Vehicle function optimization methods, devices, equipment, storage media, and program products

Abstract

This application relates to a vehicle function optimization method, apparatus, device, storage medium, and program product. The method includes: determining the vehicle function to be optimized from candidate vehicle functions based on historical behavior data associated with a target user; determining the target user's user behavior pattern based on the historical behavior data and the functional influencing factors corresponding to the function to be optimized; and optimizing the vehicle function to be optimized based on the user behavior pattern and the functional configuration information of the vehicle function to be optimized, thereby obtaining the optimized target vehicle function. This method can improve the efficiency and reliability of vehicle function optimization.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, storage medium, and program product for optimizing vehicle functions. Background Technology

[0002] With the continuous development of intelligent vehicles, in order to ensure the driver's driving experience, it is necessary to adaptively optimize and adjust the vehicle functions of the driver's vehicle.

[0003] Existing vehicle function optimization methods typically involve manually modifying settings to adaptively adjust vehicle functions. However, relying on manual parameter configuration reduces the efficiency of vehicle function optimization. Summary of the Invention

[0004] Therefore, it is necessary to provide a vehicle function optimization method, apparatus, equipment, storage medium, and program product that can improve the efficiency of vehicle function optimization in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a vehicle function optimization method, including:

[0006] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0007] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0008] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0009] In one embodiment, based on historical behavior data associated with the target user, the vehicle functions to be optimized are determined from among the candidate vehicle functions, including:

[0010] Based on the historical behavior data associated with the target users, determine the functional keywords corresponding to the historical behavior data; based on the functional keywords corresponding to the historical behavior data, determine the usage frequency of each candidate vehicle function; based on the usage frequency of each candidate vehicle function, determine the vehicle functions to be optimized.

[0011] In one embodiment, the user behavior pattern of the target user is determined based on historical behavior data and the functional influencing factors corresponding to the function to be optimized, including:

[0012] Based on the functional influencing factors corresponding to the functions to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior characteristics corresponding to each functional influencing factor; based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined.

[0013] In one embodiment, the vehicle function to be optimized is optimized based on user behavior patterns and the function configuration information of the vehicle function to be optimized, to obtain the optimized target vehicle function, including:

[0014] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, a functional optimization scheme for the vehicle functions to be optimized is determined; based on the functional optimization scheme and the vehicle functions to be optimized, a virtual vehicle function model corresponding to the vehicle functions to be optimized is constructed; based on user behavior patterns and the virtual vehicle function model, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0015] In one embodiment, the vehicle function to be optimized is optimized based on user behavior patterns and a virtual vehicle function model to obtain the optimized target vehicle function, including:

[0016] Based on user behavior patterns, relevant behavioral data related to the target user is obtained from standard behavioral data; the relevant behavioral data is input into the virtual vehicle function model to obtain the function operation results; based on the function operation results and the function optimization plan, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

[0017] In one embodiment, based on the function operation results and the function optimization scheme, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function, including:

[0018] If the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0019] Secondly, this application also provides a vehicle function optimization device, comprising:

[0020] The function determination module is used to determine the vehicle functions to be optimized from the candidate vehicle functions based on the historical behavior data associated with the target user.

[0021] The pattern determination module is used to determine the user behavior patterns of target users based on historical behavior data and the functional influencing factors corresponding to the functions to be optimized.

[0022] The function optimization module is used to optimize the functions of the vehicle to be optimized based on user behavior patterns and the function configuration information of the functions to be optimized, so as to obtain the optimized target vehicle functions.

[0023] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0024] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0025] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0026] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0028] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0029] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0030] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0031] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0032] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0033] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0034] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0035] The aforementioned vehicle function optimization method, apparatus, equipment, storage medium, and program product determine the vehicle function to be optimized from candidate vehicle functions based on historical behavior data associated with the target user. Then, based on the historical behavior data and the functional influencing factors corresponding to the function to be optimized, the user behavior pattern of the target user is determined. Subsequently, the vehicle function to be optimized is optimized based on the user behavior pattern and the functional configuration information of the vehicle function to be optimized, resulting in the optimized target vehicle function. Compared to related technologies that adaptively optimize vehicle functions by manually configuring vehicle parameters, the above method, by analyzing user behavior patterns based on historical behavior data and the functional influencing factors corresponding to the function to be optimized, and then optimizing the vehicle function to be optimized based on these user behavior patterns, can improve the efficiency and reliability of vehicle function optimization. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a vehicle function optimization method in one embodiment;

[0038] Figure 2 This is a flowchart illustrating the process of determining the vehicle function to be optimized in one embodiment.

[0039] Figure 3 This is a flowchart illustrating the process of determining a user behavior pattern in one embodiment;

[0040] Figure 4 This is a flowchart illustrating the process of determining the function of a target vehicle in one embodiment;

[0041] Figure 5 This is a flowchart illustrating the process of determining the function of a target vehicle in another embodiment;

[0042] Figure 6 This is a flowchart illustrating the vehicle function optimization method in another embodiment;

[0043] Figure 7 This is a structural block diagram of a vehicle function optimization device in one embodiment;

[0044] Figure 8 This is a structural block diagram of the vehicle function optimization device in another embodiment;

[0045] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] With the continuous development of intelligent vehicles, in order to ensure the driver's driving experience, it is necessary to adaptively optimize and adjust the vehicle functions of the driver's vehicle.

[0048] Existing vehicle function optimization methods typically involve manually modifying settings to adaptively adjust vehicle functions. However, relying on manual parameter configuration reduces the efficiency of vehicle function optimization.

[0049] Based on this, in an exemplary embodiment, such as Figure 1 As shown, a vehicle function optimization method is provided. Taking the application of this method to a function optimization device as an example, the method includes the following steps:

[0050] S101, Based on the historical behavior data associated with the target user, determine the vehicle functions to be optimized from the candidate vehicle functions.

[0051] Among them, the target user is any vehicle driver; historical behavior data is the behavior data generated by the target user while driving the vehicle, which may be the behavior data corresponding to the user's trigger operation on the corresponding screen of the in-vehicle terminal; candidate vehicle functions are the various functions that the vehicle terminal can provide, such as seat adjustment function, navigation function and intelligent recommendation function; vehicle functions to be optimized are vehicle functions that need to be optimized, such as functions that the target user uses frequently.

[0052] Optionally, based on the target user's user identification information, historical behavioral data corresponding to the target user's trigger operations on the corresponding screen of the in-vehicle terminal within a historical period can be obtained from the target vehicle driven by the target user. Subsequently, the historical behavioral data associated with the target user can be input into a trained function determination model, which then outputs the vehicle function to be optimized based on the historical behavioral data and model parameters. The function determination model is trained using sample behavioral data corresponding to the functions of each candidate vehicle.

[0053] Understandably, in order to ensure the reliability of data acquisition, data tracking can be performed in advance in each candidate vehicle function before collecting historical behavior data, so as to obtain the historical behavior data of the target user in each candidate vehicle function during historical periods.

[0054] S102, Based on historical behavior data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users.

[0055] Among them, functional influencing factors are the relevant factors that affect the operation of each candidate function. For example, functional influencing factors for navigation functions may include, but are not limited to, information such as network conditions at the current location and vehicle model; user behavior patterns are the behavior patterns of target users when using vehicle functions.

[0056] Optionally, based on the functional influencing factors corresponding to the function to be optimized, the behavioral data corresponding to the functional influencing factors in the historical behavioral data can be summarized; then, the summary results can be processed by feature extraction to determine the user behavior pattern of the target user.

[0057] S103, based on user behavior patterns and the functional configuration information of the vehicle function to be optimized, optimize the vehicle function to be optimized to obtain the optimized target vehicle function.

[0058] Among them, the functional configuration information is information related to the basic functional configuration; the target vehicle function is the vehicle function after the function of the vehicle to be optimized has been optimized.

[0059] Optionally, based on the user's user behavior pattern, within the parameter adjustment range corresponding to the functional configuration information of the vehicle function to be optimized, the parameter adjustment method is determined; then, the vehicle function to be optimized is optimized using the parameter adjustment method to obtain the optimized target vehicle function.

[0060] In addition, to provide the driving experience for the target users, related functions corresponding to the function to be optimized can be optimized based on user behavior patterns.

[0061] For example, regarding vehicle battery usage, based on the target vehicle's remaining battery power and road conditions, the historical electricity usage data of user A can be aggregated and processed to determine that user A's electricity usage characteristics are: when the road conditions are stable and the remaining battery power is greater than 40%, priority should be given to using electric energy.

[0062] Furthermore, based on the battery configuration information corresponding to the vehicle's battery usage function, the vehicle's battery usage function can be set to automatically use electric power for driving when the road environment is stable and the remaining battery power is greater than 40%. Correspondingly, in order to ensure the normal operation of the vehicle, when the remaining battery power is about to drop below 40%, the system can automatically recommend nearby charging stations to user A.

[0063] In the aforementioned vehicle function optimization method, the vehicle function to be optimized is determined from candidate vehicle functions based on historical behavior data associated with the target user. Then, based on the historical behavior data and the functional influencing factors corresponding to the function to be optimized, the user behavior pattern of the target user is determined. Subsequently, the vehicle function to be optimized is optimized based on the user behavior pattern and the functional configuration information of the vehicle function to be optimized, resulting in the optimized target vehicle function. Compared to related technologies that adaptively optimize vehicle functions by manually configuring vehicle parameters, the above method, by analyzing user behavior patterns based on historical behavior data and the functional influencing factors corresponding to the function to be optimized, and then optimizing the vehicle function to be optimized based on these user behavior patterns, can improve the efficiency and reliability of vehicle function optimization.

[0064] To ensure the accuracy of the identified vehicle functions to be optimized, based on the above embodiments, this embodiment provides an optional method for determining the vehicle functions to be optimized, such as... Figure 2 As shown, the specific steps include:

[0065] S201, Based on the historical behavior data associated with the target user, determine the functional keywords corresponding to the historical behavior data.

[0066] Among them, the functional keywords are the keywords corresponding to the functions of each candidate vehicle.

[0067] Optionally, for any historical behavior data associated with the target user, the historical behavior data can be input into a trained keyword determination model, and the keyword determination model can output the functional keywords corresponding to the historical behavior data based on the historical behavior data and model parameters.

[0068] S202, based on the functional keywords corresponding to historical behavior data, determine the usage frequency of each candidate vehicle function.

[0069] Optionally, the functional keywords corresponding to each historical behavior data can be aggregated to obtain keyword aggregation results; then, based on the correlation between each functional keyword and each candidate vehicle function, the usage frequency of each candidate vehicle function can be determined according to the keyword aggregation results.

[0070] S203, determine the vehicle functions to be optimized based on the usage frequency of each candidate vehicle function.

[0071] Optionally, the candidate vehicle functions can be sorted according to their frequency of use to obtain a function ranking order; then, the functions that appear first in the ranking order can be selected as the vehicle functions to be optimized. For example, if it is pre-set that two vehicle functions need to be optimized, the function that appears second in the ranking order can be selected as the vehicle function to be optimized.

[0072] In this embodiment of the application, the frequency of use of each candidate vehicle function is determined based on the functional keywords corresponding to historical behavior data, thereby determining the vehicle function to be optimized, which can ensure the accuracy of the determination of the vehicle function to be optimized.

[0073] To ensure the accuracy of user behavior patterns, based on the above embodiments, this embodiment provides an optional method for determining user behavior patterns, such as... Figure 3 As shown, the specific steps include:

[0074] S301. Based on the functional influencing factors corresponding to the function to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior features corresponding to each functional influencing factor.

[0075] Among them, user behavior characteristics are the behavioral characteristics of target users under the influence of functional factors.

[0076] Optionally, for any functional influencing factor corresponding to the function to be optimized, the behavioral data corresponding to the functional influencing factor can be extracted from historical behavioral data; then, the behavioral data corresponding to the functional influencing factor is input into the trained feature extraction model, and the feature extraction model outputs the user behavior features corresponding to the functional influencing factor based on the behavioral data corresponding to the functional influencing factor and the model parameters.

[0077] For example, one of the factors affecting navigation functionality is lane congestion. Based on lane congestion, user B's route selection behavior under various lane congestion conditions can be extracted from user B's historical behavior data. Subsequently, based on user B's route selection behavior under various congestion conditions, it can be determined that user B's behavioral characteristics are that, under road congestion conditions, user B prioritizes choosing routes with shorter travel times.

[0078] S302, Based on the user behavior characteristics corresponding to the influencing factors of each function, determine the user behavior pattern of the target user.

[0079] Optionally, the user behavior features corresponding to the influencing factors of each function can be combined to obtain the user behavior pattern of the target user. For example, the user behavior features corresponding to the influencing factors of each function can be concatenated according to their priority to obtain the user behavior pattern of the target user.

[0080] In this embodiment of the application, by determining the user behavior pattern of the target user based on the user behavior characteristics corresponding to each functional influencing factor, the accuracy of the user behavior pattern determination can be guaranteed.

[0081] To ensure the reasonableness of the target vehicle's functions, based on the above embodiments, this embodiment provides an optional method for determining the target vehicle's functions, such as... Figure 4 As shown, the specific steps include:

[0082] S401, based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, determine the functional optimization scheme for the vehicle functions to be optimized.

[0083] Among them, the function optimization plan is a plan to optimize the function to be optimized.

[0084] Optionally, based on user behavior patterns, the initial adjustment method corresponding to each functional parameter in the vehicle function to be optimized can be determined. Subsequently, for any initial adjustment method corresponding to a functional parameter, it can be determined whether the initial adjustment method corresponding to the functional parameter is feasible based on the functional configuration information of the vehicle function to be optimized. If so, the initial adjustment method corresponding to the functional parameter can be directly used as the parameter adjustment method in the functional optimization scheme.

[0085] If not, the initial adjustment method corresponding to the function parameter is adjusted based on the function configuration information of the vehicle function to be optimized, so as to obtain the parameter adjustment method in the function optimization scheme.

[0086] S402, based on the functional optimization scheme and the vehicle functions to be optimized, construct a virtual vehicle function model corresponding to the vehicle functions to be optimized.

[0087] Among them, the virtual vehicle function model is a virtual model that can simulate the operation of the vehicle function to be optimized.

[0088] Optionally, based on the functional optimization scheme, you can perform simple modeling of the vehicle functions to be optimized to obtain a virtual vehicle function model that can simulate the operation of the vehicle functions to be optimized.

[0089] S403 optimizes the vehicle functions to be optimized based on user behavior patterns and virtual vehicle function models to obtain optimized target vehicle functions.

[0090] Optionally, a virtual vehicle function model can be run based on user behavior patterns to obtain function operation feedback data; subsequently, the actual vehicle function to be optimized can be optimized based on the function operation feedback data to obtain the optimized target vehicle function.

[0091] In this embodiment of the application, by optimizing the vehicle function to be optimized based on user behavior patterns and virtual vehicle function models, the optimized target vehicle function can be obtained, thus ensuring the rationality of the target vehicle function.

[0092] To further ensure the rationality of the target vehicle's functions, based on the above embodiments, this embodiment provides another optional method for determining the target vehicle's functions, such as... Figure 5 As shown, the specific steps include:

[0093] S501, based on user behavior patterns, retrieves relevant behavioral data related to the target user from standard behavioral data.

[0094] Standard behavioral data consists of routine behavioral data of users collected from other vehicles; related behavioral data consists of behavioral data that is highly relevant to the target user.

[0095] Optionally, user behavior patterns can be used as an index to obtain related behavior data relevant to the target user from the correlation between standard behavior data and behavior patterns.

[0096] S502, input the associated behavioral data into the virtual vehicle function model to obtain the function operation results.

[0097] The functional operation results are obtained by simulating the functional operation using a virtual vehicle functional model.

[0098] Optionally, the associated behavioral data can be input into the virtual vehicle function model. The virtual vehicle function model can then be used to simulate the operation of the vehicle functions under the associated behavioral data, thereby obtaining the function operation results.

[0099] S503 optimizes the vehicle functions to be optimized based on the function operation results and function optimization plan, and obtains the optimized target vehicle functions.

[0100] Optionally, the feasibility of the functional optimization scheme can be verified based on the functional operation results, and the vehicle function to be optimized can be optimized according to the verification results to obtain the optimized target vehicle function.

[0101] For example, if the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained; if the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0102] Among them, abnormal operating parameters are functional parameters that are abnormal when the virtual vehicle functional model is running.

[0103] Optionally, if the function operation result is normal, it proves that the function optimization scheme meets the operation requirements. Therefore, the function optimization scheme can be directly adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function can be obtained.

[0104] If the function operation result is abnormal, it proves that the function optimization plan does not meet the operation requirements. Therefore, it is necessary to extract the abnormal operation parameters from the function operation result. Then, according to the parameter values ​​corresponding to the abnormal operation parameters, the function optimization plan is adjusted, and the adjusted function optimization plan is used to optimize the vehicle function to be optimized, so as to obtain the optimized target vehicle function.

[0105] In this embodiment of the application, by using a virtual vehicle function model to simulate vehicle functions and determining the optimized target vehicle functions based on the function operation results, the rationality of the target vehicle functions can be guaranteed.

[0106] Figure 6 This is a flowchart illustrating a vehicle function optimization method in another embodiment. Based on the above embodiments, this embodiment provides an optional example of a vehicle function optimization method. (Combined with...) Figure 6 The specific implementation process is as follows:

[0107] S601, determine the functional keywords corresponding to the historical behavior data based on the target user's associated historical behavior data.

[0108] S602 determines the usage frequency of each candidate vehicle function based on the functional keywords corresponding to historical behavior data.

[0109] S603 determines the vehicle functions to be optimized based on the usage frequency of each candidate vehicle function.

[0110] S604. Based on the functional influencing factors corresponding to the function to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior features corresponding to each functional influencing factor.

[0111] S605 determines the user behavior pattern of the target user based on the user behavior characteristics corresponding to the influencing factors of each function.

[0112] S606 determines the functional optimization scheme for the vehicle function to be optimized based on user behavior patterns and the functional configuration information of the vehicle function to be optimized.

[0113] S607, based on the functional optimization scheme and the vehicle functions to be optimized, constructs a virtual vehicle function model corresponding to the vehicle functions to be optimized.

[0114] S608, based on user behavior patterns, retrieves relevant behavioral data related to the target user from standard behavioral data.

[0115] S609, input the associated behavioral data into the virtual vehicle function model to obtain the function operation results.

[0116] S610 optimizes the vehicle functions to be optimized based on the function operation results and function optimization plan, and obtains the optimized target vehicle functions.

[0117] Optionally, if the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained; if the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0118] The specific processes of S601-S610 described above can be found in the description of the above method embodiments. Their implementation principles and technical effects are similar, and will not be repeated here.

[0119] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0120] Based on the same inventive concept, this application also provides a vehicle function optimization device for implementing the vehicle function optimization method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more vehicle function optimization device embodiments provided below can be found in the limitations of the vehicle function optimization method described above, and will not be repeated here.

[0121] In one exemplary embodiment, such as Figure 7 As shown, a vehicle function optimization device 1 is provided, including: a function determination module 10, a mode determination module 20, and a function optimization module 30, wherein:

[0122] The function determination module 10 is used to determine the vehicle functions to be optimized from the candidate vehicle functions based on the historical behavior data associated with the target user.

[0123] The pattern determination module 20 is used to determine the user behavior pattern of the target user based on historical behavior data and the functional influencing factors corresponding to the function to be optimized.

[0124] The function optimization module 30 is used to optimize the function of the vehicle to be optimized based on user behavior patterns and the function configuration information of the function to be optimized, so as to obtain the optimized target vehicle function.

[0125] In an exemplary embodiment, the function determination module 10 is specifically used for:

[0126] Based on the historical behavior data associated with the target users, determine the functional keywords corresponding to the historical behavior data; based on the functional keywords corresponding to the historical behavior data, determine the usage frequency of each candidate vehicle function; based on the usage frequency of each candidate vehicle function, determine the vehicle functions to be optimized.

[0127] In one exemplary embodiment, the pattern determination module 20 is specifically used for:

[0128] Based on the functional influencing factors corresponding to the functions to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior characteristics corresponding to each functional influencing factor; based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined.

[0129] In one exemplary embodiment, such as Figure 8 As shown, the function optimization module 30 includes:

[0130] The scheme determination unit 31 is used to determine the function optimization scheme of the vehicle function to be optimized based on the user behavior pattern and the function configuration information of the vehicle function to be optimized.

[0131] Model building unit 32 is used to build a virtual vehicle function model corresponding to the vehicle function to be optimized based on the function optimization scheme and the vehicle function to be optimized.

[0132] The function optimization unit 33 is used to optimize the vehicle function to be optimized based on user behavior patterns and virtual vehicle function models to obtain the optimized target vehicle function.

[0133] In an exemplary embodiment, the function optimization unit 33 is specifically used for:

[0134] Based on user behavior patterns, relevant behavioral data related to the target user is obtained from standard behavioral data; the relevant behavioral data is input into the virtual vehicle function model to obtain the function operation results; based on the function operation results and the function optimization plan, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

[0135] In an exemplary embodiment, the function optimization unit 33 is further configured to:

[0136] If the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0137] Each module in the aforementioned vehicle function optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0138] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle function optimization method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0139] Those skilled in the art will understand that Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0140] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0141] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0142] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0143] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0144] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0145] Based on the historical behavior data associated with the target users, determine the functional keywords corresponding to the historical behavior data; based on the functional keywords corresponding to the historical behavior data, determine the usage frequency of each candidate vehicle function; based on the usage frequency of each candidate vehicle function, determine the vehicle functions to be optimized.

[0146] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0147] Based on the functional influencing factors corresponding to the functions to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior characteristics corresponding to each functional influencing factor; based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined.

[0148] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0149] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, a functional optimization scheme for the vehicle functions to be optimized is determined; based on the functional optimization scheme and the vehicle functions to be optimized, a virtual vehicle function model corresponding to the vehicle functions to be optimized is constructed; based on user behavior patterns and the virtual vehicle function model, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0150] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0151] Based on user behavior patterns, relevant behavioral data related to the target user is obtained from standard behavioral data; the relevant behavioral data is input into the virtual vehicle function model to obtain the function operation results; based on the function operation results and the function optimization plan, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

[0152] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0153] If the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0154] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0155] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0156] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0157] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0158] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0159] Based on the historical behavior data associated with the target users, determine the functional keywords corresponding to the historical behavior data; based on the functional keywords corresponding to the historical behavior data, determine the usage frequency of each candidate vehicle function; based on the usage frequency of each candidate vehicle function, determine the vehicle functions to be optimized.

[0160] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0161] Based on the functional influencing factors corresponding to the functions to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior characteristics corresponding to each functional influencing factor; based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined.

[0162] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0163] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, a functional optimization scheme for the vehicle functions to be optimized is determined; based on the functional optimization scheme and the vehicle functions to be optimized, a virtual vehicle function model corresponding to the vehicle functions to be optimized is constructed; based on user behavior patterns and the virtual vehicle function model, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0164] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0165] Based on user behavior patterns, relevant behavioral data related to the target user is obtained from standard behavioral data; the relevant behavioral data is input into the virtual vehicle function model to obtain the function operation results; based on the function operation results and the function optimization plan, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

[0166] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0167] If the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0168] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0169] Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function;

[0170] Based on historical behavioral data and the functional influencing factors corresponding to the functions to be optimized, determine the user behavior patterns of the target users;

[0171] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0172] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0173] Based on the historical behavior data associated with the target users, determine the functional keywords corresponding to the historical behavior data; based on the functional keywords corresponding to the historical behavior data, determine the usage frequency of each candidate vehicle function; based on the usage frequency of each candidate vehicle function, determine the vehicle functions to be optimized.

[0174] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0175] Based on the functional influencing factors corresponding to the functions to be optimized, feature extraction processing is performed on historical behavior data to obtain user behavior characteristics corresponding to each functional influencing factor; based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined.

[0176] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0177] Based on user behavior patterns and the functional configuration information of the vehicle functions to be optimized, a functional optimization scheme for the vehicle functions to be optimized is determined; based on the functional optimization scheme and the vehicle functions to be optimized, a virtual vehicle function model corresponding to the vehicle functions to be optimized is constructed; based on user behavior patterns and the virtual vehicle function model, the vehicle functions to be optimized are optimized to obtain the optimized target vehicle functions.

[0178] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0179] Based on user behavior patterns, relevant behavioral data related to the target user is obtained from standard behavioral data; the relevant behavioral data is input into the virtual vehicle function model to obtain the function operation results; based on the function operation results and the function optimization plan, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

[0180] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0181] If the function operation result is normal, the function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is adopted to optimize the function of the vehicle to be optimized, and the optimized target vehicle function is obtained.

[0182] It should be noted that the data involved in this application (including but not limited to historical behavior data) is all data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0183] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0184] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0185] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing vehicle functions, characterized in that, The method includes: Based on the historical behavior data associated with the target users, identify the vehicle functions to be optimized from each candidate vehicle function; Based on the functional influencing factors corresponding to the vehicle functions to be optimized, feature extraction processing is performed on the historical behavior data to obtain user behavior features corresponding to each functional influencing factor. Based on the user behavior characteristics corresponding to each functional influencing factor, the user behavior pattern of the target user is determined; Based on the user behavior pattern and the functional configuration information of the vehicle function to be optimized, determine the functional optimization scheme for the vehicle function to be optimized. Based on the functional optimization scheme and the vehicle function to be optimized, a virtual vehicle function model corresponding to the vehicle function to be optimized is constructed. Based on the user behavior pattern and the virtual vehicle function model, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

2. The method according to claim 1, characterized in that, The step of determining the vehicle functions to be optimized from among the candidate vehicle functions based on the historical behavior data associated with the target user includes: Based on the historical behavior data associated with the target user, determine the functional keywords corresponding to the historical behavior data; Based on the functional keywords corresponding to the historical behavior data, the usage frequency of each candidate vehicle function is determined; Based on the usage frequency of each candidate vehicle's functions, the vehicle functions to be optimized are determined.

3. The method according to claim 1, characterized in that, The step of optimizing the vehicle function to be optimized based on the user behavior pattern and the virtual vehicle function model to obtain the optimized target vehicle function includes: Based on the user behavior pattern, obtain related behavior data concerning the target user from standard behavior data; The associated behavioral data is input into the virtual vehicle function model to obtain the function operation results; Based on the function operation results and the function optimization scheme, the vehicle function to be optimized is optimized to obtain the optimized target vehicle function.

4. The method according to claim 3, characterized in that, The step of optimizing the vehicle function to be optimized based on the function operation results and the function optimization scheme to obtain the optimized target vehicle function includes: If the function operation result is normal, the function optimization scheme is adopted to optimize the vehicle function to be optimized, and the optimized target vehicle function is obtained. If the function operation result is abnormal, the function optimization scheme is adjusted according to the abnormal operation parameters in the function operation result, and the adjusted function optimization scheme is used to optimize the vehicle function to be optimized, so as to obtain the optimized target vehicle function.

5. The method according to claim 1, characterized in that, The step of determining the user behavior pattern of the target user based on the user behavior characteristics corresponding to each functional influencing factor includes: Based on the priority of each functional influencing factor, the user behavior characteristics corresponding to each functional influencing factor are spliced ​​together to obtain the user behavior pattern of the target user.

6. The method according to claim 1, characterized in that, When the vehicle function to be optimized is navigation, the factors affecting the function include the network conditions at the current location and the vehicle model.

7. A vehicle function optimization device, characterized in that, The device includes: The function determination module is used to determine the vehicle functions to be optimized from the candidate vehicle functions based on the historical behavior data associated with the target user. The pattern determination module is used to perform feature extraction processing on the historical behavior data according to the functional influencing factors corresponding to the functions of the vehicle to be optimized, to obtain the user behavior characteristics corresponding to each functional influencing factor; and to determine the user behavior pattern of the target user according to the user behavior characteristics corresponding to each functional influencing factor. The function optimization module is used to determine the function optimization scheme of the vehicle function to be optimized based on the user behavior pattern and the function configuration information of the vehicle function to be optimized; construct a virtual vehicle function model corresponding to the vehicle function to be optimized based on the function optimization scheme and the vehicle function to be optimized; and optimize the vehicle function to be optimized based on the user behavior pattern and the virtual vehicle function model to obtain the optimized target vehicle function.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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