Vehicle service scenario recommendation method, system, device and storage medium

Abstract

The application discloses a vehicle service scene recommendation method, system, device and storage medium, and relates to the technical field of vehicle intelligent service. The method determines a plurality of candidate behavior feature combinations according to vehicle historical travel operation behavior data, calculates the occurrence probability of the candidate behavior feature combinations in the vehicle historical travel operation behavior data, determines a target behavior feature combination according to the occurrence probability of the plurality of candidate behavior feature combinations, statistically analyzes feature parameters in the vehicle historical travel operation behavior data, determines target feature parameters of each behavior feature in the target behavior feature combination, combines atomic services according to the target behavior feature combination and the corresponding target feature parameters, and obtains a vehicle service scene. The application determines the historical service preference of a user by analyzing vehicle historical travel operation behavior data, thereby automatically designing a vehicle service scene, reducing the difficulty of user-defined atomic service combination, and improving user experience.

Description

Technical Field

[0001] This invention relates to the field of vehicle intelligent service technology, and in particular to a method, system, device and storage medium for recommending vehicle service scenarios. Background Technology

[0002] In the field of vehicle driving services, vehicle services are currently broken down into atomic services and made available to users. Users can freely combine and define these atomic services based on their own needs, creating their own usage scenarios. For example, atomic services such as air conditioning temperature, air conditioning fan speed, seat heating, seat ventilation, and closing windows are made available, allowing users to combine these services to customize scenario modes. However, the large number of open atomic services and the complex scenario design logic increase the difficulty and cumbersome process for users to customize vehicle service scenarios, resulting in a poor user experience. Summary of the Invention

[0003] The main objective of this application is to propose a method, system, device, and storage medium for recommending vehicle service scenarios, aiming to recommend personalized vehicle service scenarios and improve user experience.

[0004] To achieve the above objectives, one aspect of this application proposes a vehicle service scenario recommendation method, comprising the following steps:

[0005] Acquire vehicle historical trip operation behavior data, wherein the vehicle historical trip operation behavior data includes multiple trip data within a preset historical time period of the vehicle, and each trip data includes behavioral characteristics and characteristic parameters recorded in chronological order;

[0006] Based on the vehicle's historical trip operation behavior data, multiple candidate behavioral feature combinations are determined, and the probability of occurrence of the candidate behavioral feature combinations in the vehicle's historical trip operation behavior data is calculated.

[0007] The target behavioral feature combination is determined based on the probability of occurrence of multiple candidate behavioral feature combinations;

[0008] Statistical analysis is performed on the feature parameters in the vehicle's historical trip operation behavior data to determine the target feature parameters for each behavior feature in the target behavior feature combination.

[0009] Based on the target behavior feature combination and the corresponding target feature parameters, atomic services are combined to obtain the vehicle service scenario.

[0010] In some embodiments, determining multiple candidate behavioral feature combinations based on the vehicle's historical trip operation behavior data includes the following steps:

[0011] The vehicle's historical trip operation behavior data is sliced ​​according to a preset time length to obtain multiple historical operation behavior slice data, wherein the historical operation behavior slice data includes at least one behavior feature;

[0012] The correlation degree of the behavioral features in the historical operation behavior slice data is obtained by combining and associating the behavioral features based on the vehicle's historical trip operation behavior data.

[0013] Based on the correlation, each behavioral feature in the historical operation behavior slice data is filtered to obtain a candidate behavioral feature combination.

[0014] In some embodiments, calculating the probability of occurrence of the candidate behavioral feature combination in the vehicle's historical trip operation behavior data includes the following steps:

[0015] The first number of historical operation behavior slices obtained after slicing the historical travel operation behavior data of the vehicle is counted.

[0016] Determine a second number of historical operation behavior slices containing all behavioral features that include combinations of candidate behavioral features;

[0017] The probability of occurrence of the candidate behavioral feature combination is determined based on the first quantity and the second quantity.

[0018] In some embodiments, the step of statistically analyzing the feature parameters in the vehicle's historical trip operation behavior data to determine the target feature parameters for each behavior feature in the target behavior feature combination includes the following steps:

[0019] Determine the discrete and non-discrete behavioral characteristics in the vehicle's historical trip operation behavior data;

[0020] Conditional probability statistical analysis is performed on the feature parameters of the discrete behavioral features to obtain a parameter lookup table for each discrete behavioral feature. Collaborative filtering analysis is performed on the feature parameters of the non-discrete behavioral features to obtain the common features of each non-discrete behavioral feature.

[0021] The target feature parameters for each behavioral feature in the target behavioral feature combination are determined based on the parameter lookup table or the common features.

[0022] In some embodiments, determining the target feature parameters of each behavioral feature in the target behavioral feature combination based on the parameter lookup table or the common features includes the following steps:

[0023] Determine the feature type of the behavioral features in the target behavioral feature combination, wherein the feature type is one of discrete behavioral features and non-discrete behavioral features;

[0024] When the feature type of the behavior feature in the target behavior feature combination is discrete behavior feature, the corresponding parameter lookup table of discrete behavior feature is queried to determine the target feature parameters of the behavior feature in the target behavior feature combination.

[0025] When the feature type of the behavioral feature in the target behavioral feature combination is a non-discrete behavioral feature, the target feature parameter of the behavioral feature in the target behavioral feature combination is determined according to the common features of the corresponding non-discrete behavioral features.

[0026] In some embodiments, the step of combining atomic services based on the target behavior feature combination and corresponding target feature parameters to obtain a vehicle service scenario includes the following steps:

[0027] The corresponding atomic service is determined based on each behavioral feature in the target behavioral feature combination, and the target feature parameters of the behavioral features are written into the parameters of the atomic service.

[0028] By combining the atomic services corresponding to all behavioral features in the target behavioral feature combination, a vehicle service scenario is obtained.

[0029] In some embodiments, the vehicle service scenario recommendation method further includes the following steps:

[0030] A service recommendation page is generated based on the vehicle service scenario, wherein each atomic service of the vehicle service scenario in the service recommendation page is displayed in the form of an operable control;

[0031] The service recommendation page is displayed on the in-vehicle terminal.

[0032] To achieve the above objectives, another aspect of this application proposes a vehicle service scenario recommendation system, including:

[0033] The first module is used to acquire vehicle historical trip operation behavior data, wherein the vehicle historical trip operation behavior data includes multiple trip data within a preset historical time period of the vehicle, and each trip data includes behavioral characteristics and characteristic parameters recorded in chronological order;

[0034] The second module is used to determine multiple candidate behavioral feature combinations based on the vehicle's historical trip operation behavior data, and to calculate the probability of occurrence of the candidate behavioral feature combinations in the vehicle's historical trip operation behavior data.

[0035] The third module is used to determine the target behavioral feature combination based on the occurrence probability of multiple candidate behavioral feature combinations;

[0036] The fourth module is used to perform statistical analysis on the feature parameters in the vehicle's historical trip operation behavior data, and to determine the target feature parameters of each behavior feature in the target behavior feature combination.

[0037] The fifth module is used to combine atomic services based on the target behavior feature combination and the corresponding target feature parameters to obtain the vehicle service scenario.

[0038] To achieve the above objectives, another aspect of the embodiments of this application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the vehicle service scenario recommendation method described in the above embodiments.

[0039] To achieve the above objectives, another aspect of the embodiments of this application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the vehicle service scenario recommendation method described in the above embodiments.

[0040] The vehicle service scenario recommendation method, system, device, and storage medium proposed in this application determine multiple candidate behavioral feature combinations based on historical vehicle trip operation behavior data, calculate the probability of occurrence of each candidate behavioral feature combination in the historical vehicle trip operation behavior data, determine the target behavioral feature combination based on the probability of occurrence of multiple candidate behavioral feature combinations, perform statistical analysis on the feature parameters in the historical vehicle trip operation behavior data, determine the target feature parameters for each behavioral feature in the target behavioral feature combination, and perform atomic service combination based on the target behavioral feature combination and the corresponding target feature parameters to obtain the vehicle service scenario. This application determines the user's historical service preferences by analyzing historical vehicle trip operation behavior data, thereby automatically designing vehicle service scenarios, reducing the difficulty for users to customize atomic service combinations, and improving user experience. Attached Figure Description

[0041] Figure 1 This is a flowchart of the vehicle service scenario recommendation method provided in the embodiments of this application;

[0042] Figure 2 yes Figure 1 A flowchart of one of the steps in step S102;

[0043] Figure 3 yes Figure 1 A flowchart of another step, S102;

[0044] Figure 4 yes Figure 1 The flowchart of step S104 in the process;

[0045] Figure 5 yes Figure 4 Another flowchart of step S403 in the process;

[0046] Figure 6 yes Figure 1 The flowchart of step S105 in the process;

[0047] Figure 7 This is a flowchart of a vehicle service scenario recommendation method provided in another embodiment of this application;

[0048] Figure 8 This is a schematic diagram of a vehicle service scenario recommendation system provided in an embodiment of this application;

[0049] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application;

[0050] Figure 10 This is a schematic diagram illustrating the overall concept of the vehicle service scenario recommendation method provided in this application embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0054] The vehicle service scenario recommendation method, system, device, and storage medium provided in this application are specifically described through the following embodiments. First, the vehicle service scenario recommendation method in this application embodiment is described.

[0055] The vehicle service scenario recommendation method provided in this application relates to the field of vehicle intelligent service technology. The vehicle service scenario recommendation method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as 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, and big data and artificial intelligence platforms; the software can be an application implementing the vehicle service scenario recommendation method, but is not limited to the above forms.

[0056] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0057] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0058] Figure 1 This is an optional flowchart of the vehicle service scenario recommendation method provided in the embodiments of this application. Figure 1The method may include, but is not limited to, steps S101 to S105.

[0059] Step S101: Obtain vehicle historical trip operation behavior data, wherein the vehicle historical trip operation behavior data includes multiple trip data within a preset historical time period of the vehicle, and each trip data includes behavioral characteristics and characteristic parameters recorded in chronological order;

[0060] Step S102: Determine multiple candidate behavioral feature combinations based on the vehicle's historical trip operation behavior data, and calculate the probability of occurrence of the candidate behavioral feature combinations in the vehicle's historical trip operation behavior data.

[0061] Step S103: Determine the target behavioral feature combination based on the occurrence probability of multiple candidate behavioral feature combinations;

[0062] Step S104: Perform statistical analysis on the feature parameters in the vehicle's historical trip operation behavior data to determine the target feature parameters for each behavior feature in the target behavior feature combination.

[0063] Step S105: Combine atomic services based on the target behavior feature combination and the corresponding target feature parameters to obtain the vehicle service scenario.

[0064] In step S101 of some embodiments, the vehicle's historical trip operation behavior data can be trip data generated by the vehicle within a certain period of time, recorded by the onboard controller or the cloud, such as trip data generated during the vehicle's driving within 3 months. In this embodiment, the trip data includes the behavioral characteristics of a series of control operations performed by the user during a certain historical trip, such as controlling the windows, adjusting the air conditioning, and adjusting the fragrance. The trip data also includes feature parameters corresponding to the behavioral characteristics. For example, the feature parameter for controlling the windows is "open" or "closed," the feature parameter for adjusting the air conditioning is the temperature value, and the feature parameter for adjusting the fragrance is the fragrance type.

[0065] In step S102 of some embodiments, since the vehicle historical trip operation behavior data includes a large number of historical behavior features, it is necessary to extract some behavior features to form candidate behavior feature combinations, and select target behavior feature combinations based on the probability of occurrence of candidate behavior feature combinations in the vehicle historical trip operation behavior data, so as to facilitate the combination of subsequent vehicle service scenarios.

[0066] Please see Figure 2 In some embodiments, step S102, which involves determining multiple candidate behavioral feature combinations based on vehicle historical trip operation behavior data, may include, but is not limited to, steps S201 to S203:

[0067] Step S201: The vehicle historical trip operation behavior data is sliced ​​according to a preset time length to obtain multiple historical operation behavior slice data, wherein the historical operation behavior slice data includes at least one behavior feature.

[0068] Step S202: Based on the vehicle's historical trip operation behavior data, perform combined association calculations on the behavioral features in the historical operation behavior slice data to obtain the association degree of the behavioral features;

[0069] Step S203: Filter each behavioral feature in the historical operation behavior slice data according to the correlation to obtain candidate behavioral feature combinations.

[0070] In this embodiment, the vehicle's historical trip operation data is sliced ​​according to a preset time length to obtain multiple historical operation behavior slice data. For example, there is a 30-minute trip data in the vehicle's historical trip operation data, with a time window of 1 minute (or other time lengths), and a rolling cycle of 10 seconds, generating a total of 175 trip slices. Among them, historical operation behavior slice data 1 is {close the car window, turn on the seat massage, play a certain song, turn on the fragrance}; historical operation behavior slice data 2 is {}, that is, the user did not perform any operation during this time period, and the historical operation behavior slice data is empty; ...; historical operation behavior slice data 175 is {shift to P gear, adjust the seat position to a certain angle, play a certain cartoon}.

[0071] Using historical operational behavior slices as units, calculate the correlation degree between each behavioral feature in the historical operational behavior slices over the past n months. If the behavioral features in the historical operational behavior slices are {A, B, C, D}, calculate the correlation degrees between A and B, A and C, A and D, B and C, B and D, and C and D respectively. Then, integrate the correlation degrees of a certain behavioral feature with the correlation degrees of other behavioral features to obtain the correlation degree of that behavioral feature. For example, calculate the average correlation degree of A and B, A and C, and A and D to obtain the correlation degree of behavioral feature A.

[0072] After obtaining the correlation degree of each behavioral feature in the historical operation behavior slice data, the behavioral features are filtered according to the preset correlation degree threshold. The behavioral features that meet the correlation degree threshold are retained, and the remaining behavioral features are filtered out, thereby generating candidate behavioral feature combinations {A, B, D}.

[0073] The correlation between any two behavioral features A and B can be calculated using the following formula:

[0074] KULC(A->B)=0.5*(P(B|A)+P(A|B));

[0075] Wherein, P(B|A) represents the probability of behavior feature B occurring given that behavior feature A has occurred; P(A|B) represents the probability of behavior feature A occurring given that behavior feature B has occurred; both P(B|A) and P(A|B) can be obtained by statistical analysis of vehicle historical trip operation behavior data.

[0076] The above behavioral feature filtering operation is performed on m non-empty historical operation behavior slices to obtain m candidate behavioral feature combinations.

[0077] Please see Figure 3 In some embodiments, step S102, which calculates the probability of occurrence of candidate behavioral feature combinations in vehicle historical trip operation behavior data, may include, but is not limited to, steps S301 to S303:

[0078] Step S301: Count the first number of historical operation behavior slice data obtained after slicing the vehicle historical trip operation behavior data;

[0079] Step S302: Determine the second number of historical operation behavior slices containing all behavioral features of candidate behavioral feature combinations.

[0080] Step S303: Determine the probability of occurrence of candidate behavioral feature combinations based on the first quantity and the second quantity.

[0081] In this embodiment, a first number of historical operation behavior slices is obtained after slicing the vehicle's historical trip operation behavior data. For example, a total of 300 historical operation behavior slices are obtained. For a candidate combination of behavior features, all historical operation behavior slices are matched to determine a second number of historical operation behavior slices that contain all behavior features of the candidate combination. For example, if the candidate combination of behavior features is {A, B} and a historical operation behavior slice is {A, B, D}, then the historical operation behavior slice is considered to contain all behavior features of the candidate combination; if the candidate combination of behavior features is {A, B} and a historical operation behavior slice is {A, C, D}, then the historical operation behavior slice is considered not to contain all behavior features of the candidate combination. By matching all historical operation behavior slices, a second number of historical operation behavior slices containing all behavior features of the candidate combination is determined. Then, the probability of occurrence of the candidate combination of behavior features is determined based on the ratio of the second number to the first number.

[0082] In step S103 of some embodiments, the probability of occurrence of each candidate behavioral feature combination is compared with a first threshold, and the candidate behavioral feature combinations with a probability of occurrence greater than the first threshold are determined as target behavioral feature combinations. For example, the first threshold can be 50%. The target behavioral feature combination represents the user's preferred combination of operations, and serves as the basis for subsequent atomic service combinations to obtain the vehicle service scenario. In another example, the candidate behavioral feature combination with the highest probability of occurrence can also be determined as the target behavioral feature combination.

[0083] In step S104 of some embodiments, after determining the target behavior feature combination, it is also necessary to confirm the target feature parameters of each behavior feature in the target behavior feature combination, so as to realize the construction of subsequent vehicle service scenarios. For example, the target behavior feature combination is {window control, seat massage control, play songs, fragrance control}, and the corresponding target feature parameters are {window closed, seat wave massage, play song 1, turn on fragrance}.

[0084] Please see Figure 4 In some embodiments, step S104 may include, but is not limited to, steps S401 to S403:

[0085] Step S401: Determine the discrete and non-discrete behavioral characteristics in the vehicle's historical trip operation behavior data;

[0086] Step S402: Perform conditional probability statistical analysis on the feature parameters of discrete behavioral features to obtain a parameter lookup table for each discrete behavioral feature; perform collaborative filtering analysis on the feature parameters of non-discrete behavioral features to obtain the common features of each non-discrete behavioral feature.

[0087] Step S403: Determine the target feature parameters for each behavioral feature in the target behavioral feature combination based on the parameter lookup table or common features.

[0088] In this embodiment, the behavioral features in the vehicle's historical trip operation data can be discrete or non-discrete. Discrete behavioral features refer to behavioral features whose feature parameters are discrete, such as window control being either open or closed, or seat massage being one of butterfly, snake, or wavy patterns. Non-discrete behavioral features refer to behavioral features whose feature parameters are not discrete, such as playing a song or a video. For discrete behavioral features, the conditional probabilities of each feature parameter in the vehicle's historical trip operation data are statistically analyzed to form a parameter lookup table for discrete behavioral features. For example, for the fragrance control behavioral feature, the conditional probability of the fragrance being open when the window is closed and the conditional probability of the fragrance being open when the window is open can be statistically analyzed. It is understood that when statistically analyzing the conditional probabilities of feature parameters, the precondition can be empty. In this case, the conditional probability of the feature parameter is the probability of the feature parameter occurring. For example, the proportion of window closing in all window control behaviors can be directly statistically analyzed, which is the conditional probability of window closing. For non-discrete behavioral features, we can statistically analyze the common characteristics of all feature parameters of non-discrete behavioral features in the vehicle's historical trip operation behavior data, or the common characteristics of feature parameters over a period of time. For example, for the behavioral feature of playing songs, we can statistically analyze the common characteristics of the type of songs played (love songs, rap, etc.), rhythm (fast, slow), etc., over a recent period. Combining the parameter lookup table or common characteristics, we can determine the target feature parameters for each behavioral feature in the target behavioral feature combination.

[0089] Please see Figure 5 In some embodiments, step S403 may include, but is not limited to, steps S501 to S503:

[0090] Step S501: Determine the feature type of the behavioral features in the target behavioral feature combination, wherein the feature type is one of discrete behavioral features and non-discrete behavioral features;

[0091] Step S502: When the feature type of the behavior feature in the target behavior feature combination is discrete behavior feature, query the parameter lookup table of the corresponding discrete behavior feature to determine the target feature parameters of the behavior feature in the target behavior feature combination.

[0092] Step S503: When the feature type of the behavior feature in the target behavior feature combination is a non-discrete behavior feature, the target feature parameters of the behavior feature in the target behavior feature combination are determined according to the common features of the corresponding non-discrete behavior features.

[0093] For example, the target behavioral feature combination includes three behavioral features: seat massage intensity, seat massage mode, and song playback. Seat massage intensity and mode are discrete behavioral features, while song playback is a non-discrete behavioral feature. For seat massage intensity, a parameter lookup table of statistically derived seat massage intensity can be consulted, and the feature parameter with the highest conditional probability can be used as the target feature parameter. For example, if seat massage intensity level 2 has the highest conditional probability, then the target feature parameter for seat massage intensity is level 2. For song playback behavioral features, based on the common features of songs played recently, a collaborative filtering algorithm is used to determine the corresponding songs from user playlists with these common features as the target feature parameter for song playback behavioral features. In this embodiment, compared to atomic services such as "play xx song," "play favorite songs," or "play songs under the specified scenario category," this embodiment can automatically update according to user habits, enabling atomic services corresponding to non-discrete behavioral features such as song or video playback to have personalized parameter update capabilities, thus improving user experience.

[0094] In step S105 of some embodiments, an atomic service is an independent unit capable of implementing a certain behavioral feature function, and atomic services with different behavioral features can be stored in the cloud. After determining the target behavioral feature combination, the corresponding atomic services can be obtained according to the behavioral features in the target behavioral feature combination, and the atomic services can be combined sequentially according to the behavioral features in the target behavioral feature combination to obtain the vehicle service scenario.

[0095] Please see Figure 6 In some embodiments, step S105 may include, but is not limited to, steps S601 to S602:

[0096] Step S601: Determine the corresponding atomic service based on each behavioral feature in the target behavioral feature combination, and write the target feature parameters of the behavioral features into the parameters of the atomic service.

[0097] Step S602: Combine the atomic services corresponding to all behavioral features in the target behavioral feature combination to obtain the vehicle service scenario.

[0098] For example, the target behavior feature combination is {window control, seat massage control, play song, fragrance control}, and the corresponding target feature parameters are {window closed, seat wave massage, play song 1, turn on fragrance}. Based on the target behavior feature combination, the window control atomic service, seat massage control atomic service, play song atomic service, and fragrance control atomic service are called, and the parameters window closed, seat wave massage, play song 1, and turn on fragrance are passed to the corresponding atomic services. The atomic services are combined in sequence to obtain a personalized vehicle service scenario.

[0099] Compared to current user-generated scene modes, this application embodiment divides the driving trip into trip slices, calculates the correlation between various behaviors in each slice, and combines behavioral features with high correlation and high frequency of occurrence to proactively recommend self-generated scene modes to users. Furthermore, this application embodiment also supports continuous updates to scene modes. For example, based on different time periods of analyzed historical trip operation behavior data, a summer scene mode can be automatically generated for users based on summer data, such as turning on seat ventilation and setting a suitable air conditioning temperature; a winter scene mode can be automatically generated based on winter data, such as turning on the appropriate seat heating level and adjusting the air conditioning temperature based on recent user actions.

[0100] Please see Figure 7 In some embodiments, the vehicle service scenario recommendation method of this application may also include, but is not limited to, steps S701 to S702:

[0101] Step S701: Generate a service recommendation page based on the vehicle service scenario, wherein each atomic service of the vehicle service scenario in the service recommendation page is displayed in the form of an operable control;

[0102] Step S702: Display the service recommendation page on the vehicle terminal.

[0103] In this embodiment, on the service recommendation page, each atomic service of the vehicle service scenario is displayed as an operable control. For example, each atomic service includes a delete control, a move control, and a parameter editing control. The delete control is used to delete the corresponding atomic service, the move control is used to change the order of the atomic services, and the parameter editing control is used to modify the feature parameters of the atomic service. When the service recommendation page is displayed on the in-vehicle terminal, users can fine-tune the cloud-recommended vehicle service scenarios to better suit their needs.

[0104] According to some embodiments of this application, please refer to Figure 10 The vehicle service scenario recommendation method of this application embodiment is illustrated by the following example:

[0105] The user's ride-hailing data from the past three months is segmented into trip slices, which are historical activity data. For example, for a 30-minute trip, the time window is 1 minute and the scrolling period is 10 seconds. A total of 175 trip slices are generated.

[0106] Calculate the correlation between each behavioral feature in the trip slice over the past 3 months. Taking trip slice 1 as an example, {close the car window, turn on the seat massage, play music, turn on the fragrance}.

[0107] From each trip segment, behavioral features that meet a threshold are saved, while other behaviors are filtered to form candidate behavioral feature combinations: {closing the window, turning on the seat massage, playing music}. This process is repeated to generate multiple candidate behavioral feature combinations.

[0108] Calculate the proportion of each candidate behavioral feature combination to the total number of slices formed in the past 3 months to obtain the occurrence probability of the candidate behavioral feature combination. Select the behavioral feature combination with the highest occurrence probability as the target behavioral feature combination for personalized atomic service combination.

[0109] Personalized atomic services are matched based on the behavioral features in the target behavioral feature combination, and the feature parameters of the behavioral features are written into the atomic services. Taking seat massage control and music playback as examples, the process of determining the feature parameters is as follows: The parameters for seat massage levels are listed: Level 1, Level 2, and Level 3. Seat massage types are listed: Butterfly, Snake, and Wave. Based on conditional probability calculations, it is determined that, assuming the car windows are closed and music is playing, the highest probability value is to activate seat massage level 2 or Wave. The parameters for playing music can be based on a playlist of songs the user has recently liked, providing services to the user and continuously updating the playlist based on recent behavior.

[0110] The corresponding personalized user scenario patterns are combined to generate vehicle service scenarios. For example, a vehicle service scenario could be closing the car windows, turning the seat massage to level two, turning the seat massage to a wave-like setting, or playing a recently favorite song.

[0111] The scenario mode received on the vehicle side is used to provide personalized recommendations to users.

[0112] Please see Figure 8 This application also provides a vehicle service scenario recommendation system, including:

[0113] The first module is used to acquire vehicle historical trip operation behavior data, which includes multiple trip data within a preset historical time period of the vehicle. Each trip data includes behavioral characteristics and characteristic parameters recorded in chronological order.

[0114] The second module is used to determine multiple candidate behavioral feature combinations based on the vehicle's historical trip operation behavior data, and to calculate the probability of occurrence of the candidate behavioral feature combinations in the vehicle's historical trip operation behavior data.

[0115] The third module is used to determine the target behavioral feature combination based on the probability of occurrence of multiple candidate behavioral feature combinations.

[0116] The fourth module is used to perform statistical analysis on the feature parameters in the vehicle's historical travel operation behavior data to determine the target feature parameters for each behavior feature in the target behavior feature combination.

[0117] The fifth module is used to combine atomic services based on the target behavior feature combination and the corresponding target feature parameters to obtain the vehicle service scenario.

[0118] It is understood that the content of the above vehicle service scenario recommendation method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above vehicle service scenario recommendation method embodiments, and the beneficial effects achieved are also the same as those achieved in the above vehicle service scenario recommendation method embodiments.

[0119] This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned vehicle service scenario recommendation method. This electronic device can be any smart terminal, including a tablet computer, an in-vehicle computer, or similar device.

[0120] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0121] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0122] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the vehicle service scenario recommendation method of the embodiments of this application.

[0123] The input / output interface 903 is used to implement information input and output;

[0124] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0125] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0126] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0127] This application embodiment also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described vehicle service scenario recommendation method.

[0128] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0130] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0131] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0132] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0133] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between systems or units, and may be electrical, mechanical, or other forms.

[0134] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for recommending a vehicle service scene, characterized in that, The method comprises the following steps: acquiring vehicle historical operation behavior data, wherein the vehicle historical operation behavior data comprises a plurality of journey data in a preset time period, and each journey data comprises behavior characteristics and characteristic parameters recorded in time sequence; determining a plurality of candidate behavior characteristic combinations according to the vehicle historical operation behavior data, and calculating occurrence probabilities of the candidate behavior characteristic combinations in the vehicle historical operation behavior data; determining a target behavior characteristic combination according to the occurrence probabilities of the plurality of candidate behavior characteristic combinations; statistically analyzing the characteristic parameters in the vehicle historical operation behavior data to determine target characteristic parameters of each behavior characteristic in the target behavior characteristic combination; combining atomic services according to the target behavior characteristic combination and the corresponding target characteristic parameters to obtain a vehicle service scenario; the statistical analysis of the characteristic parameters in the vehicle historical operation behavior data to determine the target characteristic parameters of each behavior characteristic in the target behavior characteristic combination comprises the following steps: determining discrete behavior characteristics and non-discrete behavior characteristics in the vehicle historical operation behavior data; statistically analyzing the characteristic parameters of the discrete behavior characteristics to obtain a parameter query table of each discrete behavior characteristic, and statistically analyzing the characteristic parameters of the non-discrete behavior characteristics to obtain common characteristics of each non-discrete behavior characteristic; determining the target characteristic parameters of each behavior characteristic in the target behavior characteristic combination according to the parameter query table or the common characteristics. 2.The vehicle service scenario recommendation method of claim 1, wherein, the determination of the plurality of candidate behavior characteristic combinations according to the vehicle historical operation behavior data comprises the following steps: slicing the vehicle historical operation behavior data according to a preset time length to obtain a plurality of historical operation behavior slice data, wherein the historical operation behavior slice data comprises at least one behavior characteristic; combining and correlating the behavior characteristics in the historical operation behavior slice data according to the vehicle historical operation behavior data to obtain correlation degrees of the behavior characteristics; filtering each behavior characteristic in the historical operation behavior slice data according to the correlation degrees to obtain candidate behavior characteristic combinations. 3.The vehicle service scenario recommendation method of claim 2, wherein, the calculation of the occurrence probabilities of the candidate behavior characteristic combinations in the vehicle historical operation behavior data comprises the following steps: counting a first number of the historical operation behavior slice data obtained after the slicing of the vehicle historical operation behavior data; determining a second number of historical operation behavior slice data containing all behavior characteristics of a candidate behavior characteristic combination in all the historical operation behavior slice data; determining the occurrence probability of the candidate behavior characteristic combination according to the first number and the second number.

4. The vehicle service scenario recommendation method of claim 1, wherein, the determination of the target characteristic parameters of each behavior characteristic in the target behavior characteristic combination according to the parameter query table or the common characteristics comprises the following steps: determining a characteristic type of a behavior characteristic in the target behavior characteristic combination, wherein the characteristic type is one of discrete behavior characteristics and non-discrete behavior characteristics; When the feature type of the behavior feature in the target behavior feature combination is a discrete behavior feature, a parameter query table of the corresponding discrete behavior feature is queried to determine the target feature parameter of the behavior feature in the target behavior feature combination; When the feature type of the behavior feature in the target behavior feature combination is a non-discrete behavior feature, the target feature parameter of the behavior feature in the target behavior feature combination is determined according to the common feature of the corresponding non-discrete behavior feature. 5.The vehicle service scenario recommendation method of claim 1, wherein, The atomic service combination according to the target behavior feature combination and the corresponding target feature parameter to obtain the vehicle service scene includes the following steps: According to each behavior feature in the target behavior feature combination, a corresponding atomic service is determined, and the parameter of the atomic service is written according to the target feature parameter of the behavior feature; The atomic services corresponding to all behavior features in the target behavior feature combination are combined to obtain the vehicle service scene. 6.The vehicle service scenario recommendation method of claim 1, wherein, The vehicle service scene recommendation method further includes the following steps: A service recommendation page is generated according to the vehicle service scene, wherein each atomic service of the vehicle service scene in the service recommendation page is displayed in the form of an operable control; The service recommendation page is displayed on the vehicle terminal. 7.A vehicle service scenario recommendation system, characterized in that, It includes: A first module for obtaining vehicle historical trip operation behavior data, wherein the vehicle historical trip operation behavior data includes a plurality of trip data in a historical preset time period of a vehicle, and each trip data includes behavior features and feature parameters recorded in time sequence; A second module for determining a plurality of candidate behavior feature combinations according to the vehicle historical trip operation behavior data, and calculating the occurrence probability of the candidate behavior feature combinations in the vehicle historical trip operation behavior data; A third module for determining a target behavior feature combination according to the occurrence probability of a plurality of candidate behavior feature combinations; A fourth module for statistically analyzing the feature parameters in the vehicle historical trip operation behavior data to determine the target feature parameter of each behavior feature in the target behavior feature combination; A fifth module for combining atomic services according to the target behavior feature combination and the corresponding target feature parameter to obtain a vehicle service scene; The fourth module is specifically configured to perform the following steps: The statistical analysis of the feature parameters in the vehicle historical trip operation behavior data to determine the target feature parameter of each behavior feature in the target behavior feature combination includes the following steps: Discrete behavior features and non-discrete behavior features in the vehicle historical trip operation behavior data are determined; The conditional probability statistical analysis of the feature parameters of the discrete behavior features obtains a parameter query table of each discrete behavior feature, and the collaborative filtering analysis of the feature parameters of the non-discrete behavior features obtains a common feature of each non-discrete behavior feature; The target feature parameter of each behavior feature in the target behavior feature combination is determined according to the parameter query table or the common feature.

8. An electronic device, comprising: The electronic device comprises a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory, the program being executed by the processor to realize the steps of the vehicle service scene recommendation method according to any one of claims 1 to 6.

9. A computer storage medium, the storage medium being a computer readable storage medium, for computer-readable storage, characterized in that, The storage medium stores one or more programs, and the one or more programs are executable by one or more processors to realize the steps of the vehicle service scene recommendation method according to any one of claims 1 to 6.

Images