Vehicle Endurance Warning Method, Device, Equipment and Readable Storage Medium

Abstract

The present application discloses a vehicle endurance warning method, device, equipment and readable storage medium, which relates to the technical field of vehicles. The method includes: inputting the first operating parameter of a target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle; obtaining the energy consumption mileage between the target vehicle and the first endurance station, and determining the current endurance warning level of the target vehicle according to the endurance mileage and the energy consumption mileage; and controlling the target vehicle to perform corresponding endurance warning according to the endurance warning level. The present application solves the technical problem of low intelligence in current vehicle endurance warning.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a method, device, equipment and readable storage medium for vehicle endurance warning. Background Art

[0002] The endurance mileage of a vehicle is information that a driver must master during the driving process. If the driver does not have an accurate understanding of the endurance mileage, it is possible that the vehicle runs out of power or fuel during driving and cannot move, which will bring great inconvenience to the driver.

[0003] Currently, usually when the endurance mileage of the vehicle reaches the warning endurance mileage set by default at the factory, the vehicle will give a warning. However, on the premise that it cannot be guaranteed that the vehicle can reach the destination or the nearest endurance station within the warning endurance mileage, the vehicle warning at this time does not play an effective role. Moreover, when the driver is used to going to the endurance station at a certain specific endurance mileage, this method cannot well meet the driver's behavior habits, resulting in a low intelligence of vehicle endurance warning. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment and readable storage medium for vehicle endurance warning, aiming to solve the technical problem of low intelligence of current vehicle endurance warning.

[0005] To achieve the above purpose, this application provides a method for vehicle endurance warning, and the method for vehicle endurance warning includes:

[0006] Input the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle;

[0007] Obtain the energy consumption mileage between the target vehicle and the first endurance station, and determine the current endurance warning level of the target vehicle according to the endurance mileage and the energy consumption mileage;

[0008] Control the target vehicle to perform corresponding endurance warning according to the endurance warning level.

[0009] Optionally, the step of determining the current endurance warning level of the target vehicle according to the endurance mileage and the energy consumption mileage includes:

[0010] Obtain the mileage difference between the endurance mileage and the energy consumption mileage, compare the mileage difference with a preset mileage threshold to obtain a comparison result;

[0011] Query the corresponding endurance warning level in a preset mapping table according to the comparison result.

[0012] Optionally, before the step of controlling the target vehicle to perform corresponding endurance warning according to the endurance warning level, the method further includes:

[0013] According to the real-time position of the target vehicle, detect whether there is a second endurance station between the target vehicle and the first endurance station;

[0014] If so, update the first endurance station to the second endurance station, and return to the step: obtain the energy consumption mileage between the target vehicle and the first endurance station.

[0015] Optionally, the step of inputting the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle includes:

[0016] Input the first operating parameter into the endurance prediction model to obtain the predicted value of the energy consumption per unit mileage of the target vehicle;

[0017] Obtain the current remaining energy consumption value of the target vehicle, and determine the endurance mileage based on the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage.

[0018] Optionally, before the step of inputting the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle, the method further includes:

[0019] According to preset dimension indexes, perform multi-dimensional analysis on each sample vehicle to obtain data of each dimension;

[0020] Obtain the second operating parameter of each sample vehicle, and combine each second operating parameter and the corresponding dimension data to generate a feature vector of each sample vehicle;

[0021] Iteratively train the preset endurance prediction model based on each feature vector.

[0022] Optionally, the step of combining each second operating parameter and the corresponding dimension data to generate a feature vector of each sample vehicle includes:

[0023] Based on each dimension data, determine the dimension vector of the corresponding sample vehicle;

[0024] Based on each second operating parameter, determine the data vector of the corresponding sample vehicle;

[0025] Combine the dimension vector and the data vector to obtain the feature vector.

[0026] Optionally, the target vehicle includes an in-vehicle prompting device, and the step of controlling the target vehicle to perform corresponding endurance warning according to the endurance warning level includes:

[0027] Convert the warning level into a signal to generate a prompt signal with corresponding intensity;

[0028] According to the prompt signal, control the vehicle-mounted prompt device to conduct a battery life warning.

[0029] In addition, to achieve the above object, the present application also provides a vehicle battery life warning device, which includes:

[0030] A predicted battery life mileage module, configured to input the first operating parameter of the target vehicle into a preset battery life prediction model to predict the battery life mileage of the target vehicle;

[0031] A determining battery life warning level module, configured to obtain the energy consumption mileage between the target vehicle and the first charging station, and determine the current battery life warning level of the target vehicle according to the battery life mileage and the energy consumption mileage;

[0032] A battery life warning module, configured to control the target vehicle to conduct corresponding battery life warnings according to the battery life warning level.

[0033] Optionally, the determining battery life warning level module is further configured to:

[0034] Obtain the mileage difference between the battery life mileage and the energy consumption mileage, compare the mileage difference with a preset mileage threshold to obtain a comparison result;

[0035] Query the corresponding battery life warning level in a preset mapping table according to the comparison result.

[0036] Optionally, the vehicle battery life warning device is further configured to:

[0037] Detect whether there is a second charging station between the target vehicle and the first charging station according to the real-time position of the target vehicle;

[0038] If so, update the first charging station to the second charging station, and return to the step: obtain the energy consumption mileage between the target vehicle and the first charging station.

[0039] Optionally, the predicted battery life mileage module is further configured to:

[0040] Input the first operating parameter into the battery life prediction model to obtain a predicted value of the energy consumption per unit mileage of the target vehicle;

[0041] Obtain the current remaining energy consumption value of the target vehicle, and determine the battery life mileage according to the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage.

[0042] Optionally, the vehicle battery life warning device is further configured to:

[0043] Perform multi-dimensional analysis on each sample vehicle according to the preset dimension indicators to obtain data for each dimension;

[0044] Obtain the second operating parameters of each of the sample vehicles, and combine each of the second operating parameters with the corresponding dimension data to generate a feature vector for each of the sample vehicles;

[0045] Iteratively train the preset endurance prediction model based on each of the feature vectors.

[0046] Optionally, the vehicle endurance warning device is further configured to:

[0047] Determine the dimension vector of the corresponding sample vehicle based on each of the dimension data;

[0048] Determine the data vector of the corresponding sample vehicle based on each of the second operating parameters;

[0049] Combine the dimension vector and the data vector to obtain the feature vector.

[0050] Optionally, the endurance warning module is further configured to:

[0051] Perform signal conversion on the warning level to generate a prompt signal with a corresponding intensity;

[0052] Control the in-vehicle prompt device to perform endurance warning according to the prompt signal.

[0053] This application also provides a vehicle endurance warning device, where the vehicle endurance warning includes: a memory, a processor, and a vehicle endurance warning program stored on the memory and executable on the processor. When the vehicle endurance warning program is executed by the processor, the steps of the vehicle endurance warning method as described above are implemented.

[0054] This application also provides a readable storage medium, on which a vehicle endurance warning program is stored. When the vehicle endurance warning program is executed by a processor, the steps of the vehicle endurance warning method as described above are implemented.

[0055] This application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the vehicle endurance warning method as described above are implemented.

[0056] The present application provides a method, apparatus, device and readable storage medium for vehicle endurance warning. Compared with the current method where the vehicle usually gives a warning only when the endurance mileage of the vehicle reaches the warning endurance mileage set by default at the factory, the present application first inputs the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle, making the obtained endurance mileage more accurate and further improving the accuracy of subsequent vehicle endurance warning. Then, it obtains the energy consumption mileage between the target vehicle and the first endurance station, determines the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage, and finally controls the target vehicle to give a corresponding endurance warning according to the endurance warning level, achieving the purpose of the vehicle giving a corresponding endurance warning according to the endurance warning level, overcoming the technical defect that the current vehicle only gives a warning when the endurance mileage reaches the warning endurance mileage set by default at the factory. Without ensuring that the vehicle can reach the destination or the nearest endurance station within the warning endurance mileage, the vehicle warning at this time does not play an effective role. Moreover, when the driver is used to going to the endurance station at a certain specific endurance mileage, this method cannot well meet the driving habits of the driver, resulting in low intelligence of vehicle endurance warning. Therefore, the intelligence of vehicle endurance warning is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic flowchart of the first embodiment in the vehicle endurance warning method of the present application;

[0058] Figure 2 It is a schematic diagram of the endurance warning level involved in the vehicle endurance warning method of the present application;

[0059] Figure 3 It is a schematic flowchart of the second embodiment in the vehicle endurance warning method of the present application;

[0060] Figure 4 It is a schematic diagram of the apparatus involved in the vehicle endurance warning method of the present application;

[0061] Figure 5 It is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle endurance warning method of the present application.

[0062] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] To make the above objects, features, and advantages of the present application more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0064] Embodiment 1

[0065] Currently, usually when the driving range of a vehicle reaches the warning driving range set by default at the time of factory shipment, the vehicle will give a warning. However, on the premise that it cannot be guaranteed that the vehicle can reach the destination or the nearest charging station within the warning driving range, the vehicle warning at this time does not play an effective role. Moreover, when the driver is used to going to the charging station at a certain specific driving range, this method cannot well meet the driver's behavior habits, resulting in a low intelligence level of the vehicle driving range warning.

[0066] The embodiment of the present application provides a vehicle driving range warning method. In the first embodiment of the vehicle driving range warning method of the present application, with reference to Figure 1 , the vehicle driving range warning method includes:

[0067] Step S10: Input the first operating parameter of the target vehicle into a preset driving range prediction model to predict the driving range of the target vehicle;

[0068] Step S20: Obtain the energy consumption mileage between the target vehicle and the first charging station, and determine the current driving range warning level of the target vehicle based on the driving range and the energy consumption mileage;

[0069] Step S30: Control the target vehicle to perform corresponding driving range warnings according to the driving range warning level.

[0070] In this embodiment, it should be noted that in order to make the obtained cruising range of the target vehicle more accurate, a prediction model is adopted to predict the cruising range. The preset cruising range prediction model is obtained by generating feature vectors based on the second operating parameters of each sample vehicle and multi-dimensional grouping, using the unit mileage energy consumption value as the training label, and performing iterative training. The target vehicle can be a gasoline vehicle or a new energy vehicle; the first operating parameters include the first static operating parameters and the first dynamic operating parameters of the target vehicle. Among them, the first static operating parameters refer to the operating parameters of each function and each part of the target vehicle in a stationary state, including but not limited to vehicle mass, maximum power, theoretical cruising range of the vehicle DNEDC (it may also be other cruising range test standards, such as WLTP, EPA, CLTC, etc.), maximum allowable charging power, power battery capacity. The first dynamic operating parameters refer to the operating parameters of each function and each part of the target vehicle in an operating state, including but not limited to historical operating duration, historical driving distance, slow charge times, fast charge times, charge amount for each slow charge, charge amount for each fast charge; the cruising range refers to the remaining drivable mileage of the target vehicle; the energy consumption mileage refers to the mileage between the target vehicle and the first charging station; the first charging station refers to the nearest and available gas station or charging station to the current position of the target vehicle; the cruising range warning level is used to characterize the urgency of the vehicle's cruising range warning. The higher the level, the more urgent the vehicle needs to be warned.

[0071] As an example, steps S10 to S30 include: inputting the first operating parameters of the target vehicle into the cruising range prediction model, outputting the predicted value of the unit mileage energy consumption of the target vehicle, and then calculating the cruising range of the target vehicle according to the current remaining energy consumption value and the predicted value of the unit mileage energy consumption of the target vehicle. Among them, the current remaining energy consumption value refers to the remaining power or fuel of the target vehicle, and the unit mileage energy consumption value refers to the power or fuel consumed by the target vehicle per unit mileage; during the driving process of the target vehicle, the energy consumption mileage between the target vehicle and the first charging station is obtained in real time, and according to the mileage difference between the cruising range and the energy consumption mileage, the current cruising range warning level of the target vehicle is queried in the preset mapping table. Among them, the cruising range warning level includes but not limited to preset cruising range warning, recommended cruising range warning, emergency cruising range warning, very urgent cruising range warning, about to drive out of the drivable area warning; according to the cruising range warning level, a prompt signal of corresponding intensity is generated, and the target vehicle is controlled to perform corresponding cruising range warnings according to this prompt signal.

[0072] Among them, the step of determining the current cruising range warning level of the target vehicle according to the cruising range and the energy consumption mileage includes:

[0073] Step S21: Obtain the mileage difference between the cruising range and the energy consumption mileage, compare the mileage difference with a preset mileage threshold, and obtain a comparison result;

[0074] Step S22: According to the comparison result, query the corresponding cruising range warning level in a preset mapping table.

[0075] In this embodiment, it should be noted that the preset mileage threshold is used to compare with the mileage difference during the actual driving of the vehicle, so as to obtain the corresponding cruising range warning level. Among them, the preset mileage threshold includes at least one.

[0076] As an example, steps S21 to S22 include: obtaining the mileage difference between the cruising range and the energy consumption mileage, that is, subtracting the energy consumption mileage from the cruising range, comparing the mileage difference with each preset mileage threshold one by one to obtain a comparison result; and querying the corresponding cruising range warning level in the preset mapping table, where the preset mapping table is used to represent the corresponding relationship between the comparison result and the cruising range warning level.

[0077] For example, referring to Figure 2 , assume that in the preset mapping table: "After reaching the first cruising station, the mileage difference is less than or equal to the mileage preset by the vehicle owner" corresponds to "preset cruising range warning", "After reaching the first cruising station, the mileage difference is less than or equal to 50KM" corresponds to "recommended cruising range warning", "After reaching the first cruising station, the mileage difference is less than or equal to 25KM" corresponds to "urgent cruising range warning", "After reaching the first cruising station, the mileage difference is less than or equal to 10KM" corresponds to "very urgent cruising range warning", "After reaching the first cruising station, the mileage difference is less than or equal to 5KM" corresponds to "about to drive out of the cruising range warning area", where the vehicle owner's preset mileage, 50KM, 25KM, 10KM, and 5KM belong to the preset mileage threshold. The vehicle owner's preset mileage refers to the mileage threshold set by the vehicle owner according to his own behavior habits. For example, when the vehicle owner is used to having the vehicle give a cruising range warning when the mileage difference is less than or equal to 80KM. Assume that the cruising range of the target vehicle is 100KM and the energy consumption mileage is 80KM at this time, then the mileage difference is 20KM. By comparing with each preset mileage threshold, it can be seen that it is less than or equal to the preset mileage threshold of 25KM. Then, by querying the preset mapping table, it can be known that the mileage difference less than or equal to 25KM corresponds to "urgent cruising range warning", that is, the target vehicle should be controlled to give an urgent cruising range warning at this time.

[0078] Among them, before the step of controlling the target vehicle to give a corresponding cruising range warning according to the cruising range warning level, it further includes:

[0079] Step A10: According to the real-time position of the target vehicle, detect whether there is a second cruising station between the target vehicle and the first cruising station;

[0080] Step A20, if so, update the first charging station to the second charging station, and return to the step of obtaining the energy consumption mileage between the target vehicle and the first charging station.

[0081] In this embodiment, it should be noted that in actual applications, the station information of charging stations is dynamic. It is possible that during driving, it is found that a charging station that was previously full has become available. To enable the vehicle to reach the optimal charging station more quickly, this embodiment adopts a method of real-time monitoring of charging stations. If a better charging station is found, a switch is made.

[0082] As an example, steps A10 to A20 include: based on the real-time position of the target vehicle, detecting whether there is a second charging station between the target vehicle and the first charging station, where the second charging station refers to a charging station that is closer to the target vehicle and available compared to the first charging station; if there is a second charging station, update the first charging station to the second charging station, and return to the step of obtaining the energy consumption mileage between the target vehicle and the first charging station, that is, obtaining the energy consumption mileage between the target vehicle and the second charging station.

[0083] For example, assume that the first charging station is point A. During the vehicle's journey to point A, a closer and available second charging station, point B, is found. Then, switch to the new charging station and synchronously update the energy consumption mileage to point B.

[0084] Among them, the step of inputting the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle includes:

[0085] Step S11, input the first operating parameter into the endurance prediction model to obtain the predicted value of the energy consumption per unit mileage of the target vehicle;

[0086] Step S12, obtain the current remaining energy consumption value of the target vehicle, and determine the endurance mileage based on the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage.

[0087] As an example, steps S11 to S12 include: inputting the first static operating parameter and the first dynamic operating parameter into the endurance prediction model to obtain the predicted value of the energy consumption per unit mileage of the target vehicle; obtaining the current remaining energy consumption value of the target vehicle, and calculating the product of the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage. This product is the endurance mileage, where the current remaining energy consumption value can be obtained through the on-vehicle dashboard.

[0088] Wherein, the target vehicle includes an on-board prompt device, and the step of controlling the target vehicle to perform a corresponding range warning according to the range warning level includes:

[0089] Step S31, performing signal conversion on the warning level to generate a prompt signal of corresponding intensity;

[0090] Step S32: According to the prompt signal, control the vehicle-mounted prompt device to issue a range warning.

[0091] In this embodiment, it should be noted that in order to perform vehicle range warning more intelligently, different range warning levels can be set to correspond to prompt signals of different intensities, and the stronger the intensity, the more urgent the warning is, wherein the prompt signal can be a light signal or a voice signal.

[0092] As an example, step S31 to step S32 include: converting the warning level into a digital electrical signal, converting the warning level in the form of a digital signal into a prompt electrical signal of corresponding intensity, wherein the intensity of the prompt signal corresponds one-to-one to the endurance warning level; sending the prompt signal to the vehicle control terminal, so that the vehicle control terminal controls the corresponding on-board prompt device to perform an endurance warning, wherein the on-board prompt device can be a on-board voice system or a on-board lighting system, and prompt signals of different intensities correspond to lights of different colors or different voice prompts.

[0093] For example, assuming that the current vehicle's range warning level is a very urgent range warning, and its corresponding prompt signal strength is level A, the on-board lighting system is controlled to emit a red light corresponding to the A-level signal, indicating that it is a very urgent warning state at this time. The green light can indicate the preset range warning state, or the on-board voice system can be controlled to make a voice broadcast to remind the driver that the current mileage difference is less than or equal to 10KM. Assuming that the current vehicle's range warning level is a warning of about leaving the range area, and its corresponding prompt signal strength is level A+, the on-board lighting system is controlled to emit a dark red light corresponding to the A+ level signal. If the vehicle passes the range station and there are no other range stations within the range, or the vehicle passes the range station but the range cannot support the vehicle to reach the next range station, the on-board voice system is controlled to broadcast information such as the road rescue phone number, or the location information is synchronized to the corresponding after-sales road rescue center after the owner authorizes, so as to quickly arrive and provide rescue services.

[0094] This embodiment provides a method for predicting vehicle endurance. Compared with the current method where a vehicle usually issues a warning only when its endurance mileage reaches the warning endurance mileage set by default at the factory, this embodiment first obtains the second operating parameters of each sample vehicle, establishes an endurance prediction model based on each of the second operating parameters, inputs the first operating parameters of the target vehicle into the preset endurance prediction model to predict the endurance mileage of the target vehicle. By inputting the first operating parameters into the endurance prediction model, the obtained endurance mileage can be made more accurate, further improving the accuracy of subsequent vehicle endurance warnings. Then, it obtains the energy consumption mileage between the target vehicle and the first charging station, determines the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage, and finally controls the target vehicle to issue corresponding endurance warnings according to the endurance warning level, achieving the purpose of the vehicle issuing corresponding endurance warnings according to the endurance warning level. It overcomes the technical defect that currently a vehicle only issues a warning when its endurance mileage reaches the warning endurance mileage set by default at the factory. Without ensuring that the vehicle can reach the destination or the nearest charging station within this warning endurance mileage, the vehicle warning at this time does not play an effective role. Moreover, when a driver is used to going to a charging station at a certain specific endurance mileage, this method cannot well meet the driver's behavior habits, resulting in a relatively low intelligence level of vehicle endurance warnings. Therefore, the intelligence level of vehicle endurance warnings is improved.

[0095] Embodiment 2

[0096] In the second embodiment of the vehicle endurance warning method of this application, with reference to Figure 3 , the vehicle endurance warning method includes:

[0097] Step B10: Perform multi-dimensional analysis on each sample vehicle according to preset dimension indicators to obtain data for each dimension;

[0098] Step B20: Obtain the second operating parameters of each sample vehicle, and merge each of the second operating parameters with the corresponding dimension data to generate a feature vector for each sample vehicle;

[0099] Step B30: Iteratively train the preset endurance prediction model based on each of the feature vectors.

[0100] In this embodiment, it should be noted that during the actual driving process of the vehicle, there are many subjective or objective reasons that affect its comprehensive energy consumption. For example, factors such as false labeling of energy consumption for vehicle brands and years, real-time device energy consumption of the vehicle, and traffic congestion time will all lead to an increase in comprehensive energy consumption, ultimately resulting in the vehicle being unable to reach the original target planning location or charging station. In order to make the driving range of the vehicle more in line with the actual application scenario, obtain a more accurate driving range, and thus conduct more accurate driving range warnings, this embodiment conducts multi-dimensional analysis on each of the sample vehicles and establishes the preset driving range prediction model; the second operating parameter can refer to the meaning of the first operating parameter in step S10 and will not be elaborated here.

[0101] As an example, steps B10 to B30 include: performing multi-dimensional analysis on each sample vehicle according to preset dimension indicators to obtain data for each dimension, where the preset dimension indicators include but are not limited to various vehicle models, various cities, various temperature conditions, and various road conditions; collecting data samples of all sample vehicles through a vehicle networking big data platform or a hardware device vehicle-mounted terminal, where the data sample includes second static operating parameters and second dynamic operating parameters, and combining the second static operating parameters, the second dynamic operating parameters, and the corresponding dimension data to generate a feature vector for each of the sample vehicles; using each of the feature vectors as input feature parameters of a machine learning model, using the energy consumption value per unit mileage as a training label to train the model, and finally evaluating the accuracy of the model using a preset indicator, selecting the optimal model parameters, and obtaining the driving range prediction model, where the preset indicator can adopt at least one of RMSE root mean square error, MAE mean absolute error, MSE mean square error, R2_score coefficient of determination, etc., and the machine learning model can be a random forest algorithm model.

[0102] Among them, the step of combining each of the second operating parameters and the corresponding dimension data to generate a feature vector for each of the sample vehicles includes:

[0103] Step B21, based on each of the dimension data, determining the dimension vector of the corresponding sample vehicle;

[0104] Step B22, based on each of the second operating parameters, determining the data vector of the corresponding sample vehicle;

[0105] Step B23, combining the dimension vector and the data vector to obtain the feature vector.

[0106] As an example, steps B21 to B23 include: converting each of the dimension data into a vector data format to obtain the dimension vector of the corresponding sample vehicle, where the dimension data refers to the data of each sample vehicle in different dimensions; converting each of the second static operating parameters into a vector data format to obtain the static data vector of each sample vehicle, converting each of the second dynamic operating parameters into a vector data format to obtain the dynamic data vector of each sample vehicle, and combining the static data vector and the dynamic data vector to obtain the data vector; merging the dimension vector and the data vector to obtain the feature vector of the endurance prediction model.

[0107] For example, for a sample vehicle A01, assuming its dimension data are: vehicle model A (corresponding word vector encoding is 1), city is "Changsha" (corresponding word vector encoding is 4), temperature condition is 10 - 20 degrees Celsius (corresponding word vector encoding is 5), road condition is congested road condition (corresponding word vector encoding is 2), then the dimension vector of this vehicle can be obtained as {1, 4, 5, 2}. Then, based on the vehicle's vehicle mass of 1870 kg, maximum power of 180 kw, theoretical vehicle endurance of 520 km, maximum allowable charging power of 50 kw, power battery capacity of 75 kwh, historical operation duration of 123 h, historical driving distance of 3500 km, slow charge times of 9 times, fast charge times of 2 times, slow charge power consumption of 450 kwh, and fast charge power consumption of 150 kwh, based on the above static and dynamic parameters, the data vector of this vehicle can be generated as {1870, 180, 520, 50, 75, 123, 3500, 9, 2, 450, 150}. By comprehensively merging the grouping vector and the data vector of this vehicle, the feature vector of this vehicle {1, 4, 5, 2, 1870, 180, 520, 50, 75, 123, 3500, 9, 2, 450, 150} can be obtained. In this embodiment, the text fields in the features can be processed by means of word vector encoding.

[0108] This embodiment provides a vehicle endurance warning method. First, according to preset dimension indicators, multi-dimensional analysis is performed on each sample vehicle to obtain each dimension data; the second operating parameters of each sample vehicle are obtained, and each of the second operating parameters and the corresponding dimension data are combined to generate the feature vector of each sample vehicle; based on each of the feature vectors, the preset endurance prediction model is iteratively trained, achieving the purpose of establishing an endurance prediction model by performing multi-dimensional analysis on sample vehicles, overcoming the technical defect that the actual endurance mileage of current vehicles is only determined based on the starting point without considering the influence of environmental, road condition, vehicle parameters and other influencing factors on the endurance mileage, thereby resulting in inaccurate vehicle endurance mileage. Therefore, the accuracy of vehicle endurance mileage is improved.

[0109] Embodiment Three

[0110] In addition, an embodiment of the present application further provides a vehicle endurance warning device, as Figure 4 shown, the vehicle endurance warning device includes:

[0111] A predicted endurance mileage module 10, configured to input first operating parameters of a target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle;

[0112] A determining endurance warning level module 20, configured to obtain the energy consumption mileage between the target vehicle and a first endurance station, and determine the current endurance warning level of the target vehicle according to the endurance mileage and the energy consumption mileage;

[0113] An endurance warning module 30, configured to control the target vehicle to perform corresponding endurance warnings according to the endurance warning level.

[0114] Optionally, the determining endurance warning level module 20 is further configured to:

[0115] Obtain the mileage difference between the endurance mileage and the energy consumption mileage, compare the mileage difference with a preset mileage threshold to obtain a comparison result;

[0116] Query the corresponding endurance warning level in a preset mapping table according to the comparison result.

[0117] Optionally, the vehicle endurance warning device is further configured to:

[0118] Detect whether there is a second endurance station between the target vehicle and the first endurance station according to the real-time position of the target vehicle;

[0119] If so, update the first endurance station to the second endurance station, and return to the step: obtain the energy consumption mileage between the target vehicle and the first endurance station.

[0120] Optionally, the predicted endurance mileage module 10 is further configured to:

[0121] Input the first operating parameters into the endurance prediction model to obtain a predicted value of the energy consumption per unit mileage of the target vehicle;

[0122] Obtain the current remaining energy consumption value of the target vehicle, and determine the endurance mileage according to the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage.

[0123] Optionally, the vehicle endurance warning device is further configured to:

[0124] Perform multi-dimensional analysis on each sample vehicle according to preset dimension indexes to obtain data of each dimension;

[0125] Obtain the second operating parameters of each of the sample vehicles, and merge each of the second operating parameters with the corresponding dimensional data to generate the feature vectors of each of the sample vehicles;

[0126] Iteratively train the preset endurance prediction model based on each of the feature vectors.

[0127] Optionally, the vehicle endurance warning device is further configured to:

[0128] Based on each of the dimensional data, determine the dimensional vectors of the corresponding sample vehicles;

[0129] Based on each of the second operating parameters, determine the data vectors of the corresponding sample vehicles;

[0130] Merge the dimensional vectors and the data vectors to obtain the feature vectors.

[0131] Optionally, the endurance warning module 30 is further configured to:

[0132] Perform signal conversion on the warning level to generate a prompt signal of corresponding intensity;

[0133] According to the prompt signal, control the in-vehicle prompt device to perform endurance warning.

[0134] The vehicle endurance warning device provided in this application adopts the vehicle endurance warning method in the above embodiment to solve the technical problem of the low intelligence of the current vehicle endurance warning. Compared with the prior art, the beneficial effects of the vehicle endurance warning device provided in the embodiment of this application are the same as those of the vehicle endurance warning method provided in the above embodiment, and other technical features in the vehicle endurance warning device are the same as those disclosed in the method of the above embodiment, and will not be elaborated here.

[0135] Embodiment 4

[0136] The embodiment of this application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle endurance warning method in Embodiment 1 above.

[0137] Refer to the following Figure 5 , which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.Figure 5 The illustrated electronic device is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present disclosure.

[0138] As Figure 5 shown, the electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0139] Generally, the following systems may be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. The communication device may allow the electronic device to communicate with other devices wirelessly or wireline to exchange data. Although the figure shows an electronic device having various systems, it should be understood that it is not required to implement or include all the shown systems. More or fewer systems may be alternatively implemented or included.

[0140] In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device, or installed from a storage device, or installed from the ROM. When the computer program is executed by the processing device, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0141] The electronic device provided by the present application adopts the vehicle endurance warning method in the above embodiments, and solves the technical problem of the low intelligence of the current vehicle endurance warning. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiments of the present application are the same as those of the vehicle endurance warning method provided by the above embodiments, and other technical features in the electronic device are the same as those disclosed in the above embodiment methods, and will not be elaborated herein.

[0142] It should be understood that each part of the present disclosure may be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0143] As described above, this is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

[0144] Example Five

[0145] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the vehicle endurance warning method in the first embodiment above.

[0146] The computer-readable storage medium provided by the embodiments of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0147] The above computer-readable storage medium may be included in an electronic device; or it may exist separately without being assembled into the electronic device.

[0148] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device: inputs the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle; obtains the energy consumption mileage between the target vehicle and the first endurance station, and determines the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage; and controls the target vehicle to perform corresponding endurance warnings according to the endurance warning level.

[0149] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0151] The modules described in the embodiments of the present disclosure may be implemented in software or in hardware. In some cases, the name of the module does not constitute a limitation on the unit itself.

[0152] The computer-readable storage medium provided by the present application stores computer-readable program instructions for performing the above-mentioned vehicle endurance warning method, which solves the technical problem of low intelligence of current vehicle endurance warning. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiments of the present application are the same as those of the vehicle endurance warning method provided by the above embodiments, and will not be elaborated here.

[0153] Embodiment Six

[0154] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the vehicle endurance warning method as described above.

[0155] The computer program product provided by the present application solves the technical problem of the low intelligence of the current vehicle endurance warning. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiments of the present application are the same as those of the vehicle endurance warning method provided by the above embodiments, and will not be elaborated herein.

[0156] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent scope of the present application.

Claims

1. A vehicle endurance warning method, characterized in that The vehicle endurance warning method includes: Input the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle. Among them, the preset endurance prediction model is trained based on multi-dimensional sample data, and the multi-dimensional sample data includes static vehicle data, dynamic vehicle data, ambient temperature, and real-time road conditions; Obtain the energy consumption mileage between the target vehicle and the first endurance station, and determine the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage; According to the endurance warning level, control the target vehicle to perform corresponding endurance warnings. Among them, Before the step of controlling the target vehicle to perform corresponding endurance warnings according to the endurance warning level, it further includes: According to the real-time position of the target vehicle, detect whether there is a second endurance station between the target vehicle and the first endurance station. The second endurance station refers to an endurance station that is closer to the target vehicle and available compared to the first endurance station; If so, update the first endurance station to the second endurance station, and return to the step: obtain the energy consumption mileage between the target vehicle and the first endurance station; The target vehicle includes an in-vehicle prompt device. The step of controlling the target vehicle to perform corresponding endurance warnings according to the endurance warning level includes: Perform signal conversion on the warning level to generate a prompt signal with a corresponding intensity. Among them, the prompt signal includes a light color and a voice broadcast content corresponding to the warning level; Control the in-vehicle prompt device to perform endurance warnings according to the prompt signal.

2. The vehicle endurance warning method according to claim 1, wherein, The step of determining the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage includes: Obtain the mileage difference between the endurance mileage and the energy consumption mileage, compare the mileage difference with a preset mileage threshold to obtain a comparison result; Query the corresponding endurance warning level in a preset mapping table according to the comparison result.

3. The vehicle endurance warning method according to claim 1, wherein The step of inputting the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle includes: Input the first operating parameter into the preset endurance prediction model to predict the predicted value of the energy consumption per unit mileage of the target vehicle; Obtain the current remaining energy consumption value of the target vehicle, and determine the endurance mileage based on the current remaining energy consumption value and the predicted value of the energy consumption per unit mileage.

4. The vehicle endurance warning method according to claim 1, wherein, Before the step of inputting the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle, it further includes: Perform multi-dimensional analysis on each sample vehicle according to preset dimension indicators to obtain data for each dimension; Obtain the second operating parameter of each sample vehicle, and merge each second operating parameter and the corresponding dimension data to generate a feature vector for each sample vehicle; Iteratively train the preset endurance prediction model based on each feature vector.

5. The vehicle endurance warning method according to claim 4, wherein The step of merging each second operating parameter and the corresponding dimension data to generate a feature vector for each sample vehicle includes: Based on each dimension data, determine the dimension vector of the corresponding sample vehicle; Determine the data vector of the corresponding sample vehicle based on each of the second operating parameters; Combine the dimension vector and the data vector to obtain the feature vector.

6. A vehicle endurance warning device, characterized in that, The vehicle endurance warning device includes: An estimated endurance mileage module, configured to input the first operating parameter of the target vehicle into a preset endurance prediction model to predict the endurance mileage of the target vehicle, where the preset endurance prediction model is generated based on multi-dimensional sample data, and the multi-dimensional sample data includes static vehicle data, dynamic vehicle data, ambient temperature, and real-time road conditions; A module for determining the endurance warning level, configured to obtain the energy consumption mileage between the target vehicle and the first charging station, and determine the current endurance warning level of the target vehicle based on the endurance mileage and the energy consumption mileage; An endurance warning module, configured to control the target vehicle to perform corresponding endurance warnings according to the endurance warning level, where, Before controlling the target vehicle to perform corresponding endurance warnings according to the endurance warning level, it further includes: Detect whether there is a second charging station between the target vehicle and the first charging station according to the real-time position of the target vehicle, where the second charging station refers to a charging station that is closer to the target vehicle and available compared to the first charging station; If so, update the first charging station to the second charging station, and return to the step: obtain the energy consumption mileage between the target vehicle and the first charging station, The target vehicle includes in-vehicle prompting equipment, and controlling the target vehicle to perform corresponding endurance warnings according to the endurance warning level includes: Perform signal conversion on the warning level to generate a prompting signal with a corresponding intensity, where the prompting signal includes a light color and a voice broadcast content corresponding to the warning level; Control the in-vehicle prompting equipment to perform endurance warnings according to the prompting signal.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; where, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the vehicle endurance warning method according to any one of claims 1 to 6.

8. A readable storage medium, characterized in that, A program for implementing the vehicle endurance warning method is stored on the readable storage medium, and the program for implementing the vehicle endurance warning method is executed by a processor to implement the steps of the vehicle endurance warning method according to any one of claims 1 to 6.

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