Driving style identification method, data processing method, device and equipment

Abstract

The invention provides a driving style identification method, a data processing method, a device and equipment. Relates to the technical field of intelligent driving. The method comprises the following steps: identifying a target driving style of a current user from a plurality of driving styles based on historical driving behavior data of the current user, and controlling the autonomous mobile equipment to run according to driving environment information, navigation information and the target driving style obtained in a moving process. Wherein the plurality of driving styles are obtained based on clustering analysis of the driving behavior data of the plurality of users. By means of the method, accurate recognition and personalized adaptation of the driving style of the user can be achieved, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and particularly to a method for identifying driving styles, a data processing method, a device, and a device. Background Art

[0002] Users can drive autonomous mobile devices. Different users usually have different driving styles.

[0003] In the related art, autonomous mobile devices are usually equipped with intelligent driving systems. During driving, the autonomous mobile device can drive according to the general driving style in the intelligent driving system. For example, for an intelligent driving vehicle, in the general driving style, the set following distance can be 3m.

[0004] However, in the above method, the general driving style cannot well match the user's personal driving style, resulting in a poor user experience. Summary of the Invention

[0005] This application provides a method for identifying driving styles, a data processing method, a device, and a device, which are used to solve the problem that the general driving style in the prior art cannot well match the user's personal driving style, resulting in a poor user experience.

[0006] In a first aspect, this application provides a method for identifying a driving style, which is applied to an autonomous mobile device. The method includes:

[0007] Based on the historical driving behavior data of the current user, identify the target driving style of the current user from multiple driving styles, where the multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users;

[0008] According to the driving environment information, navigation information, and the target driving style obtained during the movement, control the autonomous mobile device to drive.

[0009] In a possible implementation manner, the step of identifying the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user includes:

[0010] Determine a historical feature vector according to the historical driving behavior data;

[0011] Obtain the driving feature vectors corresponding to the multiple driving styles respectively;

[0012] Perform a matching process on the driving feature vectors corresponding to the multiple driving styles according to the historical feature vector, and determine the target driving style.

[0013] In a possible implementation manner, the step of determining a historical feature vector according to the historical driving behavior data includes:

[0014] Extract features from the historical driving behavior data based on a preset quantitative index and / or a preset feature processing model to obtain the historical feature vector;

[0015] Among them, the preset quantitative index is determined based on the driving behaviors of the multiple users in at least one driving scenario of cutting in, starting and stopping, congestion, following, and turning; the preset feature processing model is used to extract feature vectors from the driving behavior data of users.

[0016] In a possible implementation manner, the extracting features from the historical driving behavior data based on the preset quantitative index and / or the preset feature processing model to obtain the historical feature vector includes:

[0017] Extract features from the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, and determine the first historical feature vector as the historical feature vector; or,

[0018] Extract features from the historical driving behavior data based on a preset feature processing model to obtain a second historical feature vector, and determine the second historical feature vector as the historical feature vector; or,

[0019] Extract features from the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, extract features from the historical driving behavior data based on a preset feature processing model to obtain a second historical feature vector, and splice the first historical feature vector and the second historical feature vector to obtain the historical feature vector.

[0020] In a possible implementation manner, the controlling the autonomous mobile device to travel according to the driving environment information, navigation information, and the target driving style obtained during the movement includes:

[0021] Configure the target driving style in the intelligent driving model of the autonomous mobile device;

[0022] Process the driving environment information and the navigation information through the intelligent driving model to obtain a driving decision-making scheme;

[0023] Control the autonomous mobile device to travel according to the driving decision-making scheme;

[0024] Among them, the driving decision-making scheme includes at least one of planning a driving route, cutting-in processing, lane-changing timing selection, following distance, turning speed, and the time interval for starting with the autonomous mobile device in front.

[0025] In a possible implementation manner, the driving environment information includes the motion state information of the autonomous mobile device, the motion states of surrounding traffic participants, and road traffic information; and / or,

[0026] The navigation information includes map information, the position information of the autonomous mobile device, and path trajectory information.

[0027] In a possible implementation manner, the method further includes:

[0028] Receiving the multiple driving styles sent by the server;

[0029] Displaying the multiple driving styles in the visualization interface of the autonomous mobile device.

[0030] In a possible implementation manner, the method further includes:

[0031] Receiving the style description of each driving style sent by the server;

[0032] Correspondingly, the displaying the multiple driving styles in the visualization interface of the autonomous mobile device includes:

[0033] Displaying the multiple driving styles and the corresponding style description of each driving style in the visualization interface of the autonomous mobile device.

[0034] In a possible implementation manner, the method further includes:

[0035] Receiving the style description report of the current user sent by the server; the style description report includes at least one of: overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality characteristics;

[0036] In response to a first upload operation of the current user, uploading the style description report of the current user to a shared cloud platform for interaction among users of multiple autonomous mobile devices.

[0037] In a possible implementation manner, the method further includes:

[0038] In response to a second upload operation of the current user, uploading the running information of the target driving style to the shared cloud platform, where the running information includes the target driving style and at least one of the following: the number of kilometers traveled by the target driving style, average energy consumption, and running time.

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

[0040] Control the autonomous mobile device to travel according to the new driving style downloaded by the current user from the shared cloud platform, the driving environment information obtained during the movement, the navigation information, and the new driving style.

[0041] In a second aspect, the present application provides a data processing method for driving styles, the method including:

[0042] Obtain the driving behavior data of multiple users;

[0043] Perform cluster analysis on the driving behavior data of the multiple users to obtain at least one cluster;

[0044] Add corresponding driving styles to the at least one cluster respectively to determine multiple driving styles;

[0045] Send the multiple driving styles to at least one autonomous mobile device.

[0046] In a possible implementation manner, the performing cluster analysis on the driving behavior data of the multiple users to obtain at least one cluster includes:

[0047] For each user, obtain the feature vector corresponding to the user according to the driving behavior data of the user, where the feature vector includes at least one piece of data related to the driving style;

[0048] Perform cluster analysis on the feature vectors of the multiple users to obtain the at least one cluster.

[0049] In a possible implementation manner, the driving style corresponding to any cluster has a corresponding style description, and the style description is used to interpret the driving style;

[0050] The sending the multiple driving styles to at least one autonomous mobile device includes:

[0051] Send the multiple driving styles and the style description corresponding to each driving style to the at least one autonomous mobile device.

[0052] In a possible implementation manner, the method further includes:

[0053] Obtain the driving behavior data of the current user sent by the autonomous mobile device;

[0054] Based on a preset quantitative index, determine the relative driving index of the current user according to the driving behavior data of the multiple users and the driving behavior data of the current user, where the relative driving index is used to represent the ranking of the current user among the multiple users;

[0055] Among them, the preset quantitative index is determined based on the driving behavior of the user in at least one driving scenario such as cutting in line, starting and stopping, congestion, following a vehicle, and turning.

[0056] In a possible implementation manner, the method further includes:

[0057] Obtain the target personality traits corresponding to the multiple driving styles;

[0058] Add the target personality traits corresponding to each driving style to the style description corresponding to the driving style.

[0059] In a possible implementation manner, the method further includes:

[0060] Establish a style mapping relationship according to the personality traits and driving styles of the multiple users. The style mapping relationship includes the target personality traits corresponding to each driving style, and the target personality traits are the personality traits with the highest frequency of occurrence among the users corresponding to the driving style;

[0061] Correspondingly, the obtaining of the target personality traits corresponding to the multiple driving styles includes:

[0062] Based on the style mapping relationship, query and obtain the target personality traits corresponding to each driving style.

[0063] In a possible implementation manner, the method further includes:

[0064] Based on the target driving style of the current user and the style mapping relationship, determine a style description report corresponding to the target driving style of the current user. The style description report includes at least one of: overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality traits;

[0065] Send the style description report of the current user to the autonomous mobile device.

[0066] In a third aspect, the present invention provides an identification device for driving styles, and the device includes:

[0067] A first acquisition module, configured to identify the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user, and the multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users;

[0068] A first processing module, configured to control the autonomous mobile device to travel according to the driving environment information, navigation information, and the target driving style obtained during the movement.

[0069] Fourth aspect, there is provided a data processing device for driving styles, the device comprising:

[0070] A second acquisition module, configured to acquire driving behavior data of multiple users;

[0071] An analysis module, configured to perform clustering analysis on the driving behavior data of the multiple users to obtain at least one clustering cluster;

[0072] A second processing module, configured to add corresponding driving style labels to the at least one clustering cluster respectively to determine multiple driving styles;

[0073] A sending module, configured to send the multiple driving styles to at least one autonomous mobile device.

[0074] Fifth aspect, the present application provides an electronic device, comprising: a processor, a memory, and a communication interface;

[0075] The memory stores computer-executable instructions;

[0076] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspect.

[0077] Sixth aspect, the present application provides an autonomous mobile device, comprising: the electronic device according to the fifth aspect.

[0078] Seventh aspect, the present application provides a server, comprising: a processor, a memory, and a communication interface;

[0079] The memory stores computer-executable instructions;

[0080] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the second aspect.

[0081] Eighth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect or the second aspect.

[0082] Ninth aspect, the present application provides a program product, comprising: a computer program, and when the program product runs on a computer, it causes the computer to execute the method according to any one of the first aspect or the second aspect.

[0083] Tenth aspect, the present application provides a computer program, and when the computer program is executed by a processor, it is used to execute the method according to any one of the first aspect or the second aspect.

[0084] The method and device for identifying driving styles, data processing method, and equipment provided by this application. It includes identifying the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user, where the multiple driving styles are obtained through clustering analysis of the driving behavior data of multiple users. Then, according to the driving environment information, navigation information, and target driving style obtained during the movement, the autonomous mobile device can be controlled to drive. During the above process, by analyzing the historical driving behavior data of the current user, the target driving style suitable for this user can be identified, thereby providing a more personalized driving experience and improving the user experience. Description of the Drawings

[0085] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments that conform to this application, and are used together with the specification to explain the principles of this application.

[0086] Figure 1 Schematic diagram of the application scenario provided by the embodiment of this application;

[0087] Figure 2 Schematic flowchart of Embodiment 1 of the method for identifying driving styles provided by this application;

[0088] Figure 3 Schematic flowchart of Embodiment 2 of the method for identifying driving styles provided by this application;

[0089] Figure 4 Logic block diagram for determining driving decisions provided by the embodiment of this application;

[0090] Figure 5 Schematic flowchart of Embodiment 1 of the data processing method for driving styles provided by this application;

[0091] Figure 6 Schematic flowchart of Embodiment 2 of the data processing method for driving styles provided by this application;

[0092] Figure 7 Schematic diagram of a clustering result provided by the embodiment of this application;

[0093] Figure 8 Schematic flowchart of Embodiment 3 of the data processing method for driving styles provided by this application;

[0094] Figure 9 Schematic flowchart of Embodiment 4 of the data processing method for driving styles provided by this application;

[0095] Figure 10 Schematic flowchart of Embodiment 5 of the data processing method for driving styles provided by this application;

[0096] Figure 11 Schematic diagram of the sixth embodiment of the data processing method for driving style provided by the present application;

[0097] Figure 12 Schematic diagram of the third embodiment of the driving style recognition method provided by the present application;

[0098] Figure 13 Schematic diagram of the fourth embodiment of the driving style recognition method provided by the present application;

[0099] Figure 14 Schematic diagram of the structure of the first embodiment of the driving style recognition device provided by the present application;

[0100] Figure 15 Schematic diagram of the structure of the second embodiment of the driving style recognition device provided by the present application;

[0101] Figure 16 Schematic diagram of the structure of the first embodiment of the data processing device for driving style provided by the present application;

[0102] Figure 17 Schematic diagram of the structure of the second embodiment of the data processing device for driving style provided by the present application;

[0103] Figure 18 Schematic diagram of the structure of the electronic device provided by the embodiments of the present application;

[0104] Figure 19 Schematic diagram of the structure of the server provided by the embodiments of the present application. Detailed implementation manners

[0105] In the embodiments of the present application, the term "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0106] In the embodiments of the present application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present application are all information and data that have been authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0108] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Apparently, the described embodiments are only a part rather than all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts shall fall within the protection scope of this application.

[0109] Figure 1 It is a schematic diagram of the application scenario provided by the embodiment of this application. Please refer to Figure 1 , an autonomous mobile device may be deployed with an intelligent driving system. The autonomous mobile device may identify the target driving style of the current user from multiple driving styles obtained by clustering and analyzing the driving behavior data of multiple users based on the current user's driving behavior data, and control the autonomous mobile device to travel according to the target driving style.

[0110] Optionally, the autonomous mobile device may be a flying car, an intelligent driving vehicle, a mobile robot, an intelligent ship, or other devices that can move and are configured with an intelligent driving system.

[0111] For example, an intelligent driving vehicle may be deployed with an assisted driving system. The intelligent driving vehicle may identify the target driving style of the current user A as driving style 1 from driving style 1, driving style 2,..., and driving style N through the driving behavior data of the current user A, and control the intelligent driving vehicle to travel according to driving style 1.

[0112] In the related art, during the driving process, an autonomous mobile device deployed with an intelligent driving system travels according to a general driving style preset in the intelligent driving system. For example, it travels at a set following distance of 3 m. However, the general driving style cannot well match the user's personal driving style, resulting in a poor user experience.

[0113] In view of the above problems, the inventors found during the research on how to identify the user's personal driving style that during the historical period of the user driving an autonomous mobile device, the autonomous mobile device can usually record the user's historical driving behavior data, and the historical driving behavior data can well reflect the user's driving preferences for the autonomous mobile device. For example, through the historical driving behavior data, it can be reflected that some users' driving styles are sporty and can pass a section of road more efficiently; some are comfortable and can achieve very smooth acceleration and deceleration, thus making the passengers in the vehicle feel relaxed. Therefore, driving according to the general driving style set in the intelligent driving system often fails to meet the driving needs of users. Based on this, the inventors found through multiple experiments that the target driving style of the current user can be identified from multiple driving styles obtained by clustering and analyzing the driving behavior data of multiple users based on the historical driving behavior data of the current user. Furthermore, the autonomous mobile device can be controlled to drive based on the driving environment information, navigation information, and target driving style obtained during the movement to meet the driving needs of users. Based on this, the present application proposes a method for identifying a driving style to solve the problem that the general driving style cannot well match the user's personal driving style, resulting in a poor user experience.

[0114] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0115] Figure 2 It is a schematic flowchart of the first embodiment of the method for identifying a driving style provided by the present application. Please refer to Figure 2 This method is applied to an autonomous mobile device, and the method includes:

[0116] S201. Based on the historical driving behavior data of the current user, identify the target driving style of the current user from multiple driving styles, where the multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users.

[0117] The execution subject of the embodiments of the present application can be an electronic device or an identification device for driving style set in the electronic device. The identification device for driving style can be implemented by software or by a combination of software and hardware. The identification device for driving style can be a processor in the electronic device. For the convenience of understanding, in the following, the execution subject is taken as an electronic device for example, and the electronic device can be a control unit in the autonomous mobile device.

[0118] In this step, the electronic device can obtain the historical driving behavior data of the current user, and based on the historical driving behavior data, identify the target driving style of the current user from multiple driving styles obtained by clustering and analyzing the driving behavior data of multiple users. In a specific implementation, the historical feature vector can be determined according to the historical driving behavior data of the current user, the driving feature vectors corresponding to multiple driving styles are obtained, and the target driving style is determined by matching the historical feature vector among the driving feature vectors corresponding to multiple driving styles.

[0119] For ease of understanding, the following will take an intelligent driving vehicle as an example of the autonomous mobile device for illustration.

[0120] Optionally, the driving behavior data can include training data for training the end-to-end system. The end-to-end system can perform imitation learning on the training data, so as to output driving decisions according to information such as images and point clouds. Among them, the end-to-end system can be an autonomous driving system or an assisted driving system, and has an intelligent driving model.

[0121] Specifically, the driving behavior data can include at least one of: speed data, acceleration and deceleration patterns, steering behavior, braking habits, driving time and road conditions, fuel consumption data, driving routes, driving events, and environmental conditions. These data can be collected by in-vehicle sensors and intelligent devices.

[0122] For example, based on the clustering analysis of the driving behavior data of multiple users, the clustering results of 3 driving styles can be obtained, namely driving style 1, driving style 2, and driving style 3. Driving style 1 corresponds to driving feature vector 1, driving style 2 corresponds to driving feature vector 2, and driving style 3 corresponds to driving feature vector 3. The historical feature vector A can be determined according to the historical driving behavior data of the current user, and the target driving style is determined to be driving style 1 by matching the historical feature vector A among the driving feature vectors corresponding to the 3 driving styles.

[0123] S202. Control the autonomous mobile device to travel according to the driving environment information, navigation information, and target driving style obtained during the movement.

[0124] In this step, the electronic device can control the autonomous mobile device to travel according to the identified target driving style, in combination with the driving environment information and navigation information obtained by the autonomous mobile device during the travel.

[0125] In an alternative embodiment, the identified target driving style can be configured in the intelligent driving model of the autonomous mobile device. The intelligent driving model processes the driving environment information and navigation information obtained during the driving process of the autonomous mobile device to obtain a driving decision-making plan, and then controls the driving of the autonomous mobile device according to the driving decision-making plan.

[0126] Optionally, the target driving style can be configured in the intelligent driving model of the autonomous mobile device in any one of the following ways.

[0127] Method 1: In response to the selection operation of the current user in the visual interface of the autonomous mobile device. For example, the electronic device can respond to the selection operation of user A in the human-computer interaction interface of the intelligent driving vehicle and configure the identified driving style 1 in the intelligent driving model of the intelligent driving vehicle.

[0128] Method 2: In response to the voice command of the current user. For example, the electronic device can respond to the voice command of user A "Apply my personalized driving style" and configure the identified driving style 1 in the intelligent driving model of the intelligent driving vehicle.

[0129] Regarding the driving environment information, it can include the motion state information (speed, direction, etc.) of the autonomous mobile device, the motion states of surrounding traffic participants (speed, direction, etc.), and road traffic information (traffic lights, speed limits, traffic flow, etc.).

[0130] Regarding the navigation information, it can include map information, the position information of the autonomous mobile device, as well as path trajectory information and navigation trajectory information.

[0131] Regarding the driving decision-making plan, it can include at least one of, but is not limited to, planning a driving path, dealing with cutting in, choosing a lane-changing opportunity, following distance, turning speed, and the time interval for starting with the autonomous mobile device in front.

[0132] For example, the electronic device can configure driving style 1 in the intelligent driving model of the intelligent driving vehicle. The intelligent driving model processes the driving environment information and navigation information obtained during the driving process of the intelligent driving vehicle to obtain a driving decision-making plan. The driving decision-making plan can include dealing with cutting in. Then, the autonomous mobile device can be controlled to drive according to the driving decision-making plan. For example, for a vehicle that cuts in without following road traffic rules, after comprehensively considering the driving environment information and navigation information, the intelligent driving vehicle can accelerate to increase the safety distance from the cutting-in vehicle to ensure the safety of both the cutting-in vehicle and the intelligent driving vehicle.

[0133] In an embodiment of the present application, an electronic device can identify the target driving style of the current user from multiple driving styles by analyzing the historical driving behavior data of the current user. These driving styles are obtained by performing clustering analysis on the driving behavior data of multiple users. Subsequently, the driving of the autonomous mobile device can be controlled according to the driving environment information, navigation information, and target driving style obtained during the movement. In the above process, the target driving style of the current user can be identified based on the historical driving behavior data of the current user, thereby providing a more personalized driving experience for the current user and improving the user experience.

[0134] Based on the Figure 2 embodiment shown above, below, in combination with Figure 3 the following, a further detailed description of the above method for identifying driving styles will be given.

[0135] Figure 3 FIG. Figure 3 is a schematic flowchart of the second embodiment of the method for identifying driving styles provided by the present application. Please refer to

[0136] S301. Determine a historical feature vector according to the historical driving behavior data.

[0137] In this step, the electronic device can extract a historical feature vector from the obtained historical driving behavior data of the current user according to the historical driving behavior data of the current user.

[0138] In an alternative embodiment, feature extraction can be performed on the historical driving behavior data based on a preset quantitative index and / or a preset feature processing model to obtain a historical feature vector;

[0139] wherein, the preset quantitative index is determined based on the driving behaviors of multiple users in at least one driving scenario among cutting in, starting and stopping, congestion, following, and turning; the preset feature processing model is used to perform feature extraction on the driving behavior data of the user to obtain a feature vector.

[0140] Specifically, the preset quantitative index can be formulated with corresponding numerical indexes for some key scenarios, such as cutting in, starting and stopping, congestion, following, and turning, through an expert scoring system in combination with the analysis of driving scenarios by engineering and technical personnel. For example, in the cutting-in scenario, the closest distance between the intelligent driving vehicle (the vehicle itself) and other vehicles in the game can be concerned; in the starting and stopping scenario, how long after the vehicle in front starts moving, the vehicle itself also starts moving can be concerned.

[0141] Specifically, the preset feature processing model can be a pre-trained deep learning model or an artificial intelligence (AI) model.

[0142] Optionally, based on a preset quantitative index, feature extraction can be performed on historical driving behavior data to obtain a first historical feature vector, and the first historical feature vector can be determined as the historical feature vector; or,

[0143] Based on a preset feature processing model, feature extraction can be performed on historical driving behavior data to obtain a second historical feature vector, and the second historical feature vector can be determined as the historical feature vector; or,

[0144] Based on a preset quantitative index, feature extraction is performed on historical driving behavior data to obtain a first historical feature vector, and based on a preset feature processing model, feature extraction is performed on historical driving behavior data to obtain a second historical feature vector, and the first historical feature vector and the second historical feature vector are concatenated to obtain the historical feature vector.

[0145] For example, based on a preset quantitative index, feature extraction can be performed on historical driving behavior data to obtain a first historical feature vector, and based on a preset feature processing model, feature extraction can be performed on historical driving behavior data to obtain a second historical feature vector. The first historical feature vector and the second historical feature vector can include different feature sets, so as to analyze and understand the user's driving behavior from different perspectives. Furthermore, the first historical feature vector and the second historical feature vector can be concatenated to obtain the historical feature vector.

[0146] Exemplarily, the first historical feature vector can include speed features, steering features, braking features, vehicle distance keeping features, and time features; among them, the speed features can include average speed, maximum speed, frequencies of acceleration and deceleration, the steering features can include the change frequency of the steering angle and the speed during turning, the braking features can include the braking usage frequency and braking force, the vehicle distance keeping can include the average distance from the vehicle in front and the closest distance to other vehicles, and the time features can include driving time periods (such as peak periods, night driving, etc.), driving duration, and how long after the vehicle in front starts moving, the vehicle also starts moving.

[0147] Specifically, for User 1, based on preset quantitative indicators, feature extraction can be performed on the historical driving behavior data of User 1 to obtain a first historical feature vector. The first historical feature vector may include a vehicle distance keeping feature and a time feature. Among them, the vehicle distance keeping feature can be determined based on Preset Quantitative Indicator 1, and the time feature can be determined based on Preset Quantitative Indicator 2. Preset Quantitative Indicator 1 can be based on the closest distance between the intelligent driving vehicle of interest and other vehicles in the cut-in scenario during the game. Thus, the vehicle distance keeping feature can be extracted from the historical driving behavior data of User 1, and the vehicle distance keeping feature can be that the closest distance to other vehicles is 0.3 m. Preset Quantitative Indicator 2 can be based on how long after the vehicle in front starts moving, the vehicle of interest also starts moving in the start-stop scenario. Thus, the time feature can be extracted from the historical driving behavior data of User 1, and the time feature can be that the vehicle of interest starts moving 2 s after the vehicle in front starts moving.

[0148] Exemplarily, the second historical feature vector may include a behavior pattern, an environmental response feature, and a fuel consumption feature. Among them, the behavior pattern may include the number and conditions of hard accelerations and hard brakes. The environmental response feature may include driving adjustments for different weather conditions (such as rainy days and snowy days). The fuel consumption feature may include the average fuel consumption and the fuel consumption changes under different driving conditions.

[0149] Specifically, for User 1, based on a preset feature processing model, feature extraction can be performed on the historical driving behavior data of User 1 to obtain a second historical feature vector. The second historical feature vector may include an environmental response feature and a fuel consumption feature. The environmental response feature may be that User 1 reduces the speed by 20% on average when it rains, while the fuel consumption feature may be that User 1 has an average fuel consumption of 7 liters per 100 kilometers on urban roads. Among them, the preset feature processing model is a pre-set mathematical model that can output the required feature vector. Each feature can be output by one model, or all features can be output by one model.

[0150] Optionally, when performing the splicing process of the first historical feature vector and the second historical feature vector, it can be splicing the second historical feature vector on the basis of the first historical feature vector; or splicing the first historical feature vector on the basis of the second historical feature vector.

[0151] S302. Obtain the driving feature vectors corresponding to multiple driving styles, where the multiple driving styles are obtained based on the cluster analysis of the driving behavior data of multiple users.

[0152] In this step, based on the multiple driving styles obtained from the cluster analysis of the driving behavior data of multiple users, the driving feature vectors corresponding to each driving style can be further obtained.

[0153] For example, based on three driving styles obtained from clustering analysis of driving behavior data of multiple users, driving feature vectors corresponding to the three driving styles can be obtained, namely, driving feature vector 1 corresponding to driving style 1, driving feature vector 2 corresponding to driving style 2, and driving feature vector 3 corresponding to driving style 3.

[0154] In an alternative embodiment, each clustering cluster obtained by clustering can represent a driving style, and this driving style can be a high-dimensional vector. For example, driving style 1 can be represented by a 64-dimensional high-dimensional vector, and this 64-dimensional high-dimensional vector can be the center of clustering cluster 1, representing the average value or geometric center of all data points in this cluster.

[0155] S303. According to the historical feature vector, perform a matching process among the driving feature vectors corresponding to multiple driving styles to determine the target driving style.

[0156] In this step, the historical feature vector and the driving feature vectors corresponding to multiple driving styles can be matched to determine the target driving style.

[0157] In specific implementation, among the driving feature vectors corresponding to multiple driving styles, the driving style corresponding to the driving feature vector that is the same as the historical feature vector can be determined as the target driving style; or, if there is no driving feature vector that is the same as the historical feature vector among the driving feature vectors corresponding to multiple driving styles, then among the driving feature vectors corresponding to multiple driving styles, the driving style corresponding to the target feature vector with the highest similarity to the historical feature vector can be determined as the target driving style.

[0158] Optionally, any one of the similarity measurement methods such as cosine similarity, Euclidean distance, and Manhattan distance can be used to select the target feature vector with the highest similarity to the historical feature vector from the driving feature vectors corresponding to multiple driving styles.

[0159] For example, among the three driving feature vectors corresponding to three driving styles, the driving style 1 corresponding to the driving feature vector 1 that is the same as the historical feature vector can be determined as the target driving style.

[0160] For another example, the Euclidean distance measurement method can be used to select the target feature vector with the highest similarity to the historical feature vector from the three driving feature vectors corresponding to three driving styles. Furthermore, the driving style corresponding to the target feature vector can be determined as the target driving style. Among them, the target feature vector can be driving feature vector 1, and the target driving style can be driving style 1 corresponding to driving feature vector 1.

[0161] It should be noted that if the target feature vectors selected from the driving feature vectors corresponding to multiple driving styles by the similarity measurement method include at least two different driving feature vectors, the driving styles corresponding to the at least two different driving feature vectors can be recommended to the current user through the interaction interface, and the target driving style can be determined according to the user's selection.

[0162] In addition, if the target feature vectors selected from multiple driving styles by the similarity measurement method include at least two different driving feature vectors, the driving styles corresponding to the at least two different driving feature vectors can also be fused to obtain a new driving style. In specific implementation, the driving feature vectors corresponding to different driving styles can be weighted and averaged, and the weights can be set according to the usage frequency of the driving style or the user's preference. The new driving style can also be uploaded to the server or stored in the autonomous mobile device as an alternative driving style.

[0163] S304. Configure the target driving style in the intelligent driving model of the autonomous mobile device.

[0164] In this step, the electronic device can, in response to the user's selection operation in the visualization interface of the autonomous mobile device, configure the target driving style in the intelligent driving model of the autonomous mobile device.

[0165] In an alternative embodiment, the corresponding relationship with the adjustable parameters in the intelligent driving model can be identified according to the driving feature vectors of the target driving style. The adjustable parameters may include speed control, acceleration and deceleration response, steering sensitivity, braking force, distance keeping, etc. These parameters can be adjusted through the driving feature vectors to ensure that the behavior of the model is consistent with the target driving style.

[0166] In another alternative embodiment, the intelligent driving model can be designed as a modular structure, so that different driving styles can be loaded and switched as independent modules. By opening the Application Programming Interface (API), the target driving style module can be integrated into the model, allowing flexible switching of styles under different driving conditions.

[0167] S305. Process the driving environment information and navigation information through the intelligent driving model to obtain a driving decision-making plan.

[0168] In this step, an intelligent driving model configured with a target driving style can process driving environment information and navigation information during the driving process of an autonomous mobile device to obtain a driving decision-making plan. The driving decision-making plan includes, but is not limited to, at least one of planning a driving route, handling cut-ins, selecting a lane-changing timing, following distance, turning speed, and the time interval for starting with the autonomous mobile device in front.

[0169] Planning a driving route means selecting an optimal driving route based on factors such as the current geographical location, destination, real-time traffic conditions, and road restrictions.

[0170] For example, driving style 1 is a sporty style, driving style 2 is a comfortable style, and driving style 3 is an aggressive style; for the scenario of an intelligent driving vehicle traveling from the city center to the airport, different driving styles process driving environment information and navigation information through the intelligent driving model, and the possible planned driving routes obtained may be as follows:

[0171] The planned driving route corresponding to the sporty style may include: selecting a route that can provide a smooth driving experience, mainly through highways and expressways, to maintain a high speed and stability. Exemplarily, the intelligent driving vehicle can choose to immediately enter the ring expressway after departing from the city center, bypass the low-speed areas and traffic lights in the urban area, and ensure a high average speed throughout the journey.

[0172] The planned driving route corresponding to the comfortable style may include; giving priority to the smoothness and comfort of passengers, and can choose to avoid bumpy sections and busy traffic areas. Exemplarily, the intelligent driving vehicle can choose a street with good greening, avoid the noise and congestion of the highway, and can also choose to pass through some quiet residential areas to provide a more comfortable driving experience.

[0173] The planned driving route corresponding to the aggressive style may include: selecting the shortest-time route, even if it needs to pass through busy traffic areas. Exemplarily, the intelligent driving vehicle can choose a straight path directly through the city center, use real-time traffic information to quickly change lanes and overtake, choose to use small roads or shortcuts, and quickly pass through traffic-dense areas to reduce the driving time.

[0174] Handling cut-ins means deciding whether to yield or maintain the lane position according to the real-time traffic environment and vehicle dynamics when encountering other vehicles trying to insert into the current lane to ensure driving safety and smoothness.

[0175] For example, driving style 1 is a sporty style, driving style 2 is a comfortable style, and driving style 3 is an aggressive style; different driving styles process driving environment information and navigation information through the intelligent driving model, and the obtained handling of cut-ins can be as follows:

[0176] The cut-in handling corresponding to the sporty driving style may include: when encountering other vehicles attempting to cut into the current lane, selectively yielding according to the real-time traffic environment to avoid unnecessary deceleration and acceleration. Exemplarily, when the intelligent driving vehicle detects a vehicle attempting to cut in, the intelligent driving vehicle may slightly decelerate, widen the distance from the vehicle ahead, and yield sufficient space for the vehicle to smoothly cut in, thereby maintaining the smoothness of the overall traffic flow.

[0177] The cut-in handling corresponding to the comfortable driving style may include: when encountering a cut-in situation, being more inclined to actively yield to avoid discomfort caused by sudden braking or acceleration. Exemplarily, when a vehicle in the adjacent lane intends to cut in, the intelligent driving vehicle may decelerate in advance, maintain a large safety distance, and yield sufficient space for the vehicle to safely cut in, thereby ensuring a comfortable experience for the passengers in the vehicle.

[0178] The cut-in handling corresponding to the aggressive driving style may include: when encountering a cut-in situation, choosing to maintain the lane position and not actively yield unless a potential collision risk is detected. For example, when a vehicle attempts to cut in, the intelligent driving vehicle may maintain the current speed or slightly accelerate to prevent other vehicles from cutting in, thereby maintaining the driving speed and rhythm.

[0179] Lane-changing timing selection is to decide when to change lanes on a multi-lane road according to the speeds, positions of surrounding vehicles, and the navigation requirements of the destination, in order to optimize the driving efficiency or avoid obstacles.

[0180] For example, driving style 1 is the sporty driving style, driving style 2 is the comfortable driving style, and driving style 3 is the aggressive driving style; different driving styles process driving environment information and navigation information through an intelligent driving model, and the lane-changing timing selection obtained can be as follows:

[0181] The lane-changing timing selection corresponding to the sporty driving style may include: on a multi-lane road, according to the speeds and positions of surrounding vehicles, choosing to change lanes when the traffic flow is relatively stable and there is sufficient space to optimize the driving efficiency. Exemplarily, when the intelligent driving vehicle detects a slow vehicle in the front lane, the intelligent driving vehicle may smoothly change lanes on the premise of ensuring sufficient space in the adjacent lane to maintain a high driving speed and smoothness.

[0182] The lane-changing timing selection corresponding to the comfortable driving style may include: when changing lanes, being more inclined to choose the timing when the traffic flow is less and the lane-changing process is the smoothest. Exemplarily, when the intelligent driving vehicle needs to change lanes to follow the navigation instructions, the intelligent driving vehicle may wait until there is a large gap and fewer vehicles in the adjacent lane, and then slowly and smoothly change lanes.

[0183] The lane-changing timing selection corresponding to the aggressive driving style may include: When changing lanes, one can choose to quickly change lanes using a smaller gap in order to reach the destination faster. Exemplarily, when the intelligent driving vehicle identifies an obstacle or a slow vehicle in the front lane, the intelligent driving vehicle can quickly analyze the traffic flow speed and spacing in the adjacent lane and choose to change lanes in the shortest time to avoid deceleration and maintain the driving speed.

[0184] The following-distance is to dynamically adjust the safe distance from the vehicle ahead according to the current driving speed, road conditions, and traffic density to ensure sufficient reaction time and driving safety.

[0185] For example, driving style 1 is a sporty style, driving style 2 is a comfortable style, and driving style 3 is an aggressive style; the following-distances obtained by different driving styles through processing the driving environment information and navigation information by the intelligent driving model can be as follows:

[0186] The following-distance corresponding to the sporty driving style may include: While maintaining a smooth driving experience, choose a moderate following-distance according to the current driving speed and road conditions. Exemplarily, when the intelligent driving vehicle is driving on the highway at a speed of 100 km / h, the intelligent driving vehicle can choose to maintain a following-distance of 50 meters to ensure sufficient reaction time while maintaining a relatively high speed.

[0187] The following-distance corresponding to the comfortable driving style may include: Considering the smoothness of driving, choose a larger following-distance. Exemplarily, when the intelligent driving vehicle is driving on the highway at a speed of 100 km / h, the intelligent driving vehicle can choose to maintain a following-distance of 70 meters so as to smoothly adjust the speed when traffic changes, thereby ensuring the comfortable experience of passengers.

[0188] The following-distance corresponding to the aggressive driving style may include: When emphasizing fast driving and efficiency, choose a smaller following-distance. Exemplarily, when the intelligent driving vehicle is driving on the highway at a speed of 100 km / h, the intelligent driving vehicle can choose to maintain a following-distance of 30 meters so as to quickly change lanes and overtake when traffic is dense, thereby maintaining the driving speed and rhythm.

[0189] The turning speed is to select an appropriate vehicle speed according to the road curvature and vehicle dynamics when turning to ensure the stability of the vehicle and the comfort of passengers.

[0190] For example, driving style 1 is a sporty style, driving style 2 is a comfortable style, and driving style 3 is an aggressive style; the turning speeds obtained by different driving styles through processing the driving environment information and navigation information by the intelligent driving model can be as follows:

[0191] The turning speed corresponding to the sporty driving style may include: while maintaining a smooth driving experience, selecting a relatively high turning speed according to the road curvature and vehicle dynamics to ensure driving stability. Exemplarily, when an intelligent driving vehicle turns on a highway ramp with a small curvature, the vehicle may choose to pass at a speed of 40 kilometers per hour.

[0192] The turning speed corresponding to the comfortable driving style may include: considering the comfort of passengers and the smoothness of driving, selecting a relatively low turning speed. Exemplarily, when an intelligent driving vehicle turns on the same highway ramp, the vehicle may choose to pass at a speed of 30 kilometers per hour to reduce lateral acceleration and ensure a comfortable experience for passengers.

[0193] The turning speed corresponding to the aggressive driving style may include: when focusing on fast driving and efficiency, selecting an even higher turning speed. Exemplarily, when an intelligent driving vehicle turns on the same ramp, the vehicle may choose to pass at a speed of 50 kilometers per hour in order to complete the turn faster and maintain the driving speed and rhythm.

[0194] The time interval for starting after the preceding autonomous moving device is the time interval between the restart of the vehicle and the vehicle in front in the case of a traffic light or a stop state, in order to optimize traffic flow efficiency and driving safety.

[0195] For example, driving style 1 is the sporty driving style, driving style 2 is the comfortable driving style, and driving style 3 is the aggressive driving style; the time intervals for starting after the preceding autonomous moving device obtained by different driving styles through processing driving environment information and navigation information may be as follows:

[0196] The starting interval time corresponding to the sporty driving style may include: selecting a relatively short starting interval time to quickly respond to traffic signal changes. Exemplarily, when the traffic light turns green and the vehicle in front starts, the intelligent driving vehicle may start within 1 second to ensure quickly following the vehicle in front and optimizing traffic flow efficiency.

[0197] The starting interval time corresponding to the comfortable driving style may include: considering the comfort of passengers and the smoothness of driving, selecting a moderate starting interval time. Exemplarily, when the traffic light turns green and the vehicle in front starts, the intelligent driving vehicle may start within 2 seconds to ensure a smooth start and avoid passengers feeling sudden acceleration.

[0198] The starting interval time corresponding to the aggressive driving style may include: selecting an even shorter starting interval time in order to quickly respond and enter the normal driving speed. Exemplarily, when the traffic light turns green and the vehicle in front starts, the intelligent driving vehicle may start within 0.5 second in order to quickly enter the normal driving speed and maintain the driving speed and rhythm.

[0199] Optionally, the driving environment information may include, but is not limited to, the motion state information of the autonomous mobile device, the motion states of surrounding traffic participants, and road traffic information; and / or, the navigation information may include, but is not limited to, map information, the position information of the autonomous mobile device, and path trajectory information.

[0200] For example, an intelligent driving model configured with driving style 1 can process driving environment information and navigation information during the driving process of an intelligent driving vehicle to obtain a driving decision-making plan. The driving decision-making plan includes cut-in handling and the time interval for starting with the vehicle ahead.

[0201] S306. Control the autonomous mobile device to drive according to the driving decision-making plan.

[0202] For example, during the driving process of an intelligent driving vehicle, precise control of the intelligent driving vehicle can be achieved according to the cut-in handling and the time interval for starting with the vehicle ahead in the driving decision-making plan.

[0203] Exemplarily, when an intelligent driving vehicle is driving on a highway, the vehicle detects that a vehicle in the adjacent lane is trying to cut in. According to the decision of cut-in handling, the vehicle safely makes room by slightly decelerating and adjusting the distance from the vehicle ahead, ensuring smooth and safe driving. At the same time, when encountering a traffic signal, the intelligent driving vehicle can be optimized according to the time interval for starting with the vehicle ahead. When the red light turns green, the vehicle detects that the vehicle ahead has started to move and quickly starts smoothly within 1 second.

[0204] In the embodiments of the present application, the electronic device can analyze the historical driving behavior data of the current user to determine the historical feature vector. Then, obtain the driving feature vectors corresponding to multiple driving styles, which are obtained by performing clustering analysis on the driving behavior data of multiple users. According to the historical feature vector, perform a matching process among the driving feature vectors corresponding to these driving styles to determine the target driving style. The target driving style can be configured into the intelligent driving model of the autonomous mobile device. Further, the intelligent driving model can process driving environment information and navigation information, generate a driving decision-making plan, and control the driving of the autonomous mobile device. In the above process, by analyzing the historical driving behavior data of the current user and determining the target driving style, a personalized driving experience can be provided for the user, making the response and behavior of the autonomous mobile device more in line with the user's driving habits and preferences, and improving the user experience.

[0205] Figure 4 It is a logic block diagram for determining a driving decision provided by the embodiments of the present application. Please refer to Figure 4The target driving style can be configured in the end-to-end system, which can generate a driving decision plan based on comprehensive processing of driving environment information, navigation information and target driving style. The target driving style is obtained by clustering and analyzing the driving behavior data of multiple users, and an adaptive driving style category is selected from the clustering results.

[0206] During the training phase of the end-to-end system, each piece of input data not only contains driving environment information and navigation information, but also needs to be associated with the corresponding driving style. At the same time, the end-to-end system also needs to introduce the true value of the trajectory as a supervision signal, and calculate the loss function (LOSS) (such as mean square error, trajectory similarity loss, etc.) by comparing the difference between the predicted behavior trajectory output by the model and the true value of the trajectory, so as to optimize the model parameters.

[0207] In an embodiment of the present application, by clustering and analyzing multi-user driving behavior data, diverse driving styles can be determined, enabling the end-to-end system to flexibly configure the target driving style to meet the personalized needs of different users, significantly improving the adaptability of driving decisions and user experience.

[0208] Furthermore, the integrated driving environment information, navigation information and target driving style are processed in a coordinated manner, which enhances the end-to-end system's perception and decision-making capabilities for complex scenarios, making the generated driving decisions both safe and adaptable to the scenarios. In addition, the introduction of the true value of the trajectory as a supervisory signal in the end-to-end system training and the dynamic optimization of the model parameters in combination with the loss function can ensure that the behavior trajectory output by the system conforms to the real driving data, effectively improves the accuracy and reliability of driving decisions, and matches the configured driving style, effectively improving the user's driving experience.

[0209] Figure 5 This is a flow chart of the first embodiment of the method for processing driving style data provided by this application. Figure 5 , the method is applied to the server and may include:

[0210] S501: Acquire driving behavior data of multiple users.

[0211] In this step, the server may obtain the driving behavior data of multiple users in at least one of the following ways.

[0212] Method 1: Autonomous mobile devices are equipped with sensing devices (such as cameras, lidar, millimeter wave radar, accelerometers) that can record the driving behavior data of multiple users in real time. These driving behavior data can be uploaded to the server through the Internet of Vehicles system, or transferred to removable storage media and then uploaded to the server.

[0213] Method 2: Obtain the driving behavior data of multiple users from open and shared data platforms. These data platforms can usually provide data sets from multiple sources for technicians to research and use. Through APIs or data download services, the server can access the data on these data platforms to obtain the driving behavior data of multiple users.

[0214] S502. Perform clustering analysis on the driving behavior data of multiple users to obtain at least one clustering cluster.

[0215] In this step, the server performs clustering analysis on the driving behavior data of multiple users to obtain at least one clustering cluster. In a specific implementation, for each user, the server can obtain the corresponding feature vector according to the user's driving behavior data, and the feature vector can include at least one piece of data related to the driving style. Further, clustering analysis can be performed on the feature vectors of multiple users to obtain at least one clustering cluster.

[0216] Optionally, for each user, feature extraction can be performed on the driving behavior data based on a preset quantitative index and / or a preset feature processing model to obtain a feature vector;

[0217] Among them, the preset quantitative index is determined based on the driving behaviors of multiple users in at least one driving scenario including but not limited to cutting in line, starting and stopping, congestion, following, and turning; the preset feature processing model is used to perform feature extraction on the driving behavior data of the user to obtain a feature vector.

[0218] Similarly, feature extraction can be performed on the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, and the first historical feature vector is determined as the historical feature vector; or,

[0219] Feature extraction is performed on the historical driving behavior data based on the preset feature processing model to obtain a second historical feature vector, and the second historical feature vector is determined as the historical feature vector; or,

[0220] Feature extraction is performed on the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, feature extraction is performed on the historical driving behavior data based on the preset feature processing model to obtain a second historical feature vector, and the first historical feature vector and the second historical feature vector are concatenated to obtain a historical feature vector.

[0221] It should be understood that for each user, the above historical driving behavior data is essentially also the driving behavior data of this user; the feature vector of this user can be the first historical feature vector, the second historical feature vector, or a combined vector obtained by concatenating the first historical feature vector and the second historical feature vector.

[0222] Optionally, the clustering algorithm for clustering the driving behavior data of multiple users may include any one of the K-means clustering algorithm, hierarchical clustering, and DBSCAN clustering algorithm. After completing the clustering analysis, each clustering cluster may represent a typical driving style.

[0223] For example, through the K-means clustering algorithm, the feature vectors corresponding to the driving behavior data of 100 users can be clustered to obtain 3 clustering clusters, and these 3 clustering clusters respectively represent 3 driving styles.

[0224] S503. Add corresponding driving styles to at least one clustering cluster to determine multiple driving styles.

[0225] In this step, driving styles can be added to each clustering cluster in the clustering result to obtain multiple driving styles.

[0226] Since the driving behavior data belonging to the same category usually has similar behavior styles, algorithm experts can, based on the clustering result, add driving styles that conform to their behavior styles to each clustering cluster.

[0227] For example, for the 3 clustering clusters obtained through the K-means clustering algorithm, information such as the corresponding driving style names can be added respectively to obtain 3 driving styles. Among them, clustering cluster 1 can correspond to driving style 1, clustering cluster 2 can correspond to driving style 2, and clustering cluster 3 can correspond to driving style 3. Driving style 1 can be a sports style, driving style 2 can be a comfort style, and driving style 3 can be an aggressive style.

[0228] S504. Send multiple driving styles to at least one autonomous mobile device.

[0229] In a specific implementation manner, each driving style sent by the server to at least one autonomous mobile device may further include configuration parameters or configuration files of the driving style. Among them, the configuration parameters or configuration files may include, but are not limited to, speed control parameters, steering sensitivity, following distance and distance keeping, response time to obstacles, and reaction to traffic signals, etc.

[0230] For example, the server can send driving style 1, driving style 2, and driving style 3 to at least one intelligent driving vehicle. Driving style 1 can also include the configuration parameters of driving style 1, and the configuration parameters can include a maximum speed of 120 km / h, an acceleration time of 7 s for 100 km, and a minimum safe distance of 50 m from the vehicle in front when driving at high speed; Driving style 2 can also include the configuration parameters of driving style 2, and the configuration parameters can include a maximum speed of 100 km / h, an acceleration time of 10 s for 100 km, and a minimum safe distance of 70 m from the vehicle in front when driving at high speed; Driving style 2 can also include the configuration parameters of driving style 2, and the configuration parameters can include a maximum speed of 140 km / h, an acceleration time of 3 s for 100 km, and a minimum safe distance of 30 m from the vehicle in front when driving at high speed.

[0231] In an embodiment of the present application, the server can obtain the driving behavior data of multiple users, and perform clustering analysis on these data to identify at least one clustering cluster. Subsequently, each clustering cluster can be assigned a corresponding driving style to determine multiple driving styles. Finally, the server can send multiple driving styles to at least one autonomous mobile device. In the above process, by the server obtaining and analyzing the driving behavior data of multiple users and sending the results to the autonomous mobile device, a personalized driving experience can be achieved. This enables the autonomous mobile device to determine the target driving style that conforms to the driving preferences and habits of the current user based on the historical driving behavior data, thereby improving user satisfaction.

[0232] In a possible design, the method for identifying driving styles can further include: The autonomous mobile device can receive multiple driving styles sent by the server and display the multiple driving styles in the visualization interface of the autonomous mobile device.

[0233] Optionally, the user can select the target driving style from multiple driving styles in the visualization interface, fine-tune the target driving style through the visualization interface, and set personalized parameters such as acceleration, turning sensitivity, and braking intensity, so as to create a unique driving style for experience.

[0234] Furthermore, after the user selects the target driving style in the visualization interface, fine-tuning can be performed according to the actual driving experience during the experience process. The visualization interface can provide real-time data feedback such as speed, acceleration, and energy consumption to help the user make further adjustments. In addition, the system can also intelligently recommend the most suitable driving style on the visualization interface according to the current driving environment and road conditions.

[0235] In the embodiments of the present application, the autonomous mobile device can obtain multiple driving styles sent by the server and display the multiple driving styles in its visualization interface for the user to select. The user can freely select and adjust the driving style according to personal preferences, so as to obtain a more personalized driving experience.

[0236] Figure 6 It is a schematic flowchart of the second embodiment of the data processing method for driving styles provided by the present application. Please refer to Figure 6 , on the basis of the embodiment shown in Figure 5 , step S502 may include:

[0237] S601. For each user, obtain the feature vector corresponding to the user according to the driving behavior data of the user, where the feature vector includes at least one piece of data related to the driving style.

[0238] In this step, for each user, feature extraction can be performed on the driving behavior data of the user based on a preset quantitative index and / or a preset feature processing model to obtain a feature vector.

[0239] Specifically, the preset quantitative index is determined based on the driving behaviors of multiple users in at least one driving scenario including but not limited to cutting in line, starting and stopping, congestion, following a vehicle, and turning.

[0240] For example, for user 1, based on the preset quantitative index, feature extraction can be performed on the historical driving behavior data of user 1 to obtain a first historical feature vector. The first historical feature vector can include multiple numerical indexes determined based on the preset quantitative index. Exemplarily, the first historical feature vector can include numerical indexes determined based on preset quantitative index 1 and preset quantitative index 2. Among them, preset quantitative index 1 can be based on the closest distance between the intelligent driving vehicle being concerned and other vehicles in the game in the cutting-in-line scenario, so that numerical index 1 can be extracted from the historical driving behavior data of user 1, and numerical index 1 can be that the closest distance between the intelligent driving vehicle and other vehicles is 0.3m; preset quantitative index 2 can be based on how long after the vehicle in front starts moving, the vehicle of this user also starts moving in the starting and stopping scenario, so that numerical index 2 can be extracted from the historical driving behavior data of user 1, and numerical index 2 can be that the vehicle of this user starts moving 2s after the vehicle in front starts moving.

[0241] Specifically, the preset feature processing model is used to perform feature extraction on the driving behavior data of the user to obtain a feature vector.

[0242] For example, for the driving behavior data of user A, a first historical feature vector can be extracted based on a preset quantitative index, and a second historical feature vector can be extracted based on a preset feature processing model. The first historical feature vector and the second historical feature vector are concatenated to obtain a combined vector, which is used as the final feature vector of user A.

[0243] In addition, if only the preset quantitative index or the preset feature processing model is used to extract features from the driving behavior data of user A to obtain a feature vector, then the feature vector can be the first historical feature vector or the second historical feature vector.

[0244] S602. Perform clustering analysis on the feature vectors of multiple users to obtain at least one clustering cluster.

[0245] In this step, clustering analysis can be performed on the feature vectors respectively corresponding to the driving behavior data of multiple users to obtain at least one clustering cluster.

[0246] In a specific implementation, each clustering cluster can correspond to a driving style, and the driving style can be represented by the center of the clustering cluster. The vector used to represent the center of the clustering cluster can be used as the driving feature vector of the driving style.

[0247] In the embodiment of the present application, the server generates a feature vector for each user by obtaining the driving behavior data of multiple users. After clustering analysis of these feature vectors, at least one clustering cluster can be identified. Each clustering cluster is assigned a specific driving style to determine multiple driving styles. Finally, the server can send multiple driving styles to at least one autonomous mobile device. Through this process, the autonomous mobile device can determine the target driving style that best suits its driving preferences and habits based on the historical driving behavior data of the current user, providing a personalized driving experience for the user.

[0248] Figure 7 This is a schematic diagram of a clustering result provided by the embodiment of the present application. Please refer to Figure 7 The autonomous driving training data can include the driving behavior data of multiple users. After feature vectors are extracted from the driving behavior data of multiple users through the preset quantitative index in the expert scoring system, clustering analysis is performed on the feature vectors to obtain at least one clustering cluster, and corresponding driving styles are added to at least one clustering cluster respectively, so that multiple driving styles can be determined.

[0249] For example, the determined multiple driving styles may include a sporty and aggressive driving style, a sporty and comfortable driving style, a comfortable and conservative driving style, …, a cautious and conservative driving style. Among them, User 1 (Driver 1) may have a sporty and aggressive driving style, User 2 (Driver 2) may have a sporty and comfortable driving style, User 3 (Driver 3) may have a comfortable and conservative driving style, and User N (Driver N) may have a cautious and conservative driving style.

[0250] In a possible design, the driving style corresponding to any clustering cluster has a corresponding style description, and the style description is used to interpret the driving style. Then, the step of sending multiple driving styles to at least one autonomous mobile device in S504 may include: sending multiple driving styles and the style description corresponding to each driving style to at least one autonomous mobile device.

[0251] In an alternative embodiment, the algorithm expert may add a qualitative style description to the driving style corresponding to each clustering cluster according to the clustering result to realize the interpretation of the driving style.

[0252] For example, Driving Style 1 is a sporty style, and its corresponding style description is: The sporty style provides rapid acceleration response and sensitive steering operation, and tends to maintain a medium to high driving speed. When turning or changing lanes, it gives priority to balancing handling and efficiency, and is very suitable for users who pursue driving pleasure; Driving Style 2 is a comfortable style, and its corresponding style description is: The comfortable style pays attention to the smoothness of driving, the acceleration and braking processes are linear and gentle, actively maintains a safe distance from the vehicle in front, reduces sharp turns or frequent lane changes, and is suitable for scenarios that require high comfort such as family travel or long-distance driving; Driving Style 3 is an aggressive style, and its corresponding style description is: The aggressive style frequently overtakes and changes lanes, the acceleration and braking amplitudes are large, tends to drive at a speed close to the upper limit of the road speed limit, and responds quickly to dynamic changes in traffic flow, and is suitable for time-sensitive tasks or scenarios that require high traffic efficiency.

[0253] In another alternative embodiment, the style description corresponding to the driving style may further include at least one of: overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, and average vehicle speed.

[0254] Regarding the overtaking probability, it may refer to the frequency of the user performing overtaking operations during driving. For example, a user with an aggressive style may overtake once every 10 minutes on the highway to quickly overtake slow vehicles, while a user with a comfortable style may only overtake when it is safe and necessary throughout the journey to ensure driving safety.

[0255] Regarding traffic efficiency, it can refer to the efficiency of a vehicle passing through a certain section of the road within a unit of time. For example, users with an aggressive driving style can maintain a high traffic efficiency on urban roads with heavy traffic, enabling them to reach their destinations 10 minutes faster than the average time during peak hours, while users with a comfortable driving style may prioritize the smoothness of the drive, even if the travel time is slightly longer.

[0256] For driving smoothness, it can refer to the ability of an autonomous mobile device to maintain a stable state during driving, including the smoothness of acceleration, braking, and steering. For example, users with an aggressive driving style may accelerate rapidly and turn sharply more frequently, resulting in lower driving smoothness. Users with a comfortable driving style may maintain a stable vehicle speed and gentle turns during long trips to ensure passenger comfort.

[0257] For average vehicle speed, it can refer to the average driving speed of an autonomous mobile device over a period of time. For example, users with an aggressive driving style may maintain an average vehicle speed of 60 km / h on a road with a speed limit of 60 km / h to enjoy the driving pleasure while obeying traffic rules, while users with a comfortable driving style may maintain a speed of 40 km / h to ensure the comfort and safety of the ride.

[0258] For acceleration frequency, it can refer to the frequency of acceleration operations by the user during driving. For example, users with an aggressive driving style can accelerate once every 5 minutes on urban roads to quickly increase speed between traffic lights, while users with a comfortable driving style may accelerate only once every 15 minutes to maintain a smooth driving experience.

[0259] Regarding lane change frequency, it can be the frequency of a user changing lanes during driving. For example, users with an aggressive driving style may change lanes once every 2 minutes on congested urban roads to find the fastest driving route, while users with a comfortable driving style may only change lanes when necessary throughout the journey.

[0260] It should be noted that the metrics included in the style descriptions corresponding to driving styles, such as at least one of overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, and average vehicle speed, can be calculated based on the average values of the clustering results. These average values provide a representative perspective for the description of driving styles, enabling each style to reflect the typical characteristics of each cluster in the clustering analysis results, thus providing a more accurate and comprehensive interpretation of driving styles.

[0261] In an embodiment of the present application, the server can generate a style description including qualitative and / or quantitative indicators for the driving style corresponding to each clustering cluster, and send the style description to the autonomous mobile device, enabling the autonomous mobile device to select and adapt a driving style that conforms to the user's preferences and habits based on the user's historical behavior, thereby providing a personalized driving experience.

[0262] Similarly, in a possible design, the method for identifying a driving style may further include: The autonomous mobile device can receive the style description of each driving style sent, and display multiple driving styles and the corresponding style description of each driving style in the visualization interface of the autonomous mobile device.

[0263] Optionally, by displaying the detailed description of each driving style through the visualization interface, the user can better understand the characteristics and applicable scenarios of each driving style. In addition, the user can select the target driving style according to the qualitative and / or quantitative indicators (such as acceleration frequency, average vehicle speed, etc.) in the style description, combined with their own driving preferences and needs.

[0264] In an embodiment of the present application, the autonomous mobile device can receive and display multiple driving styles and their corresponding detailed style descriptions, providing an intuitive selection experience for the user through the visualization interface. In the above process, not only can the user easily understand and compare the characteristics of different driving styles, but also can select the target driving style according to personal preferences, thereby realizing a personalized driving experience.

[0265] Figure 8 It is a schematic flowchart of Embodiment 3 of the data processing method for driving styles provided by the present application. Please refer to Figure 8 , on the basis of the above embodiment, the data processing method for driving styles may further include:

[0266] S801. Obtain the driving behavior data of the current user sent by the autonomous mobile device.

[0267] For example, the server can receive the driving behavior data of the current user A sent by the autonomous mobile device.

[0268] S802. Based on the preset quantitative indicators, determine the relative driving indicators of the current user according to the driving behavior data of multiple users and the driving behavior data of the current user.

[0269] In this step, the server can determine the relative driving indicators of the current user by analyzing the driving behavior data of multiple users and the driving behavior data of the current user, based on the preset quantitative indicators. The relative driving indicators are used to represent the ranking of the current user among multiple users. Among them, the preset quantitative indicators are determined based on the driving behavior of the user in at least one driving scenario including but not limited to cutting in, starting and stopping, congestion, following, and turning.

[0270] Optionally, numerical indicators can be extracted from the driving behavior data of the current user and the driving behavior data of multiple users according to preset quantitative indicators, and the ranking of the current user among multiple users can be determined based on the comprehensive position of the numerical indicators of the current user among the numerical indicators of multiple users.

[0271] For example, the ranking of the current user among multiple users can be obtained through the following steps ①②③.

[0272] Step ①: For each driving scenario, the server can extract numerical indicators based on preset quantitative indicators. For example, in the cutting-in scenario, the cutting-in frequency in the driving behavior data of the current user A and multiple users can be extracted.

[0273] Step ②: The server can compare the numerical indicators of the current user A with the indicators of other multiple users to generate a relative ranking. For example, in the cutting-in scenario, the server can compare the cutting-in frequency of the current user A with the cutting-in frequencies of other users. Through a sorting algorithm, the server can determine the position of the current user A in the overall user group. Suppose the cutting-in frequency of the current user A is in the top 20% position among all users, that is, the current user A is more frequent in cutting-in behavior than 80% of the users.

[0274] Step ③: The server can comprehensively analyze the relative rankings in each scenario to generate a relative driving indicator, where the relative driving indicator can be the overall driving score of the current user. The overall driving score of the current user A can be calculated by weighted average or other statistical methods to provide a comprehensive evaluation of the overall driving style of the current user A, determine the relative driving indicator of the current user A among multiple users, such as the aggressiveness is in the top 10%, or the comfort level is in the top 5%.

[0275] Optionally, after determining the relative driving indicator of the current user among multiple users, the server can choose to directly send the relative driving indicator to the autonomous mobile device or terminal device of the current user. In addition, the server can also integrate the relative driving indicator into a driving style description report and send the report to the current user. So that the current user can clearly understand their overall driving score, thereby helping the current user better understand their driving style. Among them, the terminal device can be the current user's smartphone, laptop, smartwatch, or laptop computer.

[0276] In an embodiment of the present application, the server may obtain the driving behavior data of the current user sent by the autonomous mobile device, and analyze the driving behavior data of multiple users and the data of the current user based on preset quantitative indicators to determine the relative driving indicators of the current user. Through the relative driving indicators, users can understand their rankings in a larger user group, help users identify the relative positions of their driving styles, and encourage users to improve their driving behaviors to achieve higher rankings or better driving performances.

[0277] Figure 9 It is a schematic flowchart of Embodiment 4 of the data processing method for driving style provided by the present application. Please refer to Figure 9 , on the basis of the above Embodiment 3, the data processing method for driving style may further include:

[0278] S901. Obtain the target personality traits corresponding to multiple driving styles.

[0279] In this step, the server may obtain the target personality traits of the driving styles corresponding to each clustering cluster.

[0280] The personality traits may be personalized traits inferred by the server after analyzing the driving behavior data. Among them, the personality traits may include, but are not limited to: adventurous, cautious, patient, and confident.

[0281] In an optional implementation manner, the server may pre-train a personality parsing model dedicated to inferring personality traits. Through the personality parsing model, the driving behavior data of each user may be analyzed to obtain the personality traits of each user. For the driving style corresponding to any clustering cluster, the personality trait with the highest frequency of occurrence among the users included in the driving style may be selected as the target personality trait corresponding to the driving style. Optionally, the personality parsing model may be merged with a preset feature processing model into an integrated model, and while outputting the feature vector according to the driving behavior data of each user, the personality traits of the user may be output.

[0282] Exemplarily, the personality traits may also be a personality type formed by respectively selecting a personality description from the following four personality dimensions. Dimension 1 includes introversion and extroversion, Dimension 2 includes sensing and intuition, Dimension 3 includes thinking and emotion, and Dimension 4 includes judgment and perception.

[0283] For example, the server may analyze the driving behavior data of multiple users and infer that the target personality trait of driving style 1 (clustering cluster 1) is adventurous, the target personality trait of driving style 2 (clustering cluster 2) is cautious, and the target personality trait of driving style 3 (clustering cluster 3) is patient.

[0284] S902. Add the target personality traits corresponding to each driving style to the style description corresponding to the driving style.

[0285] For example, the server can add the adventurous personality traits corresponding to driving style 1 to style description 1 corresponding to driving style 1, add the cautious personality traits corresponding to driving style 2 to style description 2 corresponding to driving style 2, and add the patient personality traits corresponding to driving style 3 to style description 3 corresponding to driving style 3.

[0286] In the embodiment of the present application, the server can obtain the target personality traits corresponding to multiple driving styles and add the target personality traits corresponding to each driving style to the style description corresponding to the driving style. By combining personality traits with driving styles, it can help users better understand the relationship between driving behaviors and personality traits and provide personalized feedback to users.

[0287] In Figure 9 Based on the above - shown embodiment, below, in combination with Figure 10 the above - mentioned data processing method for driving styles will be further described in detail.

[0288] Figure 10 FIG. is a schematic flowchart of Embodiment 5 of the data processing method for driving styles provided by the present application. Please refer to Figure 10 Based on the above - mentioned Embodiment 4, the data processing method for driving styles may include:

[0289] S1001. Establish a style mapping relationship according to the personality traits and driving styles of multiple users.

[0290] In this step, the server can establish a style mapping relationship according to the personality traits and driving styles of multiple users. The style mapping relationship includes the target personality traits corresponding to each driving style, and the target personality traits are the personality traits with the highest frequency of occurrence among the users corresponding to the driving style.

[0291] Optionally, when the server obtains the driving behavior data of multiple users, it can obtain the personality traits of each user. Among them, the personality traits of each user can be selected by the user through the autonomous mobile device in the visual interface.

[0292] For example, the visual interface of the intelligent driving vehicle can provide options of multiple personality traits for the user to choose. For each user, when uploading the driving behavior data, they can select their own personality traits and upload them to the server together. Exemplarily, when user 1 uploads the driving behavior data, they can select their own adventurous personality traits and upload them to the server together.

[0293] When the server performs clustering analysis on the driving behavior data of multiple users to obtain at least one clustering cluster and further obtains the driving style corresponding to each clustering cluster, it can select the personality trait with the highest frequency of occurrence among the users corresponding to the driving style as the target personality trait corresponding to the driving style, and establish a style mapping relationship according to each driving style and the target personality trait corresponding to the driving style.

[0294] For example, based on the personality traits and driving styles of multiple users, a style mapping relationship can be established, and some of the corresponding relationships can be shown in Table 1 as follows:

[0295] Table 1

[0296] Clustering result Driving style Target personality traits Cluster 1 Driving style 1 Adventurous type Cluster 2 Driving style 2 Cautious type Cluster 3 Driving style 3 Patient type

[0297] S1002. Query and obtain the target personality trait corresponding to each driving style based on the style mapping relationship.

[0298] For example, based on the style mapping relationship, it can be queried and obtained that the target personality trait corresponding to driving style 1 is adventurous, the target personality trait corresponding to driving style 2 is cautious, and the target personality trait corresponding to driving style 3 is patient.

[0299] S1003. Add the target personality trait corresponding to each driving style to the style description corresponding to the driving style.

[0300] For example, the adventurous personality trait, the cautious personality trait, and the patient personality trait can be added to style description 1, style description 2, and style description 3 in sequence.

[0301] In the embodiment of the present application, the server can establish a style mapping relationship based on the personality traits and driving styles of multiple users. Through the style mapping relationship, the server can query and obtain the target personality trait corresponding to each driving style, and integrate these target personality traits into the corresponding driving style description. Furthermore, it can help users deeply understand the connection between their driving behaviors and personality traits, provide personalized feedback suggestions, and improve the user experience.

[0302] Figure 11 It is a schematic flowchart of Embodiment 6 of the data processing method for driving styles provided by the present application. Please refer to Figure 11 , on the basis of the above Embodiment 5, the data processing method for driving styles may include:

[0303] S1101. Determine the style description report corresponding to the target driving style of the current user based on the target driving style of the current user and the style mapping relationship.

[0304] In this step, the server can obtain the target driving style of the current user, determine the target personality trait corresponding to the target driving style based on the pre-established style mapping relationship, add the target personality trait to the style description corresponding to the target driving style, and establish a style description report exclusive to the current user. Among them, the style description report may include at least one of: overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality trait.

[0305] For example, based on the historical driving behavior data of the current user A, the target driving style of the current user A is identified as driving style 1 from multiple driving styles. Based on the pre-established style mapping relationship, it can be determined that the target personality trait corresponding to driving style 1 is adventurous. Add the adventurous personality trait to the style description 1 corresponding to driving style 1, and establish a style description report exclusive to the current user A. The style description report may include that the driving style of the current user A is an aggressive style, can maintain an average vehicle speed of 50 km / h on a road with a speed limit of 60 km / h, accelerates once every 4 minutes on urban roads, the current user A is in the top 10% of aggressiveness among multiple users with adventurous personality traits, and the personality trait is adventurous.

[0306] In an alternative embodiment, in addition to determining the target personality trait of the current user through the style mapping relationship, more detailed personality traits of the current user can also be obtained through a personality trait inference model. The personality trait inference model can accept various inputs, such as driving style, detailed driving data of the user, age, etc. Through these inputs, the model can infer and output more accurate personality traits. The personality trait inference model can be a pre-trained deep learning model or an artificial intelligence model.

[0307] For example, the personality trait inference model is an artificial intelligence model. The artificial intelligence model can analyze the behavior patterns of the current user A under different driving conditions, such as the habits of accelerating and braking, the speed when turning, and the reactions under different weather and traffic conditions. In addition, the artificial intelligence model can also consider the influence of the age of the current user A on driving habits and risk preferences. By comprehensively considering these factors, the artificial intelligence model can output more detailed and personalized personality traits of the current user A. Exemplarily, the personality traits of the current user A output by the artificial intelligence model can be: the current user A tends to drive fast when the weather is good and the traffic flow is small, showing a high risk preference; reduces the vehicle speed in bad weather and attaches importance to safety; is prone to impatience in a long traffic jam.

[0308] S1102. Send the style description report of the current user to the autonomous mobile device.

[0309] For example, the server may send the style description report of the current user A to the intelligent driving vehicle of the current user A.

[0310] In the embodiments of the present application, the server may determine the corresponding style description report based on the target driving style of the current user and the style mapping relationship, and send the style description report of the current user to the autonomous mobile device. In this way, the current user can obtain personalized driving feedback to help them better understand the connection between their driving behavior and personality traits. In addition, the style description report may include detailed driving behavior analysis, such as overtaking probability, passing efficiency, acceleration frequency, etc., so as to provide specific improvement suggestions for the current user, enhance the user experience, and enable the current user to feel higher intelligent and personalized services when using the autonomous mobile device.

[0311] Figure 12 It is a schematic flowchart of the third embodiment of the method for identifying driving style provided by the present application. Please refer to Figure 12 After the server sends the style description report of the current user to the autonomous mobile device, the method for identifying the driving style may further include:

[0312] S1201. Receive the style description report of the current user sent by the server.

[0313] In this step, the electronic device may receive the style description report of the current user sent by the server. The style description report includes at least one of overtaking probability, passing efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality trait.

[0314] For example, the electronic device may receive the style description report of the current user A sent by the server. The style description report may include that the driving style of the current user A is an aggressive style, the average vehicle speed can be maintained at 50 km / h on a road with a speed limit of 60 km / h, the vehicle accelerates once every 4 minutes on urban roads, the current user A is among the top 10% in terms of aggressiveness among multiple users with adventurous personality traits, and the personality trait is adventurous.

[0315] S1202. In response to the first upload operation of the current user, upload the style description report of the current user to the shared cloud platform.

[0316] In this step, the electronic device may, in response to the first upload operation of the current user, upload the style description report of the current user to the shared cloud platform, which is used for interaction among users of multiple autonomous mobile devices.

[0317] For a shared cloud platform, it can be a centralized data interaction center that allows users of multiple autonomous mobile devices to interact and share data. Specifically, it can include operations such as liking, commenting, communicating, and sharing.

[0318] For example, the shared cloud platform can be an Internet community where users can upload their style description reports for other users in the Internet community to comment, like, etc.

[0319] Optionally, the first upload operation of the current user can be triggered in various ways, such as manual operations by the user on the electronic device, preset automatic upload conditions (such as regular upload or upload after a specific event), etc.

[0320] In an embodiment of the present application, after the electronic device receives the style description report of the current user sent by the server, it can upload the style description report of the current user to the shared cloud platform in response to the manual operation of the current user. By uploading the style description report to the shared cloud platform, the sharing and interaction of personalized driving styles can be realized, thereby enhancing the user's sense of participation and overall experience.

[0321] Figure 13 It is a schematic flowchart of Embodiment 4 of the driving style recognition method provided by the present application. Please refer to Figure 13 , on the basis of the embodiment shown in Figure 12 , the driving style recognition method may further include:

[0322] S1301. In response to the second upload operation of the current user, upload the running information of the target driving style to the shared cloud platform.

[0323] In this step, the electronic device can upload the running information of the target driving style to the shared cloud platform in response to the second upload operation of the user. The running information includes the target driving style and at least one of the following: the number of kilometers traveled by the target driving style, the average energy consumption, and the running time.

[0324] Optionally, after the electronic device controls the autonomous mobile device to travel and run for a period of time according to the driving environment information, navigation information, and target driving style obtained during the movement, it can upload the running information of the target driving style to the shared cloud platform in response to the second upload operation of the user.

[0325] Similarly, the second upload operation can have the same triggering method as the first upload operation, such as manual operations by the user on the electronic device, preset automatic upload conditions (such as regular upload or upload after a specific event), etc.

[0326] For example, if the target driving style is Driving Style 1 and the intelligent driving vehicle deployed with Driving Style 1 has run for one week, it can respond to the manual operation of the current user A and upload the running information of Driving Style 1 to the shared cloud platform. The running information includes Driving Style 1, the running kilometers of Driving Style 1 is 200 kilometers, the average energy consumption is 7 liters / 100 kilometers, and the running time is 48 hours. Optionally, if the power source of the intelligent driving vehicle is electric energy, the average energy consumption can be expressed in kilowatt-hours / 100 kilometers.

[0327] In another alternative embodiment, the target driving style determined according to the historical driving behavior data corresponds to a driving style vector. The electronic device can respond to the third upload operation of the current user and upload the driving style vector to the shared cloud platform separately. In addition, the electronic device can also upload the driving style vector while uploading the running information of the target driving style to the shared cloud platform in response to the second upload operation of the current user. The driving style vector can be obtained by methods such as consistency matching, similarity matching, or vector fusion.

[0328] It should be noted that since the style data (such as driving style, style description) only includes style-related features (such as driving style vector), does not include specific driving scenarios and map information, nor personal privacy data, it can be shared without other de-sensitization processing, which is convenient for sharing and does not expose personal information.

[0329] S1302. Control the autonomous mobile device to travel according to the new driving style downloaded by the current user from the shared cloud platform, the driving environment information obtained during the movement, the navigation information, and the new driving style.

[0330] In this step, the electronic device can obtain the new driving style downloaded by the current user from the shared cloud platform and configure the new driving style in the intelligent driving model of the autonomous mobile device. Furthermore, the autonomous mobile device can be controlled to travel through the driving environment information and navigation information obtained during the movement of the autonomous mobile device.

[0331] In an alternative embodiment, after the current user experiences the new driving style, the current user can provide feedback on the new driving style in the shared cloud platform, including operations such as liking, commenting, communicating, and sharing.

[0332] In the embodiments of the present application, the electronic device can respond to the second upload operation of the current user and upload the running information of the target driving style to the shared cloud platform. By uploading the running information, the current user can share the actual performance of their driving style with other users on the shared cloud platform, enhancing the current user's sense of participation and interaction with other users.

[0333] Furthermore, in the driving style recognition method provided by the embodiments of the present application, the current user can browse the driving styles shared by other users on the shared cloud platform, giving the current user more options. The current user can select the driving styles of interest for download. After downloading, the new driving style can be configured and experienced on the user's own autonomous mobile device to explore different driving experiences and improve user satisfaction.

[0334] Figure 14 It is a schematic structural diagram of Embodiment 1 of the driving style recognition device provided by the present application. Please refer to Figure 14 , the driving style recognition device 10 includes:

[0335] The first acquisition module 11 is configured to identify the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user, where the multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users;

[0336] The first processing module 12 is configured to control the autonomous mobile device to travel according to the driving environment information, navigation information, and the target driving style obtained during the movement.

[0337] The driving style recognition device provided by the embodiments of the present application can execute the technical solutions shown in the above method embodiments, and its implementation principles and beneficial effects are similar, which will not be elaborated here.

[0338] In a possible implementation manner, the first acquisition module 11 is specifically configured to:

[0339] Determine a historical feature vector according to the historical driving behavior data;

[0340] Obtain the driving feature vectors respectively corresponding to the multiple driving styles;

[0341] Perform matching processing on the driving feature vectors respectively corresponding to the multiple driving styles according to the historical feature vector, and determine the target driving style.

[0342] In a possible implementation manner, the first acquisition module 11 is specifically configured to:

[0343] Extract features from the historical driving behavior data based on a preset quantitative index and / or a preset feature processing model to obtain the historical feature vector;

[0344] Wherein, the preset quantitative index is determined based on the driving behaviors of the multiple users in at least one driving scenario such as cutting in, starting and stopping, congestion, following, and turning; the preset feature processing model is used to extract feature vectors from the driving behavior data of users.

[0345] In a possible implementation manner, the first acquisition module 11 is specifically configured to:

[0346] Extract features from the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, and determine the first historical feature vector as the historical feature vector; or,

[0347] Extract features from the historical driving behavior data based on a preset feature processing model to obtain a second historical feature vector, and determine the second historical feature vector as the historical feature vector; or,

[0348] Extract features from the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, extract features from the historical driving behavior data based on a preset feature processing model to obtain a second historical feature vector, and splice the first historical feature vector and the second historical feature vector to obtain the historical feature vector.

[0349] In a possible implementation manner, the first processing module 12 is specifically configured to:

[0350] Configure the target driving style in the intelligent driving model of the autonomous mobile device;

[0351] Process the driving environment information and the navigation information through the intelligent driving model to obtain a driving decision-making scheme;

[0352] Control the autonomous mobile device to travel according to the driving decision-making scheme;

[0353] Wherein, the driving decision-making scheme includes at least one of planning a driving path, lane cutting processing, lane changing timing selection, following distance, turning speed, and the time interval for starting with the autonomous mobile device in front.

[0354] In a possible implementation manner, the driving environment information includes the motion state information of the autonomous mobile device, the motion states of surrounding traffic participants, and road traffic information; and / or,

[0355] The navigation information includes map information, the position information of the autonomous mobile device, and path trajectory information.

[0356] Figure 15 This is the structural schematic diagram of the second embodiment of the driving style recognition device provided by this application. On the basis of the Figure 14 shown embodiment, please refer to Figure 15 , the driving style recognition device 10 further includes:

[0357] A receiving module 13, configured to receive the multiple driving styles sent by the server;

[0358] A display module 14, configured to display the multiple driving styles in a visualization interface of the autonomous mobile device.

[0359] In a possible implementation manner, the receiving module 13 is further configured to receive a style description of each driving style sent by a server.

[0360] Correspondingly, the display module 14 is specifically configured to:

[0361] Display the multiple driving styles and a style description corresponding to each driving style in the visualization interface of the autonomous mobile device.

[0362] In a possible implementation manner, the receiving module 13 is further configured to receive a style description report of the current user sent by the server; the style description report includes at least one of: overtaking probability, passing efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality characteristics.

[0363] The first processing module 12 is further configured to, in response to a first upload operation of the current user, upload the style description report of the current user to a shared cloud platform, where the shared cloud platform is used for interaction among users of multiple autonomous mobile devices.

[0364] In a possible implementation manner, the first processing module 12 is further configured to, in response to a second upload operation of the current user, upload operation information of the target driving style to the shared cloud platform, where the operation information includes the target driving style and at least one of the following: the number of kilometers traveled by the target driving style, average energy consumption, and running time.

[0365] In a possible implementation manner, the first processing module 12 is further configured to control the autonomous mobile device to travel according to a new driving style downloaded by the current user from the shared cloud platform, driving environment information obtained during movement, navigation information, and the new driving style.

[0366] The driving style recognition device provided in an embodiment of this application can execute the technical solutions shown in the above method embodiments, and the implementation principles and beneficial effects are similar, and will not be elaborated here.

[0367] Figure 16 This is a schematic structural diagram of Embodiment 1 of a data processing device for driving styles provided in this application. Please refer to Figure 16 The data processing device 20 for driving styles includes:

[0368] A second acquisition module 21, configured to acquire driving behavior data of multiple users.

[0369] An analysis module 22, configured to perform clustering analysis on the driving behavior data of the multiple users to obtain at least one clustering cluster;

[0370] A second processing module 23, configured to add corresponding driving style labels to the at least one clustering cluster respectively to determine multiple driving styles;

[0371] A sending module 24, configured to send the multiple driving styles to at least one autonomous mobile device.

[0372] The data processing device for driving styles provided by the embodiments of the present application can execute the technical solutions shown in the above method embodiments, and the implementation principles and beneficial effects are similar, which will not be elaborated here.

[0373] In a possible implementation manner, the analysis module 22 is specifically configured to:

[0374] For each user, obtain a feature vector corresponding to the user according to the driving behavior data of the user, where the feature vector includes at least one piece of data related to the driving style;

[0375] Perform clustering analysis on the feature vectors of the multiple users to obtain the at least one clustering cluster.

[0376] In a possible implementation manner, there is a corresponding style description for the driving style corresponding to any clustering cluster, and the style description is used to interpret the driving style;

[0377] The sending module 24 is specifically configured to:

[0378] Send the multiple driving styles and the style description corresponding to each driving style to the at least one autonomous mobile device.

[0379] In a possible implementation manner,

[0380] The second obtaining module 21 is further configured to obtain the driving behavior data of the current user sent by the autonomous mobile device;

[0381] The second processing module 23 is further configured to determine a relative driving index of the current user based on a preset quantitative index according to the driving behavior data of the multiple users and the driving behavior data of the current user, and the relative driving index is used to represent the ranking of the current user among the multiple users;

[0382] Wherein, the preset quantitative index is determined based on the driving behavior of the user in at least one driving scenario such as cutting in line, starting and stopping, congestion, following, and turning.

[0383] In a possible implementation manner,

[0384] The second acquisition module 21 is further configured to acquire the target personality traits corresponding to the multiple driving styles;

[0385] The second processing module 23 is further configured to add the target personality traits corresponding to each driving style to the style description corresponding to the driving style.

[0386] Figure 17 This is a schematic structural diagram of the second embodiment of the data processing device for driving styles provided by the present application. On Figure 16 the basis of the shown embodiment, please refer to Figure 17 , the data processing device 20 for driving styles further includes:

[0387] A third processing module 25, configured to establish a style mapping relationship according to the personality traits and driving styles of the multiple users, where the style mapping relationship includes the target personality traits corresponding to each driving style, and the target personality traits are the personality traits with the highest occurrence frequency among the users corresponding to the driving style;

[0388] Correspondingly, the second acquisition module 21 is specifically configured to:

[0389] Query and acquire the target personality traits corresponding to each driving style based on the style mapping relationship.

[0390] In a possible implementation manner,

[0391] The third processing module 25 is further configured to determine a style description report corresponding to the target driving style of the current user based on the target driving style of the current user and the style mapping relationship, where the style description report includes at least one of: overtaking probability, traffic efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality traits;

[0392] The sending module 24 is further configured to send the style description report of the current user to the autonomous mobile device.

[0393] The data processing device for driving styles provided by the embodiments of the present application can execute the technical solutions shown in the above method embodiments, and its implementation principles and beneficial effects are similar, which will not be elaborated here.

[0394] Figure 18 This is a schematic structural diagram of the electronic device provided by the embodiments of the present application. Please refer to Figure 18 , the electronic device 30 may include a processor 31, a memory 32, and a communication interface 34. Exemplarily, the processor 31, the memory 32, and the communication interface 34 are interconnected through a bus 33.

[0395] The memory 32 stores computer-executable instructions;

[0396] The processor 31 executes the computer-executable instructions stored in the memory 32, such that the processor 31 executes the driving style processing method provided in the foregoing method embodiments.

[0397] The electronic device provided in the embodiments of the present application can execute the technical solutions shown in the foregoing embodiments, and the implementation principles and beneficial effects are similar, and will not be elaborated herein.

[0398] The embodiments of the present application provide an autonomous mobile device, including Figure 18 the electronic device shown, to implement the driving style recognition method in the foregoing embodiments, and the implementation principles and beneficial effects are similar, and will not be elaborated herein.

[0399] Figure 19 It is a schematic structural diagram of the server provided in the embodiments of the present application. Please refer to Figure 19 , the server 40 may include a processor 41, a memory 42, and a communication interface 44. Exemplarily, the processor 41, the memory 42, and the communication interface 44 are interconnected through a bus 43.

[0400] The memory 42 stores computer-executable instructions;

[0401] The processor 41 executes the computer-executable instructions stored in the memory 42, such that the processor 41 executes the data processing method for driving style provided in the foregoing method embodiments.

[0402] The server provided in the embodiments of the present application can execute the technical solutions shown in the foregoing method embodiments, and the implementation principles and beneficial effects are similar, and will not be elaborated herein.

[0403] The embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in the foregoing method embodiments.

[0404] The embodiments of the present application provide a program product, including: a computer program, when the program product runs on a computer, enabling the computer to execute the method described in the foregoing method embodiments.

[0405] The embodiments of the present application provide a computer program, when the computer program is executed by a processor, it is used to execute the method described in the foregoing method embodiments.

[0406] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0407] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0408] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0409] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0410] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0411] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0412] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0413] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0414] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for identifying a driving style, characterized in that Applied to an autonomous mobile device, the method includes: Based on the historical driving behavior data of the current user, identifying the target driving style of the current user from multiple driving styles, where the multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users; According to the driving environment information, navigation information, and the target driving style obtained during the movement, controlling the autonomous mobile device to drive.

2. The method according to claim 1, wherein The step of identifying the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user includes: Determining a historical feature vector according to the historical driving behavior data; Obtaining the driving feature vectors corresponding to the multiple driving styles respectively; Performing a matching process among the driving feature vectors corresponding to the multiple driving styles according to the historical feature vector, and determining the target driving style.

3. The method according to claim 2, wherein The step of determining a historical feature vector according to the historical driving behavior data includes: Performing feature extraction on the historical driving behavior data based on a preset quantitative index and / or a preset feature processing model to obtain the historical feature vector; Wherein, the preset quantitative index is determined based on the driving behaviors of the multiple users in at least one driving scenario such as cutting in, starting and stopping, congestion, following, and turning; the preset feature processing model is used to perform feature extraction on the driving behavior data of a user to obtain a feature vector.

4. The method according to claim 3, characterized in that, The step of performing feature extraction on the historical driving behavior data based on the preset quantitative index and / or the preset feature processing model to obtain the historical feature vector includes: Performing feature extraction on the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, and determining the first historical feature vector as the historical feature vector; or, Performing feature extraction on the historical driving behavior data based on the preset feature processing model to obtain a second historical feature vector, and determining the second historical feature vector as the historical feature vector; or, Performing feature extraction on the historical driving behavior data based on the preset quantitative index to obtain a first historical feature vector, performing feature extraction on the historical driving behavior data based on the preset feature processing model to obtain a second historical feature vector, and performing a splicing process on the first historical feature vector and the second historical feature vector to obtain the historical feature vector.

5. The method according to any one of claims 1-4, characterized in that, The step of controlling the autonomous mobile device to drive according to the driving environment information, navigation information, and the target driving style obtained during the movement includes: Configuring the target driving style in the intelligent driving model of the autonomous mobile device; Processing the driving environment information and the navigation information through the intelligent driving model to obtain a driving decision-making scheme; Controlling the autonomous mobile device to drive according to the driving decision-making scheme; Wherein, the driving decision-making scheme includes at least one of planning a driving path, cut-in processing, lane-changing timing selection, following distance, turning speed, and the time interval for starting with the autonomous mobile device in front.

6. The method according to any one of claims 1-5, characterized in that, The driving environment information includes the motion state information of the autonomous mobile device, the motion states of surrounding traffic participants, and road traffic information; and / or, The navigation information includes map information, the position information of the autonomous mobile device, and path trajectory information.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Receiving the multiple driving styles sent by the server; Displaying the multiple driving styles in a visualization interface of the autonomous mobile device.

8. The method according to claim 7, wherein The method further includes: Receiving the style descriptions of each driving style sent by the server; Correspondingly, the displaying the multiple driving styles in the visualization interface of the autonomous mobile device includes: Displaying the multiple driving styles and the style descriptions corresponding to each driving style in the visualization interface of the autonomous mobile device.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: Receiving the style description report of the current user sent by the server; the style description report includes at least one of overtaking probability, passing efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving index, and personality characteristics; In response to a first upload operation of the current user, uploading the style description report of the current user to a shared cloud platform for interaction among users of multiple autonomous mobile devices.

10. The method according to any one of claims 1-9, characterized in that, The method further includes: In response to a second upload operation of the current user, uploading the operation information of the target driving style to the shared cloud platform, where the operation information includes the target driving style and at least one of the following: the number of kilometers traveled by the target driving style, average energy consumption, and running time.

11. The method according to claim 9 or 10, characterized in that, The method further includes: Controlling the autonomous mobile device to travel according to the new driving style downloaded by the current user from the shared cloud platform, the driving environment information obtained during movement, the navigation information, and the new driving style.

12. A data processing method for driving styles, characterized in that, The method includes: Obtaining driving behavior data of multiple users; Performing cluster analysis on the driving behavior data of the multiple users to obtain at least one cluster; Adding corresponding driving styles to the at least one cluster respectively to determine multiple driving styles; Sending the multiple driving styles to at least one autonomous mobile device.

13. The method according to claim 12, wherein The performing cluster analysis on the driving behavior data of the multiple users to obtain at least one cluster includes: For each user, obtaining a feature vector corresponding to the user according to the driving behavior data of the user, where the feature vector includes at least one piece of data related to the driving style; Performing cluster analysis on the feature vectors of the multiple users to obtain the at least one cluster.

14. The method according to any one of claims 12 or 13, characterized in that, Each driving style corresponding to a cluster has a corresponding style description for interpreting the driving style; The sending the multiple driving styles to at least one autonomous mobile device includes: Sending the multiple driving styles and the style descriptions corresponding to each driving style to the at least one autonomous mobile device.

15. The method according to any one of claims 12 - 14, characterized in that, The method further includes: Obtaining the driving behavior data of the current user sent by the autonomous mobile device; Based on preset quantitative metrics, determine the relative driving metrics of the current user according to the driving behavior data of the multiple users and the driving behavior data of the current user, where the relative driving metrics are used to represent the ranking of the current user among the multiple users; Among them, the preset quantitative metrics are determined based on the driving behavior of the user in at least one driving scenario such as cutting in line, starting and stopping, congestion, following a vehicle, and turning.

16. The method according to claim 14, characterized in that, The method further includes: Obtain the target personality traits corresponding to the multiple driving styles; Add the target personality traits corresponding to each driving style to the style description corresponding to the driving style.

17. The method according to claim 16, wherein The method further includes: Establish a style mapping relationship according to the personality traits and driving styles of the multiple users. The style mapping relationship includes the target personality traits corresponding to each driving style, and the target personality traits are the personality traits with the highest frequency of occurrence among the users corresponding to the driving style; Correspondingly, the obtaining of the target personality traits corresponding to the multiple driving styles includes: Based on the style mapping relationship, query and obtain the target personality traits corresponding to each driving style.

18. The method according to claim 17, characterized in that, The method further includes: Based on the target driving style of the current user and the style mapping relationship, determine the style description report corresponding to the target driving style of the current user. The style description report includes at least one of the following: overtaking probability, passing efficiency, acceleration frequency, lane change frequency, driving smoothness, average vehicle speed, relative driving metrics, and personality traits; Send the style description report of the current user to the autonomous mobile device.

19. An identification device for driving style, characterized in that, The device includes: A first acquisition module, configured to identify the target driving style of the current user from multiple driving styles based on the historical driving behavior data of the current user. The multiple driving styles are obtained by clustering and analyzing the driving behavior data of multiple users; A first processing module, configured to control the autonomous mobile device to travel according to the driving environment information, navigation information, and the target driving style obtained during the movement.

20. A data processing device for driving style, characterized in that, The device includes: A second acquisition module, configured to acquire the driving behavior data of multiple users; An analysis module, configured to perform clustering analysis on the driving behavior data of the multiple users to obtain at least one clustering cluster; A second processing module, configured to add corresponding driving style labels to the at least one clustering cluster respectively to determine multiple driving styles; A sending module, configured to send the multiple driving styles to at least one autonomous mobile device.

21. An electronic device, characterized in that, Includes: A processor, a memory, and a communication interface; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1 to 11.

22. An autonomous mobile device, characterized in that, Includes: The electronic device according to claim 21.

23. A server, characterized in that, Includes: A processor, a memory, and a communication interface; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 12 to 18.

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, are used to implement the method according to any one of claims 1 to 18.

25. A program product, characterized in that, Including: A computer program that, when the program product runs on a computer, causes the computer to execute the method according to any one of claims 1 to 18 above.

26. A computer program, characterized in that, When the computer program is executed by a processor, it is used to execute the method according to any one of claims 1 to 18 above.

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