Vehicle playlist interaction method and related devices

Abstract

The application discloses a vehicle playlist interaction method and related equipment, and relates to the technical field of automobile electronics and intelligent cockpit, and mainly aims to solve the problems of lack of intelligence and humanity in the existing vehicle music recommendation scheme. The method comprises the following steps: determining a target trigger scene based on vehicle environment data, wherein the vehicle environment data comprises vehicle operation data and environment perception data, and the target trigger scene is used for judging the starting time of playlist interaction; generating a target recommended playlist based on the target trigger scene and human-vehicle information, wherein the human-vehicle information comprises driver attributes, passenger attributes and vehicle use scenarios; and pushing the target recommended playlist to a client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist. The application is used for the vehicle playlist interaction process.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics and smart cockpit technology, and in particular to a vehicle playlist interaction method and related equipment. Background Technology

[0002] As the level of automotive intelligence continues to improve, users' expectations for in-vehicle infotainment systems, especially for proactive and personalized services in various driving and parking scenarios, are increasing. Currently, the industry mainly relies on third-party music applications on the central control screen for in-vehicle music services. Their music recommendation function is typically triggered only after the user actively opens the application, within the application's internal page. The recommendation logic heavily depends on the user's personal historical behavior data collected within the application, such as playback history, favorites, and preferred music genres. This "passive" recommendation model has significant limitations, making it difficult for the recommendation results to deeply align with the rapidly changing actual driving scenarios and the real-time status of the occupants, thus failing to meet users' emotional and entertainment needs in specific situations. Therefore, existing in-vehicle music recommendation solutions suffer from insufficient intelligence and user-friendliness. Summary of the Invention

[0003] In view of the above problems, the present invention provides a vehicle playlist interaction method and related equipment, the main purpose of which is to solve the problem of insufficient intelligence and user-friendliness of existing in-vehicle music recommendation schemes.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides a vehicle playlist interaction method, the method comprising: The target triggering scenario is determined based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. A target recommended playlist is generated based on the target triggering scenario and the information of people and vehicles, wherein the information of people and vehicles includes driver attributes, passenger attributes, and vehicle usage scenario; The target recommended playlist is pushed to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

[0005] Optional, The vehicle operation data includes: vehicle navigation and gear status, vehicle charging / discharging status, and navigation trip duration information. The environmental perception data includes: road congestion and traffic light status, in-vehicle multimedia playback status, and user call status. Each triggering scenario has its own corresponding preconditions and triggering conditions. The prerequisites are rule-based conditions set based on the vehicle operation data and environmental perception data. The triggering conditions include instantaneous triggering conditions and timed triggering conditions.

[0006] Optionally, determining the target triggering scenario based on vehicle environmental data includes: The vehicle environment data is compared with the preconditions of each triggering scenario to obtain a comparison result, wherein the comparison result is the matching status of the vehicle environment data and the preconditions of the triggering scenario; If the comparison result shows that the vehicle environment data matches all the preconditions of the triggering scenario, the triggering scenario with all preconditions matched is determined as the initial triggering scenario, and the initial triggering scenario enters the candidate state to be triggered.

[0007] Optionally, determining the target triggering scenario based on vehicle environmental data includes: When the initially determined triggering scenario enters the candidate state to be triggered, the triggering conditions of the initially determined triggering scenario are detected; If the triggering conditions of the initially determined triggering scenario match the instantaneous triggering conditions, the initially determined triggering scenario is determined as the target triggering scenario; If the triggering conditions of the initially determined triggering scenario match the timing triggering conditions, a continuous monitoring loop will be entered. During the continuous monitoring loop, the preconditions and triggering conditions of the initially determined triggering scenario are detected; If the preconditions and triggering conditions of the initially determined triggering scenario continue to match for a preset duration, the initially determined triggering scenario is determined as the target triggering scenario.

[0008] Optionally, generating the target recommended playlist based on the target triggering scenario and the vehicle and pedestrian information includes: Obtain the information about the people and vehicles; Based on the target triggering scenario and the information about people and vehicles, an initial recommended playlist is generated through a cloud model. Based on the vehicle music application, playable music is filtered from the initial recommended playlist to determine the target recommended playlist.

[0009] Optionally, the customer feedback signal includes a first customer feedback signal, and the step of pushing the target recommended playlist to the client to obtain the customer feedback signal includes: The target recommended playlist is pushed to the client to obtain a first customer feedback signal returned by the client, wherein the first customer feedback signal includes play, ignore, and view details; The feedback operation for determining the target recommended playlist is based on the first customer feedback signal.

[0010] Optionally, the customer feedback signal includes a second customer feedback signal, and the feedback operation of determining the target recommended playlist based on the first customer feedback signal includes: When the first customer feedback signal is play, control the vehicle music application to play the target recommended playlist; If the first customer feedback signal is ignored, stop pushing the target recommended playlist to the client; When the first customer feedback signal is to view details, the target recommended playlist and recommendation reason are pushed to the client to obtain a second customer feedback signal, wherein the second customer feedback signal includes: playback control instructions and regeneration instructions for specific songs in the target recommended playlist.

[0011] Secondly, embodiments of the present invention also provide a vehicle playlist interaction device, comprising: The determining unit is used to determine the target triggering scenario based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. The generation unit is used to generate a target recommended playlist based on the target triggering scenario and the human and vehicle information, wherein the human and vehicle information includes driver attributes, passenger attributes, and vehicle usage scenario; The push unit is used to push the target recommended playlist to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

[0012] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein, when the program is executed by a processor, the steps of the above-described vehicle playlist interaction method are implemented.

[0013] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, including at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the above-described vehicle playlist interaction method.

[0014] By employing the above technical solution, the vehicle playlist interaction method and related equipment provided by this invention address the shortcomings of existing in-vehicle music recommendation solutions in terms of intelligence and user-friendliness. This invention determines the target triggering scenario based on vehicle environmental data, including vehicle operation data and environmental perception data. The target triggering scenario is used to determine the initiation timing of playlist interaction. A target recommended playlist is generated based on the target triggering scenario and vehicle and human information, including driver attributes, passenger attributes, and vehicle usage scenario. The target recommended playlist is pushed to the client to obtain customer feedback signals, which are the user's operation commands related to the target recommended playlist. In this solution, by collecting and analyzing two major categories of real-time information—vehicle operation data and environmental perception data—the appropriate time to initiate playlist interaction is dynamically determined, i.e., the target triggering scenario is identified. This binds the music service's initiation conditions to the vehicle's actual operating status and external environment, thereby reducing the recommendation service's dependence on user-initiated operations and enabling the service to automatically appear at the right time. After identifying the target triggering scenario, the system further integrates static or semi-static information about people and vehicles, including driver attributes, passenger attributes, and vehicle usage scenarios, as the decision-making basis for generating recommended content. This expands the recommendation basis from single in-application user historical behavior data to a multi-dimensional information system that integrates real-time scenario status and vehicle and passenger background, enhancing the relevance of recommended content to the current specific context. Finally, by proactively pushing the generated target recommended playlist to the client and obtaining user operation commands as feedback, a closed loop from environmental perception and intelligent recommendation to user interaction is established. This realizes the transformation of service from waiting for user initiation to proactive adaptation, reducing the degree of mismatch between recommendation service and user real-time scenario needs caused by recommendation lag and single basis.

[0015] Correspondingly, the vehicle playlist interaction device, equipment, and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1A flowchart illustrating a vehicle playlist interaction method provided by an embodiment of the present invention is shown. Figure 2 This diagram illustrates the composition of a vehicle playlist interaction device according to an embodiment of the present invention. Figure 3 This diagram illustrates the composition of a vehicle playlist interactive electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0019] To address the shortcomings of existing in-vehicle music recommendation schemes in terms of intelligence and user-friendliness, this invention provides a vehicle playlist interaction method, such as... Figure 1 As shown, the method includes: S101. Determine the target triggering scenario based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. For example, vehicle environmental data refers to the comprehensive data set involved in vehicle operation, including two main categories: vehicle operation data and environmental perception data. Vehicle operation data mainly reflects the vehicle's dynamic operating status, such as vehicle navigation and gear position, vehicle charging and discharging status, and navigation trip duration. Environmental perception data mainly reflects the real-time environmental conditions of the external road and internal cabin, such as road congestion and traffic light status, in-vehicle multimedia playback status, and user call status. The target trigger scenario refers to the specific vehicle usage situation identified by the system through analysis of the above vehicle environmental data. This scenario is used to determine the appropriate time to initiate the playlist interaction.

[0020] In this application, vehicle operation data and environmental perception data are continuously monitored and collected, and these real-time data are matched and compared with the conditions corresponding to various predefined vehicle usage scenarios. For example, when the system detects that the vehicle navigation is on, the gear is in D, the road congestion status is displayed as congested, and the in-vehicle multimedia playback status is not playing and the user's call status is not in use, the system may match the current situation as a long-term traffic jam scenario and identify it as the target trigger scenario. Through this real-time data fusion and condition matching mechanism, the system can automatically and accurately identify the timing for initiating playlist interaction.

[0021] By using the above technical solution, the target triggering scenario can be determined based on vehicle environment data, and the triggering mechanism of music recommendation can be dynamically linked with the actual operating status of the vehicle and the internal and external environment. This reduces the dependence of the recommendation service on the user's active operation and enables the service to be automatically triggered at a time that is more in line with the actual car use scenario, thereby improving the timeliness and contextual adaptability of the recommendation service.

[0022] In one embodiment, The vehicle operation data includes: vehicle navigation and gear status, vehicle charging / discharging status, and navigation trip duration information. The environmental perception data includes: road congestion and traffic light status, in-vehicle multimedia playback status, and user call status. Each triggering scenario has its own corresponding preconditions and triggering conditions. The prerequisites are rule-based conditions set based on the vehicle operation data and environmental perception data. The triggering conditions include instantaneous triggering conditions and timed triggering conditions.

[0023] For example, the aforementioned vehicle operation data refers to dynamic information reflecting the vehicle's own operating status, including vehicle navigation and gear position status, vehicle charging / discharging status, and navigation trip duration information. Vehicle navigation and gear position status indicates whether the vehicle is in navigation mode and the current gear position; vehicle charging / discharging status indicates whether the vehicle is charging or discharging; navigation trip duration information provides the remaining trip time calculated by the navigation system. Environmental perception data refers to the external road and internal cabin environment conditions acquired by the vehicle through sensors or networks, including road congestion and traffic light status, in-vehicle multimedia playback status, and user call status. Road congestion and traffic light status reflects the current level of traffic congestion and the countdown of traffic lights; in-vehicle multimedia playback status indicates the playback status of multimedia content such as music, radio, or video throughout the vehicle; user call status indicates whether the driver is using a handheld phone or an in-vehicle Bluetooth phone. Each triggering scenario corresponds to a set of preconditions and triggering conditions. The preconditions are a combination of rules set based on vehicle operation data and environmental perception data, used to determine whether the scenario has the basis for triggering. The triggering conditions include instantaneous triggering conditions and timed triggering conditions. Instantaneous triggering conditions refer to conditions that are triggered immediately after being met, while timed triggering conditions refer to conditions that need to be met continuously for a certain period of time before they can be triggered.

[0024] In this application, specific items of vehicle operation data and environmental perception data are associated with predefined trigger scenarios to set preconditions and trigger conditions for each scenario. For example, for a long-term traffic jam scenario, the preconditions might be that the vehicle navigation is on, the gear is in D, the in-vehicle multimedia playback is not playing, and the user is not on a call; the trigger condition would be that the road congestion status is displayed as congested or extremely congested, which is an instantaneous trigger condition. For a boring long-distance driving scenario, the preconditions are similar, but the trigger condition might be that the navigation trip duration information shows a remaining journey time of more than forty minutes and no multimedia playback occurs for a continuous period, which is a timed trigger condition. The system monitors these data items in real time, checks whether they match the preconditions of a specific scenario, and then further determines whether the trigger condition is met, thereby identifying the target trigger scenario.

[0025] By employing the aforementioned technical solution, and clearly defining the components of vehicle operation data and environmental perception data, and precisely binding them to the preconditions and triggering conditions of the triggering scenario, the scenario judgment process gains greater data support and clearer rules. This design reduces the possibility of scenario misjudgment due to ambiguous data sources or unclear condition definitions, improves the accuracy and reliability of trigger timing judgment, and thus provides a more solid decision-making foundation for subsequent playlist interaction initiation.

[0026] In one embodiment, determining the target triggering scenario based on vehicle environmental data includes: The vehicle environment data is compared with the preconditions of each triggering scenario to obtain a comparison result, wherein the comparison result is the matching status of the vehicle environment data and the preconditions of the triggering scenario; If the comparison result shows that the vehicle environment data matches all the preconditions of the triggering scenario, the triggering scenario with all preconditions matched is determined as the initial triggering scenario, and the initial triggering scenario enters the candidate state to be triggered.

[0027] For example, the above comparison result refers to the output obtained after comparing the vehicle environment data with the preconditions of each triggering scenario, which is used to indicate the degree of conformity between the data and the conditions; the matching status specifically describes whether each data item in the vehicle environment data is consistent with the corresponding rule in the preconditions; the initial triggering scenario refers to the intermediate state in which the system initially determines that the scenario may be triggered when the vehicle environment data is consistent with all the preconditions of a certain triggering scenario; the candidate state to be triggered indicates that the initial triggering scenario has passed the precondition check and is in the transition stage of waiting for the subsequent triggering condition judgment.

[0028] In this application, after acquiring vehicle environmental data in real time, the system logically compares it with the pre-defined preconditions for each trigger scenario to check whether the data values ​​meet the condition rules. For example, for a long-term traffic jam scenario, the system checks whether the vehicle navigation is on, the gear is in Drive, the in-vehicle multimedia playback is not playing, and the user's call status is not in use. When all conditions are met, the system generates a successful matching comparison result, marks the scenario as an initial trigger scenario, and puts it into a candidate state for triggering, preparing for the detection of subsequent trigger conditions. This step-by-step judgment mechanism ensures the orderliness and accuracy of scenario recognition.

[0029] By using the above technical solution, the scene recognition process is made more refined and phased by systematically comparing vehicle environmental data with preconditions and introducing the concepts of initial triggering scenarios and candidate states to be triggered. This reduces the risk of scene misjudgment that may be caused by a single judgment step, improves the reliability of trigger timing judgment and the stability of system decision-making, and thus provides a more solid logical foundation for the launch of playlist interaction.

[0030] In one embodiment, determining the target triggering scenario based on vehicle environmental data includes: When the initially determined triggering scenario enters the candidate state to be triggered, the triggering conditions of the initially determined triggering scenario are detected; If the triggering conditions of the initially determined triggering scenario match the instantaneous triggering conditions, the initially determined triggering scenario is determined as the target triggering scenario; If the triggering conditions of the initially determined triggering scenario match the timing triggering conditions, a continuous monitoring loop will be entered. During the continuous monitoring loop, the preconditions and triggering conditions of the initially determined triggering scenario are detected; If the preconditions and triggering conditions of the initially determined triggering scenario continue to match for a preset duration, the initially determined triggering scenario is determined as the target triggering scenario.

[0031] For example, triggering conditions include instantaneous triggering conditions and timed triggering conditions. Instantaneous triggering conditions refer to conditions that are triggered immediately after being met, while timed triggering conditions refer to conditions that need to be met continuously for a certain period of time before being triggered. Continuous monitoring loop refers to the cyclical process in which the system continuously monitors the condition status under timed triggering conditions, and preset duration refers to a pre-set time length threshold.

[0032] In this application, when an initially predetermined trigger scenario enters the candidate state for triggering, the system first detects the type of trigger condition for that scenario. If the trigger condition matches an instantaneous trigger condition, such as in a long-term traffic jam scenario where vehicle environment data shows the road congestion status as congested, the system immediately identifies the initially predetermined trigger scenario as the target trigger scenario. If the trigger condition matches a timed trigger condition, such as in a boring long-distance driving scenario, the system enters a continuous monitoring loop, in which it continuously detects whether the preconditions and trigger conditions of the initially predetermined trigger scenario maintain a matching state; if any precondition or trigger condition is detected to be mismatched during the loop, the loop is interrupted; if all conditions continue to match and a preset duration is reached, such as no multimedia playback behavior for fifteen minutes, the system finally determines the initially predetermined trigger scenario as the target trigger scenario.

[0033] It's important to note that for scenarios relying on timed trigger conditions, a dynamic time management mechanism is implemented within the continuous monitoring loop. Once the timed monitoring phase begins, it continuously monitors in real-time whether all preconditions for the initially defined trigger scenario are met. If, at any point before the preset time limit is reached, any precondition is no longer met (e.g., the user starts playing music or making a phone call midway through the timer), the system immediately resets the current timer and exits the trigger judgment process for that scenario, restarting the timer only when all conditions are met again. Furthermore, some scenarios include specific cooldown time requirements in their preconditions. For example, in the scenario of staying in the car while charging, if the user has played music or video during the current driving cycle, a full 15-minute cooldown period must be waited after the last playback before the system re-determines the multimedia playback status as "not played" and allows the precondition to be considered met. This timer reset and cooldown time mechanism together ensures the accuracy and rationality of scenario triggering, avoiding interference caused by brief fluctuations in status or immediate re-recommendation after user intervention.

[0034] By employing the above technical solutions and through a differentiated processing mechanism based on the type of triggering conditions, especially by introducing a continuous monitoring loop for timed triggering conditions, the scene trigger judgment becomes more refined and adaptive. This reduces the possibility of false triggering due to temporary fulfillment or midway changes of conditions, improves the accuracy of trigger timing and the system's adaptability to dynamic environmental changes, thereby enhancing the reliability of playlist interaction startup and the consistency of user experience.

[0035] The following lists the four target triggering scenarios defined in this application, along with their corresponding preconditions and triggering conditions: The prerequisites for the long-term traffic jam scenario are that the vehicle is in navigation mode and in drive (D) gear, no multimedia is playing in the vehicle and the driver is not using a phone. The trigger condition is that the vehicle is on a congested or extremely congested road.

[0036] The prerequisites for the "boredom during long-distance driving" scenario are that the vehicle is in D gear and in navigation mode, the remaining travel time is no less than forty minutes, and there is no multimedia playback in the vehicle. The trigger condition is that the timer starts after the prerequisites are met, and the scenario is triggered when there is no multimedia playback and no phone call is made for fifteen consecutive minutes.

[0037] The prerequisites for the long wait at traffic lights scenario are the same as those for the long traffic jam scenario: the vehicle must be in navigation mode, in Drive (D) gear, with no multimedia playback and no phone call being made. The trigger condition is that the traffic light in the current lane has at least 60 seconds remaining on the countdown timer.

[0038] The prerequisites for the in-car parking scenario while charging are that the vehicle is charging and in P gear, there is someone in the driver's seat and the nap mode is not activated, and no multimedia is playing in the vehicle (if it has been played, it needs to cool down for 15 minutes). The trigger condition is that the timer starts after the prerequisite conditions are met. It is triggered when there is no multimedia playback and no phone call within 5 consecutive minutes. If the vehicle status changes during the timer and the prerequisite conditions are not met, the timer is reset to zero.

[0039] It should be understood that the specific duration and other numerical parameters listed in this application are only a preferred embodiment. Those skilled in the art can determine them through conventional selection or limited experimentation according to actual needs, and they are not intended to limit the scope of protection.

[0040] S102. Generate a target recommended playlist based on the target triggering scenario and the vehicle and person information, wherein the vehicle and person information includes driver attributes, passenger attributes, and vehicle usage scenario; For example, vehicle and passenger information refers to the associated information actively captured by the system after the triggering scenario is determined, used to generate personalized recommended playlists. It includes driver attributes, passenger attributes, and vehicle usage scenarios. Driver attributes mainly refer to personal characteristics such as the driver's age, gender, and emotional state; passenger attributes mainly refer to the passenger composition, such as whether there are children in the vehicle; vehicle usage scenarios mainly refer to comprehensive information describing the environmental state of the vehicle, such as the usage scenario, road type, and usage time.

[0041] In this application, the process of generating a target recommended playlist based on the target triggering scenario and human / vehicle information involves the system simultaneously acquiring human / vehicle information matching the scenario after determining the target triggering scenario. For example, when the system identifies a boring long-distance driving scenario, it will simultaneously acquire information such as the driver's emotional state, whether there are children in the vehicle, and whether the current road type is a highway. Subsequently, the system combines the identifier of the target triggering scenario with this specific human / vehicle information into complete request parameters and sends them to the cloud-based big data model. The cloud-based big data model performs in-depth analysis of the received information, such as combining contexts like long-distance driving, driver fatigue, and highway, to generate a music recommendation content package containing a specific playlist and the corresponding recommendation reason for each song. This system combines specific driving scenarios with rich contextual information as the decision-making basis for the cloud-based big data model to generate recommended playlists.

[0042] By using the above technical solution, the timing of vehicle use represented by the target triggering scenario is deeply integrated with the user and environmental characteristics reflected by the vehicle and human information, and used together as input for generating recommended playlists. This expands the basis for generating recommended content from a single dimension to a multi-dimensional information association system, reducing the possibility of the recommendation results being out of touch with the current specific context, and enhancing the fit between playlist content and real vehicle use scenarios and the status of drivers and passengers, thereby improving the personalization and scenario adaptability of music recommendation services.

[0043] In one embodiment, generating the target recommended playlist based on the target triggering scenario and the vehicle and pedestrian information includes: Obtain the information about the people and vehicles; Based on the target triggering scenario and the information about people and vehicles, an initial recommended playlist is generated through a cloud model. Based on the vehicle music application, playable music is filtered from the initial recommended playlist to determine the target recommended playlist.

[0044] For example, the initial recommended playlist refers to the intermediate result generated by the cloud model based on the target triggering scenario and vehicle and pedestrian information, which includes a playlist list and recommendation reason information; the target recommended playlist refers to the final playlist that can actually be played after the initial recommended playlist has been filtered by the vehicle music application.

[0045] The aforementioned cloud-based model primarily consists of a deep learning network structure and its related parameter configuration deployed on a cloud server. This model is specifically trained to understand the complex mapping relationship between driving scenarios and music preferences. Its input information consists of a structured target trigger scenario identifier and an associated human and vehicle information dataset. The target trigger scenario identifier indicates the specific driving scenario type, while the human and vehicle information dataset contains multi-dimensional features such as driver attributes, passenger attributes, and vehicle usage scenarios. The model's output is an initial recommended playlist generated based on the input information. This playlist not only includes a list of music tracks suitable for the current scenario but also generates corresponding natural language recommendation explanations for each song. The training data comes from large-scale real-world driving scenario data collection. This data is anonymized and contains multi-dimensional labels, covering vehicle operating status, environmental perception information, driver and passenger attribute characteristics, and users' historical music interaction behavior in the corresponding scenarios. The training process employs a combination of supervised learning and reinforcement learning. First, supervised learning helps the model establish a basic association between scenario features and music content. Then, reinforcement learning continuously optimizes the recommendation strategy based on user feedback signals, enabling the model to gradually learn more attractive music recommendation patterns in different complex scenarios. Through this end-to-end modeling approach, the cloud-based model can intelligently associate abstract car usage scenario features with specific music elements, thereby generating playlist recommendations that are both technically sound and human-centered, laying the foundation for subsequent localized filtering and user interaction.

[0046] In one embodiment, the application further includes: determining a target music application for performing the search, wherein the determination process includes: Prioritize the music app that was most recently played as the target music app; If the most recently played music app does not exist, check the login status of the first music app; If the first music application is already logged in, then select the first music application as the target music application; If the first music application is not logged in, then the second music application is selected as the target music application; Based on the identified target music application, the playability of the music in the initial recommended playlist is searched and filtered.

[0047] Using the above technical solution, in the process of filtering playable music in the initial recommended playlist based on the vehicle's music application, the system employs an intelligent music application selection strategy to determine the target application for the search. Specifically, the system first checks if there is a music application that was recently played by the user. If so, it prioritizes that application as the target application for this search, thus maintaining the continuity of the user's usage habits. If the system detects that there is no recent playback record, it further checks the account login status of the first music application. When the first music application is detected to be logged in, the system selects the first music application as the target application for song search; if the first music application is not logged in, the system selects a second music application as a backup target application to perform the search task. Through this dynamic priority logic, the system can adaptively select the music service provider that is most likely to match the user's preferences and is currently available, thereby completing the playability verification and filtering of songs in the initial recommended playlist generated by the cloud model, and finally forming a target recommended playlist that the user can directly play. This search mechanism design ensures that the music recommendation service can intelligently adapt to the user's personalized usage habits and account status, improving the accuracy of playlist filtering and the consistency of the user experience.

[0048] In this application, the current vehicle and pedestrian information is first acquired in real time. Then, the identifier of the target triggering scenario, along with the vehicle and pedestrian information, is sent to a cloud-based model to request the generation of a playlist. The cloud-based model comprehensively analyzes the input information and returns an initial recommended playlist, which includes a list of suggested songs and a reason for each song's recommendation. The system then submits this initial recommended playlist to the vehicle's local music application for availability retrieval and filtering. It checks whether each song has a usable audio source, ultimately selecting songs that can be played locally to form the target recommended playlist, while maintaining the correspondence between these songs and the recommendation reason information generated by the cloud-based model.

[0049] By utilizing the above technical solution, personalized playlist suggestions are first generated using the intelligent generation capabilities of cloud-based models, and then filtered for usability by combining them with local music applications. This ensures that the recommended content is both intelligently adapted to the scene and practically playable locally, reducing the probability of unplayable songs appearing in the recommended playlist and improving the usability of the playlist and the continuity of the user experience.

[0050] S103. Push the target recommended playlist to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

[0051] For example, the client refers to the user interface such as the vehicle's central control screen; the customer feedback signal refers to the operation command generated by the user through the client for the target recommended playlist, such as play, ignore, or view details.

[0052] In this application, the system proactively pushes the target recommended playlist to the user by triggering a pop-up window in the notification center. After receiving the pop-up, the user can perform corresponding operations to generate feedback signals. For example, clicking the play button will cause the system to control the local music application to play the songs sequentially starting from the first song in the playlist; clicking the ignore button or if there is no operation timeout will cause the system to close the pop-up directly; or clicking the view details button will launch an AI mini-window to present the playlist details and recommendation reasons in a streaming effect. In the AI ​​mini-window, the user can further trigger a second operation command, such as selecting a specific song to play or clicking the regenerate switch. The system will then control the playback or re-trigger the playlist generation process according to the command.

[0053] By employing the aforementioned technical solutions, and through proactively pushing playlists and supporting multi-level user interaction commands, the service has been transformed from one-way recommendation to two-way interaction. This reduces the burden on users to manually search for music, enhances the system's responsiveness to users' real-time intentions, and thus strengthens the convenience and user engagement of the music recommendation service.

[0054] In one embodiment, the customer feedback signal includes a first customer feedback signal, and the step of pushing the target recommended playlist to the client to obtain the customer feedback signal includes: The target recommended playlist is pushed to the client to obtain a first customer feedback signal returned by the client, wherein the first customer feedback signal includes play, ignore, and view details; The feedback operation for determining the target recommended playlist is based on the first customer feedback signal.

[0055] For example, the first customer feedback signal refers to the user's initial response to the system's recommended playlist pushed through the client interface. This includes three basic command types: play, ignore, and view details. The play command indicates that the user confirms playing the recommended playlist; the ignore command indicates that the user rejects the current recommendation; and the view details command indicates that the user needs more detailed playlist information.

[0056] In this application, the system presents the target recommended playlist to the user by triggering a pop-up window in the notification center, and monitors the user's actions on this pop-up interface in real time. When the user clicks the play button, the system passes the playlist to the local music application for automatic sequential playback; when the user clicks the ignore button or there is no continuous operation for a set time, the system directly closes the pop-up window and ends the recommendation; when the user clicks the view details button, the system starts the subsequent detailed information display process. For example, in a scenario of long-term traffic jams, after the system pushes a playlist of soothing music, the user can choose to play it immediately or view the specific reasons for the recommendation based on their current mood.

[0057] By employing the above technical solutions, a corresponding response mechanism is established through the first-level operation instructions, enabling the system to quickly and accurately obtain user intent, reducing the operational complexity during the interaction process, improving the system's response efficiency to real-time user feedback, and providing differentiated interaction paths for users with different needs, thereby enhancing the flexibility of service recommendations and the user's sense of control.

[0058] In one embodiment, the customer feedback signal includes a second customer feedback signal, and the feedback operation of determining the target recommended playlist based on the first customer feedback signal includes: When the first customer feedback signal is play, control the vehicle music application to play the target recommended playlist; If the first customer feedback signal is ignored, stop pushing the target recommended playlist to the client; When the first customer feedback signal is to view details, the target recommended playlist and recommendation reason are pushed to the client to obtain a second customer feedback signal, wherein the second customer feedback signal includes: playback control instructions and regeneration instructions for specific songs in the target recommended playlist.

[0059] For example, the second customer feedback signal refers to the operation instructions generated by the user through further interaction with the client after the first customer feedback signal is to view details. These instructions include playback control instructions and regeneration instructions for specific songs in the target recommended playlist. Playback control instructions represent the user's control operations such as playing, pausing, or switching specific songs in the playlist; regeneration instructions represent the user's request for the system to regenerate the recommended playlist based on the current information.

[0060] In this application, when the system detects that the first customer feedback signal is "view details," it launches the AI ​​pop-up interface and pushes the detailed content of the target recommended playlist and the reasons for the recommendation to the client. Within the AI ​​pop-up, the user can trigger a second customer feedback signal. For example, by directly clicking the play button for a song, the system controls the vehicle's music application to play the song sequentially starting from that song. Alternatively, by clicking the regenerate switch, the system re-triggers the playlist generation process and updates the content displayed in the AI ​​pop-up. For instance, in a scenario where the user is in the car while charging, if they are not satisfied with the recommended playlist after viewing its details, they can obtain a new playlist by using the regenerate command.

[0061] It is important to note that, in terms of the graphical user interface interaction flow, this application implements a two-level progressive interactive interface: the first level is a notification center pop-up window, which proactively pushes playlist information to the user after the target recommended playlist is determined. If the user does not interact for a period of time, the pop-up window will automatically close. When the user clicks "View Details" in the pop-up window, the system launches the second-level AI mini-window interface, which presents the playlist details in a streaming display, fully showing the song list of the target recommended playlist and the corresponding recommendation reasons. Within the AI ​​mini-window, the user can directly select any specific song in the list to play and switch to other songs at any time during playback. In addition, the user can trigger a regeneration command to instruct the system to re-regenerate a new playlist list based on the memory information used when the playlist was previously generated (i.e., the same target trigger scenario and human / vehicle information). After receiving the new playlist content, the system updates the playlist list displayed in the AI ​​mini-window while keeping the original recommendation text unchanged, thus completing a closed-loop interaction and allowing the user to continuously optimize the recommendation results.

[0062] By employing the aforementioned technical solutions and supporting second-level fine-grained interactive commands, users can gain deeper control and adjust the recommended content. This reduces the likelihood of interaction interruption when a single recommendation does not match user preferences, improves the system's adaptability to dynamic user needs, and thus enhances the flexibility and user satisfaction of the music recommendation service.

[0063] It should be noted that this application also includes abnormal scenario handling. When the system detects an abnormality in a critical link during execution, such as the cloud model failing to generate a playlist due to network problems, or the local music application being unable to complete song search and filtering for some reason, the system will automatically interrupt the subsequent push process and will no longer trigger a notification center pop-up to push the target recommended playlist to the user. Instead, it will adopt a silent processing method to ensure that it will not interfere with the user when the service is unavailable or the content is incomplete, thereby maintaining the continuity of the user experience and the stability of the system.

[0064] Furthermore, as a response to the above Figure 1 In addition to the implementation of the method shown, this embodiment of the invention also provides a vehicle playlist interaction device for the above-mentioned... Figure 1 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 2 As shown, the device includes: a first acquisition unit 21, a determination unit 22, a second acquisition unit 23, and a generation unit 24, wherein... The determining unit is used to determine the target triggering scenario based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. The generation unit is used to generate a target recommended playlist based on the target triggering scenario and the human and vehicle information, wherein the human and vehicle information includes driver attributes, passenger attributes, and vehicle usage scenario; The push unit is used to push the target recommended playlist to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

[0065] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, a vehicle playlist interaction method can be implemented, addressing the shortcomings of existing in-vehicle music recommendation schemes in terms of intelligence and user-friendliness.

[0066] This invention provides a computer-readable storage medium including a stored program that, when executed by a processor, implements the vehicle playlist interaction method.

[0067] This invention provides a processor for running a program, wherein the program executes the vehicle playlist interaction method during runtime.

[0068] This invention provides an electronic device, which includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the vehicle playlist interaction method described above. This invention provides an electronic device 30, such as... Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and bus 303 connected to the processor; wherein, the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call program instructions in the memory to execute the above-mentioned vehicle playlist interaction method.

[0069] The smart electronic devices mentioned in this article can be PCs, tablets, mobile phones, etc.

[0070] This application also provides a computer program product that, when executed on a process management electronic device, is suitable for executing a program that initializes the above-described vehicle playlist interaction method steps.

[0071] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0072] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The control flow of the memory in the corresponding embodiment.

[0077] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units 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, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0080] The units described 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.

[0081] 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.

[0082] 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 several 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 program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for interacting with a vehicle's playlist, characterized in that, include: The target triggering scenario is determined based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. A target recommended playlist is generated based on the target triggering scenario and the information of people and vehicles, wherein the information of people and vehicles includes driver attributes, passenger attributes, and vehicle usage scenario; The target recommended playlist is pushed to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

2. The method according to claim 1, characterized in that, The vehicle operation data includes: vehicle navigation and gear status, vehicle charging / discharging status, and navigation trip duration information. The environmental perception data includes: road congestion and traffic light status, in-vehicle multimedia playback status, and user call status. Each triggering scenario has its own corresponding preconditions and triggering conditions. The prerequisites are rule-based conditions set based on the vehicle operation data and environmental perception data. The triggering conditions include instantaneous triggering conditions and timed triggering conditions.

3. The method according to claim 2, characterized in that, The determination of the target triggering scenario based on vehicle environmental data includes: The vehicle environment data is compared with the preconditions of each triggering scenario to obtain a comparison result, wherein the comparison result is the matching status of the vehicle environment data and the preconditions of the triggering scenario; If the comparison result shows that the vehicle environment data matches all the preconditions of the triggering scenario, the triggering scenario with all preconditions matched is determined as the initial triggering scenario, and the initial triggering scenario enters the candidate state to be triggered.

4. The method according to claim 3, characterized in that, The determination of the target triggering scenario based on vehicle environmental data includes: When the initially determined triggering scenario enters the candidate state to be triggered, the triggering conditions of the initially determined triggering scenario are detected; If the triggering conditions of the initially determined triggering scenario match the instantaneous triggering conditions, the initially determined triggering scenario is determined as the target triggering scenario; If the triggering conditions of the initially determined triggering scenario match the timing triggering conditions, a continuous monitoring loop will be entered. During the continuous monitoring loop, the preconditions and triggering conditions of the initially determined triggering scenario are detected; If the preconditions and triggering conditions of the initially determined triggering scenario continue to match for a preset duration, the initially determined triggering scenario is determined as the target triggering scenario.

5. The method according to claim 1, characterized in that, The process of generating a target recommended playlist based on the target triggering scenario and vehicle / pedestrian information includes: Obtain the information about the people and vehicles; Based on the target triggering scenario and the information about people and vehicles, an initial recommended playlist is generated through a cloud model. Based on the vehicle music application, playable music is filtered from the initial recommended playlist to determine the target recommended playlist.

6. The method according to claim 1, characterized in that, The customer feedback signal includes a first customer feedback signal, and the step of pushing the target recommended playlist to the client to obtain the customer feedback signal includes: The target recommended playlist is pushed to the client to obtain a first customer feedback signal returned by the client, wherein the first customer feedback signal includes play, ignore, and view details; The feedback operation for determining the target recommended playlist is based on the first customer feedback signal.

7. The method according to claim 6, characterized in that, The customer feedback signal includes a second customer feedback signal, and the feedback operation of determining the target recommended playlist based on the first customer feedback signal includes: When the first customer feedback signal is play, control the vehicle music application to play the target recommended playlist; If the first customer feedback signal is ignored, stop pushing the target recommended playlist to the client; When the first customer feedback signal is to view details, the target recommended playlist and recommendation reason are pushed to the client to obtain a second customer feedback signal, wherein the second customer feedback signal includes: playback control instructions and regeneration instructions for specific songs in the target recommended playlist.

8. A vehicle music playlist interactive device, characterized in that, Also includes: The determining unit is used to determine the target triggering scenario based on vehicle environment data, wherein the vehicle environment data includes vehicle operation data and environmental perception data, and the target triggering scenario is used to determine the timing of playlist interaction. The generation unit is used to generate a target recommended playlist based on the target triggering scenario and the human and vehicle information, wherein the human and vehicle information includes driver attributes, passenger attributes, and vehicle usage scenario; The push unit is used to push the target recommended playlist to the client to obtain customer feedback signals, wherein the customer feedback signals are operation instructions generated by the customer for the target recommended playlist.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, it implements the steps of the vehicle playlist interaction method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the vehicle playlist interaction method as described in any one of claims 1 to 7.

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