A method, apparatus, equipment, and medium for multi-project parallel optical cable routing planning

Abstract

This invention discloses a method, apparatus, equipment, and medium for multi-project parallel optical cable routing planning. The method includes: determining the available space for the bending sections of the optical cable ducts based on the space occupancy status, duct deformation data, and duct geometric parameters; generating a space occupancy model for each duct segment using 3D modeling technology based on the space occupancy status, duct deformation data, and available space for the bending sections; obtaining the preset optical cable origin and destination points for each project; optimizing the optical cable path using a genetic algorithm based on the space occupancy model to obtain an initial optical cable routing plan; analyzing the duct resource contention among multiple projects based on the initial optical cable routing plan; and adjusting the initial optical cable routing plan according to the duct resource contention situation to obtain the final optical cable routing plan result. This invention can rationally plan optical cable duct resources and effectively improve the accuracy of optical cable routing planning.

Description

Technical Field

[0001] This invention relates to the field of optical cable planning technology, and in particular to a method, apparatus, equipment and medium for planning optical cable routes in parallel with multiple projects. Background Technology

[0002] With the expansion of power grids and the acceleration of urbanization, optical fiber, as the lifeline of information transmission, directly affects the stability and future expansion capabilities of power distribution systems due to the rationality of its routing planning. However, traditional optical fiber routing planning methods often rely on manual experience or simple algorithms, which suffer from low accuracy and poor adaptability when dealing with multi-project parallel scenarios. Furthermore, existing technologies often ignore the impact of duct bends, leading to unreasonable and inaccurate duct resource planning. Summary of the Invention

[0003] To address the above technical problems, this invention provides a method, apparatus, equipment, and medium for planning optical cable routes in parallel with multiple projects, which can rationally plan optical cable pipeline resources and effectively improve the accuracy of optical cable route planning.

[0004] This invention provides a method for planning optical cable routes in parallel across multiple projects, comprising:

[0005] Acquire the space occupancy status, duct deformation data, and duct geometric parameters of each optical cable duct;

[0006] Based on the space occupancy status, the pipe deformation data, and the pipe geometric parameters, the available space for the bending section of the optical cable pipe is determined;

[0007] Based on the space occupancy status, the pipe deformation data, and the available space of the curved section, a space occupancy model for each pipe section is generated using 3D modeling technology.

[0008] Obtain the preset optical cable origin and destination for each project, and optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan;

[0009] Based on the initial optical cable routing plan, the pipeline resource contention among multiple projects is analyzed, and the initial optical cable routing plan is adjusted according to the pipeline resource contention to obtain the final optical cable routing plan result.

[0010] As an improvement to the above solution, determining the available space for the bending section of the optical cable duct based on the space occupancy status, the duct deformation data, and the duct geometric parameters includes:

[0011] Based on the space occupancy status and the pipe deformation data, the actual usable space of the pipe is calculated;

[0012] The location of the bend in the pipeline is determined based on the pipeline's geometric parameters, and the available space of the bend in the optical cable pipeline is obtained based on the actual available space of the pipeline and the location of the bend.

[0013] As an improvement to the above scheme, the step of obtaining the preset optical cable origin and destination for each project, and optimizing the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan includes:

[0014] Obtain the service access point and target node for each newly added optical cable in a multi-project scenario;

[0015] Based on the space occupancy model, spatial analysis is performed to identify available optical cable ducts;

[0016] Based on the available optical cable ducts, the service access points and target nodes of the newly added optical cables, and with the goal of minimizing duct occupancy, path length and bending segment risk, a genetic algorithm is used for iterative optimization to obtain the initial optical cable route plan; the bending segment risk is positively correlated with the degree of bending of the bending segment.

[0017] As an improvement to the above scheme, the step of analyzing the pipeline resource contention among multiple projects based on the initial optical cable routing plan includes:

[0018] The saturation of pipeline splicing points is obtained based on the initial optical cable routing plan, and pipeline expansion requirements are generated based on the saturation of pipeline splicing points.

[0019] Based on the pipeline expansion requirements and the preset regional permit restrictions and environmental conditions, an expansion plan is determined;

[0020] Based on the initial optical cable routing plan and the expansion plan, a pipeline resource contention analysis is performed on the optical cable routing plans of multiple projects to obtain the pipeline resource contention situation among multiple projects.

[0021] As an improvement to the above scheme, the pipeline expansion demand is also determined based on the prediction results of the saturation of the pipeline connection point, which are obtained by predicting the historical growth trend of the saturation of the pipeline connection point.

[0022] As an improvement to the above scheme, the step of performing a pipeline resource contention analysis on the fiber optic cable routing plans of multiple projects based on the initial fiber optic cable routing plan and the expansion plan, to obtain the pipeline resource contention situation among multiple projects, includes:

[0023] Based on the current available capacity of the optical cable duct and the expansion capacity in the expansion plan, the expected available capacity of the optical cable duct is obtained.

[0024] Based on the initial optical cable routing plan, the duct and fiber core requirements for each project are obtained;

[0025] For each section of the pipeline, the fiber core requirements of each project are added together to obtain the expected usage capacity;

[0026] If the pipeline's estimated usage capacity is greater than the current available capacity but less than the estimated available capacity, then the pipeline is determined to be in a first resource contention state.

[0027] If the pipeline's projected usage capacity is greater than its projected available capacity, then the pipeline is determined to be in a second resource contention state.

[0028] By combining the first resource contention status and the second resource contention status, the pipeline resource contention situation among multiple projects is obtained.

[0029] As an improvement to the above scheme, the step of adjusting the initial optical cable route plan based on the pipeline resource contention situation to obtain the final optical cable route plan result includes:

[0030] Based on the pipeline resource contention situation, the first pipeline in the first resource contention state and the second pipeline in the second resource contention state are selected.

[0031] The priority of each project is obtained, and the paths corresponding to the first and second pipelines in the initial optical cable routing plan are adjusted according to the priority of each project to obtain the final optical cable routing plan result.

[0032] The present invention also provides a multi-project parallel optical cable routing planning device, comprising:

[0033] The data acquisition module is used to acquire the space occupancy status, duct deformation data and duct geometric parameters of each optical cable duct;

[0034] The space calculation module is used to determine the available space of the curved section of the optical cable duct based on the space occupancy status, the duct deformation data and the duct geometric parameters;

[0035] The model building module is used to generate a space occupancy model for each pipe segment based on the space occupancy status, the pipe deformation data, and the available space of the curved section using three-dimensional modeling technology.

[0036] The preliminary planning module is used to obtain the preset optical cable origin and destination for each project, and to optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan.

[0037] The planning results module is used to analyze the pipeline resource contention among multiple projects based on the initial optical cable routing plan, and adjust the initial optical cable routing plan according to the pipeline resource contention to obtain the final optical cable routing plan result.

[0038] The present invention also provides a computer device, including a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor executes the computer program to implement the multi-project parallel optical cable routing planning method described above.

[0039] The present invention also provides a computer-readable storage medium storing a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the multi-project parallel optical cable routing planning method described above.

[0040] Compared to existing technologies, the beneficial effects of the multi-project parallel optical cable routing planning method, apparatus, equipment, and medium provided by this invention are as follows: By acquiring the space occupancy status, pipe deformation data, and pipe geometric parameters of each optical cable duct, the available space of the bending section of the optical cable duct is determined based on the space occupancy status, pipe deformation data, and pipe geometric parameters. Furthermore, based on the space occupancy status, pipe deformation data, and available space of the bending section, a three-dimensional modeling technique is used to generate a space occupancy model for each duct segment, achieving high-precision duct modeling. This enables accurate assessment and efficient utilization of duct space, which is beneficial for improving resource utilization. The invention also acquires the preset optical cable starting point for each project. Finally, based on the aforementioned space occupancy model, a genetic algorithm is used to optimize the optical cable path, resulting in an initial optical cable route plan. This allows for parallel and rapid automated path optimization in complex pipeline networks, improving the efficiency of optical cable route planning. Based on the initial optical cable route plan, the pipeline resource contention among multiple projects is analyzed, and the initial optical cable route plan is adjusted accordingly to obtain the final optical cable route plan. This effectively addresses resource allocation and conflict issues in parallel multi-project scenarios, not only improving the accuracy and efficiency of optical cable route planning but also significantly reducing construction risks and overall costs. It is suitable for large-scale, multi-project parallel pipeline optical cable planning. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a multi-project parallel optical cable routing planning method provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the structure of a multi-project parallel optical cable routing planning device provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 , Figure 1 This is a flowchart illustrating a multi-project parallel optical cable routing planning method provided by an embodiment of the present invention. The multi-project parallel optical cable routing planning method includes:

[0046] S1: Obtain the space occupancy status, pipe deformation data and pipe geometric parameters of each optical cable duct;

[0047] S2: Determine the available space for the bending section of the optical cable duct based on the space occupancy status, the duct deformation data, and the duct geometric parameters;

[0048] S3: Based on the space occupancy status, the pipe deformation data, and the available space of the curved section, a three-dimensional modeling technology is used to generate a space occupancy model for each pipe segment;

[0049] S4: Obtain the preset optical cable origin and destination for each project, and optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan;

[0050] S5: Based on the initial optical cable routing plan, analyze the pipeline resource contention among multiple projects, and adjust the initial optical cable routing plan according to the pipeline resource contention to obtain the final optical cable routing plan result.

[0051] Specifically, the space occupancy status of optical cable ducts includes the actual proportion of space occupied by the optical cables already laid within the duct. For example, if a duct has a total capacity of 10 optical cables and currently has 4 cables laid, then the space occupancy rate of the duct is 40%. The space occupancy status of optical cable ducts can be obtained through sensors, such as fiber optic sensing systems and ultrasonic testing equipment, to acquire information about the cable layout within the duct and thus determine the space occupancy ratio of each duct segment.

[0052] Pipeline deformation data can be collected using strain sensors, laser scanners, or other monitoring equipment. This data reflects changes in the pipeline's cross-sectional area, identifies areas with significant deformation, and records the specific locations and degrees of deformation. For example, in some areas, soil subsidence or external loads may cause a 10% reduction in the pipeline's cross-sectional area.

[0053] Pipeline geometry parameters include the diameter, length, bending radius, and material properties of the optical cable duct, which can be obtained through design drawings or on-site measurements.

[0054] As one optional embodiment, in step S2, determining the available space for the curved section of the optical cable duct based on the space occupancy status, the duct deformation data, and the duct geometric parameters includes:

[0055] Based on the space occupancy status and the pipe deformation data, the actual usable space of the pipe is calculated;

[0056] The location of the bend in the pipeline is determined based on the pipeline's geometric parameters, and the available space of the bend in the optical cable pipeline is obtained based on the actual available space of the pipeline and the location of the bend.

[0057] Specifically, based on the space occupancy status and deformation data of the optical cable duct, the actual usable space of the duct is calculated. The specific calculation formula is as follows:

[0058]

[0059] in, For the actual usable space of the pipeline, For the original space of the pipe, This refers to the proportion of space occupied by optical cables in the conduit. This represents the percentage of space reduction caused by pipe deformation.

[0060] Furthermore, the location and geometric characteristics of the bends in the pipeline are obtained based on the pipeline's geometric parameters, including curvature and angle. Based on the calculated actual usable space of the pipeline, the available space of the bends in the optical cable pipeline is determined according to their location and geometric characteristics. Specifically, since the bend angle affects the actual usable space at the bend—a larger bend angle means fewer optical cables can be accommodated inside the pipeline—an availability threshold for the space of each bend is set according to industry standards or experience. Different bends correspond to different thresholds. Based on the calculated actual usable space of the pipeline, and combined with the availability threshold of the bend space, the available space of each bend is determined, thereby improving the accuracy of subsequent modeling.

[0061] In step S3, based on the spatial occupancy status of the optical cable duct, the available space in the bending section, and the duct's geometric parameters, a three-dimensional modeling technique, such as CAD software or a BIM system, is used to generate a spatial occupancy model for each duct segment. The model visually presents the spatial distribution of the optical cable duct. This spatial occupancy model includes the following information: the proportion of optical cable space occupied in each duct segment, the actual available space in the bending section, and the specific location and influence range of the deformation area.

[0062] As one optional embodiment, in step S4, obtaining the preset optical cable origin and destination for each project, and optimizing the optical cable path using a genetic algorithm based on the space occupancy model to obtain an initial optical cable route plan, includes:

[0063] Obtain the service access point and target node for each newly added optical cable in a multi-project scenario;

[0064] Based on the space occupancy model, spatial analysis is performed to identify available optical cable ducts;

[0065] Based on the available optical cable ducts, the service access points and target nodes of the newly added optical cables, and with the goal of minimizing duct occupancy, path length and bending segment risk, a genetic algorithm is used for iterative optimization to obtain the initial optical cable route plan; the bending segment risk is positively correlated with the degree of bending of the bending segment.

[0066] Specifically, spatial analysis is performed on the space occupancy model. Based on the actual available space in the curved sections and the overall space occupancy status of the ducts, ducts with cabling capabilities are identified, resulting in usable optical cable ducts. Areas with severe deformation or insufficient space are marked as unusable resources. This embodiment quantifies the space occupancy of ducts to identify usable resources, facilitating subsequent optical cable routing planning.

[0067] Before using genetic algorithms for path optimization, it is necessary to determine the start and end points of each new optical cable, i.e., the service access point and the target node. The specific start and end points of the optical cable are determined by network planning or customer requirements. The start point is usually the fiber aggregation point / distribution box, and the end point is usually the user access point or other aggregation point of the downstream branch. In multi-project scenarios, each project has one or more pairs of start and end points, and each pair of start and end points defines a path.

[0068] The genetic algorithm uses the path corresponding to the origin and destination as chromosomes, where one chromosome represents one optical cable route, and the nodes in the path represent the entrance / exit of each optical cable duct segment. In a three-dimensional duct network based on a space occupancy model, several feasible paths are randomly or heuristically constructed as a population, ensuring that each path is connected from the origin to the destination and does not violate duct availability or deformation constraints. A fitness function is established to calculate the comprehensive cost of each path. This comprehensive cost is obtained by weighted summation of the total path length, the occupancy rate of all ducts in the path, and the bending risk value of each bend segment, where the weights can be adjusted according to business priorities. Parent individuals are selected based on the fitness function calculation results, retaining paths with low comprehensive costs. Crossover and mutation operations are performed on the paths, selecting the optimal paths to enter the next generation. This iterative process is repeated until the maximum number of iterations is reached, at which point the initial optical cable route plan is obtained based on the path with the lowest comprehensive cost in the final population. This embodiment of the invention can automatically identify available duct resources, select paths with the lowest occupancy rate and avoid areas with high deformation and bending risks, perform global optimization, improve resource utilization efficiency, and ensure network scalability.

[0069] As one optional embodiment, in step S5, the analysis of pipeline resource contention among multiple projects based on the initial optical cable routing plan includes:

[0070] The saturation of pipeline splicing points is obtained based on the initial optical cable routing plan, and pipeline expansion requirements are generated based on the saturation of pipeline splicing points.

[0071] Based on the pipeline expansion requirements and the preset regional permit restrictions and environmental conditions, an expansion plan is determined;

[0072] Based on the initial optical cable routing plan and the expansion plan, a pipeline resource contention analysis is performed on the optical cable routing plans of multiple projects to obtain the pipeline resource contention situation among multiple projects.

[0073] Specifically, the saturation of duct splice points is extracted from the initial optical cable routing plan. The saturation of duct splice points represents the ratio of the number of optical cable connections carried by a splice point to the designed capacity. If the saturation of duct splice points in a certain area exceeds a preset threshold, it is considered that there is a capacity expansion requirement in that area. Areas with duct splice point saturation greater than the preset threshold are identified as areas with capacity expansion requirements, and expansion requirements are generated.

[0074] As one optional embodiment, the pipeline expansion demand is also determined based on the predicted results of the pipeline connection point saturation, which are obtained by predicting the historical growth trend of the pipeline connection point saturation.

[0075] Specifically, the saturation data of pipeline connection points over several historical periods is collected and input into a preset time series prediction model, such as an LSTM model, to predict the saturation of connection points in future periods. When the predicted saturation of connection points is greater than a set threshold, an expansion requirement is generated for that connection point.

[0076] Furthermore, for areas with expansion needs, expansion plans are generated by combining regional permit restrictions and environmental constraints. Regional permit restrictions refer to the impact of regional policies on construction; for example, policy restrictions in urban centers may prevent immediate construction, limiting the construction time of expansion plans. Environmental conditions refer to the impact of the environment in which the construction area is located on construction; for example, winter construction in cold regions may be affected by frozen soil, or the rainy season may impact progress. Therefore, it is necessary to develop construction plans based on environmental conditions and meteorological data, scheduling expansion plans for suitable seasons. Based on regional permit restrictions and environmental conditions, the expansion time for each expansion plan can be determined.

[0077] As one optional embodiment, the step of performing pipeline resource contention analysis on the fiber optic cable routing plans of multiple projects based on the initial fiber optic cable routing plan and the expansion plan to obtain the pipeline resource contention situation among multiple projects includes:

[0078] Based on the current available capacity of the optical cable duct and the expansion capacity in the expansion plan, the expected available capacity of the optical cable duct is obtained.

[0079] Based on the initial optical cable routing plan, the duct and fiber core requirements for each project are obtained;

[0080] For each section of the pipeline, the fiber core requirements of each project are added together to obtain the expected usage capacity;

[0081] If the pipeline's estimated usage capacity is greater than the current available capacity but less than the estimated available capacity, then the pipeline is determined to be in a first resource contention state.

[0082] If the pipeline's projected usage capacity is greater than its projected available capacity, then the pipeline is determined to be in a second resource contention state.

[0083] By combining the first resource contention status and the second resource contention status, the pipeline resource contention situation among multiple projects is obtained.

[0084] As one optional embodiment, adjusting the initial optical cable routing plan based on the pipeline resource contention situation to obtain the final optical cable routing plan result includes:

[0085] Based on the pipeline resource contention situation, the first pipeline in the first resource contention state and the second pipeline in the second resource contention state are selected.

[0086] The priority of each project is obtained, and the paths corresponding to the first and second pipelines in the initial optical cable routing plan are adjusted according to the priority of each project to obtain the final optical cable routing plan result.

[0087] Specifically, based on the aforementioned space occupancy model, the current available capacity of the optical cable ducts involved in the initial optical cable routing plan is obtained. Then, combined with the expansion capacity in the expansion plan, the current available capacity and expansion capacity of each duct are summed to obtain the expected available capacity of each duct. Based on the initial optical cable routing plan, the fiber core requirements for each project's path on each duct are listed. Then, the total requirement for all projects on each duct is calculated, i.e., the fiber core requirements on each duct are summed to obtain the expected usage capacity.

[0088] Furthermore, based on the initial optical cable routing plan, the resource contention status of the pipeline is determined according to the expected usage capacity. If the expected usage capacity of a pipeline is less than its current available capacity, there is no resource contention for that pipeline, and the corresponding path does not need to be adjusted. If the expected usage capacity of a pipeline is between its current available capacity and the expected available capacity, the pipeline is in a first resource contention state, meaning there is short-term overload. Paths passing through the first pipeline in high-priority projects need to be adjusted. Specifically, a genetic algorithm is used to recalculate the paths passing through the first pipeline and adjust them to nearby or backup paths that meet the resource conditions to satisfy the needs of high-priority projects. Paths of medium and low priority are not adjusted. If the expected usage capacity of a pipeline is greater than the expected available capacity, the pipeline is in a second resource contention state. Even after expansion, there is still a resource shortage. Paths planned for the second pipeline need to be retained in order of priority until the expected usage capacity of the pipeline exceeds the expected available capacity. Paths exceeding the expected available capacity are then replanned and adjusted to nearby or backup paths that meet the resource conditions. Finally, the optical cable routing plan result after conflict coordination is obtained. When adjusting the path, it is also necessary to consider the constraints between the time of each project and the expansion time of the expansion plan to ensure that resources do not conflict.

[0089] This invention, through obtaining the spatial occupancy status, deformation data, and geometric parameters of each optical cable duct, determines the available space in the bending sections of the optical cable duct based on these parameters. Then, using 3D modeling technology, it generates a spatial occupancy model for each duct segment, achieving high-precision duct modeling. This allows for accurate assessment and efficient utilization of duct space, improving resource utilization. Furthermore, it obtains the preset optical cable start and end points for each project and, based on the spatial occupancy model, employs a genetic algorithm to... The invention optimizes optical cable routes to obtain an initial optical cable route plan, enabling parallel and rapid automated route optimization in complex pipeline networks, thus improving the efficiency of optical cable route planning. Based on the initial optical cable route plan, it analyzes the pipeline resource contention among multiple projects and adjusts the initial optical cable route plan accordingly to obtain the final optical cable route plan result. This effectively handles resource allocation and resource conflict issues in multi-project parallel scenarios, improving not only the accuracy and efficiency of optical cable route planning but also significantly reducing construction risks and overall costs. It is suitable for large-scale, multi-project parallel pipeline optical cable planning. Accordingly, the invention also provides a multi-project parallel optical cable route planning device, capable of implementing all the processes of the multi-project parallel optical cable route planning method described in the above embodiments.

[0090] Please see Figure 2 , Figure 2 This is a schematic diagram of a multi-project parallel optical cable routing planning device provided in an embodiment of the present invention. The multi-project parallel optical cable routing planning device includes:

[0091] Data acquisition module 201 is used to acquire the space occupancy status, pipe deformation data and pipe geometric parameters of each optical cable duct;

[0092] The space calculation module 202 is used to determine the available space of the bending section of the optical cable duct based on the space occupancy status, the duct deformation data and the duct geometric parameters;

[0093] The model building module 203 is used to generate a space occupancy model for each pipe segment based on the space occupancy status, the pipe deformation data and the available space of the curved section using three-dimensional modeling technology.

[0094] The preliminary planning module 204 is used to obtain the preset optical cable origin and destination for each project, and to optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan.

[0095] The planning result module 205 is used to analyze the pipeline resource contention among multiple projects based on the initial optical cable routing plan, and adjust the initial optical cable routing plan according to the pipeline resource contention to obtain the final optical cable routing plan result.

[0096] Preferably, determining the available space for the bending section of the optical cable duct based on the space occupancy status, the duct deformation data, and the duct geometric parameters includes:

[0097] Based on the space occupancy status and the pipe deformation data, the actual usable space of the pipe is calculated;

[0098] The location of the bend in the pipeline is determined based on the pipeline's geometric parameters, and the available space of the bend in the optical cable pipeline is obtained based on the actual available space of the pipeline and the location of the bend.

[0099] Preferably, the step of obtaining the preset optical cable origin and destination for each project, and optimizing the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan includes:

[0100] Obtain the service access point and target node for each newly added optical cable in a multi-project scenario;

[0101] Based on the space occupancy model, spatial analysis is performed to identify available optical cable ducts;

[0102] Based on the available optical cable ducts, the service access points and target nodes of the newly added optical cables, and with the goal of minimizing duct occupancy, path length and bending segment risk, a genetic algorithm is used for iterative optimization to obtain the initial optical cable route plan; the bending segment risk is positively correlated with the degree of bending of the bending segment.

[0103] Preferably, the step of analyzing the pipeline resource contention among multiple projects based on the initial optical cable routing plan includes:

[0104] The saturation of pipeline splicing points is obtained based on the initial optical cable routing plan, and pipeline expansion requirements are generated based on the saturation of pipeline splicing points.

[0105] Based on the pipeline expansion requirements and the preset regional permit restrictions and environmental conditions, an expansion plan is determined;

[0106] Based on the initial optical cable routing plan and the expansion plan, a pipeline resource contention analysis is performed on the optical cable routing plans of multiple projects to obtain the pipeline resource contention situation among multiple projects.

[0107] Preferably, the pipeline expansion demand is further determined based on the predicted saturation of the pipeline connection point, which is obtained by predicting the historical growth trend of the saturation of the pipeline connection point.

[0108] Preferably, the step of performing pipeline resource contention analysis on the optical cable routing plans of multiple projects based on the initial optical cable routing plan and the expansion plan to obtain the pipeline resource contention situation among multiple projects includes:

[0109] Based on the current available capacity of the optical cable duct and the expansion capacity in the expansion plan, the expected available capacity of the optical cable duct is obtained.

[0110] Based on the initial optical cable routing plan, the duct and fiber core requirements for each project are obtained;

[0111] For each section of the pipeline, the fiber core requirements of each project are added together to obtain the expected usage capacity;

[0112] If the pipeline's estimated usage capacity is greater than the current available capacity but less than the estimated available capacity, then the pipeline is determined to be in a first resource contention state.

[0113] If the pipeline's projected usage capacity is greater than its projected available capacity, then the pipeline is determined to be in a second resource contention state.

[0114] By combining the first resource contention status and the second resource contention status, the pipeline resource contention situation among multiple projects is obtained.

[0115] Preferably, adjusting the initial optical cable routing plan based on the pipeline resource contention situation to obtain the final optical cable routing plan result includes:

[0116] Based on the pipeline resource contention situation, the first pipeline in the first resource contention state and the second pipeline in the second resource contention state are selected.

[0117] The priority of each project is obtained, and the paths corresponding to the first and second pipelines in the initial optical cable routing plan are adjusted according to the priority of each project to obtain the final optical cable routing plan result.

[0118] In specific implementation, the working principle, control process and technical effects of the multi-project parallel optical cable routing planning device provided in this embodiment of the invention are the same as those of the multi-project parallel optical cable routing planning method in the above embodiments, and will not be repeated here.

[0119] See Figure 3 , Figure 3This is a structural block diagram of a computer device provided in an embodiment of the present invention. The computer device includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements the steps in the above-described embodiments of the multi-project parallel optical cable routing planning method. Alternatively, when the processor 301 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.

[0120] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0121] The computer device may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0122] The processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0123] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0124] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0125] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the multi-project parallel optical cable routing planning method described in any of the above embodiments.

[0126] This invention provides a method, apparatus, device, and medium for multi-project parallel optical cable routing planning. Its advantages lie in: acquiring the spatial occupancy status, deformation data, and geometric parameters of each optical cable duct; determining the available space in the bending sections of the optical cable duct based on the spatial occupancy status, deformation data, and geometric parameters; and then generating a spatial occupancy model for each duct segment using 3D modeling technology based on the spatial occupancy status, deformation data, and available space in the bending sections. This achieves high-precision duct modeling, enabling accurate assessment and efficient utilization of duct space, thus improving resource utilization. It also acquires the preset optical cable start and end points for each project, based on... Based on the aforementioned space occupancy model, a genetic algorithm is used to optimize the optical cable path, resulting in an initial optical cable route plan. This enables parallel and rapid automated path optimization in complex pipeline networks, improving the efficiency of optical cable route planning. Based on the initial optical cable route plan, the pipeline resource contention among multiple projects is analyzed, and the initial optical cable route plan is adjusted according to the pipeline resource contention situation to obtain the final optical cable route plan result. This effectively handles resource allocation and resource conflict issues in multi-project parallel scenarios, not only improving the accuracy and efficiency of optical cable route planning but also significantly reducing construction risks and overall costs. It is suitable for large-scale, multi-project parallel pipeline optical cable planning.

[0127] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for planning optical cable routes in parallel across multiple projects, characterized in that, include: Acquire the space occupancy status, duct deformation data, and duct geometric parameters of each optical cable duct; Based on the space occupancy status, the pipe deformation data, and the pipe geometric parameters, the available space for the bending section of the optical cable pipe is determined; Based on the space occupancy status, the pipe deformation data, and the available space of the curved section, a space occupancy model for each pipe section is generated using 3D modeling technology. Obtain the preset optical cable origin and destination for each project, and optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan; Based on the initial optical cable routing plan, the pipeline resource contention among multiple projects is analyzed, and the initial optical cable routing plan is adjusted according to the pipeline resource contention to obtain the final optical cable routing plan result.

2. The multi-project parallel optical cable routing planning method as described in claim 1, characterized in that, The step of determining the available space for the curved section of the optical cable duct based on the space occupancy status, the duct deformation data, and the duct geometric parameters includes: Based on the space occupancy status and the pipe deformation data, the actual usable space of the pipe is calculated; The location of the bend in the pipeline is determined based on the pipeline's geometric parameters, and the available space of the bend in the optical cable pipeline is obtained based on the actual available space of the pipeline and the location of the bend.

3. The multi-project parallel optical cable routing planning method as described in claim 1, characterized in that, The process of obtaining the preset fiber optic cable origin and destination for each project, and optimizing the fiber optic cable path using a genetic algorithm based on the space occupancy model to obtain the initial fiber optic cable route plan includes: Obtain the service access point and target node for each newly added optical cable in a multi-project scenario; Based on the space occupancy model, spatial analysis is performed to identify available optical cable ducts; Based on the available optical cable ducts, the service access points and target nodes of the newly added optical cables, and with the goal of minimizing duct occupancy, path length and bending segment risk, a genetic algorithm is used for iterative optimization to obtain the initial optical cable route plan; the bending segment risk is positively correlated with the degree of bending of the bending segment.

4. The multi-project parallel optical cable routing planning method as described in claim 1, characterized in that, The step of analyzing pipeline resource contention among multiple projects based on the initial optical cable routing plan includes: The saturation of pipeline splicing points is obtained based on the initial optical cable routing plan, and pipeline expansion requirements are generated based on the saturation of pipeline splicing points. Based on the pipeline expansion requirements and the preset regional permit restrictions and environmental conditions, an expansion plan is determined; Based on the initial optical cable routing plan and the expansion plan, a pipeline resource contention analysis is performed on the optical cable routing plans of multiple projects to obtain the pipeline resource contention situation among multiple projects.

5. The multi-project parallel optical cable routing planning method as described in claim 4, characterized in that, The pipeline expansion demand is also determined based on the predicted saturation of the pipeline connection points, which is obtained by predicting the historical growth trend of the saturation of the pipeline connection points.

6. The multi-project parallel optical cable routing planning method as described in claim 4, characterized in that, The step involves performing a pipeline resource contention analysis on the fiber optic cable routing plans of multiple projects based on the initial fiber optic cable routing plan and the expansion plan, to obtain the pipeline resource contention situation among the multiple projects, including: Based on the current available capacity of the optical cable duct and the expansion capacity in the expansion plan, the expected available capacity of the optical cable duct is obtained. Based on the initial optical cable routing plan, the duct and fiber core requirements for each project are obtained; For each section of the pipeline, the fiber core requirements of each project are added together to obtain the expected usage capacity; If the pipeline's estimated usage capacity is greater than the current available capacity but less than the estimated available capacity, then the pipeline is determined to be in a first resource contention state. If the pipeline's projected usage capacity is greater than its projected available capacity, then the pipeline is determined to be in a second resource contention state. By combining the first resource contention status and the second resource contention status, the pipeline resource contention situation among multiple projects is obtained.

7. The multi-project parallel optical cable routing planning method as described in claim 6, characterized in that, The step of adjusting the initial optical cable route plan based on the pipeline resource contention situation to obtain the final optical cable route plan result includes: Based on the pipeline resource contention situation, the first pipeline in the first resource contention state and the second pipeline in the second resource contention state are selected. The priority of each project is obtained, and the paths corresponding to the first and second pipelines in the initial optical cable routing plan are adjusted according to the priority of each project to obtain the final optical cable routing plan result.

8. A multi-project parallel optical cable routing planning device, characterized in that, include: The data acquisition module is used to acquire the space occupancy status, duct deformation data and duct geometric parameters of each optical cable duct; The space calculation module is used to determine the available space of the curved section of the optical cable duct based on the space occupancy status, the duct deformation data and the duct geometric parameters; The model building module is used to generate a space occupancy model for each pipe segment based on the space occupancy status, the pipe deformation data, and the available space of the curved section using three-dimensional modeling technology. The preliminary planning module is used to obtain the preset optical cable origin and destination for each project, and to optimize the optical cable path using a genetic algorithm based on the space occupancy model to obtain the initial optical cable route plan. The planning results module is used to analyze the pipeline resource contention among multiple projects based on the initial optical cable routing plan, and adjust the initial optical cable routing plan according to the pipeline resource contention to obtain the final optical cable routing plan result.

9. A computer device, characterized in that, The method includes a processor and a memory, the memory storing a computer program configured to be executed by the processor, wherein the processor, when executing the computer program, implements the multi-project parallel optical cable routing planning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the multi-project parallel optical cable routing planning method as described in any one of claims 1 to 7.

Images