Method for determining energy replenishment mode, device, and storage medium

Abstract

Embodiments of the present invention relate to the technical field of computers. Disclosed are a method for determining an energy replenishment mode, a device, and a storage medium. In the solution, a vehicle-mounted terminal can send state information of a plurality of in-vehicle electric devices and the number of passengers to a big data platform, and on the basis of these pieces of information, the big data platform calculates charging amount of electricity (i.e., target charging amount of electricity) required for a target vehicle to reach a destination, further determines a plurality of energy replenishment modes on the basis of the remaining amount of electricity and the charging amount of electricity, and marks a corresponding charging pile combination and charging time. In this way, by means of the method for automatically determining the energy replenishment mode, a user can know energy replenishment modes under different paths without planning an energy replenishment mode by himself / herself; furthermore, the user can perform energy replenishment to the vehicle on the basis of the selected path and the corresponding energy replenishment mode to reach the destination, and convenience is achieved.

Description

Method, device and storage medium for determining energy replenishment mode

[0001] This disclosure claims priority to Chinese patent application number 202310771113.0, filed on June 26, 2023, entitled “Method, device and storage medium for determining energy replenishment mode,” the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present disclosure relates to the technical field of new energy vehicles, and in particular to a method, device, and storage medium for determining an energy replenishment mode. Background Art

[0003] With the development of new energy vehicle technology, new energy vehicles are gaining increasing market recognition and gradually increasing their market share. However, the issue of battery life has long troubled users of electric vehicles. Generally, users can determine the charging time and location based on the battery level displayed on the dial or on-board terminal display, as well as the location of charging stations on an electronic map.

[0004] However, when the journey is long, it is difficult for users to plan a complete charging plan, which may cause the vehicle to break down on the road due to insufficient power, causing great inconvenience to users.

[0005] Summary of the Invention

[0006] This disclosure provides a method, device, and storage medium for determining a recharge mode, which can solve the problems of related technologies. The technical solution is as follows:

[0007] In a first aspect, a method for determining an energy replenishment mode is provided, the method comprising:

[0008] After the vehicle terminal detects the user's input operation on the destination, it sends a route acquisition request to the map platform, wherein the route acquisition request includes the identification information of the target vehicle;

[0009] The vehicle-mounted terminal obtains status information of multiple in-vehicle electrical devices and the number of passengers, and sends a request for energy replenishment method recommendation to the big data platform, wherein the status information is an activated state or an inactivated state, and the energy replenishment method recommendation request includes identification information of the target vehicle and status information of the multiple in-vehicle electrical devices;

[0010] The map platform determines multiple paths between the location of the target vehicle and the destination, and sends the paths to the big data platform and the vehicle terminal;

[0011] The big data platform determines that at least one of the status information of the multiple in-vehicle electrical devices is in a startup state, determines the in-vehicle electrical device with the startup state among the multiple in-vehicle electrical devices as a target in-vehicle electrical device, and determines the calibrated power of the target in-vehicle electrical device based on the correspondence between the vehicle identification information and the calibrated power of the in-vehicle electrical device and the identification information of the target vehicle;

[0012] The big data platform determines the total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums the calibrated powers of the electrical devices in the target vehicle to obtain a total power value of the electrical devices in the target vehicle, and determines the drivable distance of the target vehicle based on the remaining power, the total weight, the total power value, and the expected speed of the target vehicle, wherein the remaining power and the expected speed of the target vehicle are obtained by the big data platform from the vehicle networking platform;

[0013] The big data platform determines a target driving distance corresponding to each path based on the drivable distance and the path length corresponding to each path;

[0014] The big data platform determines, based on the target driving distance corresponding to each path, the expected speed of the target vehicle, and the total power value, a first energy consumed by the electrical equipment in the target vehicle corresponding to each path; and determines, based on the target driving distance corresponding to each path, the number of passengers, and identification information of the target vehicle, a second energy consumed by the target vehicle corresponding to each path; and sums the first energy and the second energy to obtain a target charging power required by the target vehicle corresponding to each path;

[0015] The big data platform inputs the remaining power, the target charging power corresponding to each path, the charging power of the multiple charging piles in each path, the spacing between the multiple charging piles in each path, and the calibrated power of the target vehicle into the energy replenishment recommendation algorithm to determine the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination, wherein the calibrated power is the power of the target vehicle when it is fully charged;

[0016] The big data platform sends the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination to the vehicle terminal;

[0017] The vehicle-mounted terminal displays the multiple paths and marks the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination.

[0018] In one possible implementation, the vehicle-mounted terminal obtains status information of multiple in-vehicle electrical devices and the number of passengers, including:

[0019] The vehicle-mounted terminal obtains status information of a plurality of in-vehicle electrical devices and the number of passengers input by a user.

[0020] In one possible implementation, the vehicle-mounted terminal obtains status information of multiple in-vehicle electrical devices, including:

[0021] The vehicle-mounted terminal detects status information of multiple in-vehicle electrical devices according to a specified period.

[0022] In one possible implementation, the big data platform determines the total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums the calibrated powers of the electrical devices in the target vehicle to obtain a total power value of the electrical devices in the target vehicle, and determines the drivable distance of the target vehicle based on the remaining power, the total weight, the total power value, and the expected speed of the target vehicle, including:

[0023] The drivable distance is determined based on the following formula: G = (n x m1 + m2) x g (1) P = P1 + P2 + ... P n (2)

[0024] Where G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard weight, m2 is the mass of the target vehicle, P is the total power value of the electrical equipment in the target vehicle, P n is the power value of any electrical device in the target vehicle, S is the drivable distance, W is the remaining power, k is the friction coefficient, and v is the expected speed of the target vehicle.

[0025] In one possible implementation, the big data platform determines the target driving distance corresponding to each path based on the drivable distance and the path length corresponding to each path, including:

[0026] The target driving distance corresponding to each path is determined by subtracting the drivable distance plus a preset reserved distance from the path length corresponding to each path.

[0027] In one possible implementation, the big data platform determines, based on the target driving distance corresponding to each path, the expected speed of the target vehicle, and the total power value, a first energy consumed by a target in-vehicle electrical device whose status information is activated among the multiple in-vehicle devices corresponding to each path; determines, based on the target driving distance corresponding to each path, the number of passengers, and identification information of the target vehicle, a second energy consumed by the target vehicle corresponding to each path; and sums the first energy and the second energy to obtain a target charging power required by the target vehicle corresponding to each path, including:

[0028] Determine the target charging power required by the target vehicle corresponding to each path based on the following formula; W2=G·k·S1 (5) W3=W1+W2 (6)

[0029] Among them, W1 is the first energy, P is the total power value of the electrical equipment in the target vehicle, S1 is the target driving distance, v is the expected speed of the target vehicle, W2 is the second energy, G is the total weight of the target vehicle, k is the friction coefficient, and W3 is the target charging power.

[0030] In one possible implementation, the big data platform inputs the remaining power, the target charging power corresponding to each path, the charging power of the multiple charging piles in each path, the distance between the multiple charging piles in each path, and the calibrated power of the target vehicle into the energy replenishment recommendation algorithm to determine the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination, including:

[0031] Arrange and combine the multiple charging piles in each path to obtain multiple basic charging pile combinations corresponding to each path;

[0032] Determining the charging time corresponding to each charging pile in each basic charging pile combination based on the charging power of the charging pile in each basic charging pile combination and the target charging power corresponding to each path;

[0033] Based on the remaining power, the charging power of the charging piles in each basic charging pile combination, the charging time corresponding to each charging pile in each basic charging pile combination, the interval distance between the multiple charging piles in each basic charging pile combination, and the calibrated power of the target vehicle, the multiple basic charging pile combinations corresponding to each path are screened to obtain a feasible charging pile combination corresponding to each path;

[0034] The charging pile combination with the shortest charging time among the feasible charging pile combinations corresponding to each path is determined as the charging pile combination corresponding to each path.

[0035] In a possible implementation, the method further includes:

[0036] The big data platform determines whether the target vehicle supports the battery swap function based on the identification information of the target vehicle. If the target vehicle supports the battery swap function, the total weight of the target vehicle is determined based on the number of passengers and the identification information of the target vehicle. The calibrated powers of the target in-vehicle devices are summed to obtain the total power value of the in-vehicle electrical devices of the target in-vehicle devices whose status information is activated. Based on the remaining power, the total weight, the total power value and the expected speed of the target vehicle, the drivable distance of the target vehicle under the remaining power is determined;

[0037] The big data platform sends the multiple battery swap stations within the drivable distance in each path to the vehicle terminal;

[0038] The on-board terminal displays the multiple paths and marks multiple battery swap stations within the drivable distance in each path.

[0039] In a second aspect, a computer device is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions; the processor executes the computer instructions stored in the memory, so that the computer device executes the method of the first aspect and its possible implementation methods.

[0040] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer program code. In response to the computer program code being executed by a computer device, the computer device executes the method of the first aspect and possible implementations thereof.

[0041] In a fourth aspect, a computer program product is provided, the computer program product comprising computer program code. In response to the computer program code being executed by a computer device, the computer device executes the method of the first aspect and possible implementations thereof.

[0042] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0043] Through the solution provided by the embodiment of the present disclosure, the vehicle terminal can send the status information of multiple in-vehicle electrical devices and the number of passengers to the big data platform. The big data platform calculates the charging power required for the target vehicle to reach the destination (i.e., the target charging power) based on this information. Furthermore, based on the remaining power and the charging power, it determines a variety of charging methods and marks the corresponding charging pile combinations and charging times. In this way, by automatically determining the charging method, users can understand the charging methods under different paths without having to plan the charging method themselves. Furthermore, users can charge the vehicle according to the selected path and the corresponding charging method to reach the destination, which is more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] FIG1 is a schematic structural diagram of an energy replenishment method recommendation system provided by an embodiment of the present disclosure;

[0046] FIG2 is a schematic structural diagram of a terminal provided by an embodiment of the present disclosure;

[0047] FIG3 is a schematic diagram of the structure of a server provided by an embodiment of the present disclosure;

[0048] FIG4 is a flow chart of a method for determining an energy replenishment mode provided in an embodiment of the present disclosure;

[0049] FIG5 is a schematic diagram of determining a target driving distance according to an embodiment of the present disclosure;

[0050] FIG6 is a schematic diagram of determining a target driving distance according to an embodiment of the present disclosure;

[0051] FIG7 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] The disclosed embodiment provides a method for determining an energy replenishment method, which is used to recommend a corresponding energy replenishment method for a user's travel route when the user is driving. The method can be implemented by an energy replenishment method recommendation system, which may include an on-board terminal, a big data platform, an Internet of Vehicles platform, a map platform, etc., as shown in Figure 1. The big data platform, the Internet of Vehicles platform, and the map platform can be the background servers of related applications. Among them, the big data platform is used to obtain relevant data from the Internet of Vehicles platform and the map platform to determine the corresponding energy replenishment method for the vehicle. The Internet of Vehicles platform is used to record the driving data of the relevant vehicles, such as speed, remaining power, etc. The map platform is used to determine the location of the vehicle and provide the vehicle with multiple paths to the destination, etc.

[0053] From the perspective of hardware composition, the structure of the vehicle terminal can be as shown in Figure 2, including a processor 210, a memory 220, a display component 230 and a communication component 240.

[0054] The processor 210 may be a CPU (central processing unit) or a SoC (system on chip), etc. The processor 110 may be used to execute various instructions involved in the method.

[0055] The memory 220 may include various volatile memories or non-volatile memories, such as an SSD (solid state disk) or a DRAM (dynamic random access memory). The memory 220 may be used to store pre-stored data, intermediate data, and result data during the process of determining the energy replenishment mode.

[0056] The display component 230 can be an independent screen, or a screen integrated with the terminal body, a projector, etc. The screen can be a touch screen or a non-touch screen. The display component is used for an operation interface, for example, a display interface of a target path, etc.

[0057] The communication component 240 may be a wired network connector, a WiFi (wireless fidelity) module, a Bluetooth module, a cellular network communication module, etc. The communication component 240 may be used to transmit data with other devices, such as servers or other terminals.

[0058] In addition to the processor and memory, the vehicle terminal may also include audio acquisition components, audio output components, etc.

[0059] The audio collection component may be a microphone for collecting the user's voice, and the audio output component may be a speaker, earphones, etc. for playing audio.

[0060] From the perspective of hardware composition, the structure of the server may be as shown in FIG3 , including a processor 310 , a memory 320 and a communication component 330 .

[0061] The processor 310 may be a CPU or a SoC, etc. The processor 310 may be used to execute various instructions involved in the method, etc.

[0062] The memory 320 may include various volatile memories or non-volatile memories, such as SSD, DRAM memory, etc. The memory 320 may be used to store pre-stored data, intermediate data, and result data in the process of determining the energy replenishment mode, for example, multiple corresponding relationships.

[0063] The communication component 330 may be a wired network connector, a WiFi module, a Bluetooth module, a cellular network communication module, etc. The communication component 330 may be used to transmit data with other devices, such as servers or other terminals.

[0064] In the field of new energy vehicles, especially electric vehicles, battery life has always been a problem for users. Therefore, when choosing a self-driving trip, people can enter the name of the destination on the in-vehicle terminal, select a route to the destination, and determine whether there are any energy refueling stations along the selected route, such as battery swap stations and charging stations.

[0065] In view of the above application scenarios, the present disclosure provides a method for determining an energy replenishment method. The processing flow of the method may be shown in FIG4 , including the following processing steps:

[0066] 401. After detecting the user's input operation on the destination, the vehicle terminal sends a route acquisition request to the map platform.

[0067] The route acquisition request includes the identification information of the target vehicle and the identification information of the destination. The identification information of the target vehicle may include the target vehicle's license plate number, frame number, model, etc. The identification information of the destination may be the name of the destination.

[0068] In practice, when a user needs to drive to a destination, they can enter the name of the destination in the map application. At this time, the vehicle terminal detects the user's input operation of the destination name and sends a route acquisition request to the map platform.

[0069] 402. The vehicle terminal obtains status information of multiple in-vehicle electrical devices and the number of passengers, and sends a request for a recommended energy replenishment method to the big data platform.

[0070] The multiple in-vehicle electrical devices may include air conditioning, audio equipment, etc. The status information indicates whether the device is activated or not. The charging method recommendation request may include identification information of the target vehicle and status information of the multiple in-vehicle electrical devices.

[0071] In practice, users can select the status information of each in-car electrical device based on their usage habits in the application that recommends charging methods. In addition, users are required to enter the number of passengers on the in-car terminal.

[0072] In other possible implementations, the vehicle terminal can periodically monitor the status of multiple in-vehicle electrical devices and the number of passengers using pressure sensors under the seats of the target vehicle. Upon detecting the user inputting a destination, the terminal can append the most recently acquired status information and number of passengers to a recommended charging method request, which is then sent to the big data platform.

[0073] 403, the map platform determines multiple paths between the target vehicle's location and the destination, and sends them to the big data platform and the vehicle terminal.

[0074] During implementation, when the map platform receives a route acquisition request, it can determine the location of the target vehicle based on the identification information of the target vehicle carried in the route acquisition request, and determine the location of the destination based on the identification information of the destination carried in the route acquisition request. Furthermore, multiple paths are planned based on the location of the target vehicle and the location of the destination and sent to the big data platform and the on-board terminal.

[0075] 404. The big data platform determines that among the status information of multiple in-vehicle electrical devices, at least one in-vehicle electrical device has status information of a startup state, and determines the in-vehicle electrical device with status information of a startup state among the multiple in-vehicle electrical devices as the target in-vehicle electrical device. Based on the correspondence between the vehicle identification information and the rated power of the in-vehicle electrical device and the identification information of the target vehicle, the rated power of the target in-vehicle electrical device is determined.

[0076] During implementation, after receiving a request for a recommended charging method, the big data platform retrieves the status information of multiple in-vehicle electrical devices from the request. If the status information of all the in-vehicle electrical devices indicates that they are not powered on, the subsequent processing in this step may be omitted. If at least one of the in-vehicle electrical devices indicates that they are powered on, the in-vehicle electrical device with the powered-on status information may be determined as the target in-vehicle electrical device.

[0077] The big data platform can store a correspondence between vehicle identification information and the rated power of in-vehicle electrical devices, as shown in Table 1. Thus, the big data platform can determine, based on the identification information of the target in-vehicle electrical devices, multiple in-vehicle electrical devices corresponding to the target vehicle and the rated power of the multiple in-vehicle electrical devices in the correspondence table. Furthermore, based on the identification information of the target in-vehicle electrical devices, the platform can determine, based on the correspondence table, the rated power of the target in-vehicle electrical devices.

[0078] Table 1

[0079] For example, if the vehicle identification information is "Type A vehicle", the above Table 1 can be used to determine the multiple in-vehicle electrical equipment corresponding to the Type A vehicle and the calibrated power of the multiple in-vehicle electrical equipment. The target in-vehicle electrical equipment is "air conditioning", and it can be determined that the calibrated power of the air conditioning of the Type A vehicle is 3kW.

[0080] 405. The big data platform determines the total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums the calibrated powers of the electrical equipment in the target vehicle to obtain the total power value of the electrical equipment in the target vehicle, and determines the drivable distance of the target vehicle based on the remaining power, total weight, total power value and expected speed of the target vehicle.

[0081] The remaining battery charge and the target vehicle's expected speed can be obtained by the big data platform from the IoV platform. The target vehicle can send its status parameters, such as driving speed and remaining battery charge, to the IoV platform at a specified frequency. The IoV platform can calculate the received driving speeds to obtain the target vehicle's average speed and update the average speed at a preset interval. In this way, the big data platform can obtain the target vehicle's average speed from the IoV platform and use it as the target vehicle's expected speed.

[0082] In implementation, the big data platform can store the corresponding relationship between the vehicle identification information and the vehicle's curb mass (i.e., net weight). In this way, the big data platform can determine the target vehicle's curb mass based on the target vehicle's identification information and the corresponding relationship between the vehicle's identification information and the vehicle's curb mass. Furthermore, the big data platform can determine the target vehicle's gross weight based on the following formula (1). At the same time, the big data platform can sum the rated power of the target vehicle's electrical equipment according to formula (2) to obtain the total power value of the target vehicle's electrical equipment. Furthermore, the big data platform can determine the target vehicle's drivable distance based on formula (3).

[0083] The calculation formula is as follows: G=(n×m1+m2)×g (1) P=P1+P2+......P n(2)

[0084] Among them, G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard weight, m2 is the mass of the target vehicle, P is the total power value of the electrical equipment in the target vehicle, P n is the power value of any target vehicle's electrical equipment, S is the drivable distance, W is the remaining power, k is the friction coefficient, and v is the expected speed of the target vehicle. It can be understood that n in formula (1) refers to the number of passengers, and P in formula (2) n The n in refers to the total number of electrical devices in the target vehicle. Therefore, the n in formula (1) is different from the n in formula (2).

[0085] In other possible implementations, the vehicle terminal can calculate the weight of each passenger using the pressure detected by the pressure sensor under the seat of the target vehicle and send it to the big data platform. In this way, the big data platform can calculate the total weight of the target vehicle based on the actual weight of the passengers, which is more accurate.

[0086] 406 , the big data platform determines the target driving distance corresponding to each path based on the drivable distance and the path length corresponding to each path.

[0087] In implementation, after calculating the drivable distance, the big data platform can compare the drivable distance with the path length of each path. If the path length of each path is less than or equal to the drivable distance, it means that the target vehicle has sufficient power and can reach the destination without recharging, so no subsequent processing is required. If there is at least one path whose path length is greater than the drivable distance, the big data platform can subtract the drivable distance from the path length corresponding to each path to obtain the target driving distance corresponding to each path, as shown in Figure 5. Alternatively, the big data platform can subtract the drivable distance plus the preset reserved distance from the path length corresponding to each path to obtain the target driving distance corresponding to each path, as shown in Figure 6. For example, the preset reserved distance can be 10 kilometers. In this way, by setting the preset reserved distance, the target vehicle can still reach the destination with a margin after arriving at the destination, or when the target vehicle consumes a large amount of power, it can still reach the destination, which is safer.

[0088] 407. The big data platform determines the first energy consumed by the electrical equipment in the target vehicle corresponding to each path based on the target driving distance corresponding to each path, the expected speed of the target vehicle, and the total power value. The big data platform determines the second energy consumed by the target vehicle corresponding to each path based on the target driving distance corresponding to each path, the number of passengers, and the identification information of the target vehicle. The first energy and the second energy are summed to obtain the target charging power required by the target vehicle corresponding to each path.

[0089] In practice, the big data platform can calculate the first energy consumed by the target vehicle's electrical equipment when the target vehicle travels the target distance according to formula (4), and calculate the second energy consumed by the target vehicle when the target vehicle travels the target distance according to formula (5). Finally, the big data platform adds the first energy and the second energy to obtain the target charging power. In this way, the target charging power required by the target vehicle corresponding to each path can be calculated.

[0090] The calculation formula is as follows: W2=G·k·S1 (5) W3=W1+W2 (6)

[0091] Among them, W1 is the first energy, S1 is the target driving distance, W2 is the second energy, and W3 is the target charging power.

[0092] At 408, the big data platform inputs the remaining power, the target charging power corresponding to each route, the charging power of multiple charging piles in each route, the interval distance between multiple charging piles in each route, and the calibrated power of the target vehicle into the energy replenishment recommendation algorithm to determine the charging pile combination corresponding to each route and the charging time corresponding to each charging pile in the charging pile combination.

[0093] Among them, the calibrated power is the power of the target vehicle when it is fully charged.

[0094] The specific steps for determining the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination can be as follows:

[0095] Step 1: Arrange and combine multiple charging piles in each path to obtain multiple basic charging pile combinations corresponding to each path.

[0096] In practice, for each path, the big data platform can identify all charging piles on the path and determine the usage status, health status, etc. of all charging piles. Then, after excluding charging piles with a usage status of "in use" or an unhealthy health status, the remaining charging piles are arranged and combined. For example, there are five charging piles on a certain path, namely charging pile A, charging pile B, charging pile C, charging pile D, charging pile E, and charging pile F. Among them, charging pile F is in use. After excluding it, the remaining five charging piles are arranged and combined to obtain the following multiple basic charging pile combinations: (A), (B), (C) ... (A, B), (A, C) ... (A, B, C) ... (A, B, C, D) ... (A, B, C, D, E).

[0097] Step 2: Determine the charging time corresponding to each charging pile in each basic charging pile combination based on the charging power of the charging pile in each basic charging pile combination and the target charging power corresponding to each path.

[0098] In implementation, for each basic charging pile combination, the big data platform can be based on the formula W3=P a t a +……+P n t n , multiple sets of numerical solutions for the charging time of each charging pile are determined. Further, a numerical solution can be randomly selected from these multiple sets of numerical solutions as the charging time combination of the basic charging pile combination, that is, the charging time corresponding to each charging pile in the basic charging pile combination is determined. Where n represents the number of the charging pile, P n Indicates the charging power of the charging pile, t n Indicates the charging time of the charging pile.

[0099] Step three: Based on the remaining power, the charging power of the charging piles in each basic charging pile combination, the charging time corresponding to each charging pile in each basic charging pile combination, the interval distance between multiple charging piles in each basic charging pile combination, and the calibrated power of the target vehicle, the multiple basic charging pile combinations corresponding to each path are screened to obtain a feasible charging pile combination corresponding to each path.

[0100] In practice, for each basic charging pile combination, the big data platform can calculate the product of the charging power and charging time of each charging pile, and use this product as the charging power of the charging pile. Then, the big data platform can determine the distance between each two charging piles and further calculate the power required to travel from one charging pile to the other according to formulas (4)-(6). This power is determined as the power consumption corresponding to the charging pile located farther back than the other of the two charging piles.

[0101] When screening, you can process based on the remaining power and the charging power and power consumption of each charging pile:

[0102] First, for the first charging pile in each basic charging pile combination, the distance from the target vehicle to the first charging pile can be determined. Based on the above formulas (4)-(6), the power required for the target vehicle to reach the first charging pile is obtained, and this power is determined as the power consumption corresponding to the first charging pile. Further, it is determined whether the remaining power is greater than or equal to the power consumption corresponding to the first charging pile. If not, the basic charging pile combination can be excluded. If so, the second rule is used for screening.

[0103] Second, for the mth charging pile in each basic charging pile combination (m is a positive integer greater than 1), the charging power of the first m charging piles can be summed, the remaining power can be added, and the sum of the power consumption of the first m charging piles can be subtracted to obtain the power consumption of the target vehicle at the mth charging pile. Further, it is determined whether the power consumption is greater than the calibrated power of the target vehicle. If so, the basic charging pile combination can be excluded. If not, when m is equal to the number of charging piles in the basic charging pile combination, the basic charging pile combination is determined as a feasible charging pile combination corresponding to the corresponding path.

[0104] Step 4: Determine the charging pile combination with the shortest charging time among the feasible charging pile combinations corresponding to each path as the charging pile combination corresponding to each path.

[0105] In practice, the above process can determine multiple feasible charging pile combinations corresponding to each path. The big data platform can sum the charging times of the charging piles in each feasible charging pile combination to obtain the charging time corresponding to each feasible charging pile combination. Furthermore, the feasible charging pile combinations are sorted from smallest to largest by charging time, and the feasible charging pile combination with the highest ranking is determined as the charging pile combination corresponding to the corresponding path. In this way, the charging pile combination corresponding to each path can be determined.

[0106] In other possible implementations, the energy replenishment recommendation algorithm may be a machine learning algorithm.

[0107] 409. The big data platform sends the charging pile combination corresponding to each route and the charging time corresponding to each charging pile in the charging pile combination to the vehicle terminal.

[0108] At 410 , the vehicle terminal displays multiple routes and marks the charging pile combination corresponding to each route and the charging time corresponding to each charging pile in the charging pile combination.

[0109] In practice, when the vehicle terminal receives multiple routes sent by the map platform, it displays the multiple routes. When it receives the charging pile combination corresponding to each route and the charging time corresponding to each charging pile in the combination, it marks the charging piles in the determined charging pile combination and the corresponding charging time on the corresponding route. Specific marking methods may include highlighting the charging piles in the charging pile combination, for example, enlarging the icons of the charging piles in the charging pile combination, marking them with special symbols, etc. At the same time, the charging pile combination number and the charging time corresponding to each charging pile may also be displayed in the lower right corner of the relevant display interface.

[0110] Since some new energy vehicles support battery swapping, it is more convenient to replenish energy through battery swapping than charging. Therefore, the embodiment of the present disclosure also provides a method for determining a battery swap station. The specific processing steps can be as follows:

[0111] Step 1: The big data platform determines whether the target vehicle supports the battery swap function based on the identification information of the target vehicle. If the target vehicle supports the battery swap function, the total weight of the target vehicle is determined based on the number of passengers and the identification information of the target vehicle. The calibrated power of the electrical equipment in the target vehicle with the status information of the start-up state is summed to obtain the total power value of the electrical equipment in the target vehicle with the status information of the start-up state. Based on the remaining power, total weight, total power value and the expected speed of the target vehicle, the drivable distance of the target vehicle with the remaining power is determined.

[0112] In implementation, the big data platform may store the correspondence between the vehicle's identification information and the energy replenishment method. In this way, the big data platform can obtain the identification information of the target vehicle from the energy replenishment method acquisition request, and based on the identification information of the target vehicle, determine whether the target vehicle supports the battery swap function in the correspondence between the vehicle's identification information and the energy replenishment method. If so, the big data platform can calculate the drivable distance of the target vehicle with the remaining power. The specific calculation method can be the same as step 405 and will not be repeated here. If not, step 406 can be processed after determining the drivable distance.

[0113] In step 2, the big data platform will determine multiple battery swap stations within the drivable distance of each route and send them to the on-board terminal.

[0114] During implementation, the big data platform identifies all battery swap stations within the drivable distance among the multiple paths provided by the map platform and sends them to the on-board terminal.

[0115] Step 3: The vehicle terminal displays multiple routes and marks multiple battery swap stations within the drivable distance of each route.

[0116] In practice, after receiving multiple routes, the vehicle terminal displays the routes and, after receiving multiple battery swap stations within the drivable distance of each route, marks the battery swap stations. Specifically, marking can be done by highlighting the multiple battery swap stations within the drivable distance, for example, by enlarging the icons of the multiple battery swap stations within the drivable distance and marking them with special symbols.

[0117] Through the method provided by the embodiment of the present disclosure, the vehicle terminal can send the status information of multiple in-vehicle electrical devices and the number of passengers to the big data platform. The big data platform calculates the charging power required for the target vehicle to reach the destination (i.e., the target charging power) based on this information. Furthermore, based on the remaining power and the charging power, multiple charging methods are determined, and the corresponding charging pile combinations and charging times are marked. In this way, by automatically determining the charging method, users can understand the charging methods under different paths without having to plan the charging method themselves. Furthermore, users can charge the vehicle according to the selected path and the corresponding charging method to reach the destination, which is more convenient.

[0118] FIG7 shows a block diagram of an electronic device 700 provided in an embodiment of the present disclosure. The electronic device 700 may be any of the terminals described in the aforementioned embodiments. The electronic device 700 may be a portable mobile terminal, such as a smartphone, a tablet computer, an MP3 player (moving picture experts group audio layer III), an MP4 player (moving picture experts group audio layer IV), a laptop computer, or a desktop computer. The electronic device 700 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal, or other similar names.

[0119] Typically, the electronic device 700 includes a processor 701 and a memory 702 .

[0120] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (digital signal processing), FPGA (field-programmable gate array), or PLA (programmable logic array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU; the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 701 may be integrated with a GPU (graphics processing unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (artificial intelligence) processor, which is used to process computing operations related to machine learning.

[0121] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is executed by the processor 701 to implement the method provided in the embodiments of the present disclosure.

[0122] In some embodiments, electronic device 700 may optionally include a peripheral device interface 703 and at least one peripheral device. Processor 701, memory 702, and peripheral device interface 703 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 703 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning assembly 708, and a power supply 709.

[0123] The peripheral device interface 703 can be used to connect at least one I / O (input / output)-related peripheral device to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0124] The radio frequency circuit 704 is used to receive and transmit RF (radio frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 704 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (wireless fidelity) networks. In some embodiments, the radio frequency circuit 704 may also include circuits related to NFC (near field communication), which is not limited in this disclosure.

[0125] Display screen 705 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, or any combination thereof. If display screen 705 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 705. These touch signals can be input as control signals to processor 701 for processing. Display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 705, located on the front panel of electronic device 700. In other embodiments, there can be at least two display screens 705, located on different surfaces of electronic device 700 or in a foldable design. In still other embodiments, display screen 705 can be a flexible display, located on a curved or foldable surface of electronic device 700. Display screen 705 can also be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 705 can be made of materials such as LCD (liquid crystal display) and OLED (organic light-emitting diode).

[0126] The camera assembly 706 is used to capture images or videos. Optionally, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (virtual reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0127] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 701 for processing, or to be input into the radio frequency circuit 704 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there can be multiple microphones, which are respectively arranged in different parts of the electronic device 700. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signals into sound waves audible to humans, but also convert the electrical signals into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0128] The positioning component 708 is used to locate the current geographic location of the electronic device 700 to implement navigation or LBS (location based service). The positioning component 708 can be a positioning component based on the GPS (global positioning system) or the Beidou system.

[0129] Power supply 709 is used to power the various components of electronic device 700. Power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0130] In some embodiments, the electronic device 700 further includes one or more sensors 710 , including but not limited to: an acceleration sensor 711 , a gyroscope sensor 712 , a pressure sensor 713 , a fingerprint sensor 714 , an optical sensor 715 , and a proximity sensor 716 .

[0131] The accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the electronic device 700. For example, the accelerometer 711 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 711. The accelerometer 711 can also be used to collect game or user motion data.

[0132] The gyroscope sensor 712 can detect the orientation and rotation angle of the electronic device 700. It can work in conjunction with the accelerometer 711 to capture the user's 3D movements of the electronic device 700. Based on the data collected by the gyroscope sensor 712, the processor 701 can implement the following functions: motion sensing (for example, changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0133] The pressure sensor 713 can be set on the side frame of the electronic device 700 and / or the lower layer of the display screen 705. When the pressure sensor 713 is set on the side frame of the electronic device 700, it can detect the user's grip signal of the electronic device 700, and the processor 701 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is set on the lower layer of the display screen 705, the processor 701 controls the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0134] The fingerprint sensor 714 is used to collect the user's fingerprint. The processor 701 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 714, or the fingerprint sensor 714 identifies the user's identity based on the collected fingerprint. When the user's identity is recognized as a trusted identity, the processor 701 authorizes the user to perform relevant sensitive operations, such as unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 can be set on the front, back, or side of the electronic device 700. When the electronic device 700 is provided with a physical button or manufacturer logo, the fingerprint sensor 714 can be integrated with the physical button or manufacturer logo.

[0135] The optical sensor 715 is used to detect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity detected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity detected by the optical sensor 715.

[0136] Proximity sensor 716, also known as a distance sensor, is typically located on the front panel of electronic device 700. Proximity sensor 716 is used to detect the distance between the user and the front of electronic device 700. In one embodiment, when proximity sensor 716 detects that the distance between the user and the front of electronic device 700 is gradually decreasing, processor 701 controls display screen 705 to switch from the screen-on state to the screen-off state. When proximity sensor 716 detects that the distance between the user and the front of electronic device 700 is gradually increasing, processor 701 controls display screen 705 to switch from the screen-off state to the screen-on state.

[0137] Those skilled in the art will appreciate that the structure shown in FIG. 7 does not limit the electronic device 700 , and may include more or fewer components than shown, or combine certain components, or adopt a different component arrangement.

[0138] In an embodiment of the present disclosure, a computer-readable storage medium is further provided, such as a memory including instructions, wherein the instructions can be executed by a processor in a terminal to perform the method for performing interactive operations in the above embodiment. The computer-readable storage medium can be non-transitory. For example, the computer-readable storage medium can be a ROM (read-only memory), a RAM (random access memory), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0139] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0140] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0141] The above descriptions are only some possible embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for determining an energy replenishment method, characterized in that: The method comprises: After the vehicle terminal detects the user's input operation on the destination, it sends a route acquisition request to the map platform, wherein the route acquisition request includes the identification information of the target vehicle; The vehicle terminal obtains status information of multiple in-vehicle electrical appliances and the number of passengers, and sends a request for energy replenishment mode recommendation to the big data platform, wherein the status information is a start-up state or an unstarted state, and the energy replenishment mode recommendation request includes identification information of the target vehicle and status information of the multiple in-vehicle electrical appliances; The map platform determines multiple paths between the location of the target vehicle and the destination, and sends the paths to the big data platform and the vehicle terminal; The big data platform determines that at least one of the status information of the multiple in-vehicle electrical devices is in a startup state, determines the in-vehicle electrical device whose status information is in a startup state among the multiple in-vehicle electrical devices as a target in-vehicle electrical device, and determines the rated power of the target in-vehicle electrical device based on the correspondence between the vehicle identification information and the rated power of the in-vehicle electrical device and the identification information of the target vehicle; The big data platform determines the total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums the calibrated powers of the electrical equipment in the target vehicle to obtain the total power value of the electrical equipment in the target vehicle, and determines the drivable distance of the target vehicle under the remaining power based on the remaining power, the total weight, the total power value and the expected speed of the target vehicle, wherein the remaining power and the expected speed of the target vehicle are obtained by the big data platform from the Internet of Vehicles platform; The big data platform determines the target driving distance corresponding to each path based on the drivable distance and the path length corresponding to each path; The big data platform determines the first energy consumed by the electrical equipment in the target vehicle corresponding to each path based on the target driving distance corresponding to each path, the expected speed of the target vehicle, and the total power value; determines the second energy consumed by the target vehicle corresponding to each path based on the target driving distance corresponding to each path, the number of passengers, and the identification information of the target vehicle; and sums the first energy and the second energy to obtain the target charging power required by the target vehicle corresponding to each path; The big data platform stores the remaining power, the target charging power corresponding to each path, The charging power of the multiple charging piles in each path, the interval distance between the multiple charging piles in each path, and the calibrated power of the target vehicle are input into the energy replenishment recommendation algorithm to determine the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination, wherein the calibrated power is the power of the target vehicle when it is fully charged; The big data platform sends the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination to the vehicle terminal; The vehicle-mounted terminal displays the multiple paths, and marks the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination.

2. The method according to claim 1, characterized in that The vehicle-mounted terminal obtains status information of multiple in-vehicle electrical devices and the number of passengers, including: The vehicle-mounted terminal obtains status information of a plurality of in-vehicle electrical appliances and the number of passengers input by a user.

3. The method according to claim 1, characterized in that The vehicle-mounted terminal obtains status information of multiple in-vehicle electrical devices, including: The vehicle-mounted terminal detects status information of a plurality of in-vehicle electrical appliances at a specified period.

4. The method according to claim 1, characterized in that: The big data platform determines the total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums the calibrated powers of the electrical equipment in the target vehicle to obtain the total power value of the electrical equipment in the target vehicle, and determines the drivable distance of the target vehicle under the remaining power based on the remaining power, the total weight, the total power value and the expected speed of the target vehicle, including: The drivable distance is determined based on the following formula; G=(n×m1+m2)×g (1) P=P1+P2+......P n (2) Where G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard weight, m2 is the mass of the target vehicle, P is the total power value of the electrical equipment in the target vehicle, P n is the power value of any target in-vehicle electrical equipment, S is the drivable distance, W is the remaining power, k is the friction The friction coefficient, v is the expected speed of the target vehicle.

5. The method according to claim 1, characterized in that The big data platform determines the target driving distance corresponding to each path based on the drivable distance and the path length corresponding to each path, including: The target driving distance corresponding to each path is determined by subtracting the drivable distance plus the preset reserved distance from the path length corresponding to each path.

6. The method according to claim 1, characterized in that The big data platform determines, based on the target driving distance corresponding to each path, the expected speed of the target vehicle, and the total power value, a first energy consumed by a target in-vehicle electrical device whose status information is activated among the multiple in-vehicle devices corresponding to each path; determines, based on the target driving distance corresponding to each path, the number of passengers, and the identification information of the target vehicle, a second energy consumed by the target vehicle corresponding to each path; and sums the first energy and the second energy to obtain a target charging power required by the target vehicle corresponding to each path, including: Determine the target charging power required by the target vehicle corresponding to each path based on the following formula; W2=G·k·S1 (5) W3=W1+W2 (6) Among them, W1 is the first energy, P is the total power value of the electrical equipment in the target vehicle, S1 is the target driving distance, v is the expected speed of the target vehicle, W2 is the second energy, G is the total weight of the target vehicle, k is the friction coefficient, and W3 is the target charging power.

7. The method according to claim 1, characterized in that The big data platform inputs the remaining power, the target charging power corresponding to each path, the charging power of multiple charging piles in each path, the interval distance between multiple charging piles in each path, and the calibrated power of the target vehicle into the energy replenishment recommendation algorithm, and determines the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination, including: Arrange and combine the multiple charging piles in each path to obtain the corresponding Multiple basic charging pile combinations; Determine the charging time corresponding to each charging pile in each basic charging pile combination based on the charging power of the charging pile in each basic charging pile combination and the target charging power corresponding to each path; Based on the remaining power, the charging power of the charging piles in each basic charging pile combination, the charging time corresponding to each charging pile in each basic charging pile combination, the interval distance between multiple charging piles in each basic charging pile combination, and the calibrated power of the target vehicle, the multiple basic charging pile combinations corresponding to each path are screened to obtain a feasible charging pile combination corresponding to each path; The charging pile combination with the shortest charging time among the feasible charging pile combinations corresponding to each path is determined as the charging pile combination corresponding to each path.

8. The method according to claim 1, characterized in that The method further comprises: The big data platform determines whether the target vehicle supports the battery swap function based on the identification information of the target vehicle. If the target vehicle supports the battery swap function, the total weight of the target vehicle is determined based on the number of passengers and the identification information of the target vehicle, the calibrated powers of the electrical equipment in the target vehicle are summed to obtain the total power value of the electrical equipment in the target vehicle whose status information is the startup state, and the drivable distance of the target vehicle under the remaining power is determined based on the remaining power, the total weight, the total power value and the expected speed of the target vehicle; The big data platform determines a plurality of battery swap stations within the drivable distance in each path and sends them to the vehicle terminal; The vehicle-mounted terminal displays the multiple paths and marks multiple battery swap stations within the drivable distance in each path.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory is used to store computer instructions; The processor executes the computer instructions stored in the memory to enable the computer device to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program code. In response to the computer program code being executed by a computer device, the computer device executes the method according to any one of claims 1 to 8.

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