PMS 3.0-based cable laying path planning method, device and equipment

Abstract

The invention provides a method, a device and equipment for planning a cable laying path based on PMS 3.0, and relates to the technical field of electric power engineering optimization. The method comprises the following steps: based on a PMS 3.0 power grid topology of a target area, an underground pipe network BIM model and geological radar data, constructing a target function considering construction cost, construction efficiency, electromagnetic interference and thermal resistance; geological radar data and the underground pipe network BIM model are integrated, and a dynamic constraint model considering obstacles and path conflicts is constructed; and generating an initial solution set of the cable laying path planning scheme, and performing iterative optimization on the initial solution set based on an NSGA-II algorithm, the target function and the dynamic constraint model to obtain a Pareto optimal solution set of the cable laying path planning scheme of the target area. According to the invention, active obstacle avoidance can replace passive rerouting in construction in a design period, and multiple effects of construction cost, influence on sensitive facilities and service life of the cable are considered at the same time.

Description

Technical Field

[0001] This invention relates to the field of power engineering optimization technology, and in particular to a cable laying path planning method, device and equipment based on PMS3.0. Background Technology

[0002] Cables are an important component of power systems. Cable laying path planning is a technical process for selecting or allocating the optimal spatial route for cables underground or in specific channels to meet the needs of power, communication and other infrastructure construction projects.

[0003] Current cable laying route planning still heavily relies on manual experience. Planners typically rely on limited topographic maps, rough sketches of underground pipelines, and fragmented on-site survey information, combined with their personal experience, to manually draw up plans and compare options. However, manual processing is difficult to efficiently and accurately integrate massive amounts of dynamically updated existing underground pipeline information. It is very easy for information omissions or misjudgments to lead to spatial conflicts between the planned route and existing facilities, or to fail to consider special geological distributions, which greatly increases construction difficulty and costs, resulting in project delays and resource waste. Summary of the Invention

[0004] This invention provides a cable laying path planning method, apparatus, and equipment based on PMS3.0 to solve the problem of optimizing the cable laying path planning effect.

[0005] In a first aspect, embodiments of the present invention provide a cable laying path planning method based on PMS3.0, comprising:

[0006] Based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area, an objective function considering construction cost, construction efficiency, electromagnetic interference and thermal resistance is constructed.

[0007] By integrating ground-penetrating radar data with underground pipeline network BIM models, a dynamic constraint model considering obstacles and path conflicts is constructed.

[0008] An initial solution set for cable laying path planning schemes is generated, and based on the NSGA-II algorithm, objective function, and dynamic constraint model, the initial solution set is iteratively optimized to obtain the Pareto optimal solution set for cable laying path planning schemes in the target area.

[0009] In one possible implementation, the formula for calculating construction costs is:

[0010] f1=L(x)·c cable +N bridge (x)·c bridge

[0011] L(x)=L steright (x)+n90° l 90° +n 45° l 45° +n s型 l s型

[0012] Where f1 is the construction cost score, L(x) is the path length, x is the cable laying path planning scheme, and c cable N represents the unit cost of the cable. bridge (x) represents the number of cable tray segments, c bridge For the unit cost of cable trays, L steright (x) represents the path length of the straight line segment, n 90° n represents the number of 90° bends. 45° n represents the number of 45° bends. s型 The number of S-shaped bends, l 90° The calculated length for a 90° bend is l. 45° The calculated length for a 45° bend is l. s型 This is the equivalent length of the S-shaped bend.

[0013] In one possible implementation, the formula for calculating construction efficiency is:

[0014]

[0015] Where f2 is the construction efficiency score, γ s Let be the geological difficulty coefficient of the s-th segment of the path, and n be the number of segments of the path.

[0016] In one possible implementation, the formula for calculating electromagnetic interference is:

[0017]

[0018] Where f3 is the electromagnetic interference score, w ij The weights are for cable types, i,j = 1, 2, ..., m, where m is the total number of cables, and V i For cable i, I is the rated voltage level. j Spacing is the rated current load of cable j. ij Shielding factor ij This refers to the cable spacing.

[0019] In one possible implementation, the formula for calculating thermal resistance is:

[0020]

[0021]

[0022] Where f4 is the thermal resistance score, fill rate(x) represents the cable tray fill ratio, α represents the thermal resistance growth coefficient, and θ represents the thermal resistance growth coefficient. fill θ is the safe threshold for cable tray fill rate. range θ is the normalization coefficient for the fill rate. range =1-θ fill A cable (x) represents the total occupied area, A bridge (x) represents the cross-sectional area inside the cable tray, a i Let be the cross-sectional area of ​​the i-th cable, i = 1, 2, ..., m, where m is the total number of cables.

[0023] In one possible implementation, the dynamic constraint model is as follows:

[0024]

[0025] Among them, efficiency score Penalty for construction efficiency, δ s Let be the obstacle penalty value of the s-th path segment, and n be the number of path segments. max λ is the maximum theoretical number of segments for the path. ef As a threshold for construction efficiency constraints, conflict penalty The overlap_ratio represents the path conflict penalty, where β is the penalty coefficient, K is the number of conflict points, and overlap_ratio is the ratio of the number of conflict points. k Let λ be the path overlap rate at the k-th conflict point. co This is the threshold for path conflict constraints.

[0026] In one possible implementation, the congestion calculation formula for the NSGA-II algorithm is as follows:

[0027]

[0028] Among them, id x Let x be the congestion level of the cable laying path planning scheme x, where x∈[1,X] and X is the population size. This is the normalized value of the qth objective function for cable laying path planning scheme x+1.

[0029] In one possible implementation, after obtaining the Pareto optimal solution set of the cable laying path planning scheme for the target area, the following is also included:

[0030] For each cable laying path planning scheme in the Pareto optimal solution set, a 3D rendering of the cable laying path planning scheme is generated based on the PMS3.0 system, and the construction process of the cable laying path planning scheme is visualized and pre-simulated using AR equipment.

[0031] Secondly, embodiments of the present invention provide a cable laying path planning device based on PMS3.0, comprising:

[0032] The target construction module is used to construct an objective function that considers construction cost, construction efficiency, electromagnetic interference and thermal resistance based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area.

[0033] The constraint construction module is used to integrate ground-penetrating radar data with underground pipeline network BIM models to build dynamic constraint models that take into account obstacles and path conflicts.

[0034] The iterative solution module generates an initial solution set for cable laying path planning schemes and iteratively optimizes the initial solution set based on the NSGA-II algorithm, objective function, and dynamic constraint model to obtain the Pareto optimal solution set for the cable laying path planning scheme in the target area.

[0035] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0036] The cable laying path planning method, device, and equipment based on PMS3.0 provided in this invention integrate the PMS3.0 power grid topology, underground pipeline BIM model, and ground-penetrating radar data of the target area. The power grid topology ensures that the path complies with electrical specifications, the BIM model integrates the three-dimensional spatial attributes of existing pipelines, and the ground-penetrating radar dynamically captures soil and rock defects and hydrological characteristics. This eliminates omissions and errors in manual information integration from the source, breaking through the limitations of traditional single-objective optimization. It constructs a multi-objective function that quantifies construction cost, construction efficiency, electromagnetic interference, and thermal resistance, and iteratively solves the problem using the NSGA-II multi-objective evolutionary algorithm. The output is a Pareto optimal solution set that balances all objectives, allowing users to select the planning scheme that best meets their needs. While actively avoiding obstacles during the design phase instead of passively rerouting during construction, it also takes into account multiple aspects such as construction cost, impact on sensitive facilities, and cable lifespan, achieving breakthrough improvements in conflict avoidance, global optimization, decision-making efficiency, and cost control. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the implementation of the cable laying path planning method based on PMS3.0 provided in this embodiment of the invention.

[0038] Figure 2 This is a schematic diagram of the cable laying path planning device based on PMS3.0 provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0041] See Figure 1 The document illustrates a flowchart of the cable laying path planning method based on PMS3.0 provided in this embodiment of the invention, detailed below:

[0042] Step 101: Based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area, construct an objective function that considers construction cost, construction efficiency, electromagnetic interference and thermal resistance.

[0043] In this embodiment, the Power Production Management System is used to store the power grid topology, equipment parameters, and operational data. The 3D model of underground pipelines constructed using Building Information Modeling (BIM) includes the spatial coordinates and attribute data of cable trays, existing cables, and underground pipelines (such as gas pipes and water supply pipes). Underground geological structure data obtained through ground-penetrating radar is used to identify geological features such as rock layers and underground obstacles.

[0044] By combining PMS3.0 power grid topology, underground pipeline network BIM model, and ground-penetrating radar data, information such as cable start and end points and voltage levels can be determined as the initial solution space for selecting cable laying path planning schemes. Furthermore, the influence of various cable laying path planning schemes on construction costs, construction efficiency, electromagnetic interference, and thermal resistance can be determined. Abstract indicators such as cost, efficiency, electromagnetic compatibility, and thermal management are transformed into calculable mathematical models, overcoming the limitations of traditional planning that relies on experience, thereby achieving multi-objective optimization of cable laying path planning schemes.

[0045] Step 102: Integrate ground-penetrating radar data with underground pipeline network BIM model to construct a dynamic constraint model that considers obstacles and path conflicts.

[0046] In this embodiment, ground-penetrating radar data and BIM model are fused in real time to update the construction difficulty coefficient and path conflict detection results.

[0047] Specifically, since the efficiency decreases when the path passes through rocky areas or obstacles, the detection data of the ground-penetrating radar is rasterized, the construction difficulty coefficient is updated in segments, and special areas such as rocky areas and old pipeline areas are treated as high-penalty areas, so as to avoid such solutions during the iteration process.

[0048] To address path conflicts, the system calculates the overlap rate between the path and existing pipelines in real time and rejects paths with high overlap rates, thereby limiting the overlap rate between cables and gas pipelines and other important facilities, and reducing the pipeline damage accident rate during construction.

[0049] Step 103: Generate an initial solution set for the cable laying path planning scheme, and iteratively optimize the initial solution set based on the NSGA-II algorithm, objective function, and dynamic constraint model to obtain the Pareto optimal solution set for the cable laying path planning scheme in the target area.

[0050] The NSGA-II algorithm takes four objectives—construction cost, construction efficiency, electromagnetic interference, and thermal resistance—as its multiple objective functions. Combining BIM cable tray capacity and ground-penetrating radar obstacle distribution, it uses the minimum cost nearest neighbor method to generate a high-quality initial solution. Starting from the starting point, it prioritizes selecting the nearest node with the lowest construction cost that meets the BIM cable tray capacity constraint to extend the path until the endpoint. This process is repeated to generate multiple differentiated paths to form the initial population.

[0051] Then, non-dominated sorting, crowding calculation, and adaptive genetic operations (crossover, mutation, elite retention) are used to iterate the population, and finally a Pareto optimal solution set is generated, which preserves the diversity of multi-objective solution sets and is suitable for dynamic trade-offs in complex scenarios.

[0052] In one possible implementation, the congestion calculation formula for the NSGA-II algorithm is as follows:

[0053]

[0054] Among them, id x Let x be the congestion level of the cable laying path planning scheme x, where x∈[1,X] and X is the population size. This is the normalized value of the qth objective function for cable laying path planning scheme x+1.

[0055] In this embodiment, the crowding index is used to measure the distribution density of candidate solutions x in the target space. This method of calculating crowding ensures that the solution set is uniformly distributed in the target space, providing diverse trade-off options.

[0056] This invention integrates the PMS3.0 power grid topology, underground pipeline BIM model, and ground-penetrating radar data of the target area. The power grid topology ensures that the path complies with electrical specifications, the BIM model integrates the three-dimensional spatial attributes of existing pipelines, and the ground-penetrating radar dynamically captures soil and rock defects and hydrological characteristics. This eliminates omissions and errors in manual information integration from the source, breaking through the limitations of traditional single-objective optimization. It constructs a multi-objective function that quantifies construction cost, construction efficiency, electromagnetic interference, and thermal resistance, and iteratively solves the problem using the NSGA-II multi-objective evolutionary algorithm. The output is a Pareto optimal solution set that balances all objectives, allowing users to select the planning scheme that best meets their needs. While actively avoiding obstacles during the design phase instead of passively rerouting during construction, it also takes into account multiple aspects such as construction cost, impact on sensitive facilities, and cable lifespan, achieving breakthrough improvements in conflict avoidance, global optimization, decision-making efficiency, and cost control.

[0057] In one possible implementation, the formula for calculating construction costs is:

[0058] f1=L(x)·c cable +N bridge (x)·c bridge

[0059] L(x)=L steright (x)+n 90° l 90° +n 45° l 45° +n s型 l s型

[0060] Where f1 is the construction cost score, L(x) is the path length, x is the cable laying path planning scheme, and c cable N represents the unit cost of the cable. bridge (x) represents the number of cable tray segments, c bridge For the unit cost of cable trays, L steright (x) represents the path length of the straight line segment, n 90° n represents the number of 90° bends. 45° n represents the number of 45° bends. s型 The number of S-shaped bends, l 90° The calculated length for a 90° bend is l. 45° The calculated length for a 45° bend is l. s型 This is the equivalent length of the S-shaped bend.

[0061] In this embodiment, bends increase the actual length of cable laying and the difficulty of construction. Converting them into equivalent straight lengths facilitates cost calculation and path comparison. The conversion factor for bends of different angles can be determined based on engineering standards and practical experience.

[0062] For each cable laying path scheme x, the three-dimensional coordinate point sequence of each cable is extracted, and the turning angle of adjacent segments is calculated. 90° bends are used for segments with turning angles between 85° and 95°, and 45° bends are used for segments with turning angles between 40° and 50°. When two consecutive 45° bends exist with a distance < 1m, an S-shaped bend is used. When the path crosses a cable tray turning section, a corresponding angle bend is automatically added. This determines the number of each type of bend in each cable laying path scheme x.

[0063] In one possible implementation, the formula for calculating construction efficiency is:

[0064]

[0065] Where f2 is the construction efficiency score, γ sLet be the geological difficulty coefficient of the s-th segment of the path, and n be the number of segments of the path.

[0066] In this embodiment, the path is divided into continuous, homogeneous geological sections, such as rock areas, soil areas, and pipeline areas, based on the obstacle distribution in the geological grid map. To facilitate construction management, when the length of a single geological section is greater than 50m, it is divided into 50m equal intervals.

[0067] Different geological types have varying degrees of impact on construction difficulty. A geological difficulty coefficient for each geological type can be calculated based on actual construction experience or historical construction data. The overall construction efficiency score is the average of the geological difficulty coefficients for each segment, which helps avoid excessively high construction efficiency scores in long-path scenarios.

[0068] By quantifying the construction efficiency score, factors affecting the construction period, such as path complexity and mechanical accessibility, can be incorporated into the cable laying route planning.

[0069] In one possible implementation, the formula for calculating electromagnetic interference is:

[0070]

[0071] Where f3 is the electromagnetic interference score, w ij The weights are for cable types, i,j = 1, 2, ..., m, where m is the total number of cables, and V i For cable i, I is the rated voltage level. j Spacing is the rated current load of cable j. ij Shielding factor ij This refers to the cable spacing.

[0072] In this embodiment, the electromagnetic induction between cables is related to distance, current, and voltage level. Specifically, the greater the voltage level difference, the stronger the interference; the larger the load current, the stronger the magnetic field; the closer the distance, the more significant the interference; and the power cable has a higher interference weight on the control cable. Therefore, for two cables, if one is a power cable and the other a control cable, the cable type weight is 2; if both are power cables or both are control cables, the cable type weight is 1. ij The minimum distance between the two cables is given. The shielding coefficient is 1.5 if there is a shielding layer and 1 if there is no shielding layer.

[0073] Electromagnetic interference control can prevent electromagnetic pollution of sensitive facilities (such as communication pipelines) by cables. At the same time, when high-voltage cables and low-voltage cables are laid in parallel, the distance between them is forced to increase, so as to meet long-term safety standards.

[0074] In one possible implementation, the formula for calculating thermal resistance is:

[0075]

[0076] Where f4 is the thermal resistance score, fill rate (x) represents the cable tray fill ratio, α represents the thermal resistance growth coefficient, and θ represents the thermal resistance growth coefficient. fill θ is the safe threshold for cable tray fill rate. range θ is the normalization coefficient for the fill rate. range =1-θ fikl A cable (x) represents the total occupied area, A bridge (x) represents the cross-sectional area inside the cable tray, a i Let be the cross-sectional area of ​​the i-th cable, i = 1, 2, ..., m, where m is the total number of cables.

[0077] In this embodiment, the cable tray specification selected in path scheme x directly determines the cross-sectional area A of the cable tray. bridge (x), the number of cables m and the cross-sectional area a of a single cable in route scheme x. i The total occupied area is determined, from which the cable tray fill rate can be calculated. rate (x).

[0078] The fill rate normalization coefficient is used to map the portion of the fill rate exceeding the threshold to the [0,1] interval, making the thermal resistance factor calculation conform to the exponential growth model. When the route scheme x chooses to bypass the high-density area, the cable tray fill rate is [fill]. rate As (x) decreases, the thermal resistance score f4 approaches 1, enabling thermal management optimization of the laying path.

[0079] In one possible implementation, the dynamic constraint model is as follows:

[0080]

[0081] Among them, efficiency score Penalty for construction efficiency, δ s Let be the obstacle penalty value of the s-th path segment, and n be the number of path segments. max λ is the maximum theoretical number of segments for the path. ef As a threshold for construction efficiency constraints, conflict penalty The overlap_ratio represents the path conflict penalty, where β is the penalty coefficient, K is the number of conflict points, and overlap_ratio is the ratio of the number of conflict points. k Let λ be the path overlap rate at the k-th conflict point. co This is the threshold for path conflict constraints.

[0082] In this embodiment, the dynamic constraint model is used to eliminate infeasible solutions such as conflicting paths and overcapacity ratios before calculating the fitness function value (i.e., the objective function value), which complements the NSGA-II algorithm in optimizing the objective function and improves optimization efficiency.

[0083] Regarding the penalty for construction efficiency, n max This indicates the maximum number of segments the path can ideally have, with a default of 50m segments. The calculation formula is: L max Let n be the maximum possible path length. max This is used to normalize obstacle penalty values, preventing excessive penalty accumulation on long paths due to numerous segments. The obstacle penalty value is correlated with ground-penetrating radar detection indicators, such as δ0 for rocky areas. s =0.7, δ in underground pipeline area s =1.2, δ of ordinary soil s =0.

[0084] The path conflict penalty coefficient β can take a relatively large value, such as 10. 6 This is used to ensure that the objective function value of conflicting paths is significantly higher than that of feasible solutions. Conflicting path overlap rate. The calculation method is overlap length / total path length, which means the degree of overlap between the planned cable path and the safety envelope of the existing pipeline in three-dimensional space. When the net distance between the two pipelines is less than the safety distance, it is considered to be an overlap.

[0085] In one possible implementation, after obtaining the Pareto optimal solution set of the cable laying path planning scheme for the target area, the following is also included:

[0086] For each cable laying path planning scheme in the Pareto optimal solution set, a 3D rendering of the cable laying path planning scheme is generated based on the PMS3.0 system, and the construction process of the cable laying path planning scheme is visualized and pre-simulated using AR equipment.

[0087] In this embodiment, each solution in the Pareto optimal solution set is a balanced solution that reflects objectives such as cost and efficiency.

[0088] For each cable laying route planning scheme, the existing cable parameters (voltage level, model) and power grid topology are first extracted from PMS3.0 and associated with the spatial coordinates of the underground pipeline network BIM model to form three-dimensional route data. Then, the route scheme is converted into a parametric model, and the preset 3D effect models of bends, cable trays, and cables are called and placed in the corresponding coordinates to generate 3D effect diagrams containing bends, cable trays, and cables. Finally, using synchronous positioning and map building technology, the virtual model is aligned with the actual site scene, and the virtual model of each step is dynamically displayed according to the construction sequence. The display interval of each step is set according to the construction difficulty of the route segment to realize the dynamic superposition display of construction steps and effectively reflect the construction efficiency of each route planning scheme.

[0089] This method enables dynamic simulation of the construction process of cable laying route planning schemes, eliminates misunderstandings of two-dimensional drawings, and improves staff's understanding of route planning schemes.

[0090] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0091] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0092] Figure 2 A schematic diagram of the cable laying path planning device based on PMS3.0 provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0093] like Figure 2 As shown, the cable laying path planning device 2 based on PMS3.0 includes:

[0094] The target construction module 21 is used to construct an objective function that considers construction cost, construction efficiency, electromagnetic interference and thermal resistance based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area.

[0095] Constraint construction module 22 is used to integrate ground-penetrating radar data with underground pipeline network BIM model to construct a dynamic constraint model that considers obstacles and path conflicts;

[0096] The iterative solution module 23 is used to generate an initial solution set for the cable laying path planning scheme, and to iteratively optimize the initial solution set based on the NSGA-II algorithm, objective function and dynamic constraint model to obtain the Pareto optimal solution set of the cable laying path planning scheme for the target area.

[0097] In one possible implementation, the formula for calculating construction costs is:

[0098] f1=L(x)·c cable +N bridge (x)·c bridge

[0099] L(x)=L steright (x)+n 90° l 90° +n 45° l 45° +n s型 l s型

[0100] Where f1 is the construction cost score, L(x) is the path length, x is the cable laying path planning scheme, and c cable N represents the unit cost of the cable. bridge (x) represents the number of cable tray segments, c bridge For the unit cost of cable trays, L steright (x) represents the path length of the straight line segment, n 90° n represents the number of 90° bends. 45° n represents the number of 45° bends. s型 The number of S-shaped bends, l 90° The calculated length for a 90° bend is l. 45° The calculated length for a 45° bend is l. s型 This is the equivalent length of the S-shaped bend.

[0101] In one possible implementation, the formula for calculating construction efficiency is:

[0102]

[0103] Where f2 is the construction efficiency score, γ s Let be the geological difficulty coefficient of the s-th segment of the path, and n be the number of segments of the path.

[0104] In one possible implementation, the formula for calculating electromagnetic interference is:

[0105]

[0106] Where f3 is the electromagnetic interference score, w ij The weights are for cable types, i,j = 1, 2, ..., m, where m is the total number of cables, and V i For cable i, I is the rated voltage level. j Spacing is the rated current load of cable j. ij xhield is the shielding factor. ij This refers to the cable spacing.

[0107] In one possible implementation, the formula for calculating thermal resistance is:

[0108]

[0109] Where f4 is the thermal resistance score, fill rate (x) represents the cable tray fill ratio, α represents the thermal resistance growth coefficient, and θ represents the thermal resistance growth coefficient. fill θ is the safe threshold for cable tray fill rate. range θ is the normalization coefficient for the fill rate. range =1-θ fill a cable (x) represents the total occupied area, A bridge (x) represents the cross-sectional area inside the cable tray, ai Let be the cross-sectional area of ​​the i-th cable, i = 1, 2, ..., m, where m is the total number of cables.

[0110] In one possible implementation, the dynamic constraint model is as follows:

[0111]

[0112] Among them, efficiency score Penalty for construction efficiency, δ s Let be the obstacle penalty value of the s-th path segment, and n be the number of path segments. max λ is the maximum theoretical number of segments for the path. ef As a threshold for construction efficiency constraints, conflict penalty The overlap_ratio represents the path conflict penalty, where β is the penalty coefficient, K is the number of conflict points, and overlap_ratio is the ratio of the number of conflict points. k Let λ be the path overlap rate at the k-th conflict point. co This is the threshold for path conflict constraints.

[0113] In one possible implementation, the congestion calculation formula for the NSGA-II algorithm is as follows:

[0114]

[0115] Among them, id x Let x be the congestion level of the cable laying path planning scheme x, where x∈[1,X] and X is the population size. This is the normalized value of the qth objective function for cable laying path planning scheme x+1.

[0116] In one possible implementation, the iterative solution module 23 is also used for:

[0117] After obtaining the Pareto optimal solution set of cable laying path planning schemes for the target area, for each cable laying path planning scheme in the Pareto optimal solution set, a 3D rendering of the cable laying path planning scheme is generated based on the PMS3.0 system, and the construction process of the cable laying path planning scheme is visualized and pre-simulated through AR equipment.

[0118] This invention integrates the PMS3.0 power grid topology, underground pipeline BIM model, and ground-penetrating radar data of the target area. The power grid topology ensures that the path complies with electrical specifications, the BIM model integrates the three-dimensional spatial attributes of existing pipelines, and the ground-penetrating radar dynamically captures soil and rock defects and hydrological characteristics. This eliminates omissions and errors in manual information integration from the source, breaking through the limitations of traditional single-objective optimization. It constructs a multi-objective function that quantifies construction cost, construction efficiency, electromagnetic interference, and thermal resistance, and iteratively solves the problem using the NSGA-II multi-objective evolutionary algorithm. The output is a Pareto optimal solution set that balances all objectives, allowing users to select the planning scheme that best meets their needs. While actively avoiding obstacles during the design phase instead of passively rerouting during construction, it also takes into account multiple aspects such as construction cost, impact on sensitive facilities, and cable lifespan, achieving breakthrough improvements in conflict avoidance, global optimization, decision-making efficiency, and cost control.

[0119] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0120] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 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 computer program 32 in electronic device 3.

[0121] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0122] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0123] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0124] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A cable laying path planning method based on PMS3.0, characterized in that, include: Based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area, an objective function considering construction cost, construction efficiency, electromagnetic interference and thermal resistance is constructed. By integrating the ground-penetrating radar data with the underground pipeline network BIM model, a dynamic constraint model considering obstacles and path conflicts is constructed. An initial solution set for cable laying path planning schemes is generated, and based on the NSGA-II algorithm, the objective function, and the dynamic constraint model, the initial solution set is iteratively optimized to obtain the Pareto optimal solution set for the cable laying path planning schemes in the target area.

2. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The formula for calculating construction costs is: f1=L(x)·c cable +N bridge (x)·c bridge L(x)=L steright (x)+n 90° l 90° +n 45° l 45° +n s型 l s型 Where f1 is the construction cost score, L(x) is the path length, x is the cable laying path planning scheme, and c cable N represents the unit cost of the cable. bridge (x) represents the number of cable tray segments, c bridge For the unit cost of cable trays, L steright (x) represents the path length of the straight line segment, n 90° n represents the number of 90° bends. 45° n represents the number of 45° bends. s型 The number of S-shaped bends, l 90° The calculated length for a 90° bend is l. 45° The calculated length for a 45° bend is l. s型 This is the equivalent length of the S-shaped bend.

3. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The formula for calculating construction efficiency is: Where f2 is the construction efficiency score, γ s Let be the geological difficulty coefficient of the s-th segment of the path, and n be the number of segments of the path.

4. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The formula for calculating electromagnetic interference is: Where f3 is the electromagnetic interference score, w ij The weights are for cable types, i,j = 1, 2, ..., m, where m is the total number of cables, and V is the weight for cable type. i For cable i, I is the rated voltage level. j Spacing is the rated current load of cable j. ij Shielding factor ij This refers to the cable spacing.

5. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The formula for calculating thermal resistance is: Where f4 is the thermal resistance score, fill rate (x) represents the cable tray fill ratio, α represents the thermal resistance growth coefficient, and θ represents the thermal resistance growth coefficient. fill θ is the safe threshold for cable tray fill rate. range θ is the normalization coefficient for the fill rate. range =1-θ fill A cable (x) represents the total occupied area, A bridge (x) represents the cross-sectional area inside the cable tray, a i Let be the cross-sectional area of ​​the i-th cable, i = 1, 2, ..., m, where m is the total number of cables.

6. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The dynamic constraint model is as follows: Among them, efficiency score Penalty for construction efficiency, δ s Let n be the obstacle penalty value for the s-th path segment, and n be the number of path segments. max λ is the maximum theoretical number of segments for the path. ef As a threshold for construction efficiency constraints, conflict penalty The overlap_ratio represents the path conflict penalty, where β is the penalty coefficient, K is the number of conflict points, and overlap_ratio is the ratio of the number of conflict points. k Let λ be the path overlap rate at the k-th conflict point. co This is the threshold for path conflict constraints.

7. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, The congestion calculation formula for the NSGA-II algorithm is as follows: Among them, id x Let x be the congestion level of the cable laying path planning scheme x, where x∈[1,X] and X is the population size. This is the normalized value of the qth objective function for cable laying path planning scheme x+1.

8. The cable laying path planning method based on PMS3.0 according to claim 1, characterized in that, After obtaining the Pareto optimal solution set of the cable laying path planning scheme for the target area, the following steps are also included: For each cable laying path planning scheme in the Pareto optimal solution set, a 3D rendering of the cable laying path planning scheme is generated based on the PMS3.0 system, and the construction process of the cable laying path planning scheme is visualized and pre-simulated using AR equipment.

9. A cable laying path planning device based on PMS3.0, characterized in that, include: The target construction module is used to construct an objective function that considers construction cost, construction efficiency, electromagnetic interference and thermal resistance based on the PMS3.0 power grid topology, underground pipeline BIM model and ground-penetrating radar data of the target area. The constraint construction module is used to integrate the ground-penetrating radar data with the underground pipeline network BIM model to construct a dynamic constraint model that considers obstacles and path conflicts. The iterative solution module is used to generate an initial solution set for the cable laying path planning scheme, and to iteratively optimize the initial solution set based on the NSGA-II algorithm, the objective function, and the dynamic constraint model to obtain the Pareto optimal solution set for the cable laying path planning scheme in the target area.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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