Vehicle base distributed heat supply station position optimization method and system

Abstract

The invention discloses a vehicle base distributed heat supply station position optimization method and system, and the method comprises the steps: carrying out the clustering of all building coordinates of a building group in a base, and setting a heat supply station in each cluster for the heat supply of buildings in the cluster; aiming at each cluster, aiming at enabling the sum of the distances from the heat supply station to other buildings in the cluster to reach the minimum value, carrying out optimization solution on the position of the heat supply station, and obtaining the optimal position of the heat supply station; and comparing the total construction cost under the design schemes of different numbers and positions of the heat supply stations, and selecting the scheme with the lowest total cost as the optimal scheme. According to the method, the position and the pipeline of the heat supply station can be directly generated through the designed building coordinates, expert experience is completely separated, and the shortest pipeline network is really realized; the method is good in universality and high in practicability, and has obvious application and popularization values; according to the method, the heat supply station position selection efficiency can be greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle depot heat supply station design, and particularly relates to a vehicle depot distributed heat supply station position optimization method and system. BACKGROUND

[0002] As an important part of urban rail transit, a vehicle depot has a large building scale, a large winter heat supply amount, and a large carbon emission amount generated by heat supply. Therefore, the vehicle depot adopting an energy-saving and low-carbon heat supply scheme is one of main measures for energy saving and carbon reduction of the urban rail transit system.

[0003] A distributed heat supply system is to set heat supply generation and consumption near a user side to fully utilize renewable energy and efficiently and energy-savingly meet the needs of the user and the surrounding area. In the determination of a heat supply scheme, the number and position of heat supply stations are an important influencing factor, and the balance of initial investment of equipment and transmission and distribution and operation cost needs to be considered. In a conventional design, heat supply stations are set in load centers as much as possible, and the distances from the heat supply stations to each heat supply point are ensured to be short as much as possible. In an actual project, selection and comparison are often determined and selected through expert experience, and quantitative comparison optimization is rarely used for site selection and pipe network decision-making. However, for a project with a large area and many heat supply points, it is difficult to determine the design number, position, and line arrangement of the heat supply stations through the method of expert experience. It can be seen that the conventional heat supply station design method is difficult to achieve the best energy-saving and carbon-reducing effect. SUMMARY

[0004] The present application provides a vehicle depot distributed heat supply station position optimization method and system to solve the problem that current vehicle depot heat supply station design relies on artificial expert experience for determination and selection and cannot achieve the best energy-saving and carbon-reducing effect.

[0005] According to a first aspect, a vehicle depot distributed heat supply station position optimization method is provided in an embodiment, and the method comprises:

[0006] S1, obtaining all buildings and building coordinates of a building group in a target vehicle depot;

[0007] S2, clustering all building coordinates of the building group in the depot and obtaining K clusters, one heat supply station is set in each cluster to supply heat to the buildings in the cluster, the number of heat supply stations is K, and the initial value of K is 1;

[0008] S3, for each cluster, optimizing and solving the position of the heat supply station to minimize the sum of distances from the heat supply station to other buildings in the cluster, and obtaining an optimal position of the heat supply station;

[0009] S4, calculating a total construction cost under a design scheme of the current number and position of heat supply stations according to the number of heat supply stations and the optimal position of the heat supply station;

[0010] S5, judging whether the number K of heat supply stations reaches a preset threshold value, if not, adding 1 to K, repeating steps S2 to S4 to obtain multiple heat supply station number and position design schemes, if yes, executing step S6;

[0011] S6, comparing the total construction costs under different heat supply station number and position design schemes, and selecting the scheme with the lowest total cost as the best scheme.

[0012] Further, clustering all building coordinates of the building group in the base and obtaining K clusters, specifically including:

[0013] Using a Gaussian mixture model for clustering:

[0014]

[0015] where λ i is the weight of the i-th class, is the probability density distribution of the i-th class, and the expression is:

[0016]

[0017] where d is the dimension number of the coordinates, and is two-dimensional, is the coordinate of the i-th class building sample point, μ i and Σ i are the mean and variance of the i-th class, respectively.

[0018] Further, the heat supply station position is optimized and solved to achieve the minimum sum of distances from the heat supply station to other buildings in the class, to obtain the optimal heat supply station position, specifically including:

[0019] Using a particle swarm optimization algorithm to optimize and solve the heat supply station position, the steps are as follows:

[0020] Initialization: randomly generate a group of particles in the search space, initialize the position and velocity of the particles;

[0021] Evaluate fitness: calculate the fitness value of each particle according to the objective function;

[0022] Update the position and velocity of the particles:

[0023] Each particle remembers its own past individual best position;

[0024] The entire particle swarm remembers the past global best position of the whole;

[0025] According to the individual best position and the global best position, update the velocity and position of each particle;

[0026] The position and velocity update formula of the particle is as follows:

[0027]

[0028] wherein v i (t) is the velocity of particle i at iteration t, x i (t) is the position of particle i at iteration t, w is the inertia weight, controlling the continuation of velocity, c1 and c2 are acceleration constants, r1 and r2 are random numbers between 0 and 1, is the historical individual best position of the particle, g best is the historical global best position of all particles;

[0029] Iteration: taking the sum of distances from the heat station position to the rest of the buildings as the objective, the position coordinates of the heat station as the candidate solution, carrying out continuous iteration optimization, repeating the evaluation and updating steps until the stopping condition is reached.

[0030] Further, the objective function is:

[0031]

[0032] wherein x p , y p are the initial position coordinates of the particle, x i , y i are the coordinates of the buildings, N is the number of the rest of the buildings, and G represents the sum of distances from the heat station position to the rest of the buildings.

[0033] Further, according to the number of heat stations and the optimal position of the heat station, the total construction cost under the current number and position design scheme of the heat station is calculated, which specifically includes:

[0034] The total cost of the current scheme is obtained by calculating the sum of the construction cost of the heat station and the cost of the heat pipe line:

[0035] Total construction cost = K x X + pipe network length x Y, wherein the pipe network length is the sum of the heat pipe lengths from the heat station to the rest of the buildings, X is the construction cost of a single heat station, and Y is the cost of each meter of pipe.

[0036] Further, the total construction costs under different number and position design schemes of the heat station are compared, and the scheme with the lowest total cost is selected as the best scheme, which specifically includes:

[0037] The number of heat stations, the position of the heat station, and the heat pipe network layout under the best scheme are output.

[0038] According to the second aspect, an embodiment provides a vehicle base distributed heat station position optimization system, which comprises:

[0039] The building coordinate acquisition module is configured to acquire all buildings and building coordinates of a building group in a target vehicle base.

[0040] The clustering module is configured to cluster all building coordinates of the building group in the base and obtain K clusters, one heating station is arranged in each cluster to provide heat for buildings in the cluster, the number of the heating stations is K, the initial value of K is 1, and it is determined whether K reaches a preset threshold value; if K does not reach the preset threshold value, the value of K is increased by 1 and the clustering is performed again.

[0041] The position optimization module is configured to optimize the position of the heating station for each cluster to minimize the sum of distances from the heating station to other buildings in the cluster.

[0042] The cost calculation module is configured to calculate the total construction cost under the current heating station number and position design scheme according to the number of the heating stations and the optimal position of the heating station.

[0043] The optimal scheme determination module is configured to compare the total construction costs under different heating station number and position design schemes, and select a scheme with the lowest total cost as the optimal scheme.

[0044] Further, the cost calculation module is specifically configured to:

[0045] The total cost of the current scheme is obtained by calculating the sum of the construction cost of the heating station and the cost of the heating pipeline route:

[0046] The total construction cost = K × X + the length of the pipeline network × Y, wherein the length of the pipeline network is the sum of the lengths of the heating pipelines from the heating station to the remaining buildings, X is the construction cost of a single heating station, and Y is the cost of each meter of pipeline.

[0047] According to the three aspects, an embodiment provides an electronic device, the device comprising: a processor and a memory;

[0048] The memory is configured to store one or more program instructions;

[0049] The processor is configured to run the one or more program instructions to perform the steps of the vehicle base distributed heating station position optimization method according to any one of the above.

[0050] According to the fourth aspect, an embodiment provides a computer-readable storage medium, the computer-readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the vehicle base distributed heating station position optimization method according to any one of the above.

[0051] The application provides a vehicle base distributed heat supply station position optimization method and system, coordinates of all buildings in a building group in the base are clustered, one heat supply station is arranged in each cluster to supply heat to the buildings in the cluster, the position of the heat supply station is optimized to obtain an optimal position of the heat supply station, the total construction cost under a current heat supply station number and position design scheme is calculated according to the number of heat supply stations and the optimal position of the heat supply station, the total construction cost under different heat supply station number and position design schemes is compared, and the scheme with the lowest total cost is selected as the best scheme. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A flow chart of a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0053] Figure 2 A specific implementation flow chart of a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0054] Figure 3 Building coordinates in a vehicle base in a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0055] Figure 4 A pipe network layout diagram of a single heat supply station to a heat supply point in a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0056] Figure 5 A pipe network layout diagram of two heat supply stations to a heat supply point in a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0057] Figure 6 A pipe network layout diagram of three heat supply stations to a heat supply point in a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0058] Figure 7 Straight line connection heat supply cost in a vehicle base distributed heat supply station position optimization method provided by an embodiment of the application;

[0059] Figure 8The broken line connection heat supply cost in a vehicle base distributed heat supply station position optimization method provided by one embodiment of the present application;

[0060] Figure 9 A logic structure schematic diagram of a vehicle base distributed heat supply station position optimization system provided by one embodiment of the present application. DETAILED DESCRIPTION

[0061] The present application will be further described below in conjunction with the drawings. In different embodiments, similar elements are denoted by similar reference numerals. In the following embodiments, many details are described in order to make the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different cases, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application being overwhelmed by too much description, and it is not necessary to describe these related operations in detail for those skilled in the art, who can fully understand the related operations according to the description in the specification and general technical knowledge in the art.

[0062] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate way to form various embodiments. At the same time, the steps or actions in the method description can also be sequentially adjusted or adjusted in a manner that is obvious to those skilled in the art. Therefore, the order in the specification and the drawings is only for the purpose of clearly describing one embodiment, and does not mean that it is the necessary order, unless otherwise stated that a certain order must be followed.

[0063] The first embodiment of the present application provides a vehicle base distributed heat supply station position optimization method, which will be described in detail below in conjunction with Figure 1 and Figure 2 .

[0064] As shown in Figure 1 , in step S1, all buildings and building coordinates of the building group in the target vehicle base are obtained.

[0065] As shown in Figure 1 , in step S2, all building coordinates of the building group in the base are clustered and K clusters are obtained, and one heat supply station is set in each cluster to supply heat to the buildings in the cluster. The number of heat supply stations is K, and the initial value of K is 1.

[0066] Specifically, the clustering algorithm that can be used includes a Gaussian mixture model (GMM), K-means clustering, fuzzy C-means, Mean Shift, DBSCAN, etc.

[0067] In this embodiment, a Gaussian mixture model is used for clustering.

[0068]

[0069] where λ i is the weight of the i-th class, is the probability density distribution of the i-th class, and the expression is:

[0070]

[0071] where d is the dimension number of the coordinates, and d = 2 in this embodiment, is the coordinate of the i-th building sample point, μ i and Σ i are the mean and variance of the i-th class, respectively.

[0072] In each building group, a heating station is established, and the heating station provides heat to other buildings in the building group.

[0073] As shown in FIG. 3, in step S3, for each cluster, the position of the heating station is optimized to obtain an optimal position of the heating station, with the objective of minimizing the sum of distances from the heating station to other buildings in the class. Figure 1

[0074] Specifically, the optimization algorithm that can be used includes a particle swarm optimization (PSO), an ant colony optimization algorithm, a bacterial foraging algorithm, a firefly algorithm, an artificial fish school algorithm, etc.

[0075] In this embodiment, the particle swarm optimization algorithm is used to select the coordinate of the heating station according to the coordinates of the buildings to be heated, and to determine the pipe network. The position of the heating station is optimized by using the particle swarm optimization algorithm, and the steps are as follows:

[0076] Initialization: A group of particles are randomly generated in the search space, and the position and speed of the particles are initialized.

[0077] Evaluation of fitness: The fitness value of each particle is calculated according to the objective function, and the objective function is:

[0078]

[0079] where x p and y p are the initial position coordinates of the particle, x i and y i ​Let N be the coordinates of the building, N be the number of other buildings, and G be the sum of the distances from the heating station to the other buildings.

[0080] Update the particle's position and velocity:

[0081] Each particle remembers its own best position in the past;

[0082] The entire particle swarm remembers the overall global best position from the past;

[0083] Update the velocity and position of each particle based on its individual best position and the global best position;

[0084] The formulas for updating the particle's position and velocity are as follows:

[0085]

[0086] Among them, v i (t) is the velocity of particle i at iteration t, x i (t) represents the position of particle i at iteration t, w is the inertial weight that controls the continuity of velocity, c1 and c2 are acceleration constants, and r1 and r2 are random numbers between 0 and 1. It is the optimal position of the particle's historical individual, g best It is the best position in the history of all particles;

[0087] Iteration: With the goal of minimizing the sum of the distances from the heating station to the other buildings, the location coordinates of the heating station are used as candidate solutions. The process involves continuous iterative optimization, repeated evaluation and update steps, until the stopping condition is met.

[0088] like Figure 1 As shown, in step S4, the total construction cost under the current design scheme of the number and location of heating stations is calculated based on the number of heating stations and the optimal location of the heating stations.

[0089] In this embodiment, the total cost of the current solution is obtained by calculating the sum of the construction cost of the heating station and the route cost of the heating pipeline:

[0090] Total construction cost = number of heating stations K × X + pipeline network length × Y, where the pipeline network length is the sum of the lengths of the heating pipelines from the heating stations to the other buildings, X is the construction cost of a single heating station, and Y is the cost per meter of pipeline.

[0091] like Figure 1 As shown, in step S5, it is determined whether the number of heating stations K has reached the preset threshold. If it has not reached the threshold, the value of K is incremented by 1, and steps S2 to S4 are repeated to obtain various design schemes for the number and location of heating stations. If the threshold is reached, step S6 is executed.

[0092] The specific implementation process is as follows: assuming that K=1, clustering is performed to obtain a cluster, and the particle swarm algorithm is used for the optimization of the heat supply station and the pipeline in the cluster, and the first optimization is completed; K+1, that is, K=2, clustering is performed to obtain two clusters, and the particle swarm algorithm is used for the optimization of the heat supply station and the pipeline in the two clusters respectively, and the second optimization is completed; K+1, that is, K=3, and the like, until K reaches the target requirement.

[0093] Specifically, if the vehicle base has a specific requirement for the number of heat supply stations, the number of heat supply stations K is determined according to the actual requirement of the vehicle base and clustering is performed. If there is a specific upper limit or lower limit of the number of heat supply stations, the cost of the number of heat supply stations that does not meet the requirement is removed, and then the costs under different numbers of heat supply stations are compared to select the number of heat supply stations and the pipeline network layout with the minimum cost.

[0094] As shown in Figure 1 , in step S6, the total construction cost under different heat supply station number and location design schemes is compared, and the scheme with the lowest total cost is selected as the best scheme.

[0095] Specifically, the number of heat supply stations, the location of the heat supply station and the heat supply pipeline network layout under the best scheme are output.

[0096] The building coordinates in the vehicle base in the embodiment are as shown in Figure 3 . The building groups shown in Figure 3 are clustered, and exemplary, clustered into 1, 2 and 3 respectively. After obtaining the clusters, the sum of the distances from the heat supply station to the heat supply point pipeline network is taken as the objective function by the particle swarm algorithm for each cluster, and the location of the heat supply station is taken as the candidate solution, wherein the connection mode of the heat supply station to the heat supply point pipeline network has a straight line connection and a polyline connection. The final result is as shown in Figure 4 , 5 and 6. The specific heat supply station coordinates and pipeline network length are shown in Table 1 and Table 2. In the embodiment, the calculation cost is shown in Table 3 and Table 4, and the cost chart is shown in 7 and Figure 8 . According to the actual price, the scheme with the minimum cost is selected.

[0097] Table 1: Heat supply station location and pipeline length of straight line connection

[0098]

[0099]

[0100] Table 2: Heat supply station location and pipeline length of polyline connection

[0101]

[0102] Table 3: Heat supply cost of straight line connection

[0103] Pipe length Cost / (ten thousand yuan) Single heating station 7120m X+0.7120Y Two heating stations 5289m 2X+0.4289Y Three heating stations 3357m 3X+0.3357Y

[0104] Table 4 linear connection heat supply cost

[0105] Pipe length Cost / (ten thousand yuan) Single heating station 8977m X+0.8977Y Two heating stations 5623m 2X+0.5623Y Three heating stations 4146m 3X+0.4146Y

[0106] Corresponding to the above disclosed vehicle base distributed heat supply station position optimization method, the embodiment of the application also discloses a vehicle base distributed heat supply station position optimization system, as shown in the figure, which specifically comprises: Figure 9

[0107] The building coordinate acquisition module is configured to acquire all buildings and building coordinates of the building group in the target vehicle base.

[0108] The clustering module is configured to cluster all building coordinates of the building group in the base and obtain K clusters, one heat supply station is arranged in each cluster to supply heat to the buildings in the cluster, the number of heat supply stations is K, the initial value of K is 1, and it is determined whether K reaches a preset threshold value, if not, the value of K is increased by 1 and then re-clustered.

[0109] The position optimization module is configured to optimize the position of the heat supply station for each cluster to minimize the sum of distances from the heat supply station to other buildings in the cluster.

[0110] The cost calculation module is configured to calculate the total construction cost under the current heat supply station number and position design scheme according to the number of heat supply stations and the optimal position of the heat supply station.

[0111] The optimal scheme determination module is configured to compare the total construction costs under different heat supply station number and position design schemes, and select the scheme with the lowest total cost as the optimal scheme.

[0112] Further, the cost calculation module is specifically configured to:

[0113] The total cost of the current scheme is obtained by calculating the sum of the construction cost of the heat supply station and the cost of the heat supply pipeline:

[0114] Total construction cost = K x X + pipeline network length x Y, wherein the pipeline network length is the sum of the lengths of the heat supply pipelines from the heat supply station to the remaining buildings, X is the construction cost of a single heat supply station, and Y is the cost of each meter of pipeline.

[0115] It should be noted that the detailed description of the vehicle base distributed heat supply station position optimization system provided by the embodiment of the application can refer to the related description of the vehicle base distributed heat supply station position optimization method provided by the embodiment of the application, which will not be repeated here.

[0116] ​In addition, the embodiment of the present application further provides an electronic device, which comprises a processor and a memory; the memory is used for storing one or more program instructions; the processor is used for running the one or more program instructions to execute the steps of the vehicle base distributed heat supply station position optimization method according to any one of the above.

[0117] It should be noted that the detailed description of the electronic device provided by the embodiment of the present application can refer to the related description of the vehicle base distributed heat supply station position optimization method provided by the embodiment of the present application, which will not be repeated here.

[0118] In addition, the embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the vehicle base distributed heat supply station position optimization method according to any one of the above.

[0119] It should be noted that the detailed description of the computer readable storage medium provided by the embodiment of the present application can refer to the related description of the vehicle base distributed heat supply station position optimization method provided by the embodiment of the present application, which will not be repeated here.

[0120] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be realized by hardware or by a computer program. When all or part of the functions in the above embodiments are realized by a computer program, the program can be stored in a computer readable storage medium, which can include read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc. The above functions are realized by executing the program by a computer. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above functions are realized.

[0121] In addition, when all or part of the functions in the above embodiments are realized by a computer program, the program can also be stored in a server, another computer, a storage medium such as a disk, an optical disk, a flash disk or a mobile hard disk, and is downloaded or copied into the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above embodiments are realized.

[0122] The above application of specific examples is used to illustrate the present application, which is only used to help understand the present application, and does not limit the present application. For those skilled in the art to which the present application belongs, according to the idea of the present application, a number of simple deductions, deformations or substitutions can be made.

Claims

1. A method for optimizing the location of a distributed heating station in a vehicle depot, characterized in that, The method includes: S1, obtain all buildings and their coordinates within the target vehicle base complex; S2, cluster all building coordinates within the base and obtain K clusters. Set up a heating station in each cluster to provide heating for the buildings in the cluster. The number of heating stations is K, and the initial value of K is 1. S3, for each cluster, optimize the location of the heating station to obtain the optimal location of the heating station by minimizing the sum of the distances from the heating station to other buildings in the same cluster. S4. Calculate the total construction cost under the current design scheme for the number and location of heating stations, based on the number of heating stations and their optimal locations. S5. Determine whether the number of heating stations K has reached the preset threshold. If not, increment K by 1 and repeat steps S2 to S4 to obtain various design schemes for the number and location of heating stations. If the threshold is reached, proceed to step S6. S6. Compare the total construction costs under different design schemes for the number and location of heating stations, and select the scheme with the lowest total cost as the best scheme.

2. The method for optimizing the location of a distributed heating station in a vehicle depot as described in claim 1, characterized in that, Clustering of all building coordinates within the site's building complex yields K clusters, specifically including: Clustering was performed using a Gaussian mixture model: In the formula λ i Let i be the weight of the i-th class. Let i be the probability density distribution of the i-th class, expressed as: In the formula, d represents the dimension of the coordinates, which is two-dimensional. The coordinates of the building sample point of type i, μ i and Σ i Let be the mean and variance of the i-th class, respectively.

3. The method for optimizing the location of a distributed heating station in a vehicle depot as described in claim 1, characterized in that, To minimize the sum of distances from the heating station to other buildings in the same category, the location of the heating station is optimized by solving a solution that yields the optimal location. This optimization includes: The location of the heating station is optimized using the particle swarm optimization algorithm, and the steps are as follows: Initialization: Randomly generate a group of particles in the search space and initialize the position and velocity of the particles; Fitness evaluation: Calculate the fitness value for each particle based on the objective function; Update the particle's position and velocity: Each particle remembers its own best position in the past; The entire particle swarm remembers the overall global best position from the past; Update the velocity and position of each particle based on its individual best position and the global best position; The formulas for updating the particle's position and velocity are as follows: Among them, v i (t) is the velocity of particle i at iteration t, x i (t) represents the position of particle i at iteration t, w is the inertial weight that controls the continuity of velocity, c1 and c2 are acceleration constants, and r1 and r2 are random numbers between 0 and 1. It is the optimal position of the particle's historical individual, g best It is the best position in the history of all particles; Iteration: With the goal of minimizing the sum of the distances from the heating station to the other buildings, the location coordinates of the heating station are used as candidate solutions. The process involves continuous iterative optimization, repeated evaluation and update steps, until the stopping condition is met.

4. The method for optimizing the location of a distributed heating station in a vehicle depot as described in claim 3, characterized in that, The objective function is: Where, x p y p Let x be the initial position coordinates of the particle. i y i Let N be the coordinates of the building, N be the number of other buildings, and G be the sum of the distances from the heating station to the other buildings.

5. The method for optimizing the location of a distributed heating station in a vehicle depot as described in claim 1, characterized in that, Based on the number of heating stations and their optimal locations, calculate the total construction cost under the current design scheme for the number and location of heating stations, specifically including: The total cost of the current plan is obtained by calculating the sum of the construction cost of the heating station and the route cost of the heating pipeline: Total construction cost = number of heating stations K × X + pipeline network length × Y, where the pipeline network length is the sum of the lengths of the heating pipelines from the heating stations to the other buildings, X is the construction cost of a single heating station, and Y is the cost per meter of pipeline.

6. The method for optimizing the location of a distributed heating station in a vehicle depot as described in claim 1, characterized in that, Compare the total construction costs under different design schemes for the number and location of heating stations, and select the scheme with the lowest total cost as the optimal scheme. Specifically, this includes: Output the number of heating stations, their locations, and the layout of the heating pipeline network under the optimal solution.

7. A system for optimizing the location of a distributed heating station in a vehicle depot, characterized in that, The system includes: The building coordinate acquisition module is used to acquire all buildings and their coordinates within the target vehicle base. The clustering module is used to cluster the coordinates of all buildings in the building complex within the site and obtain K clusters. Each cluster is set up with a heating station to provide heating to the buildings in the cluster. The number of heating stations is K, and the initial value of K is 1. It is determined whether K has reached a preset threshold. If not, the value of K is incremented by 1 and then the clustering is performed again. The location optimization module is used to optimize the location of the heating station for each cluster, with the goal of minimizing the sum of the distances from the heating station to other buildings in the cluster, and to obtain the optimal location of the heating station. The cost calculation module is used to calculate the total construction cost under the current design scheme of the number and location of heating stations, based on the number of heating stations and the optimal location of the heating stations. The optimal solution determination module is used to compare the total construction cost under different design schemes for the number and location of heating stations, and select the scheme with the lowest total cost as the optimal solution.

8. The vehicle depot distributed heating station location optimization system according to claim 7, characterized in that, The cost calculation module is specifically used for: The total cost of the current plan is obtained by calculating the sum of the construction cost of the heating station and the route cost of the heating pipeline: Total construction cost = number of heating stations K × X + pipeline network length × Y, where the pipeline network length is the sum of the lengths of the heating pipelines from the heating stations to the other buildings, X is the construction cost of a single heating station, and Y is the cost per meter of pipeline.

9. An electronic device, characterized in that, The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of the method for optimizing the location of a vehicle base distributed heating station as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for optimizing the location of a distributed heating station at a vehicle base as described in any one of claims 1 to 6.

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