Optical cable routing planning method, device, terminal equipment and storage medium

Abstract

The present application provides a method, device, terminal equipment and storage medium for planning optical cable routes. The method includes: converting the business requirements obtained from the user input interface into target parameters; optimizing the target parameters by a search algorithm based on the optical cable resource data model to obtain a search optimization result; optimizing the search optimization result by a planning algorithm to obtain a planning optimization result; and outputting the planning optimization result. The method of the present application reduces the dependence on manual experience and improves the efficiency of optical cable route planning.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an optical cable routing planning method, device, terminal equipment and storage medium. Background Art

[0002] With the continuous development of optical communication network technology, the composition of optical cable resources is becoming more and more complex, the number of resources is increasing, and the complexity of opening services for optical cables is also gradually increasing. In previous implementation plans, the opening plan was planned and set up in a coarse-to-fine, automatic-to-manual manner. It was necessary to first complete the large-granularity selection through the transmission system, and then manually configure the optical cable segments and fiber cores under the transmission system. Only after all manual links are completed can the complete configuration plan of the entire fiber core be viewed.

[0003] In possible implementation, the manual work can be reduced to a certain extent, and automatic planning can be achieved by manually configuring parameters within a small number of optical cable resource nodes. This technical solution is based on the optical cable resource parameters manually input by technicians, which is more dependent on the experience of technicians and has low efficiency in optical cable route planning. Summary of the invention

[0004] The present application provides an optical cable route planning method, apparatus, terminal equipment and storage medium, which are used to reduce manual steps, realize automatic opening of optical cable planning schemes, and improve the efficiency of optical cable route planning.

[0005] In a first aspect, the present application provides a method for planning an optical cable route, comprising:

[0006] The business requirements obtained in the user input interface are converted into target parameters; the target parameters are the ID information in the cloud resource library corresponding to the business requirements; the business requirements include one or more of the following information of the optical cable route: starting position, ending position, must-pass node, avoidance node, passing optical cable, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers;

[0007] Based on the optical cable resource data model, the target parameters are searched and optimized by the algorithm to obtain the optimal search result; the search algorithm includes the depth-first algorithm and the breadth-first algorithm;

[0008] The planning algorithm is optimized for the search optimization results to obtain the planning optimization results; the planning algorithms include A* algorithm and Dijkstra algorithm;

[0009] Output the optimal planning results.

[0010] Optionally, a search algorithm is optimized for target parameters based on the optical cable resource data model to obtain a search optimization result, including:

[0011] If the target parameters have must-pass node parameters and avoidance node parameters, the depth-first algorithm is used to search for the best algorithm to obtain the best search result;

[0012] If the target parameter exists in the path of the optical cable parameter, the search algorithm is preferentially selected according to the breadth-first algorithm to obtain the optimal search result.

[0013] Optionally, the search algorithm is optimized for the target parameter based on the optical cable resource data model to obtain the optimal search result, which also includes:

[0014] The target parameters include must-pass node parameters and avoidance node parameters. If the depth-first algorithm cannot reach the planning result, the breadth-first algorithm is used to select the optimal search algorithm to obtain the optimal search result.

[0015] The target parameters exist in the path cable parameters. If the breadth-first algorithm cannot reach the planning result, the search algorithm is optimized according to the depth-first algorithm to obtain the optimal search result.

[0016] Optionally, a planning algorithm is optimized for the search optimization result to obtain a planning optimization result, including:

[0017] Based on the A* algorithm and the Dijkstra algorithm, the search results are verified respectively to obtain the A* algorithm results and the Dijkstra algorithm results;

[0018] The optimal planning result is obtained based on the A* algorithm results and Dijkstra algorithm results, the number of nodes, the path length and the similarity of the results.

[0019] Optionally, output the planning optimization results, including:

[0020] The planning optimization results are output on the display screen in the form of a list and a GIS map.

[0021] The present application provides a method for planning an optical cable route, further comprising:

[0022] Obtain optical cable resource data from the cloud resource library;

[0023] Simplify the correlation and redundant data of the optical cable resource data to obtain the original modeling data to establish the original data model;

[0024] Based on the necessary business fields in the optical cable resource data, the original data model is supplemented for the second time to obtain

[0025] Optical cable resource data model; the necessary business fields include the optical fiber path name information, fiber core quality information in the redundant data, and number information in the association relationship.

[0026] In a second aspect, the present application provides an optical cable routing planning device, comprising:

[0027] The business requirement processing module is used to convert the business requirements obtained in the user input interface into target parameters; the target parameters are the ID information in the cloud resource library corresponding to the business requirements; the business requirements include one or more of the following information of the optical cable route: starting position, end position, must-pass node, avoidance node, passing optical cable, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers;

[0028] The search algorithm optimization module is used to optimize the search algorithm for the target parameters based on the optical cable resource data model to obtain the search optimization results; the search algorithm includes the depth first algorithm and the breadth first algorithm;

[0029] The planning algorithm optimization module is used to optimize the planning algorithm for the search optimization results to obtain the planning optimization results; the planning algorithms include A* algorithm and Dijkstra algorithm;

[0030] The planning result output module is used to output the planning optimization results.

[0031] In a third aspect, an embodiment of the present application provides a terminal device, including: a memory and a processor;

[0032] The memory is used to store computer instructions; the processor is used to execute the computer instructions stored in the memory to implement any method in the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement any one of the methods in the first aspect.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements any method in the first aspect when executed by a processor.

[0035] It should be noted that the above-mentioned computer program may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor or separately from the processor, which is not limited in this application.

[0036] The optical cable route planning method, device, terminal equipment and storage medium provided by the present application convert the business requirements obtained from the user input interface into target parameters; perform search algorithm optimization on the target parameters based on the optical cable resource data model to obtain the search optimization result; perform planning algorithm optimization on the search optimization result to obtain the planning optimization result; and output the planning optimization result. The user can automatically plan a better optical cable route planning scheme by inputting fewer business requirements. The method reduces the dependence on manual experience and improves the efficiency of optical cable route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0038] Figure 2 A schematic diagram of a process flow of an optical cable routing planning method provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of a process flow of an optical cable routing planning method provided in an embodiment of the present application;

[0040] Figure 4 A path example diagram of the optical cable routing planning method provided in an embodiment of the present application;

[0041] Figure 5 A schematic diagram of a process flow of an optical cable routing planning method provided in an embodiment of the present application;

[0042] Figure 6 A schematic diagram of the structure of an optical cable routing planning device provided in an embodiment of the present application;

[0043] Figure 7 A schematic diagram of the structure of an optical cable routing planning terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0045] First, the terms involved in this application are explained:

[0046] Optical Distribution Frame (ODF): A fiber optic distribution device designed specifically for fiber optic communication rooms. It has the functions of optical cable fixing and protection, optical cable termination, line adjustment, and optical cable core and pigtail protection.

[0047] Optical cable junction box: It is a junction equipment that provides termination and jumpering for the trunk layer optical cable and the distribution layer optical cable. After the optical cable is introduced into the optical cable junction box, it is fixed, terminated and fiber-matched, and then the trunk layer optical cable and the distribution layer optical cable are connected using jumper fibers.

[0048] In possible implementations, the optical cable routing planning scheme still has the following problems:

[0049] With the development of optical communication technology, various types of optical communication data are increasing day by day, and higher requirements are also put forward for the transmission of information. The composition of optical cable resources is becoming more and more complex, the number of resources is increasing, and the complexity of opening services on optical cables is also gradually increasing. In the existing optical cable routing planning scheme, a scheme is often adopted to complete the large-granularity selection through the transmission system first, and then the automatic selection of optical cable segments and fiber cores under the transmission system by manually configuring parameters, which reduces the manual links to a certain extent. However, this method requires the optical cable planner to manually input multiple optical cable parameters to realize automatic planning, which is more dependent on the manual experience of the technicians. At the same time, for the optical cable routing planning scheme, there are often many nodes and a lot of information data traversed, which affects the time to output the planning results, increases the amount of manual operations, and has low efficiency in optical cable routing planning.

[0050] The optical cable routing planning method based on the optical cable resource data model proposed in this application covers all existing optical cable routing conditions in real-life scenarios in the model. It can select the best search algorithm and the best planning algorithm according to the user's business request (starting position and ending position, etc.), and automatically plan multiple optical cable routing planning schemes for users. This method reduces the dependence on manual experience, and non-technical personnel can also perform optical cable routing planning based on basic business requirements. The optical cable routing planning method of this application can realize end-to-end simulation planning of long-distance optical cables based on the optical cable resource data model, up to hundreds of thousands of kilometers.

[0051] The technical solution of the present invention and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0052] Before introducing the method provided by the embodiment of the present invention in detail, first, the application scenario of the optical cable routing planning method provided by the embodiment of the present invention is introduced in detail.

[0053] The optical cable routing planning method provided in this application can be applied to Figure 1 The application scenario diagram of the optical cable routing planning system is shown in FIG. Figure 1As shown, the optical cable routing planning system includes: a server 101 and a client 102, and the client 102 can be used to receive the business requirements input by the user and display the search results of the optical cable routing planning. Exemplarily, the client displays a manual optical path simulation page, and the user fills in the business requirements based on the page, for example: fill in the starting point, the end point, add or delete the avoidance location, the must-pass location, etc. The search result page of the client 102 provides multiple optical cable routing planning schemes for the user to view. After the server 101 obtains and stores the optical cable resource data from the cloud resource library, it establishes an optical cable resource data model based on the optical cable resource data. The server 101 performs optical cable routing planning on the business requirement information input by the user based on the optical cable resource data model, and sends the search results to the client.

[0054] Based on the above optical cable routing planning system architecture, Figure 2 As shown, Figure 2 A schematic diagram of a process flow of a method for planning optical cable routing provided in an embodiment of the present application. The method may include:

[0055] S201, converting the business requirements obtained in the user input interface into target parameters; the target parameters are ID information in the cloud resource library corresponding to the business requirements. The business requirements include one or more of the following information of the optical cable route: starting position, end position, must-pass node, avoidance node, passing optical cable, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers.

[0056] The client can provide an interface for users to input business requirements and send the business requirements to the server. The server stores all the data of the existing data model queried from the cloud resource library. The server parameterizes the business requirements into the corresponding ID information in the cloud resource library.

[0057] Exemplarily, based on the business requirements input by the user on the client, such as: starting position, ending position, necessary nodes, avoidance nodes, optical cables passing through, maximum number of transfer points, fiber attenuation coefficient, number of idle optical fibers, etc., the user's business requirements are parameterized, and the starting position, ending position, necessary nodes, avoidance nodes, and optical cables passing through entered by the user are converted into the ID information corresponding to the business requirements in the resource library to obtain the target parameters. For example, the user enters the starting position and ending position in text form, and the server converts the received starting position and ending position into corresponding longitude and latitude information or ID information. It is understandable that the business requirements can be one or more of the above examples, and the present application does not limit this.

[0058] The optical cable routing planning method of the present application allows users to input fewer business requirements to implement fast and convenient optical cable routing planning. When the server obtains fewer target parameters, the target parameters can be matched and combined through matching strategies.

[0059] For example, the user inputs the starting position in text form, and the server converts the starting position into a definition table of the area to query the ID number of the area where the starting position is located, the corresponding machine room, ODF, the terminal corresponding to the ODF, the fiber core and other data information.

[0060] Exemplarily, the default combination in the matching strategy is: starting position parameter, ending position parameter, necessary node parameter, avoidance node parameter, maximum transfer point number parameter, fiber attenuation coefficient parameter, and idle fiber number parameter. If the route optical cable parameter exists, in addition to the default combination, a new combination is generated, namely: starting position parameter, ending position parameter, necessary node parameter, avoidance node parameter, route optical cable parameter, maximum transfer point number parameter, fiber attenuation coefficient parameter, and idle fiber number parameter. If the route optical cable parameter does not exist, in addition to the default combination, the server will query the route optical cables that exist at both the starting position and the ending position according to the starting position parameter and the ending position parameter, and generate a corresponding number of combinations according to the number of route optical cables, and combine them with the starting position parameter, the ending position parameter, the necessary node parameter, the avoidance node parameter, the maximum transfer point number parameter, the fiber attenuation coefficient parameter, and the idle fiber number parameter, and use them in the subsequent optical cable routing planning.

[0061] S202, based on the optical cable resource data model, a search algorithm is optimized for the target parameters to obtain a search optimization result; the search algorithm includes a depth-first algorithm and a breadth-first algorithm.

[0062] A depth-first algorithm or a breadth-first algorithm is used to search and optimize the target parameters to obtain a search optimization result; the search optimization result includes at least one planned path that can realize the opening of the optical cable route between the starting position and the ending position. The search optimization result is saved as a substitute data.

[0063] S203, optimizing the search optimization result by a planning algorithm to obtain a planning optimization result; the planning algorithm includes an A* algorithm and a Dijkstra algorithm.

[0064] The target parameters are optimized by the planning algorithm based on the A* algorithm and the Dijkstra algorithm to verify whether the planning path included in the search optimization result in step S102 meets the target parameters. If so, a better planning scheme is selected from multiple planning paths for output.

[0065] S204: Output the optimal planning result.

[0066] Exemplarily, the planning optimization results are output on the display screen of the client in the form of a list and a GIS map.

[0067] The optical cable route planning method provided in this embodiment converts the business requirements obtained in the user input interface into target parameters; performs search algorithm optimization on the target parameters based on the optical cable resource data model to obtain the search optimization result; performs planning algorithm optimization on the search optimization result to obtain the planning optimization result; and outputs the planning optimization result. In the above process, the user can automatically plan the optical cable route planning scheme by inputting fewer business requirements. The dependence on manual experience is reduced, and the efficiency of optical cable route planning is improved.

[0068] Figure 3 A flowchart of a method for planning optical cable routing is provided for an embodiment of the present application. Figure 2 Based on the illustrated embodiment, S202 includes:

[0069] Optionally, if the target parameters have must-pass node parameters and avoidance node parameters, the depth-first algorithm is used to select the optimal search algorithm to obtain the optimal search result. If the depth-first algorithm cannot achieve the planning result, the breadth-first algorithm is used to select the optimal search algorithm to obtain the optimal search result.

[0070] When searching for the optimal algorithm for the target parameters, determine whether the target parameters include the parameters of the necessary nodes and the parameters of the avoidance nodes. When the parameters of the necessary nodes and the avoidance nodes exist, the depth-first algorithm takes precedence over the breadth-first algorithm in the algorithm priority. Based on the depth-first algorithm, the search algorithm for the target parameters is optimized to obtain multiple planning paths.

[0071] For example, Figure 4 This is an example diagram of the optical cable routing planning path provided in the embodiment of the present application. Figure 4 As shown in the figure, circles represent nodes and numbers are node numbers.

[0072] There is a backtracking operation in the depth-first algorithm, which is implemented through a multi-stage determination process. Each stage corresponds to a branch. If the branch path cannot go deeper, it will return to the node just searched and select another branch path of the node. The depth-first algorithm is slower for single node search, but some nodes can be retained.

[0073] For example, if 1 is the starting position and 14 is the ending position, the search result order of the depth-first algorithm is:

[0074] Path 1: 1-2-4-14

[0075] Path 2: 1-2-5

[0076] Path 3: 1-2-6-9

[0077] Path 4: 1-2-6-10-14

[0078] Path 5: 1-3-7-11-14

[0079] Path 6: 1-3-8-12-14

[0080] Path 7: 1-3-8-13

[0081] Among them, paths 1, 4, 5 and 6 are feasible paths that can reach the end position 14.

[0082] Based on the above-mentioned depth-first algorithm, the characteristics of some nodes can be retained. When there are must-pass node parameters and avoidance node parameters in the target parameters, the avoidance nodes are excluded first, and the requirements of the must-pass nodes are preserved.

[0083] If there is no connected route in the depth-first algorithm, or the connected route cannot meet the requirements of the maximum number of transfer points, fiber attenuation coefficient or number of idle fibers, the planning result cannot be achieved. At this time, the breadth-first algorithm is performed on the target parameters.

[0084] Optionally, if the target parameter has a path cable parameter, the search algorithm is optimized according to the breadth-first algorithm to obtain the optimal search result. If the breadth-first algorithm cannot achieve the planning result, the search algorithm is optimized according to the depth-first algorithm to obtain the optimal search result.

[0085] When searching for the optimal algorithm for the target parameters, it is determined whether the target parameters include the path optical cable parameters. When the path optical cable parameters exist, the breadth-first algorithm takes precedence over the depth-first algorithm in the algorithm priority. Based on the breadth-first algorithm, the search algorithm for the target parameters is optimized to obtain multiple planning paths.

[0086] There is no backtracking operation in the breadth-first algorithm. All unvisited adjacent nodes of the current node are visited at one time, and for each adjacent node, all unvisited secondary adjacent nodes of its adjacent nodes are visited. The breadth-first algorithm is faster for searching a single node, but all nodes must be retained.

[0087] For example, if 1 is the starting position and 14 is the ending position, the order of search results of the breadth-first algorithm is:

[0088] <1,2>、<1,3>

[0089] <2,4>, <2,5>, <2,6>, <3,7>, <3,8>

[0090] <4,14>、<6,9>、<6,10>、<7,11>、<8,12>、<8,13>

[0091] <10,14>、<11,14>、<12,14>

[0092] After traversing all nodes, the reachable paths of the breadth-first algorithm are obtained: 1-2-4-14; 1-2-6-10-14; 1-3-7-11-14 and 1-3-8-12-14. If there is no avoidance node parameter in the target parameter, all nodes can be retained. At this time, according to the characteristics of the breadth-first algorithm, the path cable can be found at the fastest speed, accelerating the search process.

[0093] If there is no connected route in the breadth-first algorithm, or the connected route cannot meet the requirements of the maximum number of transfer points, fiber attenuation coefficient or number of idle fibers, the planning result cannot be achieved. At this time, the depth-first algorithm is performed on the target parameters.

[0094] The optical cable routing planning method provided in this embodiment determines the target parameters. If the target parameters have necessary node parameters and avoidance node parameters, the search algorithm is preferentially selected according to the depth-first algorithm to obtain the search optimization result. If the target parameters have path optical cable parameters, the search algorithm is preferentially selected according to the breadth-first algorithm to obtain the search optimization result. The algorithm priority is established according to the characteristics of the breadth-first algorithm and the depth-first algorithm, and the search optimization result can be generated by quickly traversing based on the characteristics of the target parameters.

[0095] It can be understood that step S203 specifically includes:

[0096] Optionally, the search optimization results are verified based on the A* algorithm and the Dijkstra algorithm respectively to obtain the A* algorithm result and the Dijkstra algorithm result.

[0097] Some planned paths in the search optimization results may not meet the requirements of the target parameters. In this case, the search optimization results can be verified by the A* algorithm results and the Dijkstra algorithm results.

[0098] The principle of the Dijkstra algorithm is to visit the node with the lowest current cumulative cost g(n), where g(n) is the cumulative cost from the initial node to the current node. Dijkstra can ensure that the node passed through must be the path with the minimum cost g(n) from the starting position to the current position. Therefore, when searching according to this principle, when it finds the target node, the path traced back must be the path with the minimum cost. The Dijkstra algorithm can not only verify the accuracy of the search and optimization results, but also obtain the minimum cost planning path corresponding to the Dijkstra algorithm results.

[0099] The A* algorithm introduces a heuristic function h(n) based on Dijkstra, where h(n) represents the estimated cost from the current node to the target node.

[0100] f(n)=g(n)+h(n)

[0101] The A* algorithm traverses each node to calculate the estimated cost, ensuring that it moves from the current node to the adjacent node with the lowest cost, thereby ensuring that the traversed path has the lowest cost. The A* algorithm can both verify the accuracy of the search and optimization results and obtain the minimum cost planning path corresponding to the A* algorithm results.

[0102] Optionally, the optimal planning result is obtained according to the A* algorithm result and the Dijkstra algorithm result, the number of nodes, the path length and the similarity of the results.

[0103] Since the A* algorithm and the Dijkstra algorithm have different pathfinding paths, different characteristic results will be presented when there are more node basic data. According to the A* algorithm and the Dijkstra algorithm, the planning paths that do not meet the target parameters in the search and optimization results are excluded. At the same time, multiple better planning schemes are selected from the search and optimization results, the A* algorithm results, and the Dijkstra algorithm results, and output to the user.

[0104] Exemplarily, poor and repeated schemes can be excluded based on the number of nodes, path length, similarity of planning schemes, etc. When the similarity between two planning schemes is too high, the planning scheme is identified as a repeated planning scheme and is excluded. Among the remaining planning schemes, multiple paths with the least number of nodes, the shortest path length, or the smallest fiber core loss are selected as the planning optimization results. It can be understood that the screening scheme of the optical cable routing planning path can be planned according to other screening rules.

[0105] The optical cable routing planning method provided in this embodiment verifies the search optimization results based on the A* algorithm and the Dijkstra algorithm respectively, and obtains the A* algorithm results and the Dijkstra algorithm results; obtains the planning optimization results according to the A* algorithm results and the Dijkstra algorithm results, the number of nodes, the path length and the similarity of the results. The method verifies the search optimization results by optimizing the planning algorithm, thereby improving the accuracy of the reachable paths in the optical cable routing planning method. Among the multiple planning paths of the search optimization results, the A* algorithm results and the Dijkstra algorithm results, multiple paths with the least number of nodes, the shortest path length and the smallest core attenuation are selected as the planning optimization results, enriching the selectivity of the optional planning paths, and providing users with multiple high-quality automatically planned optical cable routes.

[0106] Figure 5 The flowchart of establishing the optical cable resource data model provided by the embodiment of the present application is as follows. Figure 5 As shown, the method includes:

[0107] S501. Obtain optical cable resource data from a cloud resource library.

[0108] The server periodically obtains models such as management area, station, computer room, equipment placement point, optical cable, optical cable segment, fiber core, ODF, optical cross-connection box, integrated box, optical fiber splitter box, optical cable joint, provincial boundary, column frame, module, terminal, termination, fiber fusion, office optical fiber, office optical fiber routing, optical fiber optical path, optical link and optical link routing from the cloud resource library, and extracts all the optical cable resource data in the existing model. For example, the optical cable resource data can be queried and cached on the server in a 24-hour cycle through the server application, and the optical cable resource data can be updated in a timely manner.

[0109] S502: simplify the association relationship and redundant data of the optical cable resource data to obtain original modeling data to establish an original data model.

[0110] The optical cable resource data is large and complex, so the optical cable resource data is first simplified to remove fields that are not related to the optical cable routing plan. For example, the manufacturer, ownership, etc. After the data is simplified, the optical cable resource data is simplified according to the characteristics of the end-to-end connection of the optical cable. For example: the model of ODF, optical cable junction box, integrated box and optical cable splitter box associated with the terminal through column frames and modules is simplified to ODF, optical cable junction box, integrated box, optical cable splitter box directly associated with the terminal; the fiber core and terminal associated through the local optical fiber routing and optical link routing are simplified to the direct association between the terminal and the fiber core.

[0111] Redundant data processing is performed based on duplication and unavailability of the optical cable resource status, association relationship, node information, etc. For example: when the business status of the resources in the fiber core and terminal is non-idle, the relevant resources are unavailable data in this application. The entire association relationship connected end to end can be queried based on the model directly associated with the terminal and the fiber core, thereby excluding the data related to this optical cable; when two model data composed of fiber cores and terminals, the fiber cores and terminals passing through them belong to the same parent resource in sequence, then these two data are considered to be duplicate data and can be simplified.

[0112] After the correlation relationship of the optical cable resource data and the reduction of redundant data, the original data model is established.

[0113] S503, based on the necessary service fields in the optical cable resource data, the original data model is supplemented for the second time to obtain the optical cable resource data model; the necessary service fields include the optical fiber optical path name information, the fiber core quality information in the redundant data and the numbering information in the association relationship.

[0114] Because in step S502, the simplified column frames and module-related models will have numbering information; the simplified optical fiber paths will have optical fiber path ID and name information; the simplified fiber core and terminal duplicate data will contain fiber core quality information, so it is necessary to supplement the original data model again through the necessary business fields in the optical cable resource data. The server application will query the relevant results through the optical cable resource data, fill them into the original data model, and finally output the optical cable resource data model that can be used for simulation, and store all resource information as nodes in the server in the form of a bidirectional linked list.

[0115] In this embodiment, the optical cable resource data is obtained from the cloud resource library; the association relationship and redundant data of the optical cable resource data are simplified to obtain the original modeling data to establish the original data model; based on the necessary business fields in the optical cable resource data, the original data model is supplemented for a second time to obtain the optical cable resource data model. This method provides a method for modeling optical cable resource data, and the optical cable route planning system can realize automatic path planning based on the optical cable resource data model, reducing the dependence on manual experience.

[0116] Figure 6 This is a schematic diagram of the structure of an optical cable routing planning device provided in one embodiment of the present application. Figure 6 As shown, the optical cable routing planning device 60 provided in this embodiment may include:

[0117] The business requirement processing module 601 is used to convert the business requirements obtained from the user input interface into target parameters; the target parameters are the ID information in the cloud resource library corresponding to the business requirements; the business requirements include one or more of the following information of the optical cable route: starting position, ending position, must-pass nodes, avoidance nodes, passing optical cables, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers.

[0118] The search algorithm optimization module 602 is used to optimize the target parameters based on the optical cable resource data model and obtain the search optimization result; the search algorithm includes the depth first algorithm and the breadth first algorithm.

[0119] The planning algorithm optimization module 603 is used to optimize the planning algorithm for the search optimization result to obtain the planning optimization result; the planning algorithm includes the A* algorithm and the Dijkstra algorithm.

[0120] The planning result output module 604 is used to output the planning optimization result.

[0121] Optionally, the search algorithm optimization module 602 is used to perform search algorithm optimization on the target parameters based on the optical cable resource data model to obtain the search optimization result, which specifically includes:

[0122] The depth-first algorithm module is used to select the best search algorithm based on the depth-first algorithm if the target parameters have necessary node parameters and avoidance node parameters to obtain the best search result.

[0123] The breadth-first algorithm module is used to search for the best algorithm according to the breadth-first algorithm if the target parameter has the path cable parameter, and obtain the best search result.

[0124] Optionally, the search algorithm optimization module 602 is used to perform search algorithm optimization on the target parameter based on the optical cable resource data model to obtain the search optimization result, and specifically includes:

[0125] The breadth-first algorithm module is also used for target parameters that have must-pass node parameters and avoidance node parameters. If the depth-first algorithm cannot achieve the planning result, the breadth-first algorithm is used to search for the best algorithm to obtain the best search result.

[0126] The depth-first algorithm module is also used for the target parameters and the path cable parameters. If the breadth-first algorithm cannot achieve the planning result, the depth-first algorithm is used to search the algorithm to obtain the optimal search result.

[0127] Optionally, the planning algorithm optimization module 603 is used to perform planning algorithm optimization on the search optimization result to obtain the planning optimization result, which specifically includes:

[0128] The A* algorithm and Dijkstra algorithm modules are used to verify the search and optimization results based on the A* algorithm and the Dijkstra algorithm respectively, and obtain the A* algorithm results and the Dijkstra algorithm results.

[0129] The planning path optimization module is used to obtain the planning optimization result according to the A* algorithm result and the Dijkstra algorithm result, the number of nodes, the path length and the similarity of the results.

[0130] Optionally, the planning result output module 604 is used to output the planning optimization result, which specifically includes:

[0131] The planning optimization results are output on the display screen in the form of a list and a GIS map.

[0132] The present application provides an optical cable route planning device, further comprising an optical cable resource data simulation module 605, specifically comprising:

[0133] The optical cable data acquisition module is used to obtain optical cable resource data from the cloud resource library.

[0134] The optical cable data simplification module is used to simplify the association relationship and redundant data of the optical cable resource data to obtain the original modeling data to establish the original data model.

[0135] The optical cable model supplement module is used to supplement the original data model based on the necessary business fields in the optical cable resource data to obtain the optical cable resource data model; the necessary business fields include the optical fiber light path name information, the fiber core quality information in the redundant data and the numbering information in the association relationship.

[0136] The optical cable routing planning device of the embodiment of the present application can be used to execute the technical solution of the above-mentioned optical cable routing planning method embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0137] Figure 7 This is a schematic diagram of the structure of an optical cable routing planning terminal device provided in an embodiment of the present application. Figure 7 As shown, the optical cable routing planning terminal device 70 provided in this embodiment may include:

[0138] Processor 701; and

[0139] Memory 702, used to store executable instructions of the terminal device;

[0140] Among them, the processor is configured to execute the technical solution of the above-mentioned optical cable routing planning method embodiment by executing executable instructions. Its implementation principle and technical effect are similar and will not be repeated here.

[0141] In an embodiment of the present application, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned optical cable routing planning method embodiment is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0142] A computer program product is also provided in an embodiment of the present application, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned optical cable routing planning method embodiment is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0143] In the specific implementation of the above-mentioned terminal device or server, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0144] Those skilled in the art will appreciate that all or part of the steps of any of the above method embodiments may be completed by hardware associated with program instructions. The aforementioned program may be stored in a computer-readable storage medium, and when the program is executed, all or part of the steps of the above method embodiments are executed.

[0145] If the technical solution of the present application is implemented in the form of software and sold or used as a product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a computer program or a number of instructions. The computer software product enables a computer device (which can be a personal computer, a server, a network device or a similar electronic device) to perform all or part of the steps of the method described in Example 1 of the present application. The aforementioned storage medium can be a U disk, a mobile hard disk, a ROM, a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for planning optical cable routing, characterized in that: include: Convert the business requirements obtained in the user input interface into target parameters; the target parameters are ID information in the cloud resource library corresponding to the business requirements; the business requirements include one or more of the following information of the optical cable route: starting position, ending position, must-pass node, avoidance node, passing optical cable, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers; Based on the optical cable resource data model, a search algorithm is optimized for the target parameter to obtain a search optimization result; the search algorithm includes a depth-first algorithm and a breadth-first algorithm; Performing planning algorithm optimization on the search optimization result to obtain a planning optimization result; the planning algorithm includes A* algorithm and Dijkstra algorithm; Outputting the planning optimization result; The performing of a search algorithm optimization on the target parameter based on the optical cable resource data model to obtain a search optimization result includes: If the target parameters have necessary node parameters and avoidance node parameters, the search algorithm is preferentially selected according to the depth-first algorithm to obtain the search optimization result; If the target parameter has a path optical cable parameter, the search algorithm is preferentially selected according to the breadth-first algorithm to obtain the search optimization result; The performing of a search algorithm optimization on the target parameter based on the optical cable resource data model to obtain a search optimization result also includes: The target parameters include the must-pass node parameters and the avoidance node parameters. If the depth-first algorithm cannot achieve a planning result, a search algorithm is optimized according to the breadth-first algorithm to obtain the search optimization result; The target parameters include the optical cable parameters of the route. If the breadth-first algorithm cannot achieve the planning result, the search algorithm is optimized according to the depth-first algorithm to obtain the search optimization result.

2. The method according to claim 1, characterized in that The performing of planning algorithm optimization on the search optimization result to obtain the planning optimization result includes: Based on the A* algorithm and the Dijkstra algorithm, the search optimization results are verified respectively to obtain the A* algorithm result and the Dijkstra algorithm result; The planning optimization result is obtained according to the A* algorithm result and the Dijkstra algorithm result, the number of nodes, the path length and the similarity of the results.

3. The method according to claim 2, characterized in that The outputting of the planning optimization result includes: The planning optimization results are output on the display screen in the form of a list and a GIS map.

4. The method according to claim 1, characterized in that: Also includes: Acquire optical cable resource data from the cloud resource library; Simplifying the association relationship and redundant data of the optical cable resource data to obtain original modeling data to establish an original data model; Based on the necessary service fields in the optical cable resource data, the original data model is supplemented for a second time to obtain the optical cable resource data model; the necessary service fields include the optical fiber optical path name information, the fiber core quality information in the redundant data and the numbering information in the association relationship.

5. An optical cable routing planning device, characterized in that: include: The business requirement processing module is used to convert the business requirements obtained in the user input interface into target parameters; the target parameters are ID information in the cloud resource library corresponding to the business requirements; the business requirements include one or more of the following information of the optical cable route: starting position, end position, must-pass node, avoidance node, passing optical cable, maximum number of transfer points, optical fiber attenuation coefficient or number of idle optical fibers; A search algorithm optimization module is used to perform search algorithm optimization on the target parameters based on the optical cable resource data model to obtain a search optimization result; the search algorithm includes a depth-first algorithm and a breadth-first algorithm; A planning algorithm optimization module is used to optimize the search optimization result by a planning algorithm to obtain a planning optimization result; the planning algorithm includes an A* algorithm and a Dijkstra algorithm; A planning result output module, used to output the planning optimization result; The search algorithm optimization module is specifically used to: if the target parameter has a necessary node parameter and an avoidance node parameter, the search algorithm optimization is preferentially performed according to the depth-first algorithm to obtain the search optimization result; if the target parameter has a path optical cable parameter, the search algorithm optimization is preferentially performed according to the breadth-first algorithm to obtain the search optimization result; The target parameters include the must-pass node parameters and the avoidance node parameters. If the depth-first algorithm cannot achieve a planning result, a search algorithm is optimized according to the breadth-first algorithm to obtain the search optimization result; The target parameters include the optical cable parameters of the route. If the breadth-first algorithm cannot achieve the planning result, the search algorithm is optimized according to the depth-first algorithm to obtain the search optimization result.

6. A terminal device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; the processor is used to execute the computer instructions stored in the memory to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 4.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 4 when being executed by a processor.

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