Vehicle parameter configuration method, device and equipment of electric bicycle and medium

Abstract

The invention relates to the technical field of electric bicycles, and discloses a vehicle parameter configuration method and device of an electric bicycle, equipment and a medium. The method comprises the following steps: acquiring safety boundary information of a target vehicle, historical driving habit information of a user, and real-time driving preference information and real-time road condition information input by the user in real time; generating a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information; and performing vehicle parameter configuration on the target vehicle based on the vehicle parameter configuration file. According to the embodiment of the invention, the vehicle parameters of the electric bicycle can be dynamically configured according to the user characteristics of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric bicycles, and in particular to a vehicle parameter configuration method, device, equipment and medium for an electric bicycle. BACKGROUND

[0002] At present, the performance output mode (such as energy saving, standard, sports) of an electric bicycle is usually a fixed mode preset at the factory, and users can only make limited choices and cannot make deep personalized customization. Current rental and exchange electric operators mainly focus on the circulation and basic endurance of batteries and ignore the differentiation of user experience. For rental and exchange electric users, the battery and vehicle rented each time may not be the same, and the experience is difficult to maintain consistency. Therefore, there is a lack of a scheme in the related art that can dynamically and safely adjust the vehicle parameters of a vehicle according to a specific user, a specific vehicle and a specific trip, and it is impossible to achieve the best experience of "car following people". SUMMARY

[0003] The purpose of the present application is to provide a vehicle parameter configuration method, device, equipment and storage medium for an electric bicycle, which can dynamically configure the vehicle parameters of an electric bicycle according to the user characteristics of a user.

[0004] The present application provides a vehicle parameter configuration method for an electric bicycle, comprising: obtaining safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information and real-time road condition information input by the user in real time; generating a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information; configuring vehicle parameters of the target vehicle based on the vehicle parameter configuration file.

[0005] In some embodiments, the generating of the vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information comprises: extracting and fusing features of the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information to obtain comprehensive information features; inputting the comprehensive information features into a pre-trained vehicle parameter configuration model to generate target vehicle parameters based on the comprehensive information features, so as to generate vehicle parameters under the constraint of the safety boundary information while satisfying the historical driving habit dimension, the real-time driving preference dimension and the real-time road condition dimension; packaging the target vehicle parameter configuration values to obtain a vehicle parameter configuration file.

[0006] In some embodiments, the feature extraction and fusion of the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information comprises: vectorizing the historical driving habit information, the real-time driving preference information and the real-time road condition information to obtain corresponding user information vectors; based on the safety boundary information, fine-tuning the user information vectors, and combining each fine-tuned user information vector to obtain the comprehensive information feature.

[0007] In some embodiments, before the vectorization of the historical driving habit information, the real-time driving preference information and the real-time road condition information, it further comprises: determining whether the real-time driving preference information contains an active selection instruction; if it contains, giving the real-time driving preference information a higher weight than the historical driving habit information.

[0008] In some embodiments, the vehicle parameter generation based on the comprehensive information feature comprises: determining a basic vehicle configuration mode according to the real-time road condition information; based on the real-time driving preference information and the historical driving habit information, fine-tuning the vehicle parameters in the basic vehicle configuration mode to generate multiple sets of candidate vehicle parameter configuration values; matching the comprehensive information feature with multiple candidate information features to determine a target information feature that meets a preset matching condition and has the highest similarity with the comprehensive information feature; the candidate vehicle parameter configuration value corresponding to the target information feature with the highest user evaluation or the highest usage frequency is taken as the target vehicle parameter configuration value.

[0009] In some embodiments, the vehicle parameter configuration of the target vehicle based on the vehicle parameter configuration file comprises: downloading the vehicle parameter configuration file to the target vehicle, so that the control system of the target vehicle updates the vehicle parameters of the target vehicle based on the vehicle parameter configuration file; or generating a vehicle parameter package in real time based on the vehicle parameter configuration file and the driving data of the target vehicle in the driving process, downloading the vehicle parameter package to the target vehicle, so that the control system of the target vehicle updates the vehicle parameters of the target vehicle based on the vehicle parameter package.

[0010] In some embodiments, the trigger condition for updating the vehicle parameters of the target vehicle comprises at least one of: a driving mode switching operation is detected; a road type change is predicted based on navigation information; an operation behavior of the user is detected to be inconsistent with a current vehicle parameter configuration; periodic adjustment is performed at a fixed time period.

[0011] The application also provides a vehicle parameter configuration device for an electric bicycle, comprising: a first module configured to acquire safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time, and real-time road condition information; a second module configured to generate a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information; a third module configured to perform vehicle parameter configuration on the target vehicle based on the vehicle parameter configuration file.

[0012] The application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the vehicle parameter configuration method for an electric bicycle when executing the computer program.

[0013] The application also provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the vehicle parameter configuration method for an electric bicycle.

[0014] The application has the following advantages: by fusing vehicle safety limits, long-term behavior characteristics, real-time preferences, and environmental data, multi-dimensional collaborative decision-making is achieved. For example, when a user temporarily selects aggressive driving but the road condition is wet and slippery, the safety boundary is prioritized, and the response speed is only increased within the allowable range of braking performance, instead of directly applying a fixed driving mode. This solves the problem of inconsistent experience caused by frequent vehicle replacement in the rental and exchange electric field, so that different vehicles can automatically adapt to the user's habits and real-time needs. By dynamically balancing safety limits and user preferences, the operation risk caused by parameter conflicts is avoided, for example, automatic enhancement of braking recovery on a long downhill road section, which not only meets the user's energy-saving needs, but also prevents the brake system from overheating. In addition, the linkage adjustment of real-time road conditions and driving modes can reduce the user's frequent manual operations and improve driving comfort. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 An application environment diagram of the vehicle parameter configuration method for an electric bicycle provided by the application is shown.

[0016] Figure 2is a flowchart of a vehicle parameter configuration method of an electric bicycle provided by an embodiment of the present application.

[0017] Figure 3 is a flowchart of a method for generating a vehicle parameter configuration file provided by an embodiment of the present application.

[0018] Figure 4 is a flowchart of a method for generating a vehicle parameter based on comprehensive information characteristics provided by an embodiment of the present application.

[0019] Figure 5 is a structural schematic diagram of a vehicle parameter configuration device of an electric bicycle provided by an embodiment of the present application.

[0020] Figure 6 is a hardware structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0022] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown can be executed in a manner different from the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification and claims and drawings are used to distinguish similar objects, and are not intended to describe a specific order or sequence.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0024] Figure 1 is an application environment diagram of a vehicle parameter configuration method of an electric bicycle provided by an embodiment of the present application. See Figure 1The vehicle parameter configuration method of the electric bicycle is applied to a vehicle parameter configuration system of the electric bicycle. The vehicle parameter configuration system of the electric bicycle comprises a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network, and the terminal 110 can be a desktop terminal or a mobile terminal, and the mobile terminal can be at least one of a mobile phone, a tablet computer, a notebook computer and the like. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers. The terminal 110 is used to upload safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time and real-time road condition information to the server 120. The server 120 is used to acquire the safety boundary information of the target vehicle, the historical driving habit information of the user, the real-time driving preference information input by the user in real time and the real-time road condition information, generate a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information, and perform vehicle parameter configuration on the target vehicle based on the vehicle parameter configuration file.

[0025] In another embodiment, the above-mentioned vehicle parameter configuration method of the electric bicycle can be directly applied to the terminal 110, and the terminal 110 is used to acquire safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time and real-time road condition information, generate a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information, and perform vehicle parameter configuration on the target vehicle based on the vehicle parameter configuration file.

[0026] Referring to Figure 2 In an embodiment, a vehicle parameter configuration method of an electric bicycle is provided, which can be applied to a terminal and a server. In this embodiment, the vehicle parameter configuration method of the electric bicycle is exemplified by application to the terminal. The vehicle parameter configuration method of the electric bicycle comprises but is not limited to steps S201 to S204.

[0027] In step S201, safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time and real-time road condition information are acquired.

[0028] The safety boundary information refers to a limit parameter range allowed by the hardware performance of the vehicle, which can be achieved by reading the maximum load threshold of the motor, the battery and the braking system through the vehicle controller, and is used to ensure that the generated parameter configuration does not exceed the safety limit. The safety boundary information can be pre-stored in a database, and the execution subject acquires the safety boundary information by reading data from the database.

[0029] The historical driving habit information refers to the operation characteristics formed in the long-term use process of the user, which can be specifically represented by collecting the accelerator pedal opening degree, brake frequency, average speed data and establishing a behavior model, and is used to reflect the basic driving style of the user. The safety boundary information can be pre-stored in the database, and when the user uploads the lease request through the user terminal, the subject executes the user tag attached in the lease request, and reads the historical driving habit information of the user corresponding to the user tag from the database.

[0030] The real-time driving preference information refers to the temporary demand actively selected by the user at present, which can be specifically obtained by the user uploading through the user terminal, and is used to represent the driving style preference of the user at present. When the user leases the electric bicycle, the user terminal scans the two-dimensional code on the electric bicycle through the user terminal and enters the lease interaction interface, selects the driving style of this ride in the lease interaction interface, such as power type, balanced type or endurance type, and the user terminal uploads the selection result of the user to the subject, thereby obtaining the real-time driving preference information.

[0031] The real-time road condition information refers to the road conditions of the environment where the vehicle is located, which can be specifically obtained by combining GPS positioning with map data or camera recognition of road surface state, and is used to dynamically adjust the parameters to adapt to different terrains or traffic conditions. The user uploads the riding destination through the user terminal, the subject plans the path based on the starting point and destination of the ride, and analyzes the real-time road condition of the planned path in real time to obtain the real-time road condition information.

[0032] Step S202, generating a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information.

[0033] The subject extracts and fuses the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information, and takes the fused information as the basis for comprehensive decision-making. Based on the mapping relationship between the fused information and the vehicle parameters, the corresponding vehicle parameters are generated, the generated vehicle parameters are encapsulated, and finally the vehicle parameter configuration file is generated.

[0034] Step S203, configuring the vehicle parameters of the target vehicle based on the vehicle parameter configuration file.

[0035] The vehicle parameter configuration method of the electric bicycle provided in the embodiments of the present application comprises the following steps: after receiving a lease request, real-time driving preference information and a riding destination uploaded by a user terminal, determining a target user and a target vehicle based on user tag information and vehicle tag information attached to the lease request, reading safety boundary information of the target vehicle and historical driving habit information of the user from a database, and obtaining real-time road condition information based on a path between the riding destination and a starting point. The safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information are used as prediction bases, and corresponding vehicle parameters are generated based on a mapping relationship between the safety boundary information, the historical driving habit information, the real-time driving preference information, the real-time road condition information and the vehicle parameters, a vehicle parameter configuration file is generated, for example, when a user selects a sports mode on a flat road, the subject is enabled to improve motor output power within the safety boundary, and if consecutive curves are detected, the power response is automatically reduced and the braking sensitivity is enhanced, and finally the generated configuration file updates parameters through a vehicle-mounted control system, for example, adjusting a motor torque curve, an energy recovery intensity or a steering assist level. In this way, by fusing vehicle safety limits, long-term behavior characteristics, real-time preferences and environmental data, multi-dimensional collaborative decision-making is realized, for example, when a user temporarily selects aggressive driving but the road condition is wet and slippery, the safety boundary is prioritized, and the response speed is improved only within the allowable range of braking performance, instead of directly applying a fixed sports mode, thereby solving the problem of inconsistent experience caused by frequent vehicle replacement in a lease and exchange electric field, enabling different vehicles to automatically adapt parameters according to user habits and real-time needs, dynamically balancing safety limits and user preferences, and avoiding operation risks caused by parameter conflicts, for example, automatically enhancing braking recovery on a long downhill road section, which not only meets the user's energy saving needs, but also prevents the brake system from overheating. In addition, the linkage adjustment of real-time road conditions and driving modes can reduce frequent manual operation of the user and improve driving comfort.

[0036] Reference Figure 3 In an embodiment, the method of generating a vehicle parameter configuration file comprises but is not limited to steps S301 to S303.

[0037] Step S301, feature extraction and fusion are performed on the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information to obtain comprehensive information features.

[0038] Step S302, the comprehensive information features are input into a pre-trained vehicle parameter configuration model to generate target vehicle parameters based on the comprehensive information features, and target vehicle parameter configuration values are obtained.

[0039] The vehicle parameter configuration model is used to generate vehicle parameters under the constraint of the safety boundary information while meeting the historical driving habit dimension, the real-time driving preference dimension and the real-time road condition dimension.

[0040] In step S303, the target vehicle parameter configuration value is packaged to obtain a vehicle parameter configuration file.

[0041] The comprehensive information feature refers to the feature obtained by vectorization, fine-tuning and combination of multi-source heterogeneous data into a unified representation. The neural network model or linear weighting method can be used to realize the comprehensive information feature. For example, the average speed and acceleration in the historical driving habit information are converted into a multi-dimensional vector and then normalized.

[0042] The vehicle parameter configuration model refers to a parameter generation model trained by a machine learning algorithm. The deep reinforcement learning framework or decision tree model can be used to realize the vehicle parameter configuration model. For example, the safety boundary is introduced as a constraint condition in the training stage, so that the parameters output by the model are always within the range allowed by the mechanical structure.

[0043] In the feature extraction stage, the long-term behavior pattern in the historical driving habit information is quantified into a feature vector, the instantaneous operation intention in the real-time driving preference information is captured by a sliding window, and the road slope and traffic flow in the real-time road condition information are converted into numerical form. The safety boundary information is used as a hard constraint to modify the range of the above vectors. For example, the upper limit of the acceleration requested by the user is limited within the maximum output range of the motor. The fused comprehensive information feature is input into the vehicle parameter configuration model. The model calculates the fitness of different parameter combinations through a multi-layer perception mechanism. Under the premise of meeting the safety constraints, the balance point of the real-time preference and the historical habit is preferentially matched, and finally the target configuration value including the power output curve and the energy recovery intensity is output. The configuration value is serialized and packaged to form a vehicle parameter configuration file that can be transmitted. In this way, the dynamic parameter generation is realized through feature fusion and constraint optimization model. For example, when the user temporarily selects the sports mode but the historical data shows that the habit is smooth acceleration, the model will adjust the torque output curve within the safety range, which responds to the real-time preference and avoids the safety hazards caused by sudden acceleration.

[0044] In some embodiments, the safety boundary information, the historical driving habit information, the real-time driving preference information and the real-time road condition information are subjected to feature extraction and fusion, including: vectorizing the historical driving habit information, the real-time driving preference information and the real-time road condition information to obtain corresponding user information vectors; fine-tuning the user information vectors based on the safety boundary information, and combining the fine-tuned user information vectors to obtain a comprehensive information feature.

[0045] After obtaining the historical driving habit information, real-time driving preference information and real-time road condition information, first, the historical behavior data such as the acceleration pedal depth and the average speed are converted into multi-dimensional embedding vectors. Then, based on the real-time driving preference information, the user's current selected sport mode preference is converted into a binary coded vector. The road congestion degree and slope angle in the real-time road condition information are quantified into a floating point type vector. The maximum output power limit value in the safety boundary information is used to construct a scaling coefficient matrix, and the element-by-element multiplication operation is performed on the above three vectors to realize the numerical range constraint. Finally, the adjusted three groups of vectors are spliced along the feature dimension to form a multi-dimensional comprehensive information feature vector, which is used as the input data of the vehicle parameter configuration model, that is, the comprehensive information feature.

[0046] In some embodiments, before the vectorization processing of the historical driving habit information, real-time driving preference information and real-time road condition information, it further includes: judging whether the real-time driving preference information contains an active selection instruction; if it contains, giving the real-time driving preference information a higher weight than the historical driving habit information.

[0047] The active selection instruction refers to a parameter adjustment signal triggered by the user through explicit operation, such as sliding the power adjustment bar or clicking the preset mode button on the interface.

[0048] In the process of generating the vehicle parameter configuration file, the execution subject first identifies the instruction type of the user's real-time driving preference information. When it is detected that the real-time driving preference information contains an active selection instruction, such as the user manually enabling the climbing assist mode, the real-time driving preference information will be given a higher calculation weight in the subsequent vectorization processing stage. At this time, the regular acceleration habit data in the historical driving habit information will be down-weighted, ensuring that the user's currently active driving mode will have priority to affect the vehicle parameter configuration result. For example, when the user temporarily selects the load mode, even if the historical data reflects that he usually adopts the standard mode, the power output parameter will still be adjusted according to the load demand. Therefore, by identifying the active selection instruction and dynamically adjusting the weight, the temporary operation instruction can override the historical behavior data, ensuring that the parameter configuration result is real-time adapted to the user's explicit demand, solving the problem that the user's temporary driving demand is weakened by the historical data, and ensuring that the active selection instruction can have priority to affect the vehicle parameter configuration, for example, when the user suddenly needs to ride for a long distance, the actively selected endurance priority mode can directly increase the energy management weight, avoiding the system maintaining the default configuration due to the historical short-distance commuting habit. This dynamic weight adjustment mechanism enhances the real-time and initiative of the parameter configuration, so that the vehicle can quickly adapt to the user's immediate operation intention.

[0049] Referring to Figure 4 In an embodiment, the method for generating vehicle parameters based on the comprehensive information feature includes but is not limited to steps S401 to S404.

[0050] Step S401, according to real-time road condition information, determine the basic vehicle configuration mode.

[0051] Step S402, on the basis of the basic vehicle configuration mode, based on real-time driving preference information and historical driving habit information, individualize fine-tuning of vehicle parameters in the basic vehicle configuration mode to generate multiple sets of candidate vehicle parameter configuration values.

[0052] Step S403, match the comprehensive information feature with multiple candidate information features to determine the target information feature that meets the preset matching condition and has the highest similarity with the comprehensive information feature.

[0053] Step S404, take the candidate vehicle parameter configuration value corresponding to the target information feature with the highest user evaluation or the highest use frequency as the target vehicle parameter configuration value.

[0054] The basic vehicle configuration mode refers to the basic vehicle operation mode determined according to real-time road condition information, which can be realized by identifying real-time road conditions using a road condition classification model and matching standard configurations in a preset mode library, for example, flat roads correspond to energy-saving mode, and slopes correspond to power-enhancing mode. This feature is used to establish an initial parameter framework that adapts to the current road conditions, avoiding personalized adjustment from deviating from the actual road condition requirements.

[0055] Individualized fine-tuning refers to dynamically adjusting parameters in the basic configuration mode in combination with user preferences and historical behavior, which can be realized by prioritizing real-time preferences and historical data through a weight allocation algorithm. For example, when the user actively selects acceleration response, the real-time preference data weight can be increased to 1.5 times that of historical data. This feature ensures flexible adaptation of parameter configuration under the premise of ensuring safety.

[0056] Candidate information feature matching refers to calculating the similarity between the current comprehensive feature and the feature vector in the historical configuration case library, which can be realized by using cosine similarity algorithm or Euclidean distance calculation method, and selecting historical cases with similarity higher than a set threshold (for example, 0.85) and user evaluation score exceeding 4 stars. This feature optimizes configuration selection through group wisdom, improving the rationality and reliability of parameter configuration.

[0057] In the process of generating vehicle parameters, first, the basic vehicle configuration mode is determined according to real-time road condition information, for example, the stability priority mode is automatically selected when a continuous curve road condition is detected. Then, based on the user's current operation preference information (such as frequent use of the accelerator handle) and historical driving habit information (such as average speed record), the torque output parameters in the basic vehicle configuration mode are adjusted in multiple dimensions to generate multiple sets of candidate vehicle parameter configuration values. Next, the comprehensive information features including road conditions, preferences, and historical behaviors are matched with the 100,000 historical configuration data in the candidate case library to filter out the configuration scheme with the highest similarity ranking and a user score higher than the threshold score. Finally, the configuration with the most usage frequency is selected as the target vehicle parameter configuration value. In this way, through a three-layer optimization mechanism, dynamic configuration is realized. First, a basic framework for road condition adaptation is established, then a personalized adjustment layer is added, and finally a group optimization mechanism is introduced. Compared with the tedious operation of manually adjusting parameters one by one in traditional methods, the optimized configuration that is safe and conforms to the selection tendency of most users can be automatically generated, solving the problem of mismatch between vehicle parameters and user habits in the rental and exchange of electric vehicles. Under the premise of ensuring safety boundaries, through the three-stage processing of road condition adaptation, personalized optimization, and group optimization, a configuration scheme that meets the user's habits can be quickly generated for each rental of a different vehicle.

[0058] In some embodiments, based on the vehicle parameter configuration file, the vehicle parameters of the target vehicle are configured, including: issuing the vehicle parameter configuration file to the target vehicle, so that the control system of the target vehicle updates the vehicle parameters of the target vehicle based on the vehicle parameter configuration file; or generating a vehicle parameter package in real time based on the vehicle parameter configuration file and the driving data of the target vehicle in the driving process, issuing the vehicle parameter package to the target vehicle, and making the control system of the target vehicle update the vehicle parameters of the target vehicle based on the vehicle parameter package.

[0059] The vehicle parameter configuration file can be encapsulated in JSON or XML format to encapsulate parameter values and transmitted to the vehicle control system through the wireless communication module to ensure the integrity and parsability of the configuration information during transmission. Real-time generation of the vehicle parameter package means dynamically adjusting the parameter configuration according to the acceleration, slope, and battery voltage data collected during driving. Specifically, an edge computing device can be used to generate configuration update instructions locally to avoid cloud transmission delay.

[0060] The vehicle parameter profile is transmitted to the target vehicle through the wireless communication module. The control system of the vehicle parses the parameter values in the file and updates the control parameters such as motor torque limit and energy recovery intensity. When the user actively switches the driving mode, the parameter update process is triggered immediately, for example, when switching from economy mode to sports mode, the motor output power limit is automatically increased. During driving, if the navigation system predicts that the road slope in front exceeds 5%, the energy recovery level is adjusted in advance to reduce brake wear. When monitoring the user's continuous three times of rapid acceleration operation, which does not match the current economic mode parameters, a temporary parameter package is automatically generated to improve the power response. Every fixed period, such as 30 minutes, the system reanalyzes the user's operation habit data, and if it detects an average speed increase of 10%, the cruise control parameters are dynamically adjusted to adapt to the new driving style. In this way, by dynamically generating configuration files or real-time parameter packages, the same user can have a consistent driving experience on different vehicles, solving the problem of inconsistent experience caused by vehicle differences in the rental and exchange electric field, and realizing the dynamic matching of parameter configuration and user habits, real-time road conditions, for example, after the user changes the vehicle, the system automatically loads the historical preference parameters, without the need to reconfigure, and increases the energy recovery intensity in advance on long downhill road sections to avoid the risk of brake overheating.

[0061] In some embodiments, the trigger condition for updating the vehicle parameters of the target vehicle includes at least one of the following: detecting a driving mode switching operation; predicting a change in road type based on navigation information; detecting that the user's continuous operation behavior does not match the current vehicle parameter configuration; periodically adjusting at fixed time intervals.

[0062] The driving mode switching operation refers to the user's active selection of different performance output modes, which can be achieved through physical button triggering or touch interface selection, and is used to respond to the user's immediate demand for power output and energy consumption mode.

[0063] Predicting a change in road type based on navigation information refers to identifying changes in road slope and road surface material in front through path planning data, such as adjusting torque output parameters in advance when switching from flat road to mountain road section. The user's continuous operation behavior that does not match the current configuration refers to when the accelerator pedal opening degree continuously exceeds the current mode setting range, for example, the user accelerates with full opening for three times but the power response does not meet the expectation, indicating that the current parameters deviate from the driving demand.

[0064] Periodic adjustment refers to parameter optimization according to a preset interval, for example, updating the configuration every 30 days based on user usage data to adapt to gradual changes in long-term driving habits.

[0065] When the vehicle control system detects a driving mode switching operation, the parameter configuration file corresponding to the mode is immediately updated to ensure that the power output characteristics are consistent with the user's selection. When the navigation system predicts that the road type will change from urban roads to unpaved roads in the next few kilometers, the suspension damping and motor torque distribution strategy are adjusted in advance. When it is monitored that the user has performed five consecutive rapid acceleration operations within a few seconds and the current configuration is in energy saving mode, the system automatically switches to the sports mode parameter configuration. The system automatically analyzes the user's driving data for the last few days every Sunday morning, iteratively optimizing parameters such as energy recovery intensity and starting response speed. In this way, through multi-dimensional triggering mechanisms, the parameter configuration can be actively optimized according to navigation predictions, operation behavior deviation, and other data without user intervention, solving the problem of dynamic matching between the vehicle and user habits in the rental and battery replacement scenarios, reducing the user's repeated adjustment operations due to parameter mismatch, and improving the vehicle's adaptive ability.

[0066] Referring to Figure 5 The embodiments of the present application also provide a vehicle parameter configuration device of an electric bicycle, which can implement the vehicle parameter configuration method of the electric bicycle. The device comprises: A first module 501 is configured to acquire safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time, and real-time road condition information; A second module 502 is configured to generate a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information; A third module 503 is configured to perform vehicle parameter configuration on the target vehicle based on the vehicle parameter configuration file.

[0067] The specific implementation of the vehicle parameter configuration device of the electric bicycle is basically the same as that of the above-mentioned vehicle parameter configuration method of the electric bicycle, and will not be described here.

[0068] Figure 6 is a block diagram of an electronic device according to an example embodiment.

[0069] The electronic device 600 according to this embodiment of the present disclosure will be described below with reference to Figure 6 Figure 6 The displayed electronic device 600 is merely an example and should not impose any limitation on the function and use range of the embodiments of the present disclosure.

[0070] As Figure 6 ​As shown, the electronic device 600 is in the form of a general computing device. Components of the electronic device 600 can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components including the storage unit 620 and the processing unit 610, a display unit 640, and the like.

[0071] The storage unit stores program code that can be executed by the processing unit 610 to cause the processing unit 610 to perform the steps described above in the method of configuring vehicle parameters of an electric bicycle according to various example embodiments of the present disclosure.

[0072] The storage unit 620 can include a readable medium in the form of volatile storage such as random access memory (RAM) 6201 and / or cache memory 6202, and can further include a non-volatile storage such as read-only memory (ROM) 6203.

[0073] The storage unit 620 can also include a program / utility 6204 having a set of program modules 6205 such as an operating system, one or more application programs, other program modules, and program data, each of which can give the electronic device 600 its functionality, at least in part. Each of these example applications or modules, or some combination thereof, can include implementation of a network environment.

[0074] The bus 630 can represent one or more of several types of bus structures, including a storage bus or bus controller, a peripheral bus, an accelerated graphics port, an industry standard architecture bus, a video interface, a local bus using any of the bus architectures, and the like.

[0075] The electronic device 600 can also communicate with one or more external devices 600' such as a keyboard or pointing device, a Bluetooth device, etc.; and / or one or more devices that enable a user to interact with the electronic device 600; and / or one or more devices that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can be via an input / output (I / O) interface 650. Similarly, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, via a network adapter 660. The network adapter 660 can communicate with the other components of the electronic device 600 via the bus 630. It will be appreciated that the electronic device 600 can be connected to other types of computing devices using other forms of communication media. It will be appreciated that the electronic device 600 can be connected to other types of computing devices using other forms of communication media.

[0076] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the vehicle parameter configuration method of the electric bicycle.

[0077] The vehicle parameter configuration method, device, equipment and medium of the electric bicycle provided by the embodiment of the present application realize multi-dimensional collaborative decision by fusing vehicle safety limits, long-term behavior characteristics, real-time preferences and environmental data, for example, when the user temporarily selects aggressive driving but the road condition is wet and slippery, the safety boundary is preferentially guaranteed, and the response speed is only improved within the allowable range of braking performance, instead of directly applying a fixed motion mode, thereby solving the inconsistent experience problem caused by frequent replacement of vehicles in the rental and replacement electric field, enabling different vehicles to automatically adapt parameters according to user habits and real-time needs, dynamically balancing safety limits and user preferences, and avoiding operation risks caused by parameter conflicts, for example, automatically enhancing braking recovery on a long downhill road section, thereby meeting the user's energy-saving needs and preventing the brake system from overheating. In addition, the linkage adjustment of real-time road conditions and driving modes can reduce the user's frequent manual operation and improve driving comfort.

[0078] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the embodiments of the present disclosure.

[0079] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable 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.

[0080] The computer readable storage medium can include a computer-readable storage medium comprising a data signal embodied in or carried by a carrier wave or other transport mechanism and constitutes media which carries or stores the program code in a modulated data signal. Such a propagated signal can take a wide variety of forms including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable medium can be any medium that can be read by a computer including magnetic tape; magnetic disk; optical disk; optical fiber; and / or electrical signals.

[0081] Those skilled in the art can understand that the above-mentioned modules can be distributed in the device according to the description of the embodiment, and can also be changed in one or more devices different from the embodiment. The modules of the above-mentioned embodiment can be combined into one module, or can be further split into a plurality of sub-modules.

[0082] The exemplary embodiments of this disclosure are specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structure, arrangement or implementation method described herein; on the contrary, the present disclosure is intended to cover various modifications and equivalent arrangements within the spirit and scope of the appended claims.

Claims

1. A method of configuring vehicle parameters of an electric bicycle, characterized by, The method comprises the following steps: obtaining safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time, and real-time road condition information; generating a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information; configuring vehicle parameters of the target vehicle based on the vehicle parameter configuration file.

2. The method of claim 1, wherein, The step of generating the vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information comprises the following steps: extracting and fusing features of the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information to obtain comprehensive information features; inputting the comprehensive information features into a pre-trained vehicle parameter configuration model to generate vehicle parameters based on the comprehensive information features, and obtaining target vehicle parameter configuration values; the vehicle parameter configuration model is used to generate vehicle parameters under the constraint of the safety boundary information and to simultaneously satisfy the historical driving habit dimension, the real-time driving preference dimension, and the real-time road condition dimension; packaging the target vehicle parameter configuration values to obtain a vehicle parameter configuration file.

3. The method of claim 2, wherein, The step of extracting and fusing features of the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time road condition information comprises the following steps: vectorizing the historical driving habit information, the real-time driving preference information, and the real-time road condition information to obtain corresponding user information vectors; based on the safety boundary information, fine-tuning the user information vectors, and combining the fine-tuned user information vectors to obtain the comprehensive information features.

4. The method of claim 3, wherein, Before the step of vectorizing the historical driving habit information, the real-time driving preference information, and the real-time road condition information, the method further comprises the following steps: determining whether the real-time driving preference information contains an active selection instruction; if the real-time driving preference information contains the active selection instruction, assigning a higher weight to the real-time driving preference information than to the historical driving habit information.

5. The method of claim 2, wherein, The step of generating vehicle parameters based on the comprehensive information features comprises the following steps: determining a basic vehicle configuration mode according to the real-time road condition information; based on the basic vehicle configuration mode, fine-tuning vehicle parameters in the basic vehicle configuration mode based on the real-time driving preference information and the historical driving habit information to generate multiple groups of candidate vehicle parameter configuration values; matching the comprehensive information features with multiple candidate information features to determine a target information feature that meets a preset matching condition and has the highest similarity with the comprehensive information features; selecting, as the target vehicle parameter configuration value, a candidate vehicle parameter configuration value that has the highest user evaluation or the highest use frequency corresponding to the target information feature.

6. The method of claim 1, wherein, The step of configuring vehicle parameters of the target vehicle based on the vehicle parameter configuration file comprises the following steps: The vehicle parameter configuration file is sent to the target vehicle, and a control system of the target vehicle updates vehicle parameters of the target vehicle based on the vehicle parameter configuration file; or a vehicle parameter package is generated in real time based on the vehicle parameter configuration file and driving data of the target vehicle during driving, and the vehicle parameter package is sent to the target vehicle, so that the control system of the target vehicle updates the vehicle parameters of the target vehicle based on the vehicle parameter package.

7. The method of claim 6, wherein, The trigger condition for updating the vehicle parameters of the target vehicle includes at least one of the following: A driving mode switching operation is detected; It is predicted that the road type will change based on navigation information; It is detected that the user's continuous operation behavior does not match the current vehicle parameter configuration; Periodic adjustment is performed at a fixed time period.

8. A vehicle parameter configuration device for an electric bicycle, characterized by, The method comprises: A first module is configured to obtain safety boundary information of a target vehicle, historical driving habit information of a user, real-time driving preference information input by the user in real time, and real-time traffic information; A second module is configured to generate a vehicle parameter configuration file based on the safety boundary information, the historical driving habit information, the real-time driving preference information, and the real-time traffic information; A third module is configured to configure vehicle parameters of the target vehicle based on the vehicle parameter configuration file.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the vehicle parameter configuration method of the electric bicycle according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the vehicle parameter configuration method of the electric bicycle according to any one of claims 1 to 7.

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