New energy vehicle path planning method based on hierarchical contraction algorithm

Abstract

The invention discloses a new energy automobile path planning method based on a hierarchical contraction algorithm, and relates to the field of path planning. The method comprises the following steps: receiving a CH graph, vehicle endurance information and charging node information; according to the vehicle endurance information and the charging node information, path query about the charging nodes is carried out on the CH graph, and a CHChargeStation graph is generated; receiving an initial position node and an end position node; and determining a path based on a bidirectional Dijkstra algorithm by taking the starting position node and the ending position node as source points and combining a CH graph, a CHChargeStation graph and psychological threshold constraints of a user on electric quantity, and performing charging event planning according to the determined path. Compared with the prior art, the method is low in time complexity and high in path query speed, and fully considers the psychological threshold demand of the user for the remaining electric quantity of the automobile.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and more specifically, to a new energy vehicle path planning method based on a hierarchical contraction algorithm. Background Art

[0002] Against the backdrop of rising global environmental awareness and energy structure transformation, the development of new energy vehicles has received widespread attention. At the user level, how to travel smoothly has become the focus.

[0003] On the one hand, compared with traditional cars, the battery life of new energy vehicles is an issue that cannot be ignored, especially during long-distance driving, which often requires multiple charging. On the other hand, the uneven distribution of charging facilities today, or the battery life anxiety caused by car owners on the road due to concerns about insufficient power, are difficult problems encountered in route planning. When users use most navigation software to plan their own itineraries, they usually only get the route with the shortest driving distance or driving time. Where to charge and the charging level on the way still need to be carefully considered, which consumes a lot of energy and has a poor user experience. Summary of the invention

[0004] In order to overcome the defect of poor user experience described in the above-mentioned prior art, the present invention provides a new energy vehicle path planning method based on a hierarchical contraction algorithm.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a new energy vehicle path planning method based on a hierarchical contraction algorithm comprises:

[0007] Receiving a CH graph, vehicle endurance information, and charging node information; wherein the CH graph is a weighted directed graph generated on the original road network graph based on a hierarchical contraction algorithm;

[0008] According to the vehicle endurance information and charging node information, a path query about the charging node is performed on the CH graph to generate a CH_ChargeStation graph;

[0009] Receive the starting location node and the ending location node;

[0010] Taking the starting location node and the ending location node as source points, combining the CH graph, the CH_ChargeStation graph and the user's psychological threshold constraint on the power, the path is determined based on the bidirectional Dijkstra algorithm, and the charging event planning is performed according to the determined path.

[0011] In a second aspect, a computer program product comprises a computer program or computer executable instructions, wherein when the computer program or computer executable instructions are executed by a processor, the method described in the first aspect is implemented.

[0012] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0013] The present invention discloses a new energy vehicle path planning method based on a hierarchical contraction algorithm. The original road network diagram is preprocessed by the hierarchical contraction algorithm to create multiple shortcuts to form a CH diagram, which is convenient for improving the speed in the process of querying the path. At the same time, the vehicle endurance information and the user's psychological threshold constraint on the power are introduced, and the charging event planning is performed after the path is determined based on the bidirectional Dijkstra algorithm, so that the user experience is friendly. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a new energy vehicle path planning method based on a hierarchical contraction algorithm in Example 1 of the present application;

[0015] Figure 2 This is a schematic diagram of the process of generating the CH map in Example 1 of the present application. DETAILED DESCRIPTION

[0016] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged in appropriate cases, which is only to describe the distinction mode adopted by the objects of the same attributes in the embodiments of the present application when describing. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment containing a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment. The term "determine" widely covers various actions, and may include acquisition, calculation, calculation, processing, derivation, investigation, search (for example, search in a table, database or other data structure), ascertainment, and similar actions, and may also include reception (for example, receiving information), access (for example, accessing data in a memory) and similar actions, and may also include generation, creation, establishment and similar actions, as well as parsing, selection, selection and similar actions, etc. The relevant definitions of other terms will be given in the following description.

[0017] It should be noted that when an element is considered to be "connected" to another element, it can be directly connected to the other element, or connected to the other element through an intermediate element. In addition, the "connection" in the following embodiments should be understood as "electrical connection", "communication connection", etc. if there is transmission of electrical signals or data between the connected objects.

[0018] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0019] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0020] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0021] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0022] Example 1

[0023] This embodiment provides a new energy vehicle path planning method based on a hierarchical contraction algorithm. Figure 1 ,include:

[0024] Receive a CH (Contraction Hierarchies) graph, vehicle range information, and charging node information; wherein the CH graph is a weighted directed graph generated on the original road network graph based on a hierarchical contraction algorithm;

[0025] According to the vehicle endurance information and charging node information, a path query about the charging node is performed on the CH graph to generate a CH_ChargeStation graph;

[0026] Receive the starting location node and the ending location node;

[0027] Taking the starting location node and the ending location node as source points, combining the CH graph, the CH_ChargeStation graph and the user's psychological threshold constraint on the power, the path is determined based on the bidirectional Dijkstra algorithm, and the charging event planning is performed according to the determined path.

[0028] It should be noted that the time complexity of directly executing the shortest path search algorithm (such as the Dijkstra algorithm) on a complex road network diagram is often unacceptable. In this embodiment, the original road network diagram is preprocessed by a hierarchical contraction algorithm to create multiple shortcuts to form a CH diagram, which is convenient for increasing the speed of querying the path and has low time complexity; at the same time, the vehicle endurance information and the user's psychological threshold constraint on the power are introduced, and the charging event planning is performed after the path is determined based on the bidirectional Dijkstra algorithm, which provides a user-friendly experience.

[0029] In some preferred embodiments, the generation of the CH graph is described in Figure 2 ,include:

[0030] Initialize the contraction status, importance and level of all nodes on the original road network diagram, and establish a priority queue to record the node numbers and corresponding importance of all nodes;

[0031] Traversing the top nodes in the priority queue, that is, the nodes with the smallest importance, and recording them as the first top nodes, until the priority queue is empty: popping the first top node out of the priority queue, updating the importance of the first top node, and determining the contraction result of the corresponding first top node according to the updated importance and updating the priority queue;

[0032] When all nodes in the priority queue are shrunk, the original road network graph with shortcuts added is used as the CH graph according to the shrinkage result, and the direction of the edge is always from the node with less importance to the node with greater importance.

[0033] It should be understood that when searching on the CH graph, the search will always be conducted from nodes with less importance to nodes with greater importance, and when the intersection is found, the shortest path is obtained.

[0034] In some optional embodiments, determining the contraction result of the corresponding first top-level node according to the updated importance and updating the priority queue includes:

[0035] Re-determine a top-level node in the priority queue after the first top-level node is popped out, and record it as a second top-level node;

[0036] Calculating a difference between the updated importance of the first top-level node and the importance of the second top-level node;

[0037] Determine whether to shrink the first top-level node based on whether the difference is less than or not greater than a preset value: if the first top-level node is shrunk, a check witness is performed during the shrinking process, and the hierarchy of the neighboring nodes of the first top-level node is updated; otherwise, the node number of the first top-level node and the updated importance are recorded in the priority queue.

[0038] In some specific implementations, when a node x is shrunk, the level of its neighbor node y needs to be updated. The updating process is as follows: t updated level = max (original level of y, level of x + 1).

[0039] In some specific implementations, the preset value used for comparison with the difference is 10.

[0040] Furthermore, the inspection witness includes:

[0041] Traverse the neighbor nodes u of the first top-level node x:

[0042] Taking node u as the source point, the shortest path from node u to another neighbor node v is calculated based on the Dijkstra algorithm. When the shortest path does not exist and does not need to pass through the first top-level node x, a shortcut uv is added to the original road network graph.

[0043] It should be noted that u and v are both adjacent points of x, and u, v, and x are not compressed. Only when there is no path that does not pass through x and the path length is less than the length of uxv, can a shortcut uv be established, and its length is the length of uxv, that is, the shortcut uv is the shortest path from u to v.

[0044] It can be understood that the CH graph is equivalent to the original road network graph with shortcuts added.

[0045] Furthermore, the update formula of the importance is:

[0046] Importance = edge difference + 2 × number of shrinking neighbors + level

[0047] Where, edge difference = number of shortcuts added - number of original edges removed.

[0048] In some optional embodiments, the initialization process includes:

[0049] Initialize the contraction states of all nodes to uncontracted, the levels to 0, and the importances to -n, where n represents the number of nodes;

[0050] The importance and the node number of the node are put into the priority queue as a single element in the form of a tuple.

[0051] In some preferred embodiments, the vehicle range information includes a vehicle maximum mileage constraint C; and,

[0052] The performing of path query on the charging node on the CH graph includes:

[0053] Determine the charging node on the CH graph according to the charging node information;

[0054] Traversing the charging nodes: taking the charging nodes as source points and target points respectively, and combining the vehicle maximum mileage constraint to respectively determine the passable results between the charging nodes and other nodes on the CH graph; wherein the passable results are reachable without charging, reachable with charging, or disconnected, and, when the passable result between the charging node and any node on the CH graph is reachable with charging, recording the corresponding edge and weight as the first record;

[0055] Integer traverses the vehicle mileage constraint interval [1, C] and performs path queries between all charging nodes: when the road between two points is not broken and the distance is less than or equal to the current maximum mileage, the corresponding edge and weight are recorded as the second record;

[0056] According to the second record, a corresponding CH_ChargeStation graph containing only charging nodes is generated for all mileages within the vehicle mileage constraint interval [1, C].

[0057] It should be understood that the next node visited by the vehicle should be within the reachable range of the vehicle when it is at the current node, that is, the distance between the two nodes must not be greater than the maximum mileage constraint of the vehicle.

[0058] In some optional embodiments, the vehicle endurance information also includes the vehicle initial power; and,

[0059] The method combines the CH graph, the CH_ChargeStation graph and the user's psychological threshold constraint on the amount of electricity, and determines the path based on the bidirectional Dijkstra algorithm, including:

[0060] Combined with the vehicle maximum mileage constraint, the passable result from the starting position node s to the ending position node t is determined on the CH graph:

[0061] If the passable result from the starting location node s to the ending location node t is reachable without charging, then determine whether the remaining power of the vehicle when it arrives at the ending location node still meets the user's psychological threshold constraint on power according to the initial power of the vehicle, the maximum mileage constraint of the vehicle, and the user's psychological threshold constraint on power: if so, determine the path based on the bidirectional Dijkstra algorithm on the CH graph; otherwise, update the passable result from the starting location node s to the ending location node t to reachable with charging;

[0062] If the traversability result from the starting location node s to the ending location node t is that charging is required, a path is determined according to the charging node information and the CH_ChargeStation graph;

[0063] If the traversable result from the starting location node s to the ending location node t is a broken circuit, the broken circuit is returned.

[0064] Further, determining a path according to the charging node information and the CH_ChargeStation graph includes:

[0065] Determine whether the starting location node s and the ending location node t are charging nodes according to the charging node information, traverse the first k shortest paths and add them to the charging path set to be planned for charging:

[0066] If the starting location node s and the ending location node t are both charging nodes, a path st is determined based on the first record and the second record; and a three-stage planning is performed on the CH_ChargeStation graph: a first relay charging node x is traversed with s as the center point, and a second relay charging node y is traversed with t as the center point, and a first sub-path sx, a second sub-path xy and a third sub-path yt are determined based on the first record and the second record, and the path sxyt is obtained by traversing the found path st and the path sxyt determines the first k shortest paths; if the starting position node s is a common node and the ending position node t is a charging node, two-stage path planning is performed: according to the first record and the second record, combined with the vehicle maximum mileage constraint and the user's psychological threshold constraint on the power, a first relay charging node x is traversed with s as the center point, and the first sub-path sx and the second sub-path xt are determined based on the first record and the second record, and the path sxt is obtained by splicing; and the first k shortest paths are determined by traversing the found path sxt;

[0067] If the starting location node s is a charging node and the ending location node t is a common node, two-stage path planning is performed: according to the first record and the second record, combined with the maximum mileage constraint of the vehicle and the psychological threshold constraint of the user on the power, a first relay charging node x is traversed with t as the center point, and the first sub-path sx and the second sub-path xt are determined based on the first record and the second record, respectively, and the path sxt is obtained by splicing; and the first k shortest paths are determined by traversing the found path sxt;

[0068] If both the starting position node s and the ending position node t are ordinary nodes, then a three-stage path planning is performed: According to the first record and the second record, combined with the vehicle's maximum mileage constraint and the user's psychological threshold constraint on the battery level, traverse and search for a first relay charging node x with s as the center point, and traverse and search for a second relay charging node y with t as the center point in combination with the vehicle's maximum mileage constraint and the user's psychological threshold constraint on the battery level. Then, determine the first sub-path s-x, the second sub-path x-y, and the third sub-path y-t based on the first record and the second record respectively, and splice them to obtain the path s-x-y-t; and, traverse the found path s-x-y-t to determine the top k shortest paths.

[0069] In some specific implementation processes, a queue with a large top heap structure is used to traverse and determine the top k shortest paths: Initialize the maximum queue size k of the queue with the large top heap structure; Traverse the reachable paths obtained: When the current queue size size of the queue < k, directly push the currently traversed reachable path into the queue; When size = k, take out the top element of the queue and compare it with the currently traversed reachable path, and push the element with a smaller element value (i.e., path distance or travel time) into the queue. Finally, the weights weight of the paths in the queue are arranged from small to large.

[0070] It should be understood that the value of k can be set by those skilled in the art according to the actual situation. Exemplarily, it is dynamically set according to the number of reachable paths.

[0071] It should be understood that traversing and searching for a first relay charging node x in combination with the vehicle's maximum mileage constraint and the user's psychological threshold constraint on the battery level means traversing the charging nodes around the ordinary nodes.

[0072] It should also be understood that in the three-stage planning, the first relay charging node x and the second relay charging node y may coincide or may not coincide.

[0073] As a possible implementation manner, the charging time planning includes:

[0074] When the set R of charging paths for the charging plan to be made is a non-empty set:

[0075] Respectively determine the travel time, vehicle energy consumption of the top k shortest paths in the set of charging paths for the charging plan to be made, and the node power of the charging nodes on the shortest paths. Use the starting position node, the ending position node, and the charging nodes on the shortest paths as nodes, and use the current node V i The charging time required to travel to the charging node S within the reachable charging node range i As the charging time from V i To S iThe weight of the unidirectional edge is constructed, and a unidirectional graph is obtained by traversing each edge on the unidirectional graph, and the path with the shortest charging time from the starting position node to the ending position node at the charging node and the corresponding overall charging time are obtained; and the total time of the shortest path, that is, the sum of the driving time and the overall charging time, is recorded as the first total time, a variable is set to record the minimum total time min_t, that is, the minimum first total time, and the shortest path corresponding to the minimum total time min_t is recorded as the planned path; wherein the charging time is determined according to the initial power of the vehicle, the energy consumption of the vehicle and the charging power;

[0076] When the charging path set R receives the newly determined shortest path:

[0077] Determine the travel time of the newly determined shortest path, and when the travel time of the newly determined shortest path is less than the minimum total time, determine the total time of the newly determined shortest path, recorded as the second total time; and when the second total time is less than the minimum total time, replace the planned path with the newly determined shortest path, and replace the minimum total time with the second total time.

[0078] It should be noted that the path finally returned to the user is the planned path.

[0079] As another possible implementation, the charging event planning includes:

[0080] When the charging path set R to be planned is not an empty set:

[0081] Determine the travel time, vehicle energy consumption, and node power of the k shortest paths in the charging path set to be charged, respectively, taking the starting position node, the ending position node, and the charging nodes on the shortest path as nodes, and the current node V i Drive to a charging node S within the reachable charging node range i The charging time required is as follows: i Point to S i The weight of the unidirectional edge is determined, and a unidirectional graph is constructed. By traversing each edge on the unidirectional graph, the path with the shortest charging time from the starting position node to the ending position node at the charging node and the corresponding overall charging time are obtained; and the total time of the shortest path, that is, the sum of the driving time and the overall charging time, is recorded as the first total time and updated to the charging path set R to be charged, and the charging path set R to be charged is constructed as a path set R of a max-heap structure that has been charged. ′; Wherein, the charging time is determined according to the initial power of the vehicle, the energy consumption of the vehicle and the charging power, and the large top stack structure is determined according to the first total time;

[0082] When the charging path set R ′ When receiving a newly determined shortest path:

[0083] The charging path set R ′ The shortest path at the top of the heap is recorded as the top element, and the corresponding first total time is recorded as the top total time. When the travel time of the newly determined shortest path is less than or not greater than the top total time, the total time of the newly determined shortest path is determined and recorded as the second total time; and when the second total time is less than the top total time, the top element is replaced by the newly determined shortest path, the top total time is replaced by the second total time, and the path set R of the charged plan is reconstructed. ′ It should be emphasized that in this embodiment, the first k shortest paths added to the charging path set to be charged are determined based on the path distance, while in the charging event planning, the path set R of the charging plan is ′ The shortest path in is determined based on the total time (ie, the first total time).

[0084] It should be noted that the charging path set R ′ The number of elements in (i.e., the number of paths) may be the same as the number of elements in the charging path set R to be charged, or may be different (e.g., R ′ The number of elements in is less than the number of elements in R).

[0085] It should be understood that In some specific implementations, the travel time may be determined based on a preset constant speed, or may be determined based on obtaining an average speed of the determined path. It should be understood that when the determined path contains multiple sub-paths, the constant speed for each sub-path may be the same or different, and when the travel time is determined based on the average speed, it should be determined based on the corresponding average speed of each sub-path.

[0086] In some specific implementations, the vehicle energy consumption may be determined based on path distance, road grade, and traffic congestion delay index (reflecting the degree of congestion).

[0087] As a non-limiting example, vehicle energy consumption = a*path distance*road grade*traffic congestion delay index, where a is a positive proportional factor.

[0088] As a non-limiting example, the road grade may determine the corresponding quantized value according to the road attributes of the corresponding path (such as national roads, urban roads), and may also determine the quantized value according to the average communication speed when the road is unobstructed.

[0089] It should be noted that the charging power described in this embodiment can be a constant power or a power set under a charging management strategy (such as when the vehicle power is greater than 80% of its battery capacity, the charging power is gradually reduced linearly to protect the health of the vehicle battery).

[0090] In some specific implementations, the vehicle charging strategy is specified as follows: when a vehicle chooses to charge at a charging node, its power level when leaving is 100%. The planned path meets the condition that there is no time on the path when the vehicle's power level is lower than the user's psychological threshold.

[0091] Example 2

[0092] This embodiment provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor, so that the processor executes part or all of the steps of the method provided in Example 1 of the present application.

[0093] It is understood that the storage medium may be transient or non-transient. Exemplarily, the storage medium includes, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0094] Exemplarily, the processor may be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA).

[0095] Exemplarily, the read-only memory includes but is not limited to MASK ROM, PROM, EPROM, EEPROM, Flash, etc.

[0096] Exemplarily, the random access memory includes but is not limited to DRAM, SRAM, SDRAM, DDR SDRAM, etc.

[0097] In some examples, a computer program product is provided, which can be implemented in hardware, software or a combination thereof. As a non-limiting example, the computer program product can be embodied as the storage medium, or as a software product, such as an SDK (Software Development Kit).

[0098] As a non-limiting example, a computer program product is provided, the computer program product includes a computer program or a computer executable instruction, the computer program or the computer executable instruction is stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or the computer executable instruction from the computer-readable storage medium, and the processor executes the computer executable instruction, so that the electronic device performs some or all steps of the method described in the embodiment of the present application.

[0099] In some examples, a computer program is provided, comprising a computer-readable code, and when the computer-readable code is run in a computer device, a processor in the computer device executes a part or all of the steps in the method.

[0100] This embodiment also proposes an electronic device, including a memory and a processor, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and when the processor executes the at least one instruction, at least one program, a code set or an instruction set, it implements part or all of the steps of the method described in Example 1.

[0101] In some examples, a hardware entity of the electronic device is provided, including: a processor, a memory and a communication interface; wherein the processor generally controls the overall operation of the electronic device; the communication interface is used to enable the electronic device to communicate with other terminals or servers through a network; the memory is configured to store instructions and applications executable by the processor, and can also cache data to be processed or processed by the processor and various modules in the electronic device (including but not limited to image data, audio data, voice communication data and video communication data), which can be implemented by flash memory (FLASH), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or random access memory (RAM).

[0102] The processor may include one or more processing elements. Thus, the processor may include one or more integrated circuits (ICs) configured to perform the functions of the processor. In addition, each integrated circuit may include a circuit (e.g., a first circuit, a second circuit, and other circuits, etc.) configured to perform the functions of the processor.

[0103] Furthermore, data may be transmitted between the processor, the communication interface and the memory via a bus, which may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together.

[0104] It can be understood that the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described again here.

[0105] The same or similar reference numerals correspond to the same or similar components;

[0106] The terms used to describe the positional relationship in the drawings are only used for illustrative purposes and should not be construed as limiting the present application;

[0107] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0108] In different specific implementations, the method or system described in the present application can be implemented in software, hardware or a combination thereof. In addition, the order of the steps of the method can be changed, and various elements can be added, reordered, combined, omitted, modified, etc.

[0109] Obviously, the above-mentioned embodiments of the present application are merely examples for clearly illustrating the present application, and are not intended to limit the implementation methods of the present application, and are not intended to limit the present application. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. Each discrete structure / function module or unit can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part, and the structure and function of the discrete components can be implemented as a combined structure or component. It is not necessary and impossible to enumerate all the implementation methods here. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the claims of the present application.

Claims

1. A new energy vehicle path planning method based on a hierarchical contraction algorithm, characterized in that: include: Receiving a CH graph, vehicle endurance information, and charging node information; wherein the CH graph is a weighted directed graph generated on the original road network graph based on a hierarchical contraction algorithm; According to the vehicle endurance information and charging node information, a path query about the charging node is performed on the CH graph to generate a CH_ChargeStation graph; Receive the starting location node and the ending location node; Taking the starting location node and the ending location node as source points, combining the CH graph, the CH_ChargeStation graph and the user's psychological threshold constraint on the power, the path is determined based on the bidirectional Dijkstra algorithm, and the charging event planning is performed according to the determined path.

2. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 1, characterized in that: The generation of the CH graph includes: Initialize the contraction status, importance and level of all nodes on the original road network diagram, and establish a priority queue to record the node numbers and corresponding importance of all nodes; Traversing the top nodes in the priority queue, that is, the nodes with the smallest importance, and recording them as the first top nodes, until the priority queue is empty: popping the first top node out of the priority queue, updating the importance of the first top node, and determining the contraction result of the corresponding first top node according to the updated importance and updating the priority queue; When all nodes in the priority queue are shrunk, the original road network graph with shortcuts added is used as the CH graph according to the shrinkage result, and the direction of the edge is always from the node with less importance to the node with greater importance.

3. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 2, characterized in that: The step of determining the contraction result of the corresponding first top-level node according to the updated importance and updating the priority queue includes: Re-determine a top-level node in the priority queue after the first top-level node is popped out, and record it as a second top-level node; Calculating a difference between the updated importance of the first top-level node and the importance of the second top-level node; Determine whether to shrink the first top-level node based on whether the difference is less than or not greater than a preset value: if the first top-level node is shrunk, a check witness is performed during the shrinking process, and the hierarchy of the neighboring nodes of the first top-level node is updated; otherwise, the node number of the first top-level node and the updated importance are recorded in the priority queue.

4. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 3, characterized in that: The inspection witnesses include: Traverse the neighbor nodes u of the first top-level node x: Taking node u as the source point, the shortest path from node u to another neighbor node v is calculated based on the Dijkstra algorithm. When the shortest path does not exist and does not need to pass through the first top-level node x, a shortcut uv is added to the original road network graph.

5. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 4, characterized in that: The update formula of the importance is: Importance = edge difference + 2 × number of shrinking neighbors + level Where, edge difference = number of shortcuts added - number of original edges removed.

6. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to any one of claims 1 to 5, characterized in that: The vehicle endurance information includes a vehicle maximum mileage constraint C; as well as, The performing of path query on the charging node on the CH graph includes: Determine the charging node on the CH graph according to the charging node information; Traversing the charging nodes: taking the charging nodes as source points and target points respectively, and combining the vehicle maximum mileage constraint to respectively determine the passable results between the charging nodes and other nodes on the CH graph; wherein the passable results are reachable without charging, reachable with charging, or disconnected, and, when the passable result between the charging node and any node on the CH graph is reachable with charging, recording the corresponding edge and weight as the first record; Integer traverses the vehicle mileage constraint interval [1, C] and performs path queries between all charging nodes: when the road between two points is not broken and the distance is less than or equal to the current maximum mileage, the corresponding edge and weight are recorded as the second record; According to the second record, a corresponding CH_ChargeStation graph containing only charging nodes is generated for all mileages within the vehicle mileage constraint interval [1, C].

7. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 6, characterized in that: The vehicle endurance information also includes the vehicle's initial battery power; and, The method combines the CH graph, the CH_ChargeStation graph and the user's psychological threshold constraint on the amount of electricity, and determines the path based on the bidirectional Dijkstra algorithm, including: Combined with the vehicle maximum mileage constraint, the passable result from the starting position node s to the ending position node t is determined on the CH graph: If the passable result from the starting location node s to the ending location node t is reachable without charging, then determine whether the remaining power of the vehicle when it arrives at the ending location node still meets the user's psychological threshold constraint on power according to the initial power of the vehicle, the maximum mileage constraint of the vehicle, and the user's psychological threshold constraint on power: if so, determine the path based on the bidirectional Dijkstra algorithm on the CH graph; otherwise, update the passable result from the starting location node s to the ending location node t to reachable with charging; If the traversability result from the starting location node s to the ending location node t is that charging is required, a path is determined according to the charging node information and the CH_ChargeStation graph; If the traversable result from the starting location node s to the ending location node t is a broken circuit, the broken circuit is returned.

8. The new energy vehicle path planning method based on the hierarchical contraction algorithm according to claim 7 is characterized in that: The determining a path according to the charging node information and the CH_ChargeStation map includes: Determine whether the starting location node s and the ending location node t are charging nodes according to the charging node information, traverse the first k shortest paths and add them to the charging path set to be planned for charging: If the starting location node s and the ending location node t are both charging nodes, the path st is determined based on the first record and the second record; and a three-stage planning is performed on the CH_ChargeStation graph: a first relay charging node x is traversed with s as the center point, and a second relay charging node y is traversed with t as the center point, and a first sub-path sx, a second sub-path xy and a third sub-path yt are determined based on the first record and the second record, and the path sxyt is obtained by concatenating them; and the first k shortest paths are determined by traversing the found paths st and sxyt; If the starting location node s is a common node and the ending location node t is a charging node, two-stage path planning is performed: according to the first record and the second record, combined with the maximum mileage constraint of the vehicle and the psychological threshold constraint of the user on the power, a first relay charging node x is traversed with s as the center point, and the first sub-path sx and the second sub-path xt are determined based on the first record and the second record, and the path sxt is obtained by splicing; and the first k shortest paths are determined by traversing the found path sxt; If the starting location node s is a charging node and the ending location node t is a common node, two-stage path planning is performed: according to the first record and the second record, combined with the maximum mileage constraint of the vehicle and the psychological threshold constraint of the user on the power, a first relay charging node x is traversed with t as the center point, and the first sub-path sx and the second sub-path xt are determined based on the first record and the second record, respectively, and the path sxt is obtained by splicing; and the first k shortest paths are determined by traversing the found path sxt; If the starting position node s and the ending position node t are both ordinary nodes, three-segment path planning is performed: according to the first record and the second record, combined with the vehicle's maximum mileage constraint and the user's psychological threshold constraint on the amount of electricity, a first relay charging node x is traversed with s as the center point, and a second relay charging node y is traversed with t as the center point in combination with the vehicle's maximum mileage constraint and the user's psychological threshold constraint on the amount of electricity, and the first sub-path sx, the second sub-path xy and the third sub-path yt are determined based on the first record and the second record, and the path sxyt is spliced ​​to obtain; and the first k shortest paths are determined by traversing the found path sxyt.

9. A new energy vehicle path planning method based on a hierarchical contraction algorithm according to claim 8, characterized in that: The charging event planning includes: When the charging path set R to be planned is not an empty set: Determine the travel time, vehicle energy consumption, and node power of the k shortest paths in the charging path set to be charged, respectively, taking the starting position node, the ending position node, and the charging nodes on the shortest path as nodes, and the current node V i Drive to a charging node S within the reachable charging node range i The charging time required is as follows: i Point to S i The weight of the unidirectional edge is constructed, and a unidirectional graph is obtained by traversing each edge on the unidirectional graph, and the path with the shortest charging time from the starting position node to the ending position node at the charging node and the corresponding overall charging time are obtained; and the total time of the shortest path, that is, the sum of the driving time and the overall charging time, is recorded as the first total time, the minimum first total time is recorded as the minimum total time min_t, and the shortest path corresponding to the minimum total time min_t is recorded as the planned path; wherein the charging time is determined according to the initial power of the vehicle, the energy consumption of the vehicle and the charging power; When the charging path set R receives the newly determined shortest path: Determine the travel time of the newly determined shortest path, and when the travel time of the newly determined shortest path is less than the minimum total time, determine the total time of the newly determined shortest path, recorded as the second total time; and when the second total time is less than the minimum total time, replace the planned path with the newly determined shortest path, and replace the minimum total time with the second total time.

10. A computer program product comprising a computer program or computer executable instructions, characterized in that: When the computer program or computer executable instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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