Method, device and storage medium for determining a way of energy compensation

Abstract

The embodiment of the disclosure discloses a method, device and storage medium for determining a power supplementing mode, and belongs to the technical field of computers. In the scheme, a vehicle-mounted terminal can send state information of a plurality of in-vehicle electrical equipment and the number of passengers to a big data platform, and the big data platform calculates the charging amount required for a target vehicle to reach a destination (i.e., target charging amount) according to the information. Further, according to the remaining amount and the charging amount, a plurality of power supplementing modes are determined, and the corresponding charging pile combination and charging time are labeled. In this way, by automatically determining the power supplementing mode, the user can understand the power supplementing mode under different paths without planning the power supplementing mode by himself / herself. Further, the user can supplement the power of the vehicle according to the corresponding power supplementing mode according to the selected path to reach the destination, which is more convenient.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of new energy vehicles, and particularly relates to a method for determining a power supplementing mode, a device and a storage medium. BACKGROUND

[0002] With the development of new energy vehicle technology, new energy vehicles are increasingly recognized in the market, and the market share is gradually increasing. For electric vehicles, the endurance problem has always plagued users. Generally, users can determine the charging time and charging location by the power displayed on the dial or the display screen of the vehicle terminal and the location of the charging station on the electronic map.

[0003] However, when the journey is long, it is difficult for the user to plan a complete charging plan, resulting in the vehicle being stranded on the road due to insufficient power, which brings great inconvenience to the user. SUMMARY

[0004] The present disclosure provides a method for determining a power supplementing mode, a device and a storage medium, which can solve the problems of related technologies. The technical solutions are as follows:

[0005] In a first aspect, a method for determining a power supplementing mode is provided, the method comprising:

[0006] After the vehicle terminal detects a user input operation on a destination, the vehicle terminal sends a route acquisition request to a map platform, wherein the route acquisition request includes identification information of a target vehicle;

[0007] The vehicle terminal acquires state information of a plurality of in-vehicle electrical equipment and the number of passengers, and sends a power supplementing mode recommendation request to a big data platform, wherein the state information is a start state or a non-start state, and the power supplementing mode recommendation request includes the identification information of the target vehicle and the state information of the plurality of in-vehicle electrical equipment;

[0008] The map platform determines a plurality of paths between the location of the target vehicle and the destination, and sends the paths to the big data platform and the vehicle terminal;

[0009] The big data platform determines that at least one of the state information of the plurality of in-vehicle electrical equipment is in a start state, determines the in-vehicle electrical equipment with the state information in the start state as a target in-vehicle electrical equipment, and determines the rated power of the target in-vehicle electrical equipment based on the correspondence between the identification information of the vehicle and the rated power of the in-vehicle electrical equipment and the identification information of the target vehicle;

[0010] The big data platform determines a total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums up the rated power of the electric equipment in the target vehicle to obtain a total power value of the electric equipment in the target vehicle, and determines a drivable distance of the target vehicle under the residual electric quantity based on the residual electric quantity, the total weight, the total power value and an expected speed of the target vehicle, wherein the residual electric quantity and the expected speed of the target vehicle are obtained by the big data platform from a vehicle networking platform.

[0011] The big data platform determines a target driving distance corresponding to each path based on the drivable distance and a path length corresponding to each path.

[0012] The big data platform determines a first energy consumed by the electric 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 a second energy consumed by the target vehicle driving 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 up the first energy and the second energy to obtain a target charging electric quantity required by the target vehicle corresponding to each path.

[0013] The big data platform inputs the residual electric quantity, the target charging electric quantity corresponding to each path, charging power of the plurality of charging piles in each path, interval distance between the plurality of charging piles in each path, and rated electric quantity of the target vehicle into a power supplement recommendation algorithm to determine a charging pile combination corresponding to each path and a charging time corresponding to each charging pile in the charging pile combination, wherein the rated electric quantity is an electric quantity when the target vehicle is fully charged.

[0014] 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-mounted terminal.

[0015] The vehicle-mounted terminal displays the plurality of paths and labels the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination.

[0016] In a possible implementation, the vehicle-mounted terminal obtains the state information of the plurality of in-vehicle electric equipment and the number of passengers, including:

[0017] The vehicle-mounted terminal obtains the state information of the plurality of in-vehicle electric equipment and the number of passengers input by a user.

[0018] In a possible implementation, the vehicle-mounted terminal obtains the state information of the plurality of in-vehicle electric equipment, including:

[0019] The vehicle-mounted terminal detects state information of a plurality of in-vehicle electrical equipment according to a specified period.

[0020] In a possible implementation, the big data platform determines a total weight of the target vehicle based on the number of passengers and the identification information of the target vehicle, sums up the rated power of the target in-vehicle electrical equipment to obtain a total power value of the target in-vehicle electrical equipment, and determines a drivable distance of the target vehicle under the residual electric quantity based on the residual electric quantity, the total weight, the total power value and an expected speed of the target vehicle, including:

[0021] The drivable distance is determined based on the following formula:

[0022] G = (n × m1 + m2) × g (1)

[0023] P = P1 + P2 + … + P n (2)

[0024]

[0025] wherein G is the total weight of the target vehicle, n is the number of passengers, m1 is a standard body weight, m2 is the mass of the target vehicle, P is the total power value of the target in-vehicle electrical equipment, P n is the power value of any target in-vehicle electrical equipment, S is the drivable distance, W is the residual electric quantity, k is a friction coefficient, and v is the expected speed of the target vehicle.

[0026] In a possible implementation, the big data platform determines a target driving distance corresponding to each path based on the drivable distance and a path length corresponding to each path, including:

[0027] The path length corresponding to each path is subtracted by the drivable distance plus a preset reserved distance to determine the target driving distance corresponding to each path.

[0028] In a possible implementation, the big data platform determines a first energy consumed by a target in-vehicle electrical equipment corresponding to each path among the in-vehicle electrical equipment whose state information is started based on the target driving distance corresponding to each path, the expected speed of the target vehicle and the total power value, determines a 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 up the first energy and the second energy to obtain a target charging electric quantity required by the target vehicle corresponding to each path, including:

[0029] determining the target charging electric quantity required by the target vehicle corresponding to each path based on the following formula:

[0030]

[0031] W2=G·k·S1 (5)

[0032] W3=W1+W2 (6)

[0033] wherein W1 is the first energy, P is the total power value of the target in-vehicle electrical equipment, 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 electric quantity.

[0034] In a possible implementation, the big data platform inputs the residual electric quantity, the target charging electric quantity corresponding to each path, the charging power of the plurality of charging piles in each path, the interval distance between the plurality of charging piles in each path, and the rated electric quantity of the target vehicle into a power supplement 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:

[0035] performing permutation and combination on the plurality of charging piles in each path to obtain a plurality of basic charging pile combinations corresponding to each path;

[0036] determining the charging time corresponding to each charging pile in each basic charging pile combination based on the charging power of the charging piles in each basic charging pile combination and the target charging electric quantity corresponding to each path;

[0037] performing screening on the plurality of basic charging pile combinations corresponding to each path based on the residual electric quantity, 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 plurality of charging piles in each basic charging pile combination, and the rated electric quantity of the target vehicle to obtain a feasible charging pile combination corresponding to each path;

[0038] determining the charging pile combination with the shortest charging time in the feasible charging pile combinations corresponding to each path as the charging pile combination corresponding to each path.

[0039] In a possible implementation, the method further includes:

[0040] The big data platform determines whether the target vehicle supports battery swapping based on the vehicle's identification information. If the target vehicle supports battery swapping, it determines the total weight of the target vehicle based on the number of passengers and the vehicle's identification information. It then sums the rated power of the in-vehicle equipment to obtain the total power value of the in-vehicle electrical equipment whose status information indicates activation. Based on the remaining battery power, the total weight, the total power value, and the expected speed of the target vehicle, it determines the driving distance of the target vehicle with the remaining battery power.

[0041] The big data platform will identify multiple battery swapping stations within the drivable distance of each path and send them to the vehicle terminal.

[0042] The vehicle-mounted terminal displays the multiple routes and marks multiple battery swapping stations within the drivable distance of each route.

[0043] In a second aspect, a computer device is provided, comprising a memory and a processor, the memory for storing computer instructions; the processor executes the computer instructions stored in the memory to cause the computer device to perform the method of the first aspect and its possible implementations.

[0044] Thirdly, a computer-readable storage medium storing computer program code is provided, wherein, in response to the computer program code being executed by a computer device, the computer device executes the method of the first aspect and its possible implementations.

[0045] Fourthly, a computer program product is provided, the computer program product including computer program code, and a method by which the computer device executes the first aspect and its possible implementations in response to the computer program code being executed by a computer device.

[0046] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0047] The solution provided in this disclosure allows the vehicle terminal to send status information of multiple in-vehicle electrical devices and the number of passengers to a big data platform. Based on this information, the big data platform calculates the required charging power (i.e., the target charging power) for the vehicle to reach its destination. Furthermore, based on the remaining battery power and the target charging power, it determines multiple charging methods and marks the corresponding charging pile combinations and charging times. This automatic determination of charging methods eliminates the need for users to plan their own charging routes. Users can then conveniently charge their vehicles according to the selected route and the corresponding charging method to reach their destination. Attached Figure Description

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained based on these drawings without creative labor.

[0049] Figure 1 is a structural schematic diagram of a power supplement method recommendation system provided by an embodiment of the present disclosure;

[0050] Figure 2 is a structural schematic diagram of a terminal provided by an embodiment of the present disclosure;

[0051] Figure 3 is a structural schematic diagram of a server provided by an embodiment of the present disclosure;

[0052] Figure 4 is a flow schematic diagram of a method for determining a power supplement method provided by an embodiment of the present disclosure;

[0053] Figure 5 is a schematic diagram for determining a target driving distance provided by an embodiment of the present disclosure;

[0054] Figure 6 is a schematic diagram for determining a target driving distance provided by an embodiment of the present disclosure;

[0055] Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] The present disclosure provides a method for determining a power supplement method, which is used for recommending a corresponding power supplement method for a user's travel route when the user drives. The method can be implemented by a power supplement method recommendation system, which can include a vehicle-mounted terminal, a big data platform, a vehicle networking platform, a map platform, etc., and can be as shown in Figure 1 The big data platform, the vehicle networking platform and the map platform can be background servers of related application programs. The big data platform is used for obtaining relevant data from the vehicle networking platform and the map platform, and determining a corresponding power supplement method for a vehicle. The vehicle networking platform is used for recording driving data of a related vehicle, such as speed, remaining power, etc. The map platform is used for determining a position of a vehicle, and providing a vehicle with multiple paths to a destination, etc.

[0057] From the hardware composition, the structure of the vehicle-mounted terminal can be as shown in Figure 2 , which includes a processor 210, a memory 220, a display component 230 and a communication component 240.

[0058] The processor 210 can be a CPU (central processing unit) or a SoC (system on chip), etc., and the processor 110 can be used to execute various instructions involved in the method.

[0059] The memory 220 may include various volatile or non-volatile memories, such as SSD (solid state disk) and DRAM (dynamic random access memory). The memory 220 can be used to store pre-stored data, intermediate data, and result data during the process of determining the power replenishment method.

[0060] The display component 230 can be a standalone 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 operation interfaces, such as the display interface of the target path.

[0061] The communication component 240 can be a wired network connector, a WiFi (wireless fidelity) module, a Bluetooth module, a cellular network communication module, etc. The communication component 140 can be used to transmit data with other devices, such as servers or other terminals.

[0062] In addition to processors and memory, vehicle terminals may also include audio acquisition components, audio output components, etc.

[0063] The audio acquisition component can be a microphone, used to capture the user's voice. The audio output component can be a speaker, headphones, etc., used to play audio.

[0064] From a hardware perspective, the server's structure can be as follows: Figure 3 As shown, it includes a processor 310 and a memory 320.

[0065] The processor 310 can be a CPU or SoC, etc., and the processor 310 can be used to execute various instructions involved in the method.

[0066] The memory 320 may include various volatile or non-volatile memories, such as SSDs and DRAM. The memory 320 can be used to store pre-stored data, intermediate data, and result data in the process of determining the power replenishment method, such as multiple correspondences.

[0067] The communication component 330 can be a wired network connector, a WiFi module, a Bluetooth module, a cellular network communication module, etc. The communication component 330 can be used to transmit data with other devices, such as servers or other terminals.

[0068] In the field of new energy vehicle technology, especially electric vehicles, the endurance problem has been a concern for users. Therefore, when choosing to drive to a destination, users can input the name of the destination in the vehicle terminal, select a path to the destination, and determine whether there is an energy supplement station, such as a battery swap station, a charging station, etc., in the selected path.

[0069] Embodiments of the present disclosure provide a method for determining an energy supplement method for the above application scenario. The processing flow of the method can be as shown in Figure 4 The method includes the following processing steps:

[0070] 401. After detecting the user's input operation on the destination, the vehicle terminal sends a route acquisition request to the map platform.

[0071] 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 can include the license plate number, the frame number, the vehicle type, etc. of the target vehicle. The identification information of the destination can be the name of the destination.

[0072] In implementation, when a user needs to drive to a destination, the user can input the name of the destination in the map application. At this time, the vehicle terminal detects the user's input operation on the name of the destination and sends a route acquisition request to the map platform.

[0073] 402. The vehicle terminal acquires the state information of the plurality of in-vehicle electrical equipment and the number of passengers, and sends an energy supplement method recommendation request to the big data platform.

[0074] The plurality of in-vehicle electrical equipment can include air conditioners, stereos, etc. The state information is a start state or a non-start state. The energy supplement method recommendation request can include the identification information of the target vehicle and the state information of the plurality of in-vehicle electrical equipment, etc.

[0075] In implementation, the user can select the state information of each in-vehicle electrical equipment according to his own usage habits in the application program for recommending the energy supplement method. In addition, the user also needs to input the number of passengers in the vehicle terminal.

[0076] In other possible implementations, the vehicle terminal can detect the state information of the plurality of in-vehicle electrical equipment and the number of passengers through the pressure sensor under the seat of the target vehicle at a specified period. When the vehicle terminal detects the user's input operation on the destination, the vehicle terminal can add the state information of the plurality of in-vehicle electrical equipment and the number of passengers acquired in the latest period to the energy supplement method recommendation request and send it to the big data platform.

[0077] 403, the map platform determines a plurality of paths between the location of the target vehicle and the destination, and sends to the big data platform and the vehicle terminal.

[0078] In implementation, when the map platform receives the route acquisition request, the location of the target vehicle can be determined according to the identification information of the target vehicle carried in the route acquisition request, the location of the destination can be determined according to the identification information of the destination carried in the route acquisition request, and further, a plurality of paths are planned through the location of the target vehicle and the location of the destination, and sent to the big data platform and the vehicle terminal.

[0079] 404, the big data platform determines that at least one of the state information of the plurality of in-vehicle electrical equipment is in the starting state, determines the in-vehicle electrical equipment with state information in the starting state as the target in-vehicle electrical equipment, and determines the rated power of the target in-vehicle electrical equipment based on the correspondence between the identification information of the vehicle and the rated power of the in-vehicle electrical equipment and the identification information of the target vehicle.

[0080] In implementation, the big data platform receives the state information of the plurality of in-vehicle electrical equipment from the energy supplement mode recommendation request. If the state information of the plurality of in-vehicle electrical equipment is in the starting state, the subsequent processing in this step can be omitted. If at least one of the state information of the plurality of in-vehicle electrical equipment is in the starting state, the in-vehicle electrical equipment with state information in the starting state can be determined as the target in-vehicle electrical equipment.

[0081] The big data platform can store the correspondence between the identification information of the vehicle and the rated power of the in-vehicle electrical equipment, which can be as shown in Table 1. In this way, the big data platform can determine the plurality of in-vehicle electrical equipment corresponding to the target vehicle and the rated power corresponding to the plurality of in-vehicle electrical equipment according to the identification information of the target in-vehicle electrical equipment in the correspondence table, and further determine the rated power of the target in-vehicle electrical equipment according to the identification information of the target in-vehicle electrical equipment in the correspondence table.

[0082] Table 1

[0083]

[0084] For example, the identification information of the vehicle is "A type vehicle", and the plurality of in-vehicle electrical equipment corresponding to the A type vehicle and the rated power of the plurality of in-vehicle electrical equipment can be determined through the above-mentioned Table 1, and the target electrical equipment is "air conditioner", and the rated power of the air conditioner of the A type vehicle can be determined as 3kW.

[0085] 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 rated power of the electrical equipment in the target vehicle to obtain a total power value of the electrical equipment in the target vehicle, and determines the drivable distance of the target vehicle under the remaining electric quantity based on the remaining electric quantity, the total weight, the total power value, and the expected speed of the target vehicle.

[0086] The remaining electric quantity and the expected speed of the target vehicle can be obtained by the big data platform from the Internet of Vehicles platform. The target vehicle can send its own state parameters such as driving speed, remaining electric quantity, etc. to the Internet of Vehicles platform at a specified frequency. The Internet of Vehicles platform can statistically obtain the average speed of the target vehicle by updating the average speed at a preset period. In this way, the big data platform can obtain the average speed of the target vehicle from the Internet of Vehicles platform and use it as the expected speed of the target vehicle.

[0087] In implementation, the big data platform can store a correspondence between the identification information of the vehicle and the kerb mass (i.e. net weight) of the vehicle. In this way, the big data platform can determine the kerb mass of the target vehicle according to the identification information of the target vehicle in the correspondence between the identification information of the vehicle and the kerb mass of the vehicle. Further, the big data platform can determine the total weight of the target vehicle according to the following formula (1). At the same time, the big data platform can sum the rated power of the electrical equipment in the target vehicle to obtain the total power value of the electrical equipment in the target vehicle according to formula (2). Further, the big data platform can determine the drivable distance of the target vehicle according to formula (3).

[0088] The calculation formula is as follows:

[0089] G = (n x m1 + m2) x g (1)

[0090] P = P1 + P2 + … + P n (2)

[0091]

[0092] wherein G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard body 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 equipment in the target vehicle, S is the drivable distance, W is the remaining electric quantity, k is the friction coefficient, and v is the expected speed of the target vehicle.

[0093] In other possible implementations, the vehicle-mounted terminal can calculate the weight of each passenger through 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 according to the actual body weight of the passengers, which is more accurate.

[0094] 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.

[0095] 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 does not need to be charged to reach the destination, and then the subsequent processing can be skipped. 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 respectively to obtain the target driving distance corresponding to each path, as shown in formula (3). Figure 5 Alternatively, the big data platform can subtract the drivable distance plus a preset reserved distance from the path length corresponding to each path respectively to obtain the target driving distance corresponding to each path, as shown in formula (4). 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 have a margin after reaching the destination, or when the target vehicle consumes a large amount of power, it can still reach the destination, which is safer.

[0096] 407, The big data platform determines the first energy consumed by the target in-vehicle equipment 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.

[0097] In implementation, the big data platform can calculate the first energy consumed by the target in-vehicle equipment for driving the target driving distance according to formula (4), and calculate the second energy consumed by the target vehicle for driving the target driving 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.

[0098] The calculation formula is as follows:

[0099]

[0100] W2=G·k·S1 (5)

[0101] W a =W1+W2 (6)

[0102] Wherein, W1 is a first energy, S1 is a target driving distance, W2 is a second energy, and W3 is a target charging amount.

[0103] 408, the big data platform inputs the remaining amount, the target charging amount corresponding to each path, the charging power of the plurality of charging piles in each path, the interval distance between the plurality of charging piles in each path, and the calibrated amount of the target vehicle into the energy supplement 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.

[0104] Wherein, the calibrated amount is the amount of electricity when the target vehicle is fully charged.

[0105] The specific steps of 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:

[0106] Step one, arrange and combine the plurality of charging piles in each path to obtain a plurality of basic charging pile combinations corresponding to each path.

[0107] In implementation, for each path, the big data platform can determine all charging piles on the path and determine the use state, health state, etc. of all charging piles. Then, after excluding the charging piles with use state in use or health state unhealthy, the remaining charging piles are arranged and combined. For example, there are 5 charging piles on a certain path, which are charging pile A, charging pile B, charging pile C, charging pile D, charging pile E and charging pile F, wherein the use state of charging pile F is in use, which is excluded, and the remaining 5 charging piles are arranged and combined to obtain the following basic charging pile combinations: (A), (B), (C) … (A, B), (A, C) … (A, B, C) … (A, B, C, D) … (A, B, C, D, E).

[0108] Step two, based on the charging power of each charging pile in each basic charging pile combination and the target charging amount corresponding to each path, determine the charging time corresponding to each charging pile in each basic charging pile combination.

[0109] In implementation, for each basic charging pile combination, the big data platform can determine a plurality of numerical solutions of the charging time of each charging pile based on the formula W3 = P a t a + … + P n t n , further, a numerical solution can be randomly selected from the plurality 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. Wherein, n represents the label of the charging pile, P n represents the charging power of the charging pile, and tn representing the charging time of the charging pile.

[0110] Step three, based on the remaining electric quantity, the charging power of each charging pile 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 plurality of charging piles in each basic charging pile combination, and the rated electric quantity of the target vehicle, the plurality of basic charging pile combinations corresponding to each path are screened to obtain the feasible charging pile combination corresponding to each path.

[0111] In implementation, for each basic charging pile combination, the big data platform can calculate the product of the charging power and the charging time of each charging pile, and take the product as the charging electric quantity of the charging pile. Then, the big data platform can determine the distance between each two charging piles, and further calculate the electric quantity required from one charging pile to another charging pile according to formulas (4)-(6), and determine the electric quantity as the consumed electric quantity corresponding to the charging pile located at the rear of the two charging piles.

[0112] In screening, the remaining electric quantity and the charging electric quantity and the consumed electric quantity of each charging pile can be processed:

[0113] 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 first, and the electric quantity required from the target vehicle to the first charging pile is obtained based on the above formulas (4)-(6), and the electric quantity is determined as the consumed electric quantity corresponding to the first charging pile. Further, it is determined whether the remaining electric quantity is greater than or equal to the consumed electric quantity corresponding to the first charging pile. If not, the basic charging pile combination can be excluded. If yes, the screening is performed according to the second rule.

[0114] Second, for the mth charging pile (m is a positive integer greater than 1) in each basic charging pile combination, the charging electric quantity of the first m charging piles is summed, added to the remaining electric quantity, and then subtracted from the sum of the consumed electric quantity of the first m charging piles to obtain the electric quantity of the target vehicle at the mth charging pile. Further, it is determined whether the electric quantity is greater than the rated electric quantity of the target vehicle. If yes, 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 the feasible charging pile combination corresponding to the corresponding path.

[0115] Step four, the charging pile combination with the shortest charging time in the feasible charging pile combination corresponding to each path is determined as the charging pile combination corresponding to each path.

[0116] In implementation, through the above processing, a plurality of feasible charging pile combinations corresponding to each path can be determined. The big data platform can sum the charging time of the charging piles in each feasible charging pile combination to obtain the charging time corresponding to each feasible charging pile combination. Further, the feasible charging pile combinations are sorted in ascending order of the charging time, and the feasible charging pile combination with the first sorting value 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.

[0117] In other possible implementations, the energy supplement recommendation algorithm can be a machine learning algorithm.

[0118] 409, 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.

[0119] 410, the vehicle terminal displays a plurality of paths and labels the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination.

[0120] In implementation, when the vehicle terminal receives the plurality of paths sent by the map platform, it displays the plurality of paths, and when it receives the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the combination, it labels the charging piles in the determined charging pile combination and the corresponding charging time on the corresponding path. The specific labeling method can be to highlight the charging piles in the charging pile combination, for example, to enlarge the icons of the charging piles in the charging pile combination, mark special symbols, etc. At the same time, the label of the charging pile combination and the charging time corresponding to each charging pile can also be displayed in the lower right corner of the relevant display interface.

[0121] Since some new energy vehicles support battery replacement, energy supplement through battery replacement is more convenient than energy supplement through charging. Therefore, the disclosure embodiments also provide a method for determining a battery replacement station, and the specific processing steps can be as follows:

[0122] Step one, the big data platform determines whether the target vehicle supports battery replacement based on the identification information of the target vehicle. If the target vehicle supports battery replacement, the total weight of the target vehicle is determined based on the number of passengers and the identification information of the target vehicle. The total power value of the in-vehicle electrical equipment with the status information of starting is obtained by summing the rated power of the in-vehicle electrical equipment with the status information of starting. The feasible distance of the target vehicle under the remaining electric quantity is determined based on the remaining electric quantity, the total weight, the total power value and the expected speed of the target vehicle.

[0123] In implementation, the big data platform can store a correspondence between the identification information of the vehicle and the energy supplement mode. In this way, the big data platform can obtain the identification information of the target vehicle from the energy supplement mode acquisition request, and determine whether the target vehicle supports the battery swap function based on the identification information of the target vehicle in the correspondence between the identification information of the vehicle and the energy supplement mode. If yes, the big data platform can calculate the drivable distance of the target vehicle under the remaining electric quantity, and the specific calculation method can be the same as step 405, which will not be described here. If no, the processing of step 406 can be performed after the drivable distance is determined.

[0124] Step two, the big data platform sends the multiple battery swap stations in the drivable distance in each path to the vehicle terminal.

[0125] In implementation, the big data platform determines all the battery swap stations in the drivable distance in the multiple paths provided by the map platform, and sends them to the vehicle terminal.

[0126] Step three, the vehicle terminal displays the multiple paths and labels the multiple battery swap stations in the drivable distance in each path.

[0127] In implementation, the vehicle terminal displays the multiple paths after receiving them, and labels the battery swap stations after receiving the multiple battery swap stations in the drivable distance in each path. The specific labeling method can be to highlight the multiple battery swap stations in the drivable distance, for example, to enlarge the icons of the multiple battery swap stations in the drivable distance, or to mark special symbols.

[0128] Through the method provided by the embodiment of the present disclosure, the vehicle terminal can send the state information of the multiple in-vehicle electrical equipment and the number of passengers to the big data platform, and the big data platform can calculate the target charging electric quantity (i.e., the target charging electric quantity) required for the target vehicle to reach the destination according to the information, and further determine multiple energy supplement modes according to the remaining electric quantity and the charging electric quantity, and label the corresponding charging pile combination and charging time. In this way, through the method of automatically determining the energy supplement mode, the user can understand the energy supplement mode under different paths without planning the energy supplement mode by himself / herself, and further, the user can supplement the energy of the vehicle according to the corresponding energy supplement mode according to the selected path to reach the destination, which is more convenient.

[0129] Figure 7A structural block diagram of an electronic device 700 is shown. The electronic device can be each terminal in the above embodiments. The electronic device 700 can be a portable mobile terminal such as a smartphone, a tablet computer, an MP3 player, an MP4 player, a notebook computer, or a desktop computer. The electronic device 700 can also be referred to as a user equipment, a portable terminal, a laptop terminal, a desktop terminal, or other names.

[0130] Generally, the electronic device 700 includes a processor 701 and a memory 702.

[0131] The processor 701 can include one or more processing cores, such as a 4-core processor, an 8-core processor, or the like. The processor 701 can be implemented in at least one of a hardware form of a DSP, an FPGA, a PLA. The processor 701 can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also referred to as a CPU. The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 701 can be integrated with a GPU for rendering and drawing content to be displayed on a display screen. In some embodiments, the processor 701 can further include an AI processor for processing machine learning-related computing operations.

[0132] The memory 702 can include one or more computer-readable storage media, which can be non-transitory. The memory 702 can also include a high-speed random access memory and a non-volatile memory such as one or more disk storage devices, flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction for being executed by the processor 701 to implement the method provided by the embodiments of the present disclosure.

[0133] In some embodiments, the electronic device 700 can further include a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702, and the peripheral device interface 703 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 703 through a bus, a signal line, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 704, a display screen 705, a camera component 706, an audio circuit 707, a positioning component 708, and a power supply 709.

[0134] The peripheral device interface 703 can be used to connect at least one peripheral device related to input / output 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 a separate chip or circuit board, and the present embodiment is not limited in this regard.

[0135] 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 a communication network and other communication devices through 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 subscriber identity module card, and the like. The radio frequency circuit 704 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi (wireless fidelity) network. In some embodiments, the radio frequency circuit 704 can also include NFC (near field communication) related circuitry, and the present disclosure is not limited in this regard.

[0136] The display screen 705 is configured to display a UI (user interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 is also capable of capturing touch signals on or above the surface of the display screen 705. The touch signals can be input to the processor 701 as control signals for processing. At this time, the display screen 705 can also be configured to provide virtual buttons and / or virtual keyboard, also known as soft buttons and / or soft keyboard. In some embodiments, the display screen 705 can be one, arranged on the front panel of the electronic device 700; in other embodiments, the display screen 705 can be at least two, arranged on different surfaces of the electronic device 700 or in a folding design; in other embodiments, the display screen 705 can be a flexible display screen, arranged on a curved surface or a folding surface of the electronic device 700. Even, the display screen 705 can also be arranged in an irregular shape other than a rectangle, i.e., a special-shaped screen. The display screen 705 can be made of LCD (liquid crystal display), OLED (organic light-emitting diode), etc.

[0137] The camera assembly 706 is configured 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, the rear camera is at least two, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, to realize the background blur function by fusing the main camera and the depth-of-field camera, the panoramic shooting and VR (virtual reality) shooting function by fusing the main camera and the wide-angle camera, or other fusion shooting functions. In some embodiments, the camera assembly 706 can also include a flash. The flash can be a single-color-temperature flash or a dual-color-temperature flash. The dual-color-temperature flash refers to the combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0138] The audio circuit 707 can include a microphone and a speaker. The microphone is used to collect sound waves of a user and an environment, and convert the sound waves into an electrical signal input to the processor 701 for processing, or input to the radio frequency circuit 704 to realize voice communication. For the purpose of stereo sound collection or noise reduction, the microphone can be multiple, and arranged at 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 an electrical signal from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker can be a conventional diaphragm speaker, or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only can it convert an electrical signal into a sound wave audible to humans, but also can convert an electrical signal into an inaudible sound wave to humans for ranging purposes, etc. In some embodiments, the audio circuit 707 can also include a headphone jack.

[0139] The positioning component 708 is used to position the current geographic location of the electronic device 700 to realize navigation or LBS (location based service). The positioning component 708 can be a positioning component based on a GPS (global positioning system) or a Beidou system.

[0140] The power supply 709 is used to supply power to various components in the electronic device 700. The power supply 709 can be an alternating current, a direct current, a disposable battery or a rechargeable battery. When the power supply 709 includes a rechargeable battery, the rechargeable battery can be a wired charging battery or a wireless charging battery. The wired charging battery is a battery charged through a wired line, and the wireless charging battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0141] In some embodiments, the electronic device 700 further includes one or more sensors 710. The one or more sensors 710 include, but are 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.

[0142] The acceleration sensor 711 can detect the acceleration magnitude in three coordinate axes of the coordinate system established by the electronic device 700. For example, the acceleration sensor 711 can be used to detect the components of gravitational acceleration in three coordinate axes. The processor 701 can control the display screen 705 to display a user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 711. The acceleration sensor 711 can also be used for gaming or user motion data collection.

[0143] The gyroscope sensor 712 can detect the body direction and rotation angle of the electronic device 700, and can collect 3D motions of the user with respect to the electronic device 700 in cooperation with the acceleration sensor 711. The processor 701 can implement the following functions according to the data collected by the gyroscope sensor 712: motion sensing (e.g., changing a UI according to a tilt operation of the user), image stabilization during shooting, game control, and inertial navigation.

[0144] The pressure sensor 713 can be disposed 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 disposed on the side frame of the electronic device 700, the grip signal of the user with respect to the electronic device 700 can be detected, and the left-hand or right-hand recognition or shortcut operation can be performed by the processor 701 according to the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is disposed on the lower layer of the display screen 705, the operable control on the UI interface can be controlled by the processor 701 according to the pressure operation of the user with respect to the display screen 705. The operable control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0145] The fingerprint sensor 714 is used to collect the fingerprint of the user, and the identity of the user can be recognized by the processor 701 according to the fingerprint collected by the fingerprint sensor 714, or by the fingerprint sensor 714 according to the collected fingerprint. When the identity of the user is recognized as a trusted identity, the processor 701 authorizes the user to perform a related sensitive operation, which includes unlocking the screen, viewing encrypted information, downloading software, payment, and changing settings, etc. The fingerprint sensor 714 can be disposed on the front, back, or side of the electronic device 700. When the physical button or the manufacturer's logo is disposed on the electronic device 700, the fingerprint sensor 714 can be integrated with the physical button or the manufacturer's logo.

[0146] The optical sensor 715 is used to collect the ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 according to the ambient light intensity collected 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 according to the ambient light intensity collected by the optical sensor 715.

[0147] The proximity sensor 716, also referred to as a distance sensor, is usually arranged on the front panel of the electronic device 700. The proximity sensor 716 is used to collect the distance between the user and the front of the electronic device 700. In an embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the electronic device 700 gradually decreases, the display screen 705 is switched from the bright screen state to the screen-off state under the control of the processor 701; when the proximity sensor 716 detects that the distance between the user and the front of the electronic device 700 gradually increases, the display screen 705 is switched from the screen-off state to the bright screen state under the control of the processor 701.

[0148] Those skilled in the art can understand that the structure shown in the above embodiments does not constitute a limitation on the electronic device 700, and can include more or fewer components than those shown, or combine certain components, or adopt a different arrangement of components. Figure 7 Those skilled in the art can understand that the structure shown in the above embodiments does not constitute a limitation on the electronic device 700, and can include more or fewer components than those shown, or combine certain components, or adopt a different arrangement of components.

[0149] In the embodiments of the present disclosure, a computer readable storage medium, for example, a memory including instructions executable by a processor in a terminal to perform the method of performing interactive operations in the above embodiments is also provided. 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.

[0150] 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 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 the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0151] Those of ordinary skill in the art can understand that all or part of the steps of the above embodiments can be completed by hardware, or by a program instructing related hardware, and the program can be stored in a computer readable storage medium, and the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0152] The above only describes some possible embodiments of the present disclosure, and does not limit the present disclosure, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method of determining a method of energy supplementation, characterized in that, The method comprises: After the vehicle terminal detects the input operation of the user to the destination, a route acquisition request is sent to the map platform, wherein the identification information of the target vehicle is included in the route acquisition request; The vehicle terminal acquires the state information of the plurality of in-vehicle electrical equipment and the number of passengers, and sends a power supplement mode recommendation request to the big data platform, wherein the state information is a start state or a non-start state, and the identification information of the target vehicle and the state information of the plurality of in-vehicle electrical equipment are included in the power supplement mode recommendation request; The map platform determines a plurality of paths between the position of the target vehicle and the destination, and sends them to the big data platform and the vehicle terminal; The big data platform determines that at least one of the state information of the plurality of in-vehicle electrical equipment is in the start state, determines the in-vehicle electrical equipment with the state information in the start state among the plurality of in-vehicle electrical equipment as the target in-vehicle electrical equipment, determines the rated power of the target in-vehicle electrical equipment based on the correspondence between the identification information of the vehicle and the rated power of the in-vehicle electrical equipment, 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 rated power of the target in-vehicle electrical equipment to obtain the total power value of the target in-vehicle electrical equipment, and determines the drivable distance of the target vehicle under the remaining electric quantity based on the remaining electric quantity, the total weight, the total power value and the expected speed of the target vehicle, wherein the remaining electric quantity and the expected speed of the target vehicle are acquired from the Internet of Vehicles platform by the big data 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 in the plurality of paths; The big data platform determines the first energy consumed by the target in-vehicle electrical equipment 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 electric quantity required by the target vehicle corresponding to each path; The big data platform inputs the remaining electric quantity, the target charging electric quantity corresponding to each path, the charging power of the plurality of charging piles in each path, the interval distance between the plurality of charging piles in each path, and the rated electric quantity of the target vehicle into a power supplement 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 rated electric quantity is the electric quantity when the target vehicle is full of electricity; 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 labels the charging pile combination corresponding to each path and the charging time corresponding to each charging pile in the charging pile combination; The big data platform inputs the residual electric quantity, the target charging electric quantity 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 electric quantity of the target vehicle into a power supplement 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: The multiple charging piles in each path are arranged and combined to obtain multiple basic charging pile combinations corresponding to each path; The charging time corresponding to each charging pile in each basic charging pile combination is determined based on the charging power of the charging piles in each basic charging pile combination and the target charging electric quantity corresponding to each path; The multiple basic charging pile combinations corresponding to each path are screened based on the residual electric quantity, 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 electric quantity of the target vehicle to obtain feasible charging pile combinations corresponding to each path; The charging pile combination with the shortest charging time in the feasible charging pile combinations corresponding to each path is determined as the charging pile combination corresponding to each path.

2. The method of claim 1, wherein, The vehicle-mounted terminal obtains state information of multiple in-vehicle electrical equipment and the number of passengers, including: The vehicle-mounted terminal obtains state information of multiple in-vehicle electrical equipment and the number of passengers input by a user.

3. The method of claim 1, wherein, The vehicle-mounted terminal obtains state information of multiple in-vehicle electrical equipment, including: The vehicle-mounted terminal detects state information of multiple in-vehicle electrical equipment at a specified period.

4. The method of claim 1, wherein, 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 power of the target in-vehicle electrical equipment to obtain a total power value of the target in-vehicle electrical equipment, and determines the drivable distance of the target vehicle under the residual electric quantity based on the residual electric quantity, 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: wherein G is the total weight of the target vehicle, n is the number of passengers, m1 is the standard body 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 equipment 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.

5. The method of claim 1, wherein, The big data platform determines the target travel distance corresponding to each path based on the drivable distance and the path length corresponding to each path, including: The target travel 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, respectively.

6. The method of claim 1, wherein, 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 corresponding to each path, which is an in-vehicle device with state information being started, 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 traveling corresponding to each path, sums the first energy and the second energy to obtain a target charging electric quantity required by the target vehicle corresponding to each path, and includes: The target charging electric quantity required by the target vehicle corresponding to each path is determined based on the following formula: Wherein, W1 is the first energy, P is the total power value of the target in-vehicle electrical device, 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 electric quantity.

7. The method of claim 1, wherein, The method further includes: The big data platform determines whether the target vehicle supports battery swap function based on the identification information of the target vehicle. If the target vehicle supports 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 calibration power of the target in-vehicle electrical device is summed to obtain the total power value of the target in-vehicle electrical device with state information being started, and the travelable distance of the target vehicle under the remaining electric quantity is determined based on the remaining electric quantity, the total weight, the total power value, and the expected speed of the target vehicle. The big data platform sends a plurality of battery swap stations within the travelable distance in each path to the vehicle-mounted terminal. The vehicle-mounted terminal displays the plurality of paths and marks a plurality of battery swap stations within the travelable distance in each path.

8. A computer device, comprising: The computer device includes a memory and a processor, and 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 in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program code, and in response to the computer program code being executed by a computer device, the computer device executes the method in any one of claims 1-7. The computer readable storage medium stores computer program code, and in response to the computer program code being executed by a computer device, the computer device executes the method in any one of claims 1-7.

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