An electric power line trench digitalization online operation method and device

Abstract

The application relates to a power line trench digitalization online operation and maintenance method and device. The method comprises the following steps: in the case that line static sensing data of a power line trench is abnormal, robot group inspection information of the power line trench is planned according to the line static sensing data; the robot group of the power line trench is controlled to inspect the power line trench according to the robot group inspection information, and line dynamic inspection data is obtained; physical field coupling simulation is carried out on the power line trench according to the line dynamic inspection data, and line abnormal coupling analysis data is obtained; a repair process of the power line trench is calculated according to the line abnormal coupling analysis data with a constraint condition of an abnormal elimination rule set of the power line trench, and line abnormal repair data is obtained; and robot group maintenance data of the robot group under the line trench scale is calculated according to the line abnormal repair data. The method can timely eliminate the abnormality and prevent accidents.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent operation and maintenance, in particular to a power line trench digital online operation and maintenance method and device. BACKGROUND

[0002] In the traditional technology, there are mainly several ways for the intermediate cable head of the high-voltage cable laid in the power line trench and the city network cable trench: 1. rubber sheath cable intermediate joint, 2. cold shrink tube intermediate joint, 3. hot shrink tube intermediate joint, 4. cable fusion technology intermediate joint, etc. Due to the limitation of technology, the internal insulation of the cable joint is prone to rapid aging, moisture, and damage during long-term operation, which may cause joint heating, sparking, arc, and even fire, explosion, large-scale power outage accidents, personnel casualties, and a large amount of economic losses. In addition, due to the particularity of the trench, the line operation state cannot be monitored, and only manual inspection and regular temperature measurement can be used for regular inspection, which cannot timely eliminate abnormalities and prevent accidents. SUMMARY

[0003] Therefore, it is necessary to provide a power line trench digital online operation and maintenance method and device which can timely eliminate abnormalities and prevent accidents.

[0004] In a first aspect, the present application provides a power line trench digital online operation and maintenance method, comprising:

[0005] In the case that the line static sensing data of the power line trench is abnormal, the robot group patrol information of the power line trench is planned according to the line static sensing data;

[0006] According to the robot group patrol information, the robot group of the power line trench is controlled to patrol the power line trench, and line dynamic patrol data is obtained;

[0007] According to the line dynamic patrol data, physical field coupling simulation is performed on the power line trench, and line abnormal coupling analysis data is obtained;

[0008] Taking the abnormal elimination rule set of the power line trench as a constraint condition, the repair process of the power line trench is calculated according to the line abnormal coupling analysis data, and line abnormal repair data is obtained;

[0009] According to the line abnormal repair data, the robot group maintenance data of the robot group in the line trench scale is calculated.

[0010] In a second aspect, the present application further provides a power line trench digital online operation and maintenance device, comprising:

[0011] The inspection information planning module is configured to, in the case of an abnormality in the static sensing data of the power line trench, plan robot group inspection information of the power line trench according to the static sensing data of the power line trench.

[0012] The inspection data obtaining module is configured to control the robot group of the power line trench to perform inspection on the power line trench according to the robot group inspection information, and obtain line dynamic inspection data.

[0013] The trench abnormality analysis module is configured to perform physical field coupling simulation on the power line trench according to the line dynamic inspection data, and obtain line abnormality coupling analysis data.

[0014] The trench repair analysis module is configured to calculate a repair process of the power line trench according to the line abnormality coupling analysis data, with a set of abnormality exclusion rules of the power line trench as a constraint condition, and obtain line abnormality repair data.

[0015] The trench repair control module is configured to calculate robot group maintenance data of the robot group in the line trench scale according to the line abnormality repair data.

[0016] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any step of the power line trench digital and intelligent online operation and maintenance method when executing the computer program.

[0017] The power line trench digital and intelligent online operation and maintenance method and device automatically plan the inspection task of the robot group when the static sensing data of the power line trench is abnormal, realize dynamic sensing and accurate positioning of the line state, perform physical field coupling simulation on the dynamic inspection data, improve the accuracy of abnormality analysis, on this basis, combine the abnormality exclusion rules, scientifically formulate the repair process, ensure the rationality and efficiency of the repair scheme, and finally calculate the robot group maintenance data, which can guide the robot group to work collaboratively in the trench scale, effectively improve the intelligent and automatic level of the maintenance work, realize rapid response and efficient repair of the power line trench fault, timely exclude the abnormality to prevent accidents, and ensure the safety and stability of the power system operation. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technical solutions, the drawings needed to be used in the embodiment or related technical solution description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 An application environment diagram of the power line trench intelligent online operation and maintenance method in one embodiment;

[0020] Figure 2 A flowchart of the power line trench intelligent online operation and maintenance method in one embodiment;

[0021] Figure 3 A flowchart of the line abnormal coupling analysis data obtaining method in one embodiment;

[0022] Figure 4 A flowchart of the abnormal coupling simulation data obtaining method in one embodiment;

[0023] Figure 5 A flowchart of the abnormal prediction simulation data obtaining method in one embodiment;

[0024] Figure 6 A flowchart of the first line abnormal repair data obtaining method in one embodiment;

[0025] Figure 7 A flowchart of the second line abnormal repair data obtaining method in one embodiment;

[0026] Figure 8 A flowchart of the robot group maintenance data calculation method in one embodiment;

[0027] Figure 9 A structural block diagram of the power line trench intelligent online operation and maintenance device in one embodiment;

[0028] Figure 10 An internal structure diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0030] The power line trench intelligent online operation and maintenance method provided by the embodiments of the present application can be applied in the application environment as shown in Figure 1 The terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 for processing. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0031] In one exemplary embodiment, as shown in Figure 2 A power line trench intelligent online operation method is provided. The method is applied to a server in Figure 1 for example, including the following steps 202 to 210. Among them:

[0032] Step 202, in the case of abnormal line static sensing data of the power line trench, the robot group inspection information of the power line trench is planned according to the line static sensing data.

[0033] Among them, the power line trench can be a closed channel structure laid underground or on the ground, used for laying and protecting power cables, avoiding external environmental interference or mechanical damage, and its structure usually includes concrete channels, supports, drainage systems, etc.

[0034] Among them, the line static sensing data can be the environmental and cable operation state information periodically or continuously collected by fixed sensors in the non-inspection state, usually including temperature, humidity, cable displacement, voltage, current, stress and strain, gas composition, etc.

[0035] Among them, the robot group inspection information can be a set of task instructions generated for multiple robots after detecting static abnormalities, including the inspection path, task area, inspection time, acquisition parameters, obstacle avoidance strategy and coordination rules of each robot.

[0036] Specifically, in the case of abnormal static sensing data of the power line trench, the cloud algorithm is used to analyze the collected multi-source static data, identify the type of abnormal data (such as temperature rise, voltage fluctuation, displacement anomaly, etc.) and its spatial distribution characteristics. Then based on the location of the abnormal point, the relevance of the surrounding sensing nodes and the historical fault database, the possible abnormal influence range is comprehensively judged, and combined with the trench structure layout, the robot operation ability and the current state, the task allocation optimization algorithm (such as multi-objective path planning or swarm intelligence algorithm) is used to generate the robot group inspection information of the robot group, including the starting position, inspection path, key node dwell time, sensor opening strategy and task priority of each robot.

[0037] Step 204, according to the robot group inspection information, control the robot group of the power line trench to inspect the power line trench, and get the line dynamic inspection data.

[0038] Among them, the robot group can be an intelligent operation cluster composed of multiple mobile inspection or maintenance robots with different functions and collaboration capabilities. These robots have the ability of autonomous navigation, data acquisition, operation execution and collaborative communication, and can realize comprehensive sensing, analysis and processing of the internal environment of the trench according to task allocation.

[0039] The line dynamic inspection data can be real-time and high-precision information collected by the robot group through the multi-modal sensors (such as vision, infrared, gas, electromagnetic, etc.) carried by the robot group during the execution of the inspection task, and can reflect the detailed changes of the current state of the line and the pipe trench.

[0040] Specifically, according to the robot group inspection task information, the server starts unified scheduling and control of each robot in the power line pipe trench, ensuring that each robot enters the pipe trench according to the planned path and carries out inspection work in an orderly manner. During the inspection work, each robot flexibly calls various sensor modules carried by itself, such as a vision camera, an infrared thermal imager, an ultrasonic sensor, a gas detector, and an electromagnetic interference detector, to collect real-time and high-precision data of the pipe structure, the cable surface, the connection node, and the environmental parameters. At the same time, each robot synchronizes the state and cooperates with obstacle avoidance through a wireless network, ensuring information integrity and operation safety. The server can also dynamically adjust the inspection parameters, such as modifying the task strategy in real time and dispatching nearby robots to investigate in detail when a new abnormal point is found. Finally, all robots upload the collected line dynamic inspection data to the central system.

[0041] Step 206: According to the line dynamic inspection data, a physical field coupling simulation is performed on the power line pipe trench to obtain line abnormal coupling analysis data.

[0042] The physical field coupling simulation can be a process of joint modeling and simulation analysis of multiple physical fields (such as heat, electricity, force, and magnetism) in the power line pipe trench, which can reveal the multi-factor coupling mechanism behind abnormal phenomena, and assist in determining the fault cause, risk range, and development trend.

[0043] The line abnormal coupling analysis data can be the analysis results output after the physical field coupling simulation, which includes abnormal type, occurrence location, formation cause, physical evolution path, risk level, and potential impact on system operation.

[0044] Specifically, the line dynamic inspection data is input into the physical field coupling simulation module in the server, a three-dimensional digital twin model corresponding to the actual power line pipe trench is constructed, and based on the three-dimensional digital twin model, multi-physical field data such as thermal field, electric field, force field, and electromagnetic field are fused. Finite element analysis (FEA) or multi-physical field simulation algorithm is used to conduct in-depth analysis of the abnormal area. For example, the insulation aging process of a cable in a certain area under local overheating conditions, the change of electromagnetic leakage path caused by moisture, or the stress concentration phenomenon of the pipe trench caused by external soil structure disturbance. Through the coupling simulation of the mutual influence and change trend among these physical fields, the root cause of the abnormality, the evolution mechanism, and the potential impact on the surrounding structure are identified, and detailed line abnormal coupling analysis data is generated.

[0045] Step 208, with the exception of the power line trench rule set as a constraint condition, the repair process of the power line trench is calculated according to the line exception coupling analysis data, and the line exception repair data is obtained.

[0046] Among them, the exception rule set can be a rule base constructed by expert knowledge, operation and maintenance standards and historical experience, which stipulates the processing method, disposal priority, construction restriction, safety constraint and resource allocation requirement of different types of exceptions.

[0047] Among them, the line exception repair data can be an executable repair scheme generated under the analysis data and rule constraints, including repair process flow corresponding to fault type, material use, operation steps, resource demand, time estimation and operation precautions, etc., which is the technical input of the robot to execute the repair task.

[0048] Specifically, the exception type, location, cause and risk level in the line exception coupling analysis data are matched and constrained with the preset exception rule set, and these rule sets contain various fault handling priorities, safety standards, operation environment restrictions, material use specifications, etc.; Then call the built-in repair process knowledge base, combined with intelligent reasoning algorithm (such as expert system or rule-based optimization engine), generate the optimal repair process flow under the premise of meeting the constraint conditions, which includes the selected repair technology (such as local replacement, insulation reinforcement, structure support, etc.), the required materials and tools, the operation details and duration estimation of each step, and the feasibility and safety check, and the obtained line exception repair data.

[0049] Step 210, according to the line exception repair data, the robot group maintenance data of the robot group in the line trench scale is calculated.

[0050] Among them, the robot group maintenance data can be a multi-robot cooperative operation instruction set generated according to the repair task demand, robot capability and actual environment of the trench, which involves task allocation, path planning, action sequence, operation synchronization and interaction mechanism of each robot, ensuring the whole maintenance process to be automatic, efficient and safe.

[0051] Specifically, after generating the line anomaly repair data, the tools required for each repair operation, the operation space, the operation accuracy, and the execution sequence are analyzed, and task scheduling and path planning algorithms are used to reasonably allocate the roles of each robot in the maintenance process, such as operation execution, material handling, environment monitoring, or operation assistance, etc. At the same time, considering the limited space and safety requirements in the pipe trench, the robot operation path, the working window time, and the collaborative action need to be dynamically coordinated to ensure efficient and orderly collaborative work of multiple robots in a limited space, avoiding conflicts and resource waste. The final generated robot group maintenance data includes the task allocation, execution instructions, navigation path, and interaction rules of each robot in the space-time dimension.

[0052] In the above-mentioned power line trench intelligent online operation and maintenance method, when the static perception data of the power line trench is abnormal, the robot group's inspection task is automatically planned, realizing dynamic perception and accurate positioning of the line state; physical field coupling simulation is performed on the dynamic inspection data, improving the accuracy of anomaly analysis; on this basis, combined with anomaly exclusion rules, a scientific repair process is developed to ensure the rationality and efficiency of the repair scheme; and finally, the calculated robot group maintenance data can guide the collaborative work of the robot group in the trench scale, effectively improving the intelligent and automated level of maintenance work, enabling rapid response and efficient repair of power line trench faults, timely eliminating abnormalities to prevent accidents and ensure the safety and stability of the power system.

[0053] In one exemplary embodiment, as shown in Figure 3 According to the line dynamic inspection data, the power line trench is subjected to physical field coupling simulation to obtain line anomaly coupling analysis data, including steps 302 to 310. Among them:

[0054] Step 302, according to the line dynamic inspection data, identify the physical field coupling boundary conditions of the power line trench.

[0055] The physical field coupling boundary conditions can be boundary behavior constraints set for each physical field (such as heat, electricity, force, and magnetism) when performing multi-physical field simulation modeling, such as temperature fixation, electric potential setting, force application direction and size, material contact interface characteristics, etc.

[0056] Specifically, the line dynamic inspection data collected by the robot group is analyzed to extract key parameters related to physical fields such as heat, electricity, force, and magnetism, such as local temperature rise, current density, stress concentration point, humidity change, etc. On the basis of combining the pipe trench structure model and the spatial distribution of sensors, data inversion and boundary identification algorithms are used to automatically determine the boundary conditions of various physical fields as physical field coupling boundary conditions (such as fixed temperature boundary, electric potential boundary, mechanical constraint, etc.).

[0057] Step 304, according to the physical field coupling boundary conditions, the physical field coupling equation set of the power line trench is constructed.

[0058] Wherein, the physical field coupling equation set can be a mathematical expression of the interaction relationship between multiple physical phenomena, which is usually composed of basic control equations such as heat conduction, electric field distribution, mechanical response, and magnetic field change, and different physical fields are linked into a unified solving system through coupling terms. For example, the heat-electricity coupling equation considers both the joule heat generated by the current and the feedback of heat on the electrical conductivity.

[0059] Specifically, after identifying the physical field coupling boundary conditions of the power line trench, based on the actual structure, material properties and line operation environment of the trench, the corresponding basic control equations for heat, electricity, force, magnetism and other physical fields are constructed, such as Fourier's law for heat conduction, Poisson or Maxwell equation set for electric field, basic equation of elasticity for mechanical response, Ampere loop law for magnetic field, etc. These equations are associated through the introduction of coupling terms to reflect their dynamic interaction process in the trench environment. At the same time, according to the three-dimensional geometric model and discrete grid division results of the trench structure, the finite element or finite volume method is used to numerically disperse and assemble the equation set, so that the entire coupling system can be efficiently solved on the numerical simulation platform, and the physical field coupling equation set of the power line trench is obtained.

[0060] Step 306, according to the physical field coupling equation set, the abnormal coupling simulation of the power line trench is carried out, and the abnormal coupling simulation data is obtained.

[0061] Wherein, the abnormal coupling simulation can be a process of simulating the physical abnormalities (such as overheating, stress concentration, electric field distortion, etc.) existing in the power line trench based on the current line dynamic inspection data and the constructed physical field coupling model.

[0062] Wherein, the abnormal coupling simulation data can be the simulation results output in the abnormal coupling simulation process, covering the numerical distribution, interaction strength, abnormal conduction path, and physical quantity change of key regions of multiple physical fields in a specific time and space range.

[0063] Specifically, after the construction of the physical field coupling equation set is completed, the coupling equation set containing the interaction of multiple physical fields such as heat, electricity, force, and magnetism is solved by using the set boundary conditions and initial state parameters to simulate the real physical behavior under the support of the current dynamic inspection data. For example, how the heat generated by the cable under overload is diffused, whether thermal expansion and contraction cause stress concentration, or whether humidity rise causes local insulation failure and electric field distortion. The process of mutual influence of each physical field in the simulation is dynamically tracked and quantified to form key data such as abnormal propagation path, coupling strength distribution, and critical parameter change. The simulation results are output in the form of high-precision atlas and data set, forming abnormal coupling simulation data.

[0064] Step 308, according to the physical field coupling equation set and the abnormal coupling simulation data, abnormal prediction simulation is performed on the power line trench to obtain abnormal prediction simulation data.

[0065] Among them, the abnormal prediction simulation can be based on the abnormal coupling simulation, further introducing time variable and evolution model, and prospectively simulating the expansion, deterioration or transfer trend of the current abnormality over time. It is based on material aging model, heat diffusion equation, current dynamic response and other prediction mechanisms to predict the development direction and speed of potential risks in the power line trench in the future period of time, supporting early intervention and fault prevention decisions.

[0066] Among them, the abnormal prediction simulation data can be the time series output results obtained in the abnormal prediction simulation process, describing the evolution process of multiple physical field variables over time, as well as the future propagation path, influence range and possible critical points of the abnormality in the trench structure.

[0067] Specifically, on the basis of the abnormal coupling simulation data, time evolution parameters are added to the original physical field coupling equation set, and combined with material performance attenuation model, environmental change trend and historical fault evolution path, the development process of the current abnormality in the future period of time is simulated. For example, it can be predicted whether a local heat concentration will cause insulation layer breakdown in a few hours or days, or whether a stress concentration point will cause physical cracking as time evolves. The prediction simulation not only outputs the change trend and spatial distribution of each key physical quantity at future time, but also evaluates the expansion path, speed and possible cascading failure risk of the abnormality, forming structured abnormal prediction simulation data.

[0068] Step 310, fusion of abnormal coupling simulation data and abnormal prediction simulation data, to obtain line abnormal coupling analysis data.

[0069] Specifically, after obtaining abnormal coupling simulation data and abnormal prediction simulation data, the two types of simulation results are jointly analyzed by a multi-source data fusion algorithm. In the fusion process, not only the interaction characteristics of the current physical field in the static simulation are considered, but also the time trend and spatial expansion path of the abnormal evolution in the prediction simulation are integrated. Therefore, methods such as principal component analysis (PCA), Bayesian fusion, and graph neural networks are used to reduce dimensionality, extract features, and associate semantics of the multi-dimensional data, generating line abnormal coupling analysis data reflecting the fault mechanism, such as abnormal influence factors, coupling strength scores, potential fault chains, risk level matrices, etc.

[0070] In this embodiment, by combining line dynamic inspection data and multi-physical field simulation modeling, a physical field coupling equation set that conforms to the actual working conditions is constructed, realizing accurate simulation of the interaction behavior of multiple physical fields such as heat, electricity, force, and magnetism in the power line trench. Through the linkage calculation of abnormal coupling simulation and prediction simulation, not only the physical mechanism of the current abnormality can be accurately identified, but also the evolution trend and potential risk points can be predicted in advance. The line abnormal coupling analysis data obtained by fusion has stronger timeliness, accuracy, and comprehensiveness, providing high-reliability and high-value data support for abnormal diagnosis, intelligent decision-making, and preventive maintenance, significantly improving the intelligent and predictive level of power line trench abnormality identification.

[0071] In one exemplary embodiment, as shown in Figure 4 According to the physical field coupling equation set, abnormal coupling simulation of the power line trench is performed to obtain abnormal coupling simulation data, including steps 402 to 410. Among them:

[0072] Step 402, spatial partitioning of the power line trench is performed to obtain trench spatial partitioning data.

[0073] The trench spatial partitioning data can be simulation input data sets generated for each sub-region after regional division of the power line trench according to structural characteristics, physical properties, or abnormal distribution. This data typically includes three-dimensional geometric information, material parameters, boundary conditions, initial state, sensor location, etc.

[0074] Specifically, before performing abnormal coupling simulation of the power line trench, the entire line trench is spatially partitioned according to the three-dimensional geometric model of the trench, cable arrangement, heat source location, material distribution, and abnormal point density identified in the dynamic inspection data. The division method can use voxel grid-based, hierarchical region, or functional module-based methods to divide the trench into several sub-regions, each with certain physical independence and retaining coupling relationships with adjacent regions. The data of each spatial sub-region includes boundary geometric information, material parameters, sensor location, and initial conditions, which collectively constitute the "trench spatial partitioning data".

[0075] Step 404, using the physical field coupling equation set, respectively, to each trench space partition data for abnormal coupling solution, get the first coupling simulation data.

[0076] Wherein, abnormal coupling solution can be based on the established multi-physical field coupling equation set, the abnormal signs of power line trench area for numerical calculation, simulation of heat, electricity, force, magnetic and other physical field interaction.

[0077] Wherein, the first coupling simulation data can be in the trench after each space subzone independent abnormal coupling solution obtained after the simulation results of the fusion data.

[0078] Specifically, based on each sub-region of the trench space partition data, using the constructed physical field coupling equation set for independent abnormal coupling solution of each region, the process of solving is for each sub-area of the specific material properties, boundary conditions and initial physical state, respectively, to calculate the heat, electricity, force, magnetic and other physical field distribution and interaction in the region. For example, in a region, the heat generated by the cable conductor under high current density can be simulated how to conduct in the structure, and the thermal field of the surrounding area has a coupling effect. In order to improve the calculation efficiency and decoupling complexity, different partitions use regional parallel computing architecture, and each partition is solved independently, while maintaining the consistency of the variables of adjacent regions at the boundary. The simulation results of each sub-area are combined to output the first coupling simulation data, including the multi-physical field distribution of local abnormal response, coupling strength index and the spatial position of potential risk points.

[0079] Step 406, using the physical field coupling equation set, respectively, to different physical processes in the power line trench for abnormal coupling solution, get each physical coupling simulation data.

[0080] Wherein, the physical coupling simulation data can be the result of coupling simulation of different physical processes (such as heat, electricity, force, and magnetism) in the power line trench.

[0081] Specifically, the global behavior of different physical processes in the power line trench itself can be obtained by modeling and solving each type of physical process such as heat conduction, current distribution, structural stress response, and electromagnetic interference. The running state and abnormal propagation path of each physical field in the whole trench system can be grasped from a more macroscopic point of view. In the solving process, the corresponding coupling equation is used for each type of physical process, and numerical simulation is performed in the global range to simulate the influence of cable heating on the overall temperature rise, the change of electric field in the humid environment, the response of cable stress concentration area under load fluctuation, etc. This process does not depend on spatial partition, but starts from the dimension of physical mechanism to obtain the global response behavior of different physical fields. The final output of each physical coupling simulation data covers the abnormal coupling results of different physical processes.

[0082] Step 408, periodically exchange the coupling amount of each physical coupling simulation data to obtain second coupling simulation data.

[0083] Wherein, the periodic exchange can be a strategy for data interaction in multi-physical field coupling simulation, which means that the key coupling variables (such as temperature, current density, stress, etc.) are shared periodically between each physical process simulation within a set time step or simulation period, and are fed back to other physical models as new input conditions.

[0084] Wherein, the second coupling simulation data can be the global simulation output result generated after the interaction and iterative update of multiple physical fields under the periodic exchange mechanism, which comprehensively reflects the linkage effect and overall evolution path generated by coupling between multiple physical processes, and has higher dynamic consistency and global coupling accuracy.

[0085] Specifically, according to the set time step or iteration period of each physical coupling simulation data, the key coupling variables (such as joule heat caused by current, the influence of electric field on material stress, the disturbance of thermal expansion and contraction on mechanical boundary, etc.) of the physical coupling simulation data are extracted within each period, and these key coupling variables are input as boundary conditions or source terms into the simulation model of other physical processes, forming a two-way linkage between physical fields. This periodic exchange breaks the limitations of single physical process analysis, making the simulation closer to the real complex coupling behavior. Through multiple iterations and exchanges, the coupling state between each physical field is continuously corrected, and finally the second coupling simulation data is generated.

[0086] Step 410, according to the actual scene data of the power line trench, selecting abnormal coupling simulation data from the first coupling simulation data and the second coupling simulation data.

[0087] Specifically, using the actual scene data of the power line trench, the first coupling simulation data and the second coupling simulation data are screened and optimally selected to extract the most representative and engineering guiding significance abnormal coupling simulation data. Wherein the actual scene data includes sensor measured values, historical inspection records, environmental parameters (such as humidity, temperature, soil conditions), equipment operating conditions, etc., which are used as the basis for checking the simulation results. Through similarity matching, error analysis or multi-index evaluation method based on optimization algorithm, the consistency of the two groups of simulation data and the actual scene is compared, and the simulation model output most consistent with the real running state is identified from the first coupling simulation data and the second coupling simulation data. The selected abnormal coupling simulation data not only contains the current abnormal physical quantity distribution and coupling path, but also reflects the quantitative evaluation results of its potential threat to the system.

[0088] In this embodiment, by fine spatial partitioning of the power line trench, the simulation solution is more in line with the actual structure characteristics, and the abnormal coupling simulation data is obtained by coupling equation set of physical field for each partition and each type of physical process, which improves the accuracy of local abnormal identification and global coupling analysis. At the same time, the periodic exchange mechanism realizes the dynamic coupling feedback between multiple physical fields, effectively restores the real evolution process of complex coupling behavior. Finally, based on the actual scene data, the abnormal simulation data most suitable for the working condition is selected from multiple simulation results, the precise modeling and dynamic mapping of abnormal state are realized, and the reliability, timeliness and engineering adaptability of the abnormal analysis of the power line trench are significantly improved, providing high-quality data support for subsequent intelligent decision and maintenance.

[0089] In one exemplary embodiment, as shown in Figure 5 Abnormal prediction simulation of the power line trench is performed according to the physical field coupling equation set and the abnormal coupling simulation data to obtain abnormal prediction simulation data, including steps 502 to 504. Among them:

[0090] Step 502, according to the abnormal coupling simulation data and the historical abnormal data of the power line trench, setting the abnormal prediction term of the physical field coupling equation set.

[0091] Among them, the historical abnormal data can be a set of space-time data related to various abnormal events accumulated in the long-term operation and maintenance process of the power line trench through static sensors, dynamic inspection, maintenance records and other means.

[0092] Among them, the abnormal prediction term can be a mathematical extension term introduced on the basis of the existing physical field coupling equation for simulating the abnormal evolution behavior over time. It is modeled according to the abnormal coupling simulation results and historical abnormal data, and contains key factors affecting abnormal development, such as thermal diffusion speed, electrical load change trend, material aging rate, environmental disturbance response, etc.

[0093] Specifically, before abnormal prediction simulation, since the physical field coupling equation does not set the parameters of the abnormal prediction term, the physical field coupling equation set needs to be enhanced for prediction. In the specific implementation process, by comparing and analyzing the historical fault cases and the current abnormal evolution characteristics in the historical abnormal data, the key influencing factors such as temperature rise rate, current fluctuation mode, stress accumulation trend, insulation aging rate, etc. are extracted, and these factors are converted into quantifiable mathematical expressions as time-dependent terms or dynamic source terms embedded in the original physical field coupling equation. In order to improve the prediction accuracy, machine learning or statistical modeling methods are also used to fit and optimize the parameters of the abnormal development mode based on the abnormal coupling simulation data, so that the prediction term can reflect the potential path of the coupling evolution between multiple physical fields. Finally, the abnormal prediction term is formed.

[0094] At step 504, the abnormality prediction simulation of the power line trench is performed according to the abnormality prediction item, and abnormality prediction simulation data is obtained.

[0095] The abnormality prediction simulation can be a process of dynamically simulating the abnormal development trend in a future time length by time stepping, based on the enhanced multi-physical field coupling equation set, in combination with the current abnormal state and the prediction item.

[0096] Specifically, after the prediction item setting of the physical field coupling equation set is completed, the enhanced physical field coupling equation set is used to perform the abnormality prediction simulation of the power line trench, to simulate the coupling evolution process of the multi-physical field in a future period of time. In the simulation process, a time stepping algorithm is used to gradually calculate the dynamic response of the thermal, electrical, mechanical, and magnetic physical fields with time, and to track the evolution trend of the abnormal area, such as the diffusion of hot spots, the expansion of voltage instability areas, and the evolution of material stress concentration areas. According to the dynamic boundary conditions and the historical evolution mode introduced by the prediction item, potential risk diffusion paths, critical failure time points, and possible linkage chains of the abnormality are identified. The finally output abnormality prediction simulation data includes the time series of multiple physical quantities, the abnormal development path diagram, the warning threshold change of the key indicators, and the like.

[0097] In one specific embodiment, the physical field coupling equation set includes a thermal-mechanical coupling equation set, a fluid-structure coupling equation set, and an electromagnetic field coupling equation set.

[0098] The thermal-mechanical coupling equation set includes a heat transfer and heat source equation and a mechanical equilibrium equation.

[0099] The heat transfer and heat source equation has an expression of

[0100]

[0101] wherein ρ1 is the density of the solid material in the power line trench, C p (T) is the specific heat capacity of the material changing with temperature T, k(T) is the thermal conductivity of the material changing with temperature T, Γ EM (E, B, T) is an electromagnetic loss heat source term of the power line trench changing with electric field E, magnetic field B, and temperature T, is a mechanical energy dissipation heat term of the power line trench changing with strain rate and solid stress tensor σ1, T env is the temperature at the ventilation of the power line trench.

[0102] The mechanical equilibrium equation has an expression of

[0103]

[0104] where σ1(Y,ε) is the solid stress tensor varying with temperature T and solid material strain tensor ε, f ext (T) is the external body force varying with temperature T, D(T,ω(ε)) is the constitutive matrix coupled with temperature T and damage degree ω(ε), and α(T-T0) is the thermal expansion coefficient varying with temperature interval T-T0.

[0105] The fluid-structure coupling equation set includes a fluid region equation, a structure region equation, and a fluid-structure coupling boundary condition.

[0106] The expression of the fluid region equation is

[0107]

[0108] where ρ2 is the fluid density in the power line trench, v is the fluid velocity field, p is the fluid pressure, μ is the fluid dynamic viscosity, F couple (u) is the coupling force of the fluid to the solid displacement, u is the structure deformation varying with the flow passage deformation, and κ(p,T) is the permeability coefficient varying with temperature T and fluid pressure p.

[0109] The expression of the structure region equation is

[0110]

[0111] where ρ s is the structure density of the power line trench, is the structure stress tensor varying with temperature T and structure deformation u, g fluid (p,v) is the external load of the fluid to the solid varying with fluid pressure p and fluid velocity field v, D s is the elastic constitutive matrix of the structure material, and ε(u) is the structure material strain tensor varying with structure deformation u.

[0112] The expression of the fluid-structure coupling boundary condition is

[0113]

[0114] where φ(u) is the structure safety coefficient function varying with structure deformation u, and n is the interface normal vector.

[0115] The electromagnetic field coupling equation set includes Maxwell equations, coupling point flow equations, and medium relationship equations.

[0116] The expression of the Maxwell equation is

[0117]

[0118] Wherein, E is electric field intensity, H is magnetic field intensity, B is magnetic induction intensity, D is electric displacement vector, p free is free charge density, J coupled is coupling current density;

[0119] The expression of the coupling point flow equation is,

[0120] J coupled = σ2(T, ε)E + J pd (E, T, ε)

[0121] Wherein, σ2(T, ε) is the material conductivity changing with temperature T and strain tensor ε of solid material, J pd (E, T, ε) is the partial discharge current changing with electric field intensity E, temperature T and strain tensor ε of solid material.

[0122] The expression of the medium relationship equation is,

[0123] D = ε'(T, ε)E, B = μH

[0124] Wherein, ε'(T, ε) is the relative dielectric constant changing with temperature T and strain tensor ε of solid material.

[0125] In this embodiment, by combining the abnormal coupling simulation data and the historical abnormal data of the power line trench, the abnormal prediction term in the physical field coupling equation set is dynamically set, so that the model has the ability to face the future state evolution; the deep fusion of historical experience and current state is introduced to realize the accurate modeling and simulation prediction of the abnormal development trend, and then the abnormal prediction simulation is carried out by using the prediction term, which can effectively deduce the propagation path, evolution speed and potential risk point of the abnormality under the coupling of multiple physical fields, so as to realize the early identification and dynamic early warning of the fault, and significantly enhance the forward-looking, intelligent and risk prevention and control ability of the power line trench operation and maintenance management.

[0126] In an exemplary embodiment, as Figure 6 shown, the repair process of the power line trench is calculated according to the line abnormal coupling analysis data, taking the abnormal elimination rule set of the power line trench as the constraint condition, to obtain line abnormal repair data, including steps 602 to 606. Wherein:

[0127] Step 602, fault element feature extraction is performed on the line abnormal coupling analysis data to obtain trench abnormal key feature information.

[0128] Wherein, the fault element feature extraction can be in-depth processing of the line abnormal coupling analysis data to identify and extract core feature information closely related to the fault state.

[0129] The pipe trench anomaly key feature information can be a set of important information obtained by extracting the feature of the fault element, which can comprehensively describe the current abnormal state in the power line pipe trench. These information includes the spatial position of the abnormal area, the abnormal type (such as thermal runaway, structural deformation, electrical breakdown, etc.), the response relationship between the coupled physical fields, the abnormal evolution trend, and the risk level, etc.

[0130] Specifically, the line anomaly coupling analysis data is input into the feature extraction algorithm, and the feature extraction algorithm (such as pattern recognition, cluster analysis, statistical filtering, etc.) is used to identify the core elements related to the fault, including the spatial position of the abnormality, the influence range, the physical field coupling strength, the development trend, the risk level, the abnormal type (such as thermal breakdown, arc discharge, structural crack), etc. Then, through the data dimension reduction and feature fusion technology, the multi-source complex simulation data is converted into the pipe trench anomaly key feature information with discriminative power.

[0131] In step 604, according to the pipe trench anomaly key feature information, the maintenance decision analysis of the power line pipe trench is carried out with the abnormality elimination rule set of the power line pipe trench as the constraint condition, and the pipe trench maintenance decision data is obtained.

[0132] The abnormality elimination rule set can be a rule base constructed by expert knowledge, industry standards, operation and maintenance experience and safety specifications, which specifies the standard process and constraint conditions for how to handle different types of anomalies, including fault type and corresponding recommended processing method, maintenance priority, safety operation boundary, material and tool application range, environmental condition limitation, etc.

[0133] The maintenance decision analysis can be a process of evaluating and selecting multiple feasible maintenance strategies after obtaining the abnormal key feature information and referring to the abnormality elimination rule set.

[0134] The pipe trench maintenance decision data can be the output result of the maintenance decision analysis, including the maintenance strategy, priority ranking, task arrangement, recommended repair method (such as replacement, reinforcement, compensation, etc.), required resource list, operation window, etc. generated by the system according to the current abnormal situation.

[0135] Specifically, under the constraint of the power line trench anomaly elimination rule set, the trench anomaly key feature information (such as anomaly type, location, coupling strength, risk level, etc.) is matched with the processing logic in the anomaly elimination rule set, such as high-risk anomalies that need to be disposed of first, specific materials that are prohibited from being used in specific environments, and specific anomalies that need to use specific repair processes. Combined with historical maintenance cases, resource availability and on-site environmental restrictions, the risk, cost and efficiency of multiple feasible maintenance schemes are evaluated using a rule engine or a multi-factor decision algorithm based on a knowledge graph, and the optimal or alternative scheme is automatically selected to generate trench maintenance decision data, including maintenance strategy type (such as local replacement, temporary reinforcement, overall upgrade, etc.), construction priority, task timing, required materials and personnel configuration.

[0136] Step 606, according to the trench maintenance decision data, the power line trench is analyzed for maintenance parameters to obtain line anomaly repair data.

[0137] The maintenance parameter analysis can be a process of further converting the maintenance decision data into precise and operable technical parameters, according to the goals and constraints of the maintenance task, combined with the trench space model, robot operation capability, construction specifications, etc., to refine the maintenance path, operation steps, time control, tool selection, material specifications, operation coordinates, etc.

[0138] Specifically, based on the selected trench maintenance decision data, combined with the structure model of the power line trench, the robot operation capability, the environmental conditions and the safety specifications, a series of key maintenance parameters are extracted and calculated, including operation path planning, maintenance position coordinates, operation area size, material usage specifications, repair depth, repair step sequence, required tool type, resource allocation scheme and operation duration evaluation, etc. At the same time, through parameter simulation and feasibility verification, the timing coordination, spatial compatibility and robot operation stability between each link in the maintenance process are ensured. The final generated line anomaly repair data.

[0139] In this embodiment, by extracting the fault element features from the line anomaly coupling analysis data, the key abnormal features in the power line trench are accurately identified, and the nature of the fault is deeply understood; on this basis, the anomaly elimination rule set is introduced as a decision constraint, combined with the extracted key feature information, intelligent maintenance decision analysis is carried out to ensure that the maintenance scheme has high rationality and execution feasibility; further through maintenance parameter analysis, the maintenance process, material selection and operation path are refined and quantified to form precise and efficient line anomaly repair data. The whole process realizes the closed-loop optimization from anomaly identification to repair strategy formulation, significantly improves the intelligent level, response speed and operation accuracy of trench fault handling, and effectively guarantees the stable operation of the power line and the efficient allocation of maintenance resources.

[0140] In one exemplary embodiment, as shown in Figure 7 As shown in FIG. 7, according to the pipe trench maintenance decision data, the power line pipe trench is analyzed for maintenance parameters to obtain line abnormal repair data, including steps 702 to 708. Among them:

[0141] Step 702, according to the pipe trench maintenance decision data, select several material process maintenance parameters from the material process database of the power line pipe trench.

[0142] Among them, the material process database can be a knowledge base that stores various material information and construction process parameters related to the maintenance of the power line pipe trench, including performance indicators (such as strength, thermal conductivity, corrosion resistance, etc.) of physical materials such as insulating materials, reinforcing members, sealants, and corrosion-resistant coatings, and corresponding construction process parameters (such as applicable temperature and humidity conditions, operation mode, construction time limit, etc.).

[0143] Among them, the material process maintenance parameters can be detailed parameter information extracted from the material process database to guide specific maintenance operations, describing the performance of various materials in different maintenance scenarios, environmental adaptation, application method, and quantity recommendation, etc.

[0144] Specifically, according to the key information of maintenance type, fault location, working environment, and safety level provided in the pipe trench maintenance decision data, intelligent matching and screening are performed in the material process database of the power line pipe trench, which stores a large number of materials and their supporting process parameters that can be used in different maintenance scenarios, such as cable insulation materials, sealing materials, structural reinforcement, and corrosion-resistant coatings, as well as their performance indicators (such as thermal conductivity, dielectric strength, corrosion resistance, construction temperature range, curing time, etc.). Through multi-condition screening algorithm, the database is screened, and several material process combinations that are available under current maintenance conditions, meet performance requirements, and have executable process are matched, and several material process maintenance parameters such as recommended amount, applicable environmental conditions, and required construction equipment are extracted.

[0145] Step 704, according to each material process maintenance parameter, select several material process maintenance rules from the material process rule library of the power line pipe trench.

[0146] Among them, the material process maintenance rule can be a knowledge item extracted from the material process rule library, which is used to constrain and guide the combination, application, and execution of maintenance materials and construction process, including material compatibility, safety specifications, construction sequence, site environmental adaptability, process combination restrictions, etc.

[0147] Specifically, based on the maintenance parameters of various materials and processes, maintenance rules related to the selected materials are further extracted from the material and process rule library of power line trenches. This rule library contains a large number of knowledge entries based on engineering experience, industry standards, and safety specifications, used to guide the reasonable combination and construction operations of materials during actual maintenance. The rules cover material compatibility (e.g., a certain sealing agent cannot be used with a specific insulating material), environmental adaptability (e.g., performance limitations of materials under high humidity or high temperature), construction sequence (e.g., reinforcement should be completed before sealing), and safety boundaries (e.g., coating thickness must not be lower than a certain threshold). Based on the properties of the selected materials and the requirements of the maintenance task, a rule engine is used for condition matching, dependency analysis, and conflict detection to filter out several material and process maintenance rules closely related to the current material and process.

[0148] Step 706: Perform multi-objective parameter optimization on the maintenance parameters and rules of each material process to obtain initial anomaly repair data.

[0149] Among them, multi-objective parameter optimization can be an intelligent optimization process that comprehensively considers multiple objective functions (such as cost, efficiency, safety, sustainability, etc.) to weigh and select the material, process and construction parameters in the maintenance plan.

[0150] The initial anomaly repair data can be the result of multi-objective parameter optimization, including material selection, construction process, process configuration and key maintenance parameters, forming a theoretically optimal and feasible maintenance plan draft.

[0151] Specifically, a multi-objective optimization model is constructed by using all selected material and process maintenance parameters and rules as input variables and boundary conditions. The objective function typically includes maximizing maintenance effectiveness, minimizing cost, minimizing construction time, and minimizing safety risk. Intelligent optimization algorithms such as genetic algorithms, multi-objective particle swarm optimization (MOPSO), or Pareto front search are employed to perform global search and iterative adjustments across dimensions such as material combinations, process sequences, and parameter configurations. During the optimization process, the feasibility and superiority of each solution are evaluated, conflicting rule combinations and parameter schemes that do not conform to the working conditions are eliminated, and the initial anomaly repair data is finally output.

[0152] Step 708: Based on the anomaly prediction simulation data, perform statically indeterminate optimization on the initial anomaly repair data to obtain the line anomaly repair data.

[0153] Among them, statically indeterminate optimization can be a parameter optimization method based on the prediction of future operating conditions. Its core objective is to introduce redundant design and dynamic adjustment mechanism on the basis of the initial maintenance plan, so that the maintenance plan can remain stable and effective when the anomaly develops further, the environment changes or uncertainties occur.

[0154] Specifically, the initial anomaly repair data is subjected to hyperstatic optimization using abnormal prediction simulation data to improve the robustness and adaptability of the repair scheme under future complex working conditions, where hyperstatic optimization is an advanced optimization method for redundancy design and dynamic reliability, which adjusts the initial anomaly repair data again by introducing abnormal evolution trends such as future temperature rise intensification, stress continuous superposition, electric field concentration enhancement, etc. In the specific implementation process, the risk area involved in the prediction simulation, the time sequence change of the key physical variables, the potential fault chain, etc. are taken as dynamic constraint conditions to reevaluate the sustainability and fault tolerance of the initial repair scheme. For example, if the prediction shows that a certain thermal anomaly area will expand to the adjacent area, the system will automatically adjust the range of insulating materials or thicken the reinforced structure; if future electrical shock may occur, the material is optimized to enhance the electrical strength. The final line anomaly repair data not only meets the current repair needs, but also has certain forward protection ability and environmental adaptability.

[0155] In this embodiment, based on the pipe trench maintenance decision data, the matching maintenance materials and process parameters are intelligently screened from the material process database, and combined with the safety specifications and application constraints in the material process rule library, a constraint model of maintenance parameters and rules is constructed; further, a multi-objective parameter optimization method is adopted to balance among multiple dimensions such as cost, efficiency, safety, etc. to generate an initial anomaly repair scheme with optimal comprehensive performance; abnormal prediction simulation data is introduced to perform hyperstatic optimization on the initial scheme to enhance its adaptability and robustness to future fault evolution and complex working condition changes, thereby finally forming line anomaly repair data with foresight, safety and practicality. The intelligent design level of the maintenance process is improved, and the whole-process closed-loop management and control from material selection to process planning to dynamic optimization is realized.

[0156] In one exemplary embodiment, as shown in Figure 8 According to the line anomaly repair data, the robot group maintenance data of the robot group under the line pipe trench scale is calculated, including steps 802 to 806. Among them:

[0157] Step 802, according to the line anomaly repair data and the line pipe trench scale, the pipe trench maintenance envelope of each maintenance robot of the robot group in the power line pipe trench is calculated.

[0158] Among them, the maintenance robot can be an intelligent work unit deployed in the power line pipe trench, which has the functions of autonomous navigation, precise operation, fault handling, etc., and is specially used for executing various maintenance tasks such as cable replacement, insulation reinforcement, structure reinforcement, surface cleaning, etc.

[0159] The pipe trench maintenance envelope can be the maximum operating range boundary that the maintenance robot can occupy in space when performing a specific task. The envelope is jointly determined by factors such as robot size, kinematic model, task posture change, reach range, and path deviation, and represents the dynamic use area of the robot in the pipe trench space during task completion.

[0160] Specifically, according to the information about the location, required action range, maintenance process, and task duration of each maintenance task in the line anomaly repair data, and in combination with the actual environmental parameters such as the spatial scale, geometric structure, channel width, and obstacle distribution of the line pipe trench, the motion range of each maintenance robot is modeled. Then, by using the kinematic and dynamic models of the robot, the posture change, maximum reachable range of the end effector, operating radius, turning limit, and other factors of each maintenance robot under different maintenance conditions are analyzed, and a three-dimensional task trajectory of the robot inside the pipe trench is generated. In combination with the posture adjustment and space occupation that the robot can involve during task execution, the external boundary of the task space of each robot is comprehensively formed, which is defined as the pipe trench maintenance envelope. The pipe trench maintenance envelope not only identifies the dynamic operable area of the robot during the execution of the maintenance task, but also provides key spatial boundary data for subsequent safety analysis, path planning, and supply robot path avoidance.

[0161] Step 804, according to each pipe trench maintenance envelope, predicting the redundancy safety information of the robot group in the power line pipe trench.

[0162] The redundancy safety information can be the prediction and evaluation result of the required safety buffer space based on the uncertainty factors such as deviation, error, path fluctuation, and other uncertain factors that can occur during the operation of the maintenance robot. This information includes robot operation error range, minimum safety distance, obstacle avoidance area, and collaboration conflict risk point.

[0163] Specifically, based on the pipe trench maintenance envelope corresponding to each maintenance robot, multiple dynamic factors are considered, including the operation error, path tracking deviation, inertia drift when starting and stopping, robot size, mechanical arm reach limit, spatial overlap during collaborative work, and the position distribution of fixed obstacles (such as supports and cable brackets) in the pipe trench; in combination with the three-dimensional simulation environment, collision detection algorithms and multi-scenario simulation are applied to dynamically analyze the boundary changes and minimum safety distance requirements that the maintenance robot can touch during the task process. In addition, the time conflict window and obstacle avoidance mechanism during collaborative work of multiple maintenance robots are also considered, and the key areas and path overlapping points prone to interference are identified. The calculated redundancy safety information includes safety buffer zone, potential conflict area, operation redundancy rate, and risk level evaluation, etc.

[0164] At step 806, according to the redundant safety information and the line trench dimension, the maintenance supply envelope of each supply robot in the robot group in the power line trench is calculated.

[0165] Among them, the supply robot can be an auxiliary robot specially providing support services for the maintenance robot, responsible for tasks such as transporting maintenance materials, energy supply, tool replacement, waste recycling, and environmental monitoring in the trench.

[0166] Among them, the maintenance supply envelope can be the maximum dynamic space range of the supply robot that can pass through, stay in, and operate during the execution of the supply task. The envelope line comprehensively considers factors such as trench structure size, maintenance robot activity area, redundant safety information, and supply path design.

[0167] Specifically, since the supply robot needs to complete tasks such as material distribution, power supply, tool replacement, and environmental monitoring without interfering with the operation of the maintenance robot, first, the supply point and resource distribution strategy are determined, and according to the time sequence distribution of the maintenance task, the supply path and supply residence point that the supply robot can enter are planned; combined with the redundant safety information, the maintenance and supply path and supply residence point are optimized to avoid high-frequency operation areas and narrow passages of the maintenance robot, while meeting the timeliness and dynamic scheduling requirements of the supply. Based on the optimized maintenance and supply path and supply residence point, through spatial accessibility analysis, path obstacle avoidance simulation, and operation coordination modeling, the dynamic operation range boundary of each supply robot is calculated to form the maintenance supply envelope.

[0168] At step 808, the robot group maintenance data is obtained by fusing the trench maintenance envelope and the maintenance supply envelope.

[0169] Specifically, the spatial overlap, potential conflict area, and coordination node between the trench maintenance envelope and the maintenance supply envelope are analyzed, combined with the operation time window of each robot, the supply frequency, the task priority, and the path intersection point. By introducing obstacle avoidance strategies, dynamic yielding rules, and resource sharing mechanisms, the overall optimization of the group behavior of each robot is carried out, and the envelope lines that may interfere with each other are optimized to ensure that the robot group can achieve efficient collaboration, uninterrupted operation, and conflict minimization in the limited trench space. At the same time, the redundancy compensation strategy for sudden situations (such as robot failure or path blockage) is also considered in the fusion process. The final output of the robot group maintenance data includes multi-robot operation space-time arrangement diagram, task coordination sequence, obstacle avoidance action library, resource interaction protocol, and maintenance execution instruction set.

[0170] In this embodiment, by combining the line anomaly repair data and the spatial scale of the line trench, the spatial operation range of each maintenance robot in the actual operation process is accurately calculated, the maintenance envelope is formed, the dynamic modeling of the operation space is realized, the redundant safety information of the robot group in the limited space is predicted based on the envelope, the potential collision risk and the space interference area are effectively identified, and the safety and stability in the group operation process are ensured. According to the redundant safety information and the trench structure characteristics, the travel path and the operation boundary of the supply robot are reasonably planned, the supply envelope is generated, and the efficient partitioning and scheduling of the supply and maintenance tasks are realized. The maintenance data of the robot group is output by integrating the maintenance envelope and the supply envelope, so that the robot group has the ability of collaborative operation, path obstacle avoidance and dynamic deployment in the trench environment, and the intelligentization, automation and space utilization efficiency of the power line trench maintenance operation are significantly improved.

[0171] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0172] Based on the same inventive concept, the embodiments of the present application also provide an electric power line trench digital and intelligent online operation and maintenance device for implementing the above-mentioned electric power line trench digital and intelligent online operation and maintenance method, as shown in Figure 9 The device provides a solution to the implementation scheme as described above. The specific limitations of one or more electric power line trench digital and intelligent online operation and maintenance device embodiments provided below can be referred to the limitations of the electric power line trench digital and intelligent online operation and maintenance method described above, and will not be described here. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0173] In an exemplary embodiment, a computer device, which can be a server, is provided, and its internal structure diagram can be as shown inFigure 10 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface.

[0174] In an embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above-mentioned method embodiments when executing the computer program.

[0175] In an embodiment, a computer readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned method embodiments.

[0176] In an embodiment, a computer program product or a computer program is provided, including computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the above-mentioned method embodiments.

[0177] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0178] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments.

Claims

1. A method for intelligent online operation and maintenance of power line trenches, characterized in that, The method includes: In the event of anomalies in the static sensing data of the power line trench, the robot swarm inspection information of the power line trench is planned based on the static sensing data. Based on the robot swarm inspection information, the robot swarm is controlled to inspect the power line trench, and dynamic inspection data of the line is obtained. Based on the dynamic inspection data of the power line, identify the physical field coupling boundary conditions of the power line trench; Based on the physical field coupling boundary conditions, a set of physical field coupling equations for the power line trench is constructed. Based on the physical field coupling equations, an abnormal coupling simulation was performed on the power line trench to obtain abnormal coupling simulation data. Based on the physical field coupling equations and the abnormal coupling simulation data, an anomaly prediction simulation is performed on the power line trench to obtain anomaly prediction simulation data. By integrating the abnormal coupling simulation data and the abnormal prediction simulation data, line abnormal coupling analysis data is obtained; Using the set of rules for eliminating anomalies in the power line trench as constraints, the repair process of the power line trench is calculated based on the line anomaly coupling analysis data to obtain line anomaly repair data; Based on the abnormal repair data of the power line and the trench scale corresponding to the robot group, calculate the trench repair envelope of each maintenance robot in the power line trench. Based on the trench maintenance envelopes of each of the aforementioned trenches, predict the redundancy safety information of the robot swarm in the power line trenches; Based on the redundant safety information and the dimensions of the power line trench, calculate the maintenance and supply envelope of each supply robot in the robot swarm in the power line trench. The maintenance data of the robot swarm is obtained by fusing the trench maintenance envelope and the maintenance supply envelope.

2. The method according to claim 1, characterized in that, The abnormal coupling simulation of the power line trench is performed based on the physical field coupling equations to obtain abnormal coupling simulation data, including: The power line trenches are spatially partitioned to obtain spatial partition data for each trench. Using the physical field coupling equations, the abnormal coupling solution is performed on each of the trench space partition data to obtain the first coupling simulation data. Using the physical field coupling equations, abnormal coupling solutions are performed on different physical processes in the power line trench to obtain simulation data for each physical coupling. The coupling amounts of each of the aforementioned physical coupling simulation data are periodically exchanged to obtain the second coupling simulation data; Based on the actual scenario data of the power line trench, the abnormal coupling simulation data is selected from the first coupling simulation data and the second coupling simulation data.

3. The method according to claim 2, characterized in that, The step involves performing anomaly prediction simulation on the power line trench based on the physical field coupling equations and the anomaly coupling simulation data, to obtain anomaly prediction simulation data, including: Based on the abnormal coupling simulation data and the historical abnormal data of the power line trench, an abnormal prediction term is set for the physical field coupling equation set. Based on the anomaly prediction terms, anomaly prediction simulation is performed on the power line trench to obtain the anomaly prediction simulation data.

4. The method according to claim 3, characterized in that, The physical field coupling equation set includes the thermodynamic coupling equation set, the fluid structure coupling equation set, and the electromagnetic field coupling equation set. The thermodynamic coupling equation set includes heat transfer and pyrogen equations as well as mechanical equilibrium equations; The equation for heat transfer and pyrogen is expressed as follows: in, The density of solid materials in power line trenches. For materials with temperature The changing specific heat capacity, For materials with temperature The changing thermal conductivity For power line trenches following the electric field ,magnetic field and temperature The changing electromagnetic loss heat source term, For power line trenches as a function of strain rate and solid stress tensor The changing mechanical energy consumption and heat dissipation items, The temperature at the ventilation point of the power line conduit; The mechanical equilibrium equation is expressed as follows: in, As temperature and the strain tensor of solid materials The changing solid stress tensor, As temperature And the changing external physical forces, For temperature and degree of damage Coupled constitutive matrix, For varying temperature ranges The coefficient of thermal expansion changes; The fluid-structure coupling equation set includes fluid region equations, structural region equations, and fluid-structure coupling boundary conditions. The expression for the fluid domain equation is: in, The fluid density in the power line trench. For the fluid velocity field, For fluid pressure, For fluid dynamic viscosity, This represents the coupling force between the fluid and the solid displacement. This refers to the structural deformation that changes with the deformation of the flow channel. As temperature and fluid pressure And the changing permeability coefficient; The expression for the structural region equation is: in, The structural density of power line trenches, As temperature and structural deformation The changing structural stress tensor For fluid pressure and fluid velocity field The changing fluid's external load on the solid, The elastic constitutive matrix of the structural material. To adapt to structural deformation The changing structural material strain tensor; The expression for the fluid-structure interaction boundary condition is as follows: in, To adapt to structural deformation The changing structural safety factor function The interface normal vector; The electromagnetic field coupling equations include Maxwell's equations, the coupling point flow equations, and the medium relationship equations. The expression for Maxwell's equations is: in, For electric field strength, The magnetic field strength, Magnetic flux density It is the electric displacement vector. Free charge density, The coupling current density; The expression for the coupled point flow equation is, in, As temperature and the strain tensor of solid materials The changing conductivity of the material For varying electric field strength ,temperature and the strain tensor of solid materials And the changing partial discharge current; The expression for the medium relationship equation is: in, As temperature and the strain tensor of solid materials The relative permittivity changes.

5. The method according to claim 1, characterized in that, The process of repairing the power line trench is calculated based on the abnormality elimination rule set of the power line trench as a constraint, according to the line abnormality coupling analysis data, to obtain line abnormality repair data, including: Fault element features are extracted from the abnormal coupling analysis data of the line to obtain key feature information of the trench anomaly; Using the set of anomaly elimination rules for the power line trench as constraints, and based on the key anomaly characteristic information of the trench, maintenance decision analysis is performed on the power line trench to obtain trench maintenance decision data; Based on the trench maintenance decision data, maintenance parameters of the power line trench are analyzed to obtain abnormal repair data of the line.

6. The method according to claim 5, characterized in that, The step of analyzing maintenance parameters of the power line trench based on the trench maintenance decision data to obtain abnormal repair data for the line includes: Based on the trench maintenance decision data, several material and process maintenance parameters are selected from the power line trench material and process database. Based on the material process maintenance parameters, select several material process maintenance rules from the material process rule library of the power line trench; Multi-objective parameter optimization is performed on the maintenance parameters and rules of each material process to obtain initial anomaly repair data; Based on the anomaly prediction simulation data, the initial anomaly repair data is subjected to statically indeterminate optimization to obtain the line anomaly repair data.

7. A digital online operation and maintenance device for power line trenches, characterized in that, The device includes: The inspection information planning module is used to plan the robot group inspection information of the power line trench based on the static perception data of the power line trench when anomalies occur. The inspection data acquisition module is used to control the robot group of the power line trench to inspect the power line trench according to the robot group inspection information, and obtain dynamic inspection data of the line. The trench anomaly analysis module is used to identify the physical field coupling boundary conditions of the power line trench based on the dynamic inspection data of the line. Based on the physical field coupling boundary conditions, a set of physical field coupling equations for the power line trench is constructed. Based on the physical field coupling equations, an abnormal coupling simulation was performed on the power line trench to obtain abnormal coupling simulation data. Based on the physical field coupling equations and the abnormal coupling simulation data, an anomaly prediction simulation is performed on the power line trench to obtain anomaly prediction simulation data. By integrating the abnormal coupling simulation data and the abnormal prediction simulation data, line abnormal coupling analysis data is obtained; The trench repair analysis module is used to calculate the repair process of the power line trench based on the set of anomaly elimination rules of the power line trench as constraints, and obtain the line anomaly repair data. The trench repair control module is used to calculate the trench repair envelope of each repair robot in the power line trench based on the abnormal repair data of the line and the trench scale corresponding to the robot group. Based on the trench maintenance envelopes of each of the aforementioned trenches, predict the redundancy safety information of the robot swarm in the power line trenches; Based on the redundant safety information and the dimensions of the power line trench, calculate the maintenance and supply envelope of each supply robot in the robot swarm in the power line trench. The maintenance data of the robot swarm is obtained by fusing the trench maintenance envelope and the maintenance supply envelope.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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