Method and system for optimizing cable laying path in electric power engineering

Abstract

The invention relates to the technical field of power construction, in particular to a cable laying path optimization method and system in power engineering, which can improve the reasonability of cable laying path planning, reduce the construction cost and improve the power transmission stability. The method comprises the following steps: planning a cable laying path based on any two algorithms in a preset path planning algorithm database to obtain a first initial path and a second initial path; performing branch path positioning on the first initial path and the second initial path, extracting path paragraphs of which the divergence degrees are greater than a first preset threshold value, and marking the path paragraphs as branch paragraphs; obtaining a path divergence degree index by considering the ratio of the accumulated length of the divergence section to the total length of the cable laying plan; comparing the path divergence degree index with a second preset threshold value to obtain at least two optimized paths; and carrying out optimization processing on the plurality of optimized paths to obtain an optimal planning path for cable laying.

Description

Technical Field

[0001] The present invention relates to the technical field of power construction, and particularly to a method and system for optimizing the cable laying path in a power project. Background Art

[0002] With the continuous development of urban construction and the continuous growth of power demand, higher requirements are put forward for the planning of cable laying paths. A reasonable cable laying path can not only reduce construction costs, but also improve the stability and reliability of power transmission and reduce the impact on the surrounding environment.

[0003] Existing cable laying path planning methods are often relatively single, usually relying only on a certain path planning algorithm. This makes it difficult for the planned path to achieve the optimal effect when facing complex terrains, diverse surrounding environments, and strict cost control and other factors. For example, when planning the path solely based on the Geographic Information System (GIS), although the terrain and the distribution of existing buildings can be intuitively considered, there may be deficiencies in construction difficulty and cost estimation; while only using intelligent algorithms such as genetic algorithms may ignore the limitations of actual geographical conditions, resulting in the planned path being difficult to implement in actual construction.

[0004] In addition, in the existing path planning process, the differences and integration between the paths generated by different planning algorithms are rarely fully considered. The paths generated by different algorithms often act independently, lacking an effective comparison and fusion mechanism. This leads to the inability to give full play to the advantages of various algorithms when facing complex power engineering scenarios, and it is difficult to obtain the cable laying path with the optimal comprehensive performance. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method and system for optimizing the cable laying path in a power project, which can improve the rationality of cable laying path planning, reduce construction costs, and improve the stability of power transmission.

[0006] In the first aspect, the present invention provides a method for optimizing the cable laying path in a power project, the method comprising:

[0007] Based on any two algorithms in a preset path planning algorithm database, plan the cable laying path to obtain a first initial path and a second initial path;

[0008] Locate the diverging paths of the first initial path and the second initial path, extract the path segments with a divergence degree greater than a first preset threshold, and mark them as divergence segments;

[0009] Consider the ratio of the cumulative length of the divergence segments to the total length of the cable laying plan to obtain a path divergence degree index;

[0010] Compare the path divergence degree index with a second preset threshold to obtain at least two optimized paths;

[0011] Perform an optimization process on multiple optimized paths to obtain the optimal cable laying planning path.

[0012] Further, comparing the path divergence degree index with the second preset threshold includes:

[0013] In response to the path divergence degree index not exceeding the second preset threshold, mark both the first initial path and the second initial path as optimized paths;

[0014] In response to the path divergence degree index exceeding the second preset threshold, use the remaining algorithms in the preset path planning algorithm database to perform path planning on the divergent section again to obtain at least three divergent reconstruction planning paths;

[0015] Perform similarity analysis on at least three divergent reconstruction planning paths, extract the divergent reconstruction planning paths with similarity exceeding a third preset threshold between each pair, and perform overlay integration with any one of the first initial path or the second initial path to obtain optimized paths.

[0016] Further, the preset path planning algorithm database includes at least five path planning algorithms, at least including the shortest path algorithm, particle swarm optimization algorithm, genetic algorithm, ant colony algorithm, and simulated annealing algorithm.

[0017] Further, the formula for calculating the path divergence degree index is:

[0018]

[0019] where I represents the path divergence degree index; n represents the total number of divergent sections; m i represents the number of coordinate points included in the i-th divergent section; (x ij , y ij ) represents the horizontal and vertical coordinates of the j-th coordinate point in the i-th divergent section; L t represents the total length of the cable laying plan.

[0020] Further, the influencing factors for setting the first preset threshold include construction environment factors, cable laying requirement factors, algorithm characteristic factors, cost factors, and historical data factors.

[0021] Further, the method for performing an optimization process on multiple optimized paths includes:

[0022] For each optimized path, collect evaluation index data;

[0023] Score each item of the evaluation index data for each path, and summarize to obtain the total score for each path;

[0024] Assign weights to the data of each evaluation index, and calculate the comprehensive score of each path according to the weights and the total score of each path;

[0025] Compare the comprehensive scores of each path, and select the path with the highest score as the optimal planning path.

[0026] Furthermore, the factors considered in the process of optimizing multiple optimized paths include route length, construction complexity, construction cost investment, and the degree of impact on the surrounding ecological environment.

[0027] On the other hand, the present application also provides a cable laying path optimization system in a power project. The system includes:

[0028] A path planning module that plans the cable laying path based on any two algorithms in a preset path planning algorithm database to obtain a first initial path and a second initial path;

[0029] A divergence positioning module that locates the divergence paths of the first initial path and the second initial path, extracts the path segments with a divergence degree greater than a first preset threshold, and marks them as divergence segments;

[0030] An index calculation module that obtains a path divergence degree index by considering the ratio between the cumulative length of the divergence segments and the total length of the cable laying plan;

[0031] A comparison and optimization module that compares the path divergence degree index with a second preset threshold to obtain at least two optimized paths;

[0032] An optimization decision module that performs optimization processing on multiple optimized paths to obtain the optimal planning path for cable laying.

[0033] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus, and when the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0034] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0035] The beneficial effects of the present invention compared with the prior art are as follows: By selecting any two algorithms from the preset path planning algorithm database for path planning, the advantages of different algorithms are fully utilized; this diversity not only improves the flexibility of path planning, but also enables the planned path to better adapt to complex terrains and diverse surrounding environments; at the same time, the complementarity between different algorithms helps to reduce the blind spots in the planning process and improve the overall quality of path planning; the method introduces a step for locating the divergent paths of the first initial path and the second initial path, which can accurately identify the different parts between the two paths; by marking and processing the path segments with a divergence degree greater than the preset threshold, the method effectively reduces the uncertainty in path planning and improves the feasibility and reliability of the path; by considering the ratio of the cumulative length of the divergent segments to the total length of the cable laying plan, the method can quantitatively evaluate the divergence degree between paths; this provides a clear direction and basis for subsequent path optimization, helping to further improve the scientificity and accuracy of path planning; by comparing the path divergence degree index with the preset threshold for processing, the path planning scheme can be flexibly adjusted and optimized; this mechanism ensures that the path planning can generate the optimal path that meets the actual requirements when facing complex scenarios and changing needs;

[0036] By selecting any two algorithms from a preset database of at least five algorithms to plan the path, the limitations of a single algorithm are avoided; different algorithms have their own advantages and disadvantages. For example, the shortest path algorithm can quickly determine a path with a short distance, and the genetic algorithm can search for a better solution in a complex space. Combining multiple algorithms may integrate their advantages in terms of geographical conditions, cost, construction difficulty, etc., and generate an initial path that better meets the actual needs; by performing divergent path location and calculating the divergence degree index for two initial paths, the differences and their degrees between the paths generated by different algorithms can be clearly understood; this not only helps to discover the preferences of different algorithms in specific scenarios, but also provides a quantitative basis for subsequent optimization; obtaining the optimized path based on the comparison of the path divergence degree index with the second preset threshold can be flexibly adjusted according to the actual path differences, generating multiple optimized paths with different focuses, fully exploiting the potential of different algorithm combinations to adapt to the complex and changing scenarios of power engineering; optimizing by comprehensively considering the route length, construction complexity, construction cost investment, and the impact on the surrounding ecological environment among multiple optimized paths is a major improvement over traditional planning methods; it is difficult for traditional single-algorithm planning to consider multiple factors simultaneously, while this method can balance key factors and obtain the cable laying path with the best comprehensive performance, effectively reducing construction costs, improving the stability and reliability of power transmission, and reducing the impact on the surrounding environment; In summary, the above cable laying path optimization method in power engineering can improve the rationality of cable laying path planning, reduce construction costs, and improve the stability of power transmission. Brief Description of the Drawings

[0037] Figure 1 is the flowchart of the present invention;

[0038] Figure 2 is the flowchart for comparing the path divergence degree index with a second preset threshold;

[0039] Figure 3 is the structural diagram of a cable laying path optimization system in a power project. Detailed implementation manners

[0040] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage media contains computer program code.

[0041] The above-mentioned computer-readable storage media can adopt any combination of one or more computer-readable storage media. The computer-readable storage media includes: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, the computer-readable storage media can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component.

[0042] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0043] The present application describes the provided method, device, and electronic device through flowcharts and / or block diagrams.

[0044] It should be understood that each block of the flowchart and / or block diagram, as well as the combination of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, resulting in a device that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0045] These computer-readable program instructions may also be stored in a computer-readable storage medium that can cause a computer or other programmable data processing apparatus to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes instructions for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0046] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operation steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0047] The present application will be described below with reference to the accompanying drawings in the present application.

[0048] Embodiment 1: As Figures 1 to 2 shown, a method for optimizing the cable laying path in a power project of the present invention specifically includes the following steps:

[0049] S1. Based on any two algorithms in a preset path planning algorithm database, plan the cable laying path to obtain a first initial path and a second initial path;

[0050] The preset path planning algorithm database includes at least five path planning algorithms, at least including the shortest path algorithm, particle swarm optimization algorithm, genetic algorithm, ant colony algorithm, and simulated annealing algorithm;

[0051] Algorithm selection combination strategy: In actual power construction, different construction scenarios and requirements determine the selection combination of algorithms; for example, when the terrain of the construction area is relatively regular, the surrounding environmental interference is less, but the cost control is extremely strict, the shortest path algorithm and the genetic algorithm can be selected for combination; the shortest path algorithm can initially determine the theoretically shortest path, providing a basis for cost control; the genetic algorithm can optimize in combination with other factors such as construction difficulty and environmental impact on the basis of the distance factor; if the terrain of the construction area is complex, there are many obstacles, and at the same time, it is hoped to quickly obtain a better path plan, the particle swarm optimization algorithm and the ant colony algorithm can be selected for combination; the particle swarm optimization algorithm has a fast convergence speed and can quickly narrow the search range; the ant colony algorithm has unique advantages in dealing with complex space search problems, and the combination of the two can efficiently handle complex terrains;

[0052] Shortest Path Algorithm: In the power construction scenario, first, the geographical information of the construction area needs to be converted into graph structure data. Each power facility node and the cable laying route points are regarded as the nodes of the graph, and the connecting edges between the nodes represent the paths where cables can be laid. The weights of the edges can be assigned according to factors such as actual distance and terrain complexity. The shortest path algorithm starts from the starting node and continuously updates the shortest distances to other nodes, and finally finds the shortest path from the starting point to the ending point. This path is a part of the initial cable laying path planned based on the shortest path algorithm.

[0053] Particle Swarm Optimization Algorithm: In the power construction path planning, each particle is regarded as a potential solution for the cable laying path. The position of the particle corresponds to the coordinate representation of the path in space. The velocity of the particle determines its moving direction and step size in the search space. The algorithm initializes a group of particles, and each particle is randomly assigned an initial position and velocity. In each iteration, the particle adjusts its velocity and position according to its own historical best position and the global best position. For example, during the search process, the particle will adjust its moving direction according to factors such as the distance between the current position and the target position and the terrain complexity around it, and gradually search for a better cable laying path.

[0054] Genetic Algorithm: For power construction path planning, the cable laying path is encoded as a chromosome, and each gene of the chromosome can represent a node or a path segment passed by the path. By randomly generating an initial population, each individual in the population represents a possible cable laying path. Fitness evaluation is performed on each individual, and the fitness function comprehensively considers factors such as path length, construction cost, and environmental impact. For example, a path with a short length, low cost, and small environmental impact has a high fitness. Then, through selection, crossover, and mutation operations, the population is continuously evolved, making the individuals in the population gradually develop towards a better cable laying path direction, and finally obtaining the initial cable laying path based on the genetic algorithm.

[0055] Ant Colony Algorithm: An environmental model similar to the real ant colony foraging is constructed in the power construction area. Each power facility node and possible laying points are regarded as the positions that the ant colony can access. The ants move between these positions and select the next node to visit according to the pheromone concentration on the path. The higher the pheromone concentration, the greater the probability that the ant selects this path. Initially, the pheromone concentrations on each path are the same. As the ants keep moving, the ants on the shorter path pass through more times, and the pheromone accumulates quickly, attracting more ants to select this path. Through multiple rounds of iteration, the ants gradually find a better cable laying path, which is the initial path planned based on the ant colony algorithm.

[0056] Simulated Annealing Algorithm: In the power cable laying path planning, the initially randomly generated cable laying path is regarded as the current solution. A new solution is generated by randomly perturbing the current solution. Calculate the difference in the objective function between the new solution and the current solution. If the objective function value of the new solution is better, accept the new solution. If the new solution is worse, accept the new solution with a certain probability, and this probability decreases as the temperature drops. During the simulated annealing process, the temperature is initially high, and the probability of accepting a worse solution is large, which is conducive to jumping out of the local optimal solution. As the temperature gradually decreases, the algorithm is more inclined to accept better solutions and finally converges to the globally better cable laying path as the initial path.

[0057] After selecting the algorithm, the power cable laying path is planned as follows:

[0058] Input the starting point, ending point, terrain and existing building distribution information of the cable laying into the selected algorithm.

[0059] Start the selected algorithm combination for path planning calculation.

[0060] After the algorithm runs to completion, output the first initial path and the second initial path.

[0061] In this step, through a preset path planning algorithm database, which contains at least five path planning algorithms, different algorithms can be flexibly selected and combined according to the actual power construction scenarios and requirements. This strategy ensures that the path planning scheme can well adapt to various complex and changeable construction environments and specific requirements. According to factors such as the terrain, obstacle distribution, and cost control of the construction area, select a suitable algorithm combination for path planning. For example, in the case of regular terrain and strict cost control, select the combination of the shortest path algorithm and the genetic algorithm, which not only ensures the shortest path but also considers other practical factors. In complex terrain, the combination of the particle swarm optimization algorithm and the ant colony algorithm can quickly find a better path scheme, improving the planning efficiency and accuracy. Automatically performing path planning through algorithms reduces the subjectivity of manual intervention and judgment, improving the objectivity and scientific nature of the planning. At the same time, the intelligent characteristics of the algorithms make the planning process more efficient and accurate, and can handle large-scale and complex data sets, providing strong technical support for power construction. During the path planning process, multiple factors such as path length, construction cost, and environmental impact are comprehensively considered, and comprehensive evaluation and optimization are carried out through mechanisms such as fitness functions. This not only ensures the economy and feasibility of the cable laying path but also helps to reduce the impact on the surrounding environment and achieve sustainable development. The design of the preset path planning algorithm database makes the selection and combination of algorithms scalable. As new algorithms emerge or construction requirements change, algorithm combinations can be conveniently added or adjusted. At the same time, the implementation and calling methods of the algorithms are also convenient for maintenance and update, ensuring the long-term stable operation of the path planning system.

[0062] S2. Locate the divergent paths for the first initial path and the second initial path, extract the path segments with a divergence degree greater than the first preset threshold, and mark them as divergent segments;

[0063] The method for marking the divergent segments includes:

[0064] Align the first initial path and the second initial path to the same reference coordinate system; align the starting points and ending points of the two paths and ensure that they extend in the same direction; if the starting points and ending points of the paths do not exactly coincide, methods such as interpolation or resampling are required to align them spatially;

[0065] Find the parts where the two paths deviate significantly in space by calculating the position differences between the two paths at each node interval, and obtain a set of path divergence segments;

[0066] Based on the set of path divergence segments, for each path segment, compare it with the first preset threshold and calculate its divergence degree; if it exceeds the threshold, it is considered that there is a large divergence in this path segment; according to the calculated divergence degree and the set first preset threshold, extract the path segments with a divergence degree greater than the threshold;

[0067] Mark the path segments with a divergence degree greater than the first preset threshold as divergent segments;

[0068] The influencing factors for setting the first preset threshold include:

[0069] Construction environment factors: If the terrain of the construction area is complex, such as having many mountains, hills, rivers, and lakes, the paths planned by different algorithms may vary greatly. In order to capture path differences more comprehensively, the first preset threshold should be relatively low so as to extract more valuable divergent path segments for analysis; if the terrain is relatively flat and open, such as in plain areas, the path differences are relatively small, and the threshold can be appropriately increased to avoid extracting too many meaningless small divergences;

[0070] Cable laying requirement factors: If the accuracy requirement for the cable laying path is very high, such as in some special places with extremely high requirements for power transmission stability, such as data centers, hospitals, etc., a lower threshold is required to accurately find the subtle differences between different paths to ensure the accuracy of path optimization; if the accuracy requirement is relatively low, such as for the power supply lines in general residential areas, the threshold can be appropriately relaxed; for important power transmission trunks, in order to ensure the reliability and stability of power supply, more detailed analysis of path differences is required, and the threshold should be low; while for some secondary and temporary lines, the threshold can be appropriately increased;

[0071] Algorithm characteristic factors: The principles and characteristics of different algorithms determine the differences in the paths they plan. For example, the shortest path algorithm and the genetic algorithm may differ greatly, while the particle swarm optimization algorithm and the ant colony algorithm may have certain similarities in some cases. For algorithm combinations with large differences, the threshold should be appropriately lowered according to the specific situation to accurately extract divergent paths. For algorithm combinations with high similarity, the threshold can be appropriately increased. If the selected algorithm has poor stability, the results of each run may fluctuate to a certain extent. In order to avoid misjudging these fluctuations as valuable divergent paths, the threshold should be appropriately increased. For algorithms with good stability, the threshold can be relatively flexibly adjusted according to other factors.

[0072] Cost factors: Analyzing divergent paths requires a certain amount of manpower. If manpower is limited, a higher threshold can reduce the number of divergent paths that need to be processed and reduce labor costs. On the contrary, if manpower is sufficient, the threshold can be appropriately lowered. If the project is time-sensitive and path planning and optimization need to be completed in a shorter time, the threshold can be appropriately increased to reduce the number of extracted divergent path segments and improve processing efficiency. However, if time is more abundant, the threshold can be lowered for more detailed analysis.

[0073] Historical data factors: Refer to the differences in paths planned by different algorithms in previous similar power construction projects. If the path differences in previous projects are generally large, the threshold can be appropriately lowered; if the differences are small, the threshold can be increased.

[0074] In this step, by calculating the position differences of the two initial paths at each node interval, it is possible to accurately identify the parts where the two paths deviate significantly in space, that is, the divergent path segments; ensuring the accurate positioning of the divergent paths provides a reliable basis for subsequent optimization processing; the setting of the first preset threshold takes into account various factors, making the extraction of divergent paths more in line with the actual engineering requirements and enabling reasonable adjustments according to different situations; by extracting the path segments with a divergence degree greater than the first preset threshold and marking them as divergent segments, the workload of subsequent optimization processing can be significantly reduced; because only these path segments with large divergences need to be concerned, rather than analyzing the entire path one by one, thus improving the optimization efficiency; this step sets the threshold by comprehensively considering various factors and accurately extracts the divergent paths, which helps to more comprehensively consider various constraint conditions in subsequent optimization processing, so as to obtain a more reliable and stable cable laying path; by accurately identifying and optimizing the divergent paths, problems such as path conflicts and increased construction difficulty in actual construction can be avoided, thereby reducing construction risks and costs; at the same time, the optimized path can better adapt to the surrounding environment and reduce the impact on the ecological environment; as an important part of the cable laying path optimization method, this step helps to improve the overall efficiency of the entire project by accurately identifying and optimizing the divergent paths; it can reduce construction costs, improve the stability and reliability of power transmission, and reduce the impact on the surrounding environment, etc.

[0075] S3. Consider the ratio between the cumulative length of the divergent segments and the total length of the cable laying plan to obtain the path divergence degree index;

[0076] The path divergence degree index can intuitively reflect the difference degree between the two initial paths generated based on different algorithms; in power construction, this helps to understand the applicability and limitations of different algorithms in specific scenarios;

[0077] Through the path divergence degree index, construction personnel can judge whether further optimization of the path is needed and the key areas for optimization; if the index is low, it means that the difference between the two paths is small, and only local fine-tuning is required; if the index is high, it is necessary to comprehensively consider the advantages and disadvantages of different paths and conduct in-depth optimization by integrating various factors to obtain a cable laying path that better meets the actual needs;

[0078] The formula for calculating the path divergence degree index is:

[0079]

[0080] where I represents the path divergence degree index; n represents the total number of divergent segments; m i represents the number of coordinate points included in the i-th divergent segment; (x ij , y ij) represents the horizontal and vertical coordinates of the j-th coordinate point in the i-th divergence section; L t represents the total planned length of the cable laying.

[0081] In this step, by introducing the path divergence degree index, the difference degree between two initial paths generated based on different algorithms can be quantitatively reflected; enabling construction personnel to more intuitively understand the differences between paths and providing data support for subsequent optimization decisions; the path divergence degree index, as an important reference index, helps construction personnel judge whether it is necessary to further optimize the path and determine the key areas for optimization; when the index is low, it indicates that the difference between the two paths is small, and only local fine-tuning is required; while when the index is high, it is necessary to comprehensively consider the advantages and disadvantages of different paths and conduct in-depth optimization; by calculating the path divergence degree index, construction personnel can carry out work more targeted and avoid waste of resources caused by blind optimization; at the same time, for path sections with large divergences, optimization can be prioritized, thereby improving the overall construction efficiency; the calculation of the path divergence degree index helps construction personnel more comprehensively consider the applicability and limitations of different algorithms in specific scenarios, so as to select a more suitable algorithm for path planning; helps reduce uncertainties in construction and improve project quality; the path divergence degree index can also be used as an important index to evaluate the performance of different path planning algorithms; by comparing the path divergence degree indexes generated by different algorithms, construction personnel can understand the advantages and disadvantages of the algorithms, thereby providing useful feedback for algorithm improvement.

[0082] S4. Compare the path divergence degree index with a second preset threshold to obtain at least two optimized paths;

[0083] In response to the path divergence degree index not exceeding the second preset threshold, both the first initial path and the second initial path are marked as optimized paths;

[0084] When the path divergence degree index does not exceed the second preset threshold, it means that the difference between the first initial path and the second initial path is small; in power construction, these two paths are similar in most sections and can both meet the basic construction and power transmission requirements well; at this time, directly marking both the first initial path and the second initial path as optimized paths can reduce unnecessary re-planning work and improve efficiency;

[0085] In response to the path divergence degree index exceeding the second preset threshold, use the remaining algorithms in the preset path planning algorithm database to re-plan the path for the divergence section again to obtain at least three divergence reconstruction planning paths;

[0086] When the path divergence degree index exceeds the second preset threshold, it indicates that the two initial paths are quite different and each has its own advantages and disadvantages. At this time, it is necessary to further explore the potential of other algorithms to optimize the path. The various algorithms in the preset path planning algorithm database have different characteristics and advantages. When the two initial paths are significantly different, using the remaining algorithms in the database to re-plan the divergent section can introduce new ideas and methods to make up for the deficiencies of the first two algorithms. If the first two algorithms are the shortest path algorithm and the particle swarm optimization algorithm respectively, the large difference may be that the former only focuses on the shortest distance, while the latter may fall into a local optimum in the search space. At this time, a genetic algorithm or an ant colony algorithm can be selected to re-plan the divergent section to find a better path. Input the relevant information of the divergent section into the remaining algorithms in the preset path planning algorithm database for calculation. Since the principles and search methods of different algorithms are different, at least three divergent reconstruction planning paths can be obtained. These paths plan the divergent section from different angles and provide more choices for subsequent optimization.

[0087] Perform a similarity analysis on at least three of the divergent reconstruction planning paths, extract the divergent reconstruction planning paths whose pairwise similarity exceeds the third preset threshold, and perform overlay integration with any one of the first initial path or the second initial path to obtain an optimized path.

[0088] In the field of power construction, similarity measurement methods include Pearson correlation coefficient, Euclidean distance, cosine similarity, etc. These methods can calculate the similarity based on features such as the shape, length, and direction of the path.

[0089] The Pearson correlation coefficient is suitable for measuring the linear relationship between two sets of data. When the path features can be quantified as numerical values, this method can be used.

[0090] The Euclidean distance is used to measure the straight-line distance between two points. In path planning, it can be applied to calculate the distance between path feature points to evaluate the similarity of the path.

[0091] The cosine similarity is used to measure the angle between two sets of data vectors and is suitable for evaluating the similarity of path directions.

[0092] According to the selected similarity measurement method, calculate the pairwise similarity of at least three divergent reconstruction planning paths. Record the similarity values of each path with other paths for subsequent analysis.

[0093] The third preset threshold is used to judge which paths have a high enough similarity.

[0094] Compare the calculated similarity values with the third preset threshold.

[0095] Extract the divergent reconstruction planning paths with similarity exceeding the third preset threshold pairwise;

[0096] According to the characteristics of the extracted similar paths and the specific requirements of the project, select an appropriate coverage integration strategy; the coverage integration strategies include selecting the shortest path for coverage integration, selecting the path with the least construction difficulty for coverage integration, selecting the path with the lowest cost for coverage integration, etc.;

[0097] Perform coverage integration on the extracted similar paths with any one of the first initial path or the second initial path.

[0098] In this step, when the path divergence degree index does not exceed the second preset threshold, directly mark the first initial path and the second initial path as optimized paths, avoiding unnecessary replanning work and significantly improving the planning efficiency; this is particularly effective when the differences between the two paths are small and both can meet the basic construction and power transmission requirements; when the path divergence degree index exceeds the second preset threshold, by using the remaining algorithms in the preset path planning algorithm database to replan the divergent section, the potential of different algorithms can be deeply explored to make up for the deficiencies of the first two algorithms; new ideas and methods can be introduced to plan the divergent section from multiple perspectives, so as to find a better path plan; by generating at least three divergent reconstruction planning paths and performing similarity analysis on them, this step can extract the paths with similarity exceeding the third preset threshold pairwise; path diversity helps to reduce the impact on the surrounding environment while meeting the construction and power transmission requirements; according to the characteristics of the extracted similar paths and the specific requirements of the project, this step allows selecting an appropriate coverage integration strategy for path optimization; it helps to maximize the comprehensive benefits while ensuring the path optimization effect, taking into account multiple aspects such as construction difficulty, cost investment, and ecological environment impact; this step adopts a variety of similarity measurement methods and coverage integration strategies to adapt to different power construction scenarios and project requirements; it helps to improve the generality and practicality of the path planning method.

[0099] S5. Perform optimization processing on multiple said optimized paths to obtain the optimal cable laying planning path; the factors considered during the optimization processing of multiple said optimized paths include route length, construction complexity, construction cost investment, and the degree of impact on the surrounding ecological environment;

[0100] The method for obtaining the optimal cable laying planning path includes:

[0101] For each optimized path, collect evaluation index data, which includes route length data, construction complexity data, construction cost input data, and ecological environment impact data; the route length data is used to evaluate the total length of the path, and a shorter path usually means lower material consumption and transportation costs; the construction complexity data is used to consider factors such as terrain adaptability, avoidance of underground pipelines, and crossing obstacles. The higher the construction complexity, the greater the construction difficulty and the longer the construction period; the construction cost input data includes various costs such as material costs, labor costs, and equipment rental costs, and is an important indicator for evaluating the economy of the path; the ecological environment impact data is used to evaluate the impact degree of the path on the surrounding land, vegetation, water sources, etc., to ensure that the path planning meets environmental protection requirements;

[0102] Score each path item by item and summarize the total score of each path; according to the evaluation indexes, set reasonable quantitative standards and scoring rules for each evaluation index of each path;

[0103] According to the actual situation and project requirements, allocate reasonable weights to each evaluation index; the weight reflects the importance of each index in the evaluation system; calculate the comprehensive score of each path according to the weight and the score of each path; the higher the comprehensive score, the better the comprehensive performance of the path;

[0104] Compare the comprehensive scores of each path and select the path with the highest score as the optimal planning path.

[0105] In this step, by optimizing multiple optimized paths and comprehensively considering multiple factors such as route length, construction complexity, construction cost input, and the impact degree on the surrounding ecological environment, this step ensures the comprehensiveness and scientificity of the path planning; helps to avoid a single factor dominating the decision-making, so as to obtain a more balanced and reasonable planning scheme; by collecting evaluation index data and scoring and summarizing each path item by item, this step can quantify the economy and feasibility of the path; a shorter path length means lower material consumption and transportation costs, and a reasonable construction complexity helps to control the construction difficulty and the construction period; in addition, the evaluation of construction cost input and ecological environment impact also helps to ensure the economy and environmental protection of the path planning; this step ensures the objectivity and fairness of the evaluation process by setting reasonable quantitative standards and scoring rules, and by allocating reasonable weights to each evaluation index; helps to reduce the influence of subjective factors on the decision-making and improve the reliability and accuracy of the path planning scheme; by comparing the comprehensive scores of each path and selecting the path with the highest score as the optimal planning path, this step realizes the optimized decision-making and resource allocation; helps to ensure the most effective use of limited resources while meeting the requirements of power transmission stability and reliability; by ensuring that the path planning meets environmental protection requirements, it helps to reduce environmental damage and pollution and promote the coordinated development of economy and environment.

[0106] Embodiment 2: As Figure 3 shown, a cable laying path optimization system in a power project of the present invention specifically includes the following modules;

[0107] A path planning module plans the cable laying path based on any two algorithms in a preset path planning algorithm database to obtain a first initial path and a second initial path;

[0108] A divergence positioning module performs divergence path positioning on the first initial path and the second initial path, extracts path segments with a divergence degree greater than a first preset threshold, and marks them as divergence segments;

[0109] An index calculation module obtains a path divergence degree index by considering the ratio between the cumulative length of the divergence segments and the total planned length of the cable laying;

[0110] A comparison and optimization module compares the path divergence degree index with a second preset threshold to obtain at least two optimized paths;

[0111] An optimization decision-making module performs optimization processing on multiple optimized paths to obtain an optimal cable laying planning path.

[0112] Through the path planning module, this system can select any two algorithms from the preset path planning algorithm database for path planning; this diversity ensures that the advantages of different algorithms can be comprehensively considered during the planning process, such as the terrain and landform analysis ability of the GIS algorithm and the global search ability of the intelligent algorithm, so as to achieve complementarity between algorithms and improve the comprehensive performance of path planning;

[0113] The divergence positioning module can accurately identify the divergence segments between the two initial paths and quantify the impact of these divergences on the overall path planning through the index calculation module; the quantitative evaluation provides a clear direction and basis for subsequent path optimization;

[0114] The comparison and optimization module can flexibly adjust and optimize the path planning scheme by comparing the path divergence degree index with the preset threshold; it ensures that the path planning can generate the optimal path that meets the actual requirements when facing complex terrains, diverse surrounding environments, and cost control requirements;

[0115] The optimization decision-making module can comprehensively evaluate multiple optimized paths, considering various factors such as route length, construction complexity, construction cost investment, and the impact on the surrounding ecological environment, so as to select the cable laying path with the best comprehensive performance; this efficient optimization ability ensures the economy, feasibility, and environmental protection of the path planning scheme;

[0116] The system reduces manual participation and subjective judgment through automated and intelligent means, improving the efficiency and accuracy of path planning. At the same time, the system can also be flexibly adjusted according to actual needs, ensuring the adaptability and flexibility of the path planning scheme. During the planning process, the system fully considers the impact of the surrounding environment. By optimizing the path planning scheme, it reduces the damage to the ecological environment caused by construction, promotes the rational use of resources and environmental protection.

[0117] In summary, the cable laying path optimization system in this power project can improve the rationality of cable laying path planning, reduce construction costs, and improve the stability of power transmission by adopting multi-algorithm fusion, detailed branch positioning and analysis, quantitative evaluation methods, effective comparison and optimization mechanisms, and comprehensive optimization decision-making processes.

[0118] The various change methods and specific embodiments of the cable laying path optimization method in the power project in the foregoing Embodiment 1 are equally applicable to the cable laying path optimization system in the power project of this embodiment. Through the foregoing detailed description of the cable laying path optimization method in the power project, those skilled in the art can clearly know the implementation method of the cable laying path optimization system in the power project of this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0119] In addition, this application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it realizes each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0120] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for optimizing cable laying paths in electric power engineering, characterized in that: The method comprises: Based on any two algorithms in a preset path planning algorithm database, the cable laying path is planned to obtain a first initial path and a second initial path; Positioning divergent paths of the first initial path and the second initial path, extracting path segments with divergence degrees greater than a first preset threshold, and marking them as divergent segments; Considering the ratio between the cumulative length of the divergent sections and the total length of the cable laying plan, a path divergence degree index is obtained; Compare the path divergence index with a second preset threshold to obtain at least two optimized paths; Optimization processing is performed on the multiple optimized paths to obtain the optimal planned path for cable laying.

2. A method for optimizing cable laying paths in electric power engineering according to claim 1, characterized in that: Comparing the path divergence index with a second preset threshold value includes: In response to the path divergence index not exceeding the second preset threshold, marking both the first initial path and the second initial path as optimized paths; In response to the path divergence index exceeding the second preset threshold, the remaining algorithms in the preset path planning algorithm database are used to perform path planning again on the divergent section to obtain at least three divergent reconstructed planning paths; Perform similarity analysis on at least three of the divergent reconstruction planning paths, extract the divergent reconstruction planning paths whose similarities between each other exceed a third preset threshold, and overlay and integrate them with any one of the first initial path or the second initial path to obtain an optimized path.

3. A method for optimizing cable laying paths in electric power engineering according to claim 1, characterized in that: The preset path planning algorithm database includes at least five path planning algorithms, including at least the shortest path algorithm, particle swarm optimization algorithm, genetic algorithm, ant colony algorithm and simulated annealing algorithm.

4. A method for optimizing cable laying paths in electric power engineering according to claim 1, characterized in that: The formula for calculating the path divergence index is: Where I represents the path divergence index; n represents the total number of divergent segments; m i Indicates the number of coordinate points contained in the i-th divergence paragraph; (x ij ,y ij ) represents the horizontal and vertical coordinates of the jth coordinate point in the i-th divergence paragraph; L t Indicates the total planned length of the cable laying.

5. A method for optimizing cable laying paths in electric power engineering according to claim 1, characterized in that: The influencing factors for setting the first preset threshold include construction environment factors, cable laying requirement factors, algorithm characteristic factors, cost factors and historical data factors.

6. A method for optimizing cable laying paths in electric power engineering according to claim 1, characterized in that: The method for performing optimization processing on a plurality of the optimization paths comprises: For each optimization path, collect evaluation metric data; Score the evaluation indicator data of each path item by item and summarize them to get the total score of each path; Assign weights to each evaluation indicator data, and calculate the comprehensive score of each path based on the weights and the total score of each path; Compare the comprehensive scores of each path and select the path with the highest score as the optimal planning path.

7. A method for optimizing cable laying paths in electric power engineering according to claim 6, characterized in that: Factors considered in the process of optimizing the multiple optimization paths include route length, construction complexity, construction cost investment, and the impact on the surrounding ecological environment.

8. A cable laying path optimization system in electric power engineering, characterized in that: The system comprises: A path planning module plans the cable laying path based on any two algorithms in a preset path planning algorithm database to obtain a first initial path and a second initial path; a divergence locating module, which locates divergent paths of the first initial path and the second initial path, extracts path segments whose divergence degree is greater than a first preset threshold, and marks them as divergent segments; An index calculation module, taking into account the ratio between the cumulative length of the divergent sections and the total length of the cable laying plan, to obtain a path divergence degree index; A comparison and optimization module compares the path divergence index with a second preset threshold to obtain at least two optimized paths; The optimization decision module performs optimization processing on the multiple optimization paths to obtain the optimal planning path for cable laying.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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