A decision-making method, device, system and component for finding an energy replenishment site

Abstract

Embodiments of the present invention relate to a decision-making method, device, system and component for finding an energy replenishment site. The method includes: when a vehicle is replenishing energy, including refueling or charging, sending an energy replenishment information list to a cloud server, the energy replenishment information list including a vehicle-side ID, energy type, real-time remaining planned path information and remaining drivable mileage data; obtaining information of a candidate energy replenishment site pushed by the cloud according to the energy replenishment information list; calculating and generating an energy replenishment positive effect index of the candidate energy replenishment site based on the information of the candidate energy replenishment site; if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indices is greater than a preset deviation threshold, selecting the candidate energy replenishment site with the maximum energy replenishment positive effect index as the target energy replenishment site; if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indices is not greater than the preset deviation threshold, selecting the target energy replenishment site according to a preset priority list.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a decision-making method, device, system and component for finding an energy replenishment site. Background Art

[0002] Currently, when an autonomous vehicle runs out of energy and needs to recharge while driving, it usually searches for nearby gas stations or charging stations based on a real-time map. The vehicle then decides which recharging station to go to based on its own location, direction, and current energy status. The decision is usually based on time priority or distance priority.

[0003] When deciding where to refuel, autonomous vehicles only consider their own status and the locations and types of refueling stations identified on the map. They lack access to the real-time operational status of refueling stations. After selecting a refueling station based on their preferences and arriving, autonomous vehicles often face long queues or unavailable refueling stations due to maintenance. In these situations, autonomous vehicles may need to replan their route and search for other refueling stations, or they may run out of energy to reach the newly planned refueling station, forcing them to wait in line for a long time. Therefore, finding optimal refueling stations and improving refueling efficiency for autonomous vehicles has become an urgent problem that needs to be solved. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and provide a decision-making method, device, system and components for finding energy replenishment sites, so as to solve the problem of low energy replenishment efficiency of autonomous driving vehicles in the existing technology.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present invention provides a decision-making method for finding an energy replenishment site, the decision-making method comprising:

[0006] Sending a list of energy replenishment information generated based on the vehicle's real-time data to the cloud server. The list includes the vehicle ID, energy type, real-time remaining planned route information, and remaining mileage data.

[0007] Obtaining information of a candidate energy replenishment site pushed by the cloud according to the energy replenishment information list, wherein the information of the candidate energy replenishment site includes a site ID, location information, energy replenishment type, site rating information, site vacancy rate, and surrounding service facility information of the candidate energy replenishment site;

[0008] Calculate and generate the energy replenishment positive effect index of the energy replenishment site to be selected according to the information of the energy replenishment site to be selected;

[0009] If the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is greater than the preset deviation threshold, the candidate energy replenishment site with the maximum energy replenishment positive effect index is selected as the target energy replenishment site;

[0010] If the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indices is not greater than the preset deviation threshold, the target energy replenishment site is selected according to the preset priority list.

[0011] In a possible implementation, if the energy replenishment type is electric energy, the information of the site to be replenished further includes information on the type of charging pile of the site to be selected.

[0012] In a possible implementation, the items in the preset priority list include direct current, alternating current, and integrated AC / DC.

[0013] In a possible implementation, if the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, before selecting the target energy replenishment site according to the preset priority list, the decision-making method further includes:

[0014] The preset priority list is updated according to the additional service request information, wherein the additional service request information includes that toilets, and / or convenience stores, and / or maintenance stations need to be configured around the energy charging station.

[0015] In a possible implementation, the calculating and generating the energy replenishment positive effect index of the to-be-selected energy replenishment site according to the to-be-selected energy replenishment site information includes:

[0016] The first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle end, the location information of the energy replenishment site, and the real-time remaining planned path information;

[0017] generating a second index component of the energy replenishment positive effect index according to the site vacancy rate;

[0018] The third index component of the energy replenishment positive effect index is calculated and generated according to the energy replenishment site location information and the real-time remaining planned path information.

[0019] In a possible implementation, the first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle, the location information of the energy replenishment site, and the real-time remaining planned path information, specifically:

[0020] The vehicle's real-time location is used as the starting point, and the location of the charging station is used as the end point to plan the charging route to be selected.

[0021] Find the end point of the path overlap according to the to-be-selected supplementary drivable path and the real-time remaining planned path information;

[0022] A third distance is generated by summing a first distance from the end point of the path overlap to the location of the to-be-selected refueling site and a second distance from the vehicle end from the location of the to-be-selected refueling site back to the path point of the real-time remaining planned path;

[0023] Calculating a fourth distance between the path coincident endpoint and the path point;

[0024] A ratio or difference is calculated between the third distance and the fourth distance, wherein the ratio or difference is negatively correlated with the first index component.

[0025] In a possible implementation, the third index component of the energy replenishment positive effect index is calculated and generated according to the energy replenishment site location information and the real-time remaining planned path information, specifically:

[0026] Calculate the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path;

[0027] If the distance is not greater than the vehicle-side cruising range, the third index component is greater than zero;

[0028] If the distance is greater than the vehicle-side cruising range, the third index component is equal to zero.

[0029] In one possible implementation, if the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, after selecting the target energy replenishment site according to the preset priority list, the decision-making method further includes:

[0030] Sending the site ID of the selected target energy replenishment site to the cloud server;

[0031] The charging pile information reserved by the cloud server through the site ID of the target charging site is obtained.

[0032] In a possible implementation, the reserved charging pile information includes the pile position locking duration.

[0033] In a possible implementation, after obtaining the charging pile information reserved by the cloud server through the site ID of the target charging site, the decision-making method further includes:

[0034] Planning a target energy replenishment path according to the location information of the target energy replenishment site, wherein the end point of the target energy replenishment path is the predetermined location of the target energy replenishment site;

[0035] After driving to the predetermined location of the target recharging site according to the planned target recharging path, the vehicle sends an arrival confirmation message to the site end;

[0036] Establish a communication connection with the field end and receive field end control requests;

[0037] Drive to the reserved charging station according to the dispatching control instructions of the field end.

[0038] In a possible implementation, after driving to the predetermined location of the target recharging site according to the planned target recharging path and before sending arrival confirmation information to the cloud and the site, the decision-making method further includes:

[0039] Obtaining a first time duration for arriving at the target energy replenishment site in real time;

[0040] If it is determined based on the first time duration that the target charging site cannot be reached within the pile position locking time duration, the charging information list generated based on the real-time data of the vehicle side is resent to the cloud server.

[0041] In a possible implementation, the calculating and generating the energy replenishment positive effect index of the candidate energy replenishment site according to the candidate energy replenishment site information further includes:

[0042] The fourth index component of the energy replenishment positive effect index is calculated based on the real-time position information of the vehicle, the remaining drivable mileage data, the energy replenishment site location information and the real-time remaining planned path information.

[0043] In one possible implementation, the fourth index component of the energy replenishment positive effect index is calculated based on the vehicle-side real-time location information, the remaining drivable mileage data, the energy replenishment station location information, and the real-time remaining planned path information, specifically:

[0044] Calculating a second time required to travel from the location of the energy replenishment site along the real-time remaining planned path to an end point of the real-time remaining planned path;

[0045] Calculating a third time required to travel from the vehicle's real-time location to the energy recharging station location;

[0046] Calculating a fourth duration, the minimum duration required for the vehicle to be recharged, based on the second duration, the third duration, and the remaining mileage data;

[0047] If the second duration is greater than the vehicle-side endurance duration, the fourth index component is equal to zero;

[0048] If the second duration is not greater than the vehicle-side endurance duration, the total duration of the second duration, the third duration and the fourth duration is calculated, and the total duration is negatively correlated with the size of the fourth index component.

[0049] A second aspect of an embodiment of the present invention provides a decision device for finding an energy replenishment site for implementing the decision method for finding an energy replenishment site described in the first aspect of the embodiment of the present invention, the decision device comprising:

[0050] A first sending module, configured to send a list of energy replenishment information generated based on real-time vehicle-side data to a cloud server, the list of energy replenishment information including the vehicle-side ID, energy type, real-time remaining planned route information, and remaining mileage data;

[0051] a first acquisition module, configured to acquire information of a candidate energy replenishment site pushed by the cloud according to the energy replenishment information list, wherein the information of the candidate energy replenishment site includes a site ID, location information, energy replenishment type, site rating information, site vacancy rate, and surrounding service facility information of the candidate energy replenishment site;

[0052] a first data processing module, which calculates and generates an energy replenishment positive effect index of the to-be-selected energy replenishment site based on the to-be-selected energy replenishment site information;

[0053] A decision module, wherein the decision module is configured to select the candidate energy replenishment site with the maximum energy replenishment positive effect index as the target energy replenishment site if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indices is greater than a preset deviation threshold;

[0054] The decision module is further configured to select a target energy replenishment site according to a preset priority list if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is not greater than a preset deviation threshold.

[0055] In a possible implementation, if the energy replenishment type is electric energy, the information of the site to be replenished further includes information on the type of charging pile of the site to be selected.

[0056] In a possible implementation, the items in the preset priority list include direct current, alternating current, and integrated AC / DC.

[0057] In a possible implementation, if the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, before selecting the target energy replenishment site according to the preset priority list, the decision-making device further includes: a second data processing module;

[0058] The second data processing module is used to update the preset priority list according to additional service request information, where the additional service request information includes the need to configure toilets, and / or convenience stores, and / or maintenance stations around the charging station.

[0059] In a possible implementation, the calculating and generating the energy replenishment positive effect index of the to-be-selected energy replenishment site according to the to-be-selected energy replenishment site information includes:

[0060] The first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle end, the location information of the energy replenishment site, and the real-time remaining planned path information;

[0061] generating a second index component of the energy replenishment positive effect index according to the site vacancy rate;

[0062] The third index component of the energy replenishment positive effect index is calculated and generated according to the energy replenishment site location information and the real-time remaining planned path information.

[0063] In a possible implementation, the first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle, the location information of the energy replenishment site, and the real-time remaining planned path information, specifically:

[0064] The vehicle's real-time location is used as the starting point, and the location of the charging station is used as the end point to plan the charging route to be selected.

[0065] Find the end point of the path overlap according to the to-be-selected supplementary drivable path and the real-time remaining planned path information;

[0066] A third distance is generated by summing a first distance from the end point of the path overlap to the location of the to-be-selected refueling site and a second distance from the vehicle end from the location of the to-be-selected refueling site back to the path point of the real-time remaining planned path;

[0067] Calculating a fourth distance between the path coincident endpoint and the path point;

[0068] A ratio or difference is calculated between the third distance and the fourth distance, wherein the ratio or difference is negatively correlated with the first index component.

[0069] In a possible implementation, the third index component of the energy replenishment positive effect index is calculated and generated according to the energy replenishment site location information and the real-time remaining planned path information, specifically:

[0070] Calculate the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path;

[0071] If the distance is not greater than the vehicle-side cruising range, the third index component is greater than zero;

[0072] If the distance is greater than the vehicle-side cruising range, the third index component is equal to zero.

[0073] In one possible implementation, if the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, after selecting the target energy replenishment site according to the preset priority list, the decision device further includes a second sending module and a second obtaining module:

[0074] The second sending module is used to send the site ID of the selected target energy replenishment site to the cloud server;

[0075] The second acquisition module is used to obtain the charging pile information reserved by the cloud server through the site ID of the target charging site.

[0076] In a possible implementation, the reserved charging pile information includes the pile position locking duration.

[0077] In a possible implementation, after obtaining the charging pile information reserved by the cloud server through the site ID of the target charging site, the decision-making device includes a planning module, a third sending module, a receiving module, and an execution module:

[0078] The planning module is used to plan a target energy replenishment path according to the location information of the target energy replenishment site, and the end point of the target energy replenishment path is a predetermined location of the target energy replenishment site;

[0079] The third sending module is used to send arrival confirmation information to the site after driving to the predetermined location of the target energy replenishment site according to the planned target energy replenishment path;

[0080] The receiving module is used to establish a communication connection with the field end and receive the field end control request;

[0081] The execution module is used to drive to the reserved charging pile according to the dispatching control instruction of the field end.

[0082] In a possible implementation, after driving to the predetermined location of the target recharging site according to the planned target recharging path and before sending arrival confirmation information to the cloud and the site, the decision-making device further includes a third acquisition module:

[0083] The third acquisition module is used to obtain a first time duration for reaching the target energy replenishment site in real time;

[0084] The first sending module is further configured to resend the charging information list generated based on the vehicle-side real-time data to the cloud server if it is determined based on the first time duration that the target charging site cannot be reached within the pile position locking time duration.

[0085] In a possible implementation, the calculating and generating the energy replenishment positive effect index of the candidate energy replenishment site according to the candidate energy replenishment site information further includes:

[0086] The fourth index component of the energy replenishment positive effect index is calculated based on the real-time position information of the vehicle, the remaining drivable mileage data, the energy replenishment site location information and the real-time remaining planned path information.

[0087] In one possible implementation, the fourth index component of the energy replenishment positive effect index is calculated based on the vehicle-side real-time location information, the remaining drivable mileage data, the energy replenishment station location information, and the real-time remaining planned path information, specifically:

[0088] Calculating a second time required to travel from the location of the energy replenishment site along the real-time remaining planned path to an end point of the real-time remaining planned path;

[0089] Calculating a third time required to travel from the vehicle's real-time location to the energy recharging station location;

[0090] Calculating a fourth duration, the minimum duration required for the vehicle to be recharged, based on the second duration, the third duration, and the remaining mileage data;

[0091] If the second duration is greater than the vehicle-side endurance duration, the fourth index component is equal to zero;

[0092] If the second duration is not greater than the vehicle-side endurance duration, the total duration of the second duration, the third duration and the fourth duration is calculated, and the total duration is negatively correlated with the size of the fourth index component.

[0093] A third aspect of an embodiment of the present invention provides a decision system for finding an energy replenishment site, wherein the decision system includes the decision device for finding an energy replenishment site according to the first aspect.

[0094] A fourth aspect of an embodiment of the present invention provides a decision-making component for finding an energy replenishment site, the decision-making component comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the decision-making method for finding an energy replenishment site as described in the first aspect of the embodiment of the present invention.

[0095] Embodiments of the present invention provide a decision-making method, device, system, and component for finding a recharging site. By obtaining information about potential recharging sites pushed from the cloud, the method calculates the recharging positive effect index of the potential recharging sites, and selects the target recharging site based on the value of the recharging positive effect index, thereby improving the recharging efficiency of autonomous vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is a schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention;

[0097] Figure 2 This is a second schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention;

[0098] Figure 3 This is a third schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention;

[0099] Figure 4This is a fourth schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention;

[0100] Figure 5 This is a fifth schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention;

[0101] Figure 6 This is one of the module structure diagrams of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention;

[0102] Figure 7 This is a second module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention;

[0103] Figure 8 This is a third module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention;

[0104] Figure 9 This is a fourth module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention;

[0105] Figure 10 This is a fifth module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention;

[0106] Figure 11 This is a module structure diagram of a decision system for finding an energy replenishment site provided in the third embodiment of the present invention;

[0107] Figure 12 This is a module structure diagram of a decision-making component for finding an energy replenishment site provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0108] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0109] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0110] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0111] In the present invention, "module", "device", "system" and the like refer to related entities applied to a computer, such as hardware, a combination of hardware and software, software or software in execution, etc. Specifically, for example, an element can be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program and / or a computer. In addition, an application or script program running on a server, or a server can all be an element. One or more elements can be in an execution process and / or thread, and an element can be localized on a computer and / or distributed between two or more computers, and can be run by various computer-readable media. An element can also communicate through local and / or remote processes based on a signal having one or more data packets, for example, a signal from a data packet interacting with another element in a local system, a distributed system, and / or a signal from a network on the Internet that interacts with other systems via signals.

[0112] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include" and "comprise" include not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, the elements defined by the phrase "include..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0113] Example 1

[0114] Embodiment 1 of the present invention provides a decision-making method for finding a charging station, and the execution subject of this application is a vehicle-side control center. Figure 1 This is a schematic diagram of a decision method for finding a recharging site provided in the first embodiment of the present invention, such as Figure 1 As shown, this decision-making method mainly includes the following steps:

[0115] Step 110 , sending a list of energy replenishment information generated based on the real-time vehicle-side data to a cloud server, wherein the list of energy replenishment information includes the vehicle-side ID, energy type, real-time remaining planned route information, and remaining mileage data.

[0116] Among them, the energy type can be any energy available to the vehicle, which can be a single energy or a mixed energy; single energy includes fuel energy, electric energy, hydrogen energy, etc.; mixed energy can include any combination of at least two single energy sources, such as a mixed energy of fuel and electricity, or a mixed energy of fuel and hydrogen, or a mixed energy of fuel, electricity and hydrogen.

[0117] Among them, the planned path information is the path information planned by the vehicle from the starting point to the end point, and the real-time remaining planned path information is the remaining planned path information from the real-time position of the vehicle to the end point during the driving process.

[0118] Among them, the remaining mileage data is the mileage data that the vehicle can still travel when the energy is exhausted, which is calculated based on the remaining energy on the vehicle side.

[0119] Step 120: Obtain the to-be-selected energy replenishment site information pushed by the cloud according to the energy replenishment information list, wherein the to-be-selected energy replenishment site information includes the site ID, location information, energy replenishment type, site rating information, site vacancy rate, and surrounding service facility information of the to-be-selected energy replenishment site.

[0120] Specifically, there are one or more recharging sites to be selected, wherein the recharging types of the recharging sites to be selected include single energy recharging sites or mixed energy recharging sites; the site rating information is the rating submitted by historical vehicle-side users for various services of the site after completing recharging at the site; the site vacancy rate is calculated and generated by the site based on the real-time recharging vehicle situation and the scheduled recharging vehicle situation at the site; the surrounding service facility information includes service facilities within a preset distance range, such as toilets, convenience stores and maintenance stations, as well as the coordinate information of the service facilities on the driving map.

[0121] In a possible implementation, if the energy replenishment type is electric energy, the information of the site to be replenished further includes information on the type of charging pile of the site to be selected.

[0122] Among them, the types of charging piles include DC charging piles, AC charging piles, and AC / DC integrated charging piles. DC charging piles can charge quickly but have an impact on the life of the rechargeable battery. AC charging piles have a small charging current and a long charging time, which have little impact on the battery life. When time is limited, the vehicle side can give priority to DC charging piles to improve energy replenishment efficiency. If time is sufficient, AC charging piles can be selected to protect the battery life.

[0123] Step 130: Calculate and generate the energy replenishment positive effect index of the candidate energy replenishment site based on the candidate energy replenishment site information.

[0124] Specifically, the higher the positive effect index of the energy replenishment site to be selected, the greater the benefit can be obtained by replenishing energy at the energy replenishment site, and it is the best choice.

[0125] In one possible implementation, step 130 specifically includes steps A1 to A3:

[0126] Step A1: Calculate and generate a first index component of the energy replenishment positive effect index based on the real-time location information of the vehicle, the location information of the energy replenishment site, and the real-time remaining planned path information.

[0127] Specifically, a to-be-selected recharging route is planned with the real-time location of the vehicle as the starting point and the location of the recharging station as the end point; a path overlap end point is found based on the to-be-selected recharging route and the real-time remaining planned path information; a first distance from the path overlap end point to the to-be-selected recharging station location and a second distance from the vehicle end from the to-be-selected recharging station location back to the path point of the real-time remaining planned path are summed to generate a third distance; a fourth distance between the path overlap end point and the path point of the real-time remaining planned path is calculated; a ratio or difference is calculated between the third distance and the fourth distance, and the ratio or difference is negatively correlated with the first exponential component.

[0128] In a specific example, assume that the first 20 kilometers of the planned alternative recharging route from the vehicle's real-time location to the recharging station overlap. At 20 kilometers, the routes diverge, and the path point at this point is recorded as the path overlap endpoint. The first distance from the path overlap endpoint to the alternative recharging station is 1 kilometer. The second distance from the vehicle's location to the path point on the real-time remaining planned path is 1.5 kilometers. The sum of the first and second distances is a third distance, which is 2.5 kilometers. The fourth distance between the path overlap endpoint and the path point on the real-time remaining planned path is 0.5 kilometers. The ratio of the third distance to the fourth distance is 5, or the difference is 2 kilometers. The size of the ratio or difference between the third and fourth distances represents the additional mileage consumed by the vehicle to reach the alternative recharging station relative to the original planned route, with larger values ​​indicating a disadvantage.

[0129] Step A2: generating a second index component of the energy replenishment positive effect index according to the site vacancy rate.

[0130] Specifically, the station vacancy rate is calculated by the station based on the real-time and scheduled vehicle availability at the station. The station vacancy rate is positively correlated with the magnitude of the second exponential component. A higher station vacancy rate indicates fewer vehicles are recharging, resulting in less queuing at the recharging station and reduced recharging time.

[0131] Step A3: Calculate and generate a third index component of the energy replenishment positive effect index based on the energy replenishment site location information and the real-time remaining planned path information.

[0132] Specifically, the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path is calculated; if the distance is not greater than the vehicle-side cruising range, the third exponent component is greater than zero; if the distance is greater than the vehicle-side cruising range, the third exponent component is equal to zero.

[0133] When the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path is less than the vehicle's endurance range, it indicates that the vehicle no longer needs to recharge after reaching the final destination after the charging is completed; when the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path is greater than the vehicle's endurance range, it indicates that the vehicle still needs to recharge before reaching the final destination after the current charging is completed, and the selection of a candidate charging station that no longer needs to recharge is better than the selection of a candidate charging station that needs to be recharged again.

[0134] In another possible implementation, step 130, after step A3, further includes:

[0135] Step A4, calculating and generating a fourth index component of the positive effect index of the recharging according to the real-time position information of the vehicle, the remaining drivable mileage data, the position information of the recharging station and the real-time remaining planned path information.

[0136] Specifically, the second duration required to travel from the location of the recharging station along the real-time remaining planned path to the end point of the real-time remaining planned path is calculated; the third duration required to travel from the real-time location of the vehicle to the location of the recharging station is calculated; the fourth duration, which is the minimum time required for recharging on the vehicle side, is calculated based on the second duration, the third duration and the remaining drivable mileage data; if the autonomous driving vehicle has a time limit for traveling to the final destination, during the recharging process, the minimum amount of energy that needs to be replenished can be calculated based on the real-time remaining planned path plus a preset safety threshold for recharging, thereby saving the total vehicle-side driving time.

[0137] If the second duration is greater than the vehicle's endurance, the fourth exponential component is equal to zero; at this time, after the vehicle has replenished enough energy, it still needs to replenish energy before reaching the destination, which consumes a long total driving time.

[0138] If the second duration is no greater than the vehicle's endurance, the total of the second, third, and fourth durations is calculated. This total duration is negatively correlated with the magnitude of the fourth exponential component. If the second duration is no greater than the vehicle's endurance, this indicates that when recharging at the candidate recharging station, only a portion of the energy can be replenished based on the real-time remaining planned route distance, thus saving total driving time. The shorter the total duration, the more favorable the selection of the candidate recharging station.

[0139] Step 140 : If the difference between the maximum positive energy replenishment effect index and the other positive energy replenishment effect indexes is greater than a preset deviation threshold, the candidate energy replenishment site with the maximum positive energy replenishment effect index is selected as the target energy replenishment site.

[0140] Specifically, a positive energy replenishment effect index is calculated for each candidate energy replenishment site, and the sites are sorted according to their numerical values. The larger the positive energy replenishment effect index is, the more favorable it is to select the candidate energy replenishment site as the target energy replenishment site.

[0141] Step 150 : If the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, a target energy replenishment site is selected according to a preset priority list.

[0142] If the difference between the maximum positive energy replenishment effect index and one or more other positive energy replenishment effect indices is no greater than a preset deviation threshold, it indicates that the benefit of selecting the candidate energy replenishment site with the maximum positive energy replenishment effect index as the target energy replenishment site is not significantly different from selecting the candidate energy replenishment site with the other one or more positive energy replenishment effect indices as the target energy replenishment site. The target energy replenishment site can be further optimized using a preset priority list.

[0143] In one possible implementation, if the energy replenishment type is electric energy, the items in the preset priority list include DC, AC, and AC / DC. In a specific example, the priority of the preset priority list is DC first, then AC / DC, and finally AC.

[0144] In one possible scenario, Figure 2 This is a second schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention, as shown in FIG. Figure 2 As shown, before step 150, the decision-making method further includes:

[0145] Step 210 : updating the preset priority list according to the additional service request information, wherein the additional service request information includes the need to configure toilets, and / or convenience stores, and / or maintenance stations around the charging station.

[0146] While the vehicle is driving, temporary additional service needs may arise. When these needs arise, an additional service request is generated, and the preset priority list is updated based on the additional service request. In a specific example, the additional service request indicates the need for restrooms near the recharging station. If the preset priority list before the update prioritizes DC, then AC / DC hybrid, and finally AC, the updated preset priority list prioritizes restrooms, then DC, then AC / DC hybrid, and finally AC. Real-time updates to the preset priority list further optimize target recharging stations.

[0147] In another possible solution, if the energy replenishment type is electric energy, Figure 3 This is a third schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention, as shown in FIG. Figure 3 As shown, after step 150, the decision-making method further includes:

[0148] Step 310: Send the site ID of the selected target charging site to the cloud server.

[0149] Step 320: Obtain the charging pile information reserved by the cloud server through the site ID of the target charging site.

[0150] After the vehicle side selects the site ID of the target charging station, the cloud server sends a charging pile reservation application to the field side corresponding to the site ID according to the site ID of the target charging station. The reservation application includes the vehicle side ID of the reserved vehicle side. The field side binds the vehicle side ID and the idle charging pile. After binding, other vehicles are unavailable. When the vehicle side arrives at the target charging station and establishes communication with the field side, the bound charging pile is released to the vehicle side. In one possible implementation, the reserved charging pile information includes the pile position locking time. The pile position locking time of the target charging station can be set according to the vacancy rate of the station. The vacancy rate is positively correlated with the pile position locking time. By setting the pile position locking time, it can be avoided that the vehicle side cannot reach the charging pile in time due to external factors and overtime occupancy affects the operating efficiency of the target charging station.

[0151] In another possible solution, Figure 4 This is a fourth schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention, as shown in FIG. Figure 4 As shown, after step 320, the decision-making method further includes:

[0152] Step 410 : planning a target charging path according to the location information of the target charging site, with the end point of the target charging path being the predetermined location of the target charging site.

[0153] Step 420: After driving to the predetermined location of the target charging station according to the planned target charging route, send arrival confirmation information to the station end.

[0154] Step 430: Establish a communication connection with the field end and receive a field end control request.

[0155] Step 440: Drive to the reserved charging station according to the dispatch control instruction of the field end.

[0156] Specifically, when the vehicle arrives at the target charging station and receives the control request from the field, the vehicle performs a communication handshake and releases control to the field. The field obtains the location of the charging pile based on the charging pile ID bound to the vehicle, and generates a scheduling control instruction based on the location of the charging pile. In one possible implementation, the scheduling control instruction includes steering wheel control instructions, vehicle speed, throttle, brake, gear and other instructions.

[0157] In another possible solution, Figure 5 This is a fifth schematic diagram of a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention, as shown in FIG. Figure 5 As shown, before step 420, the decision-making method further includes:

[0158] Step 510: Obtain a first time duration to reach the target energy replenishment site in real time.

[0159] Step 520: If it is determined based on the first time period that the target charging site cannot be reached within the pile position locking time period, the process returns to step 110.

[0160] Specifically, when the vehicle takes too long to reach the target charging station within the charging station lock time due to environmental factors, the vehicle will search for the charging station again based on the real-time data of the vehicle to avoid the situation where the target charging station has unbound the bound charging pile, resulting in failure to recharge in time and need to queue for recharging.

[0161] Example 2

[0162] Figure 6 This is one of the module structure diagrams of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention. The device is a decision-making device for finding an energy replenishment site that can implement a decision-making method for finding an energy replenishment site provided in the first embodiment of the present invention. Figure 6 As shown, the decision-making device includes: a first sending module 700 , a first acquiring module 701 , a first data processing module 702 and a decision-making module 703 .

[0163] The first sending module 700 is used to send a list of energy replenishment information generated based on the real-time data of the vehicle to the cloud server. The list of energy replenishment information includes the vehicle ID, energy type, real-time remaining planned path information and remaining mileage data.

[0164] The first acquisition module 701 is used to obtain the information of the energy replenishment site to be selected pushed by the cloud according to the energy replenishment information list, and the information of the energy replenishment site to be selected includes the site ID, location information, energy replenishment type, site rating information, site vacancy rate and surrounding service facility information of the energy replenishment site to be selected.

[0165] The first data processing module 702 calculates and generates an energy replenishment positive effect index of the to-be-selected energy replenishment site according to the to-be-selected energy replenishment site information.

[0166] The decision module 703 is configured to select the candidate energy replenishment site with the maximum energy replenishment positive effect index as the target energy replenishment site if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is greater than a preset deviation threshold;

[0167] The decision module 703 is further configured to select a target energy replenishment site according to a preset priority list if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is not greater than a preset deviation threshold.

[0168] The energy replenishment positive effect index of the selected energy replenishment site is calculated and generated according to the information of the selected energy replenishment site, including:

[0169] The first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle, the location information of the energy replenishment site and the real-time remaining planned path information; the second index component of the energy replenishment positive effect index is generated based on the site vacancy rate; and the third index component of the energy replenishment positive effect index is calculated based on the location information of the energy replenishment site and the real-time remaining planned path information.

[0170] Furthermore, the first index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle, the location information of the energy replenishment site, and the real-time remaining planned path information, specifically:

[0171] A to-be-selected recharging route is planned with the vehicle's real-time location as the starting point and the location of the recharging station as the end point. A path overlapped with the to-be-selected recharging route and the real-time remaining planned path information are found. A first distance from the path overlapped with the end point to the to-be-selected recharging station location and a second distance from the vehicle's location from the to-be-selected recharging station back to the path point of the real-time remaining planned path are summed to generate a third distance. A fourth distance between the path overlapped with the end point and the path point is calculated. A ratio or difference is calculated between the third distance and the fourth distance, where the ratio or difference is negatively correlated with the first exponential component.

[0172] Furthermore, the third index component of the energy replenishment positive effect index is calculated based on the energy replenishment site location information and the real-time remaining planned path information, specifically:

[0173] Calculate the distance from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path;

[0174] If the distance is not greater than the vehicle's endurance range, the third index component is greater than zero;

[0175] If the distance is greater than the vehicle's endurance range, the third exponent component is equal to zero.

[0176] If the energy replenishment type is electric energy, the information about the charging station to be selected also includes the type of charging pile of the charging station to be selected. The items in the preset priority list include DC, AC, and AC / DC integrated.

[0177] Furthermore, the energy replenishment positive effect index of the selected energy replenishment site is calculated based on the information of the selected energy replenishment site, and further includes:

[0178] The fourth index component of the energy replenishment positive effect index is calculated based on the real-time location information of the vehicle, the remaining mileage data, the location information of the energy replenishment station and the real-time remaining planned path information.

[0179] Furthermore, the fourth index component of the positive effect index of the recharging is calculated based on the real-time location information of the vehicle, the remaining mileage data, the location information of the recharging station, and the real-time remaining planned path information. Specifically, it is:

[0180] Calculate the second duration required to travel from the location of the charging station along the real-time remaining planned path to the end point of the real-time remaining planned path; calculate the third duration required from the real-time location of the vehicle to the location of the charging station; calculate the fourth duration, which is the minimum duration required for charging on the vehicle side, based on the second duration, the third duration and the remaining drivable mileage data; if the second duration is greater than the vehicle side's endurance time, the fourth index component is equal to zero; if the second duration is not greater than the vehicle side's endurance time, calculate the total duration of the second, third and fourth durations, and the total duration is negatively correlated with the size of the fourth index component.

[0181] In an optional solution, if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is not greater than the preset deviation threshold, before selecting the target energy replenishment site according to the preset priority list, Figure 7 The second module structure diagram of a decision-making device for finding a recharging site provided in the second embodiment of the present invention is as follows: Figure 7 As shown, the decision-making device also includes a second data processing module 704.

[0182] The second data processing module 704 is configured to update the preset priority list according to the additional service request information, where the additional service request information includes the need to configure toilets, and / or convenience stores, and / or maintenance stations around the charging station.

[0183] In another optional solution, if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is not greater than the preset deviation threshold, after selecting the target energy replenishment site according to the preset priority list, Figure 8 The third module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention is as follows: Figure 8 As shown, the decision-making device further includes a second sending module 705 and a second obtaining module 706:

[0184] The second sending module 705 is used to send the site ID of the selected target charging site to the cloud server.

[0185] The second acquisition module 706 is used to obtain the charging pile information reserved by the cloud server through the site ID of the target charging site.

[0186] Among them, the reserved charging pile information includes the pile locking time.

[0187] In another optional solution, after obtaining the charging pile information reserved by the cloud server through the site ID of the target charging site, Figure 9This is a fourth module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention, as shown in FIG. Figure 9 As shown, the decision-making device further includes a planning module 707 , a third sending module 708 , a receiving module 709 and an execution module 710 .

[0188] The planning module 707 is used to plan a target energy replenishment path according to the location information of the target energy replenishment site, and the end point of the target energy replenishment path is the predetermined location of the target energy replenishment site.

[0189] The third sending module 708 is used to send arrival confirmation information to the site after driving to the predetermined location of the target charging site according to the planned target charging path.

[0190] The receiving module 709 is used to establish a communication connection with the field end and receive a field end control request.

[0191] The execution module 710 is used to drive to the reserved charging pile according to the dispatch control instruction of the field end.

[0192] In another optional solution, after driving to the predetermined location of the target recharging station according to the planned target recharging path, before sending the arrival confirmation information to the cloud and the site, Figure 10 The fifth module structure diagram of a decision-making device for finding an energy replenishment site provided in the second embodiment of the present invention is as follows: Figure 10 As shown, the decision-making device further includes a third acquisition module 711.

[0193] The third acquisition module 711 is used to obtain the first time duration of reaching the target energy replenishment site in real time.

[0194] The first sending module 700 is further configured to resend the charging information list generated based on the vehicle-side real-time data to the cloud server if it is determined based on the first time duration that the target charging site cannot be reached within the pile position locking time duration.

[0195] The second embodiment of the present invention provides a decision-making device for finding an energy replenishment site, which is used to execute the steps of the method provided in the first embodiment of the present invention. Its implementation principle and technical effects are similar and will not be repeated here.

[0196] It should be noted that it should be understood that the division of the various modules of the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by a processing element; or they can all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the first acquisition module can be a separately established processing element, or it can be integrated into a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called by a processing element of the above device to perform the functions of the above acquisition module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. During implementation, each step of the method provided by the embodiment of the present invention or each module of the device provided by the embodiment of the present invention can be completed by the hardware integrated logic circuit in the processor element or by instructions in the form of software.

[0197] For example, the module of the device provided by the embodiment of the present invention can be one or more integrated circuits configured as the method provided by the embodiment of the present invention, such as: one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module of the device provided by the embodiment of the present invention is implemented in the form of a processing element scheduling program code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules of the device provided by the embodiment of the present invention can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0198] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the methods provided in the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The above-mentioned computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the above-mentioned computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, Bluetooth, microwave, etc.) means. The above-mentioned computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The above-mentioned available medium can be a magnetic medium (such as a floppy disk, hard disk, tape), an optical medium (such as a high-density digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0199] Example 3

[0200] Figure 11 This is a module structure diagram of a decision system for finding a refueling site provided by the third embodiment of the present invention, such as Figure 11 As shown, the system of the third embodiment of the present invention may specifically include: Figure 6-10 The decision-making device for finding a recharging site is shown.

[0201] Example 4

[0202] Figure 12 This is a module structure diagram of a decision-making component for finding a recharge site provided by the fourth embodiment of the present invention. This component is an electronic component, electronic device or server that implements the decision-making method for finding a recharge site provided by the first embodiment of the present invention. Figure 12As shown, the component 600 may include: a processor 61 (e.g., a CPU) and a memory 62; the memory 62 stores instructions executable by at least one processor 61. The instructions are executed by at least one processor 61 to enable at least one processor 61 to perform the method for pushing the location of a recharging station as provided in the first embodiment of the present invention. Preferably, the components involved in the fourth embodiment of the present invention may also include: a transceiver 63, a power supply 64, a system bus 65, and a communication port 66. The transceiver 63 is coupled to the processor 61, the system bus 65 is used to achieve communication between components, and the communication port 66 is used to connect and communicate between the component and other peripheral devices.

[0203] exist Figure 12 The system bus mentioned in the figure can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory (Non-Volatile Memory), such as at least one disk storage.

[0204] The above-mentioned processor can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0205] Embodiments of the present invention provide a decision-making method, device, system, and component for finding a recharging site. The method obtains information about the candidate recharging sites pushed from the cloud, calculates the recharging positive effect index of the candidate recharging sites, and selects the target recharging site based on the value of the recharging positive effect index, thereby improving the recharging efficiency of the autonomous driving vehicle. The method further selects the target recharging site by setting a preset priority list. The method further selects the target recharging site by updating the preset priority list based on additional service request information.

[0206] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0207] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0208] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A decision-making method for finding a refueling site, characterized in that: The decision-making method includes: Sending a list of energy replenishment information generated based on the vehicle's real-time data to the cloud server. The list includes the vehicle ID, energy type, real-time remaining planned route information, and remaining mileage data. Obtaining information of a potential charging station pushed by the cloud based on the charging information list, the information including the station ID, location information, charging type, station rating information, station vacancy rate, and surrounding service facility information of the potential charging station; wherein, if the charging type is electric energy, the information also includes information on the type of charging station at the potential charging station; Calculate and generate the energy replenishment positive effect index of the energy replenishment site to be selected according to the information of the energy replenishment site to be selected; If the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is greater than the preset deviation threshold, the candidate energy replenishment site with the maximum energy replenishment positive effect index is selected as the target energy replenishment site; If the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than the preset deviation threshold, the target energy replenishment site is selected according to the preset priority list, wherein, if the energy replenishment type is electric energy, the list items of the preset priority list include direct current, alternating current, and integrated AC / DC; The calculating and generating the energy replenishment positive effect index of the to-be-selected energy replenishment site according to the to-be-selected energy replenishment site information includes: The first index component of the energy replenishment positive effect index is calculated and generated based on the real-time vehicle position information, the position information of the energy replenishment site, and the real-time remaining planned path information, specifically: planning a to-be-selected energy replenishment driving path with the real-time vehicle position as the starting point and the position of the energy replenishment site as the end point; finding a path overlap end point based on the to-be-selected energy replenishment driving path and the real-time remaining planned path information; summing a first distance from the path overlap end point to the to-be-selected energy replenishment site position and a second distance from the vehicle position from the to-be-selected energy replenishment site position back to a path point on the real-time remaining planned path to generate a third distance; calculating a fourth distance between the path overlap end point and the path point; and calculating a ratio or difference between the third distance and the fourth distance, where the ratio or difference is negatively correlated with the first index component; generating a second index component of the energy replenishment positive effect index according to the site vacancy rate; The third index component of the recharging positive effect index is calculated based on the recharging station location information and the real-time remaining planned path information. Specifically, the distance from the location of the recharging station along the real-time remaining planned path to the end point of the real-time remaining planned path is calculated; if the distance is not greater than the vehicle-side cruising range, the third index component is greater than zero; if the distance is greater than the vehicle-side cruising range, the third index component is equal to zero.

2. The decision-making method for finding an energy replenishment site according to claim 1, characterized in that: If the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than the preset deviation threshold, before selecting the target energy replenishment site according to the preset priority list, the decision-making method further includes: The preset priority list is updated according to the additional service request information, wherein the additional service request information includes that toilets, and / or convenience stores, and / or maintenance stations need to be configured around the energy charging station.

3. The decision-making method for finding an energy replenishment site according to claim 1, characterized in that: If the difference between the maximum energy replenishment positive effect index and the other energy replenishment positive effect indexes is not greater than a preset deviation threshold, after selecting the target energy replenishment site according to the preset priority list, the decision-making method further includes: Sending the site ID of the selected target energy replenishment site to the cloud server; The charging pile information reserved by the cloud server through the site ID of the target charging site is obtained, wherein the reserved charging pile information includes the pile position locking time.

4. The decision-making method for finding an energy replenishment site according to claim 3, characterized in that: After obtaining the charging pile information reserved by the cloud server through the site ID of the target charging site, the decision-making method further includes: Planning a target energy replenishment path according to the location information of the target energy replenishment site, wherein the end point of the target energy replenishment path is the predetermined location of the target energy replenishment site; After driving to the predetermined location of the target recharging site according to the planned target recharging path, the vehicle sends an arrival confirmation message to the site end; Establish a communication connection with the field end and receive field end control requests; Drive to the reserved charging station according to the dispatching control instructions of the field end.

5. The decision-making method for finding an energy replenishment site according to claim 4, characterized in that: After driving to the predetermined location of the target energy replenishment site according to the planned target energy replenishment path and before sending arrival confirmation information to the cloud and the site, the decision-making method further includes: Obtaining a first time duration for arriving at the target energy replenishment site in real time; If it is determined based on the first time duration that the target charging site cannot be reached within the pile position locking time duration, the charging information list generated based on the real-time data of the vehicle side is resent to the cloud server.

6. The decision-making method for finding an energy replenishment site according to claim 1, characterized in that: The step of calculating and generating the energy replenishment positive effect index of the candidate energy replenishment site according to the candidate energy replenishment site information further includes: The fourth index component of the energy replenishment positive effect index is calculated based on the real-time position information of the vehicle, the remaining drivable mileage data, the energy replenishment site location information and the real-time remaining planned path information, specifically: calculating the second time required to travel from the position of the energy replenishment site along the real-time remaining planned path to the end point of the real-time remaining planned path; calculating the third time required from the real-time position of the vehicle to the position of the energy replenishment site; calculating the fourth time for the minimum time required for energy replenishment on the vehicle side based on the second time, the third time and the remaining drivable mileage data; if the second time is greater than the vehicle-side endurance time, the fourth index component is equal to zero; if the second time is not greater than the vehicle-side endurance time, calculating the total time of the second time, the third time and the fourth time, and the total time is negatively correlated with the size of the fourth index component.

7. A decision-making device for finding a refueling site, characterized in that: The decision-making device comprises: A first sending module, configured to send a list of energy replenishment information generated based on real-time vehicle-side data to a cloud server, the list of energy replenishment information including the vehicle-side ID, energy type, real-time remaining planned route information, and remaining mileage data; a first acquisition module, configured to acquire information of a to-be-selected energy replenishment site pushed by the cloud according to the energy replenishment information list, the information of the to-be-selected energy replenishment site including a site ID, location information, energy replenishment type, site rating information, site vacancy rate, and surrounding service facility information of the to-be-selected energy replenishment site; wherein, if the energy replenishment type is electric energy, the information of the to-be-selected energy replenishment site also includes information on a charging pile type of the to-be-selected energy replenishment site; a first data processing module, which calculates and generates an energy replenishment positive effect index of the to-be-selected energy replenishment site based on the to-be-selected energy replenishment site information; A decision module, wherein the decision module is configured to select the candidate energy replenishment site with the maximum energy replenishment positive effect index as the target energy replenishment site if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indices is greater than a preset deviation threshold; The decision module is further configured to select a target energy replenishment site according to a preset priority list if the difference between the maximum energy replenishment positive effect index and other energy replenishment positive effect indexes is not greater than a preset deviation threshold, wherein if the energy replenishment type is electric energy, the items in the preset priority list include direct current, alternating current, and integrated AC / DC; The first data processing module calculates and generates the energy replenishment positive effect index of the to-be-selected energy replenishment site according to the to-be-selected energy replenishment site information, including: The first index component of the energy replenishment positive effect index is calculated and generated based on the real-time vehicle position information, the position information of the energy replenishment site, and the real-time remaining planned path information, specifically: planning a to-be-selected energy replenishment driving path with the real-time vehicle position as the starting point and the position of the energy replenishment site as the end point; finding a path overlap end point based on the to-be-selected energy replenishment driving path and the real-time remaining planned path information; summing a first distance from the path overlap end point to the to-be-selected energy replenishment site position and a second distance from the vehicle position from the to-be-selected energy replenishment site position back to a path point on the real-time remaining planned path to generate a third distance; calculating a fourth distance between the path overlap end point and the path point; and calculating a ratio or difference between the third distance and the fourth distance, where the ratio or difference is negatively correlated with the first index component; generating a second index component of the energy replenishment positive effect index according to the site vacancy rate; The third index component of the recharging positive effect index is calculated based on the recharging station location information and the real-time remaining planned path information. Specifically, the distance from the location of the recharging station along the real-time remaining planned path to the end point of the real-time remaining planned path is calculated; if the distance is not greater than the vehicle-side cruising range, the third index component is greater than zero; if the distance is greater than the vehicle-side cruising range, the third index component is equal to zero.

8. A decision-making system for finding energy replenishment sites, characterized in that: The decision-making system includes a decision-making device 7 for finding an energy replenishment site.

9. A decision-making component for finding a recharging site, characterized in that: The decision-making component includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the decision-making method for finding an energy replenishment site according to any one of claims 1 to 6.

Images