Tray line layout optimization method and system based on internet of things

Abstract

The present invention relates to the technical field of tray line layout optimization, and provides a tray line layout optimization method and system based on Internet of Things. The method comprises: receiving from a user terminal a circuit topological structure diagram of a preset area to perform cable relationship assignment; constructing a cable layout tree diagram; extracting an i-th level cable set for multi-level clustering analysis; generating a cable set clustering result; setting wiring constraint directions and wiring constraint areas; setting tray distance constraint parameters; generating a three-dimensional model of the preset area; on the basis of the clustering result and under the described constraints, performing cable layout optimization in the three-dimensional model; generating a tray line layout optimization scheme; and sending the tray line layout optimization scheme to the user terminal. The present invention solves the technical problems in conventional tray line layout methods of an unsatisfactory layout result caused by relatively vague layout constraints and layout requirements, and low layout efficiency caused by excessive manual intervention and lack of automation and intelligence in the layout process.

Description

A bridge line layout optimization method and system based on the Internet of Things Technical Field

[0001] The present invention relates to the technical field of bridge line layout optimization, and in particular to a bridge line layout optimization method and system based on the Internet of Things. Background Art

[0002] Bridge line installation is a method for laying power, communications, and other lines inside or outside buildings. Bridge line installation uses a bridge to support and protect the lines, enabling them to stably traverse various environments, such as walls, ceilings, and floors. Bridge line installation requires precise design to ensure a rational layout of the lines. The appropriate bridge type and installation location must be selected based on the building's structure and layout. However, existing bridge line installation techniques are limited by site constraints, resulting in vague layout constraints and requirements. This leads to less than ideal and accurate installation results, and the installation process requires significant manual intervention and lacks automation and intelligence, resulting in low efficiency.

[0003] Therefore, a bridge line layout optimization method is needed to solve the wiring problems existing in the prior art and improve the efficiency, accuracy and automation of bridge line layout.

[0004] Summary of the Invention

[0005] This application provides an Internet of Things-based bridge line layout optimization method and system, aiming to solve the technical problems that traditional bridge line layout methods are subject to site restrictions, have vague layout constraints and layout requirements, resulting in less than ideal layout results, and have a lot of manual intervention in the layout process, lack of automation and intelligence, resulting in low layout efficiency.

[0006] In view of the above problems, the present application provides a bridge line layout optimization method and system based on the Internet of Things.

[0007] The first aspect disclosed in the present application provides a method for optimizing cable tray layout based on the Internet of Things, the method comprising: receiving a circuit topology structure diagram of a preset area from a user terminal to perform cable relationship allocation and construct a cable layout tree diagram; extracting the i-th level cable set according to the cable layout tree diagram to perform multi-level clustering analysis and generate a cable set clustering result; setting a horizontal plane wiring constraint direction and a horizontal plane wiring constraint area, setting a vertical plane wiring constraint direction and a vertical plane wiring constraint area; setting a cable tray distance constraint parameter; collecting an image of a preset area based on an image sensor to perform three-dimensional space modeling and generate a three-dimensional model of the preset area; based on the cable set clustering result, optimizing cable layout in the three-dimensional model of the preset area under the constraints of the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, the vertical plane wiring constraint area and the cable tray distance constraint parameter, and generating a cable tray line layout optimization plan; and sending the cable tray line layout optimization plan to the user terminal.

[0008] Another aspect disclosed in the present application provides a bridge line layout optimization system based on the Internet of Things, which is used for the above method, and the system includes: a cable relationship allocation module, which is used to receive a circuit topology structure diagram of a preset area from a user terminal to perform cable relationship allocation and construct a cable layout tree diagram; a multi-level clustering analysis module, which is used to extract the i-th level cable set according to the cable layout tree diagram, perform multi-level clustering analysis, and generate a cable set clustering result; a constraint area setting module, which is used to set the horizontal plane wiring constraint direction and the horizontal plane wiring constraint area, and set the vertical plane wiring constraint direction and the vertical plane wiring constraint area; a constraint parameter setting module, which is used to set the bridge distance constraint parameter; a three-dimensional space modeling module, which is used to collect a preset area image based on an image sensor to perform three-dimensional space modeling and generate a preset area three-dimensional model; a cable layout optimization module, which is used to extract the i-th level cable set according to the cable layout tree diagram, perform multi-level clustering analysis, and generate a cable set clustering result; a constraint area setting module, which is used to set the horizontal plane wiring constraint direction and the horizontal plane wiring constraint area, and set the vertical plane wiring constraint direction and the vertical plane wiring constraint area; a constraint parameter setting module, which is used to set the bridge distance constraint parameter; a three-dimensional space modeling module, which is used to perform three-dimensional space modeling based on the image sensor to collect the preset area image, and generate a preset area three-dimensional model; a cable layout optimization module, which is used to perform three-dimensional space modeling based on the cable set clustering result Cable layout optimization is performed in the three-dimensional model of the preset area under the constraints of the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, the vertical plane wiring constraint area and the bridge distance constraint parameters to generate a bridge line layout optimization plan; an optimization plan sending module is used to send the bridge line layout optimization plan to a user terminal.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By applying Internet of Things technology, cable relationships are allocated based on the circuit topology diagram and a cable layout tree diagram is constructed, realizing the automation of the cable layout process. Multi-level clustering analysis is performed on the cable collection extracted from the cable layout tree diagram to generate clustering results for the cable collection, improving the accuracy and efficiency of the layout plan. By setting the direction and area of ​​horizontal and vertical wiring constraints, the wiring path and position can be more accurately controlled, improving the layout quality. By setting the bridge distance constraint parameters and combining the characteristics of three-dimensional space modeling and preset areas, the bridge line layout is optimized under the constraints, effectively solving the difficulties of bridge distance constraints. In summary, this bridge line layout optimization method based on the Internet of Things improves the efficiency, accuracy and automation of bridge line layout, and provides an optimized solution for bridge line layout.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG1 is a flow chart of a bridge line layout optimization method based on the Internet of Things provided by an embodiment of the present application;

[0013] FIG2 is a schematic diagram of the structure of a bridge line layout optimization system based on the Internet of Things provided in an embodiment of the present application.

[0014] Explanation of the accompanying drawings: cable relationship allocation module 10, multi-level clustering analysis module 20, constraint area setting module 30, constraint parameter setting module 40, three-dimensional space modeling module 50, cable layout optimization module 60, optimization plan sending module 70. DETAILED DESCRIPTION

[0015] The embodiments of the present application provide an Internet of Things-based bridge line layout optimization method to solve the technical problems that the traditional bridge line layout method is subject to site restrictions, has vague layout constraints and layout requirements, resulting in less than ideal layout results, and has a lot of manual intervention in the layout process, lacks automation and intelligence, and leads to low layout efficiency.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0017] Example 1

[0018] As shown in FIG1 , an embodiment of the present application provides an IoT-based bridge line layout optimization method, which is applied to an IoT-based bridge line layout optimization system. The method includes:

[0019] Receive the circuit topology diagram of the preset area from the user terminal to perform cable relationship allocation and build a cable layout tree diagram;

[0020] Furthermore, receiving a circuit topology diagram of a preset area from a user terminal to perform cable relationship allocation and construct a cable layout tree diagram includes:

[0021] Extracting line series connection output nodes and line parallel connection output nodes according to the circuit topology diagram;

[0022] According to the line serially connected node, extracting a node upstream cable and a node downstream cable from the circuit topology diagram, wherein current flows from the node upstream cable through the line serially connected node to the node downstream cable;

[0023] Extracting a plurality of parallel cables from the circuit topology diagram according to the parallel output node of the line, wherein the output positions of the plurality of parallel cables are the parallel output nodes of the line;

[0024] The node upstream cable is set as the mother cable of the node downstream cable, the multiple parallel cables are set as cables of the same level, and the cable layout tree diagram is constructed.

[0025] Obtain a circuit topology diagram for a preset area from the user terminal. The preset area is a user-defined area where bridge wiring is required. The diagram should include various devices, such as servers, switches, routers, and the connections between them. In the circuit topology diagram, find all directly connected components without any other components interposed between them. These connected components form a series combination. By marking, identify one of the endpoints of the series combination as the series output node. In the circuit topology diagram, find all components that share the same two nodes. These components are connected in parallel. By marking, identify the other end of these shared nodes as the parallel output node.

[0026] In the circuit topology diagram, based on the information of the extracted line series output nodes, these nodes represent the locations where the current flow direction changes. Starting from the series output node, find the cables connected to it. These cables are the upstream cables of the node because they deliver current to the node; continue along the circuit topology diagram to find the cables flowing out of the series output node. These cables are the downstream cables of the node because they receive the current flow from the node.

[0027] In the circuit topology diagram, based on the information about the extracted parallel output nodes, which represent locations where current is split or connected in parallel, all cables connected to the parallel output node are found. These cables are parallel cables because they simultaneously receive current from the node. The extracted parallel cable output locations are marked as the locations of the parallel output nodes, indicating that the current is split from this node into multiple parallel cables.

[0028] Since the node upstream cable transports current to the node and transports current to the node downstream cable through the node, the node upstream cable is set as the mother cable of the node downstream cable; for multiple parallel cables, they are set as cables of the same level, which means that they have the same function and are regarded as cables of the same level when laid.

[0029] According to the cable layout dendrogram, extracting the i-th level cable set to perform multi-level cluster analysis and generate a cable set clustering result;

[0030] Furthermore, according to the cable layout dendrogram, the i-th level cable set is extracted for multi-level cluster analysis to generate a cable set clustering result, including:

[0031] Performing first-level clustering on the i-th level cable set according to cable usage characteristics to generate a first cable set clustering result;

[0032] Performing secondary clustering on the first cable set clustering result according to a preset voltage deviation to generate a second cable set clustering result;

[0033] The second cable set clustering result is subjected to three-level clustering according to the access load distance deviation threshold to generate the cable set clustering result.

[0034] According to the above analysis process, the cable layout tree diagram is divided into multiple levels, and the i-th level cable set is located according to the hierarchical relationship, where i represents a specific level, i is an integer and i>0.

[0035] For the i-th level cable set, data related to cable usage characteristics are collected, including cable load information (cables with different loads), power supply methods (two-circuit power cables supplying power to the same load), emergency lighting cables, and other lighting cables. A clustering algorithm, such as k-means clustering, is used to cluster the i-th level cable set according to cable usage characteristics. In this process, cables with similar uses are divided into the same cluster cluster based on cable usage characteristics. After completing the first-level clustering, the clustering result of the first cable set is obtained. Under this clustering result, the cables in each cluster have similar cable usage characteristics, and the cable uses of different clusters are also different.

[0036] Collect data related to the preset voltage deviation of the cable, including characteristics such as the cable's rated voltage, actual measured voltage, and voltage loss. Set a preset voltage deviation based on actual conditions and specific needs. The preset voltage deviation is used to determine whether to classify two cables into the same cluster. For the first cable set clustering result, use the defined preset voltage deviation to perform secondary clustering on each cluster of the first cable set. During the clustering process, the cable preset voltage deviation characteristics are used as the main basis to divide the cable groups. Cables with similar preset voltage deviations are classified into the same category to generate a clustering result for the second cable set, where the cables in each cluster are similar in terms of preset voltage deviation.

[0037] Data related to access load distance deviation is collected, including information about the cable access load's location, the actual measured distance, the preset distance, and other characteristics. Based on the actual situation and the tolerable error range, a access load distance deviation threshold is defined. This threshold will be used to determine whether to classify two cables into the same cluster. Using the defined access load distance deviation threshold, each cluster of the second cable set is clustered at the third level. In this process, cables with similar distance deviations are grouped into the same cluster, using the access load distance deviation characteristic as the primary basis.

[0038] After three-level clustering, the final clustering result of the cable set is obtained. The clustering result shows that the cables in each cluster are similar in terms of cable usage, voltage deviation, and access load distance deviation. After the multi-level clustering process, more refined adjustment and management are provided for cable layout.

[0039] Set the horizontal plane routing constraint direction and horizontal plane routing constraint area, set the vertical plane routing constraint direction and vertical plane routing constraint area;

[0040] Furthermore, setting the horizontal plane routing constraint direction and the horizontal plane routing constraint area, and setting the vertical plane routing constraint direction and the vertical plane routing constraint area include:

[0041] The horizontal plane wiring constraint direction includes the vertical direction of the beam and the parallel direction of the beam;

[0042] The vertical plane wiring constraint direction includes a direction perpendicular to the room column and a direction parallel to the room column;

[0043] Based on the Internet of Things device, the non-wiring identification area of ​​the preset area is collected, and the horizontal plane wiring constraint area and the vertical plane wiring constraint area are set.

[0044] Considering the importance of aesthetically pleasing cable routing, constraints are imposed on both routing direction and routing area. Specifically, for horizontal routing constraints, the location and orientation of beams in the room are determined. These beams are structural elements parallel to the ground. The beams are then divided into several routing areas to better control the direction of the cables. Cabling planning is performed within each routing area, with the cable path determined. Cables are then routed perpendicular to the beams, meaning they are routed along a line at a 90° angle to the beams. Similarly, cables are routed parallel to the beams, meaning they are routed along a line parallel to the beams or along the edges of the beams, to maintain aesthetics.

[0045] During the entire layout process, CAD software can be used to plan the cable layout direction and area. At the same time, according to the actual situation, you can consult the advice and opinions of professional cable wiring engineers to obtain the best layout results.

[0046] Determine the location and orientation of any columns in the room. Columns are structural elements perpendicular to the ground, used to support a building or divide spaces. Define a routing area around each column to better control the cable's route. Plan the cables within each routing area, determining the cable path and routing them perpendicular to the columns (that is, along a line at a 90° angle to the columns). Similarly, route cables parallel to the columns (that is, parallel to the columns) or along the edges of the columns for aesthetic reasons.

[0047] IoT devices, such as sensors or cameras, can be used to identify pre-defined areas where cabling is not permitted. By removing these areas from the pre-defined areas, the system can identify areas requiring horizontal cabling constraints, such as floors and ceilings. Within each constraint area, rules can be set to restrict the direction of cable routing, for example, requiring cables to run along wall edges or avoid crossing specific areas.

[0048] Identify areas requiring vertical routing constraints, including vertical structures like walls, columns, and beams. Within each constraint area, set rules to restrict cable routing, such as requiring cables to run along wall edges or parallel to columns. By defining horizontal and vertical routing constraint areas, you can ensure that cable routing meets aesthetic and functional requirements while avoiding interference or damage to other structures.

[0049] Set bridge distance constraint parameters;

[0050] Setting cable tray distance constraint parameters can help control the minimum distance between cable trays to ensure good installation and operating conditions. For example, the distance between non-power cable trays and power cable trays (without shielding covers) is greater than or equal to 0.5m. When there is no shielding cover between them, the distance requirement is larger to maintain sufficient space between power cables and non-power cables to reduce interference and mutual impact.

[0051] The distance between cable trays with shielding covers is greater than or equal to 0.3m. When cable trays are used with shielding covers, a smaller distance needs to be maintained to ensure good protection and insulation performance. The minimum distance of 0.3m can provide sufficient spacing and reduce mutual interference between shielding covers.

[0052] The distance between control cable trays (without shielding cover) is greater than or equal to 0.2m. For the distance between control cable trays, when there is no shielding cover between them, a smaller distance requirement can be adopted. The minimum distance of 0.2m can provide enough space to make the wiring more neat and easy to maintain and manage.

[0053] The distance between power cable trays (without shielding cover) is greater than or equal to 0.3m. A certain interval also needs to be maintained between power cable trays, which helps reduce interference between power cables and ensures normal power transmission and safe operation.

[0054] The settings of these parameters are determined according to actual needs and standards. By setting appropriate bridge distance constraint parameters, the reliability, stability and security of the wiring system can be ensured.

[0055] Based on the image sensor, the image of the preset area is collected to perform three-dimensional space modeling and generate a three-dimensional model of the preset area;

[0056] Use a suitable image sensor, such as a high-definition camera, to capture images of the preset area, ensuring that a sufficient number of images are obtained from different angles and positions to cover the entire area. Using computer vision technology, key feature points are extracted from the captured images. These features can be edges, corners, textures, and other significant points or areas in the image. Use a feature matching algorithm to match the features extracted from different images and find the correspondence between them. By using the matched feature points and combining the internal and external parameters of the camera, the position of each feature point in three-dimensional space is calculated. The collection of these three-dimensional points is the point cloud. Based on the obtained point cloud data, use three-dimensional modeling software, such as Autodesk Maya, to generate a three-dimensional model of the preset area, including converting the point cloud into a mesh, performing surface reconstruction, and generating a three-dimensional model of the preset area, which includes the geometric shape of the room, building or other target area, walls, columns, ceilings and other structural elements.

[0057] Based on the cable set clustering result, cable layout optimization is performed in the three-dimensional model of the preset area under the constraints of the horizontal wiring constraint direction, the horizontal wiring constraint area, the vertical wiring constraint direction, the vertical wiring constraint area and the bridge distance constraint parameters, and a bridge line layout optimization plan is generated;

[0058] Based on the above constraints, cable layout optimization is performed in the three-dimensional model of the preset area. Through optimization algorithms and search methods, the best cable layout scheme that meets the constraints is found. According to the optimized cable layout scheme, the path and connection method of the bridge line layout are determined. Taking into account the convenience of installation and maintenance of the bridge, as well as factors such as the crossing between cables, the optimal bridge line layout scheme is generated.

[0059] Through the above steps, based on the clustering results, routing constraints, and bridge distance constraints, the cable routing of the 3D model of the preset area can be optimized, and the corresponding bridge line routing optimization plan can be generated, which helps to improve the efficiency and reliability of cable routing.

[0060] Furthermore, based on the cable set clustering result, cable layout optimization is performed in the three-dimensional model of the preset area under the constraints of the horizontal wiring constraint direction, the horizontal wiring constraint area, the vertical wiring constraint direction, the vertical wiring constraint area, and the bridge distance constraint parameters, and a bridge line layout optimization plan is generated, including:

[0061] Constraining the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, and the vertical plane wiring constraint area in the three-dimensional model of the preset area to generate a wiring analysis digital twin model;

[0062] performing a wiring path analysis on a first cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate a first set of cable laying paths, wherein the first set of cable laying paths has a first set of path constraint region sequences;

[0063] Repeat the analysis, performing a wiring path analysis on an Nth cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate an Nth group of cable routing paths, wherein the Nth group of cable routing paths has an Nth group of path constraint region sequences;

[0064] Taking the first group of path constraint area sequences to the Nth group of path constraint area sequences as position constraints, the first group of cable laying paths to the Nth group of cable laying paths are optimized for bridge fixed positions based on the bridge distance constraint parameters to generate the bridge line layout optimization plan.

[0065] Open the 3D modeling software and apply the horizontal and vertical wiring constraint directions, as well as the horizontal and vertical wiring constraint areas, to the pre-built 3D model of the pre-defined area. For example, use the 3D modeling software's area selection tool to draw a polygon to define the constraint area, ensuring that the area is appropriate and covers the required wiring constraint area. After the constraints are set, a wiring analysis digital twin model is generated, which contains the cable routing path that meets the constraint requirements.

[0066] By generating a cabling analytical digital twin model, it is possible to better visualize and analyze the effects of cable routing, assess its compatibility with existing structures, and make necessary adjustments and optimizations.

[0067] Based on the cable set clustering results, a random cluster is extracted as the first cable set clustering result. Each cluster represents a group of cables with similar target locations, and multiple cables within the same group can be routed in a single cable tray. Based on the number of cables in each cluster and the access requirements of the target location, the placement of a plug-in board is determined at an appropriate location. A plug-in board is a device that provides a common power point for the cables in the cluster, simplifying wiring and providing convenient connections. This plug-in board is the target access location for the corresponding cluster.

[0068] For the first set of generated cable routing paths, define a sequence of path constraint areas. These constraint areas can be fixed-width areas that limit the routing direction of cables on walls or other structures. For example, a cable can be routed from a certain location on the wall along a certain direction. The routing constraint width in this direction is the path constraint width.

[0069] In the routing analysis digital twin model, path analysis and routing planning are performed based on the clustering results, plug-in board positions, and path constraint area sequences. The optimal routing path from the plug-in board position to the target position of each cable is calculated to meet the constraint requirements. Based on the results of path analysis and routing planning, the first set of cable routing paths are generated. These paths integrate clustering, plug-in board positions, and path constraint area sequences, and ensure that the cables are routed within an area that meets the constraint width.

[0070] The above analysis process is repeated to traverse all N clustering results in the cable set clustering result, and a corresponding cable laying path is generated for each clustering result, and each laying path includes a path constraint area sequence.

[0071] According to the first group of path constraint area sequences to the Nth group of path constraint area sequences, the position constraints of the cable routing path on the bridge are determined. These constraints include the positions of the starting and ending points of the cable routing path on the bridge, as well as the positions of the intermediate nodes on the bridge.

[0072] Based on the position constraints and bridge distance constraint parameters, an optimization algorithm is used to optimize the bridge fixed position from the first group of cable laying paths to the Nth group of cable laying paths, including adjusting the position of the cable laying paths on the bridge to minimize the path length, reduce bends and intersections, etc., while meeting the constraint requirements. According to the results of the bridge fixed position optimization, an optimized scheme for the bridge line layout is generated. This scheme combines the position constraints from the first group of path constraint area sequences to the Nth group of path constraint area sequences, and achieves more efficient and reliable line layout by optimizing the layout position of the cables on the bridge.

[0073] Furthermore, taking the first group of path constraint area sequences to the Nth group of path constraint area sequences as position constraints, optimizing the bridge fixed positions of the first group of cable laying paths to the Nth group of cable laying paths based on the bridge distance constraint parameters, and generating the bridge line layout optimization plan, includes:

[0074] Obtaining a cable model list and a cable quantity list based on the first cable set clustering result, and matching the cable model list and the cable quantity list based on a bridge size calibration table to obtain a first bridge size calibration result;

[0075] Repeat the analysis until a cable model list and a cable quantity list are obtained based on the Nth cable set clustering result, and the cable model list and the cable quantity list are matched based on the bridge size calibration table to obtain the Nth bridge size calibration result;

[0076] Performing random path combination on the first group of cable laying paths up to the Nth group of cable laying paths to generate N laying path extraction results, wherein the N laying path extraction results correspond one-to-one to the first group of cable laying paths up to the Nth group of cable laying paths;

[0077] Based on the first bridge size calibration result to the Nth bridge size calibration result, generating bridge size identifications for the N routing path extraction results, generating N bridge size identification results, and storing the N routing path extraction results and the N bridge size identification results jointly based on the bridge distance constraint parameter to set as a first bridge line routing plan;

[0078] Repeating a preset number of times to obtain H bridge line layout schemes, wherein the first bridge line layout scheme belongs to the H bridge line layout schemes;

[0079] Construct bridge layout fitness evaluation function:

[0080] Among them, f(z k) represents the fitness of the kth bridge line layout scheme. The fitness of any group of bridge line layout schemes includes N layout path extraction results, d j (z k ) represents the length of the i-th cable model of the k-th group of layout path extraction results, c j represents the cost of the i-th cable model, M represents the total number of cable models, Y represents the number of shielding partitions, w1 and w2 represent the preset weight parameters, α and β represent the normalization adjustment parameters, z k Characterize the kth group layout path extraction results;

[0081] Based on the bridge layout fitness evaluation function and the H bridge line layout schemes, the bridge fixed position is optimized to generate the bridge line layout optimization scheme.

[0082] Based on the first cable set clustering results, identify the cables included in the cluster and record the cable models and corresponding quantities to form a cable model list and a cable quantity list. Prepare a bridge size calibration table that contains different cable models and their corresponding bridge size calibration results, including bridge thickness and bridge width. This calibration table is pre-established and lists common cable models and their corresponding bridge size calibration results.

[0083] Match the cable model and cable quantity list with the tray size calibration table. Based on the cable model, find the corresponding tray size calibration result in the calibration table. If there are multiple cable models, determine the required tray size based on the cable quantity. Based on the matching results, obtain the size calibration result of the first tray, including tray thickness and tray width. This helps determine the appropriate tray size for the required cables, allowing for correct wiring planning and design.

[0084] Repeat the above process, traverse all N cable set clustering results, and obtain N bridge size calibration results.

[0085] Using a random algorithm, the first through Nth groups of cable routing paths are combined, randomly shuffling and selecting the path order to ensure that each path group participates in the combination process without omissions or duplications. Based on the results of the random path combination, N routing path extraction results are generated. Each routing path extraction result is composed of the first through Nth groups of cable routing paths combined in a random order. The N generated routing path extraction results correspond one-to-one with the first through Nth groups of cable routing paths. That is, each routing path in the routing path extraction result corresponds to the corresponding first through Nth groups of cable routing paths, maintaining path consistency and accuracy. This helps to obtain multiple possible routing solutions, providing more options and flexibility.

[0086] Based on the first bridge size calibration result to the Nth bridge size calibration result, for each layout path extraction result, according to the bridge size requirements required for the path, the corresponding calibration result is selected from the first bridge size calibration result to the Nth bridge size calibration result as the bridge size identification, and according to the bridge size identification generated for the N layout path extraction results, N bridge size identification results are formed, and each bridge size identification result corresponds to the corresponding layout path extraction result.

[0087] The N routing path extraction results and the N bridge size identification results are stored together to form a first bridge line routing plan. Appropriate data structures, such as lists or matrices, can be used to store this information to ensure its relevance and ease of access. This helps ensure that the required bridge size for each routing path meets actual requirements and provides a complete routing plan, including the associated information between paths and bridges.

[0088] According to actual conditions and specific needs, a preset number of repetitions H is defined. Within the preset number of repetitions, the process of obtaining the first bridge line layout scheme is repeated H-1 times, thereby obtaining a total of H schemes, including the first bridge line layout scheme.

[0089] The above fitness evaluation function takes into account multiple factors such as the length, cost, number of shielding partitions and preset weight parameters of the cable model, and ensures the relative importance of each indicator through normalization and weight adjustment. The weight parameters and normalization adjustment parameters can be adjusted according to actual needs and specific scenarios to adapt to different optimization goals and constraints. The fitness evaluation function is used to quantitatively evaluate different bridge line layout schemes based on multiple factors. The smaller the fitness of the evaluation result, the better the corresponding scheme.

[0090] The fixed position of the bridge is used as the optimization variable, and minimizing the fitness evaluation function is the optimization objective. The H obtained bridge and line layout schemes are used as the initial solution set. An appropriate optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, is selected, and relevant parameters such as the number of iterations and mutation probability are set. In each generation, each scheme is evaluated based on the bridge layout fitness evaluation function, and the best individuals are selected as the parents of the next generation. New schemes are updated and generated. When the preset number of iterations is reached or the stopping condition is met, the optimization process is terminated. After the optimization process is completed, the scheme with the lowest fitness evaluation function score is output as the optimized scheme for the bridge and line layout.

[0091] Furthermore, based on the bridge layout fitness evaluation function, optimizing the bridge fixed position based on the H bridge line layout schemes to generate the bridge line layout optimization scheme further includes:

[0092] For the H bridge line layout schemes, calling the bridge layout fitness evaluation function for mapping, and generating H bridge line layout fitness scores;

[0093] Sorting the H bridge line layout schemes from small to large according to the H bridge line layout fitness scores to generate H bridge line layout scheme sorting results;

[0094] Extracting the first three bridge line layout schemes from the sorted results of the H bridge line layout schemes;

[0095] For any layout path extraction result of any one of the first three cable tray layout plans, random mutation is performed based on the first group of cable layout paths up to the Nth group of cable layout paths to generate a cable tray layout expansion plan, wherein any mutation only mutates a preset number of layout path extraction results, the preset number being equal to round(1 / 5*N), where round() is a round-up function;

[0096] Based on the bridge layout fitness evaluation function, the bridge fixed position of the bridge line layout expansion plan is optimized to generate the bridge line layout optimization plan.

[0097] Based on the H bridge line layout schemes obtained, for each bridge line layout scheme, the bridge layout fitness evaluation function is used to calculate its fitness score according to the definition and calculation process of the fitness evaluation function. The calculation process is repeated to calculate the fitness scores of all H bridge line layout schemes to obtain H bridge line layout fitness scores.

[0098] The H bridge line layout plans and the corresponding fitness scores are combined into a list of key-value pairs, and the key-value pair list is sorted from small to large according to the scores to obtain a sorted key-value pair list, which represents the result of the H bridge line layout plans being arranged from small to large according to the fitness scores.

[0099] According to the sorting results of the H bridge line layout schemes obtained, the first three key-value pairs in the list are selected, and the scheme parts in these three key-value pairs are extracted, that is, the first three bridge line layout schemes are obtained, that is, the three schemes with the smallest fitness scores in the sorting results. These schemes perform well in terms of fitness scores and can be further studied and analyzed to determine the optimal bridge line layout scheme.

[0100] Select any one of the first three bridge line layout schemes, select any layout path in the selected scheme, mutate the selected layout path, that is, randomly change some or all of the path nodes, generate an expanded bridge line layout scheme, repeat this step, and mutate a preset number of layout path extraction results. The preset number is round(1 / 5*N), which indicates that the preset number is obtained by rounding up one-fifth of the total number of paths (N), where round() represents a rounding-up function, that is, for the decimal value obtained by 1 / 5*N, the nearest integer larger than the decimal value is taken. For example, when N is 32, 1 / 5*N is 6.4. The round() function is used to round 6.4, and the result is 7. Therefore, in this case, the preset number is 7, that is, 7 layout path extraction results are mutated each time. In this way, it is possible to determine how many layout paths should be mutated each time, and the number of mutated paths for each scheme may be different.

[0101] The new routing paths obtained after mutation replace the corresponding paths in the original solution, and this step is repeated until the Nth group of cable routing paths has been mutated. This can increase the diversity of solutions and provide more possibilities for further optimization or selection of the best solution.

[0102] For the obtained bridge and line layout expansion plan, the initial bridge position is first determined. The fitness score of the current plan is calculated using the bridge layout fitness evaluation function. The optimization process begins, and the plan is improved by continuously adjusting the bridge position. In each iteration, the bridge position is mutated and the fitness score of the adjusted plan is calculated. If the new plan is better than the previous one, it is selected as the current best plan; otherwise, the original plan is retained. The iterative process is repeated until the predetermined optimization stopping conditions are met, such as reaching the maximum number of iterations or the fitness score tends to be stable. The final bridge and line layout optimization plan is output. This plan is obtained by optimizing the fixed bridge position and has a better fitness score.

[0103] Furthermore, based on the first bridge size calibration result to the Nth bridge size calibration result, generating bridge size identifications for the N routing path extraction results, generating N bridge size identification results, and storing the N routing path extraction results and the N bridge size identification results jointly based on the bridge distance constraint parameter to set as a first bridge line routing plan, including:

[0104] Based on the N bridge frame size identification results, a distance-free combination is performed to calibrate the first space occupancy size information of any layout road section;

[0105] When each of the first space occupied dimension information satisfies the spatial dimension constraint information of the layout section, a full distance constraint combination is performed on the N bridge size identification results based on the bridge distance constraint parameter to calibrate the second space occupied dimension information of any layout section;

[0106] When any of the second space occupation size information does not satisfy the layout section space size constraint information, performing a partial distance constraint combination on the N bridge size identification results based on the bridge distance constraint parameter; and

[0107] Mark the shielding partition configuration for any two bridges without distance constraints;

[0108] When any one of the first space occupation size information does not satisfy the layout road section space size constraint information, the N layout path extraction results are eliminated.

[0109] Collect the dimensional identification results of N bridge frames, which include the length, width, height and other dimensional information of each bridge frame. According to the specific layout requirements of the road section, these bridge frames are combined to form a complete bridge frame system. During the combination process, no distance constraints are considered to ensure that there is no interference or collision between adjacent bridge frames. After the combination is completed, the first space occupancy dimension information is determined by measuring the maximum length, width and height of the entire bridge frame system for any layout section. Using the determined first space occupancy dimension information, combined with the shape and design of the bridge frame system, the space occupancy volume of the entire bridge frame system is calculated, such as the volume formula for calculating a rectangular parallelepiped.

[0110] Based on actual conditions and specific needs, spatial dimension constraint information for the road section is defined, including restrictions on length, width, height, and other aspects. The first spatial occupancy dimension of each bridge is compared with this spatial dimension constraint. When all dimensions meet the dimension constraint, the aforementioned bridge distance constraint parameter is retrieved and used to perform a full distance constraint combination on the N bridge dimension identification results. This ensures that the distance between each pair of adjacent bridges meets the set constraint conditions to avoid interference or collision. After completing the full distance constraint combination, the maximum length, width, and height of the entire bridge system are measured to determine the second spatial occupancy dimension information for any road section.

[0111] The second spatial occupancy dimension information for each bridge is compared with the spatial dimensional constraints of the route. If any bridge's dimensions do not meet the constraints, the N bridge dimensions are combined using the bridge distance constraint parameters to apply a partial distance constraint. In this case, only the distances between some adjacent bridges must meet the constraints, not all. This helps to meet the spatial dimensional constraints of the route as much as possible and optimizes the bridge system layout.

[0112] The function of the shielding partition is to create a partition between the bridges to prevent interference or collision. Specifically, arbitrarily select two bridges that need to be marked with the shielding partition configuration, analyze the design and layout requirements of the bridge, including requirements in terms of space dimensions, cable directions, etc., and determine the type and size of the shielding partition based on the relative position and requirements between the bridges. The shielding partition can be fixed or adjustable, and the appropriate material and shape can be selected according to actual needs. The shielding partition is placed between the two bridges to create a physical isolation layer to ensure that the shielding partition and the bridge are firmly installed and will not move or tilt. Through this step, any two bridges without distance constraints can be marked with the shielding partition configuration, which helps to ensure the stability and reliability of the bridge system and reduce the risk of interference.

[0113] If the first spatial occupancy dimension information for any layout path does not meet the size constraints, these paths must be eliminated because they cannot fit within the specified spatial dimensions. Paths that do not meet the size constraints are removed from the N layout path extraction results. This can be achieved by marking or excluding these paths. A new round of layout path extraction is performed based on the remaining layout paths, ensuring that the new extracted results meet the spatial dimension constraints of the layout segment to obtain a layout solution that meets the requirements.

[0114] The bridge line layout optimization plan is sent to the user terminal.

[0115] The derived bridge line layout optimization plan is sent to the user terminal through a suitable communication method, such as via email, file transfer protocol (FTP), cloud storage service, etc. The user can receive the optimization plan data on his terminal device and implement it.

[0116] In summary, the method and system for optimizing bridge line layout based on the Internet of Things provided by the embodiments of the present application have the following technical effects:

[0117] 1. By applying IoT technology, cable relationships are allocated based on the circuit topology diagram and a cable layout tree diagram is constructed, thus automating the cable layout process.

[0118] 2. Multi-level cluster analysis is performed on the cable sets extracted from the cable layout dendrogram to generate clustering results for the cable sets, thereby improving the accuracy and efficiency of the layout plan;

[0119] 3. By setting the direction and area of ​​horizontal and vertical routing constraints, you can more accurately control the routing path and position, improving routing quality.

[0120] 4. Set the bridge distance constraint parameters, combine the characteristics of three-dimensional space modeling and preset areas, optimize the bridge line layout under the constraint conditions, and effectively solve the difficulties of bridge distance constraints.

[0121] In summary, this IoT-based bridge line layout optimization method has achieved the goal of improving the efficiency, accuracy and automation of bridge line layout, and provided an optimized solution for bridge line layout.

[0122] Example 2

[0123] Based on the same inventive concept as the bridge line layout optimization method based on the Internet of Things in the aforementioned embodiment, as shown in FIG2 , the present application provides a bridge line layout optimization system based on the Internet of Things, the system comprising:

[0124] A cable relationship allocation module 10 is configured to receive a circuit topology diagram of a preset area from a user terminal, perform cable relationship allocation, and construct a cable layout tree diagram;

[0125] A multi-level cluster analysis module 20 is used to extract the i-th level cable set according to the cable layout dendrogram, perform multi-level cluster analysis, and generate a cable set clustering result;

[0126] A constraint region setting module 30, the constraint region setting module 30 is used to set a horizontal plane wiring constraint direction and a horizontal plane wiring constraint region, and to set a vertical plane wiring constraint direction and a vertical plane wiring constraint region;

[0127] A constraint parameter setting module 40, wherein the constraint parameter setting module 40 is used to set the bridge distance constraint parameters;

[0128] A three-dimensional space modeling module 50 is configured to perform three-dimensional space modeling based on an image sensor capturing an image of a preset area, thereby generating a three-dimensional model of the preset area;

[0129] A cable layout optimization module 60 is configured to optimize cable layout in the three-dimensional model of the preset area based on the cable set clustering result and under the constraints of the horizontal wiring constraint direction, the horizontal wiring constraint area, the vertical wiring constraint direction, the vertical wiring constraint area, and the bridge distance constraint parameters, and generate a bridge line layout optimization plan;

[0130] The optimization solution sending module 70 is used to send the bridge line layout optimization solution to the user terminal.

[0131] Furthermore, the system further includes a tree diagram construction module for performing the following steps:

[0132] Extracting line series connection output nodes and line parallel connection output nodes according to the circuit topology diagram;

[0133] According to the line serially connected node, extracting a node upstream cable and a node downstream cable from the circuit topology diagram, wherein current flows from the node upstream cable through the line serially connected node to the node downstream cable;

[0134] Extracting a plurality of parallel cables from the circuit topology diagram according to the parallel output node of the line, wherein the output positions of the plurality of parallel cables are the parallel output nodes of the line;

[0135] The node upstream cable is set as the mother cable of the node downstream cable, the multiple parallel cables are set as cables of the same level, and the cable layout tree diagram is constructed.

[0136] Furthermore, the system further includes a clustering result generation module to perform the following operation steps:

[0137] Performing first-level clustering on the i-th level cable set according to cable usage characteristics to generate a first cable set clustering result;

[0138] Performing secondary clustering on the first cable set clustering result according to a preset voltage deviation to generate a second cable set clustering result;

[0139] The second cable set clustering result is subjected to three-level clustering according to the access load distance deviation threshold to generate the cable set clustering result.

[0140] Furthermore, the system further includes a wiring constraint area setting module to perform the following operation steps:

[0141] The horizontal plane wiring constraint direction includes the vertical direction of the beam and the parallel direction of the beam;

[0142] The vertical plane wiring constraint direction includes a direction perpendicular to the room column and a direction parallel to the room column;

[0143] Based on the Internet of Things device, the non-wiring identification area of ​​the preset area is collected, and the horizontal plane wiring constraint area and the vertical plane wiring constraint area are set.

[0144] Furthermore, the system further includes a layout optimization solution generation module to perform the following operation steps:

[0145] Constraining the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, and the vertical plane wiring constraint area in the three-dimensional model of the preset area to generate a wiring analysis digital twin model;

[0146] performing a wiring path analysis on a first cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate a first set of cable laying paths, wherein the first set of cable laying paths has a first set of path constraint region sequences;

[0147] Repeat the analysis, performing a wiring path analysis on an Nth cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate an Nth group of cable routing paths, wherein the Nth group of cable routing paths has an Nth group of path constraint region sequences;

[0148] Taking the first group of path constraint area sequences to the Nth group of path constraint area sequences as position constraints, the first group of cable laying paths to the Nth group of cable laying paths are optimized for bridge fixed positions based on the bridge distance constraint parameters to generate the bridge line layout optimization plan.

[0149] Furthermore, the system further includes a layout optimization solution generation module to perform the following operation steps:

[0150] Obtaining a cable model list and a cable quantity list based on the first cable set clustering result, and matching the cable model list and the cable quantity list based on a bridge size calibration table to obtain a first bridge size calibration result;

[0151] Repeat the analysis until a cable model list and a cable quantity list are obtained based on the Nth cable set clustering result, and the cable model list and the cable quantity list are matched based on the bridge size calibration table to obtain the Nth bridge size calibration result;

[0152] Performing random path combination on the first group of cable laying paths up to the Nth group of cable laying paths to generate N laying path extraction results, wherein the N laying path extraction results correspond one-to-one to the first group of cable laying paths up to the Nth group of cable laying paths;

[0153] Based on the first bridge size calibration result to the Nth bridge size calibration result, generating bridge size identifications for the N routing path extraction results, generating N bridge size identification results, and storing the N routing path extraction results and the N bridge size identification results jointly based on the bridge distance constraint parameter to set as a first bridge line routing plan;

[0154] Repeating a preset number of times to obtain H bridge line layout schemes, wherein the first bridge line layout scheme belongs to the H bridge line layout schemes;

[0155] Construct bridge layout fitness evaluation function:

[0156] Among them, f(z k ) represents the fitness of the kth bridge line layout scheme. The fitness of any group of bridge line layout schemes includes N layout path extraction results, d j (z k ) represents the length of the i-th cable model of the k-th group of layout path extraction results, c j represents the cost of the i-th cable model, M represents the total number of cable models, Y represents the number of shielding partitions, w1 and w2 represent the preset weight parameters, α and β represent the normalization adjustment parameters, z k Characterize the kth group layout path extraction results;

[0157] Based on the bridge layout fitness evaluation function and the H bridge line layout schemes, the bridge fixed position is optimized to generate the bridge line layout optimization scheme.

[0158] Furthermore, the layout optimization solution generation module further includes the following steps:

[0159] For the H bridge line layout schemes, calling the bridge layout fitness evaluation function for mapping, and generating H bridge line layout fitness scores;

[0160] Sorting the H bridge line layout schemes from small to large according to the H bridge line layout fitness scores to generate H bridge line layout scheme sorting results;

[0161] Extracting the first three bridge line layout schemes from the sorted results of the H bridge line layout schemes;

[0162] For any layout path extraction result of any one of the first three cable tray layout schemes, random mutation is performed based on the first group of cable layout paths up to the Nth group of cable layout paths to generate a cable tray layout expansion scheme, wherein any mutation only mutates a preset number of layout path extraction results, the preset number being equal to round(1 / 5*N), where round() is a round-up function;

[0163] Based on the bridge layout fitness evaluation function, the bridge fixed position of the bridge line layout expansion plan is optimized to generate the bridge line layout optimization plan.

[0164] Furthermore, the system further includes a first layout plan generating module to perform the following operation steps:

[0165] Based on the N bridge frame size identification results, a distance-free combination is performed to calibrate the first space occupancy size information of any layout road section;

[0166] When each of the first space occupied dimension information satisfies the spatial dimension constraint information of the layout section, a full distance constraint combination is performed on the N bridge size identification results based on the bridge distance constraint parameter to calibrate the second space occupied dimension information of any layout section;

[0167] When any of the second space occupation size information does not satisfy the layout section space size constraint information, performing a partial distance constraint combination on the N bridge size identification results based on the bridge distance constraint parameter; and

[0168] Mark the shielding partition configuration for any two bridges without distance constraints;

[0169] When any one of the first space occupation size information does not satisfy the layout road section space size constraint information, the N layout path extraction results are eliminated.

[0170] Through the above detailed description of a bridge line layout optimization method based on the Internet of Things in this specification, those skilled in the art can clearly understand a bridge line layout optimization system based on the Internet of Things in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0171] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimization method for bridge cable laying based on the Internet of Things, characterized in that Applied to the bridge line layout optimization system based on the Internet of Things, the method includes: Receiving a circuit topology structure diagram of a preset area from a user terminal to perform cable relationship allocation and constructing a cable layout tree diagram; According to the cable layout tree diagram, extracting the cable set of the i-th layer for multi-level clustering analysis to generate a cable set clustering result; Setting the horizontal plane wiring constraint direction and the horizontal plane wiring constraint area, and setting the vertical plane wiring constraint direction and the vertical plane wiring constraint area; Setting the bridge distance constraint parameter; Based on the image sensor to collect the preset area image for three-dimensional space modeling to generate a three-dimensional model of the preset area; Based on the cable set clustering result, under the constraints of the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, the vertical plane wiring constraint area and the bridge distance constraint parameter, perform cable layout optimization in the three-dimensional model of the preset area to generate a bridge line layout optimization plan; Sending the bridge line layout optimization plan to the user terminal.

2. The method according to claim 1, characterized in that Receiving a circuit topology structure diagram of a preset area from a user terminal to perform cable relationship allocation and constructing a cable layout tree diagram, including: According to the circuit topology structure diagram, extracting the line series connection out nodes and the line parallel connection out nodes; According to the line series connection out nodes, extracting the upstream cable of the node and the downstream cable of the node from the circuit topology structure diagram, where the current flows from the upstream cable of the node through the line series connection out node to the downstream cable of the node; According to the line parallel connection out nodes, extracting multiple parallel cables from the circuit topology structure diagram, where the connection positions of the multiple parallel cables are the line parallel connection out nodes; Setting the upstream cable of the node as the mother cable of the downstream cable of the node, setting the multiple parallel cables as cables of the same layer, and constructing the cable layout tree diagram.

3. The method according to claim 1, wherein According to the cable layout tree diagram, extracting the cable set of the i-th layer for multi-level clustering analysis to generate a cable set clustering result, including: Performing first-level clustering on the cable set of the i-th layer according to the cable use characteristics to generate a first cable set clustering result; Performing second-level clustering on the first cable set clustering result according to a preset voltage deviation to generate a second cable set clustering result; Performing third-level clustering on the second cable set clustering result according to an access load distance deviation threshold to generate the cable set clustering result.

4. The method according to claim 1, wherein Setting the horizontal plane wiring constraint direction and the horizontal plane wiring constraint area, and setting the vertical plane wiring constraint direction and the vertical plane wiring constraint area, including: The horizontal plane wiring constraint direction includes the vertical direction of the roof beam and the parallel direction of the roof beam; The vertical plane wiring constraint direction includes the vertical direction of the column and the parallel direction of the column; Based on the Internet of Things device, collecting the non-wiring identification area of the preset area, and setting the horizontal plane wiring constraint area and the vertical plane wiring constraint area.

5. The method according to claim 1, wherein Based on the cable set clustering result, cable layout optimization is performed in the three-dimensional model of the preset area under the constraints of the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, the vertical plane wiring constraint area and the bridge distance constraint parameters, and a bridge line layout optimization plan is generated, including: Constraining the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction and the vertical plane wiring constraint area in the three-dimensional model of the preset area to generate a wiring analysis digital twin model; Performing a wiring path analysis on a first cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate a first group of cable laying paths, wherein the first group of cable laying paths has a first group of path constraint area sequences; Repeat the analysis, perform wiring path analysis on the Nth cable set clustering result of the cable set clustering result in the wiring analysis digital twin model to generate an Nth group of cable laying paths, wherein the Nth group of cable laying paths has an Nth group of path constraint area sequences; The first group of path constraint area sequences to the Nth group of path constraint area sequences are used as position constraints, and based on the bridge distance constraint parameters, the bridge fixed position optimization is performed on the first group of cable laying paths to the Nth group of cable laying paths to generate the bridge line layout optimization plan.

6. The method according to claim 5, wherein Taking the first group of path constraint area sequences to the Nth group of path constraint area sequences as position constraints, optimizing the fixed positions of the bridges for the first group of cable laying paths to the Nth group of cable laying paths based on the bridge distance constraint parameters, and generating the bridge line laying optimization plan, including: According to the first cable set clustering result, a cable model list and a cable quantity list are obtained, and the cable model list and the cable quantity list are matched based on the bridge size calibration table to obtain a first bridge size calibration result; Repeat the analysis until a cable model list and a cable quantity list are obtained according to the Nth cable set clustering result, and the cable model list and the cable quantity list are calibrated based on the bridge size calibration table. Perform matching to obtain the Nth bridge size calibration result; Performing random path combination on the first group of cable laying paths to the Nth group of cable laying paths to generate N laying path extraction results, wherein the N laying path extraction results correspond one-to-one to the first group of cable laying paths to the Nth group of cable laying paths; Based on the first bridge size calibration result to the Nth bridge size calibration result, generating bridge size identifications for the N layout path extraction results, generating N bridge size identification results, and based on the bridge distance constraint parameter, jointly storing the N layout path extraction results and the N bridge size identification results, and setting them as a first bridge line layout plan; Repeating a preset number of times to obtain H bridge line layout schemes, wherein the first bridge line layout scheme belongs to the H bridge line layout schemes; Construct a fitness evaluation function for bridge erection layout: Among them, f(z k ) represents the fitness of the k-th cable tray line layout plan. The fitness of any group of cable tray line layout plans includes N cable layout path extraction results, d j (z k ) represents the length of the i-th cable type in the k-th cable layout path extraction result, c j represents the cost of the i-th cable type, M represents the total number of cable types, Y represents the number of shielding partitions, w1 and w2 represent preset weight parameters, α and β represent normalization adjustment parameters, z k represents the k-th cable layout path extraction result; Based on the bridge layout fitness evaluation function and based on the H bridge line layout schemes, the bridge fixed position is optimized to generate the bridge line layout optimization scheme.

7. The method according to claim 6, wherein Based on the bridge layout fitness evaluation function, optimizing the bridge fixed position based on the H bridge line layout schemes, generating the bridge line layout optimization scheme, further comprising: For the H bridge line layout schemes, calling the bridge layout fitness evaluation function for mapping, and generating H bridge line layout fitness scores; Sorting the H bridge line layout schemes from small to large according to the H bridge line layout fitness scores, and generating H bridge line layout scheme sorting results; Extracting the first three bridge line layout schemes from the sorting results of the H bridge line layout schemes; For any layout path extraction result of any one of the three bridge line layout schemes, random mutation is performed based on the first group of cable layout paths to the Nth group of cable layout paths to generate a bridge line layout expansion scheme, wherein any mutation only mutates a preset number of layout path extraction results, the preset number is equal to round(1 / 5*N), and round() is a rounding up function; Based on the bridge layout fitness evaluation function, the bridge fixed position is optimized for the bridge line layout expansion plan to generate the bridge line layout optimization plan.

8. The method according to claim 6, characterized in that, Based on the first bridge size calibration result to the Nth bridge size calibration result, a bridge size identification is generated for the N layout path extraction results, and N bridge size identification results are generated. Based on the bridge distance constraint parameter, the N layout path extraction results and the N bridge size identification results are jointly stored and set as a first bridge line layout plan, including: Based on the N bridge frame size identification results, a distance-free combination is performed to calibrate the first space occupancy size information of any layout section; When each of the first space occupied dimension information satisfies the spatial dimension constraint information of the layout section, the N bridge frame dimension identification results are subjected to full distance constraint combination based on the bridge frame distance constraint parameter to calibrate the second space occupied dimension information of any layout section; When any of the second space occupation dimension information does not satisfy the layout section space dimension constraint information, performing partial distance constraint combination on the N bridge frame dimension identification results based on the bridge frame distance constraint parameter; and Mark the shielding partition configuration for any two bridges without distance constraints; When any of the first space occupation size information does not satisfy the layout section space size constraint information, the N layout path extraction results are eliminated.

9. An optimized system for laying bridge rack lines based on the Internet of Things, characterized in that, A bridge line layout optimization method based on the Internet of Things for implementing any one of claims 1 to 8, comprising: A cable relationship allocation module, the cable relationship allocation module is used to receive a circuit topology diagram of a preset area from a user terminal to perform cable relationship allocation and construct a cable layout tree diagram; Multi-level clustering analysis module, which is used to extract the cable set at the i-th level according to the cable layout tree diagram for multi-level clustering analysis and generate the clustering result of the cable set; Constraint area setting module, which is used to set the horizontal plane wiring constraint direction and the horizontal plane wiring constraint area, and set the vertical plane wiring constraint direction and the vertical plane wiring constraint area; Constraint parameter setting module, which is used to set the cable tray distance constraint parameter; Three-dimensional space modeling module, which is used to perform three-dimensional space modeling based on the images collected by the image sensor in the preset area and generate a three-dimensional model of the preset area; Cable layout optimization module, which is used to optimize the cable layout in the three-dimensional model of the preset area under the constraints of the horizontal plane wiring constraint direction, the horizontal plane wiring constraint area, the vertical plane wiring constraint direction, the vertical plane wiring constraint area and the cable tray distance constraint parameter based on the clustering result of the cable set, and generate an optimized cable tray line layout plan; Optimized plan sending module, which is used to send the optimized cable tray line layout plan to the user terminal.

Images