A vehicle parking scene acquisition method, device, equipment and medium

Abstract

The application provides a vehicle use scene acquisition method, device, equipment and medium, the vehicle use scene acquisition method comprises: acquiring human-computer interaction data and user perception data; the human-computer interaction data and the user perception data are preprocessed, and use car behavior sequence data is acquired;Acquire the information of the boundary switching event of multiple main scenes;According to the information of the boundary switching event of the main scene, the use car behavior subsequence data of different main scenes is acquired;According to the use car behavior subsequence data, use car behavior mode sequence data is acquired;The use car behavior mode sequence data is clustered, and multiple use car scenes are acquired;And multiple use car scenes are aggregated, and the whole life cycle scene map of user use car is acquired.Through the vehicle use scene acquisition method disclosed by the application, the utilization rate of vehicle-mounted data is improved, and the driving experience of user use car is improved.

Description

Technical Field

[0001] This application relates to the field of automotive service technology, specifically to a method, device, equipment, and medium for acquiring vehicle usage scenarios. Background Technology

[0002] Currently, the intelligence, connectivity, and automation of automobiles are creating new interactions and experiences between users and vehicles, transforming cars from simple transportation tools into carriers of diversified travel experiences. The demands for human-vehicle interaction scenarios are becoming increasingly diverse. To enhance user experience, automakers are developing numerous automotive services across various usage scenarios. In the future, different usage scenarios and models will lead to overall changes in vehicle design, with the in-car interaction environment shifting from the cockpit to various everyday scenarios. A key issue is how to best recreate the usage scenarios, understand user needs, and meet the essential needs of users in specific situations. Summary of the Invention

[0003] In view of the shortcomings of the prior art described above, the present invention provides a method for obtaining vehicle usage scenarios to solve the problem of how to reproduce the usage scenario to the greatest extent, understand user needs, and meet the essential needs of users in specific scenarios.

[0004] This invention provides a method for obtaining vehicle usage scenarios, the method comprising:

[0005] Acquire human-computer interaction data and user perception data;

[0006] The human-computer interaction data and the user perception data are preprocessed to obtain vehicle use behavior sequence data;

[0007] Obtain information on boundary switching events from multiple main scenes;

[0008] Based on the information of the boundary switching event of the main scene, the vehicle use behavior sequence data is segmented to obtain vehicle use behavior sub-sequence data for different main scenes;

[0009] Based on the vehicle usage behavior subsequence data, obtain vehicle usage behavior pattern sequence data;

[0010] Cluster the vehicle usage behavior pattern sequence data to obtain multiple vehicle usage scenarios; and

[0011] Multiple vehicle usage scenarios are aggregated to obtain a full lifecycle scenario map of user vehicle usage.

[0012] In one embodiment of the present invention, obtaining the vehicle usage behavior sequence data includes the following steps:

[0013] Obtain the event tracking data items and collection specifications for user vehicle usage behavior; and

[0014] Based on the embedded data items and collection specifications of the user's vehicle usage behavior, the human-computer interaction data and the user perception data are integrated to obtain non-redundant behavioral event data.

[0015] In one embodiment of the present invention, obtaining the vehicle usage behavior sequence data further includes the following steps:

[0016] The non-redundant behavioral event data is sorted by time to obtain long-sequence data of daily user behavioral events; and

[0017] The user's daily behavioral event long sequence data is aggregated and encoded to obtain the vehicle usage behavior sequence data.

[0018] In one embodiment of the present invention, obtaining the vehicle usage behavior pattern sequence data includes the following steps:

[0019] Based on the vehicle usage behavior subsequence data, obtain multiple vehicle usage behavior pattern subsequence data;

[0020] Obtain the support of multiple subsequence data of the aforementioned vehicle usage behavior patterns.

[0021] In one embodiment of the present invention, obtaining the vehicle usage behavior pattern sequence data further includes the following steps:

[0022] Determine whether the support of the vehicle use behavior pattern subsequence data is less than a preset value. If the support of the vehicle use behavior pattern subsequence data is greater than or equal to the preset value, then obtain the vehicle use behavior pattern sequence data based on the vehicle use behavior pattern subsequence data.

[0023] In one embodiment of the present invention, obtaining multiple vehicle usage scenarios includes the following steps:

[0024] Obtain the similarity of the vehicle usage behavior pattern sequence data;

[0025] Obtain clustering thresholds for multiple main scenarios; and

[0026] Based on the similarity of the vehicle use behavior pattern sequence data and the clustering thresholds of multiple main scenarios, the vehicle use behavior pattern sequence data is clustered to obtain multiple vehicle use scenarios.

[0027] In one embodiment of the present invention, obtaining a full lifecycle scenario map of a user's vehicle usage further includes the following steps:

[0028] Statistical analysis is performed on multiple vehicle usage scenarios to obtain information on high-frequency scene segmentation events within these scenarios; and

[0029] Based on the information of the high-frequency scene separation event, it is determined whether the boundary switching event of the main scene needs to be updated. If the boundary switching event of the main scene does not need to be updated, multiple vehicle use scenarios are aggregated to obtain the full lifecycle scene map of the user's vehicle use.

[0030] This invention provides a device for acquiring vehicle usage scenarios, the device comprising:

[0031] The vehicle data acquisition module is used to acquire human-computer interaction data and user perception data;

[0032] The behavior sequence data acquisition module is used to preprocess the human-computer interaction data and the user perception data to acquire vehicle use behavior sequence data;

[0033] The switching event acquisition module is used to acquire information about switching events in multiple main scenes;

[0034] The behavior subsequence data acquisition module is used to segment the vehicle use behavior sequence data according to the information of the main scene switching event, and acquire vehicle use behavior subsequence data for different main scenes;

[0035] The vehicle usage mode acquisition module is used to acquire vehicle usage mode sequence data based on the vehicle usage behavior sub-sequence data.

[0036] The vehicle usage scenario acquisition module is used to cluster the vehicle usage behavior pattern sequence data to obtain vehicle usage scenarios; and

[0037] The scene map acquisition module is used to aggregate multiple vehicle usage scenarios to obtain a scene map of the entire lifecycle of a user's vehicle usage.

[0038] The present invention provides an electronic device, the electronic device comprising:

[0039] At least one processor;

[0040] A storage device for storing at least one program, which, when executed by the at least one processor, causes the electronic device to implement the vehicle usage scenario acquisition method of any of the above-mentioned methods.

[0041] The present invention provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed by a computer processor, causes the computer to perform the vehicle usage scenario acquisition method described in any of the above claims.

[0042] The beneficial effects of this invention are as follows: This invention reversely integrates human-computer interaction data and user perception data to reconstruct the vehicle usage scenario, which helps to more realistically understand the user's vehicle usage scenarios throughout the entire life cycle, uncover valuable behavioral patterns of the vehicle in human-computer interaction, avoid misjudgment of the user's real vehicle usage habits caused by artificially defined patterns, and help evaluate and improve the vehicle system, interaction, cockpit, and dashboard, so as to better provide users with scenario-defined functions and improve the user's vehicle usage experience.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0045] Figure 1 This is an exemplary embodiment of the present application illustrating the application environment of a method for obtaining vehicle usage scenarios;

[0046] Figure 2 This is a flowchart illustrating a method for obtaining vehicle usage scenarios, as shown in an exemplary embodiment of this application.

[0047] Figure 3 This is a flowchart illustrating a method for obtaining vehicle usage behavior sequence data in an exemplary embodiment of this application;

[0048] Figure 4 This is a flowchart illustrating a method for obtaining vehicle usage behavior pattern sequence data, as shown in an exemplary embodiment of this application.

[0049] Figure 5 This is a flowchart illustrating a method for obtaining a vehicle usage scenario, as shown in an exemplary embodiment of this application.

[0050] Figure 6 This is a flowchart illustrating an exemplary embodiment of the present application of a method for obtaining a user vehicle's full lifecycle scenario map.

[0051] Figure 7 This is a schematic diagram illustrating the structure of a vehicle usage scenario acquisition device, as shown in an exemplary embodiment of this application.

[0052] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0053] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0054] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0055] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0056] First, it's important to clarify that user behavior sequences, also known as "time-series user behavior," refer to the chronological record of each step a user takes in a specific activity within a given time period. Behavior sequence analysis is a method for analyzing all behavior sequences. Its purpose is to reveal more nuanced information hidden beneath statistical data, reconstruct specific user scenarios, and identify opportunities to improve user experience by observing specific behavioral characteristics. Furthermore, "event tracking" is a term in the data acquisition field, referring to the techniques and implementation processes involved in capturing, processing, and transmitting specific user behaviors or events. The essence of event tracking is to first listen for events during application operation, and then judge and capture the events that require attention when they occur.

[0057] Figure 1 This is a schematic diagram illustrating the application environment of a method for obtaining vehicle usage scenarios, as shown in an exemplary embodiment of this application. Figure 1As shown, in some embodiments, sensor 110 acquires user perception data, including vehicle information, speed information, and information about the vehicle's surrounding environment. Sensor 110 uploads the acquired user perception data to the vehicle usage mode and usage scenario acquisition module 130. The vehicle interaction system 120 acquires human-computer interaction data and uploads it to the vehicle usage mode and usage scenario acquisition module 130. The vehicle usage mode and usage scenario acquisition module 130 processes the human-computer interaction data and user perception data to acquire vehicle usage modes and usage scenarios, and obtains a scene map by combining the usage scenarios. When user 140 drives the vehicle, the vehicle usage mode and usage scenario acquisition module 130 matches the scene map with the driving scene to obtain scene information, and provides personalized recommendation services to user 140 based on the scene, realizing scene-based definition functionality and improving user 140's driving experience. The embodiments of this application do not limit the specific function of the vehicle usage mode and usage scenario acquisition module 130. Figure 1 The vehicle usage mode and scenario acquisition module 130 shown can be, for example, an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. No limitation is imposed here. The field sensor 110 and the vehicle interaction system 120 can upload data to the vehicle usage mode and scenario acquisition module 130 via wireless networks such as 3G (third-generation mobile information technology), 4G (fourth-generation mobile information technology), and 5G (fifth-generation mobile information technology). The embodiments of this application do not limit this and can be configured according to actual needs.

[0058] In some embodiments, services based on vehicle usage scenarios mainly rely on user-defined or manually set scenarios to execute preset functions, without analyzing, acquiring, or managing user usage scenarios based on actual user usage. To address these issues, embodiments of this application propose a method, apparatus, device, and medium for acquiring vehicle usage scenarios, which will be described in detail below.

[0059] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for obtaining vehicle usage scenarios according to an exemplary embodiment of this application. In some embodiments, this method can be applied to... Figure 1The implementation environment shown is specifically executed by the vehicle usage scenario acquisition module 120 within that implementation environment. It should be understood that this method can also be applied to other exemplary implementation environments and executed by devices in other implementation environments; this embodiment does not limit the implementation environment to which this method is applicable.

[0060] For example, the vehicle usage scenario acquisition module 120 to which the vehicle usage scenario acquisition method disclosed in this embodiment is applicable may have an SDK (Software Development Kit, which is a set of development tools for building application software for specific software packages, software frameworks, operating systems, etc.) installed, and the method disclosed in this embodiment is specifically implemented as one or more functions provided by the SDK.

[0061] like Figure 2 As shown, in an exemplary embodiment, the method for obtaining vehicle usage scenarios includes at least steps S210 to S270, which are described in detail below:

[0062] Step S210: Obtain human-computer interaction data and user perception data;

[0063] First, it should be noted that, based on the data items and data collection specifications related to user vehicle usage behavior, through... Figure 1 The vehicle's sensors 110, in-vehicle application port, smartwatch, or other sensing components acquire a large amount of human-machine interaction data and user perception data, and upload this data to the cloud. The user perception data includes information such as temperature and humidity within the cockpit.

[0064] Step S220: Preprocess the human-computer interaction data and user perception data to obtain vehicle usage behavior sequence data.

[0065] After receiving human-machine interaction data and user perception data, the cloud forwards them, for example, through a distributed data processing platform (KAFKA), and performs preprocessing. Preprocessing involves correlating and integrating the human-machine interaction data and user perception data according to user vehicle behavior data points and collection specifications, and merging multiple human-machine interaction data sources and user perception data sources to remove redundant and duplicate user behavior event data. Then, each user's vehicle behavior events are sorted chronologically to obtain daily long-sequence data. This daily long-sequence data contains a series of user perception events and human-machine interaction events. User perception events include changes in temperature and humidity in the cockpit, changes in the number of people in the vehicle, and whether there is traffic congestion. Human-machine interaction events include vehicle location, remotely turning on the air conditioning, Bluetooth unlocking / locking, activating the voice assistant, destination navigation, seat heating, seat ventilation, changing the car wallpaper, turning on the air conditioning, turning on music, opening WeChat, turning on / off the driver assistance system, turning on the radio, adjusting the exterior mirrors, opening / closing the doors, and raising / lowering the windows.

[0066] Then, independent codes are developed for the long-sequence data of daily user behavior events. By aggregating and uniformly encoding the long-sequence data of daily user behavior events, encoded long-sequence data of daily user behavior events, i.e., vehicle usage behavior sequence data, can be obtained. Each vehicle usage behavior sequence data includes a unique vehicle identifier, user perception events, and human-computer interaction behavior events. This completes the process of constructing, uniformly encoding, integrating, and serializing vehicle usage behavior events. By constructing, uniformly encoding, integrating, and serializing a large amount of collected vehicle data, and fully integrating data from multiple data sources, we can make full use of the large amount of collected human-vehicle interaction data and user perception data to mine user vehicle usage habits.

[0067] Step S230: Obtain information on boundary switching events of multiple main scenes.

[0068] A vehicle usage scenario is a collection of user vehicle-related behavioral events within a certain time and space range, defining a combination of user and car-related behaviors. To segment vehicle usage behavior sequence data under different scenarios, the data can be divided using boundary switching events of multiple main scenarios. The user vehicle-related behavioral events such as finding the car, opening and closing the driver's door, entering navigation, ignition, starting parking, and turning off the engine serve as the initial boundary switching events of the main scenarios. Since user vehicle usage events extend over time, and each user uses the car multiple times a day, with multiple usage scenario switching during a single use, the segmentation points for user vehicle usage behavior sequence data need to be updated based on specific vehicle usage behavior events to further segment the data.

[0069] Step S240: Based on the information of the boundary switching event of the main scene, the vehicle use behavior sequence data is segmented to obtain vehicle use behavior sub-sequence data for different main scenes.

[0070] Based on the boundary switching events of the main scene, an overlapping boundary cutting method, also known as a partial boundary incomplete cutting method, is adopted to cut the vehicle behavior sequence data. For example, when cutting the vehicle behavior sequence data S, the sub-sequence data after cutting is retained, for example, {S1, S2, ..., S...}. m}, and retain the subsequence data, for example, {S1-S2, ..., S}. m-1 -S m}, where S1-S2 are the edge data of the vehicle use behavior sequence between S1 and S2. These two types of sub-sequence data are, for example, vehicle use behavior sub-sequence data of different main scenarios.

[0071] Step S250: Obtain vehicle use behavior pattern sequence data based on vehicle use behavior sub-sequence data.

[0072] There are some commonalities among the vehicle usage behavior subsequence data. Therefore, based on the vehicle usage behavior subsequence data with commonalities, multiple vehicle usage behavior pattern subsequence data are obtained. For example, vehicle usage behavior subsequence data L1 includes {behavior A, environment A}, {behavior B}, {behavior C}, {behavior D, environment M}, {behavior E}, and {behavior F}. Vehicle usage behavior subsequence data L2 includes {behavior A, environment A}, {behavior X}, {behavior C}, {behavior D}, and {behavior F}. This common subsequence data L3, which shares the same commonalities with vehicle usage behavior subsequence data L1 and L2, includes {behavior A, environment A}, {behavior C}, {behavior D}, and {behavior F}. This common subsequence data L3 is declared as vehicle usage behavior pattern subsequence data L3. Through the vehicle usage behavior subsequence data, multiple vehicle usage behavior pattern subsequence data are obtained, and the support of the multiple vehicle usage behavior pattern subsequence data is calculated, for example, expressed as support(L) = n, where n is the support. The support of the vehicle behavior pattern subsequence data refers to the number of all sequence data included in the subsequence data. For example, the support of the vehicle behavior pattern subsequence data L3 is 4, which can be represented as support(L3) = 4. A minimum support is also set for the vehicle behavior pattern subsequence data, for example, minsup. If the support of a vehicle behavior pattern subsequence data is less than the minimum support, it is deleted. If the support of a vehicle behavior pattern subsequence data is greater than or equal to the minimum support, it is considered a high-frequency vehicle behavior pattern sequence data. By mining and obtaining a large amount of user vehicle behavior sequence data, some high-frequency vehicle behavior pattern sequence data can be obtained. Simultaneously, the average interval time of vehicle behavior events corresponding to different vehicle behavior sequences in the vehicle behavior pattern sequence data can be obtained.

[0073] Step S260: Cluster the vehicle usage behavior pattern sequence data to obtain multiple vehicle usage scenarios.

[0074] The similarity of vehicle usage behavior pattern sequences in each main scenario is obtained, and a clustering threshold is set for each main scenario. The similarity between two behavior pattern sequences is measured by calculating the temporal distance between the events corresponding to any two sequences. Based on the similarity of the vehicle usage behavior pattern sequences in each main scenario and the clustering threshold, when the similarity value of two vehicle usage behavior pattern sequences is less than the threshold, they are placed in the same scenario for unsupervised clustering to form a vehicle usage scenario. A main scenario contains many sub-scenarios, and each sub-scenarios contains many similar vehicle usage behavior pattern sequences. For example, the cluster centers of the sub-scenarios can be used as the main vehicle usage behavior patterns for each scenario.

[0075] Step S270: Aggregate multiple vehicle usage scenarios to obtain a full lifecycle scenario map of user vehicle usage.

[0076] The system analyzes high-frequency scene segmentation events in each vehicle usage scenario to determine if updating the main scenario's boundary switching events is necessary. If updating the main scenario's boundary switching events is required, steps S230, S240, S250, and S260 are repeated. This process continues until, based on the high-frequency scene segmentation events in each vehicle usage scenario, updating the main scenario's boundary switching events is no longer necessary. Then, multiple vehicle usage scenarios are aggregated to construct a full lifecycle scenario map for user vehicle usage. This map consists of multiple vehicle usage scenarios, each containing a sequence of user vehicle usage behavior patterns arranged chronologically. The scenario map serves as a dashboard for the automaker to view the user's full lifecycle behavior pattern sequence data. When applying the scenario map, the system calculates the similarity between real-time vehicle usage behavior sequence data and the behavior pattern sequence data in each vehicle usage scenario to automatically identify vehicle usage scenarios in real time. Based on the scenario map, the system obtains user vehicle usage behavior sequence data, selectable vehicle usage behavior sequence data, and time intervals between different behavior events within that scenario, providing personalized recommendation services to users and realizing the scenario definition function. For example, a user's vehicle entry sequence includes actions such as opening the door, adjusting the seat, fastening the seatbelt, closing the door, and lowering the window. When the in-vehicle system recognizes the entry scenario, after the user completes the actions of opening the door and adjusting the seat, the system recommends services corresponding to the user's subsequent usage patterns based on the sequence of usage behaviors. This invention starts from the user's actual vehicle usage data and reverse-engineers the user's usage scenarios and behavioral patterns, avoiding misjudgments of the user's actual usage habits caused by manually defined patterns.

[0077] The entire user vehicle lifecycle includes trip preparation, vehicle driving, stops, parking, driving experience, and other situations. The user vehicle lifecycle scenario map comprises several major scenarios and multiple sub-scenarios. Each sub-scenarios has a business definition, such as garage scenario, summer scenario, seating scenario, traffic jam scenario, late-night scenario, and phone call scenario. These sub-scenarios are composed of user vehicle behavior pattern sequence data, and each user vehicle behavior pattern sequence data is composed of ordered chained user behavior events. The cluster centers of sub-scenarios can fully represent the general user behavior habits in that scenario, while user vehicle behavior pattern sequence data farther from the cluster centers can represent alternative and unique personalized needs among users. By depicting the user vehicle lifecycle scenario map and exploring the sub-scenarios under each core major scenario, automakers can better understand user needs, grasp user vehicle behavior habits in different scenarios, and define vehicle functions based on scenarios.

[0078] Figure 3 This is a flowchart illustrating a method for obtaining vehicle usage behavior sequence data, as shown in an exemplary embodiment of this application. Figure 3 As shown, in an exemplary embodiment, the method for obtaining vehicle behavior sequence data includes at least steps S310 to S350, which are described in detail below:

[0079] Step S310: Obtain the data items and collection specifications of the user's vehicle usage behavior.

[0080] Set up the data points and collection specifications for vehicle usage behavior data according to the needs of the collected data.

[0081] Step S320: Obtain human-computer interaction data and user perception data.

[0082] Based on the data points and collection specifications related to user vehicle usage behavior, Figure 1 The sensor 110 and human-computer interaction system 120 shown acquire human-computer interaction data and user perception data.

[0083] Step S330: Based on the embedded data items of user vehicle usage behavior and the collection specification information, integrate the human-computer interaction data and user perception data to obtain non-redundant behavioral event data.

[0084] Based on the data points and collection specifications of user vehicle usage behavior, human-computer interaction data and user perception data are correlated and integrated. Multiple human-computer interaction data sources and user perception data sources are merged to remove redundant and duplicate user behavior event data and obtain non-redundant behavior event data.

[0085] Step S340: Sort the non-redundant behavioral event data by time to obtain long-sequence data of daily user behavioral events.

[0086] The behavioral event data is sorted according to the chronological order of the occurrence of the behavioral events to obtain long-sequence data of user daily behavioral events.

[0087] Step S350: Aggregate and encode the long sequence data of daily user behavior events to obtain vehicle usage behavior sequence data.

[0088] Independent coding is applied to the long-sequence data of daily user behavior events. By aggregating and uniformly coding the long-sequence data of daily user behavior events, the coded long-sequence data of daily user behavior events, i.e., vehicle usage behavior sequence data, can be obtained. Each vehicle usage behavior sequence data includes the vehicle's unique identifier, user perception events, and human-computer interaction behavior events.

[0089] Figure 4This is a flowchart illustrating a method for obtaining vehicle usage behavior pattern sequence data, as shown in an exemplary embodiment of this application. Figure 4 As shown, in an exemplary embodiment, the method for obtaining vehicle behavior pattern sequence data includes at least steps S410 to S442, which are detailed below:

[0090] Step S410: Obtain vehicle usage behavior subsequence data for different main scenarios.

[0091] Based on the boundary switching events of multiple main scenarios, the vehicle usage behavior sequence data is partially and incompletely segmented to obtain vehicle usage behavior sub-sequence data under different main scenarios.

[0092] Step S420: Obtain multiple vehicle use behavior pattern subsequence data based on the vehicle use behavior subsequence data.

[0093] Among the various vehicle behavior subsequence data, some commonalities exist. Therefore, commonalities are extracted from these commonalities, forming a commonalities subsequence data set, which can be called a vehicle behavior pattern subsequence data set. For example, vehicle behavior subsequence data L1 includes {behavior A, environment A}, {behavior B}, {behavior C}, {behavior D, environment M}, {behavior E}, and {behavior F}. Vehicle behavior subsequence data L2 includes {behavior A, environment A}, {behavior X}, {behavior C}, {behavior D, environment N}, {behavior E}, and {behavior Z}. The commonalities shared by vehicle behavior subsequence data L1 and L2 are also present in subsequence data L3, which includes {behavior A, environment A}, {behavior C}, {behavior D}, and {behavior F}. This commonalities subsequence data L3 is declared as vehicle behavior pattern subsequence data L3.

[0094] Step S430: Obtain the support of multiple vehicle usage behavior pattern subsequence data.

[0095] The support of the subsequence data of the vehicle behavior pattern is the number of all sequence data included in the subsequence data of the vehicle behavior pattern. For example, the subsequence data of the vehicle behavior pattern L includes {behavior A, environment A}, {behavior B}, {behavior C}, {behavior D}, {behavior E} and {behavior F}, so the support of the subsequence data of the vehicle behavior pattern L is 6, which can be represented as support(L) = 6.

[0096] Step S440: Determine whether the support of the vehicle use behavior pattern subsequence data is less than a preset value.

[0097] Set the minimum support of the vehicle use behavior pattern subsequence data, for example, minsup, as a preset value, and determine whether the support of the vehicle use behavior pattern subsequence data is less than the set value. If the support of the vehicle use behavior pattern subsequence data is greater than or equal to the set value, execute step S441; if the support of the vehicle use behavior pattern subsequence data is less than the set value, execute step S442.

[0098] Step S441: Obtain vehicle usage behavior pattern sequence data.

[0099] If the support of a subsequence of vehicle usage behavior patterns is greater than or equal to a set value, i.e., the minimum support, then this subsequence of vehicle usage behavior patterns is considered vehicle usage behavior pattern sequence data, i.e., high-frequency vehicle usage behavior sequence data. The time intervals between different sequences within the vehicle usage behavior pattern sequence data can also be obtained.

[0100] Step S442: Delete the vehicle usage behavior pattern subsequence data.

[0101] If the support of the vehicle use behavior pattern subsequence data is less than the set value, it means that the vehicle use behavior pattern subsequence data is not high-frequency vehicle use behavior sequence data, and the vehicle use behavior pattern subsequence data is deleted.

[0102] Figure 5 This is a flowchart illustrating a method for obtaining a vehicle usage scenario, as shown in an exemplary embodiment of this application. Figure 5 As shown, in an exemplary embodiment, the method for obtaining multiple vehicle usage scenarios includes at least steps S510 to S530, which are detailed below:

[0103] Step S510: Obtain the similarity of the vehicle usage behavior pattern sequence data.

[0104] The similarity between two behavior pattern sequences can be measured by calculating the temporal distance between events corresponding to any two sequences in multiple vehicle usage behavior pattern sequence data.

[0105] Step S520: Obtain the clustering thresholds for multiple main scenes.

[0106] Clustering threshold is a critical condition for clustering data of vehicle usage behavior patterns. Each main scenario has its own clustering threshold.

[0107] Step S530: Based on the similarity of the vehicle use behavior pattern sequence data and the clustering threshold of multiple main scenarios, cluster the vehicle use behavior pattern sequence data to obtain multiple vehicle use scenarios.

[0108] When the similarity value of two vehicle usage behavior pattern sequences is less than a threshold, they are placed in the same scenario for unsupervised clustering to form a vehicle usage scenario. A main scenario contains many sub-scenarios, and each sub-scenarios contains many similar vehicle usage behavior pattern sequences. For example, the cluster center of a sub-scenarios can be used as the main vehicle usage behavior pattern for each scenario.

[0109] Figure 6 This is a flowchart illustrating a method for obtaining a user's vehicle usage lifecycle scenario map, as shown in an exemplary embodiment of this application. Figure 6 As shown, in an exemplary embodiment, the method for obtaining a user's vehicle usage lifecycle scenario map includes at least steps S610 to S632, which are detailed below:

[0110] Step S610: Obtain multiple vehicle usage scenarios.

[0111] Step S620: Statistical analysis is performed on multiple vehicle usage scenarios to obtain high-frequency scenario segmentation events in multiple vehicle usage scenarios.

[0112] Step S630: Based on the high-frequency scene separation event, determine whether it is necessary to update the boundary switching event of the main scene.

[0113] The system determines whether the boundary switching event of the main scene needs to be updated by checking whether the high-frequency scene separation event matches the boundary switching event of the main scene. If the high-frequency scene separation event matches the boundary switching event of the main scene, the boundary switching event of the main scene does not need to be changed, and step S631 is executed. If the high-frequency scene separation event does not match the boundary switching event of the main scene, the boundary switching event of the main scene needs to be changed, and step S632 is executed.

[0114] Step S631: Aggregate multiple vehicle usage scenarios to obtain a full lifecycle scenario map of user vehicle usage.

[0115] Step S632: If it is necessary to update the boundary switching event of the main scene, then obtain multiple car use scenarios again based on the updated boundary switching event of the main scene, until the high-frequency scene separation event in the multiple car use scenarios does not require updating the boundary switching event of the main scene. Then, aggregate the multiple car use scenarios to obtain the full life cycle scene map of the user's car use.

[0116] Figure 7 This is a schematic diagram illustrating the structure of a vehicle usage scenario acquisition device, as shown in an exemplary embodiment of this application. This device can be applied to... Figure 1 The device is shown in the application environment and is specifically configured in the vehicle usage scenario acquisition module 120. This device can also be applied to other exemplary implementation environments and specifically configured in other devices; this embodiment does not limit the implementation environment to which the device is applicable.

[0117] like Figure 7 As shown, the exemplary data security storage device includes:

[0118] The system includes: a vehicle data acquisition module 710 for acquiring human-computer interaction data and user perception data; a behavior sequence data acquisition module 720 for preprocessing the human-computer interaction data and user perception data to acquire vehicle usage behavior sequence data; a switching event acquisition module 730 for acquiring information on switching events of multiple main scenarios; a behavior sub-sequence data acquisition module 740 for segmenting the vehicle usage behavior sequence data according to the information on the switching events of the main scenarios to acquire vehicle usage behavior sub-sequence data for different main scenarios; a vehicle usage mode acquisition module 750 for acquiring vehicle usage behavior mode sequence data according to the vehicle usage behavior sub-sequence data; a vehicle usage scenario acquisition module 760 for clustering the vehicle usage behavior mode sequence data to acquire multiple vehicle usage scenarios; and a scenario map acquisition module 770 for aggregating multiple vehicle usage scenarios to acquire a scenario map of the entire lifecycle of user vehicle usage.

[0119] Figure 8 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 8 The computer system 800 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0120] like Figure 8 As shown, the computer system 800 includes a Central Processing Unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 802 or programs loaded from storage portion 808 into Random Access Memory (RAM) 803, such as performing the methods described in the above embodiments. The RAM 803 also stores various programs and data required for system operation. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An Input / Output (I / O) interface 805 is also connected to the bus 804.

[0121] The following components are connected to I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 810 as needed so that computer programs read from it can be installed into storage section 808 as needed.

[0122] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit (CPU) 801, it performs various functions defined in the system of this application.

[0123] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof. The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and application structures according to various embodiments of this application. Each block in a flowchart or block diagram can represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each box in a block diagram or flowchart, as well as combinations of boxes in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0125] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the vehicle usage scenario acquisition method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0126] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the vehicle usage scenario acquisition method provided in the various embodiments above.

[0127] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for obtaining vehicle usage scenarios, characterized in that, The method includes: Acquire human-computer interaction data and user perception data; The human-computer interaction data and the user perception data are preprocessed to obtain vehicle use behavior sequence data; Obtain information on boundary switching events from multiple main scenes; Based on the information of the boundary switching event of the main scene, the vehicle use behavior sequence data is segmented to obtain vehicle use behavior sub-sequence data for different main scenes; Based on the vehicle usage behavior subsequence data, obtain vehicle usage behavior pattern sequence data; specifically including: based on the vehicle usage behavior subsequence data, obtain multiple vehicle usage behavior pattern subsequence data; obtain the support of the multiple vehicle usage behavior pattern subsequence data; determine whether the support of the vehicle usage behavior pattern subsequence data is less than a preset value; if the support of the vehicle usage behavior pattern subsequence data is greater than or equal to the set value, then obtain the vehicle usage behavior pattern sequence data based on the vehicle usage behavior subsequence data. Cluster the vehicle usage behavior pattern sequence data to obtain multiple vehicle usage scenarios; and Multiple vehicle usage scenarios are aggregated to obtain a full lifecycle scenario map of user vehicle usage.

2. The method for obtaining vehicle usage scenarios according to claim 1, characterized in that, Obtaining the vehicle usage behavior sequence data includes the following steps: Obtain the event tracking data items and collection specifications for user vehicle usage behavior; and Based on the embedded data items and collection specifications of the user's vehicle usage behavior, the human-computer interaction data and the user perception data are integrated to obtain non-redundant behavioral event data.

3. The method for obtaining vehicle usage scenarios according to claim 2, characterized in that, Obtaining the vehicle usage behavior sequence data further includes the following steps: The non-redundant behavioral event data is sorted by time to obtain long-sequence data of daily user behavioral events; and The user's daily behavioral event long sequence data is aggregated and encoded to obtain the vehicle usage behavior sequence data.

4. The method for obtaining vehicle usage scenarios according to claim 1, characterized in that, Obtaining multiple usage scenarios includes the following steps: Obtain the similarity of the vehicle usage behavior pattern sequence data; Obtain clustering thresholds for multiple main scenarios; and Based on the similarity of the vehicle use behavior pattern sequence data and the clustering thresholds of multiple main scenarios, the vehicle use behavior pattern sequence data is clustered to obtain multiple vehicle use scenarios.

5. The method for obtaining vehicle usage scenarios according to claim 1, characterized in that, Obtaining a full lifecycle scenario map of a user's vehicle usage also includes the following steps: Statistical analysis is performed on multiple vehicle usage scenarios to obtain information on high-frequency scene segmentation events within these scenarios; and Based on the information of the high-frequency scene separation event, it is determined whether the boundary switching event of the main scene needs to be updated. If the boundary switching event of the main scene does not need to be updated, multiple vehicle use scenarios are aggregated to obtain the full lifecycle scene map of the user's vehicle use.

6. A device for acquiring vehicle usage scenarios, characterized in that, The device includes: The vehicle data acquisition module is used to acquire human-computer interaction data and user perception data; The behavior sequence data acquisition module is used to preprocess the human-computer interaction data and the user perception data to acquire vehicle use behavior sequence data; The switching event acquisition module is used to acquire information about switching events in multiple main scenes; The behavior subsequence data acquisition module is used to segment the vehicle use behavior sequence data according to the information of the main scene switching event, and acquire vehicle use behavior subsequence data for different main scenes; The vehicle usage pattern acquisition module is used to acquire vehicle usage pattern sequence data based on the vehicle usage behavior sub-sequence data; specifically, it includes: acquiring multiple vehicle usage pattern sub-sequence data based on the vehicle usage behavior sub-sequence data; acquiring the support of the multiple vehicle usage pattern sub-sequence data; determining whether the support of the vehicle usage pattern sub-sequence data is less than a preset value; if the support of the vehicle usage pattern sub-sequence data is greater than or equal to the preset value, then acquiring the vehicle usage pattern sequence data based on the vehicle usage pattern sub-sequence data. The vehicle usage scenario acquisition module is used to cluster the vehicle usage behavior pattern sequence data to acquire multiple vehicle usage scenarios; and The scene map acquisition module is used to aggregate multiple vehicle usage scenarios to obtain a scene map of the entire lifecycle of a user's vehicle usage.

7. An electronic device, the electronic device comprising: At least one processor; A storage device for storing at least one program, which, when executed by the at least one processor, causes the electronic device to implement the method for obtaining the vehicle usage scenario according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by the computer's processor, causes the computer to perform the method for obtaining the vehicle usage scenario as described in any one of claims 1 to 5.

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