Cable routing optimization method and device based on Dijkstra algorithm

Abstract

The invention discloses a cable routing optimization method and device based on a Dijkstra algorithm, and the method comprises the steps: obtaining equipment information and a pipeline supporting structure, such as communication bridge information, in a target layout region, firstly extracting bridge path points to construct a main path network, and constructing a main path network; a Dijkstra algorithm is utilized to calculate a trunk optimal path from a starting point to an end point, such as a trunk route from a subway station equipment room to a climbing bridge frame, a local path, such as a vertical line or an obstacle avoidance path from camera equipment to the bridge frame, is dynamically generated in combination with an equipment type, and finally the trunk path and the local path are automatically combined at a common node. And outputting a final scheme through iterative optimization. According to a traditional scheme, a whole-course path needs to be planned for each device independently, but according to the scheme, only local connection needs to be calculated after a trunk path is reused, repeated calculation is remarkably reduced, the multi-device cable generation efficiency is remarkably improved, meanwhile, path conflicts caused by manual inflection point adding are avoided, and the path precision and the engineering reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of cable routing technology, and more specifically, to a cable routing optimization method and device based on the Dijkstra algorithm. Background Art

[0002] In building electrical and communications engineering, efficient and automatic routing of communication cables within complex spaces (such as building structures containing cable trays, equipment, and other obstacles) is a key requirement. The key lies in finding the optimal or feasible routing path while avoiding obstacles.

[0003] Currently, the mainstream method is highly dependent on manual operation: designers need to manually set path inflection points on the drawings and avoid blockages by repeatedly adding inflection points based on real-time observation of the spatial relationship between cables and obstacles (such as pipes and structural beams). Its essence is an iterative process that relies on manual judgment and simple geometric collision detection.

[0004] However, this method has significant flaws: the entire manual operation makes the planning process extremely cumbersome and inefficient; it has poor adaptability to complex and changeable obstacle environments and is prone to path conflicts; it is unable to effectively handle the collaborative wiring between multiple devices, lacks global optimization capabilities, and is difficult to quickly generate optimized multi-cable paths. Summary of the Invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a cable routing optimization method and device based on the Dijkstra algorithm, which can automatically identify obstacles and add inflection points according to the extracted direction, thereby significantly improving accuracy without affecting operational efficiency.

[0006] To achieve the above object, according to a first aspect of the present invention, a cable routing optimization method based on the Dijkstra algorithm is provided, the method comprising: Acquire a target layout area; the target layout area includes equipment information and pipeline support structure information; the pipeline support structure information includes a starting point, a path point, and an end point; the equipment information includes equipment type information and equipment points; Obtaining an initial path list based on the target layout area and using the Dijkstra algorithm to obtain the optimal trunk path; the initial path list is a collection of all routing paths formed by sequentially connecting starting points, path points, and end points; Obtain the local optimal path between equipment points and pipeline support structures; The common nodes in the trunk optimal path and the local optimal path are merged based on the device type information, and the merged path is iteratively optimized based on a preset convergence condition to obtain a cable routing optimization solution.

[0007] Furthermore, the obtaining of an initial path list based on the target layout area and the use of the Dijkstra algorithm to obtain the optimal trunk path include: Get the reachable information of all starting points, waypoints and destinations; Connecting all path points point-to-point based on the reachable information to obtain a multi-line segment set distributed throughout the pipeline support structure; Constructing an initial path list based on the starting point, the end point and the polyline segment set; The shortest path from the starting point to the end point is calculated using the Dijkstra algorithm according to the initial path list to obtain the optimal trunk path.

[0008] Furthermore, the initial path list is constructed based on the starting point, the end point and the multi-segment set; Acquire a path tree for the target layout area by constructing a pathfinding undirected graph based on the starting point, the end point, and the multi-segment set; Based on the path tree, a recursive algorithm is used to traverse all the connection relationships between the starting point, the end point and the multi-segment set to determine the reachability from the starting point to the end point, and obtain the set of all feasible paths between the starting point and the end point to obtain an initial path list.

[0009] Furthermore, determining the path points based on the target layout area includes: The intersection of the shortest path from the starting point to the pipeline support structure and the pipeline support structure is determined as a path point; Determine the intersection of the shortest path from the end point to the pipeline support structure and the pipeline support structure as a path point; The intersection point between the pipeline support structure and the pipeline support structure is determined as a path point.

[0010] The merging of the common nodes in the trunk optimal path and the local optimal path based on the device type information includes: Parsing device type information of the starting point and the end point, wherein the device type information includes point-to-point devices, group devices, and devices with a forwarding relationship; In a case where the device type of the device point is a point-to-point device, merging common nodes in the trunk optimal path and the local optimal path; In a case where the device type of the device point is a group device, merging the trunk optimal path, the local optimal path, and the common nodes in the group connection; In a case where the device type of the device point is a device with a transit relationship, the trunk optimal path, the local optimal path, and the common nodes in the transit relationship connection are merged.

[0011] Furthermore, the local optimal path between the equipment point and the pipeline support structure is obtained by at least one of the following methods: an automatic optimal path based on a shortest path algorithm, a vertical projection path from the equipment point to the steel trough, and a custom path drawn by manual interaction.

[0012] Furthermore, obtaining the target layout area includes: Obtain the device points in the target layout area; determine whether the device points are grouped, if so, determine the device points as grouped devices; otherwise, determine whether the device points are switched, if so, determine the device points as switched devices, otherwise, determine the device points as point-to-point devices.

[0013] According to a second aspect of the present invention, there is also provided a cable routing optimization device based on the Dijkstra algorithm, the device comprising: The first processing module is configured to obtain a target layout area; the target layout area includes equipment information and pipeline support structure information; the pipeline support structure information includes a starting point, a path point, and an end point; the equipment information includes equipment type information and equipment points; A second processing module is configured to obtain an initial path list based on the target layout area and to obtain a trunk optimal path using the Dijkstra algorithm; the initial path list is a set of all routing paths formed by sequentially connecting starting points, path points, and end points; The third processing module is used to obtain the local optimal path between the equipment point and the pipeline support structure; A merging module is used to merge the common nodes in the trunk optimal path and the local optimal path based on the device type information, and iteratively optimize the merged path based on a preset convergence condition to obtain a cable routing optimization solution.

[0014] According to a third aspect of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the above-mentioned cable routing optimization method based on the Dijkstra algorithm when running.

[0015] According to a fourth aspect of the present invention, an electronic device is also provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the cable routing optimization method based on the Dijkstra algorithm through the computer program.

[0016] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art: The present invention provides a cable routing optimization method based on the Dijkstra algorithm. By obtaining the equipment information and pipeline support structure in the target layout area, such as communication bridge information, the bridge path points are first extracted to construct a trunk path network. The Dijkstra algorithm is used to calculate the optimal trunk path from the start point to the end point at one time, such as the trunk route from the subway station equipment room to the climbing bridge. Then, local paths are dynamically generated based on the equipment type, such as the vertical line or obstacle-avoiding path from the camera device to the bridge. Finally, the trunk path and the local path are automatically merged at the common node, and the final solution is output through iterative optimization. The traditional solution requires a separate full path for each device, while the present solution only needs to calculate local connections after reusing the trunk path, which significantly reduces repeated calculations and significantly improves the efficiency of cable generation for multiple devices. At the same time, it avoids path conflicts caused by manually adding inflection points, improving path accuracy and engineering reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of an optional cable routing optimization method based on the Dijkstra algorithm provided in an embodiment of the present application; Figure 2 A schematic diagram of an optional path tree framework provided in an embodiment of the present application; Figure 3 A partial schematic diagram of an optional device grouping relationship connection provided in an embodiment of the present application; Figure 4 A partial schematic diagram of an optional wiring effect provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0020] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0021] Dijkstra's algorithm is a classic algorithm for computing single-source shortest paths. It works on weighted directed or undirected graphs, where the weights must be non-negative. It uses a greedy strategy to gradually expand the shortest path tree, starting from the starting point. Each time, the nearest unprocessed node is selected and the shortest paths to its neighbors are updated.

[0022] Weighted / undirected graphs: These graphs consist of nodes and directed / undirected edges, each with a weight (or cost). When moving from one node to another, the weights between the connected nodes are calculated.

[0023] Cable Tray: A cable tray is a structured cabling system used to support and protect communication cables, including electrical and optical cables. It is widely used in buildings, data centers, and industrial facilities. It provides an organized routing for cables, facilitating installation, maintenance, and management.

[0024] Greedy strategy: An algorithmic design concept that selects the current optimal local solution at each step in the hope of eventually reaching the global optimal solution. It does not backtrack or consider the consequences of future steps, but only makes the best choice based on the current state.

[0025] Path tree: A graph theory concept often used to describe the structure of the shortest path or specific path from a starting point to all other nodes. It is a tree structure in which each node represents a vertex in the graph and each edge represents a path from one node to another.

[0026] According to one aspect of the embodiment of the present application, a cable routing optimization method based on the Dijkstra algorithm is provided. Figure 1 The cable routing optimization method based on the Dijkstra algorithm provided in an embodiment of the present application is described.

[0027] Figure 1 This is a flow chart of an optional cable routing optimization method based on the Dijkstra algorithm provided in an embodiment of the present application. Figure 1 As shown, the process of the method may include the following steps: S102: Acquire a target layout area.

[0028] The target layout area includes equipment information and pipeline support structure information; the pipeline support structure information includes the starting point, path point and end point; and the equipment information includes equipment type information and equipment point.

[0029] S104, obtaining an initial path list based on the target layout area, and using the Dijkstra algorithm to obtain the optimal trunk path; the initial path list is a collection of all routing paths formed by sequentially connecting starting points, path points, and end points.

[0030] S106, obtaining the local optimal path between the equipment point and the pipeline support structure.

[0031] S108 , merging the common nodes in the trunk optimal path and the local optimal path based on the device type information, and iteratively optimizing the merged path based on a preset convergence condition to obtain a cable routing optimization solution.

[0032] Pipeline support structures are physical facilities that support and secure cables, including communication bridges, cable trenches, and integrated pipeline corridors. In this embodiment, pipeline support structure information includes the starting points, path points, and endpoints of cables in the area to be laid out. Device information includes the device type and location information of connected devices in the area to be laid out.

[0033] An optional cable routing optimization method based on the Dijkstra algorithm in the embodiments of this application can be applied to communication cable layout and routing, power cable trench planning, substation internal pipeline layout; water supply and drainage pipeline networks, drainage pipeline networks, and integrated pipeline corridors; spatial layout and dynamic coordination of HVAC pipelines, and multi-disciplinary integrated pipeline corridors, such as urban underground integrated pipeline corridors. The following describes a specific embodiment of this application using the application of communication cable layout and routing as an example.

[0034] In the target layout area, such as the communication layer drawing of a subway station, first select the CAD drawing range that contains equipment (such as switches, routers) and pipeline support structures (such as communication bridges, steel troughs). By parsing the drawing layers, the geometric path of the bridge is extracted as the basis of the backbone path, and the coordinates of the equipment and its type are identified. Based on the Dijkstra algorithm, with the starting equipment room and the end climbing frame as the source points, all path points, such as the shortest path between bridge intersections and equipment connection points, are calculated to generate the backbone optimal path. For local equipment, such as cameras in grouped equipment, their local optimal paths to the nearest bridge are calculated independently. Finally, the backbone path and the local path are connected at common nodes (such as Figure 2Nodes A and H) are merged and iterative optimization is performed. For example, the path length threshold can be ≤ 10% redundancy. The final cable routing solution is output to achieve multi-device path reuse bridge trunk (for example Figure 4 green path).

[0035] Based on the contents of the above embodiments, as an optional embodiment, the present invention further provides a cable routing optimization method based on the Dijkstra algorithm, which obtains an initial path list based on the target layout area and uses the Dijkstra algorithm to obtain the optimal trunk path, including: obtaining reachable information of all starting points, path points and end points; performing point-to-point connection of all path points based on the reachable information to obtain a multi-segment set distributed throughout the pipeline support structure; constructing an initial path list based on the starting point, end point and multi-segment set; and using the Dijkstra algorithm to calculate the shortest path connecting the starting point to the end point based on the initial path list to obtain the optimal trunk path.

[0036] In this embodiment, the reachability information of the path points is automatically generated through the topological relationship: the bridge line segment endpoints, turning points and equipment projection points constitute a node set (for example Figure 2 The distance between adjacent nodes is used as the edge weight, such as the actual distance in the CAD drawing. The program automatically connects all reachable nodes to form a multi-segment set (for example Figure 3 The yellow path is constructed to construct an initial path list that includes the starting equipment room, the ending climbing frame, and intermediate nodes (such as B, C, and D). The Dijkstra algorithm uses the starting point as the source and gradually expands the shortest path tree: it prioritizes the current shortest path node (for example, starting point → A → D has the lowest weight), updates neighboring nodes (for example, D → B has a weight of 15, D → F has a weight of 8), and finally selects the minimum-weight path from the starting point to the ending point, resulting in the optimal backbone path (for example, path 2: starting point → A → D → F → H → ending point, which has the lowest total weight).

[0037] Based on the contents of the above embodiments, as an optional embodiment, the present invention also provides a cable routing optimization method based on the Dijkstra algorithm, which constructs an initial path list according to the starting point, end point and multi-segment set; obtains a path tree for the target layout area by constructing a path-finding undirected graph based on the starting point, end point and multi-segment set; and uses a recursive algorithm based on the path tree to traverse the connection relationship of all starting points, end points and multi-segment sets to determine the reachability from the starting point to the end point, and obtains all feasible path sets between the starting point and the end point to obtain an initial path list.

[0038] In this embodiment, the construction of the initial path list relies on an undirected graph data structure: nodes are path points, such as device connection point A, bridge intersection point F, etc., and edges are reachable line segments, such as AD, DF, etc. All path combinations are traversed through a recursive algorithm: starting from the starting device 1, a depth-first search is performed for the adjacent node A, and then the adjacent node D of A is recursively accessed until the end point. If a path exists, for example, path 1: device 1->A->D->B->C->E->G->H->end point; path 2: device 1->A->D->F->H->end point; path 3: device 1->A->D->B->C->F->H->end point device, then it is added to the feasible path set; if the path is interrupted, for example, when encountering an unconnected node, it is traced back to the previous node. The final output is an initial path list containing all feasible paths.

[0039] Based on the contents of the above embodiments, as an optional embodiment, the present invention further provides a method for determining a path point based on the target layout area in a cable routing optimization method based on the Dijkstra algorithm, comprising: determining the intersection of the shortest path from the starting point to the pipeline support structure and the pipeline support structure as a path point; determining the intersection of the shortest path from the end point to the pipeline support structure and the pipeline support structure as a path point; and determining the intersection of the obtained pipeline support structure and the pipeline support structure as a path point.

[0040] In this embodiment, waypoint determination can be divided into three categories: Equipment-bridge intersection: Calculate the perpendicular from the equipment point (such as equipment 1) to the nearest bridge (when there is no obstruction) or the nearest reachable point along the edge of the obstacle (when encountering a wall) to generate intersection A. Bridge-bridge intersections: Identify bridge intersections (e.g. Figure 2 B, C, E, etc.); End point association point: projection point H from the end equipment (such as climbing frame) to the bridge frame.

[0041] In this embodiment, if the path from the device 3 to the bridge encounters a ventilation pipe (obstacle), the node C is found along the edge of the pipe instead of being projected directly.

[0042] Based on the contents of the above embodiments, as an optional embodiment, the present invention also provides a cable routing optimization method based on the Dijkstra algorithm, which merges the common nodes in the trunk optimal path and the local optimal path based on the device type information, including: parsing the device type information of the starting point and the end point, the device type information including point-to-point devices, group devices and devices with a transfer relationship; when the device type of the device point is a point-to-point device, merging the common nodes in the trunk optimal path and the local optimal path; when the device type of the device point is a group device, merging the common nodes in the trunk optimal path, the local optimal path and the group connection; when the device type of the device point is a device with a transfer relationship, merging the common nodes in the trunk optimal path, the local optimal path and the transfer relationship connection.

[0043] Common node merging involves joining the device path and the trunk path at intersections (such as nodes A and H) to reduce duplicate calculations.

[0044] In this embodiment, the merging logic is determined by the device type, including: for point-to-point devices, such as independent servers, the backbone path (e.g., A→D→F→H) and the local path (e.g., device 1→A) are directly merged. For grouped devices, such as camera groups, the grouped main device path (e.g., device 2→B) is merged with the backbone path (e.g., B→C→F→H) and the grouped internal connection (e.g., device 3→device 2). For switching devices such as signal converters, the switching device paths (e.g., device 4→E) are first merged, and then the backbone path (e.g., E→G→H) and the switching relationship line (e.g., sensor→device 4) are linked. It should be noted that grouped devices refer to logically associated device groups (e.g., Figure 2 Devices 2-3 in the middle), the switching device refers to the device that needs to relay the signal (for example, device 4 is connected to the sensor).

[0045] Based on the contents of the above embodiments, as an optional embodiment, the present invention also provides a cable routing optimization method based on the Dijkstra algorithm, in which the local optimal path between the equipment point and the pipeline support structure is obtained by at least one of the following methods: an automatic optimization path based on the shortest path algorithm, a vertical projection path from the equipment point to the steel trough, and a custom path drawn by manual interaction.

[0046] In this embodiment, the local optimal path can support multiple generation methods, including automatic optimization, Dijkstra algorithm, vertical projection, and custom drawing. Specifically, automatic optimization can be set to the default format; Dijkstra algorithm can be used to avoid obstacles (for example, bypassing the edge of the wall to the bridge); vertical projection can be used to directly take the vertical line from the device point to the bridge when there is no obstruction (for example, Figure 2Device 1 → A); Using custom drawing, the user manually draws a path, such as avoiding specific areas, and records a sequence of coordinate points. Based on the contents of the above embodiments, as an optional embodiment, the present invention also provides a method for obtaining a target layout area in a cable routing optimization method based on the Dijkstra algorithm, comprising: obtaining a device point within the target layout area; determining whether the device point is grouped, and if so, determining the device point as a grouped device; otherwise, determining whether the device point is switched, and if so, determining the device point as a switched device; otherwise, determining the device point as a point-to-point device.

[0047] In this embodiment, device type classification can be logically divided into the following: first, identify the device point, such as a switch block in a CAD drawing. If the device attribute table is marked as "grouped," for example, cameras with the same group number, they are classified as grouped devices. If the device connection table shows "signal input → output," such as sensor → converter → host, they are classified as transfer devices. All other devices are considered point-to-point devices. Grouped devices treat all devices in the same group as having the same path starting point, while transfer devices treat the converter after transfer as the path starting point.

[0048] In summary, this application realizes the automatic generation of trunk common cable paths within the space based on the spatial characteristics within a certain area, through a scheme combining eigenvalue extraction and path planning, combined with an adaptive topology algorithm based on path points. By selecting the single-source shortest path of local equipment, the routing optimization scheme based on the Dijkstra algorithm can realize the rapid generation of cable paths for multiple devices within the range area. This application automatically generates the final cable route by fitting the common nodes of the trunk optimal path and the local optimal path, and based on pre-set iterative conditions, further optimizes the system path to obtain the best planning scheme.

[0049] According to another aspect of the embodiments of the present application, a control device for implementing the above-mentioned cable routing optimization method based on the Dijkstra algorithm is also provided. The device may include: The first processing module is configured to obtain a target layout area; the target layout area includes equipment information and pipeline support structure information; the pipeline support structure information includes a starting point, a path point, and an end point; the equipment information includes equipment type information and equipment points; A second processing module is configured to obtain an initial path list based on the target layout area and to obtain a trunk optimal path using the Dijkstra algorithm; the initial path list is a set of all routing paths formed by sequentially connecting starting points, path points, and end points; The third processing module is used to obtain the local optimal path between the equipment point and the pipeline support structure; A merging module is used to merge the common nodes in the trunk optimal path and the local optimal path based on the device type information, and iteratively optimize the merged path based on a preset convergence condition to obtain a cable routing optimization solution.

[0050] It should be noted here that the examples and scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules as part of the device can run in a hardware environment, can be implemented by software, and can also be implemented by hardware, where the hardware environment includes a network environment.

[0051] According to another aspect of the embodiments of the present application, a storage medium is further provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of any of the above-mentioned cable routing optimization methods based on the Dijkstra algorithm in the embodiments of the present application.

[0052] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: S1, obtaining a target layout area; the target layout area includes equipment information and pipeline support structure information; the pipeline support structure information includes a starting point, a path point, and an end point; the equipment information includes equipment type information and equipment points; S2, obtaining an initial path list based on the target layout area, and using the Dijkstra algorithm to obtain the optimal trunk path; the initial path list is a set of all routing paths formed by sequentially connecting starting points, path points, and end points; S3, obtaining the local optimal path between the equipment point and the pipeline support structure; S4, merging the common nodes in the trunk optimal path and the local optimal path based on the device type information, and iteratively optimizing the merged path based on a preset convergence condition to obtain a cable routing optimization solution.

[0053] Among them, computer-readable storage media may include, but are not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0054] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0055] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0057] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0059] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disk, etc., various media that can store program code.

[0060] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0061] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0062] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cable routing optimization method based on Dijkstra algorithm, characterized in that: include: Acquire a target layout area; the target layout area includes equipment information and pipeline support structure information; The pipeline support structure information includes a starting point, a path point, and an end point; The device information includes device type information and device point; Obtaining an initial path list based on the target layout area and using the Dijkstra algorithm to obtain the optimal trunk path; the initial path list is a collection of all routing paths formed by sequentially connecting starting points, path points, and end points; Obtain the local optimal path between equipment points and pipeline support structures; The common nodes in the trunk optimal path and the local optimal path are merged based on the device type information, and the merged path is iteratively optimized based on a preset convergence condition to obtain a cable routing optimization solution.

2. The cable routing optimization method based on Dijkstra algorithm according to claim 1, characterized in that: The obtaining of an initial path list based on the target layout area and obtaining a trunk optimal path using the Dijkstra algorithm includes: Get the reachable information of all starting points, waypoints and destinations; Connecting all path points point-to-point based on the reachable information to obtain a multi-line segment set distributed throughout the pipeline support structure; Constructing an initial path list based on the starting point, the end point and the polyline segment set; The shortest path from the starting point to the end point is calculated using the Dijkstra algorithm according to the initial path list to obtain the optimal trunk path.

3. The cable routing optimization method based on Dijkstra algorithm according to claim 2, characterized in that: said constructing an initial path list according to said starting point, end point and multi-line segment set; Acquire a path tree for the target layout area by constructing a pathfinding undirected graph based on the starting point, the end point, and the multi-segment set; Based on the path tree, a recursive algorithm is used to traverse all the connection relationships between the starting point, the end point and the multi-segment set to determine the reachability from the starting point to the end point, and obtain the set of all feasible paths between the starting point and the end point to obtain an initial path list.

4. The cable routing optimization method based on Dijkstra algorithm according to claim 2, characterized in that: The determining of the path point based on the target layout area includes: The intersection of the shortest path from the starting point to the pipeline support structure and the pipeline support structure is determined as a path point; Determine the intersection of the shortest path from the end point to the pipeline support structure and the pipeline support structure as a path point; The intersection point between the pipeline support structure and the pipeline support structure is determined as a path point.

5. The cable routing optimization method based on Dijkstra algorithm according to claim 1, characterized in that: The merging of the common nodes in the trunk optimal path and the local optimal path based on the device type information includes: Parsing device type information of the starting point and the end point, wherein the device type information includes point-to-point devices, group devices, and devices with a forwarding relationship; In a case where the device type of the device point is a point-to-point device, merging common nodes in the trunk optimal path and the local optimal path; In a case where the device type of the device point is a group device, merging the trunk optimal path, the local optimal path, and the common nodes in the group connection; In a case where the device type of the device point is a device with a transit relationship, the trunk optimal path, the local optimal path, and the common nodes in the transit relationship connection are merged.

6. The cable routing optimization method based on Dijkstra algorithm according to claim 1, characterized in that: The local optimal path between the equipment point and the pipeline support structure is obtained by at least one of the following methods: an automatic optimization path based on a shortest path algorithm, a vertical projection path from the equipment point to the steel trough, and a custom path drawn by manual interaction.

7. The cable routing optimization method based on Dijkstra algorithm according to claim 1, characterized in that: The obtaining of the target layout area includes: Obtain the device points in the target layout area; determine whether the device points are grouped, if so, determine the device points as grouped devices; otherwise, determine whether the device points are switched, if so, determine the device points as switched devices, otherwise, determine the device points as point-to-point devices.

8. A cable routing optimization device based on Dijkstra algorithm, characterized in that: include: A first processing module is used to obtain a target layout area; The target layout area includes equipment information and pipeline support structure information; The pipeline support structure information includes the starting point, path point and end point; the equipment information includes equipment type information and equipment point; A second processing module is configured to obtain an initial path list based on the target layout area and obtain an optimal trunk path using a Dijkstra algorithm; The initial path list is a collection of all routing paths formed by sequentially connecting the starting point, path point and end point; The third processing module is used to obtain the local optimal path between the equipment point and the pipeline support structure; A merging module is used to merge the common nodes in the trunk optimal path and the local optimal path based on the device type information, and iteratively optimize the merged path based on a preset convergence condition to obtain a cable routing optimization solution.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the cable routing optimization method based on the Dijkstra algorithm according to any one of claims 1 to 7 when the program is executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the cable routing optimization method based on the Dijkstra algorithm according to any one of claims 1 to 7 through the computer program.

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