Optimal path generation method, system and equipment of power communication optical path and medium

Abstract

The invention relates to the technical field of optical fiber communication, and discloses a power communication optical path optimal path generation method, system, device and medium, and the method comprises the steps: obtaining digital information containing optical cables and communication optical paths, and constructing a network topological graph with each power plant site as a node and each optical cable as an edge; searching the network topological graph to obtain a plurality of target paths under different constraint conditions; wherein the constraint conditions comprise a shortest optical cable path, a minimum jumper connection path and a minimum loss path; and determining the path corresponding to the lowest cost from the target paths as the optimal path. The method has the beneficial effect that the dynamic optimization of the path is realized.

Description

Technical Field

[0001] The present application relates to the field of optical fiber communication technology, and in particular to a method, system, device and medium for generating an optimal path of a power communication optical path. Background Art

[0002] With the modernization and intelligentization of power systems, power communication networks play an increasingly important role. Power communication networks not only need to ensure the reliability and real-time performance of data transmission, but also meet the growing demand for data transmission. In power communications, optical cables and communication optical paths are the main media for data transmission. Therefore, how to optimize the use of these optical cables and communication optical paths and achieve dynamic path optimization has become the key to improving the field of power communications. Summary of the invention

[0003] The present application provides a method, system, device and medium for generating an optimal path for a power communication optical path, which solves the technical problem of how to optimize the use of these optical cables and communication optical paths, and achieves the technical effect of realizing dynamic optimization of the path.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, an embodiment of the present application provides a method for generating an optimal path of a power communication optical path, the method comprising:

[0006] Obtain digital information including optical cables and communication optical paths, and construct a network topology map with each power plant site as a node and each optical cable as an edge;

[0007] Searching the network topology diagram to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path, and the least loss path;

[0008] The path with the lowest cost is determined from the target paths as the optimal path.

[0009] The present embodiment provides an optimal path generation method for an electric power communication optical path, by obtaining digital information of optical cables and communication optical paths, and constructing a network topology diagram. The accuracy and completeness of subsequent search paths are ensured. By treating each power plant site as a node and each optical cable as an edge, the network structure can be clearly displayed, providing a solid foundation for subsequent path optimization. Then, by searching the network topology diagram, considering different constraints (shortest optical cable path, least jump path, minimum loss path), multiple target paths are generated. This step makes it possible to evaluate and compare paths under different optimization objectives, ensuring that various potential optimization needs are considered. Selecting the path with the lowest cost from multiple target paths as the optimal path can accurately evaluate the actual cost of each path and find the optimal solution after comprehensively considering various constraints.

[0010] Optionally, the digital information includes optical cable information and communication optical path information;

[0011] The optical cable information includes name, number, power plant starting point, power plant terminal, length, type, fiber core number; and the cabinet number, optical distribution unit number, unit rack number and port number corresponding to each fiber core number;

[0012] The communication optical path information includes the power plant starting point site, the power plant terminal site, the power plant starting point optical fiber equipment, the power plant terminal optical fiber equipment, the platform, the multiplexing length, the number of jump fibers, and the optical cable segment.

[0013] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0014] In the case where the constraint condition is the shortest optical cable path, searching the network topology diagram to find all paths from the starting point site of the power plant to the ending point site of the power plant;

[0015] The total optical cable lengths of the paths are compared, and the path corresponding to the shortest total optical cable length is selected as the target path.

[0016] This embodiment searches the network topology diagram to find all the paths from the power plant starting point to the power plant terminal point. The total optical cable lengths of these paths are compared, and the shortest one is selected as the target path. This ensures that the path with the shortest optical cable length is selected under the shortest optical cable path constraint, thereby reducing the amount of optical cable used and related costs.

[0017] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0018] In the case where the constraint condition is a minimum hop path, searching the network topology map to find all paths from the power plant starting point site to the power plant terminal site;

[0019] The total hop counts of the paths are compared, and a path corresponding to the minimum total hop count is selected as the target path.

[0020] This embodiment searches the network topology map under the constraint of the least hop path to find all the paths from the power plant starting point to the power plant terminal point. The found paths are compared and the path with the least total hops is selected as the target path. This ensures that the number of hops in the signal transmission process is reduced in the path selection, thereby improving the efficiency and stability of the network.

[0021] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0022] In the case where the constraint condition is a minimum loss path, searching the network topology graph to find all paths from the starting point site of the power plant to the terminal site of the power plant;

[0023] The total losses of the paths are compared, and the path corresponding to the minimum total loss is selected as the target path.

[0024] Under the constraint of the minimum loss path, this embodiment searches the network topology map to find all paths from the power plant starting point to the power plant terminal point. The found paths are compared and the path with the minimum total loss is selected as the target path. This ensures that the path with the minimum signal loss is selected in the path selection, thereby improving the signal quality and transmission efficiency.

[0025] Optionally, the total loss is determined as follows: total loss=(optical cable length×0.4)+(number of fiber jumpers×2×0.4).

[0026] Optionally, determining the path corresponding to the lowest cost from the target path as the optimal path includes:

[0027] The multiple target paths are input into a preset machine learning model, with minimizing cost as the objective function, to obtain the optimal path.

[0028] This embodiment inputs multiple target paths into the machine learning model and uses minimizing cost as the objective function, which enables the model to automatically calculate and identify the path with the lowest cost, thereby improving the efficiency and accuracy of path selection.

[0029] In a second aspect, an embodiment of the present application provides an optimal path generation system for a power communication optical path, the system comprising:

[0030] A topology construction module is used to obtain digital information including optical cables and communication optical paths, and to construct a network topology with each power plant site as a node and each optical cable as an edge;

[0031] A target path determination module, used to search the network topology diagram to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path and the minimum loss path;

[0032] The optimal path determination module is used to determine the path corresponding to the lowest cost from the target path as the optimal path.

[0033] The present embodiment provides an optimal path generation system for an electric power communication optical path, which obtains digital information of optical cables and communication optical paths and constructs a network topology diagram. The accuracy and completeness of subsequent search paths are ensured. By treating each power plant site as a node and each optical cable as an edge, the network structure can be clearly displayed, providing a solid foundation for subsequent path optimization. Then, by searching the network topology diagram and considering different constraints (shortest optical cable path, least jump path, minimum loss path), multiple target paths are generated. This step makes it possible to evaluate and compare paths under different optimization objectives, ensuring that various potential optimization needs are considered. By selecting the path with the lowest cost from multiple target paths as the optimal path, the actual cost of each path can be accurately evaluated, and the optimal solution after comprehensive consideration of various constraints can be found.

[0034] Optionally, the digital information includes optical cable information and communication optical path information;

[0035] The optical cable information includes name, number, power plant starting point, power plant terminal, length, type, fiber core number; and the cabinet number, optical distribution unit number, unit rack number and port number corresponding to each fiber core number;

[0036] The communication optical path information includes the power plant starting point site, the power plant terminal site, the power plant starting point optical fiber equipment, the power plant terminal optical fiber equipment, the platform, the multiplexing length, the number of jump fibers, and the optical cable segment.

[0037] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0038] In the case where the constraint condition is the shortest optical cable path, searching the network topology diagram to find all paths from the starting point site of the power plant to the ending point site of the power plant;

[0039] The total optical cable lengths of the paths are compared, and the path corresponding to the shortest total optical cable length is selected as the target path.

[0040] This embodiment searches the network topology diagram to find all the paths from the power plant starting point to the power plant terminal point. The total optical cable lengths of these paths are compared, and the shortest one is selected as the target path. This ensures that the path with the shortest optical cable length is selected under the shortest optical cable path constraint, thereby reducing the amount of optical cable used and related costs.

[0041] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0042] In the case where the constraint condition is a minimum hop path, searching the network topology map to find all paths from the power plant starting point site to the power plant terminal site;

[0043] The total number of hops of the first path is compared, and the path corresponding to the minimum total number of hops is selected as the target path.

[0044] This embodiment searches the network topology map under the constraint of the least hop path to find all the paths from the power plant starting point to the power plant terminal point. The found paths are compared and the path with the least total hops is selected as the target path. This ensures that the number of hops in the signal transmission process is reduced in the path selection, thereby improving the efficiency and stability of the network.

[0045] Optionally, searching the network topology graph to obtain multiple target paths under different constraints includes:

[0046] In the case where the constraint condition is a minimum loss path, searching the network topology graph to find all paths from the starting point site of the power plant to the terminal site of the power plant;

[0047] The total losses of the paths are compared, and the path corresponding to the minimum total loss is selected as the target path.

[0048] Under the constraint of the minimum loss path, this embodiment searches the network topology map to find all paths from the power plant starting point to the power plant terminal point. The found paths are compared and the path with the minimum total loss is selected as the target path. This ensures that the path with the minimum signal loss is selected in the path selection, thereby improving the signal quality and transmission efficiency.

[0049] Optionally, the total loss is determined as follows: total loss=(optical cable length×0.4)+(number of fiber jumpers×2×0.4).

[0050] Optionally, the optimal path determination module includes:

[0051] The multiple target paths are input into a preset machine learning model, with minimizing cost as the objective function, to obtain the optimal path.

[0052] This embodiment inputs multiple target paths into the machine learning model and uses minimizing cost as the objective function, which enables the model to automatically calculate and identify the path with the lowest cost, thereby improving the efficiency and accuracy of path selection.

[0053] In a third aspect, an embodiment of the present application provides a computer device, including:

[0054] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned method for generating the optimal path of the power communication optical path by executing the computer instructions.

[0055] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to enable a computer to execute the above-mentioned optimal path generation method for the power communication optical path. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A flowchart of a method for generating an optimal path for a power communication optical path provided in an embodiment of the present application;

[0058] Figure 2 A flow chart of the shortest optical cable path provided for an embodiment of the present application;

[0059] Figure 3 A flow chart of the minimum jump path provided by an embodiment of the present application;

[0060] Figure 4 A flow chart of the minimum loss path provided by an embodiment of the present application;

[0061] Figure 5 A flowchart of step S5 provided in an embodiment of the present application;

[0062] Figure 6 A block diagram of an optimal path generation system for a power communication optical path provided in an embodiment of the present application;

[0063] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0065] According to an embodiment of the present application, an embodiment of a method for generating an optimal path for an optical path of a power communication is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0066] In this embodiment, a method for generating an optimal path of a power communication optical path is provided. Figure 1 A flowchart of a method for generating an optimal path for a power communication optical path provided in an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0067] Step S1, obtaining digital information including optical cables and communication optical paths, and constructing a network topology map with each power plant site as a node and each optical cable as an edge.

[0068] Specifically, first of all, it is necessary to collect detailed information on all communication optical paths and optical cables. The collected digital information can be determined by design parameters or parameters provided by suppliers. Including the name of the optical cable, the number of the optical cable, the starting site of the power plant, the terminal site of the power plant, the length of the optical cable, the type of optical cable, the fiber core number, etc. At the same time, the screen (cabinet) number, optical distribution unit (frame) number, unit rack (disk) number and port number corresponding to each fiber core number are also collected. The communication optical path information includes the starting site of the power plant, the terminal site of the power plant, the optical fiber equipment at the starting point of the power plant, the optical fiber equipment at the terminal of the power plant, the platform, the multiplexing length, the number of jump fibers, the optical cable segment and other information. Among them, the optical cable segment is all the optical cables passed by a communication optical path. Then, use professional network topology drawing tools, such as Visio, ProcessOn, etc., to create a network topology diagram. In the figure, each power plant site is represented as a node, and each optical cable is represented as an edge connecting two nodes. The attributes of the nodes and edges should include all the relevant information collected above.

[0069] Step S3, searching the network topology map to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path and the least loss path.

[0070] Specifically, the network topology graph can be searched using the Dijkstra algorithm, A* algorithm or breadth-first search (BFS) to calculate all possible paths from the power plant starting point to the power plant terminal. Shortest optical cable path: Find the path with the shortest optical cable length from the power plant starting point to the power plant terminal. The length of the edge can be used as the weight to run the Dijkstra or A* algorithm to select the path with the shortest total optical cable length. Minimum hop path: Find the path with the least total hops from the power plant starting point to the power plant terminal. The edge weight can be fixed to 1 and the BFS algorithm can be used to find the path with the least total hops. Minimum loss path: Find the path with the least total loss from the power plant starting point to the power plant terminal. The edge loss value can be used as the weight to run the Dijkstra or A* algorithm to find the path with the least total loss among all possible paths.

[0071] Step S5: determine the path corresponding to the lowest cost from the target paths as the optimal path.

[0072] Specifically, when determining the optimal path, in addition to considering the shortest path, the least hops, and the least loss, the path with the lowest cost is also an important consideration. In the path optimization problem, supervised learning algorithms can be used to predict path costs. For example, by training a model with historical data, the relationship between path characteristics (such as length, number of hops, loss, etc.) and cost is learned. Then, this model is used to predict the cost of new paths, so as to find the path with the lowest cost among these target paths as the final optimal path.

[0073] The present embodiment provides an optimal path generation method for an electric power communication optical path, by obtaining digital information of optical cables and communication optical paths, and constructing a network topology diagram. The accuracy and completeness of subsequent search paths are ensured. By treating each power plant site as a node and each optical cable as an edge, the network structure can be clearly displayed, providing a solid foundation for subsequent path optimization. Then, by searching the network topology diagram, considering different constraints (shortest optical cable path, least jump path, minimum loss path), multiple target paths are generated. This step makes it possible to evaluate and compare paths under different optimization objectives, ensuring that various potential optimization needs are considered. Selecting the path with the lowest cost from multiple target paths as the optimal path can accurately evaluate the actual cost of each path and find the optimal solution after comprehensively considering various constraints.

[0074] Figure 2 A flowchart of the shortest optical cable path provided in an embodiment of the present application may include the following steps:

[0075] Step S311, when the constraint condition is the shortest optical cable path, the network topology map is searched to find out all the paths from the starting site of the power plant to the ending site of the power plant.

[0076] Step S313, comparing the total optical cable lengths of the paths, and selecting the path corresponding to the shortest total optical cable length as the target path.

[0077] Specifically, the starting point of the power plant is selected as the starting node, and it is used as the current node, and the terminal of the power plant is used as the target node. A graph search algorithm, such as the Dijkstra algorithm or the A* algorithm, is applied to traverse the network topology graph. These algorithms can find the shortest path from the starting node to the target node. Among them, the Dijkstra algorithm is applicable to graphs with non-negative weights, and the shortest path tree is gradually expanded through a greedy strategy until the target node is visited. The A* algorithm is a heuristic search algorithm that uses a heuristic function to estimate the best path from the current node to the target node. During the search process, the distance from the starting node to the target node is recorded, and all possible paths are explored and the search is stopped. Among all the paths found from the starting point of the power plant to the terminal of the power plant, their total optical cable lengths are compared. The path with the shortest total optical cable length is selected as the target path.

[0078] and Figure 1 Compared with the embodiment shown in FIG. 1 , this embodiment searches the network topology diagram to find all paths from the power plant starting point to the power plant terminal point. The total optical cable lengths of these paths are compared, and the shortest one is selected as the target path. This ensures that the path with the shortest optical cable length is selected while satisfying the shortest optical cable path constraint, thereby reducing the amount of optical cable used and related costs.

[0079] Figure 3 A flowchart of the minimum jump path provided in an embodiment of the present application may include the following steps:

[0080] Step S321, when the constraint condition is the path with the least hops, search the network topology map to find out all the paths from the starting point site of the power plant to the ending point site of the power plant.

[0081] Step S323, comparing the total hop counts of the paths, and selecting the path corresponding to the minimum total hop count as the target path.

[0082] Specifically, the starting point of the power plant is selected as the starting node, and it is used as the current node, and the terminal point of the power plant is used as the target node. A graph search algorithm, such as the Dijkstra algorithm, the A* algorithm, or the BFS algorithm, is applied to traverse the network topology graph. These algorithms can find the path with the least total jumps from the starting node to the target node. During the search process, the total number of jumps from the starting node to the target node is recorded, and the search is stopped after all possible paths have been explored. Among all the paths found from the starting point of the power plant to the terminal point of the power plant, their total number of jumps is compared. The path with the least total number of jumps is selected as the target path.

[0083] and Figure 1 Compared with the embodiment shown in FIG. 1 , this embodiment searches the network topology diagram under the constraint of the least hop path to find all paths from the power plant starting point site to the power plant terminal site. The found paths are compared, and the path with the least total hop number is selected as the target path. This ensures that the number of hops in the signal transmission process is reduced in the path selection, thereby improving the efficiency and stability of the network.

[0084] Figure 4 A flowchart of a minimum loss path provided in an embodiment of the present application may include the following steps:

[0085] Step S331, when the constraint condition is the minimum loss path, search the network topology map to find out all the paths from the starting point site of the power plant to the terminal site of the power plant.

[0086] Step S333: compare the total losses of the paths, and select the path corresponding to the minimum total loss as the target path.

[0087] Specifically, the starting point of the power plant is selected as the starting node, and it is used as the current node, and the terminal of the power plant is used as the target node. A graph search algorithm, such as the Dijkstra algorithm or the A* algorithm, is applied to traverse the network topology graph. These algorithms can find the path with the smallest total loss from the starting node to the target node. During the search process, the total loss from the starting node to the target node is recorded, and the search is stopped after all possible paths have been explored. Among all the paths found from the starting point of the power plant to the terminal of the power plant, their total losses are compared. The path with the smallest total loss is selected as the target path.

[0088] In some embodiments, the total loss is determined as follows: total loss = (cable length × 0.4) + (number of fiber jumpers × 2 × 0.4).

[0089] Specifically, for each path, obtain the cable length and the number of fiber jumpers, and for each path, use the above formula to calculate the total loss. Compare all calculated total losses. Select the path with the smallest total loss as the target path. Assume there are three paths, Path 1: Cable length 10 km, fiber jumper 1. Path 2: Cable length 15 km, fiber jumper 2. Path 3: Cable length 20 km, fiber jumper 1.

[0090] Calculation shows that the total loss of path 1 = (10×0.4)+(1×2×0.4)=4+0.8=4.8; the total loss of path 2 = (15×0.4)+(2×2×0.4)=6+1.6=7.6; the total loss of path 3 = (20×0.4)+(1×2×0.4)=8+0.8=8.8; comparing these total losses, it can be seen that optical path 1 has the smallest total loss, so it will be the target path.

[0091] and Figure 1 Compared with the embodiment shown in FIG. 1 , this embodiment searches the network topology map to find all paths from the power plant starting point site to the power plant terminal site under the constraint of the minimum loss path. The found paths are compared, and the path with the minimum total loss is selected as the target path. This ensures that the path with the minimum signal loss is selected in the path selection, thereby improving the signal quality and transmission efficiency.

[0092] Figure 5 The flowchart of step S5 provided in the embodiment of the present application may include the following steps:

[0093] Step S51, input multiple target paths into a preset machine learning model, and use minimizing cost as the objective function to obtain the optimal path.

[0094] To determine the optimal path, machine learning methods can be used to optimize path planning. This involves taking multiple target paths as input features and training them with path cost minimization as the objective function. Specifically, collect all possible path data, including features such as cable length, number of fiber jumpers, and loss for each path. Extract useful features from the path data, which may include total cable length, total number of jumpers, total loss, etc. Define an objective function, namely the path cost, which includes the weighted sum of total cable length, total number of jumpers, and total loss. Select a machine learning model such as decision tree, random forest, support vector machine, or neural network. Use the collected path data and the defined objective function to train the model. Evaluate the performance of the model through methods such as cross-validation to ensure that the model can effectively predict the lowest cost path. Use the trained model to predict new path data to find the optimal path with the lowest cost.

[0095] and Figure 1Compared with the embodiment shown, this embodiment inputs multiple target paths into the machine learning model and uses minimizing cost as the objective function, which enables the model to automatically calculate and identify the path with the lowest cost, thereby improving the efficiency and accuracy of path selection.

[0096] Accordingly, please refer to Figure 6 A block diagram of an optimal path generation system for a power communication optical path provided in an embodiment of the present application, the system comprising:

[0097] The topology construction module S101 is used to obtain digital information including optical cables and communication optical paths, and to construct a network topology with each power plant site as a node and each optical cable as an edge;

[0098] The target path determination module S103 is used to search the network topology map to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path and the minimum loss path;

[0099] The optimal path determination module S105 is used to determine the path corresponding to the lowest cost from the target path as the optimal path.

[0100] In some embodiments, the digitized information includes optical cable information and communication optical path information;

[0101] The optical cable information includes name, number, power plant starting point, power plant terminal, length, type, fiber core number; and the cabinet number, optical distribution unit number, unit rack number and port number corresponding to each fiber core number;

[0102] The communication optical path information includes the power plant starting point site, the power plant terminal site, the power plant starting point optical fiber equipment, the power plant terminal optical fiber equipment, the platform, the multiplexing length, the number of fiber jumpers, and the optical cable segment.

[0103] In some embodiments, the network topology graph is searched to obtain multiple target paths under different constraints, including:

[0104] Under the constraint of the shortest optical cable path, the network topology is searched to find all the paths from the starting point of the power plant to the end point of the power plant;

[0105] The total optical cable lengths of the paths are compared, and the path corresponding to the shortest total optical cable length is selected as the target path.

[0106] In some embodiments, the network topology graph is searched to obtain multiple target paths under different constraints, including:

[0107] Under the constraint of the least hop path, the network topology is searched to find all the paths from the starting point of the power plant to the terminal point of the power plant;

[0108] The total number of hops of the paths is compared, and the path corresponding to the minimum total number of hops is selected as the target path.

[0109] In some embodiments, the network topology graph is searched to obtain multiple target paths under different constraints, including:

[0110] Under the constraint of minimum loss path, search the network topology graph to find all the paths from the starting point of the power plant to the end point of the power plant;

[0111] The total losses of the paths are compared, and the path corresponding to the smallest total loss is selected as the target path.

[0112] In some embodiments, the total loss is determined as follows: total loss = (cable length × 0.4) + (number of fiber jumpers × 2 × 0.4).

[0113] In some embodiments, the optimal path determination module S105 includes:

[0114] Multiple target paths are input into the preset machine learning model, with minimizing cost as the objective function to obtain the optimal path.

[0115] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0116] In this embodiment, an optimal path generation system for a power communication optical path is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0117] See also Figure 7 , Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Figure 7As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0118] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0119] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0120] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0122] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0123] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0124] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0125] The systems or modules described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0126] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and systems. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0134] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for generating an optimal path for a power communication optical path, characterized in that: The method comprises: Obtain digital information including optical cables and communication optical paths, and construct a network topology map with each power plant site as a node and each optical cable as an edge; Searching the network topology diagram to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path, and the least loss path; The path with the lowest cost is determined from the target paths as the optimal path.

2. The method according to claim 1, characterized in that The digital information includes optical cable information and communication optical path information; The optical cable information includes name, number, power plant starting point, power plant terminal, length, type, fiber core number; and the cabinet number, optical distribution unit number, unit rack number and port number corresponding to each fiber core number; The communication optical path information includes the power plant starting point site, the power plant terminal site, the power plant starting point optical fiber equipment, the power plant terminal optical fiber equipment, the platform, the multiplexing length, the number of jump fibers, and the optical cable segment.

3. The method according to claim 1, characterized in that The searching of the network topology graph to obtain multiple target paths under different constraints includes: In the case where the constraint condition is the shortest optical cable path, searching the network topology diagram to find all paths from the starting point site of the power plant to the ending point site of the power plant; The total optical cable lengths of the paths are compared, and the path corresponding to the shortest total optical cable length is selected as the target path.

4. The method according to claim 1, characterized in that: The searching of the network topology graph to obtain multiple target paths under different constraints includes: In the case where the constraint condition is a minimum hop path, searching the network topology map to find all paths from the power plant starting point site to the power plant terminal site; The total hop counts of the paths are compared, and a path corresponding to the minimum total hop count is selected as the target path.

5. The method according to claim 1, characterized in that The searching of the network topology graph to obtain multiple target paths under different constraints includes: In the case where the constraint condition is a minimum loss path, searching the network topology graph to find all paths from the starting point site of the power plant to the terminal site of the power plant; The total losses of the paths are compared, and the path corresponding to the minimum total loss is selected as the target path.

6. The method according to claim 5, characterized in that The total loss is determined as follows: total loss=(optical cable length×0.4)+(number of fiber jumpers×2×0.4).

7. The method according to claim 1, characterized in that The step of determining the path corresponding to the lowest cost from the target path as the optimal path includes: The multiple target paths are input into a preset machine learning model, with minimizing cost as the objective function, to obtain the optimal path.

8. An optimal path generation system for a power communication optical path, characterized in that: The system comprises: A topology construction module is used to obtain digital information including optical cables and communication optical paths, and to construct a network topology with each power plant site as a node and each optical cable as an edge; A target path determination module, used to search the network topology diagram to obtain multiple target paths under different constraints; wherein the constraints include the shortest optical cable path, the least jump path and the minimum loss path; The optimal path determination module is used to determine the path corresponding to the lowest cost from the target path as the optimal path.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the optimal path generation method for the power communication optical path according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the optimal path generation method for a power communication optical path according to any one of claims 1 to 7.

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