Method and device for determining residual energy and product

Abstract

The invention provides a residual energy determination method and device, electronic equipment, a storage medium and a computer program product, relates to the technical field of computers, in particular to the technical fields of map navigation, intelligent driving and the like, and can be applied to a map navigation scene. According to the specific implementation scheme, the method comprises the following steps: determining basic energy consumption data required for a vehicle to pass through a unit area according to environmental data of the unit area related to a navigation path; according to the driving style data corresponding to the vehicle, correcting the basic energy consumption data to obtain corrected energy consumption data; and according to the corrected energy consumption data, determining residual energy data after the vehicle passes through the unit area. The energy consumption data of the vehicle in the unit area can be accurately predicted in combination with the environment data and the driving style data, the accuracy of the residual energy data determined based on the corrected energy consumption data is improved, and the risk of energy exhaustion caused by inaccurate residual energy prediction can be effectively avoided.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to technical fields such as map navigation and intelligent driving, and in particular to a method, device, electronic device, storage medium and computer program product for determining residual energy, which can be applied in map navigation scenarios. Background Art

[0002] Existing electric vehicle navigation systems usually only consider the remaining power provided by the vehicle's battery system and the distance to the destination for navigation planning, which poses the risk of battery depletion due to inaccurate power predictions. Summary of the Invention

[0003] The present disclosure provides a method, apparatus, electronic device, storage medium, and computer program product for determining residual energy.

[0004] According to a first aspect, a method for determining remaining energy is provided, comprising: determining basic energy consumption data required for a vehicle to pass through the unit area based on environmental data of the unit area involved in a navigation path; correcting the basic energy consumption data based on driving style data corresponding to the vehicle to obtain corrected energy consumption data; and determining the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

[0005] According to the second aspect, a device for determining remaining energy is provided, comprising: an energy consumption determination unit, configured to determine basic energy consumption data required for a vehicle to pass through a unit area based on environmental data of the unit area involved in a navigation path; an energy consumption correction unit, configured to correct the basic energy consumption data based on driving style data corresponding to the vehicle to obtain corrected energy consumption data; and an energy determination unit, configured to determine remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

[0006] According to a third aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method as described in any implementation of the first aspect.

[0008] According to a fifth aspect, a computer program product is provided, comprising: a computer program, which implements the method described in any implementation manner of the first aspect when executed by a processor.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure. Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied; Figure 2 is a flow chart of an embodiment of a method for determining residual energy according to the present disclosure; Figure 3 A schematic diagram of an application scenario of the method for determining residual energy according to this embodiment; Figure 4 is a flow chart of another embodiment of a method for determining residual energy according to the present disclosure; Figure 5 is a structural diagram of an embodiment of a device for determining residual energy according to the present disclosure; Figure 6 It is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0011] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0012] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0013] Figure 1 An exemplary architecture 100 is shown to which the method and apparatus for determining residual energy disclosed herein can be applied.

[0014] like Figure 1As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The communication connections between terminal devices 101, 102, and 103 constitute a topological network, and network 104 is used to provide a medium for communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0015] The terminal devices 101, 102, and 103 can be hardware devices or software that support network connection for data interaction and data processing. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to computer terminal devices such as car computers, smart phones, tablet computers, e-book readers, laptop computers and desktop computers. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made here.

[0016] Server 105 may be a server that provides various services, such as a backend processing server that generates navigation routes based on navigation requests from terminal devices 101, 102, and 103, and predicts the vehicle's remaining energy data by combining environmental data and driving style data during navigation. For example, server 105 may be a cloud server.

[0017] It should be noted that a server can be either hardware or software. When a server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When a server is software, it can be implemented as multiple software programs or software modules (for example, software or software modules used to provide distributed services), or as a single software program or software module. This is not specifically limited here.

[0018] It should also be noted that the remaining energy determination method provided in the embodiments of the present disclosure is generally executed by a server, but this does not exclude the possibility of execution by a terminal device, or the server and terminal device cooperating with each other. Accordingly, the various components (e.g., various units) included in the remaining energy determination device can be entirely provided in the server, entirely provided in the terminal device, or provided separately in the server and the terminal device.

[0019] It should be understood that Figure 1The number of terminal devices, networks, and servers described is merely illustrative. Any number of terminal devices, networks, and servers may be provided as needed. When the electronic device on which the remaining energy determination method is running does not need to transmit data with other electronic devices, the system architecture may include only the electronic device (e.g., terminal device or server) on which the remaining energy determination method is running.

[0020] Please refer to Figure 2 , Figure 2 A data processing flow diagram of a method for determining residual energy provided by an embodiment of the present disclosure. In process 200, the following steps are included: Step 201 : determining basic energy consumption data required for a vehicle to pass through a unit area according to environmental data of the unit area involved in the navigation path.

[0021] In this embodiment, the execution subject of the method for determining the remaining energy (for example, Figure 1 The server in the navigation path may obtain the environmental data of the unit area involved in the navigation path remotely or locally through a wired network connection or a wireless network connection, and determine the basic energy consumption data required for the vehicle to pass through the unit area based on the environmental data of the unit area involved in the navigation path.

[0022] The unit areas involved in the navigation path are the unit areas that intersect with the navigation path. In this embodiment, the geographic space can be divided based on a fixed size to obtain unit areas, for example, a unit area is a grid with a side length of 500 meters; the geographic space can also be divided based on different sizes to obtain unit areas, for example, a unit area under one size is a grid with a side length of 500 meters, and a unit area under another size is a grid with a side length of 300 meters; the geographic space can also be divided based on the difference in environmental data, where adjacent areas with consistent or similar environmental data are divided into one unit area, and areas with large differences in environmental data are divided into different unit areas.

[0023] The environmental data of the unit area mainly includes meteorological environmental data such as temperature, wind speed, wind direction, air density, and humidity, road environmental data such as road slope, road curvature radius, and road flatness, and traffic environmental data such as speed limit and traffic congestion. The vehicle in this embodiment is generally a new energy vehicle such as an electric vehicle driven by electric energy.

[0024] As an example, for each unit area involved in the navigation route, the basic energy consumption data required for the vehicle to pass through the unit area is determined based on the environmental data of the unit area. For example, while determining the navigation route, each unit area involved in the navigation route is determined, and for each unit area, the basic energy consumption data required for the vehicle to pass through the unit area is determined based on the environmental data of the unit area.

[0025] As another example, in order to improve the timeliness of the process of determining basic energy consumption data and reduce the data processing pressure of the determination process, a preset number of unit areas starting from the current unit area where the vehicle is located on the subsequent navigation path can be determined during the navigation process; for each unit area in the preset number of unit areas, the basic energy consumption data required for the vehicle to pass through the unit area is determined based on the environmental data of the unit area, and the remaining energy data of the vehicle after passing through the unit area is determined based on subsequent steps 202 and 203. In this example, for each unit area, the basic energy consumption data of the preset number of unit areas starting from the unit area can be predicted, or when the last area in the preset number of unit areas is reached, the basic energy consumption data of the preset number of unit areas starting from the current unit area is predicted.

[0026] The preset number may be determined based on at least one of the following: environmental data of the unit area, remaining energy data of the vehicle after passing through the unit area (determined in subsequent step 203 ), and a setting operation for the preset number.

[0027] For example, the preset number can be determined based on environmental data for the unit area. Specifically, the preset number can be determined based on temperature changes, road gradients, continuous ramps, continuous curves, and other road complexity. The greater the temperature change and the more complex the road conditions, the larger the preset number will be.

[0028] For another example, the data quantity may be determined based on the remaining energy data of the vehicle after it passes through the unit area, wherein the size of the remaining energy data is positively correlated with the size of the preset quantity.

[0029] For another example, the execution entity can determine a preset number based on the above-mentioned multiple data. Specifically, the execution entity can determine the initial number based on the setting operation for the preset number; normalize the environmental data of the unit area and the remaining energy data of the vehicle after passing through the unit area, and determine the adjustment coefficient for the initial number based on the normalized data; adjust the initial number by the adjustment coefficient to obtain the preset number. The number of unit areas to be processed can be flexibly determined based on at least one of the environmental data of the unit area, the remaining energy data of the vehicle after passing through the unit area, and the setting operation for the preset number, thereby improving the matching degree between the preset number and the actual situation and helping to enhance the user's navigation experience.

[0030] Specifically, the environmental data and dimensions of the unit area can be input into the energy consumption prediction model, which determines the basic energy consumption data required for vehicles to pass through the unit area. The energy consumption prediction model is used to characterize the corresponding relationship between the environmental data and dimensions of the unit area and the basic energy consumption data.

[0031] Energy consumption prediction models can be trained using neural network models such as recurrent neural networks and long short-term memory networks. First, a training sample set is obtained. These training samples include sample environment data, sample size, and basic energy consumption data labels. Then, a machine learning algorithm is used, using the sample environment data and sample size as input and the basic energy consumption data labels corresponding to the input sample environment data and sample size as the desired output, to train the energy consumption prediction model.

[0032] In some optional implementations of this embodiment, the execution subject may further perform the following operation before executing step 201: determine the environmental data of the unit area by combining the map data and meteorological data of the unit area.

[0033] As an example, first, the above-mentioned execution entity calls the high-precision map API (Application Programming Interface) to obtain road environment data such as road slope and curvature radius, traffic environment data such as speed limit and real-time traffic congestion index, and accesses the meteorological agency API to obtain hourly temperature, wind speed, humidity and other meteorological environment data within a specified time period in the future (for example, 72 hours).

[0034] The meteorological data is then spatially interpolated by unit area, ensuring that each unit area has corresponding meteorological data. The timestamps of the meteorological, traffic, and road data are then synchronized to UTC (Universal Time Coordinated) to eliminate time differences between data from different data sources and ensure consistency across the temporal dimension. The IQR (Interquartile Range) algorithm is then used to filter sudden changes in the battery's State of Charge (SOC), and a Kalman filter is used to smooth the temperature prediction series.

[0035] Finally, according to the meteorological environment data, road environment data and traffic environment data associated with the unit area, an environmental parameter set at the segment (navigation segment in the unit area) level is established.

[0036] This implementation combines the map data and meteorological data of the unit area to determine the environmental data of the unit area, thereby improving the efficiency and comprehensiveness of obtaining the environmental data.

[0037] In some optional implementations of this embodiment, the execution entity may perform step 201 as follows: First, based on the environmental data of the unit area and the basic attribute parameters of the vehicle, determine the air resistance energy consumption data, rolling resistance energy consumption data, and grade resistance energy consumption data required for the vehicle to pass through the unit area. The air resistance energy consumption data represents the energy consumed by the vehicle to overcome air resistance, the rolling resistance energy consumption data represents the energy consumed by the vehicle to overcome rolling resistance, and the grade resistance energy consumption data represents the energy consumed by the vehicle to overcome grade resistance. Then, the air resistance energy consumption data, the rolling resistance energy consumption data, and the grade resistance energy consumption data are combined to obtain basic energy consumption data.

[0038] Basic attribute parameters of a vehicle include, for example, frontal area, drag coefficient, mass and other attribute parameters.

[0039] In this implementation, air resistance energy consumption Determined as follows: The formula for calculating air resistance is:

[0040] in, is the air density, A is the frontal area of ​​the vehicle, is the vehicle drag coefficient, is the speed of the vehicle. The energy consumed to overcome air resistance is:

[0041] Rolling resistance energy consumption Determined as follows: rolling resistance for:

[0042] in, is the vehicle mass, is the acceleration due to gravity, is the rolling resistance coefficient, is the road slope angle. Within the distance D, the energy consumed to overcome the rolling resistance is:

[0043] Slope resistance energy consumption Determined as follows: Slope resistance for:

[0044] The energy consumed to overcome the slope resistance within the distance D is:

[0045] The basic energy consumption data is obtained by combining the air resistance energy consumption data, rolling resistance energy consumption data and slope resistance energy consumption data using the following formula: :

[0046] In this implementation, a specific prediction method for basic energy consumption data is provided, which combines air resistance energy consumption data, rolling resistance energy consumption data and slope resistance energy consumption data to improve the prediction accuracy of basic energy consumption data.

[0047] Step 202 : Correct the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data.

[0048] In this embodiment, the execution entity may correct the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data.

[0049] In this embodiment, driving style data can be collected from an EDR (Event Data Recorder). Driving style data represents the driver's driving style and includes, for example, accelerator pedal data such as accelerator pedal depth and accelerator pedal change rate; brake pedal data such as brake pedal depth and brake pedal change rate; speed data such as speed fluctuation, average speed, and speed stability; and steering data such as steering angle and steering frequency.

[0050] As an example, feature extraction is performed on driving style data to obtain driving feature data; the degree of driver's driving aggression of the vehicle is determined based on the driving feature data; a correction coefficient is determined based on the degree of driving aggression; and basic energy consumption data is corrected by the correction coefficient to obtain corrected energy consumption data.

[0051] For example, driving aggression is divided into different levels, each corresponding to a different numerical range and correction factor. After determining the numerical value representing the degree of driving aggression (driving aggression index), the numerical range within which it falls is determined, thereby determining the aggression level; and then determining the correction factor corresponding to the aggression level. There is a positive correlation between the numerical range, aggression level, and correction factor.

[0052] As another example, the driving style data and basic energy consumption data are input into a pre-trained energy consumption correction model, which then outputs corrected energy consumption data. The energy consumption correction model is used to characterize the corresponding relationship among the driving style data, basic energy consumption data, and corrected energy consumption data.

[0053] The energy consumption correction model can be trained using neural network models such as recurrent neural networks and long short-term memory networks. First, a training sample set is obtained. The training sample includes sample driving style data, sample basic energy consumption data, and corrected energy consumption data labels. Then, a machine learning algorithm is used to train the energy consumption correction model, using the sample driving style data and sample basic energy consumption data as input and the corrected energy consumption data labels corresponding to the input sample driving style data and sample basic energy consumption data as the desired output.

[0054] In some optional implementations of this embodiment, the execution entity may perform step 202 as follows: The first step is to determine a correction coefficient under at least one preset correction dimension.

[0055] The correction coefficient is used to characterize the degree of correction of the corrected energy consumption data.

[0056] The at least one preset correction dimension may include, for example, a load correction dimension and a vehicle speed correction dimension. For the load correction dimension, the vehicle's current actual weight may be detected by a vehicle weight sensor, and a correction coefficient for the load correction dimension may be determined based on the principle that the actual weight and the correction coefficient for the load correction dimension are positively correlated.

[0057] For the vehicle speed correction dimension, the higher the speed of new energy vehicles such as electric vehicles, the greater their energy consumption. The correction coefficient under the vehicle speed correction dimension can be determined based on the principle that the vehicle speed and the correction coefficient under the vehicle speed correction dimension are positively correlated.

[0058] In the second step, the basic energy consumption data is corrected by combining the driving style data and the correction coefficient to obtain the corrected energy consumption data.

[0059] As an example, the execution entity can normalize driving style data, such as the number of sudden accelerations, sudden braking, and average vehicle speed, to generate a driving aggression index to represent the degree of driving aggression. A higher driving aggression index indicates a more aggressive driving style and a higher degree of driving aggression.

[0060] Multiply the driving aggressiveness index by the sum of the correction coefficients under multiple preset correction dimensions to obtain the correction ratio of the basic energy consumption data. Correct the basic energy consumption data according to the correction ratio to obtain the corrected energy consumption data.

[0061] In this implementation, the influence of at least one preset correction dimension on the basic energy consumption data is referred to, and the basic energy consumption data is corrected in combination with the driving style data. Based on comprehensive influencing factors, the accuracy of the corrected energy consumption data is improved.

[0062] In some optional implementations of this embodiment, the at least one preset correction dimension includes a driving style dimension or a traffic condition dimension.

[0063] In this implementation, the execution entity may perform the first step to determine the correction coefficient under at least one preset correction dimension in the following manner: Method 1: In response to a traffic congestion index of a unit area being lower than a first preset congestion index, a correction coefficient under the driving style dimension is determined according to a driving aggressiveness index represented by the driving style data.

[0064] The traffic congestion index (CTI) can be determined based on parameters such as the vehicle's idling time percentage, speed, and start-stop frequency. A higher CTI indicates more severe traffic congestion. A CTI below a first preset CTI indicates clear traffic within the unit area. When traffic is clear, driving style data such as the number of sudden accelerations, sudden braking, and average speed should be considered.

[0065] As an example, the driving style data, including the number of sudden accelerations, the number of sudden brakes, and the average vehicle speed, are normalized to obtain a driving aggressiveness index. A corresponding relationship between the driving aggressiveness index and the energy consumption increase ratio (correction coefficient) is established through a machine learning algorithm (such as linear regression). Based on this corresponding relationship, the correction coefficient under the driving style dimension is determined according to the driving aggressiveness index.

[0066] Method 2: In response to the traffic congestion index of the unit area being higher than a second preset congestion index, a correction coefficient under the traffic condition dimension is determined according to the traffic congestion index.

[0067] The traffic congestion index being higher than the second preset congestion index indicates that the traffic in the unit area is congested. At this time, it is necessary to focus on the traffic situation in the unit area.

[0068] Based on the traffic congestion index provided by the map application API, a correction coefficient based on the traffic situation dimension is introduced. The traffic congestion index and the correction coefficient based on the traffic situation dimension are positively correlated.

[0069] In this implementation, a process for determining the correction coefficient under different situations is provided, which improves the adaptability of the correction coefficient to the actual situation and improves the accuracy of the correction coefficient.

[0070] In some optional implementations of this embodiment, the at least one preset correction dimension includes a driving style dimension and a traffic condition dimension.

[0071] In this implementation, the execution entity may determine the correction coefficient for at least one preset correction dimension by: in response to a unit area traffic congestion index being between a first preset congestion index and a second preset congestion index, and a driving aggressiveness index represented by the driving style data being higher than the preset aggressiveness index, determining the correction coefficient for the driving style dimension based on the driving style data, and determining the correction coefficient for the traffic condition dimension based on the traffic congestion index. The first preset congestion index is lower than the second preset congestion index.

[0072] A driving aggressiveness index is determined based on parameters such as the number of sudden accelerations, sudden braking, and average vehicle speed in the driving style data. If the driving aggressiveness index represented by the driving style data is higher than a preset aggressiveness index, it indicates that the driver has a relatively aggressive driving style.

[0073] The traffic congestion index of the unit area is between the first preset congestion index and the second preset congestion index, indicating that there is a certain degree of congestion on the roads in the unit area.

[0074] If the traffic congestion index for the unit area is between a first preset congestion index and a second preset congestion index, and the driving aggressiveness index represented by the driving style data is higher than the preset aggressiveness index, it is necessary to simultaneously determine a correction factor for both the driving style dimension and the traffic condition dimension. The process for determining the correction factor for both the driving style dimension and the traffic condition dimension in this implementation can be referenced to the above implementation and is not further described here.

[0075] In this implementation, the execution subject may perform the second step to obtain the corrected energy consumption data in the following manner:

[0076] in, Indicates basic energy consumption data, represents the correction coefficient under the driving style dimension, Indicates the driving aggressiveness index, Indicates the correction coefficient under the traffic condition dimension, Indicates corrected energy consumption data.

[0077] In this implementation, a specific energy consumption data correction method is provided. Based on the correction coefficient under the driving style dimension and the correction coefficient under the traffic condition dimension, the driving aggressiveness index representing the driving style data is corrected to correct the basic energy consumption data, further improving the accuracy of the corrected energy consumption data.

[0078] In some optional implementations of this embodiment, the execution entity may perform the second step to obtain the corrected energy consumption data in the following manner, including: First, the basic energy consumption data is corrected by combining the driving style data and the correction coefficient to obtain the initial corrected energy consumption data; then, the energy consumption of the auxiliary equipment required for the area through which the vehicle passes is determined; finally, the initial corrected energy consumption data and the energy consumption of the auxiliary equipment are combined to obtain the corrected energy consumption data.

[0079] Auxiliary equipment energy consumption is, for example, the power consumption caused by the operation of auxiliary equipment such as the vehicle air conditioner, vehicle display screen, and vehicle refrigerator.

[0080] As an example, first, use the above formula to calculate , which is used as the initial corrected energy consumption data; then, the power of the vehicle air conditioner is calculated using the following formula :

[0081] in, is the power of the car air conditioner at standard temperature, Indicates the ambient temperature. Energy consumption of auxiliary equipment such as vehicle air conditioners It can be calculated based on driving time and air conditioning power: =

[0082] in, Indicates the vehicle's travel distance, for example, the length of the navigation segment within the unit area; Indicates the vehicle's speed.

[0083] Finally, the corrected energy consumption data is obtained by combining the initial corrected energy consumption data and the auxiliary equipment energy consumption. :

[0084] In this implementation, the energy consumption of the vehicle's auxiliary equipment is further referenced during the correction process of the basic energy consumption, thereby further improving the accuracy and comprehensiveness of the corrected energy consumption data.

[0085] Step 203: Determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

[0086] In this embodiment, the execution entity may determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

[0087] The remaining energy data of the vehicle after passing through the previous unit area is subtracted from the corrected energy consumption data of the unit area to obtain the remaining energy data of the vehicle after passing through the unit area.

[0088] In an example of determining the basic energy consumption data of each unit area involved in the navigation path at one time, for the first unit area in the navigation path, the initial remaining energy consumption of the vehicle is subtracted from the corrected energy consumption data of the first unit area to determine the remaining energy data of the vehicle after passing through the first unit area; for each unit area after the first unit area, the remaining energy data of the vehicle after passing through the previous unit area is subtracted from the corrected energy consumption data of the unit area to obtain the remaining energy data of the vehicle after passing through the unit area.

[0089] In an example of determining the basic energy consumption data for a preset number of unit areas at one time, in response to the vehicle passing through the preset number of unit areas in the previous prediction operation, the vehicle's current actual remaining energy data is subtracted from the corrected energy consumption data of the first unit area among the preset number of unit areas in the current prediction operation to obtain the remaining energy data after the vehicle passes through the first unit area. For unit areas other than the first unit area among the preset number of unit areas, the vehicle's remaining energy data after passing through the previous unit area is subtracted from the corrected energy consumption data of the unit area to obtain the remaining energy data after the vehicle passes through the unit area. This iterative process is repeated until the remaining energy data for all unit areas in the navigation path are determined.

[0090] In order to improve the accuracy of the remaining energy data, the above execution entity can calculate the remaining energy data by the following formula: :

[0091] in, Indicates corrected energy data, the unit is kWh; Indicates the nominal capacity of the energy supply device (usually a battery). Indicates the ambient temperature of the unit area; Indicates the battery health, the value range is (0-1), Indicates the length of the navigation segment within the unit area.

[0092] In some optional implementations of this embodiment, the above-mentioned execution entity can also perform the following operations: for a preset number of unit areas, determine the display method of the navigation section in the navigation path within the unit area based on the remaining energy data corresponding to the unit area, and display the navigation section using the display method.

[0093] Different navigation sections corresponding to different remaining energy data can be displayed using different colors, brightness, and other display methods. As an example, navigation sections in different unit areas are marked with different colors. The color of the navigation section corresponds to the remaining energy data corresponding to the unit area in which it is located. The remaining energy data is represented by the state of charge value, and multiple state of charge value ranges are set. For example, a navigation section with remaining energy data in the range of [100%, 80%) is marked in green; a navigation section with remaining energy data in the range of [80%, 60%) is marked in yellow; a navigation section with remaining energy data in the range of [60%, 40%) is marked in orange; a navigation section with remaining energy data in the range of [40%, 20%) is marked in light red; and a navigation section with remaining energy data in the range of [20%, 0%) is marked in dark red.

[0094] For another example, the navigation segments can be displayed with different brightness levels in the above example. For navigation segments with a remaining energy reading between [100% and 80%), a bright green color is used; for navigation segments with a remaining energy reading between [20% and 0%), a low-brightness dark red is used. As the remaining energy reading decreases, the brightness of the navigation segment decreases.

[0095] In order to improve the display effect, a color gradient function is used to process the color of the navigation section between adjacent unit areas to achieve a color gradient effect, and a brightness gradient function is used to process the brightness of the navigation section to achieve a brightness gradient effect.

[0096] In this implementation, different display modes are used to display navigation sections with different remaining energy data, so that vehicle-related personnel (drivers, passengers) can intuitively view the remaining energy status of the vehicle, thereby improving the information acquisition efficiency and experience of vehicle-related personnel.

[0097] In some optional implementations of this embodiment, the execution entity may perform step 203 as follows: First, based on the environmental data of the unit area, the remaining energy data of the vehicle after passing through the previous unit area of ​​the unit area is corrected to obtain the corrected energy data; then, based on the corrected energy data and the corrected energy consumption data, the remaining energy data of the vehicle after passing through the unit area is determined.

[0098] As an example, first, the following formula is used to correct the remaining energy data of the vehicle after passing through the previous unit area of ​​the unit area based on the environmental data of the unit area to obtain the corrected energy data:

[0099] in, Indicates corrected energy data, the unit is kWh; Indicates the nominal capacity of the battery, Indicates the ambient temperature of the unit area; Indicates the battery health, the value range is (0-1); and Battery parameters, set by the battery manufacturer, such as:

[0100] Then, the corrected energy consumption data is subtracted from the corrected energy data to determine the remaining energy data of the vehicle after passing through the unit area.

[0101] In this implementation, before determining the remaining energy data, it is necessary to correct the remaining energy data after the vehicle passes through the previous unit area of ​​the unit area according to the environmental data of the unit area, thereby further improving the accuracy of the remaining energy data.

[0102] Continue to see Figure 3 , Figure 3 This is a schematic diagram 300 of an application scenario of the method for determining the remaining energy according to the present embodiment. User 301 requests a navigation path from the origin to the destination from server 303 through a navigation application in terminal device 302, and drives the vehicle according to the navigation path. During the navigation process, the server determines the basic energy consumption data required for the vehicle to pass through the unit area based on the environmental data of the unit area involved in the navigation path; corrects the basic energy consumption data based on the driving style data corresponding to the vehicle to obtain the corrected energy consumption data; and determines the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data. Among them, the remaining energy data after passing through unit area 3031 is determined to be 80%, the remaining energy data after passing through unit area 3032 is 75%, and the remaining energy data after passing through unit area 3033 is 72%.

[0103] In this embodiment, a method for determining remaining energy is provided. The method determines basic energy consumption data required for a vehicle to pass through a unit area based on environmental data of the unit area involved in a navigation path; corrects the basic energy consumption data based on driving style data corresponding to the vehicle to obtain corrected energy consumption data; and determines the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data. Thus, by combining the environmental data and the driving style data, the energy consumption data of the vehicle in the unit area can be accurately predicted, thereby improving the accuracy of the remaining energy data determined based on the corrected energy consumption data and effectively avoiding the risk of energy depletion due to inaccurate remaining energy prediction.

[0104] In some optional implementations of this embodiment, the execution subject may further perform the following operation: in response to the distance between the vehicle and the target unit area being less than a preset distance threshold, determining a charging navigation path for the vehicle.

[0105] The target unit area is a unit area whose remaining energy data is lower than the remaining energy threshold.

[0106] As an example, the preset distance threshold represents the side length of a unit area, that is, when the vehicle reaches the previous unit area of ​​the target unit area, the charging navigation path of the vehicle is determined.

[0107] The remaining energy threshold can be determined based on the current ambient temperature, for example:

[0108] in, Indicates the remaining energy threshold, Indicates the mean ambient temperature.

[0109] As an example, in response to the distance between the vehicle and the target unit area being less than a preset distance threshold, the above-mentioned execution entity can determine the target charging pile closest to the current position of the vehicle, and determine the charging navigation path with the current position of the vehicle as the starting point and the target charging pile as the end point.

[0110] The energy replenishment data of the vehicle at the charging pile is determined by the following formula:

[0111] in, Indicates the target energy replenishment value set for the vehicle, generally the target charge amount (for example, 80%). Indicates the maximum charge level of the charging station. Based on the charge level and charging power, the charging time can also be estimated, allowing for real-time adjustments to the estimated time of arrival at the destination during navigation, improving the accuracy of information predictions throughout the entire navigation process.

[0112] In this implementation, the charging navigation route can be provided to the user in a timely manner based on the remaining energy data, helping the user to find the charging pile conveniently, further improving the user experience and convenience in the navigation process.

[0113] Continue to refer Figure 4 , shows a schematic process 400 of another embodiment of the method for determining residual energy according to the present disclosure. In the process 400, the following steps are included: Step 401 : Determine environmental data of a unit area by combining map data and weather data of the unit area involved in the navigation path.

[0114] Step 402 : Determine the air resistance energy consumption data, rolling resistance energy consumption data, and slope resistance energy consumption data required for the vehicle to pass through the unit area based on the environmental data of the unit area and the basic attribute parameters of the vehicle.

[0115] Among them, the air resistance energy consumption data represents the energy consumption caused by the vehicle overcoming air resistance, the rolling resistance energy consumption data represents the energy consumption caused by the vehicle overcoming rolling resistance, and the slope resistance energy consumption data represents the energy consumption caused by the vehicle overcoming slope resistance.

[0116] Step 403 : combining the air resistance energy consumption data, the rolling resistance energy consumption data, and the slope resistance energy consumption data to obtain basic energy consumption data.

[0117] Step 404 : determining a correction coefficient in the driving style dimension based on the driving style data corresponding to the vehicle, and determining a correction coefficient in the traffic condition dimension based on the traffic condition in the unit area.

[0118] Step 405 , combining the driving style data and the correction coefficient, correcting the basic energy consumption data to obtain initial corrected energy consumption data.

[0119] Step 406 : Determine the energy consumption of auxiliary equipment required by the area through which the vehicle passes.

[0120] Step 407: Combine the initial corrected energy consumption data and the auxiliary equipment energy consumption to obtain corrected energy consumption data.

[0121] Step 408 , based on the environmental data of the unit area, correct the remaining energy data of the vehicle after passing through the previous unit area of ​​the unit area to obtain corrected energy data.

[0122] Step 409 : Determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy data and the corrected energy consumption data.

[0123] Step 410 : In response to the distance between the vehicle and the target unit area being less than a preset distance threshold, determining a charging navigation path for the vehicle.

[0124] The target unit area is a unit area whose remaining energy data is lower than the remaining energy threshold.

[0125] Compared with the above-mentioned process 200, the process 400 of the method for determining the remaining energy in this embodiment specifically describes the prediction process of the basic energy consumption data, the correction process of the basic energy consumption data, and the determination process of the charging navigation path, which further improves the accuracy of the remaining energy data determined based on the corrected energy consumption data, can effectively avoid the risk of energy depletion due to inaccurate remaining energy prediction, and improves the user experience and convenience during the navigation process.

[0126] Continue to refer Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for determining residual energy. Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0127] like Figure 5 As shown, the remaining energy determination device 500 includes: an energy consumption determination unit 501 is configured to determine the basic energy consumption data required for the vehicle to pass through the unit area based on the environmental data of the unit area involved in the navigation path; the energy consumption correction unit 502 is configured to correct the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain the corrected energy consumption data; the energy determination unit 503 is configured to determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

[0128] In some optional implementations of this embodiment, the energy consumption correction unit 502 is further configured to: determine a correction coefficient under at least one preset correction dimension, wherein the correction coefficient is used to characterize the degree of correction of the corrected energy consumption data; and correct the basic energy consumption data by combining the driving style data and the correction coefficient to obtain the corrected energy consumption data.

[0129] In some optional implementations of this embodiment, at least one preset correction dimension is a driving style dimension or a traffic condition dimension, and the energy consumption correction unit 502 is further configured to: in response to the traffic congestion index of the unit area being lower than the first preset congestion index, determine the correction coefficient under the driving style dimension according to the driving aggressiveness index represented by the driving style data; in response to the traffic congestion index of the unit area being higher than the second preset congestion index, determine the correction coefficient under the traffic condition dimension according to the traffic congestion index, wherein the first preset congestion index is lower than the second preset congestion index.

[0130] In some optional implementations of this embodiment, at least one preset correction dimension includes a driving style dimension and a traffic condition dimension, and the energy consumption correction unit 502 is further configured to: in response to the traffic congestion index of the unit area being between a first preset congestion index and a second preset congestion index, and the driving aggressiveness index represented by the driving style data being higher than the preset aggressiveness index, determine a correction coefficient under the driving style dimension according to the driving aggressiveness index, and determine a correction coefficient under the traffic condition dimension according to the traffic congestion index, wherein the first preset congestion index is lower than the second preset congestion index.

[0131] In some optional implementations of this embodiment, the energy consumption correction unit 502 is further configured to: correct the basic energy consumption data in combination with the driving style data and the correction coefficient to obtain initial corrected energy consumption data; determine the energy consumption of auxiliary equipment required for the vehicle's travel area; and obtain corrected energy consumption data in combination with the initial corrected energy consumption data and the energy consumption of the auxiliary equipment.

[0132] In some optional implementations of this embodiment, the energy determination unit 503 is further configured to: correct the remaining energy data of the vehicle after passing through the previous unit area of ​​the unit area based on the environmental data of the unit area to obtain corrected energy data; and determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy data and the corrected energy consumption data.

[0133] In some optional implementations of this embodiment, the energy consumption determination unit 501 is further configured to: determine the air resistance energy consumption data, rolling resistance energy consumption data and slope resistance energy consumption data required for the vehicle to pass through the unit area based on the environmental data of the unit area and the basic attribute parameters of the vehicle, wherein the air resistance energy consumption data represents the energy consumption caused by the vehicle overcoming air resistance, the rolling resistance energy consumption data represents the energy consumption caused by the vehicle overcoming rolling resistance, and the slope resistance energy consumption data represents the energy consumption caused by the vehicle overcoming slope resistance; and combine the air resistance energy consumption data, rolling resistance energy consumption data and slope resistance energy consumption data to obtain basic energy consumption data.

[0134] In some optional implementations of this embodiment, the unit area is a preset number of unit areas on the subsequent navigation path, starting from the current unit area where the vehicle is located, and the preset number is determined based on at least one of the following: environmental data of the unit area, remaining energy data of the vehicle after passing through the unit area, and setting operations for the preset number.

[0135] In some optional implementations of this embodiment, the above-mentioned device also includes: a display unit (not shown in the figure), which is configured to determine the display method of the navigation section in the navigation path within the unit area according to the remaining energy data corresponding to the unit area for a preset number of unit areas, and display the navigation section using the display method.

[0136] In this embodiment, a device for determining remaining energy is provided. The device determines basic energy consumption data required for a vehicle to pass through a unit area based on environmental data of the unit area involved in a navigation path; corrects the basic energy consumption data based on driving style data corresponding to the vehicle to obtain corrected energy consumption data; and determines the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data. Thus, by combining the environmental data and the driving style data, the energy consumption data of the vehicle in the unit area can be accurately predicted, thereby improving the accuracy of the remaining energy data determined based on the corrected energy consumption data and effectively avoiding the risk of energy depletion due to inaccurate remaining energy prediction.

[0137] According to an embodiment of the present disclosure, the present disclosure also 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 that can be executed 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 implement the method for determining the remaining energy described in any of the above embodiments when executing.

[0138] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the method for determining the residual energy described in any of the above embodiments when executed.

[0139] An embodiment of the present disclosure provides a computer program product, which, when executed by a processor, can implement the method for determining the residual energy described in any of the above embodiments.

[0140] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0141] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. Computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0142] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0143] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the remaining energy determination method. For example, in some embodiments, the remaining energy determination method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the remaining energy determination method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the remaining energy determination method in any other suitable manner (e.g., via firmware).

[0144] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable residual energy determination device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on 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), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0148] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0149] A computer system may include a client and a server. The client and server are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, establishing a client-server relationship. The server can be a cloud server, also known as a cloud computing server or cloud host. This server is a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and virtual private server (VPS) services. It can also be a server in a distributed system or a server integrated with blockchain.

[0150] According to the technical solution of the embodiment of the present disclosure, a method and device for determining remaining energy are provided, which determine the basic energy consumption data required for a vehicle to pass through the unit area based on the environmental data of the unit area involved in the navigation path; correct the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data; and determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data. Therefore, the energy consumption data of the vehicle in the unit area can be accurately predicted by combining the environmental data and the driving style data, thereby improving the accuracy of the remaining energy data determined based on the corrected energy consumption data, and effectively avoiding the risk of energy depletion due to inaccurate remaining energy prediction.

[0151] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions provided by this disclosure can be achieved. This is not a limitation herein.

[0152] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for determining residual energy, comprising: Determining basic energy consumption data required for the vehicle to pass through the unit area based on environmental data of the unit area involved in the navigation path; Correcting the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data; The remaining energy data of the vehicle after passing through the unit area is determined according to the corrected energy consumption data.

2. The method according to claim 1, wherein The step of correcting the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data includes: Determining a correction coefficient under at least one preset correction dimension, wherein the correction coefficient is used to represent a degree of correction of the corrected energy consumption data; The basic energy consumption data is corrected by combining the driving style data and the correction coefficient to obtain the corrected energy consumption data.

3. The method according to claim 2, wherein: At least one of the preset correction dimensions is a driving style dimension or a traffic condition dimension, and The determining of the correction coefficient under at least one preset correction dimension includes: In response to a traffic congestion index of the unit area being lower than a first preset congestion index, determining a correction coefficient under the driving style dimension according to a driving aggressiveness index represented by the driving style data; In response to the traffic congestion index of the unit area being higher than a second preset congestion index, a correction coefficient under the traffic condition dimension is determined according to the traffic congestion index, wherein the first preset congestion index is lower than the second preset congestion index.

4. The method according to claim 2, wherein: At least one of the preset correction dimensions includes a driving style dimension and a traffic situation dimension, and The determining of the correction coefficient under at least one preset correction dimension includes: In response to a traffic congestion index of the unit area being between a first preset congestion index and a second preset congestion index, and a driving aggression index represented by the driving style data being higher than the preset aggression index, a correction coefficient under the driving style dimension is determined based on the driving aggression index, and a correction coefficient under the traffic condition dimension is determined based on the traffic congestion index, wherein the first preset congestion index is lower than the second preset congestion index.

5. The method according to claim 2, wherein: The step of combining the driving style data and the correction coefficient to correct the basic energy consumption data to obtain the corrected energy consumption data includes: Combining the driving style data and the correction coefficient, correcting the basic energy consumption data to obtain initial corrected energy consumption data; determining energy consumption of auxiliary equipment required for the vehicle to travel through the area; The corrected energy consumption data is obtained by combining the initial corrected energy consumption data and the auxiliary equipment energy consumption.

6. The method according to claim 1, wherein The determining, based on the corrected energy consumption data, the remaining energy data of the vehicle after passing through the unit area includes: Correcting the remaining energy data of the vehicle after passing through the previous unit area of ​​the unit area according to the environmental data of the unit area to obtain corrected energy data; The remaining energy data of the vehicle after passing through the unit area is determined according to the corrected energy data and the corrected energy consumption data.

7. The method according to claim 1, wherein The determining, based on the environmental data of the unit area involved in the navigation path, the basic energy consumption data required for the vehicle to pass through the unit area includes: determining, based on the environmental data of the unit area and the basic attribute parameters of the vehicle, air resistance energy consumption data, rolling resistance energy consumption data, and slope resistance energy consumption data required for the vehicle to pass through the unit area, wherein the air resistance energy consumption data represents energy consumption caused by the vehicle overcoming air resistance, the rolling resistance energy consumption data represents energy consumption caused by the vehicle overcoming rolling resistance, and the slope resistance energy consumption data represents energy consumption caused by the vehicle overcoming slope resistance; The basic energy consumption data is obtained by combining the air resistance energy consumption data, the rolling resistance energy consumption data and the slope resistance energy consumption data.

8. The method according to claim 1, wherein The unit area is a preset number of unit areas on a subsequent navigation path, starting from the current unit area where the vehicle is located, and the preset number is determined based on at least one of the following: environmental data of the unit area, remaining energy data of the vehicle after passing through the unit area, and setting operations for the preset number.

9. The method according to claim 8, wherein Also includes: For a preset number of the unit areas, a display mode of the navigation sections in the navigation path within the unit areas is determined according to the remaining energy data corresponding to the unit areas, and the navigation sections are displayed in the display mode.

10. A device for determining residual energy, comprising: an energy consumption determination unit configured to determine basic energy consumption data required for the vehicle to pass through the unit area according to environmental data of the unit area involved in the navigation path; an energy consumption correction unit configured to correct the basic energy consumption data according to the driving style data corresponding to the vehicle to obtain corrected energy consumption data; The energy determination unit is configured to determine the remaining energy data of the vehicle after passing through the unit area based on the corrected energy consumption data.

11. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 9.

13. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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