63 results about "ANT" patented technology

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention relates to the technical field of machine path planning, and discloses an island ant colony path planning method for an unmanned trolley, which comprises the following steps: after generating a grid map, decomposing global path search into path exploration between adjacent islands; non-uniform pheromone initialization is adopted between adjacent islands, local paths between the adjacent islands are searched respectively, and the position of a next grid is selected in the searching process by combining an improved node transition probability with a heuristic function and pheromone concentration; and when global pheromone distribution is updated, an optimal path is rewarded, and a worst path is punished. And combining the local paths of the island pairs to obtain global paths, and selecting the global path with the shortest length as the optimal driving path. According to the method, a segmented island strategy is adopted, the complexity of single search is reduced, the early random probing process is shortened through non-uniform initialization pheromones, the convergence speed is increased, the number of deadlock ants is effectively reduced, and the precision, convergence and stability of path planning are improved.

The invention belongs to the related technical field of underwater gravity matching navigation, and discloses a submersible vehicle route planning method, application and equipment based on an improved ant colony algorithm, and the method comprises the steps: (1) constructing an adaptability index matrix based on gravity adaptability characteristics, and carrying out the normalization and information entropy weighting to obtain comprehensive adaptability characteristics; (2) constructing an environment grid model according to the comprehensive adaptability characteristics; (3) taking a free space as a search area, carrying out path search based on an environment grating model and an improved ant colony algorithm, and further obtaining a track node sequence meeting the adaptability constraint, the path continuity and the shortest path target to complete track planning; wherein the improved ant colony algorithm is obtained by introducing an elite ant strategy to enhance the globally optimal path pheromone of the ant colony algorithm and adaptively adjusting a pheromone concentration factor, a heuristic function factor and a pheromone volatilization coefficient in the ant colony algorithm by using a particle swarm optimization algorithm. The search efficiency and the automation degree are improved.

The invention relates to the technical field of real estate, and discloses an ant colony algorithm and neural network combined house price data prediction method, which comprises the following steps: S1, multi-source data acquisition, S2, data preprocessing, S3, ant colony algorithm parameter optimization, S4, neural network dual-stage training and S5, adaptive optimization feedback. According to the house price data prediction technology combining the ant colony algorithm and the neural network, neural network model parameters are optimized by adopting the ant colony algorithm; in the house price prediction model, the parameters are combined to form a path, and ants traverse different parameter combination paths; along with iteration, pheromones on a better parameter combination path are continuously accumulated and enhanced, and ants are guided to explore more; in this way, the parameter space can be fully traversed, local optimum caused by parameter random initialization of a traditional neural network is avoided, and therefore the model can obtain a better parameter combination, and the prediction precision is remarkably improved.

The invention discloses a multi-logistics vehicle collaborative scheduling method based on a guided customer selection ant colony system, and belongs to the technical field of computing intelligence. According to the method, a feasible solution scheme is constructed for a multi-logistics-vehicle cooperative scheduling optimization problem by maintaining ant teams with the same quantity as the logistics vehicles, wherein each ant is responsible for constructing a service path of one logistics vehicle. After each ant randomly selects the initial customer of the respective service path, selecting the customer which is finally served for each ant based on a final customer selection method of the minimum service cost; then, the ant colony system fuses the current customer information and the customer selection mechanism of the customer information of the final service, and constructs the service path of the logistics vehicle path by path and customer by customer, so that the constructed path is more directional; and finally, in combination with 2-opt and a point insertion local search strategy, optimizing service paths of all logistics vehicles. According to the method, the optimal multi-logistics-vehicle cooperative scheduling scheme is constructed, and meanwhile, the service cost between the logistics vehicles can be well balanced.

The invention relates to a storage AGV path planning method based on a hierarchical collaborative ant colony algorithm, belongs to the field of storage robot path planning, and aims to solve the problem of realizing better path planning in a complex storage environment. The method comprises the following steps: constructing an environment model of intelligent storage based on a grid method; an ant colony algorithm is improved on the basis of a hierarchical cooperative system, an ant colony is divided into scout ants responsible for exploration and foraging ants responsible for optimization, a heuristic function comprehensively considering the distance, the corner and a new exploration area is constructed, an improved pheromone updating strategy based on ant layering and the pheromone concentration on a self-adaptive volatilization factor updating path are adopted, and a new exploration area is obtained. The transfer strategy fuses three modes of random selection, probability selection and determinacy selection, and finally an improved ant colony algorithm is obtained; an improved algorithm is utilized to plan a global path for each AGV, and secondary optimization is performed in a targeted manner, so that the path length is shorter, and the smoothness is higher. And an effective solution is provided for AGV path planning in a complex storage environment.

The application provides a ship path planning method and device for a near-shore environment and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: rasterizing a near-shore environment image to obtain a sailing space model; setting initial parameters of an improved potential field-ant colony algorithm and initializing a path taboo table, then determining path point pheromone distribution and a combined potential field force, calculating a transfer probability of an ant transferring to other nodes to determine a next node, and updating the taboo table; after an ant traverses all nodes to reach a target point, updating the pheromone distribution and recording a traversal path; after all ants traverse all nodes, obtaining traversal paths of all ants and determining an optimal path. The application improves the potential field-ant colony algorithm, introduces potential field information, and dynamically adjusts a heuristic information function; adjusts a path selection probability optimization algorithm structure; and considers the sailing characteristics of a ship itself to ensure the demand for autonomous sailing of the ship.

The invention provides a runoff hydropower station annual maintenance and power generation plan joint optimization method based on an improved ant colony algorithm, and belongs to the technical field of hydropower station optimization scheduling. According to the method, for the multi-target conflict problem of abandoned water loss, peak regulation capacity insufficiency and base load vacancy in an annual maintenance plan of a runoff hydropower station, a comprehensive target function containing abandoned water penalty, peak regulation vacancy penalty and base load vacancy penalty is constructed, and combined optimization is achieved by adopting an improved multi-group coevolution ant colony algorithm. The core of the algorithm lies in that ant colonies are divided into common ant colonies and elite ant colonies, the search capability is improved through local and global pheromone updating and diffusion mechanisms, and the process is optimized to determine the optimal overhaul time period of a to-be-overhauled unit and the optimal operation mode of a non-overhaul unit. Example verification shows that the method can significantly reduce abandoned water loss and base load vacancy, ensures sufficient peak regulation capacity, saves annual operation cost of the hydropower station, and is suitable for intelligent decision-making of the large radial flow type hydropower station.

The invention relates to the technical field of multi-objective optimization control, in particular to an improved ant lion algorithm-based multi-objective optimization control method for an air source heat pump air conditioning system, which comprises the following steps of: constructing a triple constraint fitness function; the method has the beneficial effects that the limitation of single target optimization of a traditional control method is broken through by constructing a triple constraint fitness function comprising a system energy consumption item, a comfort item and a system comprehensive penalty item. By dynamically adjusting the correction coefficient, the relation of energy consumption, comfort level and stability can be flexibly balanced according to actual requirements, the extreme situation that comfort level is sacrificed for energy conservation or excessive energy consumption is caused for comfort is avoided, and the optimal comprehensive performance of the system is achieved.

This invention proposes an optimized method for drone data acquisition based on matrix completion and trust assessment, applicable to drone forensics in IoT network defense. First, in matrix completion, the sampling point locations and data packet acquisition times are constructed into a matrix, and matrix completion technology is used to recover all information and select sampling points. Second, in the drone flight trajectory, the selected sampling points and the Elite Ant Trail Optimization (EATO) algorithm are combined to optimize the drone's flight trajectory. Third, in the trust evolution mechanism, the comprehensive trust level of sensor nodes is obtained through dual evaluation by neighboring nodes and the drone. The proposed method can solve the security risks and resource consumption problems of data acquisition from IoT smart devices, effectively identify malicious nodes, optimize the accuracy of trust assessment, improve network security, and reduce drone energy consumption.

The invention discloses a multi-SoC cooperative task allocation method based on quantum ant colony optimization. The method comprises the following steps: acquiring a node resource portrait set and a task attribute set for each system-on-chip node in a multi-SoC system; constructing a task-resource mapping graph; obtaining a quantum coding task set; loading the quantum coding task set to a search state space of quantum ant individuals, setting a pheromone matrix and a quantum rotation angle matrix, and obtaining an initial population of a quantum ant colony; obtaining an updated quantum state population; calculating a delay index, a power consumption index and a reliability index of each deterministic task allocation result according to the node resource portrait set, and screening out a candidate feasible solution set meeting real-time constraint; and obtaining a global scheduling scheme. According to the method, the modeling granularity and the distribution precision of task distribution under multi-domain heterogeneous resources are remarkably improved, the problems of bandwidth contention and delay mutation in cross-node and cross-domain migration are effectively avoided, and the real-time performance and the high adaptability of task distribution are achieved.

The invention discloses a dynamic equipment layout method based on a quasi-object strategy and a multi-target ant colony optimization algorithm. The method comprises the steps of layout initialization, layout legalization operation, a movement strategy based on reference equipment, configuration optimization operation and Pareto optimal configuration selection based on a maximum and minimum target distance method. N configurations Xl (l = 1, 2,..., n) are randomly generated, each ant l represents one configuration Xl, and initial configurations of the n ants are obtained; performing legalization operation on the configuration X1 by adopting a gradient method based on dynamic step length, executing a movement strategy based on reference equipment to obtain a group of compact and legal configurations, and recording the configurations as a configuration library BL; selecting all non-dominated configurations in the set BL, and storing the non-dominated configurations in an external document CS; and for the current ant l, generating a random number o, and judging the size relationship between the random number o and the parameter p to select an optimization strategy, when o is greater than p, selecting to execute a local search strategy based on improved pseudo-random proportion, otherwise, using a global optimization strategy based on ecological niche.

The invention discloses a parking lot recommendation method based on an improved ant colony algorithm, and the method comprises the steps: S1, setting the number of ants and a pheromone matrix, and determining a weight parameter of a heuristic function; s2, in the current iteration, each ant starts from a starting point, a next node is selected by adopting a roulette strategy according to the pheromone concentration between the nodes and a heuristic function value until the next node reaches an end point, a complete path is formed, and in the path construction process, part of ants execute a random walking strategy to perform global exploration; s3, after all the ants complete path construction, a pheromone matrix is updated uniformly according to the path advantages and disadvantages of all the ants, and the updating process does not include a pheromone volatilization step; and S4, the steps S2 to S3 are executed repeatedly until the iteration termination condition is met, the global optimal path in all iterations is output as a recommendation result, and the recommendation result at least comprises the target parking lot, the distance and the passing time information. The method is suitable for parking lot recommendation.

This invention relates to the field of temperature control, specifically to a method for optimizing simulation parameters of photonic debonding for wafer-level packaging. The method includes: obtaining the value range of simulation parameters for photonic debonding; dividing the data into a grid disk based on the value range, where each node of the grid disk corresponds to a parameter set; constructing an objective function for an ant colony algorithm; for each iteration of the ant colony algorithm, calculating the dynamic pheromone evaporation coefficient of the node where each ant is located to control the pheromone decay rate in the next iteration; filtering all parameter sets during the iteration process to obtain a globally optimal parameter set; and completing parameter optimization. The globally optimal parameter set is used for temperature control of photonic debonding. This invention can obtain optimal simulation parameters through an ant colony algorithm to improve the accuracy of controlling the temperature of photonic debonding.

The invention discloses an electrical impedance tomography detection method for a dike termite channel, which relates to dike detection and comprises the following steps: vertically burying an electrode array in a dike, establishing a three-dimensional geometric model in finite element analysis software, and obtaining a dike internal conductivity distribution diagram by adopting a dynamic electrical impedance tomography algorithm. And extracting a suspected ant cave feature position based on the conductivity distribution diagram, carrying out field verification on the suspected ant cave feature position, and confirming the existence and position of the ant cave. According to the method, the dynamic electrical impedance tomography algorithm is combined with finite element model construction, so that three-dimensional visual detection of the ant-suffering channel in the dam is realized, the limitation that a detection area cannot be fully covered by traditional manual drilling is solved, and the space precision and range integrity of ant-suffering positioning are improved.

The application discloses a fracture azimuth fitting method and device based on seismic data, and relates to the technical field of petroleum geophysical exploration. Ant body attribute data is acquired, grid cell division is performed on the ant body attribute data, the size of a local window is defined, grid quality evaluation is performed in the local window through a grid quality evaluation function, and the data quality of the grid cell is acquired; the size of a fitting window is selected, the spatial weight of each grid cell is acquired based on the spatial position of data, the quality weight of each grid cell is acquired based on the data quality of the grid cell, and a comprehensive weight is acquired; a double-target optimization function containing fitting accuracy and spatial smoothness is constructed, a constraint condition is designed, the fitting parameter vector in the double-target optimization function is solved, the optimal fitting parameter vector is acquired, and the fracture azimuth of the center of each fitting window is acquired. The problem of how to realize high-precision and high-robustness extraction of the fracture azimuth is solved.

1.The name of the design product: ant ecological box (ant center). 2.The use of the design product: for ant breeding. 3.The design points of the design product: in shape. 4.The picture or photo that best shows the design points: perspective view. 5.Other circumstances that need to be explained: the A part is transparent material.

The invention discloses a multi-logistics vehicle cooperation method with transportation limitation based on a pheromone sharing ant colony system, and the method comprises the following steps: firstly, obtaining task and vehicle information, constructing a cost and authority matrix, and building a synchronous optimization model; secondly, setting ant colony parameters, and performing greedy initialization on an information matrix; thirdly, initializing a plurality of ant colonies and distributing a task taboo table; then, the ant colony selects tasks through a state transition equation according to the authority and the taboo table, winning ants are screened according to the cost probability, pheromones are updated, and a preliminary scheme is formed; secondly, optimizing the scheme by using a local search algorithm fused by 2-opt and point migration; then, evaluating an adaptive value, updating a global optimal scheme and global pheromones, and sharing the pheromones to adjacent edges; and finally, iteration is carried out until convergence, and a final cooperation scheme is output. According to the invention, efficient scheduling under transportation limitation is realized, and the cost difference between vehicles is reduced while the total cost is minimized.

The invention discloses a path planning method and system based on an improved self-adaptive ant colony algorithm, and the method comprises the following steps: initializing, loading traveling salesman problem instance data of a plurality of places based on basic parameters and dynamic parameters of the improved self-adaptive ant colony algorithm, constructing a distance matrix between the places based on Euclidean distance, and performing path planning on the distance matrix. Initializing a symmetric pheromone matrix; iterative optimization is carried out, each ant starts from a randomly selected initial site, a complete path is constructed step by step through an improved state transition probability rule, the path length of each ant is calculated, and the current iterative optimal path and the length thereof are recorded; iteration is terminated, and when the number of iterations reaches the preset value, the algorithm stops running; according to the method, statistics is carried out on results of multiple rounds of operation, the average optimal distance of the multiple rounds of operation and the minimum distance in the multiple rounds of operation are obtained, namely, the optimal planning path is obtained, a self-adaptive volatilization mechanism is introduced, the pheromone volatilization rate is dynamically adjusted according to the number of iterations, and the local optimum problem is solved.

The invention discloses a hydraulic power plant auxiliary equipment scheduling and energy efficiency optimization method based on an ant colony algorithm, and relates to the technical field of hydraulic power plant auxiliary equipment scheduling. The method comprises the following steps: collecting related data of auxiliary equipment of the hydraulic power plant, wherein the related data comprises a real-time load demand of a power grid, operation condition data of each auxiliary equipment, energy efficiency expression data and operation constraint parameters; related index calculation is carried out based on the related data, and the scheduling demand of the auxiliary machine equipment is identified; establishing an objective function and constraint conditions of auxiliary equipment scheduling; and each ant represents a candidate auxiliary equipment scheduling scheme, and an optimal auxiliary equipment scheduling scheme is solved through an ant colony algorithm. The invention provides an efficient and automatic hydraulic power plant auxiliary equipment scheduling and energy efficiency optimization solution, the intelligent level and energy efficiency performance of auxiliary equipment system scheduling are improved by reducing manual intervention and expert dependence, and the method has wide application prospects and remarkable engineering value.

The embodiment of the invention provides a path planning method and device, and the method comprises the steps: controlling each ant in each initial ant colony to start from a starting station, determining a next passing station in candidate stations according to a multi-target cost value and pheromone until a termination station is reached, obtaining an initial path corresponding to the current ant, and carrying out the control of the initial ant colony. And determining a total multi-target cost value corresponding to each initial path, determining an initial optimal path of the ant colony according to the total multi-target cost value, locally updating pheromones in the traffic network model, and performing cross updating on the pheromones according to the initial optimal path of each ant colony under the condition that all the initial ant colonies complete one-time iteration. And obtaining the updated pheromone, repeatedly executing the step of obtaining the initial path corresponding to the current ant until a termination condition is met, obtaining a plurality of ant colony update optimal paths, and displaying the optimal paths to the terminal equipment. According to the method, the defects of path planning and the like in combination with actual traffic conditions are effectively overcome, and the dynamic adaptability of path planning is remarkably improved.

Embodiments of the present application provide a path planning method and device. For each initial ant colony, each ant in the initial ant colony is controlled to start from a starting station, and the next passing station is determined in candidate stations according to multi-objective generation value and pheromone, until the terminal station is reached, to obtain an initial path corresponding to the current ant. The total multi-objective generation value corresponding to each initial path is determined, the initial optimal path of the ant colony is determined according to the total multi-objective generation value, and the pheromone in the traffic network model is locally updated. In the case that all initial ant colonies complete one iteration, the pheromone is cross-updated according to the initial optimal path of each ant colony, to obtain updated pheromone. The step of obtaining the initial path corresponding to the current ant is repeatedly executed until the termination condition is met, to obtain multiple ant colony updated optimal paths and display them to a terminal device. The method effectively solves the deficiencies in path planning combined with actual traffic conditions and significantly improves the dynamic adaptability of path planning.

The invention discloses an improved ant colony algorithm-based arctic channel path planning method, which belongs to the technical field of navigation, and comprises the following steps of: (1) calculating a navigable area of an icebreaker in an arctic region by combining ice condition data, meteorological data and the level of the icebreaker; (2) initializing pheromone concentration for effective nodes in the navigable area; (3) finding out a next-step transferable node set naavi of the kth ant starting from the current node i at the moment t, and calculating the probability of the ant starting from the node i to the node j in the naavi according to the pheromone concentration; completing one iteration; and (4) updating the pheromone concentration on the path from the node i to the node j, and entering the next iteration until a termination condition is met. According to the north pole channel path planning method based on the improved ant colony algorithm, the initial pheromone concentration is optimized while the characteristics of the ant colony algorithm are reserved, the early blind search is remarkably reduced, the calculation amount is reduced, and the search efficiency is improved.

The application relates to the technical field of power distribution network automation, in particular to a power distribution network transfer path optimization decision method and system; the method comprises the following steps: finding an optimal transfer path connected to a healthy power supply for a to-be-transferred load in a power distribution network based on an improved ant colony algorithm; the improved ant colony algorithm comprises a dynamic heuristic factor for guiding ants; for the dynamic heuristic factor of any line, the dynamic heuristic factor is inversely proportional to the physical impedance and the fluctuation penalty factor of the line. The application can improve the stability of the load after the load in an affected area is transferred, and reduces the occurrence of secondary faults.

The application discloses a kind of based on the cooperative inspection method, device, terminal equipment and storage medium of heterogeneous multi-machine combination, by according to collection point and supply point, the undirected connected graph of inspection area is constructed, according to the number of robots, initial position and initial resource, constructs several ant colonies, and according to undirected connected graph, the ant colony is operated several times population exploration, to make each ant colony generate several path sets, and from several path sets, confirm a target path set, finally according to target path set, determine the inspection task of each inspection robot.Therefore, the present application solves the problem of robot endurance by constructing undirected connected graph with supply points and collection points, and improves the computational efficiency of the algorithm by constructing several ant colonies simultaneously for population exploration operation in a concurrent manner, solving the defects of insufficient computing power and insufficient robot endurance when large-scale inspection is performed in the current power intelligent inspection.

The invention provides an ant colony algorithm-based man-vehicle mixed multi-view path optimization method and system under dike burst flood, and the method comprises the steps: S1, processing a non-structural network submerged by flood, and then employing dike burst flood submerged information calculated by employing one-dimensional and two-dimensional hydrodynamic models as an initial condition for constraining an evacuation path; s2, initializing an evacuation road network, to be specific, building an evacuation road network model according to a graph theory method and city real road network information, and searching a flood avoidance multi-target optimization path from an evacuation point to an evacuation terminal point in the road network model; s3, initializing ant colony algorithm parameters, including initializing ant positions, calculating the number of to-be-evacuated objects at each evacuation source point by m ants representing different attributes according to a population thermodynamic analysis method, then initializing parameters, providing an improved heuristic formula, placing the parameters at each initial position, setting a non-dominated solution set NDS, and calculating the number of to-be-evacuated objects at each evacuation source point; and the local optimum convergence speed of the algorithm is reduced by using a pheromone updating formula.

The application discloses a kind of flood-avoiding transfer multi-objective optimization analysis method under the consideration of road congestion constraint, including constructing road network undirected graph;The shortest distance of each node to the settlement area is calculated;Ant colony and road network parameter are initialized, the pheromone concentration and road network congestion degree of last round training are reserved;All ant colonies are traversed, if not traversed, let k=k+1, all ants in ant colony k are traversed again;For each ant, the next node is selected according to the heuristic function, pheromone concentration and congestion degree, until it reaches the settlement area or empty node;Determine whether to meet the training termination condition, if meet, output pheromone concentration, settlement area matching result, and carry out result diagnosis.The application shortens transfer time and reduces transfer batch based on ant colony algorithm framework as optimization target, solves the multi-objective optimization problem of transfer area and settlement area matching and path selection under the constraint of road congestion.

The invention discloses an EIT image reconstruction quality evaluation-based dike termite nest identification method, and relates to the technical field of dike termite nest identification, and the method comprises the following steps: multi-channel EIT data collection, ant nest multi-physical field positive problem solving, image reconstruction based on a Tikhonov regularization algorithm, image reconstruction quality function calculation and image reconstruction quality evaluation. By designing a multi-channel EIT data acquisition scheme, full-coverage acquisition of electrical signals of the whole dike is realized, weak conductivity abnormal signals caused by ant caves are effectively reserved, comprehensive and pure original data support is provided for subsequent positive problem solving and image reconstruction, and the method has the advantages of being high in accuracy and high in reliability. The method ensures that signals of deep or tiny ant caves are not covered by noise, and solves the problems of limited data acquisition range, large noise interference and difficulty in capturing weak electrical characteristics of the ant caves in the dike in the existing detection technology.

The invention discloses a multi-constraint optimization method of an ant colony algorithm in transformer substation path planning. The method comprises the steps of optimizing a pheromone updating mechanism, introducing an elite ant strategy, establishing a multi-constraint adaptive model, and performing path fine adjustment in combination with a genetic algorithm. A dynamic pheromone updating mechanism is adopted, the pheromone volatilization rate is adjusted according to the search progress, and the exploration speed is increased in the initial stage of the algorithm; the high-quality path is enhanced in the later stage of search, and the convergence precision is improved; an elite ant strategy is introduced, and an ant colony is guided to search towards a global optimal direction through enhanced updating of a historical optimal solution, so that the quality of path planning is further improved; according to the method, a multi-constraint adaptability model is constructed, and in the ant colony optimization process, a qualified route is obtained through normalization processing and a punishment mechanism of constraint conditions in combination with environmental constraints, terrain adaptability and shortest distance optimization factors. Compared with a pheromone updating mechanism of a traditional ant colony algorithm, the method can find a better path more quickly, and the calculation time is shortened.

The invention relates to the technical field of power distribution network automation, in particular to an optimization decision-making method and system for a power distribution network transfer path. The method comprises the following steps: searching an optimal transfer path connected to a healthy power supply for a load to be transferred in a distribution network based on an improved ant colony algorithm; the improved ant colony algorithm comprises dynamic heuristic factors for guiding ants; the dynamic heuristic factor of any line is inversely proportional to the physical impedance and the fluctuation penalty factor of the line. According to the invention, the stability of the affected area after load transfer can be improved, and the occurrence of secondary faults is reduced.