Charging path recommendation method and device, equipment, medium and program product

Abstract

The invention relates to a charging path recommendation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring first equipment information of target charging equipment and second equipment information of candidate power supply equipment; according to at least one item of the battery condition and the position represented by the first equipment information and at least one item of the position and the charging cost represented by the second equipment information, the cost consumed when the target charging equipment is charged by using each candidate power supply equipment is predicted; obtaining charging cost prediction information corresponding to each candidate power supply device; generating a candidate recommendation index corresponding to the candidate power supply equipment according to the charging cost prediction information corresponding to the candidate power supply equipment; screening target power supply equipment from the candidate power supply equipment according to the candidate recommendation indexes corresponding to the candidate power supply equipment; and performing charging path recommendation according to the moving path between the target power supply equipment and the target charging equipment. By adopting the method, the charging path recommendation accuracy can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, and in particular, relates to a charging path recommendation method and device, computer equipment, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] With the popularity of charging equipment, such as electric vehicles and the like, the demand of users for charging services is also increasing. In order to facilitate better charging of charging equipment, a charging path is usually recommended to the user through a navigation APP or the like.

[0003] At present, the charging path is usually recommended to the user according to the position of each power supply equipment relative to the charging equipment, but this dimension is relatively single, and the deep needs of the user cannot be mined, so that the recommended charging path does not match the needs of the user, that is, the accuracy of the charging path recommendation is low. SUMMARY

[0004] Therefore, it is necessary to provide a charging path recommendation method, device, computer equipment, computer readable storage medium, and computer program product capable of improving the charging path recommendation efficiency in view of the above technical problems.

[0005] In a first aspect, the present application provides a charging path recommendation method, which comprises:

[0006] obtaining first equipment information of a target charging equipment, and second equipment information of at least one candidate power supply equipment;

[0007] predicting the cost consumed when the target charging equipment is charged by each candidate power supply equipment according to at least one of the battery status and the position represented by the first equipment information, and according to at least one of the position and the charging cost represented by the second equipment information, to obtain charging cost prediction information corresponding to each candidate power supply equipment;

[0008] generating a candidate recommendation index corresponding to each candidate power supply equipment according to the charging cost prediction information corresponding to each candidate power supply equipment;

[0009] selecting a target power supply equipment from each candidate power supply equipment according to the candidate recommendation index corresponding to each candidate power supply equipment;

[0010] recommending a charging path according to a moving path between the target power supply equipment and the target charging equipment.

[0011] In a second aspect, the present application further provides a charging path recommendation device, which comprises:

[0012] obtaining a first device information of a target charging device and a second device information of at least one candidate power supply device;

[0013] predicting, according to at least one of a battery status and a location represented by the first device information and at least one of a location and a charging cost represented by the second device information, a cost consumed when the target charging device uses each of the candidate power supply devices to charge, to obtain charging cost prediction information corresponding to each of the candidate power supply devices;

[0014] generating, according to the charging cost prediction information corresponding to each of the candidate power supply devices, a candidate recommendation index corresponding to each of the candidate power supply devices;

[0015] screening, according to the candidate recommendation index corresponding to each of the candidate power supply devices, a target power supply device from the candidate power supply devices;

[0016] recommending, according to a moving path between the target power supply device and the target charging device, a charging path.

[0017] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the processor implements the following steps when executing the computer program:

[0018] obtaining a first device information of a target charging device and a second device information of at least one candidate power supply device;

[0019] predicting, according to at least one of a battery status and a location represented by the first device information and at least one of a location and a charging cost represented by the second device information, a cost consumed when the target charging device uses each of the candidate power supply devices to charge, to obtain charging cost prediction information corresponding to each of the candidate power supply devices;

[0020] generating, according to the charging cost prediction information corresponding to each of the candidate power supply devices, a candidate recommendation index corresponding to each of the candidate power supply devices;

[0021] screening, according to the candidate recommendation index corresponding to each of the candidate power supply devices, a target power supply device from the candidate power supply devices;

[0022] recommending, according to a moving path between the target power supply device and the target charging device, a charging path.

[0023] In a fourth aspect, the present application provides a computer readable storage medium, having a computer program stored thereon, wherein the computer program is executed by a processor to implement the following steps:

[0024] obtaining first device information of a target charging device and second device information of at least one candidate power supply device;

[0025] predicting, according to at least one of a battery condition and a location represented by the first device information and at least one of a location and a charging cost represented by the second device information, a cost consumed when the target charging device is charged by each of the candidate power supply devices, to obtain charging cost prediction information corresponding to each of the candidate power supply devices;

[0026] generating a candidate recommendation index corresponding to each of the candidate power supply devices according to the charging cost prediction information corresponding to each of the candidate power supply devices;

[0027] selecting a target power supply device from the candidate power supply devices according to the candidate recommendation index corresponding to each of the candidate power supply devices;

[0028] recommending a charging path according to a moving path between the target power supply device and the target charging device.

[0029] In a fifth aspect, the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0030] obtaining first device information of a target charging device and second device information of at least one candidate power supply device;

[0031] predicting, according to at least one of a battery condition and a location represented by the first device information and at least one of a location and a charging cost represented by the second device information, a cost consumed when the target charging device is charged by each of the candidate power supply devices, to obtain charging cost prediction information corresponding to each of the candidate power supply devices;

[0032] generating a candidate recommendation index corresponding to each of the candidate power supply devices according to the charging cost prediction information corresponding to each of the candidate power supply devices;

[0033] selecting a target power supply device from the candidate power supply devices according to the candidate recommendation index corresponding to each of the candidate power supply devices;

[0034] recommending a charging path according to a moving path between the target power supply device and the target charging device.

[0035] The charging path recommendation method, device, computer device, computer readable storage medium, and computer program product described above obtain first device information of a target charging device and second device information of at least one candidate power supply device. The cost consumed when the target charging device uses each candidate power supply device to charge is predicted according to at least one of the battery status and the location represented by the first device information and at least one of the location and the charging cost represented by the second device information, to obtain charging cost prediction information corresponding to each candidate power supply device. A candidate recommendation index corresponding to each candidate power supply device is generated according to the charging cost prediction information corresponding to each candidate power supply device. A target power supply device is selected from each candidate power supply device according to the candidate recommendation index corresponding to each candidate power supply device. A charging path is recommended according to the movement path between the target power supply device and the target charging device.

[0036] In this way, when the charging cost is predicted, not only the power supply dimension of the candidate power supply device is considered, but also the charging dimension of the target charging device itself is considered. The charging cost prediction information obtained by prediction takes various factors into consideration, improving the prediction accuracy of the charging cost prediction information. The charging cost prediction information is the basis for generating the candidate recommendation index, so the generation accuracy of the candidate recommendation index is improved. The target power supply device and the movement path selected according to the candidate recommendation index are adapted to the deep needs of the user, thereby improving the accuracy of the charging path recommendation. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0038] Figure 1 An application environment diagram of the charging path recommendation method in an embodiment;

[0039] Figure 2 A flowchart of the charging path recommendation method in an embodiment;

[0040] Figure 3 A flowchart of the step of predicting the cost consumed when the target charging device uses each candidate power supply device to charge according to at least one of the battery status and the location represented by the first device information and at least one of the location and the charging cost represented by the second device information, to obtain charging cost prediction information corresponding to each candidate power supply device.

[0041] Figure 4 a flowchart of a step of generating a candidate recommendation index corresponding to each of the candidate power supply devices according to the charging cost prediction information corresponding to each of the candidate power supply devices in an embodiment;

[0042] Figure 5 a structural block diagram of a charging path recommendation device in an embodiment;

[0043] Figure 6 an internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0044] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0045] It should be noted that the terms "first", "second" and the like used herein can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used herein are intended to cover non-exclusive inclusion. The term "multiple" used herein refers to two or more. The term "and / or" used herein refers to one of the options or any combination of multiple options.

[0046] The charging path recommendation method provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment is shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The server 104 obtains the first device information of the target charging device, and the second device information of at least one candidate power supply device; according to at least one of the battery condition and the position represented by the first device information, and according to at least one of the position and the charging cost represented by the second device information, the cost consumed when each candidate power supply device is used to charge the target charging device is predicted respectively, and the charging cost prediction information corresponding to each candidate power supply device is obtained; according to the charging cost prediction information corresponding to each candidate power supply device, the candidate recommendation index corresponding to each candidate power supply device is generated; according to the candidate recommendation index corresponding to each candidate power supply device, the target power supply device is selected from each candidate power supply device; according to the moving path between the target power supply device and the target charging device, the charging path recommendation is carried out. Among them, the server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal 102 can be deployed in the target charging device, and the terminal 102 can belong to the user in the target charging device.

[0047] In an exemplary embodiment, as shown in Figure 2 , a charging path recommendation method is provided, and the method is applied to the server 104 in Figure 1 for example, including the following steps 202 to 208. Among them:

[0048] Step 202, obtaining the first device information of the target charging device, and the second device information of at least one candidate power supply device.

[0049] Among them, the target charging device in step 202 is a device waiting for charging, for example, can be electric vehicles and so on, the target charging device is obtained according to the response charging instruction, or the device whose battery condition is lower than the preset power threshold, which is not limited here. The first device information includes at least one of the charging device position and the battery information, the charging device position includes at least one of the charging device starting position and the charging device target position (representing the moving target of the target charging device), and the battery information includes at least one of the battery state of charge, the battery pack temperature and the battery thermal management state.

[0050] The candidate power supply device in step 202 is a power supply device in an open state (with power supply capability), for example, a charging pile or the like. The second device information includes at least one of power supply information, a power supply location, and a power supply environment, the power supply information includes at least one of historical power supply output power and power supply power distribution information, and the power supply environment is used to represent a power supply environment temperature.

[0051] In step 204, the cost consumed by each candidate power supply device when charging the target charging device is predicted according to at least one of the battery condition and the location represented by the first device information, and at least one of the location and the charging cost represented by the second device information, to obtain charging cost prediction information corresponding to each candidate power supply device.

[0052] In step 204, the charging cost prediction information includes at least one of time cost prediction information and economic cost prediction information.

[0053] In step 206, a candidate recommendation index corresponding to each candidate power supply device is generated according to the charging cost prediction information corresponding to each candidate power supply device.

[0054] In step 206, the charging cost prediction information includes time cost prediction information and economic cost prediction information, and the charging cost represented by the charging cost prediction information corresponding to each candidate power supply device is inversely related to the candidate recommendation index.

[0055] For example, step 206 includes: for each candidate power supply device, fusing the time cost prediction information and the economic cost prediction information of the candidate power supply device to obtain fusion information, and generating the candidate recommendation index of the candidate power supply device according to the fusion information, wherein the charging cost represented by the fusion information is inversely related to the candidate recommendation index.

[0056] In step 208, the target power supply device is selected from the candidate power supply devices according to the candidate recommendation index corresponding to each candidate power supply device.

[0057] In step 208, the number of target power supply devices can be one or more, which is not limited here.

[0058] For example, step 208 includes: ranking the candidate power supply devices according to the candidate recommendation index corresponding to each candidate power supply device to obtain a ranking result, and selecting the target power supply device from the candidate power supply devices according to the ranking result.

[0059] As an embodiment, the target power supply device is selected from the candidate power supply devices according to the ranking result, including: selecting the target power supply device with the highest candidate recommendation index from the candidate power supply devices according to the ranking result.

[0060] As another example, the screening of the target power supply device from the candidate power supply devices according to the ranking result comprises: obtaining a preset number, wherein the preset number can be set by the user as needed; and screening the target power supply device from the candidate power supply devices according to the ranking result.

[0061] In step 210, the charging path recommendation is performed according to the moving path between the target power supply device and the target charging device.

[0062] Illustratively, step 210 comprises: generating the moving path between the target power supply device and the target charging device according to the positions corresponding to the target power supply device and the target charging device respectively; and recommending the moving path between the target power supply device and the target charging device.

[0063] Further, as an example, the recommendation of the moving path between the target power supply device and the target charging device comprises: displaying the moving path between the target power supply device and the target charging device on the display screen disposed on the target charging device.

[0064] As another example, the recommendation of the moving path between the target power supply device and the target charging device comprises: sending the moving path between the target power supply device and the target charging device to the user terminal in the target charging device.

[0065] Optionally, the above method further comprises: obtaining other recommendation indexes corresponding to the candidate power supply devices and other charging devices, wherein the other charging devices are charging devices belonging to the same type of devices as the target charging device; and in the case that there is a target recommendation index with an index difference between the candidate recommendation index corresponding to the target power supply device and the target recommendation index greater than a preset difference threshold in the other recommendation indexes, updating the charging path recommendation according to the candidate power supply device corresponding to the target recommendation index.

[0066] In this way, when the target charging device has moved according to the recommended charging path or has not moved, considering that the road conditions and other information change in real time, the charging path recommendation can be updated according to the recommendation of other charging devices belonging to the same type of devices as the target charging device after the recommendation.

[0067] In the above-mentioned charging path recommendation method, when predicting charging costs, not only the power supply dimension of candidate power supply equipment is considered, but also the charging dimension of the target charging equipment itself. This ensures that the predicted charging costs take into account various factors, improving the accuracy of the predicted charging costs. Since the predicted charging costs are the basis for generating candidate recommendation indices, the accuracy of generating candidate recommendation indices is improved. This ensures that the target power supply equipment and travel paths selected based on the candidate recommendation indices are adapted to the user's deep needs, thereby improving the accuracy of charging path recommendations.

[0068] In one exemplary embodiment, such as Figure 3 As shown, step 204 includes steps 302 to 304. Wherein:

[0069] Step 302: Based on the battery status and location represented by the first device information and the location represented by the second device information, predict the time cost incurred when the target charging device uses each candidate power supply device to charge, and obtain time cost prediction information.

[0070] As an embodiment, step 302 includes: predicting the travel time cost incurred by the target charging device when charging using each candidate power supply device based on the location represented by the first device information and the location represented by the second device information, to obtain travel time cost information; predicting the queuing time cost incurred by the target charging device when charging using each candidate power supply device based on the usage flow status represented by the second device information, to obtain queuing time cost information; and predicting the charging waiting time cost incurred by the target charging device when charging using each candidate power supply device based on the battery status represented by the first device information and the power supply status represented by the second device information, to obtain charging process time cost information.

[0071] Furthermore, based on the locations represented by the first device information and the second device information, the travel time cost incurred by the target charging device when using each candidate power supply device for charging is predicted to obtain travel time cost information. This includes: based on the locations represented by the first device information and the second device information, predicting the road conditions during the process of the target charging device moving to each candidate power supply device to obtain predicted road condition information; and based on the predicted road condition information, predicting the travel time cost incurred by the target charging device when using each candidate power supply device for charging to obtain travel time cost information. The degree of congestion represented by the predicted road condition information is positively correlated with the travel time cost represented by the travel time cost information.

[0072] The second device information further includes historical usage flow, number of charging devices navigating to the candidate power supply device, and crowd heat data of an environment where the candidate power supply device is located.

[0073] The usage flow status represented by the second device information is evaluated according to the historical usage flow, the number of charging devices navigating to the candidate power supply device, and the crowd heat data of the environment where the candidate power supply device is located, represented by the second device information. The historical usage flow is positively correlated with the usage flow corresponding to the usage flow status. The number of charging devices navigating to the candidate power supply device is positively correlated with the usage flow corresponding to the usage flow status. The heat area size corresponding to the crowd heat data of the environment where the candidate power supply device is located is positively correlated with the usage flow corresponding to the usage flow status. The usage flow corresponding to the usage flow status is positively correlated with the queuing time cost represented by the queuing time cost information.

[0074] As an embodiment, the charging waiting time cost consumed when the target charging device charges using each candidate power supply device is predicted according to the battery status represented by the first device information and the power supply status represented by the second device information, to obtain charging process time cost information, including: the time required for charging the target charging device using each candidate power supply device is obtained according to the battery status represented by the first device information, the power supply status represented by the second device information, and a battery charging model, to obtain a predicted charging duration; the charging process time cost information is generated according to the predicted charging duration, wherein the predicted charging duration is positively correlated with the charging time cost represented by the charging process time cost information.

[0075] In step 304, the economic cost consumed when the target charging device charges using each candidate power supply device is predicted according to the charging cost represented by the second device information, to obtain economic cost prediction information.

[0076] In this embodiment, the time cost consumed when the target charging device charges using each candidate power supply device is predicted according to the battery status and the position represented by the first device information, and the position represented by the second device information, to obtain time cost prediction information; the economic cost consumed when the target charging device charges using each candidate power supply device is predicted according to the charging cost represented by the second device information, to obtain economic cost prediction information. The charging cost is predicted from two dimensions of time cost and economic cost, so that the predicted economic cost prediction information is more accurate.

[0077] In one exemplary embodiment, as shown in FIG. 4, Figure 4 Step 206 includes steps 402 to 406. Wherein:

[0078] At step 402, the time cost prediction information corresponding to each candidate power supply device is weighted by a first preset weight to obtain first weighted information.

[0079] At step 404, the economic cost prediction information corresponding to each candidate power supply device is weighted by a second preset weight to obtain second weighted information.

[0080] The first preset weight and the second preset weight can be set by the user as needed or can be experience values, which are not limited herein.

[0081] The weighting manner can be, but is not limited to, summation weighting and product weighting.

[0082] Optionally, before the time cost prediction information corresponding to each candidate power supply device is weighted by the first preset weight to obtain the first weighted information, the method further includes at least one of the following: in a case where the battery power represented by the first device information is lower than a preset power threshold, the first preset weight is increased; in a case where there is a device arrival time constraint corresponding to the position represented by the first device information, the first preset weight is increased.

[0083] The preset power threshold can be set by the user as needed or can be an experience value.

[0084] In this way, in a case where the battery power represented by the first device information is lower than the preset power threshold, it is indicated that the endurance of the target charging device is low, and at this time, the first preset weight is increased, so that the time cost prediction information accounts for a higher proportion in the generation of the candidate recommendation index, thereby the candidate recommendation index of the candidate power supply device corresponding to a lower time cost can be higher, and the generation accuracy of the candidate recommendation index is improved.

[0085] In this way, in a case where there is a device arrival time constraint corresponding to the position represented by the first device information, it is indicated that the target charging device needs to arrive at the position represented by the first device information within the device arrival time constraint corresponding to the position, and at this time, the first preset weight is increased, so that the time cost prediction information accounts for a higher proportion in the generation of the candidate recommendation index, thereby the candidate recommendation index of the candidate power supply device corresponding to a lower time cost can be higher, and the generation accuracy of the candidate recommendation index is improved.

[0086] At step 406, the first weighted information and the second weighted information of each candidate power supply device are fused to obtain a candidate recommendation index corresponding to each candidate power supply device.

[0087] Exemplarily, the step 406 comprises: fusing the first weighted information and the second weighted information of each candidate power supply device to obtain fusion information of each candidate power supply device, and generating a candidate recommendation index corresponding to each candidate power supply device according to the fusion information of each candidate power supply device, wherein the charging cost represented by the fusion information is inversely related to the candidate recommendation index.

[0088] In the embodiment, the first weighted information is obtained by weighting the time cost prediction information corresponding to each candidate power supply device through a first preset weight, and the second weighted information is obtained by weighting the economic cost prediction information corresponding to each candidate power supply device through a second preset weight. The candidate recommendation index corresponding to each candidate power supply device is obtained by fusing the first weighted information and the second weighted information of each candidate power supply device. Considering that different users may have different focuses on the charging time dimension and the charging economic dimension, the first preset weight and the second preset weight are set to adjust the proportion of the charging time dimension and the charging economic dimension participating in the decision of the candidate recommendation index, thereby improving the generation accuracy of the candidate recommendation index.

[0089] As a detailed embodiment, the first device information of the target charging device and the second device information of at least one candidate power supply device are obtained. The mobile time cost consumed when the target charging device uses each candidate power supply device for charging is predicted according to the position represented by the first device information and the position represented by the second device information, to obtain mobile time cost information. The queuing time cost consumed when the target charging device uses each candidate power supply device for charging is predicted according to the use flow condition represented by the second device information, to obtain queuing time cost information. The charging waiting time cost consumed when the target charging device uses each candidate power supply device for charging is predicted according to the battery condition represented by the first device information and the power supply state represented by the second device information, to obtain charging process time cost information.

[0090] Further, according to the charging cost represented by the second device information, the economic cost consumed when each candidate power supply device is used to charge the target charging device is predicted to obtain economic cost prediction information; the time cost prediction information corresponding to each candidate power supply device is weighted by a first preset weight to obtain first weighted information; the economic cost prediction information corresponding to each candidate power supply device is weighted by a second preset weight to obtain second weighted information; the first weighted information and the second weighted information of each candidate power supply device are fused to obtain a candidate recommendation index corresponding to each candidate power supply device; the target power supply device is selected from the candidate power supply devices according to the candidate recommendation index corresponding to each candidate power supply device; the charging path recommendation is performed according to the movement path between the target power supply device and the target charging device; the other recommendation index corresponding to each candidate power supply device and other charging devices is obtained, wherein the other charging devices are charging devices of the same type as the target charging device; in the case that there is a target recommendation index with an index difference between the candidate recommendation index corresponding to the target power supply device greater than a preset difference threshold in the other recommendation indexes, the charging path recommendation is updated according to the candidate power supply device corresponding to the target recommendation index.

[0091] In this way, when the charging cost is predicted, not only the power supply dimension of the candidate power supply device is considered, but also the charging dimension of the target charging device itself is considered, so that the charging cost prediction information obtained by prediction considers various factors, improving the prediction accuracy of the charging cost prediction information. The charging cost prediction information is the basis for generating the candidate recommendation index, so the generation accuracy of the candidate recommendation index is improved, and the target power supply device and the movement path selected by the candidate recommendation index are adapted to the deep needs of the user, thereby improving the accuracy of the charging path recommendation.

[0092] It should be understood that, although each step in the flowchart involved in the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by combination are within the scope of protection of the present application.

[0093] Based on the same inventive concept, the embodiments of the present application also provide a charging path recommendation device for implementing the charging path recommendation method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more charging path recommendation device embodiments provided below can refer to the limitations of the charging path recommendation method described above, which will not be repeated here.

[0094] In one exemplary embodiment, as shown in Figure 5 a charging path recommendation device is provided, comprising: an acquisition module, a prediction module, a generation module, a screening module and a recommendation module, wherein:

[0095] The acquisition module is configured to acquire first device information of a target charging device and second device information of at least one candidate power supply device.

[0096] The prediction module is configured to predict, according to at least one of the battery status and the location represented by the first device information, and according to at least one of the location and the charging cost represented by the second device information, the cost consumed when the target charging device is charged by each candidate power supply device, to obtain charging cost prediction information corresponding to each candidate power supply device.

[0097] The generation module is configured to generate a candidate recommendation index corresponding to each candidate power supply device according to the charging cost prediction information corresponding to each candidate power supply device.

[0098] The screening module is configured to screen a target power supply device from each candidate power supply device according to the candidate recommendation index corresponding to each candidate power supply device.

[0099] The recommendation module is configured to recommend a charging path according to a moving path between the target power supply device and the target charging device.

[0100] In one embodiment, the charging cost prediction information includes time cost prediction information and economic cost prediction information; the prediction module is further configured to predict, according to the battery status and the location represented by the first device information, and according to the location represented by the second device information, the time cost consumed when the target charging device is charged by each candidate power supply device, to obtain the time cost prediction information; and predict, according to the charging cost represented by the second device information, the economic cost consumed when the target charging device is charged by each candidate power supply device, to obtain the economic cost prediction information.

[0101] In one of the embodiments, the time cost prediction information comprises mobile time cost information, queuing time cost information and charging process time cost information; the prediction module is further configured to: predict, according to the location represented by the first device information and the location represented by the second device information, the mobile time cost consumed when the target charging device uses each candidate power supply device for charging, to obtain the mobile time cost information; predict, according to the usage traffic condition represented by the second device information, the queuing time cost consumed when the target charging device uses each candidate power supply device for charging, to obtain the queuing time cost information; and predict, according to the battery condition represented by the first device information and the power supply state represented by the second device information, the charging waiting time cost consumed when the target charging device uses each candidate power supply device for charging, to obtain the charging process time cost information.

[0102] In one of the embodiments, the generation module is further configured to: perform weighting processing on the time cost prediction information corresponding to each candidate power supply device by using a first preset weight, to obtain first weighting information; perform weighting processing on the economic cost prediction information corresponding to each candidate power supply device by using a second preset weight, to obtain second weighting information; and fuse the first weighting information and the second weighting information of each candidate power supply device, to obtain the candidate recommendation index corresponding to each candidate power supply device.

[0103] In one of the embodiments, before performing the weighting processing on the time cost prediction information corresponding to each candidate power supply device by using the first preset weight, the generation module is further configured to perform at least one of the following: increase the first preset weight in a case where the battery power represented by the first device information is lower than a preset power threshold; and increase the first preset weight in a case where there is a device arrival time constraint corresponding to the location represented by the first device information.

[0104] In one of the embodiments, after performing the charging path recommendation according to the mobile path between the target power supply device and the target charging device, the apparatus further comprises an update module configured to: obtain other recommendation indexes corresponding to each candidate power supply device and other charging devices, wherein the other charging devices are charging devices of the same type as the target charging device; and in a case where there is a target recommendation index with an index difference between the candidate recommendation index corresponding to the target power supply device and the target recommendation index greater than a preset difference threshold in the other recommendation indexes, update the charging path recommendation according to the candidate power supply device corresponding to the target recommendation index.

[0105] Each of the modules in the charging path recommendation apparatus can be implemented by software, hardware, or a combination thereof, in whole or in part. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be invoked and executed by a processor to perform operations corresponding to the modules.

[0106] In an example embodiment, a computer device, which can be a terminal, has an internal structure as shown in Figure 6 The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input apparatus. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input apparatus are connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, Near Field Communication (NFC), or other technologies. The computer program is executed by the processor to implement a charging path recommendation method. The display unit of the computer device is configured to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input apparatus of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball, or a touchpad arranged on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0107] Those skilled in the art can understand that Figure 6 The structure shown in the above description is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0108] In an example embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0109] In an exemplary embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0110] In an exemplary embodiment, a computer program product is provided, and the computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0111] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the related data need to comply with relevant regulations.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., and is not limited thereto.

[0113] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered as the scope of the present application.

[0114] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A charging path recommendation method, characterized in that, The method includes: Obtain first device information of the target charging device, and second device information of at least one candidate power supply device; Based on at least one of the battery status and location represented by the first device information, and based on at least one of the location and charging cost represented by the second device information, the cost incurred by the target charging device when charging using each of the candidate power supply devices is predicted, thereby obtaining charging cost prediction information corresponding to each of the candidate power supply devices; Based on the charging cost prediction information corresponding to each candidate power supply device, a candidate recommendation index is generated for each candidate power supply device. Based on the candidate recommendation index corresponding to each of the candidate power supply devices, the target power supply device is selected from each of the candidate power supply devices; A charging path is recommended based on the movement path between the target power supply device and the target charging device.

2. The method according to claim 1, characterized in that, The charging cost prediction information includes time cost prediction information and economic cost prediction information; The step of predicting the cost incurred by the target charging device when charging using each of the candidate power supply devices based on at least one of the battery status and location represented by the first device information, and based on at least one of the location and charging cost represented by the second device information, to obtain charging cost prediction information corresponding to each of the candidate power supply devices, includes: Based on the battery status and location represented by the first device information, and the location represented by the second device information, the time cost consumed when the target charging device is charged using each of the candidate power supply devices is predicted to obtain time cost prediction information. Based on the charging cost represented by the second device information, the economic cost incurred when the target charging device is charged using each of the candidate power supply devices is predicted to obtain economic cost prediction information.

3. The method according to claim 2, characterized in that, The time cost prediction information includes travel time cost information, queuing time cost information, and charging process time cost information; The step of predicting the time cost of charging the target charging device using each of the candidate power supply devices based on the battery status and location represented by the first device information and the location represented by the second device information, to obtain time cost prediction information, includes: Based on the location represented by the first device information and the location represented by the second device information, the travel time cost incurred by the target charging device when charging using each of the candidate power supply devices is predicted to obtain travel time cost information. Based on the usage flow status represented by the second device information, the queuing time cost incurred when the target charging device is charged using each of the candidate power supply devices is predicted to obtain queuing time cost information. Based on the battery status represented by the first device information and the power supply status represented by the second device information, the charging waiting time cost incurred when the target charging device is charged using each of the candidate power supply devices is predicted to obtain charging process time cost information.

4. The method according to claim 2, characterized in that, Based on the charging cost prediction information corresponding to each candidate power supply device, a candidate recommendation index is generated for each candidate power supply device, including: The time cost prediction information corresponding to each candidate power supply device is weighted by a first preset weight to obtain the first weighted information. The economic cost prediction information corresponding to each candidate power supply device is weighted by the second preset weight to obtain the second weighted information. The first weighted information and the second weighted information of each candidate power supply device are fused to obtain the candidate recommendation index corresponding to each candidate power supply device.

5. The method according to claim 4, characterized in that, Before obtaining the first weighted information by weighting the time cost prediction information corresponding to each candidate power supply device using a first preset weight, the method further includes at least one of the following: If the battery level represented by the first device information is lower than a preset power threshold, the first preset weight is increased. If there is a device arrival time constraint corresponding to the location represented by the first device information, the first preset weight is increased.

6. The method according to claim 1, characterized in that, After recommending a charging path based on the movement path between the target power supply device and the target charging device, the method further includes: Obtain other recommendation indices between each candidate power supply device and other charging devices, wherein the other charging devices are charging devices that belong to the same type of device as the target charging device; If, among the other recommendation indices, there exists a target recommendation index whose index difference with the candidate recommendation index corresponding to the target power supply device is greater than a preset difference threshold, the charging path recommendation is updated based on the candidate power supply device corresponding to the target recommendation index.

7. A charging path recommendation device, characterized in that, The device includes: The acquisition module is used to acquire first device information of the target charging device and second device information of at least one candidate power supply device. The prediction module is used to predict the cost incurred by the target charging device when it is charged using each of the candidate power supply devices, based on at least one of the battery status and location represented by the first device information, and at least one of the location and charging cost represented by the second device information, so as to obtain the charging cost prediction information corresponding to each of the candidate power supply devices. The generation module is used to generate a candidate recommendation index for each candidate power supply device based on the charging cost prediction information corresponding to each candidate power supply device. The filtering module is used to filter target power supply devices from the candidate power supply devices according to the candidate recommendation index corresponding to each candidate power supply device; The recommendation module is used to recommend charging paths based on the movement path between the target power supply device and the target charging device.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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