Power line pipe trench digital intelligence online operation and maintenance method and device

Abstract

The invention relates to a digital intelligent online operation and maintenance method and device for a power line pipe trench. The method comprises the following steps: under the condition that line static sensing data of the electric power line pipe trench is abnormal, planning robot group inspection information of the electric power line pipe trench according to the line static sensing data; according to the robot group inspection information, controlling a robot group of the electric power line pipe trench to inspect the electric power line pipe trench to obtain line dynamic inspection data; performing physical field coupling simulation on the power line pipe trench according to the line dynamic inspection data to obtain line abnormal coupling analysis data; by taking the abnormality elimination rule set of the power line pipe trench as a constraint condition, calculating a repair process of the power line pipe trench according to the line abnormality coupling analysis data to obtain line abnormality repair data; and calculating robot group maintenance data of the robot group under the line pipe trench scale according to the line abnormity repair data. By adopting the method, abnormity can be eliminated in time so as to prevent accidents.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent operation and maintenance, and particularly to a digital and intelligent online operation and maintenance method and device for power line trenches. Background Art

[0002] In traditional technologies, there are mainly several ways to connect the middle cable heads of high-voltage cables laid in power line trenches and urban network cable trenches: 1. Intermediate joint of rubber-sheathed cable, 2. Intermediate joint of cold-shrinkable tube, 3. Intermediate joint of heat-shrinkable tube, 4. Intermediate joint of cable fusion technology, etc.; due to technical limitations, they are prone to heat generation during long-term operation of the line, resulting in rapid aging, moisture absorption, and damage of the internal insulation of the cable joint, causing problems such as heat generation, sparking, and arc at the joint, leading to fires, explosions, large-scale power outages, casualties, and significant economic losses. Moreover, due to the particularity of the trench, the operating state of the line cannot be monitored, and only manual inspection and regular temperature measurement can be used for routine inspection, and abnormalities cannot be eliminated in time to prevent accidents. Summary of the Invention

[0003] Based on this, it is necessary to provide a digital and intelligent online operation and maintenance method and device for power line trenches that can eliminate abnormalities in time to prevent accidents.

[0004] In a first aspect, the present application provides a digital and intelligent online operation and maintenance method for power line trenches, including:

[0005] In the case where the line static perception data of the power line trench is abnormal, according to the line static perception data, plan the inspection information of the swarm of robots for the power line trench;

[0006] According to the inspection information of the swarm of robots, control the swarm of robots for the power line trench to inspect the power line trench to obtain line dynamic inspection data;

[0007] According to the line dynamic inspection data, perform physical field coupling simulation on the power line trench to obtain line abnormal coupling analysis data;

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

[0009] According to the line abnormal repair data, calculate the maintenance data of the swarm of robots at the scale of the line trench.

[0010] In a second aspect, the present application further provides a digital and intelligent online operation and maintenance device for power line trenches, including:

[0011] An inspection information planning module, configured to plan the inspection information of the swarm of robots for the power line trench according to the static perception data of the line when the static perception data of the power line trench appears abnormal;

[0012] An inspection data acquisition module, configured to control the swarm of robots for the power line trench to inspect the power line trench according to the inspection information of the swarm of robots, and obtain dynamic line inspection data;

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

[0014] A trench repair analysis module, configured to calculate the repair process of the power line trench according to the line abnormality coupling analysis data with the abnormal elimination rule set of the power line trench as a constraint condition, and obtain line abnormality repair data;

[0015] A trench repair control module, configured to calculate the maintenance data of the swarm of robots at the scale of the line trench according to the line abnormality repair data.

[0016] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, any step of a method for digital intelligent online operation and maintenance of a power line trench is implemented.

[0017] The above method and device for digital intelligent online operation and maintenance of a power line trench automatically plan the inspection tasks of the swarm of robots when the static perception data of the power line trench is abnormal, realizing dynamic perception and accurate positioning of the line state; performing physical field coupling simulation on the dynamic inspection data, improving the accuracy of abnormality analysis; on this basis, combining the abnormal elimination rules, scientifically formulating the repair process to ensure the rationality and efficiency of the maintenance plan; finally, the calculated maintenance data of the swarm of robots can guide the swarm of robots to cooperate at the scale of the trench, effectively improving the intelligent and automated level of the maintenance operation, being able to achieve rapid response and efficient repair of power line trench faults, promptly eliminating abnormalities to prevent accidents, and ensuring the safety and stability of the operation of the power system. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is an application environment diagram of the digital and intelligent online operation and maintenance method for the power line trench in an embodiment;

[0020] Figure 2 It is a schematic flowchart of the digital and intelligent online operation and maintenance method for the power line trench in an embodiment;

[0021] Figure 3 It is a schematic flowchart of the method for obtaining the line anomaly coupling analysis data in an embodiment;

[0022] Figure 4 It is a schematic flowchart of the method for obtaining the abnormal coupling simulation data in an embodiment;

[0023] Figure 5 It is a schematic flowchart of the method for obtaining the abnormal prediction simulation data in an embodiment;

[0024] Figure 6 It is a schematic flowchart of the method for obtaining the first type of line anomaly repair data in an embodiment;

[0025] Figure 7 It is a schematic flowchart of the method for obtaining the second type of line anomaly repair data in an embodiment;

[0026] Figure 8 It is a schematic flowchart of the method for calculating the maintenance data of the swarm of robots in an embodiment;

[0027] Figure 9 It is a structural block diagram of the digital and intelligent online operation and maintenance device for the power line trench in an embodiment;

[0028] Figure 10 It is the internal structure diagram of a computer device in an embodiment. Detailed implementation manners

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

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

[0031] In an exemplary embodiment, as Figure 2 shown, a digital and intelligent online operation and maintenance method for a power line trench is provided. Taking the server in Figure 1 as an example, the method includes the following steps 202 to 210. Among them:

[0032] Step 202, when the line static perception data of the power line trench is abnormal, plan the inspection information of the robot group for the power line trench according to the line static perception data.

[0033] Among them, the power line trench can be an enclosed channel structure laid underground or on the ground surface, used for laying and protecting power cables to avoid external environmental interference or mechanical damage. Its structure usually includes a concrete channel, brackets, a drainage system, etc.

[0034] Among them, the line static perception data can be the environmental and cable operation status information regularly 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 inspection information of the robot group can be a set of task instructions generated for multiple robots after detecting static anomalies, and the content includes the inspection path, task area, inspection time, acquisition parameters, obstacle avoidance strategy, and cooperation rules of each robot, etc.

[0036] Specifically, when the static perception data of the power line trench is abnormal, use cloud algorithms to analyze the multi-source static data collected, identify the types of abnormal data (such as temperature rise, voltage fluctuation, displacement anomaly, etc.) and their spatial distribution characteristics. Then, based on the location of the abnormal point, the relevance of the surrounding perception nodes, and the historical fault database, comprehensively judge the possible abnormal influence range, and combine the trench structure layout, robot operation ability and current state, and use a task allocation optimization algorithm (such as multi-objective path planning or swarm intelligence algorithm) to generate the inspection information of the robot group for the power line trench, including the starting position, inspection path, residence duration at key nodes, sensor activation strategy, and task priority of each robot.

[0037] Step 204, according to the inspection information of the robot group, control the robot group of the power line trench to inspect the power line trench to obtain 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 cooperation capabilities. These robots have the capabilities of autonomous navigation, data acquisition, operation execution, and cooperative communication, and can achieve comprehensive perception, analysis, and processing of the internal environment of the trench according to the task assignment.

[0039] Among them, the line dynamic inspection data can be real-time and high-precision information collected by a swarm of robots during the inspection task through the multi-modal sensors (such as vision, infrared, gas, electromagnetic, etc.) they carry, reflecting the detailed changes in the current state of the line and the pipe trench, and data showing such changes.

[0040] Specifically, according to the inspection task information of the swarm of robots, the server starts the unified scheduling and control of each robot in the power line pipe trench to ensure that each robot enters the pipe trench orderly according to the planned path to carry out the inspection operation. During the inspection operation, each robot flexibly calls its own multiple sensor modules, such as visual cameras, infrared thermal imagers, ultrasonic sensors, gas detectors, and electromagnetic interference detectors, etc., according to the task assignment, to collect real-time and high-precision data on the pipe trench structure, cable surface, connection nodes, and environmental parameters. At the same time, each robot synchronizes its state and collaboratively avoids obstacles through a wireless network to ensure information integrity and operation safety. The server can also dynamically adjust the inspection parameters. For example, when a new abnormal point is found, the task strategy is modified in real time, and nearby robots are dispatched to conduct a detailed investigation. Finally, all robots upload the line dynamic inspection data they have collected to the central system.

[0041] Step 206: Perform physical field coupling simulation on the power line pipe trench according to the line dynamic inspection data to obtain line abnormal coupling analysis data.

[0042] Among them, physical field coupling simulation can be a process of jointly modeling and simulating multiple physical fields (such as heat, electricity, force, magnetism, etc.) in the power line pipe trench, used to reveal the multi-factor coupling mechanism behind abnormal phenomena, and a method to assist in judging the cause of faults, risk scope, and their development trends.

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

[0044] Specifically, input the line dynamic inspection data into the physical field coupling simulation module in the server to construct a three-dimensional digital twin model corresponding to the actual power line pipe trench. Based on the three-dimensional digital twin model, fuse multi-physical field data such as thermal field, electric field, force field, and electromagnetic field, and use finite element analysis (FEA) or multi-physical field simulation algorithms to deeply analyze the abnormal area. For example, simulate 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 in the pipe trench due to external soil structure disturbance. Through the coupling simulation of the mutual influence and change trend between these physical fields, identify the root cause, evolution mechanism of the abnormality, and its potential impact on the surrounding structure, and generate detailed line abnormal coupling analysis data.

[0045] Step 208: Using the set of abnormal elimination rules for the power line trench as a constraint condition, calculate the repair process for the power line trench based on the line abnormal coupling analysis data to obtain line abnormal repair data.

[0046] Among them, the set of abnormal elimination rules can be a rule base constructed from expert knowledge, operation and maintenance standards, and historical experience, which stipulates the handling methods, disposal priorities, construction restrictions, safety constraints, resource allocation requirements, etc. for different types of abnormalities.

[0047] Among them, the line abnormal repair data can be an executable repair plan generated under the analysis data and rule constraints, and the content includes the repair process flow, materials used, operation steps, resource requirements, duration estimation, operation precautions, etc. corresponding to the fault type, which is the technical input for the robot to execute the repair task.

[0048] Specifically, match and judge the abnormal type, location, cause, and its risk level included in the line abnormal coupling analysis data with the preset set of abnormal elimination rules. These rule sets include various fault handling priorities, safety standards, operation environment restrictions, material usage specifications, etc.; then call the built-in repair process knowledge base, combine with intelligent reasoning algorithms (such as expert systems or rule-based optimization engines), and automatically generate the optimal repair process flow on the premise of meeting the constraint conditions. The process includes selected repair technologies (such as local replacement, insulation reinforcement, structural support, etc.), required materials and tools, operation details and duration estimation of each step, and conduct feasibility and safety verification to obtain the line abnormal repair data.

[0049] Step 210: According to the line abnormal repair data, calculate the maintenance data of the robot swarm under the scale of the line trench.

[0050] Among them, the maintenance data of the robot swarm can be a multi-robot collaborative operation instruction set generated according to the repair task requirements, robot capabilities, and the actual environment of the trench, and the content involves the task allocation, path planning, action sequence, operation synchronization, and interaction mechanism of each robot to ensure the automation, efficiency, and safety of the entire maintenance process.

[0051] Specifically, after generating the line anomaly repair data, analyze the tools, working space, operation precision, and execution order required for each repair operation, and use task scheduling and path planning algorithms to reasonably allocate the roles of each robot during the maintenance process, such as operation execution, material handling, environmental monitoring, or operation assistance. At the same time, considering the limited space and safety requirements in the pipe trench, it is necessary to dynamically coordinate the operation paths, working window times, and collaborative actions of the robots to ensure that multiple robots can operate efficiently and orderly in a limited space, avoiding conflicts and resource waste. The final generated machine group maintenance data includes the task allocation, execution instructions, navigation paths, and interaction rules of each robot in the time-space dimension.

[0052] In the above method for digital online operation and maintenance of a power line pipe trench, when the static perception data of the power line pipe trench is abnormal, the inspection tasks of the machine group are automatically planned to achieve dynamic perception and accurate positioning of the line status; physical field coupling simulation is performed on the dynamic inspection data to improve the accuracy of anomaly analysis; on this basis, combined with the anomaly elimination rules, the repair process is scientifically formulated to ensure the rationality and efficiency of the maintenance plan; the final calculated machine group maintenance data can guide the machine group to operate collaboratively at the pipe trench scale, effectively improving the intelligent and automated level of the maintenance operation, enabling rapid response and efficient repair of power line pipe trench faults, timely eliminating anomalies to prevent accidents, and ensuring the safety and stability of the power system operation.

[0053] In an exemplary embodiment, as Figure 3 shown, according to the line dynamic inspection data, physical field coupling simulation is performed on the power line pipe trench 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 pipe trench.

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

[0056] Specifically, analyze the line dynamic inspection data collected by the machine group, extract the key parameters related to physical fields such as heat, electricity, force, and magnetism, such as local temperature rise, current density, stress concentration points, humidity changes, etc. On the basis of combining the pipe trench structure model and the sensor spatial distribution, use data inversion and boundary recognition algorithms to automatically determine the boundary conditions of various physical fields as the physical field coupling boundary conditions (such as fixed temperature boundary, electric potential boundary, mechanical constraints, etc.).

[0057] Step 304: Construct a physical field coupling equation set for the power line trench according to the physical field coupling boundary conditions.

[0058] Among them, the physical field coupling equation set can be a mathematical expression of the interaction relationship between multiple physical phenomena. It 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 connected into a unified solution system through coupling terms. For example, the thermoelectric coupling equation takes into account 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 operating environment of the trench, corresponding basic control equations are constructed for physical fields such as heat, electricity, force, and magnetism. For example, Fourier's law is used for heat conduction, Poisson's or Maxwell's equations are used for the electric field, the basic equations of elasticity are used for mechanical response, and Ampere's circuital law is used for the magnetic field. These equations are then correlated by introducing coupling terms to reflect the dynamic process of their interaction 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 method or finite volume method is used to numerically discretize and assemble the equation set, enabling the entire coupling system to be efficiently solved on a numerical simulation platform to obtain the physical field coupling equation set for the power line trench.

[0060] Step 306: Conduct an abnormal coupling simulation on the power line trench according to the physical field coupling equation set to obtain abnormal coupling simulation data.

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

[0062] Among them, the abnormal coupling simulation data can be the simulation results output during the abnormal coupling simulation, covering information such as the numerical distribution, interaction intensity, abnormal conduction path, and physical quantity changes in key areas of multiple physical fields within a specific time and space range.

[0063] Specifically, after constructing the physical field coupling equations, the coupling equations that include the interaction relationships of multiple physical fields such as heat, electricity, force, and magnetism are solved by using the set boundary conditions and initial state parameters to simulate the real physical behavior supported by the current dynamic inspection data. For example, it can simulate how the heat generated by a cable under overload spreads, whether thermal expansion and contraction cause stress concentration, or whether an increase in humidity will lead to local insulation failure and electric field distortion. The process of mutual influence among various physical fields in the simulation will be dynamically tracked and quantified to form key data such as abnormal propagation paths, coupling strength distributions, and critical parameter changes. The simulation results are output in the form of high-precision maps and data sets, constituting abnormal coupling simulation data.

[0064] Step 308, based on the physical field coupling equations and the abnormal coupling simulation data, perform an abnormal prediction simulation on the power line trench to obtain abnormal prediction simulation data.

[0065] Among them, the abnormal prediction simulation can, on the basis of the abnormal coupling simulation, further introduce time variables and evolution models to prospectively simulate the possible expansion, deterioration, or transfer trends of the current abnormality over time. It is based on prediction mechanisms such as material aging models, heat diffusion equations, and current dynamic responses to estimate the development direction and speed of potential risks in the power line trench in a 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 during the abnormal prediction simulation, which describe the evolution process of multi-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 equations, and combined with the material property degradation model, environmental change trends, and historical fault evolution paths, the development process of the current abnormality in a future period of time is simulated. For example, it can predict whether the continuous temperature rise of a local heat concentration will cause the insulation layer to break down within a few hours or days, or whether the structural stress concentration point will cause physical cracking over time. The prediction simulation not only outputs the change trends and spatial distributions of key physical quantities at future times, but also can evaluate the expansion path, speed of the abnormality, and the risk of possible chain faults, forming structured abnormal prediction simulation data.

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

[0069] Specifically, after obtaining the abnormal coupling simulation data and abnormal prediction simulation data, the two types of simulation results are jointly analyzed through 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 of the possible evolution of the abnormality and the spatial expansion path in the prediction simulation are integrated. Therefore, methods such as principal component analysis (PCA), Bayesian fusion, and graph neural networks are used to reduce the dimension of multi-dimensional data, extract features, and establish semantic associations, 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 the line dynamic inspection data with the multi-physical field simulation modeling, a physical field coupling equation set that conforms to the actual working conditions is constructed to accurately simulate the interaction behaviors of multiple physical fields such as heat, electricity, force, and magnetism in the power line trench. By using the linked calculation of abnormal coupling simulation and prediction simulation, not only can the current physical mechanism of the abnormality be accurately identified, but also its 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 highly reliable and valuable data support for abnormal diagnosis, intelligent decision-making, and preventive maintenance, and significantly improving the intelligent and predictive level of abnormal identification in power line trenches.

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

[0072] Step 402, perform spatial partitioning on the power line trench to obtain the trench spatial partition data.

[0073] Among them, the trench spatial partition data can be a simulation input data set generated for each sub-region after dividing the power line trench according to structural characteristics, physical properties, or abnormal distributions. This data usually includes three-dimensional geometric information, material parameters, boundary conditions, initial states, sensor positions, etc.

[0074] Specifically, before performing the abnormal coupling simulation on the power line trench, according to the three-dimensional geometric model of the trench, the cable layout, the heat source position, the material distribution, and the abnormal point density identified in the dynamic inspection data, the entire line trench is spatially partitioned. The partitioning method can adopt methods based on voxel grids, hierarchical regions, or functional modules to divide the trench into several sub-regions. Each sub-region has a certain physical independence and retains the coupling relationship with adjacent regions. The data of each spatial sub-region includes boundary geometric information, material parameters, sensor positions, and initial conditions, which together constitute the "trench spatial partition data".

[0075] Step 404: Using the physical field coupling equations, perform abnormal coupling solutions for the spatial partition data of each pipe trench respectively to obtain the first coupled simulation data.

[0076] Among them, the abnormal coupling solution can be based on the established multi - physical field coupling equations to perform numerical calculations on the power line pipe trench area with abnormal signs, simulating the interaction behaviors between physical fields such as heat, electricity, force, and magnetism.

[0077] Among them, the first coupled simulation data can be the fusion data of the simulation results obtained by independently performing abnormal coupling solutions for each spatial sub - area after dividing the pipe trench.

[0078] Specifically, based on the spatial partition data of each sub - region of the pipe trench, use the constructed physical field coupling equations to perform independent abnormal coupling solutions for each region. The solution process is to calculate the distribution and interaction effects of physical fields such as heat, electricity, force, and magnetism in the region respectively according to the specific material properties, boundary conditions, and initial physical states in each sub - region. For example, in a certain region, it can simulate how the heat generated by the cable conductor under high current density conducts in the structure and couples with the thermal fields in the surrounding areas. To improve the calculation efficiency and decouple complexity, different partitions adopt a regional parallel computing architecture to independently solve each partition, while maintaining the consistency of variable interaction at the boundaries of adjacent regions. Converge the simulation results of each sub - region to output the first coupled simulation data, including the multi - physical field distribution with local abnormal responses, the coupling strength index, and the spatial positions of potential risk points.

[0079] Step 406: Using the physical field coupling equations, perform abnormal coupling solutions for different physical processes in the power line pipe trench respectively to obtain the physical coupled simulation data for each.

[0080] Among them, the physical coupled simulation data can be the results generated by independently modeling and performing coupled simulations for different physical processes (such as heat, electricity, force, magnetism) in the power line pipe trench on a global scale.

[0081] Specifically, for the global behaviors of different physical processes in the power line pipe trench, by separately modeling and solving various physical processes such as heat conduction, current distribution, structural stress response, and electromagnetic interference, the operating states and abnormal propagation paths of each physical field in the entire pipe trench system can be grasped from a more macroscopic perspective. During the solution process, corresponding coupling equations are used for each type of physical process, and numerical simulations are performed within the entire domain to simulate, for example, the impact of cable heating on the overall temperature rise, the changes in the electric field in a humid environment, and the response of the cable stress - concentration area under load fluctuations. This process does not depend on spatial partitioning but starts from the dimension of physical mechanisms to obtain the global response behaviors of different physical fields. The finally output "physical coupled simulation data for each" covers the abnormal coupling results between different physical processes.

[0082] Step 408: Periodically exchange the coupling amounts of the physical coupling simulation data to obtain the second coupling simulation data.

[0083] Among them, the periodic exchange can be a strategy for realizing data interaction in multi-physics field coupling simulation, which means that within a set time step or simulation period, key coupling variables (such as temperature, current density, stress, etc.) are regularly shared among the simulations of various physical processes and fed back into other physical models as new input conditions.

[0084] Among them, the second coupling simulation data can be the global simulation output result generated after the interaction and iterative update of multi-physics fields under the periodic exchange mechanism. It comprehensively reflects the linkage effect and overall evolution path caused by the coupling among multiple physical processes, and has higher dynamic consistency and global coupling accuracy.

[0085] Specifically, according to the set exchange time step or iteration period of each physical coupling simulation data, extract the key coupling variables of the physical coupling simulation data (such as Joule heat caused by current, the influence of electric field on material stress, the perturbation of mechanical boundary caused by thermal expansion and contraction, etc.) within each period, and use these key coupling variables as boundary conditions or source terms to input into the simulation models of other physical processes, forming a two-way linkage between physical fields. This periodic exchange breaks the limitation of single physical process analysis and makes the simulation closer to the real complex coupling behavior. Through multiple rounds of iteration and exchange, continuously correct the coupling state between physical fields, and finally generate the second coupling simulation data.

[0086] Step 410: Select abnormal coupling simulation data from the first coupling simulation data and the second coupling simulation data according to the actual scenario data of the power line trench.

[0087] Specifically, use the actual scenario data of the power line trench to screen and optimize the selection of the first coupling simulation data and the second coupling simulation data to extract the most representative and engineering guiding abnormal coupling simulation data. The actual scenario data includes sensor measured values, historical inspection records, environmental parameters (such as humidity, temperature, soil conditions), equipment operating conditions, etc., and these data are used as the verification basis for the simulation results. By methods such as similarity matching, error analysis or multi-index evaluation based on optimization algorithms, compare the consistency of the two sets of simulation data with the actual scenario, and identify the simulation model output that best conforms to the real operating state from the first coupling simulation data and the second coupling simulation data. The finally selected abnormal coupling simulation data not only includes 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 performing refined spatial zoning on the power line trench, the simulation solution is made more consistent with the actual structural characteristics. Combining the physical field coupling equations, the abnormal coupling solutions are separately carried out for each zone and various physical processes, improving the accuracy of local anomaly recognition and global coupling analysis. At the same time, the periodic exchange mechanism realizes the dynamic coupling feedback between multiple physical fields, effectively restoring the true evolution process of complex coupling behaviors. Finally, based on the actual scenario data, the abnormal simulation data that best matches the working conditions is selected from multiple groups of simulation results, realizing the accurate modeling and dynamic mapping of abnormal states, thus significantly enhancing the reliability, timeliness, and engineering adaptability of the abnormal analysis of power line trenches, and providing high-quality data support for subsequent intelligent decision-making and maintenance.

[0089] In an exemplary embodiment, as Figure 5 shown, according to the physical field coupling equations and the abnormal coupling simulation data, an abnormal prediction simulation is carried out on the power line trench to obtain abnormal prediction simulation data, including steps 502 to 504. Among them:

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

[0091] Among them, the historical abnormal data can be a spatio-temporal data set related to various abnormal events accumulated through means such as static sensors, dynamic inspections, and maintenance records during the long-term operation and maintenance of the power line trench.

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

[0093] Specifically, before performing the abnormal prediction simulation, since the parameters of the abnormal prediction term are not set in the physical field coupling equations, it is necessary to first enhance the prediction of the physical field coupling equations. In the specific implementation process, by comparing and analyzing historical abnormal data such as historical fault cases and current abnormal evolution characteristics, key influencing factors are extracted, such as temperature rise rate, current fluctuation pattern, stress accumulation trend, insulation aging rate, etc., and these factors are converted into quantifiable mathematical expression forms and embedded into the original physical field coupling equations as time-related terms or dynamic source terms. To improve the prediction accuracy, machine learning or statistical modeling methods are also used to fit and optimize the abnormal development mode in combination with the abnormal coupling simulation data, so that the prediction term can reflect the potential path of the coupling evolution between multiple physical fields. The finally formed abnormal prediction term.

[0094] Step 504: Perform an abnormal prediction simulation on the power line trench according to the abnormal prediction items to obtain abnormal prediction simulation data.

[0095] Among them, the abnormal prediction simulation can be a process of dynamically simulating the abnormal development trend within a certain future time duration through a time-stepping method by combining the enhanced multi-physics field coupling equations, the current abnormal state, and the prediction items.

[0096] Specifically, after setting the prediction items for the physical field coupling equations, use the enhanced physical field coupling equations to perform an abnormal prediction simulation on the power line trench to simulate the coupled evolution process of multiple physical fields within a future period of time. During the simulation process, adopt a time-stepping algorithm to gradually calculate the dynamic responses of physical fields such as heat, electricity, force, and magnetism changing with time, and track the evolution trend of the abnormal area, such as the spread of hot spots, the expansion of voltage instability areas, and the evolution of material stress concentration areas, etc.; also identify potential risk diffusion paths, critical failure time points, and possible linkage chains of abnormalities according to the dynamic boundary conditions and historical evolution patterns introduced by the prediction items. The finally output abnormal prediction simulation data includes time series of multiple physical quantities, abnormal development path diagrams, changes in warning thresholds of key indicators, etc.

[0097] In a specific embodiment, the physical field coupling equations include 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 expression of the heat transfer and heat source equation is

[0100]

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

[0102] The expression of the mechanical equilibrium equation is

[0103]

[0104] Among them, σ1(Y, ε) is the solid stress tensor that varies with temperature T and the strain tensor ε of the solid material, f ext (T) is the external body force that varies with temperature T, D(T, ω(ε)) is the constitutive matrix that couples temperature T and the damage degree ω(ε), and α(T - T0) is the thermal expansion coefficient that varies with the temperature range T - T0;

[0105] The fluid-structure coupling equations include the fluid domain equation, the structure domain equation, and the fluid-structure coupling boundary conditions;

[0106] The expression of the fluid domain equation is,

[0107]

[0108] Among them, 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 on the solid displacement, u is the structural deformation that varies with the deformation of the flow channel, and κ(p, T) is the permeability coefficient that varies with temperature T and fluid pressure p;

[0109] The expression of the structure domain equation is,

[0110]

[0111] Among them, ρ s is the structural density of the power line trench, is the structural stress tensor that varies with temperature T and structural deformation u, g fluid (p, v) is the external load of the fluid on the solid that varies with fluid pressure p and fluid velocity field v, D s is the elastic constitutive matrix of the structural material, and ε(u) is the structural material strain tensor that varies with structural deformation u;

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

[0113]

[0114] Among them, φ(u) is the structural safety factor function that varies with structural deformation u, and n is the interface normal vector;

[0115] The electromagnetic field coupling equations include Maxwell's equations, the coupling point current equations, and the medium relation equations;

[0116] The expression of Maxwell's equations is,

[0117]

[0118] Among them, E is the electric field strength, H is the magnetic field strength, B is the magnetic induction intensity, D is the electric displacement vector, and ρ free is the free charge density, and J coupled is the coupled current density;

[0119] The expression of the coupled point current equation is

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

[0121] Among them, σ2(T, ε) is the material conductivity that varies with temperature T and the strain tensor ε of the solid material, and J pd (E, T, ε) is the partial discharge current that varies with the electric field strength E, temperature T, and the strain tensor ε of the solid material;

[0122] The expression of the medium relation equation is

[0123] D = ε′(T, ε)E, B = μH

[0124] Among them, ε′(T, ε) is the relative permittivity that varies with temperature T and the strain tensor ε of the solid material.

[0125] In this embodiment, by combining the abnormal coupling simulation data with the historical abnormal data of the power line trench, the abnormal prediction item 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 integration of historical experience and the current state is introduced to realize the accurate modeling and simulation prediction of the abnormal development trend. Furthermore, using this prediction item to carry out the abnormal prediction simulation can effectively deduce the propagation path, evolution speed, and potential risk points of the abnormality under the multi-physical field coupling, so as to realize the early identification and dynamic warning of faults, and significantly enhance the forward-looking, intelligence, and risk prevention and control capabilities of the power line trench operation and maintenance management.

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

[0127] Step 602, extract the fault element characteristics from the line abnormal coupling analysis data to obtain the key characteristic information of the trench abnormality.

[0128] Among them, the extraction of fault element characteristics can be to deeply process the line abnormal coupling analysis data, and identify and refine the core characteristic information closely related to the fault state.

[0129] Among them, the key characteristic information of the trench can be an important information set obtained through fault element feature extraction and capable of comprehensively describing the current abnormal state in the trench of the power line. These information include the spatial location of the abnormal area, the type of abnormality (such as thermal runaway, structural deformation, electrical breakdown, etc.), the response relationship between coupled physical fields, the abnormal evolution trend, and the risk level, etc.

[0130] Specifically, input the line abnormal coupling analysis data into the feature extraction algorithm, and use the feature extraction algorithm (such as pattern recognition, clustering analysis, statistical filtering, etc.) to identify the core elements related to the fault, including the spatial location of the abnormality, the influence range, the physical field coupling intensity, the development trend, the risk level, the type of abnormality (such as thermal breakdown, arc discharge, structural crack), etc.; then through data dimensionality reduction and feature fusion technology, convert the multi-source complex simulation data into discriminative key characteristic information of the trench abnormality.

[0131] Step 604, taking the abnormal elimination rule set of the power line trench as the constraint condition, based on the key characteristic information of the trench abnormality, conduct maintenance decision analysis on the power line trench to obtain trench maintenance decision data.

[0132] Among them, the abnormal elimination rule set can be a rule base constructed by expert knowledge, industry standards, operation and maintenance experience, and safety specifications, which stipulates the standard processes and constraint conditions for how to handle different types of abnormalities. The content includes the fault type and the corresponding recommended treatment methods, maintenance priorities, safety operation boundaries, the applicable scope of materials and tools, environmental condition limitations, etc.

[0133] Among them, the maintenance decision analysis can be a process in which the system evaluates and selects multiple feasible maintenance strategies after obtaining the key characteristic information of the abnormality and referring to the abnormal elimination rule set.

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

[0135] Specifically, under the constraint of the abnormal elimination rule set of the power line trench, the key feature information of the trench abnormality (such as abnormality type, location, coupling strength, risk level, etc.) is matched with the processing logic in the abnormal elimination rule set. For example, high-risk abnormalities need to be disposed of first, specific materials are prohibited from being used in specific environments, and specific abnormalities require specific repair processes. Combining historical maintenance cases, resource availability, and on-site environmental restrictions, using a rule engine or a multi-factor decision-making algorithm based on a knowledge graph, evaluate the risks, costs, and efficiencies of multiple feasible maintenance plans, automatically screen out the optimal or alternative plans, and generate trench maintenance decision data, including the type of maintenance strategy (such as partial replacement, temporary reinforcement, overall upgrade, etc.), construction priority, task timing, required materials, and personnel allocation.

[0136] Step 606: According to the trench maintenance decision data, perform maintenance parameter analysis on the power line trench to obtain line abnormality repair data.

[0137] Among them, the maintenance parameter analysis can be a process of further converting the maintenance decision data into precise and operable technical parameters. According to the objectives and constraints of the maintenance tasks, combined with the trench space model, robot operation ability, construction specifications, etc., refine specific parameters such as the maintenance path, operation steps, time control, tool selection, material specifications, and operation coordinates.

[0138] Specifically, based on the selected trench maintenance decision data, combined with the structural model of the power line trench, robot operation ability, environmental conditions, and safety specifications, extract and calculate a series of key maintenance parameters, including operation path planning, maintenance position coordinates, operation area size, material usage specifications, repair depth, repair step sequence, required tool types, resource allocation plans, and operation duration assessment, etc. At the same time, through parameter simulation and feasibility verification, ensure the timing coordination, spatial compatibility, and robot operation stability among all links during the maintenance process. Finally, generate the line abnormality repair data.

[0139] In this embodiment, by extracting the fault element characteristics from the line abnormality coupling analysis data, accurately identify the key abnormality characteristics in the power line trench, and achieve an in-depth understanding of the essence of the fault; on this basis, introduce the abnormal elimination rule set as a decision constraint, combined with the extracted key feature information, carry out intelligent maintenance decision analysis to ensure that the maintenance plan has high rationality and execution feasibility; further through maintenance parameter analysis, refine and quantify the maintenance process flow, material selection, and operation path, and form accurate and efficient line abnormality repair data. The overall process realizes the closed-loop optimization from abnormality 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 an exemplary embodiment, as Figure 7 shown, according to the trench repair decision data, perform a repair parameter analysis on the power line trench to obtain line anomaly repair data, including steps 702 to 708. Among them:

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

[0142] Among them, the material process database can be a knowledge base storing various material information and construction process parameters related to the repair of power line trenches, including performance indicators (such as strength, thermal conductivity, corrosion resistance, etc.) of physical materials such as insulating materials, reinforcement members, sealants, and anti-corrosion coatings, as well as construction process parameters corresponding to these materials (such as applicable temperature and humidity conditions, operation methods, construction time limits, etc.).

[0143] Among them, the material process repair parameters can be detailed parameter information extracted from the material process database for guiding specific repair operations, describing the performance performance, adapted environment, application methods, and quantity recommendations of various materials in different repair scenarios.

[0144] Specifically, according to the key information such as repair type, fault location, working environment, and safety level provided in the trench repair decision data, enter the material process database of the power line trench for intelligent matching and screening. The material process database stores a large number of materials and their supporting process parameters available for different repair scenarios, such as cable insulation materials, plugging materials, structural reinforcement members, anti-corrosion coatings, etc., as well as their performance indicators (such as thermal conductivity, dielectric strength, corrosion resistance, construction temperature range, curing time, etc.). Screen the database through a multi-condition screening algorithm, match several material process combinations that are available under the current repair conditions, meet the performance requirements, and the process is executable, and extract several material process repair parameters, such as recommended dosage, applicable environmental conditions, required construction equipment, etc.

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

[0146] Among them, the material process repair rules can be knowledge entries extracted from the material process rule library, used to constrain and guide the combination, application, and execution of repair materials and construction processes, and it includes aspects such as material compatibility, safety specifications, construction sequence, on-site environment adaptability, and process combination limitations.

[0147] Specifically, according to each material process maintenance parameter, further extract the maintenance rules related to the selected material from the material process rule library of the power line trench. The material process rule library contains a large number of knowledge entries formulated based on engineering experience, industry standards, and safety specifications, which are used to guide the reasonable combination and construction operation of materials in the actual maintenance process. The rule content covers material compatibility (such as a certain sealant cannot be used in common with a specific insulating material), environmental adaptability (such as the performance limitations of materials under high humidity or high temperature), construction sequence (such as reinforcement should be completed before sealing), safety boundaries (such as the coating thickness shall not be lower than a certain threshold), etc. According to the attributes of the selected materials and the requirements of the maintenance task, through the rule engine for condition matching, dependency analysis, and conflict detection, several material process maintenance rules closely related to the current material process are screened out.

[0148] Step 706, perform multi-objective parameter optimization on each material process maintenance parameter and each material process maintenance rule to obtain initial abnormal 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 conduct overall trade-offs and optimizations on the materials, processes, and construction parameters in the maintenance plan.

[0150] Among them, the initial abnormal repair data can be the result of multi-objective parameter optimization, which includes content such as material selection, construction process, process configuration, and key maintenance parameters, constituting a theoretically optimal and implementable draft maintenance plan.

[0151] Specifically, take all the selected material process maintenance parameters and material process maintenance rules as input variables and boundary conditions to construct a multi-objective optimization model. The objective functions usually include maximizing maintenance effect, minimizing cost, minimizing construction time, minimizing safety risks, etc. Adopt intelligent optimization algorithms such as genetic algorithms, multi-objective particle swarm optimization (MOPSO), or Pareto front search to conduct global search and iterative adjustment on dimensions such as material combination, process sequence, and parameter configuration. During the optimization process, evaluate the feasibility and superiority of each group of solutions, exclude conflict rule combinations and parameter solutions that do not conform to the working conditions, and finally output the initial abnormal repair data.

[0152] Step 708, according to the abnormal prediction simulation data, perform hyperstatic optimization on the initial abnormal repair data to obtain line abnormal repair data.

[0153] Among them, hyperstatic optimization can be a parameter optimization method based on future working condition prediction. Its core goal is to introduce redundant design and dynamic adjustment mechanisms on the basis of the initial maintenance plan, so that the maintenance plan still maintains stability and effectiveness when the abnormality further develops, the environment changes, or uncertainty factors appear.

[0154] Specifically, using the abnormal prediction simulation data, perform statically indeterminate optimization on the initial abnormal repair data to enhance the robustness and adaptability of the maintenance plan under future complex working conditions. Statically indeterminate optimization is an advanced optimization method for redundant design and dynamic reliability. It performs secondary adjustment on the initial abnormal repair data by introducing abnormal evolution trends (such as increasing future temperature rise, continuous stress superposition, enhanced electric field concentration, etc.). In the specific implementation process, the risk areas involved in the prediction simulation, the time-series changes of key physical variables, potential fault chains, etc. are used as dynamic constraint conditions to re-evaluate the sustainability and fault tolerance of the initial repair plan. For example, if the prediction shows that a certain thermal abnormal area will expand to adjacent areas, the system automatically adjusts the range of insulating materials or thickens and strengthens the structure; if there may be an electrical shock in the future, the material selection is optimized to enhance the electrical strength resistance. The finally generated line abnormal repair data not only meets the current maintenance requirements but also has a certain degree of advanced protection ability and environmental adaptability.

[0155] In this embodiment, based on the trench maintenance decision data, intelligently screen and match the maintenance materials and process parameters from the material process database, and combine the safety specifications and application constraints in the material process rule library to construct a constraint model for maintenance parameters and rules; further adopt a multi-objective parameter optimization method to balance among multi-dimensional objectives such as cost, efficiency, and safety, and generate an initial abnormal repair plan with optimal comprehensive performance; introduce abnormal prediction simulation data to perform statically indeterminate optimization on the initial plan, enhancing its adaptability and robustness to future fault evolution and complex working condition changes, thereby finally forming line abnormal repair data with foresight, safety, and practicality. It improves the intelligent design level of the maintenance process and realizes the full-process closed-loop control from material selection to process planning and then to dynamic optimization.

[0156] In an exemplary embodiment, as Figure 8 shown, according to the line abnormal repair data, calculate the swarm robot maintenance data of the swarm robots at the line trench scale, including steps 802 to 806. Among them:

[0157] Step 802, according to the line abnormal repair data and the line trench scale, calculate the trench maintenance envelope of each maintenance robot of the swarm robots in the power line trench.

[0158] Among them, the maintenance robot can be an intelligent operation unit deployed in the power line trench, with functions such as autonomous navigation, precise operation, and fault handling, and is dedicated to performing various maintenance tasks, such as cable replacement, insulation reinforcement, structure reinforcement, surface cleaning, etc.

[0159] Among them, the maintenance envelope of the pipeline trench can be the boundary of the maximum operating range that a maintenance robot may occupy in space when performing a specific task. This envelope is jointly determined by factors such as the robot's size, kinematic model, posture change during operation, extension range, and path deviation, and represents the dynamic usage area of the pipeline trench space by the robot during the task completion process.

[0160] Specifically, based on the information in the line anomaly repair data about the location of each maintenance task, the required action range, the maintenance process flow, and the operation duration, etc., and combined with the actual environmental parameters such as the spatial scale, geometric structure, channel width, and obstacle distribution of the line pipeline trench, the motion range of each maintenance robot is modeled. Then, using the robot's kinematic and dynamic models, factors such as the posture change of each maintenance robot under different maintenance working conditions, the maximum reachable range of the end effector, the operation radius, and the steering limit are analyzed to generate its three-dimensional operation trajectory inside the pipeline trench. Combining the posture adjustment and space occupancy that the robot may be involved in during the task execution process, the external boundary of the operation space of each robot is comprehensively formed and defined as the maintenance envelope of the pipeline trench. The maintenance envelope of the pipeline trench is not only used to identify the dynamic operable area of the robot when performing maintenance tasks, but also provides key spatial boundary data for subsequent safety analysis, path planning, and path avoidance of supply robots.

[0161] Step 804, predict the redundant safety information of the robot group in the power line pipeline trench according to each maintenance envelope of the pipeline trench.

[0162] Among them, the redundant safety information can be the prediction and evaluation results of the required safety buffer space based on the uncertain factors such as the offset, error, and path fluctuation that may occur during the operation of the maintenance robot. This information includes the robot operation error range, the minimum safety distance, the obstacle avoidance area, the collaborative conflict risk points, etc.

[0163] Specifically, based on the maintenance envelope of the pipeline trench corresponding to each maintenance robot, multiple dimensions of dynamic factors are comprehensively considered, including the operation error of each maintenance robot during the task execution, the path tracking offset, the inertial drift during startup and stop, the size of the maintenance robot body, the extension limit of the robotic arm, the space overlap during collaborative operation, and the position distribution of fixed obstacles (such as brackets and cable trays) in the pipeline trench; combined with the three-dimensional simulation environment, applying collision detection algorithms and multi-scene simulations, dynamically analyze the boundary changes and minimum safety distance requirements that the maintenance robot may touch during the task process. In addition, the time conflict window and obstacle avoidance mechanism during the collaborative operation of multiple maintenance robots are also considered to identify the key areas and path coincidence points prone to interference. The calculated redundant safety information includes safety buffer areas, potential conflict areas, operation redundancy rates, and risk level evaluations, etc.

[0164] Step 806: Based on the redundant safety information and the dimensions of the line trench, calculate the maintenance supply envelope of each supply robot in the machine group for the power line trench.

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

[0166] Among them, the maintenance supply envelope can be the maximum dynamic space range where the supply robot can pass, stay, and operate during the supply task. This envelope comprehensively considers factors such as the trench structure dimensions, the activity area of the maintenance robot, 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 disturbing the operation of the maintenance robot, first determine the supply points and resource distribution strategies, and plan the supply paths and supply stay points that the supply robot can enter according to the time sequence distribution of the maintenance tasks; combined with the redundant safety information, optimize the supply paths and supply stay points to avoid high-frequency maintenance operation areas and narrow channels, while meeting the requirements of supply timeliness and dynamic scheduling. Based on the optimized supply paths and supply stay points, through spatial reachability analysis, path obstacle avoidance simulation, and operation coordination modeling, calculate the dynamic operation range boundary for each supply robot to form the maintenance supply envelope.

[0168] Step 808: Integrate the trench maintenance envelope and the maintenance supply envelope to obtain the machine group maintenance data.

[0169] Specifically, analyze the spatial overlap, potential conflict areas, and cooperation nodes between the trench maintenance envelope and the maintenance supply envelope, combined with the operation time windows, supply frequencies, task priorities, and path intersections of each robot. By introducing obstacle avoidance strategies, dynamic yielding rules, and resource sharing mechanisms, overall optimize the group behavior of each robot, optimize the envelopes that may interfere with each other, and ensure that the machine group can achieve efficient cooperation, uninterrupted operation, and conflict minimization in the limited trench space. At the same time, consider the redundant replacement strategy for emergencies (such as robot failure or path blockage) during the integration process. The final output machine group maintenance data includes the multi-robot operation space-time arrangement diagram, task cooperation sequence, obstacle avoidance action library, resource interaction protocol, and maintenance execution instruction set.

[0170] In this embodiment, by combining the line anomaly repair data with the spatial scale of the line trench, the spatial operation range of each maintenance robot during actual operation is accurately calculated to form a maintenance envelope line, realizing dynamic modeling of the operation space. Further, based on this envelope line, redundant safety information of the robot group in a limited space is predicted, potential collision risks and spatial interference areas are effectively identified, ensuring safety and stability during group operation. According to the redundant safety information and the trench structure characteristics, the travel path and operation boundary of the supply robot are reasonably planned to generate a supply envelope line, thereby realizing efficient zoning and scheduling of supply and maintenance tasks. By integrating the maintenance and supply envelope lines, complete maintenance data of the robot group is output, enabling the robot group to have the capabilities of collaborative operation, path obstacle avoidance, and dynamic allocation in the trench environment, significantly improving the intelligence, automation, and spatial utilization efficiency of the power line trench maintenance operation.

[0171] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially according to the indication of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0172] Based on the same inventive concept, an embodiment of the present application also provides a power line trench digital and intelligent online operation and maintenance device for implementing the above-mentioned power line trench digital and intelligent online operation and maintenance method, as Figure 9 shown, including: an inspection information planning module 902, an inspection data obtaining module 904, a trench anomaly analysis module 906, a trench repair analysis module 908, and a trench repair control module 910. The implementation solution provided by this device to solve problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power line trench digital and intelligent online operation and maintenance device provided below can refer to the limitations on a power line trench digital and intelligent online operation and maintenance method in the above text, and will not be repeated here. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

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

[0174] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are implemented.

[0175] In one embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0176] In one embodiment, a computer program product or a computer program is provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the 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 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 this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0178] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. 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 method embodiments.

Claims

1. A digital online operation and maintenance method for power line trenches, characterized in that The method includes: In the case where the line static perception data of the power line trench is abnormal, plan the inspection information of the swarm of robots for the power line trench according to the line static perception data; Control the swarm of robots for the power line trench to inspect the power line trench according to the inspection information of the swarm of robots, and obtain line dynamic inspection data; Perform physical field coupling simulation on the power line trench according to the line dynamic inspection data, and obtain line abnormal coupling analysis data; Taking the abnormal elimination rule set of the power line trench as a constraint condition, calculate the repair process of the power line trench according to the line abnormal coupling analysis data, and obtain line abnormal repair data; Calculate the maintenance data of the swarm of robots at the scale of the line trench according to the line abnormal repair data.

2. The method according to claim 1, characterized in that, The performing physical field coupling simulation on the power line trench according to the line dynamic inspection data to obtain line abnormal coupling analysis data includes: Identify the physical field coupling boundary conditions of the power line trench according to the line dynamic inspection data; Construct a physical field coupling equation set of the power line trench according to the physical field coupling boundary conditions; Perform abnormal coupling simulation on the power line trench according to the physical field coupling equation set, and obtain abnormal coupling simulation data; Perform abnormal prediction simulation on the power line trench according to the physical field coupling equation set and the abnormal coupling simulation data, and obtain abnormal prediction simulation data; Fuse the abnormal coupling simulation data and the abnormal prediction simulation data to obtain the line abnormal coupling analysis data.

3. The method according to claim 2, characterized in that The performing abnormal coupling simulation on the power line trench according to the physical field coupling equation set to obtain abnormal coupling simulation data includes: Perform spatial partitioning on the power line trench to obtain data of each trench spatial partition; Use the physical field coupling equation set to perform abnormal coupling solution on the data of each trench spatial partition respectively, and obtain first coupling simulation data; Use the physical field coupling equation set to perform abnormal coupling solution on different physical processes in the power line trench respectively, and obtain physical coupling simulation data; Perform periodic exchange on the coupling amounts of the physical coupling simulation data to obtain second coupling simulation data; Select the abnormal coupling simulation data from the first coupling simulation data and the second coupling simulation data according to the actual scene data of the power line trench.

4. The method according to claim 3, wherein The performing abnormal prediction simulation on the power line trench according to the physical field coupling equation set and the abnormal coupling simulation data to obtain abnormal prediction simulation data includes: Set the abnormal prediction term of the physical field coupling equation set according to the abnormal coupling simulation data and the historical abnormal data of the power line trench; Perform abnormal prediction simulation on the power line trench according to the abnormal prediction term, and obtain the abnormal prediction simulation data.

5. The method according to claim 4, characterized in that, 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; The thermal-mechanical coupling equation set includes a heat transfer and heat source equation and a mechanical equilibrium equation; The expression of the heat transfer and heat source equation is as follows: Among them, ρ1 is the density of solid materials in the power line trench, C p (T) is the specific heat capacity of the material varying with temperature T, k(T) is the thermal conductivity of the material varying with temperature T, Γ EM (e, B, T) is the electromagnetic loss heat source term of the power line trench varying with electric field E, magnetic field B and temperature T, is the mechanical energy dissipation heat term of the power line trench varying with the strain rate and the solid stress tensor σ1, T env is the temperature at the ventilation place of the power line trench; The expression of the mechanical equilibrium equation is as follows: where, σ1(T, ε) is the solid stress tensor varying with temperature T and the strain tensor ε of the solid material, f ext (T) is the external body force varying with temperature T, D(T, ω(ε)) is the constitutive matrix coupling temperature T and the damage degree ω(ε), and α(T - T0) is the thermal expansion coefficient varying with the temperature range T - T0; The fluid-structure coupling equation set includes a fluid region equation, a structure region equation, and a fluid-structure coupling boundary condition; The expression of the fluid region equation is as follows: 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 on the solid displacement, u is the structural deformation that changes with the deformation of the flow channel, and k(p,T) is the permeability coefficient that changes with the temperature T and the fluid pressure p; The expression of the structure region equation is as follows: Among them, ρ s is the structural density of the power line trench, is the structural stress tensor that varies with temperature T and structural deformation u, g fluid (p, v) is the external load of the fluid on the solid that varies with fluid pressure p and fluid velocity field v, D s is the elastic constitutive matrix of the structural material, and ε(u) is the structural material strain tensor that varies with structural deformation u; The expression of the fluid-structure coupling boundary condition is as follows: where φ(u) is a structural safety factor function that varies with the structural deformation u, and n is the interface normal vector; The electromagnetic field coupling equation set includes Maxwell's equations, the coupling point current equation, and the medium relation equation; The expression of Maxwell's equations is as follows: where E is the electric field strength, H is the magnetic field strength, B is the magnetic induction intensity, D is the electric displacement vector, ρ free is the free charge density, and J coupled is the coupled current density; The expression of the coupling point current equation is as follows: J coupled = σ2(T, ε)E + J pd (E, T, ε) where σ2(T, ε) is the material conductivity varying with temperature T and the strain tensor ε of the solid material, and J pd (E, T, ε) is the partial discharge current varying with the electric field strength E, temperature T, and the strain tensor ε of the solid material; The expression of the medium relation equation is as follows: D = ε′(T, ε)E, B = μH where ε′(T, ε) is the relative permittivity that varies with the temperature T and the solid material strain tensor ε.

6. The method according to claim 2, characterized in that Taking the set of abnormal exclusion rules of the power line trench as a constraint condition, calculate the repair process of the power line trench according to the line abnormal coupling analysis data to obtain line abnormal repair data, including: Extract the fault element characteristics from the line abnormal coupling analysis data to obtain the key characteristic information of the trench abnormality; Taking the set of abnormal exclusion rules of the power line trench as a constraint condition, conduct a maintenance decision analysis on the power line trench according to the key characteristic information of the trench abnormality to obtain trench maintenance decision data; According to the trench maintenance decision data, conduct a maintenance parameter analysis on the power line trench to obtain the line abnormal repair data.

7. The method according to claim 6, characterized in that, The conducting a maintenance parameter analysis on the power line trench according to the trench maintenance decision data to obtain the line abnormal repair data includes: According to the trench maintenance decision data, select several material process maintenance parameters from the material process database of the power line trench; According to each of the material process maintenance parameters, select several material process maintenance rules from the material process rule library of the power line trench; Conduct a multi-objective parameter optimization on each of the material process maintenance parameters and each of the material process maintenance rules to obtain initial abnormal repair data; According to the abnormal prediction simulation data, conduct a hyperstatic optimization on the initial abnormal repair data to obtain the line abnormal repair data.

8. The method according to claim 1, wherein The calculating the machine group maintenance data of the machine group under the scale of the line trench according to the line abnormal repair data includes: According to the line abnormal repair data and the line trench scale, calculate the trench maintenance envelope of each maintenance robot in the machine group in the power line trench; According to each of the trench maintenance envelopes, predict the redundant safety information of the machine group in the power line trench; According to the redundant safety information and the line trench scale, calculate the maintenance supply envelope of each supply robot in the machine group in the power line trench; Fuse the trench maintenance envelope and the maintenance supply envelope to obtain the machine group maintenance data.

9. An intelligent online operation and maintenance device for power line trenches, characterized in that, The device includes: An inspection information planning module, configured to plan the inspection information of the swarm of robots for the power line trench according to the static line perception data of the power line trench when the static line perception data of the power line trench is abnormal; An inspection data acquisition module, configured to control the swarm of robots for the power line trench to inspect the power line trench according to the inspection information of the swarm of robots, so as to obtain dynamic line inspection data; A trench anomaly analysis module, configured to perform physical field coupling simulation on the power line trench according to the dynamic line inspection data, so as to obtain line anomaly coupling analysis data; A trench repair analysis module, configured to calculate the repair process of the power line trench according to the line anomaly coupling analysis data with the abnormal elimination rule set of the power line trench as a constraint condition, so as to obtain line anomaly repair data; A trench repair control module, configured to calculate the maintenance data of the swarm of robots at the scale of the line trench according to the line anomaly repair data.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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