Vehicle machine music control method, system, device and equipment and storage medium

Abstract

The invention relates to the technical field of vehicle signal intelligent control, and discloses a vehicle machine music control method, system, device and equipment and a storage medium, and the method comprises the steps: analyzing a music playing state through a machine learning algorithm, obtaining a music playing state analysis result, combining with a complex road segment database, monitoring and analyzing a current road condition, and obtaining a control result; and obtaining a road condition analysis result, and controlling the vehicle machine music according to the music playing state analysis result and the road condition analysis result. Due to the fact that the machine learning algorithm and the complex road section database are integrated, the music playing state and the current road condition can be accurately analyzed, the vehicle machine music is controlled, and the driving experience and the driving safety are improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle signal intelligent control, and in particular to a vehicle music control method, system, device, equipment and storage medium. Background Art

[0002] As the number of car usage scenarios increases, people pay more and more attention to the experience of using the car, and music, as an effective tool for regulating emotions, appears more and more in people's car use. However, playing music while driving will bring many safety hazards: when the road conditions are more complicated, if the volume of the music is too high, or the driver is too immersed in the music, especially the music with lyrics, it may reduce the driving concentration and affect the perception of surrounding vehicles, pedestrians and the environment, such as decreased reaction ability and inability to hear the horn. The existing car music control technology considers the influencing factors in a relatively one-sided way. Summary of the invention

[0003] The main purpose of this application is to provide a car music control method, system, device, equipment and storage medium, aiming to solve the technical problem that the existing car music control technology lacks diversity in considering influencing factors.

[0004] To achieve the above objectives, the present application proposes a car music control method, the method comprising: Analyze the music playing status through a machine learning algorithm to obtain the music playing status analysis result; Combined with complex road section database, monitor and analyze current road conditions to obtain road condition analysis results; Controlling the car music according to the music playing status analysis result and the road condition analysis result; The step of controlling the car music according to the music playing state analysis result and the road condition analysis result comprises: When the music playing state analysis result is that the playing volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the traffic condition analysis result; When the traffic condition analysis result indicates that the flow of people and / or vehicles is large, it is determined that the traffic condition complexity within the preset road section is high; In combination with the complex road section database, when the road condition analysis result shows that there are warning signs, and / or relevant news reports, and / or the road section is an accident-prone section within the preset road section, the preset road section is determined to be a high-risk section; When the road condition analysis result shows that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the warning information is broadcast through the vehicle voice system, and the vehicle music is adjusted and controlled.

[0005] In one embodiment, when the road condition analysis result indicates that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the step of broadcasting warning information through the vehicle voice system and adjusting and controlling the vehicle music includes: When the road condition analysis result indicates that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, personalized warning information is broadcast through the vehicle voice system in combination with a machine learning algorithm; Integrate multimodal interaction technology, and select corresponding music adjustment strategies based on the multimodal interaction technology to adjust and control the car music.

[0006] In one embodiment, the step of analyzing the music playing status of the vehicle computer by a machine learning algorithm to obtain the music playing status analysis result includes: Preprocessing the relevant data of the music playing status by using a machine learning algorithm to obtain the preprocessed music playing data; Extracting music features and playback status features from the music playback status of the vehicle computer, and using the music features and the playback status features to train a machine learning model; The preprocessed music playback data is input into the machine learning model to obtain the music playback status analysis result.

[0007] In one embodiment, after the step of controlling the car music according to the music playing state analysis result and the road condition analysis result, the step further includes: Use the vehicle computer perception system to monitor the road conditions within the preset road section and obtain the road condition monitoring results; Matching the preset road section range with the complex road section database, and obtaining a matching result by determining whether there are warning signs within the preset road section range, and / or whether there are relevant news reports, and / or whether it is an accident-prone road section; updating the traffic condition analysis result according to the traffic condition monitoring result and the matching result; When the road condition analysis result shows that the preset road section is a low-risk road section, the music playing state is restored.

[0008] In one embodiment, after the step of combining the complex road section database, monitoring and analyzing the current road condition, and obtaining the road condition analysis result, the following step is further included: Preprocessing the current road condition related data to obtain the preprocessed current road condition data; Based on a machine learning algorithm, the pre-processed current road condition data and driving route related information are input into a road condition prediction model to obtain a prediction result; According to the prediction result, the prediction information is broadcasted through the vehicle voice system, and before entering the next preset road section range, the vehicle music is controlled in advance according to the preset time threshold.

[0009] In addition, to achieve the above purpose, the present application also proposes a car music control system, which includes: a music analysis module, a road condition monitoring module, a voice broadcast module and a controller; The road condition monitoring module is connected to the vehicle computer perception system; The controller is used to execute the vehicle music control method according to any one of claims 1 to 5.

[0010] In addition, to achieve the above-mentioned purpose, the present application also proposes a car music control device, the car music control device comprising: The music analysis module is used to analyze the music playing status through a machine learning algorithm to obtain the music playing status analysis result; The road condition analysis module is used to monitor and analyze the current road condition in combination with the complex road section database to obtain the road condition analysis results; A music control module, used to control the car music according to the music playing state analysis result and the road condition analysis result; The music control module is also used for the step of controlling the car music according to the music playback status analysis result and the road condition analysis result, including: when the music playback status analysis result is that the playback volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the road condition analysis result; when the road condition analysis result is that the pedestrian flow and / or vehicle flow are large, determining that the road condition complexity within the preset road section is high; in combination with the complex road section database, when the road condition analysis result is that there are warning signs within the preset road section, and / or there are related news reports, and / or it is an accident-prone section, determining that the preset road section is a high-risk section; when the road condition analysis result is that the road condition complexity is high, and / or there are warning signals within the preset road section, and / or the high-risk section, broadcasting warning information through the car voice system, and adjusting and controlling the car music.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a car music control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the car music control method described above.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the vehicle music control method described above are implemented.

[0013] The technical solution proposed in this application analyzes the music playing status through a machine learning algorithm to obtain the music playing status analysis results, combines with the complex road section database, monitors and analyzes the current road conditions, obtains the road condition analysis results, and controls the car music according to the music playing status analysis results and the road condition analysis results. Because this application integrates the machine learning algorithm with the complex road section database, it can accurately analyze the music playing status and the current road conditions, thereby controlling the car music and improving the driving experience and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0016] Figure 1 A flowchart of the first embodiment of the vehicle music control method of the present application is provided; Figure 2 This is a schematic diagram of the car music control process for this application; Figure 3 A flowchart of the second embodiment of the vehicle music control method of the present application is provided; Figure 4 A flowchart of the third embodiment of the vehicle music control method of the present application is provided; Figure 5 This is a schematic diagram of the car music control system architecture for this application; Figure 6 This is a schematic diagram of the module structure of the vehicle music control device according to an embodiment of the present application; Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle music control method in the embodiment of the present application.

[0017] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0019] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0020] Existing car music control technology has a rather one-sided consideration of influencing factors. Playing music while driving will bring many safety hazards: when the road conditions are more complicated, if the volume of the music is too high, or the driver is too immersed in the music, especially the music with lyrics, it may reduce driving concentration and affect the perception of surrounding vehicles, pedestrians and the environment, such as decreased reaction ability and inability to hear the horn.

[0021] Therefore, in order to overcome the above-mentioned defects, the present application provides a solution, which, by integrating machine learning algorithms with complex road section databases, can accurately analyze the music playback status and current road conditions, thereby controlling the car music and improving the driving experience and driving safety.

[0022] It should be noted that the execution subject of each embodiment of the present application may be a computing service system with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic system capable of realizing the above functions, a car music control system, etc. The following embodiments are described below taking the car music control system as an example.

[0023] Based on this, the present application embodiment provides a car music control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle music control method of the present application.

[0024] In this embodiment, the vehicle music control method includes steps S10 to S30: Step S10, analyzing the music playing status through a machine learning algorithm to obtain a music playing status analysis result.

[0025] It should be noted that machine learning algorithms refer to a class of algorithms that can automatically learn from data and improve their performance. They do not require explicit programming to perform specific tasks, but instead make decisions or predictions by analyzing input data and identifying patterns therein.

[0026] It is understandable that the music playing state is interpreted and processed using a machine learning algorithm to extract useful information or patterns and obtain the music playing state analysis results. The objects of interpretation and processing may include the volume, type, and number of times the user has listened to the music.

[0027] Step S20, combining with the complex road section database, monitoring and analyzing the current road condition to obtain a road condition analysis result.

[0028] It should be noted that the complex road section database is a database that stores information related to many complex road sections (such as traffic congestion points, accident-prone areas, construction areas, etc.). This information may include the geographical location of the road section, historical traffic data, road conditions, traffic rules, etc. In addition, the current road conditions refer to the real-time traffic conditions on the road, including traffic flow, vehicle speed, whether the road is congested, whether there are accidents or construction, etc.

[0029] Analysis using complex road section databases and current road condition monitoring data may involve a variety of technical means such as data cleaning, pattern recognition, and statistical analysis. The final road condition analysis results may include the degree of congestion in the road section, the accident risk level, the recommended driving route, etc.

[0030] Step S30, controlling the car music according to the music playing status analysis result and the road condition analysis result.

[0031] It should be understood that the conclusions obtained from analyzing various states during music playback (such as volume, number of plays, pause / resume times, etc.) reflect the user's music preferences, behavior patterns and possible emotional states, and the conclusions obtained from analyzing real-time traffic conditions on the road (such as traffic flow, vehicle speed, road congestion level, etc.) provide information about road capacity and possible traffic risks.

[0032] The music playback status analysis results and road condition analysis results serve as input information, providing a basis for controlling the car music. The system can make decisions based on this information, such as automatically turning off the music or reducing the music volume according to the degree of traffic congestion, and can make immediate adjustments based on the current road conditions and music playback status.

[0033] For ease of understanding, refer to Figure 2 This invention is provided for illustration, but is not intended to limit the present application. Figure 2 This is a diagram of the car music control process. The car monitors the car music playing status in real time, and uses machine learning algorithms to analyze and determine whether the music playing may distract the driver. If so, it monitors the road conditions in real time to determine whether the road conditions are complex and whether traffic accidents are likely to occur. If so, it adjusts the car music, such as lowering the music volume, and voice broadcasts the road conditions. In addition, if it is detected that a complex road section has been passed, the music playing status can be restored.

[0034] The step S30 further includes: When the music playing state analysis result is that the playing volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the traffic condition analysis result; When the traffic condition analysis result indicates that the flow of people and / or vehicles is large, it is determined that the traffic condition complexity within the preset road section is high; In combination with the complex road section database, when the road condition analysis result shows that there are warning signs, and / or relevant news reports, and / or the road section is an accident-prone section within the preset road section, the preset road section is determined to be a high-risk section; When the road condition analysis result shows that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the warning information is broadcast through the vehicle voice system, and the vehicle music is adjusted and controlled.

[0035] It should be noted that the preset volume threshold is usually set based on factors such as the vehicle's internal environment, road noise level, and user preferences. When the volume of music played in the car is higher than this value, it may affect the driver's perception of ambient sound and the driver's driving concentration. Interfering music refers to the type of music currently being played that is identified by the system as potentially interfering with driving, which may include music with a more exciting rhythm, drastic volume changes, or music with lyrics and plots that are easily distracting.

[0036] It is understandable that the basis for judging whether the currently playing music is likely to affect the driver's concentration and perception is: (1) playback volume: if the volume exceeds the threshold, it may affect the user's perception of the environment outside the vehicle; (2) music type: if the music is more exciting, or the music contains lyrics, plot, etc., it is more likely to distract the user; (3) the number of times the music is listened to: the more times the user listens to a piece of music, and the more times the number of times exceeds the preset threshold, the more familiar the user is with the music, and the more likely the music is to distract the user.

[0037] Understandably, in areas with dense pedestrian traffic, such as commercial areas, tourist attractions, or near transportation hubs, high pedestrian traffic may cause pedestrians to cross the road frequently, increasing the difficulty and uncertainty of vehicle driving. In addition, pedestrians may also interact with non-motorized vehicles (such as bicycles and electric vehicles), further complicating the road conditions.

[0038] Road sections with heavy traffic are often accompanied by traffic congestion, frequent lane changes, and reduced speed. These factors not only reduce the traffic efficiency of the road, but also increase the risk of traffic accidents. Especially during peak hours or under special weather conditions, the increase in traffic volume may make the road conditions more complicated.

[0039] It should be understood that when the traffic analysis results show a heavy flow of people, the complexity of the traffic will increase accordingly due to frequent and unpredictable pedestrian activities. When the traffic analysis results show a heavy flow of vehicles, the complexity of the traffic will increase accordingly due to the interaction between vehicles and competition for resources (such as lanes, traffic signals, etc.).

[0040] It should be noted that warning signs are usually used to indicate potential dangers or situations that require attention; news reports may involve information related to road safety such as traffic accidents, road construction, weather disasters, etc.; accident-prone sections refer to sections where traffic accidents have occurred more frequently in history.

[0041] If there are warning signs within the preset road section, it usually means that there may be some form of danger or risk in the road section. Construction sections, sections near schools, collapsed sections, and other sections that require special attention from drivers generally have warning signs; if there are relevant news reports about the preset road section in the recent period, especially reports related to traffic accidents or road safety issues, then the risk level of the road section may be increased accordingly. Combined with the complex road section database and the road condition analysis results, when there are warning signs within the preset road section, there are relevant news reports, and it is a road section with frequent accidents, the preset road section can be determined as a high-risk road section.

[0042] It is understood that real-time traffic data, including pedestrian flow, vehicle flow, road conditions, and warning signals such as horns and flashing lights issued by other vehicles when meeting other vehicles, are collected and analyzed. Based on the analysis results, the system determines the complexity of the road conditions and checks whether there are warning signals or high-risk sections within the preset road section.

[0043] When the system determines that the road conditions are complex, there are warning signals, or it is a high-risk section, the warning mechanism is triggered immediately. The warning information is broadcast in clear and accurate language through the car voice system, such as "The road conditions ahead are complex, please slow down" or "Pay attention to the warning signals ahead, please drive carefully" or "You have entered a high-risk section, please stay alert."

[0044] At the same time, the car music is automatically adjusted and controlled. If the car music is playing, the system may lower the music volume or even pause it.

[0045] In order to further improve the adaptability of the vehicle music control method, when the road condition analysis result shows that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the warning information is broadcasted through the vehicle voice system, and the vehicle music is adjusted and controlled, including: When the road condition analysis result indicates that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, personalized warning information is broadcast through the vehicle voice system in combination with a machine learning algorithm; Integrate multimodal interaction technology, and select corresponding music adjustment strategies based on the multimodal interaction technology to adjust and control the car music.

[0046] It is understandable that the data such as the driver's driving habits, preferences, historical feedback, etc. are collected, and the data are analyzed using machine learning algorithms to establish a personalized warning model for the driver. When the road condition analysis results show that the complexity is high, there are warning signals or high-risk sections, the system generates warning information suitable for the driver based on the personalized warning model. Among them, the warning information can include different tones, intonations, words, etc. to match the driver's preferences and driving style.

[0047] The generated personalized warning information will be broadcast to the driver in voice form through the vehicle voice system. The broadcast content may include road condition description, recommended actions, safety tips, etc.

[0048] It should be noted that multimodal interaction technology refers to the behavior of people interacting with objects by merging and unifying information in different modal channels. It emphasizes that intelligent systems capture information about people and the environment through multiple channels, or present information through multiple channels, that is, humans usually use multiple senses to perceive and understand the world in daily life. Therefore, devices or systems should also have multimodal perception and interaction capabilities to provide a more natural and intuitive interactive experience.

[0049] It should be understood that multiple interactive technologies are integrated, such as voice recognition, gesture recognition, facial recognition, etc. Through these technologies, the driver's status and intention can be monitored in real time, for example, the driver's instructions can be received through voice recognition technology, the driver's actions can be perceived through gesture recognition technology, and the driver's emotions and attention level can be judged through facial recognition technology.

[0050] According to the information obtained by multimodal interaction technology, the corresponding music adjustment strategy is selected. If the driver is in a state of tension or anxiety, the system can choose soothing music to relieve stress. If the driver needs to concentrate on dealing with complex road conditions, the system can choose quieter or slower music. If the driver is enjoying the music, but the warning information needs to be broadcast urgently, the system can automatically lower the volume of the music or pause it.

[0051] In addition, the system also has an intelligent adjustment and feedback mechanism. When adjusting the music, it can monitor the driver's reaction and feedback in real time. If the driver is not satisfied with the adjustment results, he can make adjustments through voice commands or other interactive methods. The system can also continuously learn and optimize the music adjustment strategy based on the driver's feedback.

[0052] This embodiment analyzes the music playing status through a machine learning algorithm to obtain the music playing status analysis result, combines with the complex road section database, monitors and analyzes the current road condition, obtains the road condition analysis result, and controls the car music according to the music playing status analysis result and the road condition analysis result. Since this application integrates the machine learning algorithm with the complex road section database, it can accurately analyze the music playing status and the current road condition, thereby controlling the car music and improving the driving experience and driving safety.

[0053] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 After step S30, the method may further include steps S311 to S314: Step S311, using the vehicle-computer perception system to monitor the road conditions within a preset road section to obtain road condition monitoring results.

[0054] It should be noted that the vehicle-computer perception system can include key components such as on-board cameras (capturing images, identifying the environment), millimeter-wave radars (measuring distance and speed), lidars (three-dimensional modeling, high-precision perception), and ultrasonic sensors (close-range detection, parking assistance).

[0055] The vehicle uses built-in cameras, radars, sensors and other equipment to monitor the road conditions within the preset road section in real time, including but not limited to traffic volume, vehicle speed, road conditions (such as slippery, potholes, etc.), pedestrian and non-motor vehicle activities, etc. The monitored data is processed through algorithms to generate road condition monitoring results, which may include information such as the complexity of road conditions, traffic congestion, and potential danger points.

[0056] Step S312, matching the preset road section range with the complex road section database, and obtaining a matching result by determining whether there are warning signs within the preset road section range, and / or whether there are relevant news reports, and / or whether it is an accident-prone section.

[0057] It is understandable that the information within the preset road section range is matched with the complex road section database. The matching content includes but is not limited to the existence of warning signs, related news reports, records of accident-prone sections, etc. Based on the matching situation, a matching result is generated, and the matching result may include specific information such as whether there are warning signs, whether there are related news reports, whether it is an accident-prone section, etc.

[0058] Step S313: updating the traffic condition analysis result according to the traffic condition monitoring result and the matching result.

[0059] It is understandable that the road condition monitoring results and matching results are comprehensively analyzed, and the road condition analysis results are dynamically updated taking into account the monitored real-time road condition information and the historical information in the database. The updated road condition analysis results may include different risk levels such as low-risk sections, medium-risk sections, and high-risk sections.

[0060] Step S314, when the road condition analysis result is that the preset road section is a low-risk road section, the music playing state is restored.

[0061] It should be understood that when the road condition analysis result shows that the preset road section is a low-risk section, the system determines that the current driving environment is relatively safe. At this time, the system can restore the music playback status, allowing the driver to drive in a relaxed and pleasant environment.

[0062] In addition, when the road condition analysis results indicate a medium-risk or high-risk section, the system should take corresponding warning measures. For example, the system can broadcast warning information through the vehicle voice system and reduce or pause music playback to reduce interference to the driver and improve driving safety.

[0063] This embodiment uses the vehicle-computer perception system to monitor the road conditions of preset sections, and combines it with the complex section database for matching analysis. When the section is determined to be low-risk, the system intelligently restores the music playback status, effectively avoiding driving safety hazards caused by music interference and improving safety and convenience during driving.

[0064] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 , the step S10 may include steps S101 to S103: Step S101, preprocessing the relevant data of the music playing status through a machine learning algorithm to obtain preprocessed music playing data.

[0065] It should be noted that the steps of preprocessing the relevant data of the music playing status through the machine learning algorithm may include data cleaning, feature extraction, data transformation and normalization.

[0066] Step S102, extracting music features and playback status features from the music playback status of the vehicle computer, and using the music features and the playback status features to train a machine learning model.

[0067] It should be noted that music features usually refer to the inherent properties of music, such as rhythm, melody, timbre, volume, and music genre. These features can be extracted through professional audio analysis tools or libraries. Playback status features focus on dynamic information during music playback, such as play / pause status, current playback progress, number of repeated playbacks, and user interaction records with playback controls. This information can be obtained from the playback log of the vehicle computer or user behavior data.

[0068] Step S103, input the preprocessed music playback data into the machine learning model to obtain a music playback status analysis result.

[0069] For ease of understanding, the following examples are given for illustration, but are not intended to limit the present application. The process of obtaining the music playing status analysis result may be: (1) Feature extraction a. Musical Features Audio attributes: including rhythm, volume, pitch, timbre, harmonic complexity, etc. These features can be extracted from music files using audio analysis software or libraries.

[0070] Metadata: such as song title, artist, album, genre, release year, etc. This information can usually be obtained from the music file's tags or database.

[0071] b.Playback status characteristics Playback behavior: including the number of plays, pauses, skips, and repeats. These characteristics reflect the user's preferences and interaction patterns for music.

[0072] Time characteristics: such as playback time (time of the day, days of the week), playback duration, etc.

[0073] (2) Data preprocessing The extracted features are cleaned, transformed, and normalized to ensure data quality and consistency. The feature values ​​are converted into a format suitable for processing by machine learning algorithms.

[0074] (3) Machine learning model training Choose the appropriate machine learning algorithm (such as classification algorithm, regression algorithm, clustering algorithm, etc.) according to the specific application scenario (such as predicting the user's next favorite song, classifying the user's preferred music genre, etc.).

[0075] Use the extracted music features and playback status features as input to train the machine learning model.

[0076] During the training process, the performance of the model is optimized through cross-validation, adjustment of hyperparameters, and other methods.

[0077] (4) Model evaluation and selection Use the validation set to evaluate the trained model. Evaluation indicators may include accuracy, recall, F1 score, AUC, etc. Select the best model based on the evaluation results.

[0078] (5) Music playback status analysis The preprocessed music playback data is input into the trained machine learning model. The model outputs the analysis results of the music playback status based on the input features. This may include: predicting the next song that the user may like; classifying the user's current or future music preferences; analyzing the user's playback behavior patterns, such as which time periods the user prefers to listen to music, which types of music are more popular, etc.

[0079] (6) Application of results and feedback The analysis results are applied to the car's music recommendation system, playlist generation, personalized interface design, etc., and user feedback on the recommendation results is collected for continuous improvement of models and algorithms.

[0080] This embodiment uses a machine learning algorithm to pre-process the data related to the music playing status, effectively removes noise and retains key information. On this basis, the music features and playing status features are carefully extracted from the music playing status of the car computer, and these feature data are used to train the machine learning model. By inputting the pre-processed music playing data, the model can analyze the music playing status in real time, improving the intelligent level of music playing.

[0081] In the third embodiment, after step S20, the following steps may also be included: Preprocessing the current road condition related data to obtain the preprocessed current road condition data; Based on a machine learning algorithm, the pre-processed current road condition data and driving route related information are input into a road condition prediction model to obtain a prediction result; According to the prediction result, the prediction information is broadcasted through the vehicle voice system, and before entering the next preset road section range, the vehicle music is controlled in advance according to the preset time threshold.

[0082] It should be understood that the construction of a road condition prediction model first requires the collection of raw data on road conditions from multiple channels, including but not limited to traffic flow, road congestion, weather conditions, and construction information. These data are thoroughly cleaned, integrated, and feature extracted, and features that are critical to prediction are extracted. Then, the model is trained based on the specific prediction goals and data characteristics and the current road condition data after preprocessing.

[0083] It should be noted that during the training process, the model can be continuously optimized through cross-validation, adjusting hyperparameters, etc. Afterwards, the model is fully evaluated using the validation set to ensure that it has good generalization capabilities. Once the model meets the performance requirements, it can be deployed in actual applications for accurate prediction of real-time traffic conditions.

[0084] It is understandable that the pre-processed current traffic data is input into the trained traffic prediction model to obtain the prediction results, which may include the degree of congestion, travel time, recommended speed, etc. in the future. According to the prediction results, the traffic prediction information is broadcast through the car voice system, such as "the road ahead is expected to be congested for 20 minutes, please plan the route in advance."

[0085] Before entering the next preset road section, the car music is controlled according to the preset time threshold (such as 10 minutes before the congested section), which can include lowering the volume, switching to a soothing music style, or pausing the playback. At the same time, user feedback on broadcast information and music control is collected to continuously optimize the model and algorithm, and regularly update the road condition data source and model.

[0086] For ease of understanding, refer to Figure 5 This invention is provided for illustration, but is not intended to limit the present application. Figure 5 The diagram is a schematic diagram of the car music control system architecture, which includes: (1) music analysis module, (2) road condition analysis module, and (3) voice broadcast module.

[0087] Specifically, the music analysis module is used to determine the status of music playing on the vehicle computer. It combines the volume, music type, user's preferences and habits for playing music, and uses machine learning to evaluate the music status to determine whether the current music playing status may affect the user's concentration and perception; the road condition analysis module uses the vehicle perception system to monitor the traffic and pedestrian flow on the road and capture warning signals, and at the same time matches the current road section with the complex road section database to determine whether there are warning signs on the current road section, whether there are relevant news reports (such as construction on that day), and whether it is an accident-prone section, so as to comprehensively determine whether the current road conditions are prone to traffic accidents; the voice broadcast module combines the analysis results of the road condition analysis module to warn the driver through the vehicle computer voice, and at the same time turns off or reduces the current vehicle computer music volume.

[0088] This embodiment pre-processes the current road condition data and applies machine learning algorithms to predict road conditions, and combines the driving route information to make the prediction results more reliable. The vehicle voice system broadcasts the prediction information in real time, so that the driver can understand the road conditions in advance, make reasonable driving plans, and effectively avoid congestion. Before entering the preset road section, the vehicle music is intelligently controlled according to the prediction results and the preset time threshold, which not only improves driving efficiency, but also enhances driving comfort and intelligence.

[0089] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle music control method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0090] This application also provides a car music control device, please refer to Figure 6 , the vehicle music control device comprises: The music analysis module 10 is used to analyze the music playing status through a machine learning algorithm to obtain a music playing status analysis result; The road condition analysis module 20 is used to monitor and analyze the current road condition in combination with the complex road section database to obtain the road condition analysis result; The music control module 30 is used to control the car music according to the music playing state analysis result and the road condition analysis result; The music control module 30 is also used for the step of controlling the car music according to the music playback status analysis result and the road condition analysis result, including: when the music playback status analysis result is that the playback volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the road condition analysis result; when the road condition analysis result is that the pedestrian flow and / or vehicle flow are large, determining that the road condition complexity within the preset road section is high; in combination with the complex road section database, when the road condition analysis result is that there are warning signs within the preset road section, and / or there are related news reports, and / or it is an accident-prone section, determining that the preset road section is a high-risk section; when the road condition analysis result is that the road condition complexity is high, and / or there are warning signals within the preset road section, and / or the high-risk section, broadcasting warning information through the car voice system, and adjusting and controlling the car music.

[0091] The car music control device provided by the present application adopts the car music control method in the above embodiment, which can solve the technical problem that the existing car music control technology lacks diversity in the consideration of influencing factors. Compared with the prior art, the beneficial effects of the car music control device provided by the present application are the same as the beneficial effects of the car music control method provided by the above embodiment, and the other technical features in the car music control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0092] The present application provides a vehicle music control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle music control method in the above-mentioned embodiment one.

[0093] Reference below Figure 7 , which shows a schematic diagram of the structure of a car music control device suitable for implementing the embodiment of the present application. The car music control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Desctions: tablet computers), PMPs (Portable Media Players: portable multimedia players), car terminals (such as car navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The car music control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0094] like Figure 7As shown, the vehicle music control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle music control system are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle music control device to communicate with other systems wirelessly or by wire to exchange data. Although the figure shows a vehicle music control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0095] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0096] The car music control device provided by the present application adopts the car music control method in the above embodiment, which can solve the technical problem that the existing car music control technology lacks diversity in the consideration of influencing factors. Compared with the prior art, the beneficial effects of the car music control device provided by the present application are the same as the beneficial effects of the car music control method provided by the above embodiment, and the other technical features in the car music control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0097] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0099] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle music control method in the above-mentioned embodiment.

[0100] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0101] The computer-readable storage medium may be included in the vehicle music control device; or may exist independently without being assembled into the vehicle music control device.

[0102] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle music control device, the vehicle music control device: analyzes the music playing status through a machine learning algorithm to obtain the music playing status analysis result, combines with the complex road section database, monitors and analyzes the current road conditions, obtains the road condition analysis result, and controls the vehicle music according to the music playing status analysis result and the road condition analysis result.

[0103] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0106] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle music control method, and can solve the technical problem that the existing vehicle music control technology lacks diversity in the consideration of influencing factors. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the vehicle music control method provided by the above-mentioned embodiment, and will not be repeated here.

[0107] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned vehicle music control method when executed by a processor.

[0108] The computer program product provided by the present application can solve the technical problem that the existing vehicle music control technology lacks diversity in the consideration of influencing factors. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the vehicle music control method provided by the above embodiment, which will not be repeated here.

[0109] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A car music control method, characterized in that: The method comprises the following steps: Analyze the music playing status through a machine learning algorithm to obtain the music playing status analysis result; Combined with complex road section database, monitor and analyze current road conditions to obtain road condition analysis results; Controlling the car music according to the music playing status analysis result and the road condition analysis result; The step of controlling the car music according to the music playing state analysis result and the road condition analysis result comprises: When the music playing state analysis result is that the playing volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the traffic condition analysis result; When the traffic condition analysis result indicates that the flow of people and / or vehicles is large, it is determined that the traffic condition complexity within the preset road section is high; In combination with the complex road section database, when the road condition analysis result shows that there are warning signs, and / or relevant news reports, and / or the road section is an accident-prone section within the preset road section, the preset road section is determined to be a high-risk section; When the road condition analysis result shows that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the warning information is broadcast through the vehicle voice system, and the vehicle music is adjusted and controlled.

2. The vehicle music control method according to claim 1, characterized in that: When the road condition analysis result indicates that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, the step of broadcasting warning information through the vehicle voice system and adjusting and controlling the vehicle music includes: When the road condition analysis result indicates that the road condition is highly complex, and / or there is a warning signal within the preset road section, and / or the high-risk road section, personalized warning information is broadcast through the vehicle voice system in combination with a machine learning algorithm; Integrate multimodal interaction technology, and select corresponding music adjustment strategies based on the multimodal interaction technology to adjust and control the car music.

3. The vehicle music control method according to claim 1, characterized in that: The step of analyzing the music playing status of the vehicle computer by using a machine learning algorithm to obtain the music playing status analysis result includes: Preprocessing the relevant data of the music playing status by using a machine learning algorithm to obtain the preprocessed music playing data; Extracting music features and playback status features from the music playback status of the vehicle computer, and using the music features and the playback status features to train a machine learning model; The preprocessed music playback data is input into the machine learning model to obtain the music playback status analysis result.

4. The vehicle music control method according to claim 1, characterized in that: After the step of controlling the car music according to the music playing state analysis result and the road condition analysis result, the method further includes: Use the vehicle computer perception system to monitor the road conditions within the preset road section and obtain the road condition monitoring results; Matching the preset road section range with the complex road section database, and obtaining a matching result by determining whether there are warning signs within the preset road section range, and / or whether there are relevant news reports, and / or whether it is an accident-prone road section; updating the traffic condition analysis result according to the traffic condition monitoring result and the matching result; When the road condition analysis result shows that the preset road section is a low-risk road section, the music playing state is restored.

5. The vehicle music control method according to claim 1, characterized in that: After the step of combining the complex road section database, monitoring and analyzing the current road condition, and obtaining the road condition analysis result, the following steps are also included: Preprocessing the current road condition related data to obtain the preprocessed current road condition data; Based on a machine learning algorithm, the pre-processed current road condition data and driving route related information are input into a road condition prediction model to obtain a prediction result; According to the prediction result, the prediction information is broadcasted through the vehicle voice system, and before entering the next preset road section range, the vehicle music is controlled in advance according to the preset time threshold.

6. A car music control system, characterized in that: The vehicle music control system includes: a music analysis module, a road condition monitoring module, a voice broadcast module and a controller; The road condition monitoring module is connected to the vehicle computer perception system; The controller is used to execute the vehicle music control method according to any one of claims 1 to 5.

7. A car music control device, characterized in that: The vehicle music control device comprises: The music analysis module is used to analyze the music playing status through a machine learning algorithm to obtain the music playing status analysis result; The road condition analysis module is used to monitor and analyze the current road condition in combination with the complex road section database to obtain the road condition analysis results; A music control module, used to control the car music according to the music playing state analysis result and the road condition analysis result; The music control module is also used for the step of controlling the car music according to the music playback status analysis result and the road condition analysis result, including: when the music playback status analysis result is that the playback volume exceeds a preset volume threshold, and / or the music type is disruptive music, and / or the number of times the music is listened to exceeds a preset number threshold, parsing the road condition analysis result; when the road condition analysis result is that the pedestrian flow and / or vehicle flow are large, determining that the road condition complexity within the preset road section is high; in combination with the complex road section database, when the road condition analysis result is that there are warning signs within the preset road section, and / or there are related news reports, and / or it is an accident-prone section, determining that the preset road section is a high-risk section; when the road condition analysis result is that the road condition complexity is high, and / or there are warning signals within the preset road section, and / or the high-risk section, broadcasting warning information through the car voice system, and adjusting and controlling the car music.

8. A car music control device, characterized in that: The vehicle music control device includes: a memory, a processor, and a vehicle music control program stored in the memory and executable on the processor. When the vehicle music control program is executed by the processor, the vehicle music control method according to any one of claims 1 to 5 is implemented.

9. A storage medium, characterized in that: The storage medium stores a vehicle music control program, and when the vehicle music control program is executed by the processor, the vehicle music control method according to any one of claims 1 to 5 is implemented.

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