New energy charging pile intelligent scheduling construction method and system based on green energy saving

Abstract

The invention relates to the technical field of scheduling planning, and discloses a new energy charging pile intelligent scheduling construction method and system based on green energy saving. According to the method, geographic position data of a charging pile planning area are collected in real time, the collected geographic position data of the charging pile planning area are processed, after processing is completed, map modeling is carried out based on the obtained processed geographic position data, and the charging pile planning area is obtained based on the obtained map modeling. The new energy charging pile construction position is preliminarily planned through a path planning algorithm, after the preliminary planning is completed, the traffic flow of each road in a charging pile planning area is collected, new energy vehicle data is estimated, and finally, the new energy charging pile is scheduled through an intelligent scheduling algorithm based on the preliminarily planned new energy charging pile construction position and the estimated new energy vehicle data. The new energy charging pile construction positions and number are calculated, and the rationality of intelligent scheduling of the new energy charging piles is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling planning, and specifically to an intelligent scheduling construction method and system for new energy charging piles based on green energy conservation. Background Art

[0002] With the development of new energy technologies, more and more new energy vehicles are put into use. However, due to issues such as the charging duration of new energy vehicles, the loss of charging piles, and the cost of charging piles, it is necessary to configure charging piles that meet the demand at appropriate locations.

[0003] The existing publicly applied patent CN119398399A obtains the usage data of electric vehicles in the target area, conducts predictive analysis of charging demand based on the usage data of electric vehicles to obtain a charging demand prediction model; at the same time, based on the charging demand prediction model, it conducts reasonable analysis according to different traffic flows, different driving patterns, and different charging demands; obtains the geographical information data of the target area; constructs a spatial analysis model based on the geographical information data, and conducts an evaluation of the candidate positions of charging piles according to the charging demand distribution map data, thereby obtaining suitable charging pile positions. However, since this invention only generally formulates the candidate positions of charging piles through a spatial analysis model, it ignores the actual accessibility between vehicles and charging piles, and ignores the loss of charging piles and the cost of the number of charging piles, having great limitations. Summary of the Invention

[0004] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides an intelligent scheduling construction method and system for new energy charging piles based on green energy conservation, which have the advantages of being reasonable, efficient, energy-saving, real-time, etc., and solve the problem of intelligent site selection and configuration of new energy charging piles.

[0005] (II) Technical Solutions To solve the above technical problem of intelligent site selection and configuration of new energy charging piles, the present invention provides the following technical solutions: The present invention discloses an intelligent scheduling construction method for new energy charging piles based on green energy conservation, which specifically includes the following steps: S1. Real-time collect the geographical location data of the charging pile planning area, and process the collected geographical location data of the charging pile planning area to obtain the processed geographical location data; S2. Conduct map modeling based on the obtained processed geographical location data; S3. Based on the obtained map modeling, preliminarily plan the construction positions of new energy charging piles through a path planning algorithm; S4. After the preliminary planning is completed, collect the traffic flow of each road in the charging pile planning area and estimate the new energy vehicle data; S5. Based on the preliminary planned construction locations of new energy charging piles and the estimated data of new energy vehicles, calculate the construction locations and quantities of new energy charging piles through an intelligent scheduling algorithm; S51. Collect the charging data of charging piles with different capacities, and construct a charging pile demand model based on the estimated data of new energy vehicles; S52. Based on the estimated data of new energy vehicles and the constructed charging pile demand model, accurately plan and calculate the construction locations and quantities of new energy charging piles through an intelligent scheduling algorithm.

[0006] In the present invention, geographical location data of the charging pile planning area is collected in real time, and the collected geographical location data of the charging pile planning area is processed. After the processing is completed, map modeling is performed based on the obtained processed geographical location data. Based on the obtained map modeling, the construction locations of new energy charging piles are preliminarily planned through a path planning algorithm. After the preliminary planning is completed, the traffic flow of each road in the charging pile planning area is collected, and the data of new energy vehicles is estimated. Finally, based on the preliminary planned construction locations of new energy charging piles and the estimated data of new energy vehicles, the construction locations and quantities of new energy charging piles are calculated through an intelligent scheduling algorithm, improving the rationality of the intelligent scheduling of new energy charging piles.

[0007] Preferably, the step of collecting the geographical location data of the charging pile planning area in real time and processing the collected geographical location data of the charging pile planning area to obtain the processed geographical location data includes the following steps: Collecting the geographical location data of the charging pile planning area in real time includes: road network nodes and road section data; Set the set of road network nodes in the charging pile planning area: ; where n represents the number of road network nodes, represents the nth road network node, represents the set of road network nodes; Each road network node includes: identification data, latitude data, and longitude data ; where represents the Id identification of the nth road network node, represents the longitude data of the nth road network node, represents the latitude data of the nth road network node; Set the set of road section data in the charging pile planning area as: ; where represents the set of road section data in the charging pile planning area, represents the mth road section data in the charging pile planning area; Road section data It is represented by a triple wherein represents the starting point of the road segment data represents the end point of the road segment data represents the distance of the road segment; Summarize the geographical location data of the charging pile planning area collected in real time for each item, and obtain the processed geographical location data.

[0008] The present invention improves the reliability of geographical location data processing by collecting the geographical location data of the charging pile planning area in real time, processing the collected geographical location data of the charging pile planning area, and summarizing the obtained geographical location data by means of data standardization storage.

[0009] Preferably, the map modeling based on the obtained processed geographical location data includes the following steps: Select the starting point position in the processed geographical location data and connect it to the target point to form a visibility graph; Set that the visibility graph is composed of two sets of road segment data and road network nodes, and use to represent; Take the distance of the road segment in the road segment data as the weight of the road segment data in the visibility graph, and determine the map modeling based on the two sets of road segment data and road network nodes in the visibility graph and the weight of the road segment data.

[0010] Preferably, the preliminary planning of the new energy charging pile construction location based on the obtained map modeling includes the following steps: Set the starting point and the target point, and divide the target point into small target nodes based on the starting point; S31. rasterize the obtained map modeling, and find the shortest path through the A* algorithm according to the coordinate position of the new energy vehicle in the raster space and the coordinate position of the arrival point input by the system; S32. Based on the shortest path from the calculated start node to the target node, set the adjacent shortest distance of the new energy charging pile, and select appropriate nodes based on the set adjacent shortest distance to complete the preliminary planning of the new energy charging pile construction location.

[0011] Preferably, the finding of the shortest path through the A* algorithm according to the coordinate position of the new energy vehicle in the raster space and the coordinate position of the arrival point input by the system includes the following steps: The A* algorithm predicts the shortest path by predicting the cost consumed by the path in the predicted simulation raster; Comprehensive cost The calculation method is as follows: ; wherein Represents the true cost from the current node to the target node, Represents the estimated cost from the current node to the target node; It is set that when the estimated cost of a path is less than or equal to the true cost from the current node to the target node, this path is set as the optimal path; Every time a small target node is reached, the current target node is taken as the current node, and the next small target node is taken as the new target node, and the optimal path is continuously searched by iteration until the last small target node is reached.

[0012] The present invention completes the search for the shortest path of each target point by means of map modeling, setting the starting point and the target point and gradually dividing the path between the set starting point and the target point, and at the same time by rasterizing the obtained map modeling, which improves the effectiveness of the charging pile location planning.

[0013] Preferably, after the preliminary planning is completed, the traffic flow of each road in the charging pile planning area is collected, and the steps for estimating new energy vehicle data include: Set the monitoring interval and record the vehicle data existing in the corresponding section based on the real-time monitoring results; The formula for estimating new energy vehicle data is as follows: ; Wherein, Represents the estimated new energy vehicle data per unit time, Represents time, Represents the number of new energy vehicles passing through the section within the set monitoring interval.

[0014] The present invention improves the efficiency of vehicle data calculation by setting the monitoring interval, recording the vehicle data existing in the corresponding section based on the real-time monitoring results, and calculating the estimated new energy vehicle data per unit time through the number of new energy vehicles passing through the section within the set monitoring interval.

[0015] Preferably, the steps for collecting the charging data of charging piles with different capacities and constructing a charging pile demand model based on the estimated new energy vehicle data include: The charging pile demand model is as follows: ; Wherein, Represents the charging pile demand model, Represents the longest service life of the charging pile, Represents the cost of a single charging pile, Represents the location of the charging pile, Represents the set of preliminary planned new energy charging pile construction locations, , when When it is 1, it means that a charging pile is built at the th charging pile siting location, otherwise it is 0. represents the th charging pile siting location, and represents the maintenance cost of a single charging pile. represents the distance from the i-th area to the th charging pile siting location for charging.

[0016] Preferably, based on the estimated new energy vehicle data and constructing a charging pile demand model, accurately planning and calculating the construction location and quantity of new energy charging piles through an intelligent scheduling algorithm includes the following steps: S521. Initialize the parameters of the particle swarm optimization algorithm; Set that each particle represents a set of data on the construction location and quantity of new energy charging piles, set the population size, the maximum number of iterations , the random position of the particle , the particle velocity and the inertia factor ; S522. Calculate the fitness of each particle; The fitness calculation formula for each particle is as follows: ; Among them, represents the particle fitness, represents the charging pile demand model; S523. Update the best position of a single particle; S524. Update the best position of the population; For each calculated particle, compare the fitness of its current position with the fitness of the best position passed by the particles in its population. If the fitness of the current position is greater than the fitness of the best position passed by the particles in its population, then take the current position as the current best position . If the fitness of the current position is less than or equal to the fitness of the best position passed by the particles in its population, then do not change the current best position ; S525. Update the inertia factor, and update the positions and velocities of all particles based on the updated inertia factor; The inertia factor update formula is as follows: ; Among them, represents the inertia factor at the start of iteration, represents the inertia factor at the end of iteration, represents the current iteration number, Indicates the maximum number of iterations; S526. Repeat steps S52 - S55 until the maximum number of iterations is reached, and output the inertia factor corresponding to the best position; Set the output best position as the calculated location and quantity data of new energy charging piles.

[0017] Preferably, the update of the best position of a single particle includes the following steps: The velocity update formula of a single particle is as follows: ; Wherein, represents the velocity of the particle in the th iteration process, represents the velocity of the particle in the th iteration process, represents the inertia factor, represents the position of the particle in the th iteration process, , represent the acceleration constants, , represent random numbers within the interval [0, 1], represents the personal extreme value of the particle , represents the global extreme value of all particles; The position update formula of a single particle is as follows: ; Wherein, represents the position of the particle in the th iteration process; For each calculated particle, compare the fitness of its current position with the fitness of its passed best position . If the fitness of the current position is greater than the fitness of its passed best position , then take the current position as the current best position . If the fitness of the current position is less than or equal to the fitness of its passed best position , then do not change the current best position .

[0018] By collecting the charging data of charging piles with different capacities, constructing a charging pile demand model based on the estimated new energy vehicle data, and determining the best positions and quantities of each charging pile through the particle swarm optimization algorithm, the present invention improves the real-time performance of intelligent scheduling of new energy charging piles.

[0019] The present invention also discloses an intelligent scheduling construction of a new energy charging pile based on green energy conservation, which is used to implement an intelligent scheduling construction method of a new energy charging pile based on green energy conservation. The system includes: a geographical location acquisition module, a preliminary planning module, a new energy vehicle traffic monitoring module, and a charging pile planning module; The geographical location acquisition module is used to collect and process the geographical location data of the charging pile planning area in real time; The preliminary planning module is used to preliminarily plan the construction location of the new energy charging pile through a path planning algorithm; The new energy vehicle traffic monitoring module is used to collect the traffic flow of each road in the charging pile planning area and estimate the new energy vehicle traffic flow; The charging pile planning module is used to plan the construction of the charging pile according to the preliminary planned construction location of the new energy charging pile and the estimated new energy vehicle data.

[0020] (III) Beneficial effects Compared with the prior art, the present invention provides an intelligent scheduling construction method and system of a new energy charging pile based on green energy conservation, having the following beneficial effects: 1. The invention collects the geographical location data of the charging pile planning area in real time, processes the collected geographical location data of the charging pile planning area. After the processing is completed, map modeling is carried out based on the obtained processed geographical location data, and based on the obtained map modeling, the construction location of the new energy charging pile is preliminarily planned through a path planning algorithm. After the preliminary planning is completed, the traffic flow of each road in the charging pile planning area is collected, and the new energy vehicle data is estimated. Finally, based on the preliminary planned construction location of the new energy charging pile and the estimated new energy vehicle data, through an intelligent scheduling algorithm, the construction location and quantity of the new energy charging pile are calculated, improving the rationality of the intelligent scheduling of the new energy charging pile.

[0021] 2. The invention collects the geographical location data of the charging pile planning area in real time, processes the collected geographical location data of the charging pile planning area, and summarizes the obtained geographical location data through a data standardization storage method, improving the reliability of the geographical location data processing.

[0022] 3. The invention completes the search for the shortest path of each target point by setting an initial point and a target point and gradually dividing the path between the set initial point and target point according to the obtained map modeling, and at the same time by rasterizing the obtained map modeling, improving the effectiveness of the charging pile location planning.

[0023] 4. The invention improves the efficiency of vehicle data calculation by setting a monitoring interval and recording the vehicle data existing in the corresponding section based on the real-time monitoring results, and calculating the estimated new energy vehicle data per unit time through the number of new energy vehicles passing through the section within the set monitoring interval.

[0024] 5. The invention improves the real-time performance of intelligent scheduling of new energy vehicle chargers by collecting the charging data of chargers with different capacities, constructing a charger demand model based on the estimated new energy vehicle data, and determining the optimal positions and quantities of each charger through a particle swarm optimization algorithm based on the constructed charger demand model. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic structural diagram of the intelligent scheduling process of the new energy vehicle charger of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Embodiment 1 Please refer to Figure 1 , this embodiment discloses a construction method for intelligent scheduling of new energy vehicle chargers based on green energy conservation, which specifically includes the following steps: S1. Real-time collect the geographical location data of the charger planning area, and process the collected geographical location data of the charger planning area to obtain the processed geographical location data; S2. Perform map modeling based on the obtained processed geographical location data; S3. Based on the obtained map modeling, preliminarily plan the construction positions of new energy vehicle chargers through a path planning algorithm; S4. After the preliminary planning is completed, collect the traffic flow of each road in the charger planning area and estimate the new energy vehicle data; S5. Based on the preliminarily planned construction positions of new energy vehicle chargers and the estimated new energy vehicle data, calculate the construction positions and quantities of new energy vehicle chargers through an intelligent scheduling algorithm; S51. Collect the charging data of chargers with different capacities, and construct a charger demand model based on the estimated new energy vehicle data; S52. Based on the estimated new energy vehicle data and the constructed charger demand model, accurately plan and calculate the construction positions and quantities of new energy vehicle chargers through an intelligent scheduling algorithm; Further, please refer toFigure 1 Collect the geographical location data of the charging pile planning area in real time, and process the collected geographical location data of the charging pile planning area to obtain the processed geographical location data, including the following steps: Collecting the geographical location data of the charging pile planning area in real time includes: road network nodes and road section data; Set the set of road network nodes in the charging pile planning area: ; Among them, n represents the number of road network nodes, represents the nth road network node, represents the set of road network nodes; Each road network node includes: identification data, latitude data, and longitude data ; Among them, represents the Id identification of the nth road network node, represents the longitude data of the nth road network node, represents the latitude data of the nth road network node; Set the set of road section data in the charging pile planning area as: ; Among them, represents the set of road section data in the charging pile planning area, represents the mth road section data in the charging pile planning area; Road section data is represented by a triple , where represents the starting point of the road section data, represents the end point of the road section data, represents the distance of the road section; Summarize the various real-time collected geographical location data of the charging pile planning area to obtain the processed geographical location data; Furthermore, please refer to Figure 1 , map modeling based on the obtained processed geographical location data includes the following steps: Select the starting point and the target point in the processed geographical location data and connect them to form a visibility graph; Set that the visibility graph is composed of two sets of road section data and road network nodes, and use to represent; Take the distance of the road section in the road section data as the weight of the road section data in the visibility graph, and determine the map modeling based on the two sets of road section data and road network nodes in the visibility graph and the weight of the road section data; Furthermore, please refer to Figure 1, based on the obtained map for modeling, the preliminary planning of the construction locations of new energy charging piles through a path planning algorithm includes the following steps: Set the starting point and the target point, and divide the target point into small target nodes based on the starting point; S31. rasterize the obtained map model, and find the shortest path through the A* algorithm according to the coordinate positions of new energy vehicles in the grid space and the coordinate positions of the arrival points input by the system; The A* algorithm predicts the shortest path by predicting the cost consumed by the path in the simulated grid; Comprehensive cost The calculation method is as follows: ; Among them, represents the real cost from the current node to the target node, represents the estimated cost from the current node to the target node; Set that when the estimated cost of a path is less than or equal to the real cost from the current node to the target node, set this path as the optimal path; Every time a small target node is reached, set the current target node as the current node and the next small target node as the new target node, and continuously iterate to find the optimal path until the last small target node is reached; S32. Based on the shortest path from the calculated start node to the target node, set the adjacent shortest distance of the new energy charging pile, and select appropriate nodes based on the set adjacent shortest distance to complete the preliminary planning of the construction location of the new energy charging pile; Furthermore, please refer to Figure 1 , after the preliminary planning is completed, collect the traffic flow of each road in the charging pile planning area, and estimate the new energy vehicle data, including the following steps: Set the monitoring interval, and record the vehicle data existing in the corresponding section based on the real-time monitoring results; The formula for estimating new energy vehicle data is as follows: ; Among them, represents the estimated new energy vehicle data per unit time, represents time, represents the number of new energy vehicles passing through the section within the set monitoring interval; Furthermore, please refer to Figure 1 , collect the charging data of charging piles with different capacities, and construct a charging pile demand model based on the estimated new energy vehicle data, including the following steps: The charging pile demand model is as follows: ; Among them, Represents the charging pile demand model, Represents the maximum service life of the charging pile, Represents the cost of a single charging pile, Represents the location of the charging pile, Represents the set of preliminary planned construction locations of new energy charging piles, , when is 1, otherwise it is 0, indicating that a charging pile is built at the th charging pile location, Table The number of charging piles built at the th charging pile location, Represents the maintenance cost of a single charging pile, Represents the distance from the ith area to the th charging pile location for charging; Figure 1 Furthermore, please refer to S521. Initialize the parameters of the particle swarm optimization algorithm; Set each particle to represent a set of data on the construction location and quantity of new energy charging piles, set the population size, and the maximum number of iterations , the random position of the particle , the particle velocity and the inertia factor ; S522. Calculate the fitness of each particle; The fitness calculation formula for each particle is as follows: ; Among them, Represents the particle fitness, Represents the charging pile demand model; S523. Update the best position of a single particle; The update formula for the velocity of a single particle is as follows: ; Among them, Represents the particle at the th iteration process, Represents the particle at the th iteration process, Represents the inertia factor, Represents the particle at the th iteration process, , represents the acceleration constant, and represents a random number within the interval [0, 1], represents a particle 's individual extreme value, represents the global extreme value of all particles; The position update formula for a single particle is as follows: ; wherein, represents the position of the particle in the th iteration process; For each calculated particle, compare the fitness of its current position with the fitness of its best position passed through . If the fitness of the current position is greater than the fitness of its best position passed through , then take the current position as the current best position . If the fitness of the current position is less than or equal to the fitness of its best position passed through , then do not change the current best position ; S524. Update of the group best position; For each calculated particle, compare the fitness of its current position with the fitness of the best position passed through by the particles in its population . If the fitness of the current position is greater than the fitness of the best position passed through by the particles in its population , then take the current position as the current best position . If the fitness of the current position is less than or equal to the fitness of the best position passed through by the particles in its population , then do not change the current best position ; S525. Update the inertia factor, and update the positions and velocities of all particles based on the updated inertia factor; The inertia factor update formula is as follows: ; wherein, represents the inertia factor at the start of iteration, represents the inertia factor at the final iteration, represents the current iteration number, represents the maximum iteration number; S526. Repeat steps S52 - S55 until the maximum iteration number is reached, and output the inertia factor corresponding to the best position; Set the output best position as the calculated new energy charging pile construction position and quantity data; Embodiment 2 Please refer to Figure 1 , this embodiment also discloses an intelligent scheduling construction of new energy charging piles based on green energy conservation, which is used to implement the intelligent scheduling construction method of new energy charging piles based on green energy conservation. The system includes: a geographical location acquisition module, a preliminary planning module, a new energy vehicle traffic flow monitoring module, and a charging pile planning module; The geographical location acquisition module is used to collect and process the geographical location data of the charging pile planning area in real time; The preliminary planning module is used to preliminarily plan the construction locations of new energy charging piles through a path planning algorithm; The new energy vehicle traffic flow monitoring module is used to collect the traffic flow of each road in the charging pile planning area and estimate the new energy vehicle traffic flow; The charging pile planning module is used to plan the construction of charging piles according to the preliminary planned construction locations of new energy charging piles and the estimated new energy vehicle data.

[0028] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent scheduling and construction of new energy charging piles based on green energy saving, characterized in that: The following steps are involved: S1. Collecting geographical location data of the charging pile planning area in real time, and processing the collected geographical location data of the charging pile planning area to obtain processed geographical location data; S2. performing map modeling based on the processed geographic location data; S3. Based on the obtained map modeling, the location of the new energy charging pile construction is preliminarily planned through the path planning algorithm; S4. After the preliminary planning is completed, the traffic volume of each road in the charging pile planning area is collected and the data of new energy vehicles is estimated; S5. Based on the preliminary planning of the construction location of new energy charging piles and the estimated new energy vehicle data, the construction location and quantity of new energy charging piles are calculated through the intelligent scheduling algorithm; S51, collecting charging data of charging piles of different capacities, and building a charging pile demand model based on the estimated new energy vehicle data; S52. Based on the estimated new energy vehicle data and the construction of a charging pile demand model, the location and number of new energy charging piles are accurately planned and calculated through an intelligent scheduling algorithm.

2. According to claim 1, a method for intelligent scheduling and construction of new energy charging piles based on green energy saving is characterized in that: The real-time collection of geographical location data of the charging pile planning area and processing the collected geographical location data of the charging pile planning area to obtain the processed geographical location data include the following steps: Real-time collection of geographic location data of the charging pile planning area includes: road network nodes and road section data; Set the road network node set for collecting charging pile planning areas: ; Where n represents the number of nodes in the road network. represents the nth road network node, Represents a set of road network nodes; Each road network node Includes: identification data, latitude data, and longitude data ; in, Indicates the ID of the nth network node. Represents the longitude data of the nth road network node, Represents the latitude data of the nth road network node; The road section data set of the charging pile planning area is set as: ; in, Represents the road segment data set of the charging pile planning area, Represents the data of the mth road section in the charging pile planning area; Road segment data By a triple It indicates that, Indicates the starting point of the road segment data. Indicates the end point of the road segment data. Indicates the distance of the road segment; Summarize the various real-time collected geographic location data of the charging pile planning area to obtain processed geographic location data.

3. According to claim 1, a method for intelligent scheduling and construction of new energy charging piles based on green energy saving is characterized in that: The map modeling based on the processed geographic location data comprises the following steps: Select the starting point and the target point in the processed geographic location data and connect them to form a visible graph; Assume that the visible graph consists of two sets: road segment data and road network nodes. express, Represents a visible graph; The distance of the road section in the road section data is used as the weight of the road section data in the visible graph, and the map modeling is determined based on two sets of the road section data and the road network nodes in the visible graph and the weight of the road section data.

4. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 1 is characterized in that: The preliminary planning of the construction location of the new energy charging piles based on the obtained map modeling by means of a path planning algorithm comprises the following steps: Set the initial point and target point, and divide the target point into small target nodes based on the initial point; S31, rasterizing the obtained map modeling, and finding the shortest path through the A* algorithm according to the coordinate position of the new energy vehicle in the grid space and the coordinate position of the arrival point input by the system; S32. Based on the calculated shortest path from the start node to the target node, set the adjacent shortest distance between new energy charging piles, and select appropriate nodes based on the set adjacent shortest distance to complete the preliminary planning of the construction location of the new energy charging piles.

5. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 4 is characterized in that: The method of finding the shortest path by using the A* algorithm according to the coordinate position of the new energy vehicle in the grid space and the coordinate position of the arrival point input by the system includes the following steps: The A* algorithm predicts the shortest path by predicting the cost consumed by taking a path in a simulated grid; Comprehensive cost The calculation method is as follows: ； in, Indicates the actual cost from the current node to the target node, Indicates the estimated cost from the current node to the target node; When there is a path whose estimated cost is less than or equal to the actual cost from the current node to the target node, this path is set as the optimal path; Every time a small target node is reached, the current target node is used as the current node, and the next small target node is used as the new target node. The optimal path is continuously searched iteratively until the last small target node is reached.

6. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 1 is characterized in that: After the preliminary planning is completed, collecting the traffic volume of each road in the charging pile planning area and estimating the new energy vehicle data includes the following steps: Set monitoring intervals and record vehicle data on the corresponding road section based on real-time monitoring results; The formula for estimating new energy vehicle data is as follows: ； in, Indicates the estimated new energy vehicle data per unit time, Indicates time, Indicates the number of new energy vehicles passing through the road section within the set monitoring interval.

7. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 1 is characterized in that: The collecting of charging data of charging piles of different capacities and building a charging pile demand model based on estimated new energy vehicle data includes the following steps: The charging pile demand model is as follows: ； in, represents the charging pile demand model, Indicates the maximum service life of the charging pile. Represents the cost of a single charging pile, Indicates the location of the charging pile. It indicates the preliminary planning of the location set of new energy charging piles. ,when When it is 1, it means The charging piles are built at the selected location, otherwise it is 0. Table The number of charging piles to be built at each charging pile site selection location, Indicates the maintenance cost of a single charging pile, Indicates the distance from the i-th region to the The distance for charging at the location of each charging station.

8. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 1 is characterized in that: The method of accurately planning and calculating the location and quantity of new energy charging piles based on the estimated new energy vehicle data and the construction of a charging pile demand model through an intelligent scheduling algorithm includes the following steps: S521, initialization of particle swarm optimization algorithm parameters; Set each particle to represent a set of new energy charging pile construction location and quantity data, set the group size, and the maximum number of iterations , random position of particles , particle speed and the inertia factor ; S522, calculating the fitness of each particle; The fitness calculation formula for each particle is as follows: ； in, represents the particle fitness, represents the charging pile demand model; S523, updating the best position of a single particle; S524, updating of the optimal position of the group; For each particle calculated, the fitness of its current position is compared with the best position that the particle in its population has passed. If the fitness of the current position is greater than the best position that the particle in the population has passed through, The current position is taken as the current best position. , if the fitness of the current position is less than or equal to the best position that the particle in its population has passed The fitness of , then the current best position will not be changed ; S525, updating the inertia factor, and updating the positions and velocities of all particles based on the updated inertia factor; The inertia factor update formula is as follows: ； in, represents the inertia factor at the beginning of iteration, represents the inertia factor at the final iteration, Indicates the current iteration number, Indicates the maximum number of iterations; S526, repeating steps S52-S55 until the maximum number of iterations is reached, and outputting the inertia factor corresponding to the optimal position; The optimal output position is set to the calculated new energy charging pile construction location and quantity data.

9. The method for intelligent scheduling and construction of new energy charging piles based on green energy saving according to claim 8 is characterized in that: The updating of the optimal position of a single particle comprises the following steps: The formula for updating the velocity of a single particle is as follows: ； in, Represents particles In the The speed during the iteration, Represents particles In the The speed during the iteration, represents the inertia factor, Represents particles In the The position during the iteration, , is the acceleration constant, , represents a random number in the interval [0,1], Represents particles The individual extreme value of Represents the global extreme value of all particles; The formula for updating the position of a single particle is as follows: ； in, Represents particles In the The position during the iteration; For each particle calculated, the fitness of its current position is compared with the best position it has passed. If the fitness of the current position is greater than the best position it has passed, The current position is taken as the current best position. , if the fitness of the current position is less than or equal to the best position it has passed The fitness of , then the current best position will not be changed .

10. A system for implementing the green and energy-saving new energy charging pile intelligent scheduling construction method according to any one of claims 1 to 9, characterized in that: include: Geographic location acquisition module, preliminary planning module, new energy vehicle flow monitoring module, and charging pile planning module: The geographic location acquisition module is used to collect and process the geographic location data of the charging pile planning area in real time; The preliminary planning module is used to preliminarily plan the construction location of new energy charging piles through a path planning algorithm; The new energy vehicle flow monitoring module is used to collect the vehicle flow of each road in the charging pile planning area and estimate the new energy vehicle flow; The charging pile planning module is used to plan the construction of charging piles based on the preliminary planned new energy charging pile construction locations and estimated new energy vehicle data.

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