step in realizing cable line reliability analysis and fault prediction. By initiating the Monte Carlo simulation RAMS model, multi-dimensional simulation calculations are performed on the cable line's reliability, availability, maintainability, and safety. Reliability reflects the cable's ability to operate normally within a specified time; availability reflects the proportion of time the cable is in a usable state; maintainability involves the ease and time required for fault repair; and safety focuses on the existence of potential safety hazards during operation. The generated fault probability distribution matrix presents the probability of faults occurring in each section within different operating cycles, providing directional guidance for subsequent fault location and making fault investigation more targeted. This step transforms a large amount of parameter information into quantified probability data, overcoming the subjectivity of traditional experience-based judgments and providing a scientific theoretical basis for maintenance decisions.

[0033] In practice, the topology, lengths of each section, material properties, and recent operating parameters collected by S1 are first retrieved from the model database built by S2. These parameters include the average conductor temperature over the past 30 days and the impedance value under maximum load. The simulation period for the Monte Carlo simulation RAMS model is set to one year, divided into 12 monthly cycles, with 10,000 random simulations performed in each cycle. The model's input parameters include the conductor temperature fluctuation range (20°C to 90°C based on historical data), the variation range of the insulation dielectric loss factor (0.001 to 0.05), and the possible values ​​of the sheath grounding current (0A to 3A). In each simulation, the model randomly generates a set of parameter combinations, which are then substituted into the calculation logic for reliability, availability, maintainability, and safety to obtain the fault occurrence under that set of parameters. For example, when the conductor temperature is consistently above 70°C and the dielectric loss factor is greater than 0.03, the probability of an insulation aging fault increases. After 12 cycles of simulation, the results are statistically analyzed to generate a fault probability distribution matrix with cable segments as rows and monthly periods as columns. The values ​​in the matrix represent the probability of a cable segment failing in a certain month. For example, the failure probability of the first cable segment in the 6th month is 0.08, and the failure probability of the second segment in the 8th month is 0.05, etc., providing a clear fault probability reference for subsequent steps.

[0034] S4. Based on the fault probability distribution matrix obtained in S3, the bridge balance fault location model is called to perform fault point pre-location calculation. By adjusting the ratio of standard resistor to variable resistor in the model, the preliminary range of the fault section is determined.

[0035] Specifically, step S4 is a crucial step in fault location. Based on the fault probability distribution matrix generated in step S3, it calls the bridge-balanced fault location model to perform pre-location calculations for the fault point. The core of this step lies in utilizing the bridge balance principle. By adjusting the ratio of standard resistance to variable resistance in the model, the bridge balance state is found, and the location of the fault point is deduced. The fault probability distribution matrix provides information on high-incidence fault sections for this step, eliminating the need for indiscriminate inspection of the entire line and greatly improving location efficiency. The determined preliminary fault section range provides a foundation for subsequent human-machine collaborative correction, reducing the blind spots of manual inspection. It serves as a bridge connecting simulation calculations and actual fault location, and the accuracy of its calculations directly affects the subsequent maintenance work.

[0036] In practice, the cable segments with a fault probability exceeding 0.05 are first extracted from the fault probability distribution matrix generated by S3. Let's assume the second and third cable segments are extracted. The parameters of these two cable segments, including length, impedance, and material properties, are retrieved from the model's basic database and used as input parameters for the bridge-balanced fault location model. After the model starts, the initial standard resistance is set to 10Ω, and the variable resistance's adjustment range is 0 to 20Ω. The variable resistance is gradually changed through an automatic adjustment module, while the voltage difference across the bridge is monitored in real time. When the voltage difference is less than 0.01V, the bridge is considered balanced, and the ratio of the standard resistance to the variable resistance is recorded, for example, 1:1.2. Based on this ratio and the total cable length and impedance distribution, the model calculates the initial distance from the fault point to the test end. For example, for the second cable segment, the initial fault location is calculated to be 350 meters from the test end. Based on the cable line topology, the preliminary location is determined to be in the middle section of the second cable segment. The output includes the cable segment number, the preliminary judgment of the fault type as an insulation fault, and a confidence level range of 60% to 75%, which prepares for the human-machine collaborative correction in step S5.

[0037] S5, combined with the human-machine collaborative interaction interface, dynamically compares the preliminary positioning results output by S4 with the parameters collected in real time. The maintenance personnel input supplementary parameters from the on-site investigation to iteratively correct the fault location results.

[0038] Specifically, step S5 is the core of achieving human-machine collaborative operation. It dynamically compares the preliminary location results output by S4 with real-time collected parameters, and incorporates supplementary parameters from on-site inspections input by maintenance personnel to iteratively correct the fault location results. This step fully leverages the efficiency of machine computation and the practicality of human experience. Dynamic comparison can promptly detect deviations between the preliminary location results and the actual operating status, while supplementary parameters from maintenance personnel introduce information such as the on-site environment and installation conditions that are difficult for the model to fully encompass. The iterative correction mechanism ensures that the fault location results continuously approach the actual fault point, overcoming the limitations of relying solely on model computation or manual judgment, making fault location more accurate, and providing a reliable basis for subsequent maintenance work.

[0039] In practice, the initial location results output by S4—namely, the second cable segment located 350 meters from the test end, an insulation fault, and a confidence level of 60% to 75%—are first converted into visual markers displayed on the cable topology map of the human-machine collaborative interface. Simultaneously, real-time operating parameters at this location are displayed on the interface, such as the current conductor temperature of 65℃, the insulation dielectric loss factor of 0.035, and the sheath grounding current of 0.8A. After viewing this information through the interface, maintenance personnel, carrying portable testing equipment, conduct on-site investigations of the section to collect supplementary parameters, including a soil moisture content of 25% near the fault point, the cable laying method being direct burial at a depth of 0.8 meters, and no recent construction records in the area. These supplementary parameters are then input into the interactive interface, and the system transmits them to the data fusion module. The module performs correlation analysis between the supplementary parameters and the real-time operating parameters, discovering that high soil moisture may affect insulation performance, thus adjusting the environmental impact coefficient in the bridge-balanced fault location model. After the first correction, the fault location is adjusted to 360 meters from the test end, and the confidence level increases to 80%. The result was compared with the historical fault data of the cable segment output by the Monte Carlo simulation RAMS model. The deviation was 5 meters, which is within the allowable deviation range of 10 meters. Therefore, the location was finally determined as the corrected fault point.

[0040] S6 generates a comprehensive maintenance instruction that includes fault handling plans, spare parts requirements, and maintenance operation sequences based on the corrected fault location results and the maintenance priority ranking output by the Monte Carlo simulation RAMS model.

[0041] Specifically, step S6 is the output stage of the entire maintenance method. Based on the corrected fault location results and the maintenance priority ranking output by the Monte Carlo simulation RAMS model, a comprehensive maintenance instruction is generated. This step transforms the results of the previous steps into a specific maintenance action plan. The corrected fault location results clarify the target location for maintenance, and the maintenance priority ranking determines the urgency and sequence of maintenance tasks. The comprehensive maintenance instruction includes fault handling plans, spare parts requirements, and maintenance operation sequences, providing maintenance personnel with comprehensive and specific operational guidance and avoiding arbitrariness and blindness in maintenance work. This step achieves closed-loop management from data acquisition and model calculation to maintenance execution, ensuring efficient and orderly maintenance work and ultimately improving the operational reliability of cable lines.

[0042] In practice, the fault location result after S5 correction is first obtained, namely, an insulation fault in the second cable segment 360 meters from the test end. Simultaneously, the maintenance priority ranking output from the Monte Carlo simulation RAMS model is retrieved. This ranking is determined based on fault probability, impact range, and repair difficulty; the fault point is rated as Level 1. Based on the fault type being an insulation fault, the fault handling plan is determined to replace the insulation layer of the damaged cable segment, specifically including steps such as excavating a cable trench, removing the old insulation layer, wrapping with a new insulation layer, and conducting insulation testing. Spare parts requirements are determined based on cable specifications, including a cross-sectional area of ​​240... Five meters of cross-linked polyethylene insulation tape, two tubes of insulating sealant, and one megohmmeter for testing were provided. The maintenance work sequence was arranged based on the line load. Real-time load data showed the current load was 60% of the rated load, and it was projected that the load would drop below 30% between 2:00 AM and 5:00 AM the following day; therefore, the maintenance work was scheduled for this period. After the comprehensive maintenance instruction was generated, it was sent to the maintenance team's terminal equipment through the system. The instruction clearly indicated the estimated time for each step, such as 1 hour for excavating the cable trench, 1.5 hours for replacing the insulation layer, and 0.5 hours for testing, ensuring that maintenance personnel could carry out their work in an orderly manner according to plan.

[0043] Preferably, in S3, during the simulation of cable line reliability using the Monte Carlo simulation RAMS model, a reliability degradation model based on cable conductor temperature and operating time is introduced. The model formula is as follows: ,in, For cable lines during operation and conductor temperature The reliability coefficient is below. The initial reliability coefficient of the cable. The time decay coefficient, The temperature effect index, The rated operating temperature of the cable is used; simultaneously, this model incorporates the dielectric loss factor of the cable insulation layer. The availability parameter is adjusted using the following formula: ,in, This is the corrected availability coefficient. The initial availability coefficient, This is the influence coefficient of dielectric loss.

[0044] Specifically, by introducing a reliability degradation model based on cable conductor temperature and operating time, and an availability correction model incorporating the dielectric loss factor of the insulation layer, the accuracy of the Monte Carlo simulation RAMS model in simulating cable line reliability and availability is improved. The technical parameters involved in the reliability degradation model include the initial reliability coefficient of the cable, which is typically determined based on performance tests at the time of cable delivery and ranges from 0.95 to 0.99; the time degradation coefficient, which is related to the cable material and operating environment, and for cross-linked polyethylene insulated cables, its value is generally between 0.001 and 0.005 per year; the temperature effect index, which reflects the accelerating effect of temperature on reliability degradation and typically ranges from 1.2 to 2.0; and the rated operating temperature, which is determined according to the cable type, such as 90℃ for 10kV cables. The introduction of these parameters allows the model to realistically reflect the changes in cable reliability under different operating times and conductor temperatures. For example, when the conductor temperature is consistently higher than the rated operating temperature, the reliability coefficient will decrease more rapidly over time. The initial availability coefficient in the availability correction model is typically set to 0.98 to 0.99. The dielectric loss influence coefficient, related to the insulation material characteristics, ranges from 5 to 10. The availability coefficient is dynamically corrected using real-time monitoring data of the insulation dielectric loss factor. As the dielectric loss factor increases, the availability coefficient decreases accordingly, thus making the availability parameters output by the model more closely reflect the actual operating state of the cable. During implementation, basic parameters such as the initial reliability coefficient and rated operating temperature of the cable are first retrieved from the model's foundation database. Combined with historical conductor temperature data collected in step S1 and real-time dielectric loss factor, these parameters are input into the corresponding model. Iterative calculations are then performed using the computational module of the Monte Carlo simulation RAMS model to obtain the corrected reliability and availability parameters. This provides more accurate input for the subsequent generation of the fault probability distribution matrix, thereby improving the scientific rigor and accuracy of the entire simulation process.

[0045] Preferably, in S4, the pre-location calculation of the fault point in the bridge balance fault location model adopts the dual-bridge comparison method to construct a fault point distance calculation model, and the model formula is: ,in, The distance from the fault point to the test end. This is the total length of the cable line. The line resistance from the fault point to the test terminal. These are the adjustable calibration resistors on both sides of the bridge. The resistance is a standard resistor; during the calculation process, the model automatically incorporates the sheath grounding current. The resistance parameter is compensated using the following formula: ,in, The line resistance after compensation. This is the influence coefficient of grounding current.

[0046] Specifically, the accuracy of fault location pre-positioning is improved by constructing a fault point distance calculation model and introducing a resistance compensation model that incorporates sheath grounding current. The technical parameters involved in the fault point distance calculation model include the total length of the cable line, which is determined through design drawings and on-site measurement calibration, achieving an accuracy of ±0.5 meters; the line resistance from the fault point to the test end can be initially obtained through bridge measurement; the adjustable calibration resistors on both sides of the bridge have a wide adjustment range, typically 0 to 100Ω, with an adjustment accuracy of 0.01Ω; the standard resistor uses a high-precision resistor with an error range within ±0.01%. The reasonable setting of these parameters is the foundation for ensuring the accuracy of distance calculation. By adjusting the adjustable calibration resistors to achieve a balanced bridge state, the distance from the fault point to the test end can be calculated using the resistance ratio at this point and the total cable length. Meanwhile, considering the impact of sheath grounding current on line resistance measurement, the grounding current influence coefficient in the introduced resistance compensation model is determined based on the cable sheath material and grounding method. For copper sheathed cables, its value is generally between 0.002 and 0.01. The line resistance is compensated and corrected by real-time acquisition of sheath grounding current values. When the sheath grounding current increases, the compensated line resistance increases accordingly, thereby eliminating the interference of grounding current on the measurement results. In implementation, the bridge configuration program of the bridge balance fault location model is first started, and the initial value of the standard resistance is set. Then, the adjustable calibration resistor is adjusted through the automatic adjustment module, while the voltage balance at both ends of the bridge is monitored. When balance is achieved, the relevant resistance parameters are recorded. The preliminary fault point distance is calculated based on the total cable length. Then, based on the acquired sheath grounding current value, the line resistance is corrected using the compensation model, the fault point distance is recalculated, and finally, the preliminary fault point location result after compensation is output, effectively improving the accuracy of fault location in the presence of sheath grounding current interference.

[0047] Preferably, in S5, when iteratively correcting the fault location results, a human-machine collaborative correction model is established. This model weights and fuses the on-site survey parameters input by the maintenance personnel with the output parameters of the bridge balance fault location model. The fusion formula is as follows: ,in, For the final fault location probability, The probability of fault location is calculated for the model. This refers to the probability of fault location determined by maintenance personnel based on on-site parameters. These are the weight coefficients calculated by the model and those judged manually, respectively, and they satisfy... The weighting coefficients are determined by the historical positioning error rate output from the Monte Carlo simulation RAMS model. Dynamic adjustment, the adjustment formula is: ,in, This is the error impact factor.

[0048] Specifically, by establishing a human-machine collaborative correction model and a dynamic weight coefficient adjustment model, the effective fusion of model calculation results and human judgment is achieved, improving the reliability of fault location. In the human-machine collaborative correction model, the fault location probability calculated by the model is output by the bridge balance fault location model, with a value between 0 and 1, reflecting the model's confidence in locating the fault point. The fault location probability judged by maintenance personnel based on field parameters is determined by combining the maintenance personnel's experience and field survey information, also between 0 and 1. The value of the weight coefficient directly affects the fusion result. The sum of the model-calculated weight coefficient and the human judgment weight coefficient is 1. Initially, the model-calculated weight coefficient is typically set to 0.7 to 0.8, and the human judgment weight coefficient is 0.2 to 0.3. The historical location error rate in the dynamic weight coefficient adjustment model is obtained based on historical data output by the Monte Carlo simulation RAMS model, reflecting the model's error level in past locations. The error impact factor is determined according to the accuracy requirements of the maintenance system, with a value between 0.5 and 1.0. When the historical location error rate increases, the model-calculated weight coefficient decreases accordingly, while the human judgment weight coefficient increases, thus achieving dynamic weight allocation. During implementation, the fault location probability calculated by the bridge balance fault location model and the on-site judgment probability input by maintenance personnel are first input into the human-machine collaborative correction model. At the same time, the historical location error rate is retrieved from the Monte Carlo simulation RAMS model, and the current weight coefficient is calculated by adjusting the model through weight coefficients. Then, the two probabilities are weighted according to the fusion formula to obtain the final fault location probability. If the confidence level of the calculation result does not reach the preset threshold, the calculation is repeated to obtain the manual judgment probability again until the final fault location result that meets the requirements is obtained. This fully leverages the advantages of human-machine collaboration and improves the robustness of fault location.

[0049] Preferably, in S6, when generating integrated operation and maintenance instructions, a maintenance job timing optimization model is constructed based on the maintenance priority ranking output by the Monte Carlo simulation RAMS model. The model formula is as follows: ,in, To maintain the optimal start time for the job. To maintain the cost coefficient, For the safety factor of the power system, The current availability factor of the cable line. As a fault influencing factor, The difficulty level of repairing the fault point is determined; simultaneously, the real-time load current of the cable line is considered. The maintenance time is adjusted using the following formula: ,in, This is the revised maintenance duration. Based on the maintenance duration, This is the load influence factor. This is the rated load current.

[0050] Specifically, scientific planning of maintenance operations is achieved by constructing a maintenance operation timing optimization model and a maintenance duration correction model. The technical parameters involved in the maintenance operation timing optimization model include: a maintenance cost coefficient, which comprehensively considers cost factors such as labor, equipment, and materials, and its value is determined according to the maintenance type; for example, the cost coefficient for insulation repair is typically between 500 and 1000. The power system safety coefficient reflects the degree of impact of maintenance operations on system safety, with a value between 1.1 and 1.5. The current availability coefficient of cable lines is output in real time by the Monte Carlo simulation RAMS model, ranging from 0.9 to 1.0. The fault impact factor is related to the location and type of the fault point; the fault impact factor for important sections ranges from 0.3 to 0.5. The fault repair difficulty coefficient is determined according to the complexity of the fault, with 0.2 to 0.3 for simple faults and 0.6 to 0.8 for complex faults. These parameters work together to calculate the optimal start time, ensuring a balance between cost, safety, and availability in maintenance operations. The baseline maintenance time in the maintenance time correction model is determined based on historical maintenance data; for example, the baseline maintenance time for insulation replacement is 2 to 4 hours. The load influence coefficient reflects the impact of load size on maintenance time, with a value between 0.1 and 0.3. The rated load current is determined based on the cable's rated current carrying capacity, such as 240... The rated load current of the cable is approximately 400A. The maintenance duration is corrected by comparing the real-time load current with the rated load current; the larger the load, the longer the corrected maintenance duration. During implementation, relevant parameters are first retrieved from the model, and the optimal start time for the maintenance operation is calculated using a maintenance operation timing optimization model. Simultaneously, the corrected maintenance duration is calculated based on the real-time load current using a maintenance duration correction model. Then, combined with fault handling plans and spare parts requirements, a comprehensive maintenance instruction is generated, including operation time, duration, steps, and required resources, ensuring that maintenance operations are carried out efficiently, economically, and safely.

[0051] Preferably, in S2, when constructing the basic database of the model, a correlation model between cable line material properties and environmental parameters is introduced, and the model formula is: ,in, For cable conductors at ambient temperature and humidity The conductivity at that point The conductivity under standard conditions. Standard ambient temperature, For temperature coefficient, The humidity coefficient is used; simultaneously, the cable length parameters stored in the database need to be verified through the line impedance value. The verification is performed using the following formula: ,in, For the first The verification length of the cable segment. This is the impedance value of this section of cable. Where is the cross-sectional area of ​​the conductor. For conductor resistivity, This is the length correction factor.

[0052] Specifically, by introducing a correlation model between cable material properties and environmental parameters, and a cable length verification model, the accuracy and reliability of parameters in the database are improved. In the correlation model between material properties and environmental parameters, the conductivity under standard conditions is determined based on the conductor material; the standard conductivity of copper conductors is 58 S / m. The standard ambient temperature is typically set at 25℃. The temperature coefficient reflects the effect of temperature on conductivity; the temperature coefficient for copper is 0.00393 / ℃. The humidity coefficient is related to the effect of ambient humidity on conductor performance, ranging from 0.0001 to 0.001. The actual conductivity of the conductor is calculated using real-time data on ambient temperature and humidity, enabling the database to dynamically reflect the impact of environmental changes on cable material properties. The conductor cross-sectional area in the cable length verification model is obtained from cable parameters, such as 240 mm². The resistivity of a conductor is an inherent property of the material; the resistivity of copper at 20℃ is 1.72 × 10⁻⁶. Ω•m; The length correction factor considers the influence of laying method and temperature on length. The correction factor for directly buried cables is 1.02 to 1.05. The cable length stored in the database is verified and corrected by the line impedance value to ensure the accuracy of the length parameters. In the implementation process, the collected ambient temperature and humidity data are first input into the correlation model to calculate the actual conductivity of the conductor and update it in the database. At the same time, the impedance value, conductor cross-sectional area, resistivity and other parameters of each cable segment are retrieved from the database. The verification length is calculated by the length verification model and compared with the original entered length. If the deviation exceeds ±1%, the length parameter in the database is updated with the verification length. Through the application of these two models, the model base database can more accurately reflect the actual properties and status of the cable, providing reliable data support for subsequent model calculations.

[0053] Preferably, S3 includes the following sub-steps: S31, dividing the cable line parameters in the model base database constructed in S2 according to preset operating condition intervals, each interval corresponding to a set of parameter combinations including conductor temperature range, load current range, and ambient temperature range, and initializing the simulation scenario by traversing the parameter combinations; S32, based on the divided parameter combinations, calling the random number generation module of the Monte Carlo simulation RAMS model to generate a fault triggering parameter sequence that conforms to a normal distribution, the sequence including random variables such as insulation aging rate, joint contact resistance fluctuation value, and external force damage probability; S33, substituting the randomly generated fault triggering parameter sequence into the reliability, availability, maintainability, and safety calculation sub-modules, and obtaining the state parameters in each simulation cycle through iterative calculation, the state parameters including the number of fault occurrences, average repair time, and safety margin value; S34, performing statistical analysis on the state parameters of all simulation cycles, constructing a distribution matrix with time as the horizontal axis and fault probability as the vertical axis, each element in the matrix corresponding to the fault occurrence probability under preset time nodes and parameter combinations.

[0054] Specifically, step S3 uses four sub-steps to perform multi-dimensional simulation calculations of the cable line using the Monte Carlo simulation RAMS model. In step S31, the preset operating condition ranges are determined based on cable design standards and operating experience. The conductor temperature range is typically set to -20℃ to 100℃, divided into 5℃ intervals; the load current range is 0% to 120% of the rated load, divided into 10% intervals; and the ambient temperature range is -30℃ to 60℃, divided into 5℃ intervals. The parameter combination for each range serves as the basic input for the simulation scenario, ensuring coverage of various operating conditions that the cable may face. The random number generation module in step S32 follows a normal distribution. The mean of the insulation aging rate is set to 0.01 to 0.05 per year, with a standard deviation of 0.005 to 0.01. The mean of the joint contact resistance fluctuation is 0 Ω, with a standard deviation of 0.001 to 0.01 Ω. The mean of the probability of external force damage is 0.0001 to 0.001 per year, with a standard deviation of 0.00005 to 0.0005. The introduction of these random variables makes the simulation closer to the impact of uncertainties in reality. In the calculation submodule of step S33, the number of fault occurrences is counted by the cumulative number of random event triggers. The average repair time is determined based on historical maintenance records, with 2 to 4 hours for simple faults and 8 to 24 hours for complex faults. The safety margin value is calculated by the difference between the current parameter and the safety threshold. For example, the conductor temperature safety margin is the difference between the rated temperature and the actual temperature. The iterative calculation cycle for these state parameters is set to 1 hour to ensure that short-term and long-term trends are captured. The statistical analysis in step S34 uses the sliding window method, with the horizontal axis of time divided by days and the vertical axis of failure probability having an accuracy of 0.001. Each element in the matrix is ​​calculated by the ratio of the number of failures occurring under the corresponding time node and parameter combination to the total number of simulations. The resulting failure probability distribution matrix provides a quantitative risk reference for subsequent failure localization. The entire implementation process is automated through the model calculation engine, and the output data of each sub-step is stored in real time in a temporary database to provide input for the next sub-step, ensuring the continuity and accuracy of the simulation calculation.

[0055] Preferably, S4 includes the following sub-steps: S41, extracting segment parameters whose fault probability exceeds a preset threshold from the fault probability distribution matrix generated in S3, using these parameters as input to the bridge balance fault location model, and starting the bridge configuration program of the model; S42, changing the ratio of standard resistance to variable resistance through the model's automatic adjustment module, while monitoring the voltage difference across the bridge, and recording the resistance ratio and corresponding line parameters when the voltage difference is less than the set balance threshold; S43, based on the recorded resistance ratio and line parameters, calling the fault point distance calculation sub-module, and calculating the preliminary distance value between the fault point and the test end by combining the total length of the cable line and the impedance value of each segment; S44, performing segment matching on the preliminary distance value, mapping it to specific cable line segments, and outputting the preliminary location result including the segment number, fault type, and confidence range.

[0056] Specifically, the four sub-steps in step S4 together constitute the fault point pre-location process of the bridge balance fault location model. In step S41, the preset fault probability threshold is set according to the importance of the cable; it is usually 0.05 for trunk cables and 0.08 for branch cables. The segment parameters extracted from the fault probability distribution matrix include cable segment number, length, impedance, etc. After these parameters are imported into the model, the bridge circuit configuration program will automatically match the corresponding bridge circuit parameters. In step S42, the balance threshold is set to 0.001 to 0.01V, and the adjustment step size of the automatic adjustment module is 0.01Ω. After each adjustment, the voltage difference across the bridge is monitored at 0.1-second intervals until a balance is reached. The recorded resistance ratio is accurate to four decimal places. The corresponding line parameters include the current conductor temperature and ambient temperature, providing an environmental correction basis for subsequent calculations. The fault location distance calculation submodule in step S43 combines the total length of the cable line and the impedance values ​​of each segment. The accuracy of the total length is ±0.1 meters, and the accuracy of the impedance value is ±0.001Ω. The calculation process takes into account the wave impedance characteristics and transmission loss of the cable, and the accuracy of the preliminary distance calculation can reach ±1 meter. The segment matching in step S44 is based on the cable line topology database. The segment numbering adopts a combination format of numbers and letters, such as L1-1, L1-2, etc. The fault type is determined based on the impedance change characteristics and historical data, including insulation faults, short circuit faults, poor contact, etc. The confidence range is determined by calculating the dispersion of multiple measurement results, usually from 50% to 90%. The output preliminary location results are stored in a structured data format, including timestamps, fault point coordinates, and related parameters, laying the foundation for human-machine collaborative correction in step S5. The entire process is realized through the model's automation module, and manual intervention is only required in abnormal situations to ensure positioning efficiency and accuracy.

[0057] Preferably, S5 includes the following sub-steps: S51, converting the preliminary location result output by S4 into a visualized cable line topology map marker, displaying the location of the fault section, relevant operating parameters, and model calculation basis in the human-machine collaborative interaction interface; S52, the maintenance personnel input supplementary parameters obtained from the on-site survey through the interactive interface, including soil moisture near the fault point, cable laying method, and recent construction records, which are transmitted to the data fusion module in real time; S53, the data fusion module performs correlation analysis between the supplementary parameters and the real-time collected cable operating parameters, and performs the first correction to the preliminary location result by adjusting the environmental impact coefficient in the bridge balance fault location model; S54, comparing the first correction result with the historical fault data output by the Monte Carlo simulation RAMS model, if the deviation value exceeds the allowable range, returning to S52 to re-obtain supplementary parameters until the deviation value is within the allowable range, and outputting the final fault location correction result.

[0058] Specifically, the four sub-steps in step S5 form a complete closed loop from result display to final correction. Step S51's visualized topology map uses a GIS map as its base, with a scale of 1:500 to 1:2000. Faulty sections are highlighted in red, and the size of the markers is proportional to the fault probability. Relevant operating parameters displayed include conductor temperature curves for the past hour, real-time dielectric loss factor, and sheath grounding current, with a data refresh rate of 10 seconds per second. The model calculation basis is displayed in text boxes, including the algorithm used, input parameter values, and a summary of the calculation process, allowing users to clearly understand the source of the location results. Step S52's interactive interface uses a touch-screen design, supporting handwriting input and data import. The input format for supplementary parameters has specific requirements: soil moisture is expressed as a percentage, accurate to 1%; a drop-down menu selects cable laying methods, including direct burial, conduit, tunnel, and cable tray; recent construction records require information such as construction time, scope, and type. After verification, supplementary parameters are sent to the data fusion module via an encrypted transmission protocol, with transmission delay controlled within 1 second. The data fusion module in step S53 uses a weighted average algorithm. The initial value of the environmental impact coefficient is set according to the laying method: 1.2 for direct-buried cables, 1.1 for conduit cables, and 1.0 for tunnel cables. During correction, the coefficient is dynamically adjusted based on the deviation of the supplementary parameters, with an adjustment step of 0.01. The result after the first correction must meet the requirement that the deviation from the preliminary location result is within ±5 meters. Step S54 retrieves historical fault data from the historical database of the Monte Carlo simulation RAMS model, with the time range set to the past 3 years. The deviation value is calculated using the Euclidean distance formula, and the allowable range is determined based on the cable length: ±5 meters for cables shorter than 1000 meters and ±0.5% of the length for cables longer than 1000 meters. If the deviation exceeds the allowable range, the power system will issue a prompt and lock the re-entry interface until the deviation value meets the requirements. The final output correction result includes detailed information such as latitude and longitude coordinates, fault type, and correction basis, ensuring that maintenance personnel can accurately locate the fault point.

[0059] The bridge balance fault location model in this invention is a technical model for locating cable fault points based on the bridge balance principle. It determines the balance state by adjusting the ratio of standard resistance to variable resistance in the model and monitoring the voltage difference across the bridge. Then, it calculates the preliminary location of the fault point from the test end by combining parameters such as the total length of the cable line and the impedance values ​​of each section. Furthermore, it can introduce sheath grounding current to compensate and correct the resistance parameters. The model's function is to accurately pinpoint the preliminary range of the fault section based on the fault probability distribution matrix output by the Monte Carlo simulation RAMS model, providing a foundation for subsequent human-machine collaborative correction. Its significance lies in overcoming the problems of traditional fault location methods relying on a single parameter and having large errors. By combining multi-dimensional line parameters and a dynamic compensation mechanism, it significantly improves the accuracy and efficiency of cable fault location, providing key technical support for rapid fault diagnosis and reducing power outage time.

[0060] The Monte Carlo simulation RAMS model in this invention is a simulation model for multi-dimensional analysis of cable line reliability, availability, maintainability, and safety. It generates a normally distributed sequence of fault-triggered parameters, such as insulation aging rate and joint contact resistance fluctuations, and iteratively calculates these parameters in corresponding submodules to obtain the fault probability distribution matrix and related state parameters for different operating cycles. The model simulates and predicts the operating status of cable lines under different loads and environmental conditions, outputting key information such as fault probability distribution and maintenance priorities. It provides guidance for high-fault sections in bridge-balanced fault location models and provides quantitative basis for human-machine collaborative correction and maintenance decisions. Its significance lies in incorporating uncertainties in cable operation into the analysis, achieving a scientific assessment of the cable line's operating status throughout its entire life cycle, changing the traditional experience-based maintenance model, laying the foundation for developing accurate and efficient maintenance plans, and contributing to improving the reliability and economy of cable line operation.

[0061] like Figure 2 As shown, a cable line operation and maintenance method based on human-machine collaborative drive is proposed. This method is implemented through different units, including:

[0062] The distributed multi-parameter synchronous acquisition unit integrates a fiber optic grating sensor and a Hall current sensor, which can acquire the conductor temperature and sheath grounding current operating parameters of the cable line under different load conditions in real time. Its output is connected to the input of the model basic database construction unit through a high-speed data bus.

[0063] The model base database construction unit has a built-in parameter classification and storage module and a topology mapping submodule. It can classify and process the received parameters and build a database containing the length and material properties of each cable segment. Its output end is connected to the input end of the Monte Carlo simulation RAMS operation unit and the bridge balance fault location unit through a bidirectional data interface.

[0064] The Monte Carlo simulation RAMS operation unit is equipped with a random number generator and a multi-dimensional operation submodule. It can perform reliability and availability simulation operations based on the received database parameters. Its output is connected to the input of the bridge balance fault location unit through a data transmission link, and its other output is connected to the input of the human-machine collaborative interaction correction unit through a wireless communication module.

[0065] The bridge balancing fault location unit includes a bridge circuit adjustment module and a fault point calculation submodule. It can pre-locate the fault point based on the received simulation calculation results. Its output is connected to the input of the human-machine collaborative interaction correction unit through an Ethernet interface.

[0066] The human-machine collaborative interaction correction unit is equipped with a touch-screen interactive interface and a data fusion processor. It can receive fault pre-location results and perform iterative correction by combining supplementary parameters input by maintenance personnel. Its output is connected to the input of the integrated maintenance instruction generation unit via an industrial Ethernet.

[0067] The integrated operation and maintenance instruction generation unit integrates an instruction compilation module and a priority sorting submodule. It can receive the corrected fault location results and generate integrated operation and maintenance instructions that include fault handling solutions, spare parts requirements, and maintenance operation sequences.

[0068] A human-machine collaborative approach to cable line operation and maintenance offers significant advantages in fault location, effectively overcoming the large errors of traditional methods. This method collects multi-dimensional real-time operating parameters through distributed sensing devices and combines them with a bridge-balanced fault location model. It no longer relies on parameters at a single moment but incorporates long-term cable reliability data and environmental parameter changes. After iterative model calculations, maintenance personnel input supplementary parameters from on-site inspections for correction, resulting in more accurate fault location and precise pinpointing of the fault section. This solves the problem of inaccurate location in traditional methods under complex operating conditions.

[0069] In maintenance decision-making, this method is highly scientific and overcomes the shortcomings of traditional decision-making methods that lack scientific rigor. Based on the fault probability distribution matrix and maintenance priority ranking output by the Monte Carlo simulation RAMS model, combined with the corrected fault location results, comprehensive operation and maintenance instructions are generated. This approach quantitatively analyzes the reliability and availability of different maintenance schemes, avoids the blindness of fixed-cycle maintenance, optimizes the timing of maintenance operations and spare parts configuration, realizes dynamic maintenance based on actual operating conditions, and improves the rationality of maintenance decisions.

[0070] Furthermore, the human-machine collaboration mode is a prominent advantage of this method. Through the human-machine collaborative interface, the preliminary results calculated by the model are dynamically compared and iteratively corrected with manually supplemented parameters. This leverages the model's efficient processing capabilities for complex data while incorporating the field experience of maintenance personnel. This collaborative mechanism makes fault location more aligned with actual conditions, maintenance decisions more feasible, and comprehensively improves the efficiency and reliability of cable line operation and maintenance.

[0071] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for cable line operation and maintenance based on human-machine collaborative drive, characterized in that, Includes the following steps: S1, real-time operating parameters of the cable line under different load conditions are collected by a distributed sensing device. The parameters include cable conductor temperature, insulation dielectric loss factor, sheath grounding current, line impedance value and ambient temperature. S2. Import the collected parameters into the initial parameter configuration module of the bridge balance fault location model to build a basic database of the model, including the cable line topology, the length of each cable segment and the material properties. In constructing the basic database of the model, which includes the cable line topology, the length of each cable segment, and the material properties, a correlation model between the cable line material properties and environmental parameters is introduced. The model formula is as follows: ,in, For cable conductors at ambient temperature and humidity The conductivity at that point The conductivity under standard conditions. Standard ambient temperature, For temperature coefficient, The humidity coefficient is used; simultaneously, the cable length parameters stored in the database need to be verified through the line impedance value. The verification is performed using the following formula: ,in, For the first The verification length of the cable segment. This is the impedance value of this section of cable. Where is the cross-sectional area of ​​the conductor. For conductor resistivity, This is a length correction factor; S3, start the Monte Carlo simulation RAMS model to perform multi-dimensional simulation calculations on the reliability, availability, maintainability and safety of the cable line, and generate a fault probability distribution matrix in different operating cycles; In the process of simulating the reliability of cable lines using the RAMS model in Monte Carlo simulation, a reliability degradation model based on cable conductor temperature and operating time is introduced. The model formula is as follows: ,in, For cable lines during operation and conductor temperature The reliability coefficient is below. The initial reliability coefficient of the cable. The time decay coefficient, The temperature effect index, The rated operating temperature of the cable is used; simultaneously, this model incorporates the dielectric loss factor of the cable insulation layer. The availability parameter is adjusted using the following formula: ,in, This is the corrected availability coefficient. The initial availability coefficient, This is the influence coefficient of dielectric loss; S4. Based on the fault probability distribution matrix obtained in S3, the bridge balance fault location model is called to perform the fault point pre-location calculation. By adjusting the ratio of the standard resistor to the variable resistor in the model, the preliminary range of the fault section is determined. Among them, the bridge balance fault location model uses the dual-bridge comparison method to perform fault point pre-location calculations, and constructs a fault point distance calculation model. The model formula is as follows: ,in, The distance from the fault point to the test end. This is the total length of the cable line. The line resistance from the fault point to the test terminal. These are the adjustable calibration resistors on both sides of the bridge. The resistance is a standard resistor; during the calculation process, the model automatically incorporates the sheath grounding current. Compensate for resistance parameters; S5, combined with the human-machine collaborative interaction interface, dynamically compares the preliminary positioning results output by S4 with the parameters collected in real time. The maintenance personnel input supplementary parameters from the on-site investigation to iteratively correct the fault location results. In the process of iteratively correcting the fault location results, a human-machine collaborative correction model is established. This model weights and fuses the on-site survey parameters input by maintenance personnel with the output parameters of the bridge balance fault location model. The fusion formula is as follows: ,in, For the final fault location probability, The probability of fault location is calculated for the model. This refers to the probability of fault location determined by maintenance personnel based on on-site parameters. These are the weight coefficients calculated by the model and those judged manually, respectively, and they satisfy... The weighting coefficients are determined by the historical positioning error rate output from the Monte Carlo simulation RAMS model. Dynamic adjustment, the adjustment formula is: ,in, This is the error impact factor; S6, based on the corrected fault location results and the maintenance priority ranking output by the Monte Carlo simulation RAMS model, generates a comprehensive operation and maintenance instruction that includes fault handling plan, spare parts requirements and maintenance operation sequence. Specifically, when generating comprehensive maintenance instructions that include fault handling plans, spare parts requirements, and maintenance operation sequences, a maintenance operation sequence optimization model is constructed based on the maintenance priority ranking output by the Monte Carlo simulation RAMS model; simultaneously, the real-time load current of the cable lines is considered. The maintenance time is adjusted using the following formula: ,in, This is the revised maintenance duration. Based on the maintenance duration, This is the load influence factor. This is the rated load current.

2. The cable line operation and maintenance method based on human-machine collaborative drive according to claim 1, characterized in that, S3 includes the following steps: S31, Divide the cable line parameters in the model base database constructed in S2 according to preset operating condition intervals. Each interval corresponds to a set of parameter combinations including conductor temperature range, load current range, and ambient temperature range. Initialize the simulation scenario by traversing the parameter combinations; S32, Based on the divided parameter combinations, call the random number generation module of the Monte Carlo simulation RAMS model to generate a fault triggering parameter sequence that conforms to a normal distribution. This sequence includes random variables such as insulation aging rate, joint contact resistance fluctuation value, and external force damage probability; S33, Substitute the randomly generated fault triggering parameter sequence into the reliability, availability, maintainability, and safety calculation submodules. Obtain the state parameters in each simulation cycle through iterative calculation. The state parameters include the number of fault occurrences, average repair time, and safety margin value; S34, Perform statistical analysis on the state parameters of all simulation cycles and construct a distribution matrix with time as the horizontal axis and fault probability as the vertical axis. Each element in the matrix corresponds to the fault occurrence probability under preset time nodes and parameter combinations.

3. The cable line operation and maintenance method based on human-machine collaborative drive according to claim 2, characterized in that, S4 includes the following sub-steps: S41, extracting segment parameters whose fault probability exceeds a preset threshold from the fault probability distribution matrix generated in S3, using these parameters as input to the bridge balance fault location model, and starting the model's bridge configuration program; S42, changing the ratio of standard resistance to variable resistance through the model's automatic adjustment module, while monitoring the voltage difference across the bridge, recording the resistance ratio and corresponding line parameters when the voltage difference is less than the set balance threshold; S43, based on the recorded resistance ratio and line parameters, calling the fault point distance calculation sub-module, and calculating the preliminary distance value between the fault point and the test end by combining the total length of the cable line and the impedance value of each segment; S44, performing segment matching on the preliminary distance value, mapping it to specific cable line segments, and outputting the preliminary location result including the segment number, fault type, and confidence range.

4. The cable line operation and maintenance method based on human-machine collaborative drive according to claim 3, characterized in that, S5 includes the following steps: S51, converting the preliminary location results output from S4 into a visualized cable line topology map, displaying the location of the fault section, relevant operating parameters, and model calculation basis in the human-machine collaborative interaction interface; S52, maintenance personnel input supplementary parameters obtained from on-site surveys through the interactive interface, including soil moisture near the fault point, cable laying method, and recent construction records, which are transmitted to the data fusion module in real time; S53, the data fusion module performs correlation analysis between the supplementary parameters and the real-time collected cable operating parameters, and performs the first correction to the preliminary location results by adjusting the environmental impact coefficient in the bridge balance fault location model; S54, comparing the first correction result with the historical fault data output by the Monte Carlo simulation RAMS model, if the deviation exceeds the allowable range, returning to S52 to re-acquire supplementary parameters until the deviation is within the allowable range, and outputting the final fault location correction result.

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