Distributed multi-target path planning method and system

Abstract

The invention provides a multi-target path planning method and system based on distribution, and the method comprises the steps: converting a to-be-laid physical space of a cable into a geometric space, and constructing a space model; converting a multi-objective optimization problem into a general mathematical model; constructing a cable path planning model by taking the cable weight, the bundling ratio and the laying path density as optimization targets; and introducing an economic index, constructing a model in combination with an objective function, and realizing distributed planning of a cable path. The method has the advantages of being accurate in applicable scene, high in model precision, high in planning efficiency and high in multi-objective optimization capability.

Description

Technical Field

[0001] This invention relates to the field of cable route planning technology, and in particular to a distributed multi-objective route planning method and system. Background Technology

[0002] In the field of cable path planning, traditional methods often improve convergence speed and global optimization capabilities by dynamically adjusting pheromone factors and search range, or combine genetic algorithms to optimize ring network connections to achieve dual optimization of construction and operating costs. Among these, ant colony optimization (ACO)-based path planning methods are widely used. Their core principle is that ants interact with the environment using pheromones; on short paths, ants travel back and forth more frequently, accumulating more pheromones, attracting more ants to gather, ultimately approaching the shortest path. This algorithm follows rules such as perception range, environmental information, foraging, movement, obstacle avoidance, and pheromone dispersal, and possesses characteristics such as positive feedback mechanisms, inter-individual indirect communication, distributed computing, and heuristic probabilistic search. It can be used to establish path planning models based on target tasks and determine path planning schemes. However, existing technologies have significant drawbacks: Low computational efficiency: In complex networks (such as the channel topology of thousands of cables in a large power plant), the increase in the number of nodes will lead to an exponential increase in the amount of computation, making it difficult to complete the planning efficiently.

[0003] Slow convergence speed and prone to getting trapped in local optima: Although the ant colony algorithm can find the global optimum, the classic version requires an average of 72 iterations, which takes a long time to converge and is prone to getting trapped in local optima. It is necessary to introduce correction mechanisms such as genetic mutation, which increases the complexity of the algorithm.

[0004] Weak multi-objective optimization capability: Most algorithms take "shortest path" as the single objective, ignoring the coordinated optimization of economy (such as number of bends and construction costs), safety (such as electromagnetic interference) and maintainability (such as reserved maintenance space), and cannot meet the multi-dimensional needs of real-world scenarios. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] Therefore, the first objective of this invention is to propose a distributed multi-objective path planning method.

[0007] The second objective of this invention is to propose a distributed multi-objective path planning system.

[0008] The third objective of this invention is to provide an electronic device.

[0009] The fourth objective of this invention is to provide a computer-readable storage medium.

[0010] The fifth objective of this invention is to provide a computer program product.

[0011] To achieve the above objectives, a first aspect of the present invention proposes a distributed multi-objective path planning method, comprising: Optionally, the physical space to be laid in the cable is transformed into a geometric space, the geometric space is divided into uniform or non-uniform discrete units, the units are processed using a grid algorithm, the laying space is divided equally along three coordinate axes, and a corresponding spatial model is constructed based on the positional relationship between each grid point and the product components. The objective functions for different types of targets in the cable path are transformed, converting the multi-objective optimization problem into a general mathematical model; Obtain the cable layout design requirements, take cable weight, proportion, and laying path density as optimization objectives, and construct the corresponding cable path planning model based on the mathematical model; Economic indicators are introduced, and a corresponding path planning model is constructed based on the objective function and economic indicators. The path planning model is then used to realize distributed planning of cable paths.

[0012] Optionally, the objective function for different types of targets in the cable path is transformed, converting the multi-objective optimization problem into a general mathematical model, including:

[0013]

[0014]

[0015] In the formula, for Dimensional decision space The decision vector in the decision space is mapped through the decision space. Switch to In the target space of dimension, For the target number, , , respectively, the number of inequalities and equality constraints, when , When all values ​​are 0, the above problem will be transformed into a multi-objective optimization problem without constraints.

[0016] Optionally, the cable layout design requirements are obtained, and cable weight, proportion, and laying path density are used as optimization objectives. Based on a mathematical model, a corresponding cable path planning model is constructed, including:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] In the formula, The objective function representing the cable weight is... The objective function represents the proportion of bundled cables. This represents the objective function for determining the density of the laying path. This indicates a wall-hugging constraint, meaning the path is attached to the wall. Conversely, if the value is not zero, the path does not pass through the high-temperature area. Conversely, if the value is not zero, the path does not pass through a strong electromagnetic region. Conversely, if the value is not 0, the port extension meets the requirements. Conversely, it is not 0. To ensure the path meets the minimum bending radius requirement, For the number of branch points, This refers to the number of terminals. This is a cable layout scheme.

[0024] Optional, also includes: Regarding the objective function for cable weight, the cable weight includes the weight of bundled cables and the weight of discrete cables. Using the branch information of a single cable, the type and quantity of cables contained in each bundle segment are calculated. Based on the length of each bundle segment, the total weight of the bundled cables is calculated using the following formula:

[0025] In the formula, Indicates the number of bundled sections. Indicates the length of the bundled section. This refers to the number of cables included in the bundle section. For the first section of the binding Linear density of the cable; The weight of the discrete cables is calculated using the cable type and length of each discrete cable:

[0026] In the formula, Indicates the number of discrete cables. For the length of the discrete cable, Indicates the first Linear density of a cable.

[0027] Optional, also includes: Regarding the objective function for the proportion of bundled cables, the corresponding objective function is calculated based on the number and length of the bundled sections and the number of cables in each bundled section:

[0028] Regarding the objective function for dense path laying:

[0029]

[0030] In the formula, Indicates the number of node units through which the cable passes. For dense space of nodes; Indicates the number of adjacent node elements. Indicates the volume of the unit.

[0031] Optionally, economic indicators are introduced, and a corresponding path planning model is constructed based on the objective function and economic indicators. This path planning model is then used to achieve distributed planning of cable paths, including:

[0032] in, Indicates the path planning length. Indicates the cross-sectional area of ​​the cable. The function representing the unit price of cables. Indicates the average slope of the path. This represents the slope cost coefficient. Indicates the average maintenance time; This represents the cost of cable installation per unit length. This represents the average maintenance cost.

[0033] To achieve the above objectives, a second aspect of the present invention proposes a distributed multi-objective path planning system, comprising: The first module is used to transform the physical space to be laid in the cable into a geometric space, divide the geometric space into uniform or non-uniform discrete units, process the units using a grid algorithm, divide the laying space equally along the three coordinate axes, and construct the corresponding spatial model based on the positional relationship between each grid point and the product components. The second module is used to transform the objective function for different types of targets in the cable path, converting the multi-objective optimization problem into a general mathematical model; The third module is used to obtain the cable layout design requirements, taking cable weight, proportion, and laying path density as optimization objectives, and constructing the corresponding cable path planning model based on the mathematical model. The fourth module is used to introduce economic indicators, construct the corresponding path planning model based on the objective function and economic indicators, and use the path planning model to realize distributed planning of cable paths.

[0034] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0035] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0036] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0037] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: Precise application scenarios: Specifically designed for photovoltaic cable route planning, providing new ideas for photovoltaic cable laying and meeting the actual needs of the industry.

[0038] High model accuracy: By using spatial discretization modeling and multi-objective function transformation, the model finds a layout scheme that satisfies the specified objective in a constrained decision space. Combined with economic indicators to optimize the mathematical model, the model's processing effect and accuracy are greatly improved.

[0039] High planning efficiency: The distributed planning approach effectively reduces the amount of computation in complex networks, avoids the exponential growth of computation as the number of nodes increases, and improves planning efficiency.

[0040] Strong multi-objective optimization capability: It takes into account multiple dimensions of objectives such as cable weight, bundling ratio, path density and economy, and solves the limitations of single-objective optimization of traditional algorithms, meeting the requirements of reliability, economy and maintainability in real-world scenarios.

[0041] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0042] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a distributed multi-objective path planning method provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of a distributed multi-objective path planning system provided in an embodiment of the present invention. Detailed Implementation

[0043] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0044] To overcome the shortcomings of existing technologies, this invention provides a distributed multi-objective path planning method and system. This method addresses the problems of low computational efficiency, slow convergence, and weak multi-objective optimization capabilities of traditional algorithms by spatial discretization, multi-objective function construction, and the introduction of economic indicators, thereby achieving efficient, accurate, and multi-dimensional optimization planning of cable paths.

[0045] The main principle of the technical solution of this invention is to transform the physical space of cable laying into a computable geometric space through an innovative architecture of "spatial discretization modeling + multi-objective function transformation + economic index fusion". This constructs a planning model that takes into account weight, bundling ratio, path density and economy. Combined with distributed planning, it achieves efficient optimization of cable path in complex scenarios, fundamentally solving the industry pain points of traditional path planning.

[0046] Figure 1 This is a flowchart illustrating a distributed multi-objective path planning method provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: Step S1: The physical space to be laid in the cable is converted into a geometric space. The geometric space is divided into uniform or non-uniform discrete units. The units are processed using a grid algorithm. The laying space is divided equally along the three coordinate axes. Based on the positional relationship between each grid point and the product components, a corresponding spatial model is constructed.

[0047] In this embodiment, the core objective of step S1 is to convert the physical space of the cable to be laid into a geometric space, and to discretize this geometric space, thereby providing a reasonable solution framework for subsequent path planning and optimization. Specifically, step S1 includes the following sub-steps: First, the physical space where cables are to be laid is typically a three-dimensional space. The cable laying path must avoid obstacles, and the optimal path must be selected based on the actual site conditions. In order to ensure that the subsequent path planning process can be carried out within a controllable range, this three-dimensional physical space must be transformed into a geometric space to facilitate subsequent calculations and optimization.

[0048] Secondly, in geometric space, the area is divided into uniform or non-uniform discrete units using a grid algorithm. The grid algorithm is a common spatial discretization method that divides the entire space into many small units by setting a grid in three-dimensional space. Depending on the application requirements, either a uniform or non-uniform grid can be selected. A uniform grid divides the space at even intervals and is suitable for relatively simple environments, while a non-uniform grid can adjust the unit size according to the distribution of obstacles or the complexity of cable paths, allowing for a more precise depiction of complex environments.

[0049] Then, the cable path planning space can be equally divided along three coordinate axes (X-axis, Y-axis, and Z-axis). Specifically, in each coordinate axis direction, the entire space is divided into multiple small units based on the actual size of the space and the cable layout requirements. In this way, the three-dimensional space is divided into countless small units, each of which can be regarded as a grid point, thus forming a discretized spatial model.

[0050] Next, the system will construct a corresponding spatial model based on the positional relationship between each grid point and the product components. This spatial model is the foundation of cable route planning, which determines which areas are suitable for cable laying and which areas are unsuitable for cable laying due to obstacles, equipment, or other factors, thus providing effective constraints for subsequent cable route planning.

[0051] It is worth noting that the cable route planning problem is often considered a combined optimization problem of paths and topology in three-dimensional space. In this type of optimization problem, the primary technical challenge is how to discretize the continuous three-dimensional space into a search space that can be optimized. By using a grid algorithm and spatial discretization methods, this embodiment provides an accurate and efficient solution framework for the subsequent route planning problem. The key to this process lies in how to effectively transform the three-dimensional physical space into a geometric space and divide it into multiple small units through reasonable discretization, thereby providing a clear decision space for the route planning optimization algorithm.

[0052] Step S2 involves transforming the objective functions for different types of targets along the cable path, converting the multi-objective optimization problem into a general mathematical model.

[0053] In this embodiment, the main purpose of step S2 is to transform the multi-objective optimization problem in cable route planning into a general mathematical model so that it can be solved using standard optimization methods. To achieve this goal, this step appropriately transforms the objective function so that it conforms to the mathematical form of a multi-objective optimization problem and can be effectively solved using existing optimization algorithms. Specifically, step S2 includes the following: First, the cable route planning problem involves multiple objectives, such as minimizing cost, minimizing route length, and minimizing maintenance costs. These objectives are often in competition; that is, during optimization, improvements in some objectives may lead to the deterioration of others. Therefore, this step requires integrating these multiple objectives into a unified optimization problem and using a reasonable mathematical expression to handle the relationships between these multiple objectives.

[0054] To transform multi-objective optimization problems into a general mathematical model, the following mathematical form can be used:

[0055]

[0056]

[0057] In the formula, for Dimensional decision space The decision vector in the decision space is mapped through the decision space. Switch to In the target space of dimension, For the target number, , , respectively, the number of inequalities and equality constraints, when , When all values ​​are 0, the above problem will be transformed into a multi-objective optimization problem without constraints.

[0058] Through the above transformation, this embodiment successfully transforms the multi-objective optimization problem of cable route planning into a standard mathematical optimization problem. In this way, the cable route planning problem can be solved using existing multi-objective optimization algorithms (such as the weighted summation method, the ε-constraint method, etc.), thus providing a theoretical basis and solution framework for achieving efficient and reasonable route planning.

[0059] In practical applications, multiple objectives may need to be weighted and combined according to specific circumstances to transform them into a single optimization objective. The determination of weights typically depends on actual needs, such as cable laying budget, construction time, and maintenance frequency. Using this method, this embodiment can flexibly address various optimization requirements, providing strong support for cable route planning and design.

[0060] Step S3: Obtain the cable layout design requirements, take cable weight, proportion, and laying path density as optimization objectives, and construct the corresponding cable path planning model based on the mathematical model.

[0061] When planning cable routes, it is necessary to consider the spatial structure of the cables and related laying process rules. The cables actually laid are cable bundles composed of multiple individual cables bundled together. The total weight of the cables affects the overall weight and center of gravity of the electromechanical products and indirectly reflects the cable length. Considering that all multi-branched cables require clamping and fixing during manufacturing, bundling the cables can effectively reduce the number of clamping devices needed. A larger proportion of bundled cable sections facilitates bundling manufacturing and reduces the workload and difficulty of laying during actual installation; therefore, ensuring a certain proportion of bundled cable sections is essential.

[0062] Specifically, this embodiment of the invention obtains the cable layout design requirements, takes cable weight, proportion, and laying path density as optimization objectives, and constructs a corresponding cable path planning model based on a mathematical model, as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] In the formula, The objective function representing the cable weight is... The objective function represents the proportion of bundled cables. This represents the objective function for determining the density of the laying path. This indicates a wall-hugging constraint, meaning the path is attached to the wall. Conversely, if the value is not zero, the path does not pass through the high-temperature area. Conversely, if the value is not zero, the path does not pass through a strong electromagnetic region. Conversely, if the value is not 0, the port extension meets the requirements. Conversely, it is not 0. To ensure the path meets the minimum bending radius requirement, For the number of branch points, This refers to the number of terminals. This is a cable layout scheme.

[0070] In one embodiment of the present invention, regarding the objective function for cable weight, the cable weight includes the weight of bundled cables and the weight of discrete cables. Using the branch information traversed by a single cable, the type and quantity of cables contained in each bundle segment are calculated. Based on the length of each bundle segment, the total weight of the bundled cables is calculated using the following formula:

[0071] In the formula, Indicates the number of bundled sections. Indicates the length of the bundled section. This refers to the number of cables included in the bundle section. For the first section of the binding Linear density of the cable; The weight of the discrete cables is calculated using the cable type and length of each discrete cable:

[0072] In the formula, Indicates the number of discrete cables. For the length of the discrete cable, Indicates the first Linear density of a cable.

[0073] In another embodiment of the present invention, regarding the objective function for the proportion of bundled cables, the corresponding objective function is calculated based on the number and length of the bundled sections and the number of cables in the bundled sections:

[0074] Regarding the objective function for dense path laying:

[0075]

[0076] In the formula, Indicates the number of node units through which the cable passes. For dense space of nodes; Indicates the number of adjacent node elements. Indicates the volume of the unit.

[0077] Step S4: Introduce economic indicators, construct the corresponding path planning model based on the objective function and economic indicators, and use the path planning model to realize distributed planning of cable paths.

[0078] In this embodiment, the main purpose of step S4 is to introduce economic indicators, combine them with the objective function, construct an economic optimization model for cable route planning, and realize distributed planning of cable routes based on this model. Specifically, step S4 defines a new objective function to incorporate the economic factors of cable routes into the optimization considerations, thereby optimizing the cable route laying scheme while meeting engineering constraints, achieving the goal of reducing costs and improving efficiency.

[0079] In cable routing planning, the economic indicators primarily focus on the cost of cable laying, including material costs, construction costs, and maintenance costs. In this step, we quantify the economic performance of cable routing planning by establishing an objective function that incorporates these economic factors.

[0080] The objective function can be defined as:

[0081] in, Indicates the path planning length. Indicates the cross-sectional area of ​​the cable. The function representing the unit price of cables. Indicates the average slope of the path. This represents the slope cost coefficient. Indicates the average maintenance time; This represents the cost of cable installation per unit length. This represents the average maintenance cost.

[0082] The objective function described above demonstrates that the model comprehensively considers multiple economic factors, including the direct costs of the cable (materials and installation), additional construction costs (slope impact), and long-term operating costs (maintenance expenses). This objective function not only considers the initial costs of cable installation but also the long-term maintenance costs, thus achieving full life-cycle economic optimization of the cable route.

[0083] Introducing economic indicators is crucial for optimizing cable route planning models. By quantifying factors such as route length, cross-sectional area, and slope, economic indicators help designers and engineers find the optimal cable route layout while considering costs and construction difficulties. Especially for large-scale cable laying systems, the introduction of economic indicators can significantly reduce the overall project cost and improve resource utilization efficiency.

[0084] In summary, this application's embodiments, by introducing economic indicators, construct a comprehensive optimization model based on an objective function that includes path planning, construction costs, and maintenance costs, providing a more scientific and accurate decision-making basis for cable path planning. Furthermore, the application of distributed path planning methods enables efficient and flexible cable path planning optimization even in large-scale systems.

[0085] To achieve the above embodiments, the present invention also proposes a distributed multi-objective path planning system. Figure 2 This is a schematic diagram of a distributed multi-objective path planning system provided as an embodiment of the present invention. Figure 2 As shown, the device includes: The first module is used to transform the physical space to be laid in the cable into a geometric space, divide the geometric space into uniform or non-uniform discrete units, process the units using a grid algorithm, divide the laying space equally along the three coordinate axes, and construct the corresponding spatial model based on the positional relationship between each grid point and the product components. The second module is used to transform the objective function for different types of targets in the cable path, converting the multi-objective optimization problem into a general mathematical model; The third module is used to obtain the cable layout design requirements, taking cable weight, proportion, and laying path density as optimization objectives, and constructing the corresponding cable path planning model based on the mathematical model. The fourth module is used to introduce economic indicators, construct the corresponding path planning model based on the objective function and economic indicators, and use the path planning model to realize distributed planning of cable paths.

[0086] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0087] To implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0088] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0089] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0090] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0091] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0092] This invention is intended to provide implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0093] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0094] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0095] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0097] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0099] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0100] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0101] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A distributed-based multi-objective path planning method, characterized by, The method comprises the following steps: a physical space to be laid in the cable is converted into a geometric space, the geometric space is divided into uniform or non-uniform discrete units, the units are processed by using a grid algorithm, the laying space is equally divided along three coordinate axes, and a corresponding space model is constructed according to the positional relationship between each grid point and a product part; a target function for different types of targets in the cable path is converted, and a multi-target optimization problem is converted into a general mathematical model; layout design requirements of the cable are obtained, the cable weight, proportion, and laying path density are taken as optimization targets, and a corresponding cable path planning model is constructed based on the mathematical model; economic indexes are introduced, a corresponding path planning model is constructed according to the target function and the economic indexes, and distributed planning of the cable path is realized by using the path planning model.

2. The method of claim 1, wherein, The target function for different types of targets in the cable path is converted, and a multi-target optimization problem is converted into a general mathematical model, comprising: where is a decision vector in a decision space which is transformed into a target space of dimensionality by a mapping vector , is the number of targets, , , is the number of inequality and equality constraints, respectively, and when , both are zero, the above problem is transformed into a multi-objective optimization problem without constraints.

3. The method of claim 2, wherein, layout design requirements of the cable are obtained, the cable weight, proportion, and laying path density are taken as optimization targets, and a corresponding cable path planning model is constructed based on the mathematical model, comprising: In the formula, represents the cable weight target function, represents the cable bundling proportion target function, represents the laying path density target function, represents the wall adhesion constraint, the path adheres to , otherwise 0, the path does not pass through a high-temperature area , otherwise 0, the path does not pass through a strong electromagnetic area , otherwise 0, the port extension meets the requirements , otherwise 0, is the path that meets the minimum bending radius requirement, is the number of branch points, is the number of terminal connections, is the cable layout scheme.

4. The method of claim 3, wherein, Further comprising: Regarding the cable weight target function, the cable weight includes bundled cable weight and discrete cable weight, the type and quantity of the cable contained in each bundled section are calculated through branch information passed by a single cable, and the total weight of the bundled cable is calculated according to the length of each bundled section according to the following formula: wherein denotes the number of bundling sections, denotes the length of the bundling section, is the number of cables contained in the bundling section, is the linear density of the cable of the i-th cable in the bundling section; The weight of the discrete cable is calculated by using the type and length of each discrete cable: wherein represents the number of discrete cables, is the length of the discrete cable, represents the linear density of the cable.

5. The method of claim 4, wherein, Further comprising: Regarding the bundled cable proportion target function, the number and length of the bundled sections and the number of cables in the bundled sections are used to calculate the corresponding proportion target function: Regarding the laying path density target function: wherein represents the number of cable passing node units, is a dense space of nodes; represents the number of adjacent node units, represents the volume of the unit.

6. The method of claim 5, wherein, Economic indexes are introduced, a corresponding path planning model is constructed according to the target function and the economic indexes, and distributed planning of the cable path is realized by using the path planning model, comprising: wherein, represents a path planning length, represents a cable cross-sectional area, represents a cable unit price function, represents a path average slope, represents a slope cost coefficient, represents an average maintenance time; represents a cable installation construction cost per unit length, represents an average maintenance cost.

7. A distributed-based multi-objective path planning system, comprising: Comprising: A first module is configured to convert a physical space to be laid in the cable into a geometric space, divide the geometric space into uniform or non-uniform discrete units, process the units by using a grid algorithm, equally divide the laying space along three coordinate axes, and construct a corresponding space model according to the positional relationship between each grid point and a product part; A second module is configured to convert a target function for different types of targets in the cable path, and convert a multi-target optimization problem into a general mathematical model; A third module is configured to obtain layout design requirements of the cable, take the cable weight, proportion, and laying path density as optimization targets, and construct a corresponding cable path planning model based on the mathematical model; A fourth module is configured to introduce economic indexes, construct a corresponding path planning model according to the target function and the economic indexes, and realize distributed planning of the cable path by using the path planning model.

8. An electronic device, comprising: Comprising: a processor, and a memory connected with the processor in communication; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to realize the method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer-executable instructions which, when executed by a processor, implement the method of any one of claims 1-6.

10. A computer program product, characterised in that, A computer program which, when executed by a processor, implements the method of any one of claims 1-6.

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