Vehicle-mounted noise prediction identification and noise reduction system and method

Abstract

The invention relates to the technical field of noise prediction and recognition, in particular to a vehicle-mounted noise prediction and recognition and noise reduction system and method, and the system comprises an information obtaining module which is used for obtaining sound data information of a current position environment inside and outside a vehicle and monitoring whether the interior of the vehicle reaches a noise standard or not, acquiring environment information of a map navigation route and a surrounding area of the route, acquiring road condition information of a vehicle driving road, and acquiring a current state of the vehicle and a current state of a user; the noise source is actively predicted through the noise prediction identification and noise reduction system, the noise reduction function is started before the noise occurs, different strategy instructions are predicted and executed according to different driving scenes, road conditions, vehicle states and the like, the interference of the noise to the space in the vehicle is reduced as much as possible, and the driving safety is improved. Therefore, the driving safety is ensured, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of noise prediction and recognition, and more specifically, to a vehicle-mounted noise prediction, recognition and noise reduction system and method. Background Art

[0002] The sound insulation effect of the vehicle cabin space currently still relies on the physical sound insulation of the vehicle, such as doors and windows, sound insulation strips, sound insulation cotton, etc. Even the sound insulation effect that smart cars can currently achieve is only to recognize noise and automatically close the windows. When the owner notices that it is very noisy and actively raises the windows or the car recognizes the noise and then closes the windows, etc., the noise has actually brought a lot of noise interference and influence to the owner or user, which may cause the driver to be distracted or unable to concentrate on driving, bring safety hazards, and affect the rest and conversation of people in the car. Therefore, the present invention provides a vehicle-mounted noise prediction, recognition and noise reduction system and method to solve the above problems. Summary of the invention

[0003] The purpose of the present invention is to provide a vehicle-mounted noise prediction, identification and noise reduction system and method to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A vehicle-mounted noise prediction, recognition and noise reduction system, comprising: The information acquisition module is used to obtain the sound data information of the current location environment inside and outside the vehicle and monitor whether the noise standard is met in the vehicle, obtain the map navigation route and the environmental information of the surrounding area of ​​the route, obtain the road condition information of the vehicle's driving road, and obtain the current status of the vehicle and the current status of the user; The information acquisition module acquires the noise area that the vehicle will pass through through the map navigation route and the environmental information of the area around the route, and acquires the passing vehicles that will generate noise through the road condition information; The strategy matching module is used to match the strategy according to the current state of the vehicle and the current state of the user when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, and trigger the strategy instruction before the noise occurs; the strategy execution module is used to execute the strategy instruction to perform active noise reduction of the vehicle; The information acquisition module includes a vehicle-mounted noise recognition sensor, which acquires sound data information inside and outside the vehicle; The information acquisition module also includes a vehicle-mounted camera and a laser sensor, which acquires information about the type of vehicles traveling on the road through the vehicle-mounted camera and the laser sensor, and determines whether the vehicle is a vehicle that will generate noise based on the acquired vehicle type information; The information acquisition module acquires the vehicle's current location information through a map navigation route, identifies surrounding land use information during vehicle travel, and acquires a noisy area that the vehicle will pass through based on the map navigation route and the land use information.

[0005] A preferred technical solution of the present application: the noise standards include quiet environment, normal environment, low-level noise, medium-level noise and dangerous noise; Among them, the quiet environment is 30dB to 40dB, the normal environment is 50dB to 60dB, the low-level noise is above 60dB to 70dB, the medium-level noise is above 70dB to 90dB, and the dangerous noise is above 90dB.

[0006] A preferred technical solution of the present application: the noise area includes urban areas, highways, construction sites, high-speed railways, seaside and racetracks.

[0007] A preferred technical solution of the present application: the road condition information acquired by the information acquisition module also includes road jams, road construction, traffic accidents, and other noise-related information. When a vehicle is about to pass through a road related to noise, the strategy matching module will trigger a strategy instruction before the noise occurs.

[0008] A preferred technical solution of the present application: When the vehicle is about to encounter noise, the triggering process of the strategy instruction is as follows: Extract features from land use information and road condition information around the vehicle's route, including: Extracting noise areas and non-noise areas from the land use information, wherein the non-noise areas include open land, residential areas, office buildings and commercial buildings; Extracting vehicle characteristics and road characteristics from the road condition information, wherein the vehicle characteristics include: noisy vehicles and normal vehicles; and the road characteristics include: unobstructed roads, traffic jams, road construction and traffic accidents; The above features are encoded using 0~16 respectively; The number of extracted features is , denoted as ,in, is the feature vector, represents the first eigenvector, represents the second eigenvector, represents the third eigenvector, Indicates feature vectors; Define label vector Indicates whether the vehicle is about to experience noise: =1 means that noise will be encountered; =-1 means no noise will be encountered; Construct an SVM model. The goal of the SVM model is to find a hyperplane in the feature space to maximize the interval between samples of different categories. For the linearly separable case, the decision function of the SVM model is It is expressed as: ; in, is the weight vector; for The transpose of is the bias term; Construct a logistic regression model, the decision function of the logistic regression model It is expressed as: ; in, is the sigmoid function, represents a linear combination, = + ; is the weight vector; for The transpose of is the bias term; is the base of natural logarithms; The decision function of the SVM model is combined with the decision function of the logistic regression model using the weighted average method to obtain the combined function : ; in, is the weight parameter; 0 1; The label vector of the combined function Classification rules: ; when When , it is predicted as positive, that is, the vehicle will encounter noise, triggering the strategy instruction; when , then the prediction is negative, that is, the vehicle will not encounter noise.

[0009] A preferred technical solution of the present application: the current state of the vehicle acquired by the information acquisition module is the current driving mode of the vehicle, and the driving mode includes a sports mode, a racing mode and a supercar mode; The current state of the user acquired by the information acquisition module is the behavior state of the user in the current vehicle, and the behavior state includes talking, listening to music, resting and driving; The strategy matching module matches corresponding strategy instructions according to the current state of the vehicle and the current state of the user.

[0010] A preferred technical solution of the present application: the sound data information acquired by the vehicle-mounted noise identification sensor includes the noise level emitted by the vehicle itself and the noise level transmitted to the vehicle, wherein the noise emitted by the vehicle itself includes engine noise, tire noise and wind noise.

[0011] A preferred technical solution of the present application: the policy execution module includes software execution policy and hardware execution policy; The software execution strategy includes calling the vehicle noise reduction function, and displaying and voice prompting when noise is about to occur or has occurred; The hardware execution strategy includes opening and closing windows, adjusting the size of sealing strips, unfolding sound-absorbing cloth, adjusting vehicle suspension, adjusting tire pressure and switching hybrid engines.

[0012] The present application also provides a method for predicting, identifying and reducing vehicle noise, comprising the following steps: S1. The vehicle noise recognition sensor of the information acquisition module obtains the noise level outside the vehicle and the noise level transmitted to the vehicle in real time, and monitors in real time whether the noise level inside the vehicle meets the noise standard; S2. Obtain the map navigation route and the land use information of the surrounding area of ​​the route through the information acquisition module, determine the current vehicle location information, and obtain the surrounding noise areas during the vehicle driving process, including urban areas, highways, construction sites, high-speed rails, seaside and racetracks; S3, obtaining information about the type of vehicles traveling on the road through the vehicle-mounted camera and laser sensor of the information acquisition module, determining whether the vehicle is a vehicle that will generate noise based on the obtained vehicle type information, and obtaining information about whether there is a traffic jam, road construction, traffic accident, and other road condition information related to noise on the road; S4, obtaining a driving mode of the vehicle and a behavior state of a user in the vehicle through an information acquisition module, wherein the driving modes include a sports mode, a racing mode, and a supercar mode, and the behavior states of the user include talking, listening to music, resting, and driving; S5, combining the information acquired by the information acquisition module in steps S1 to S4 with the map navigation route information, the vehicle's current location information, and the vehicle's surrounding area information actively provided by the user to form an independent database for information sharing; S6. According to the information in the database, when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, the strategy matching module performs strategy matching according to the current state of the vehicle and the current state of the user, and triggers the strategy instruction before the noise occurs to realize noise prediction and recognition; S7. After receiving the strategy instruction, the strategy execution module executes the software execution strategy and / or the hardware execution strategy. The software execution strategy includes calling the vehicle noise reduction function, and the interface display prompts and voice prompts when the noise is about to occur or has occurred. The hardware execution strategy includes opening and closing the windows, adjusting the size of the sealing strip, unfolding the sound-absorbing cloth, adjusting the vehicle suspension, adjusting the tire pressure and switching the hybrid engine, so as to enable the noise reduction function before the noise occurs.

[0013] A preferred technical solution of the present application: In step S6, when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or is about to pass by a vehicle that generates noise, the strategy matching module has the following matching strategies: When the vehicle is in racing mode or supercar mode, the policy command is not triggered. When the vehicle is in sports mode, the policy command is matched with the behavior of the user in the vehicle. When the user in the vehicle is currently listening to music, the strategy instruction will not be triggered. When the user in the vehicle is currently talking, resting, or driving, it matches the current state of the vehicle in sports mode and triggers the strategy instruction before noise occurs.

[0014] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: The present invention actively predicts the source of noise through the noise prediction recognition and noise reduction system, and enables the noise reduction function before the noise occurs. At the same time, it predicts and executes different strategy instructions for different driving scenarios, road conditions, and vehicle status, etc., to minimize the interference of noise on the vehicle interior space, thereby ensuring driving safety while improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is further described below in combination with the embodiments.

[0017] See also Figure 1 , an embodiment of the present application provides a vehicle-mounted noise prediction, recognition and noise reduction system, comprising: The information acquisition module is used to obtain the sound data information of the current location environment inside and outside the vehicle and monitor whether the noise standard is met in the vehicle, obtain the environmental information of the map navigation route and the surrounding area of ​​the route, obtain the road condition information of the vehicle's driving road, and obtain the current status of the vehicle and the current status information of the user; Noise standards include quiet environment, normal environment, low-level noise, medium-level noise and dangerous noise; Among them, the quiet environment is 30dB to 40dB, the normal environment is 50dB to 60dB, the low-level noise is above 60dB to 70dB, the noisy environment in the car affects rest, the medium-level noise is above 70dB to 90dB, which will affect conversation and rest in the car, and the dangerous noise is above 90dB, which will affect driving; The information acquisition module acquires the noise area that the vehicle will pass through through the map navigation route and the environmental information of the area around the route, and acquires the passing vehicles that will generate noise through the road condition information; The information acquisition module includes a vehicle-mounted noise recognition sensor, which acquires sound data information inside and outside the vehicle through the vehicle-mounted noise recognition sensor; The sound data information obtained by the vehicle noise recognition sensor includes the noise level emitted by the vehicle itself and the noise level transmitted to the vehicle. The noise emitted by the vehicle itself includes engine noise, tire noise and wind noise. The information acquisition module also includes a vehicle-mounted camera and a laser sensor, which acquire information about the type of vehicles traveling on the road through the vehicle-mounted camera and the laser sensor, and determine whether the vehicle is a vehicle that will generate noise based on the acquired vehicle type information. The vehicle-mounted camera and the laser sensor acquire information such as images and analyze the information through a program to acquire information about the type and speed of vehicles around the vehicle, and determine whether the vehicle is a vehicle that will generate noise based on different types of vehicles. Specifically, the information acquisition module acquires the current location information of the vehicle through the map navigation route, identifies the surrounding land use information during the vehicle's driving, acquires public land use information and land status information for analysis and identification of the current land use status, and acquires the area with noise that the vehicle will pass through based on the map navigation route and land use information; The noise areas include urban areas, highways, construction sites, high-speed rail stations, seaside areas and racetracks; The road condition information acquired by the information acquisition module also includes traffic jams, road construction, traffic accidents, and other noise-related information. When a vehicle is about to pass through a road related to noise, the strategy matching module will trigger a strategy instruction before the noise occurs. Specifically, when the vehicle is about to encounter noise, the triggering process of the strategy instruction is as follows: Extract features from land use information and road condition information around the vehicle's route, including: Extracting noise areas and non-noise areas from land use information, where non-noise areas include open land, residential areas, office buildings and commercial buildings; Extract vehicle characteristics and road characteristics from the road condition information. Vehicle characteristics include: noisy vehicles and normal vehicles; road characteristics include: smooth roads, traffic jams, road construction and traffic accidents. During specific implementation, they can be expanded according to actual conditions. The above features are encoded using 0~16 respectively; The number of extracted features is , denoted as ,in, is the feature vector, represents the first eigenvector, represents the second eigenvector, represents the third eigenvector, Indicates feature vectors; Define label vector Indicates whether the vehicle is about to experience noise: =1 means that noise will be encountered; =-1 means no noise will be encountered; Construct an SVM model, where SVM stands for support vector machine. The goal of the SVM model is to find a hyperplane in the feature space that maximizes the interval between samples of different categories. For the linearly separable case, the decision function of the SVM model is It is expressed as: ; in, is the weight vector; for The transpose of is the bias term; Specifically, the SVM model uses a loss function to measure the difference between the model's predicted value and the actual label. The loss function Defined as: ; in, is the sample size; For the The actual label of the sample, which takes the value of 1 or -1; For the The feature vector of samples; is a regularization parameter used to control the complexity of the model; The weight vector of the SVM model and the bias term It is obtained by solving the following optimization problem: ; in, is a penalty parameter used to balance the complexity of the model and the training error; Construct a logistic regression model, the decision function of the logistic regression model It is expressed as: ; in, is the sigmoid function. Specifically, the sigmoid function is an S-shaped growth curve; represents a linear combination, = + ; is the weight vector; for The transpose of is the bias term; is the base of natural logarithms; = Indicates that The number is mapped to the interval [-1,1]; when When the value of is large, is close to 0, so Close to 1; when When the value of is very small, approaches infinity, so Close to -1; when =0, =0; The decision function of the SVM model is combined with the decision function of the logistic regression model using the weighted average method to obtain the combined function : ; in, is the weight parameter; 0 1; The label vector of the combined function Classification rules: ; when When , it is predicted as positive, that is, the vehicle will encounter noise, triggering the strategy instruction; when , then the prediction is negative, that is, the vehicle will not encounter noise.

[0018] The current state of the vehicle acquired by the information acquisition module is the driving mode of the current vehicle, and the driving mode includes a sports mode, a racing mode, and a supercar mode; The current state of the user acquired by the information acquisition module is the behavior state of the user in the current car, and the behavior state includes talking, listening to music, resting and driving; The strategy matching module matches the corresponding strategy instructions according to the current state of the vehicle and the current state of the user.

[0019] The strategy matching module is used to match the strategy according to the current state of the vehicle and the current state of the user when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, and trigger the strategy instruction before the noise occurs; Specifically, when the noise level in the vehicle is about to reach the noise standard or the vehicle is about to pass through a noise area or pass by a vehicle that generates noise, the strategy matching module has the following matching strategies: When the vehicle is in racing mode or supercar mode, the policy command is not triggered. When the vehicle is in sports mode, the policy command is matched with the behavior of the user in the vehicle. When the user in the vehicle is currently listening to music, the strategy instruction will not be triggered. When the user in the vehicle is currently talking, resting, or driving, it matches the current state of the vehicle in sports mode and triggers the strategy instruction before noise occurs.

[0020] A strategy execution module is used to execute strategy instructions to perform vehicle active noise reduction; Specifically, the policy execution module includes software execution policy and hardware execution policy; The software execution strategy includes calling the vehicle noise reduction function, and displaying interface prompts and voice prompts when noise is about to occur or has occurred; Hardware execution strategies include opening and closing windows, adjusting the size of sealing strips, deploying sound-absorbing cloth, adjusting vehicle suspension, adjusting tire pressure and switching hybrid engines.

[0021] This embodiment provides a method for predicting, identifying and reducing vehicle noise, including the following steps: S1. The vehicle noise recognition sensor of the information acquisition module obtains the noise level outside the vehicle and the noise level transmitted to the vehicle in real time, and monitors in real time whether the noise level inside the vehicle meets the noise standard; S2. Obtain the map navigation route and the land use information of the surrounding area of ​​the route through the information acquisition module, determine the current vehicle location information, and obtain the surrounding noise areas during the vehicle driving process, including urban areas, highways, construction sites, high-speed rails, seaside and racetracks; S3, obtaining information about the type of vehicles traveling on the road through the vehicle-mounted camera and laser sensor of the information acquisition module, determining whether the vehicle is a vehicle that will generate noise based on the obtained vehicle type information, and obtaining information about whether there is a traffic jam, road construction, traffic accident, and other road condition information related to noise on the road; S4, obtaining a driving mode of the vehicle and a behavior state of a user in the vehicle through an information acquisition module, wherein the driving modes include a sports mode, a racing mode, and a supercar mode, and the behavior states of the user include talking, listening to music, resting, and driving; S5, combining the information acquired by the information acquisition module in steps S1 to S4 with the map navigation route information, the vehicle's current location information, and the vehicle's surrounding area information actively provided by the user to form an independent database for information sharing; S6. According to the information in the database, when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, the strategy matching module performs strategy matching according to the current state of the vehicle and the current state of the user, and triggers the strategy instruction before the noise occurs to realize noise prediction and recognition; In step S6, when the noise level in the vehicle is about to reach the noise standard or the vehicle is about to pass through a noise area or is about to pass by a vehicle that generates noise, the strategy matching module has the following matching strategies: When the vehicle is in racing mode or supercar mode, the policy command is not triggered. When the vehicle is in sports mode, the policy command is matched with the behavior of the user in the vehicle. When the user in the vehicle is currently listening to music, the policy instruction is not triggered. When the user in the vehicle is currently talking, resting, or driving, the policy instruction is triggered before noise occurs, matching the vehicle's current state in sports mode. S7. After receiving the strategy instruction, the strategy execution module executes the software execution strategy and / or the hardware execution strategy. The software execution strategy includes calling the vehicle noise reduction function, and the interface display prompts and voice prompts when the noise is about to occur or has occurred. The hardware execution strategy includes opening and closing the windows, adjusting the size of the sealing strip, unfolding the sound-absorbing cloth, adjusting the vehicle suspension, adjusting the tire pressure and switching the hybrid engine, so as to enable the noise reduction function before the noise occurs.

[0022] The present invention actively predicts the source of noise through the noise prediction recognition and noise reduction system, and enables the noise reduction function before the noise occurs. At the same time, it predicts and executes different strategy instructions for different driving scenarios, road conditions, and vehicle status, etc., to minimize the interference of noise on the vehicle interior space, thereby ensuring driving safety while improving the user experience.

[0023] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

[0024] In addition, it should be understood that although this specification is described according to implementation modes, not every implementation mode contains only independent technical solutions. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should take the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A vehicle noise prediction, recognition and noise reduction system, characterized in that: include: The information acquisition module is used to obtain the sound data information of the current location environment inside and outside the vehicle and monitor whether the noise standard is met in the vehicle, obtain the map navigation route and the environmental information of the surrounding area of ​​the route, obtain the road condition information of the vehicle's driving road, and obtain the current status of the vehicle and the current status of the user; The information acquisition module acquires the noise area that the vehicle will pass through through the map navigation route and the environmental information of the area around the route, and acquires the passing vehicles that will generate noise through the road condition information; The strategy matching module is used to match the strategy according to the current state of the vehicle and the current state of the user when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, and trigger the strategy instruction before the noise occurs; A strategy execution module is used to execute strategy instructions to perform vehicle active noise reduction; The information acquisition module includes a vehicle-mounted noise recognition sensor, which acquires sound data information inside and outside the vehicle through the vehicle-mounted noise recognition sensor; The information acquisition module also includes a vehicle-mounted camera and a laser sensor, which acquires information about the type of vehicles traveling on the road through the vehicle-mounted camera and the laser sensor, and determines whether the vehicle is a vehicle that will generate noise based on the acquired vehicle type information; The information acquisition module acquires the vehicle's current location information through a map navigation route, identifies the surrounding land use information during the vehicle's travel, and acquires the area where the vehicle will pass and has noise based on the map navigation route and the land use information.

2. The vehicle noise prediction, recognition and noise reduction system according to claim 1, characterized in that: The noise standards include quiet environment, normal environment, low-level noise, medium-level noise and dangerous noise; Among them, the quiet environment is 30dB to 40dB, the normal environment is 50dB to 60dB, the low-level noise is above 60dB to 70dB, the medium-level noise is above 70dB to 90dB, and the dangerous noise is above 90dB.

3. A vehicle noise prediction, identification and noise reduction system according to claim 1 or 2, characterized in that: The noise areas include urban areas, highways, construction sites, high-speed railways, seaside areas and racetracks.

4. The vehicle noise prediction, recognition and noise reduction system according to claim 3, characterized in that: The road condition information acquired by the information acquisition module also includes road congestion, road construction, traffic accidents, and other noise-related information. When a vehicle is about to pass through a road related to noise, the strategy matching module will trigger a strategy instruction before the noise occurs.

5. The vehicle noise prediction, recognition and noise reduction system according to claim 4, characterized in that: When the vehicle is about to encounter noise, the triggering process of the strategy command is as follows: Extract features from land use information and road condition information around the vehicle's route, including: Extracting noise areas and non-noise areas from the land use information, wherein the non-noise areas include open land, residential areas, office buildings and commercial buildings; Extracting vehicle characteristics and road characteristics from the road condition information, wherein the vehicle characteristics include: noisy vehicles and normal vehicles; and the road characteristics include: unobstructed roads, traffic jams, road construction and traffic accidents; The above features are encoded using 0~16 respectively; The number of extracted features is , denoted as ,in, is the feature vector, represents the first eigenvector, represents the second eigenvector, represents the third eigenvector, Indicates feature vectors; Define label vector Indicates whether the vehicle is about to experience noise: =1 means that noise will be encountered; =-1 means no noise will be encountered; Construct an SVM model. The goal of the SVM model is to find a hyperplane in the feature space to maximize the interval between samples of different categories. For the linearly separable case, the decision function of the SVM model is It is expressed as: ； in, is the weight vector; for The transpose of is the bias term; Construct a logistic regression model, the decision function of the logistic regression model It is expressed as: ； in, is the sigmoid function, represents a linear combination, = + ; is the weight vector; for The transpose of is the bias term; is the base of natural logarithms; The decision function of the SVM model is combined with the decision function of the logistic regression model using the weighted average method to obtain the combined function : ； in, is the weight parameter; 0 1; The label vector of the combined function Classification rules: ； when When , it is predicted as positive, that is, the vehicle will encounter noise, triggering the strategy instruction; when , then the prediction is negative, that is, the vehicle will not encounter noise.

6. A vehicle noise prediction, identification and noise reduction system according to claim 1 or 5, characterized in that: The current state of the vehicle acquired by the information acquisition module is the current driving mode of the vehicle, and the driving mode includes a sports mode, a racing mode, and a supercar mode; The current state of the user acquired by the information acquisition module is the behavior state of the user in the current vehicle, and the behavior state includes talking, listening to music, resting and driving; The strategy matching module matches corresponding strategy instructions according to the current state of the vehicle and the current state of the user.

7. The vehicle noise prediction, identification and noise reduction system according to claim 6, characterized in that: The sound data information acquired by the vehicle-mounted noise recognition sensor includes the noise level emitted by the vehicle itself and the noise level transmitted to the vehicle, wherein the noise emitted by the vehicle itself includes engine noise, tire noise and wind noise.

8. A vehicle noise prediction, identification and noise reduction system according to claim 1 or 7, characterized in that: The policy execution module includes software execution policy and hardware execution policy; The software execution strategy includes calling the vehicle noise reduction function, and displaying and voice prompting when noise is about to occur or has occurred; The hardware execution strategy includes opening and closing windows, adjusting the size of sealing strips, unfolding sound-absorbing cloth, adjusting vehicle suspension, adjusting tire pressure and switching hybrid engines.

9. A method for predicting, identifying and reducing vehicle noise, characterized in that: The following steps are involved: S1. The vehicle noise recognition sensor of the information acquisition module obtains the noise level outside the vehicle and the noise level transmitted to the vehicle in real time, and monitors in real time whether the noise level inside the vehicle meets the noise standard; S2. Obtain the map navigation route and the land use information of the surrounding area of ​​the route through the information acquisition module, determine the current vehicle location information, and obtain the surrounding noise areas during the vehicle driving process, including urban areas, highways, construction sites, high-speed rails, seaside and racetracks; S3, obtaining information about the type of vehicles on the road through the vehicle-mounted camera and laser sensor of the information acquisition module, determining whether the vehicle is a vehicle that will generate noise based on the obtained vehicle type information, and obtaining information about whether there is a traffic jam, road construction, traffic accident, and other road condition information related to noise on the road; S4, obtaining a driving mode of the vehicle and a behavior state of a user in the vehicle through an information acquisition module, wherein the driving modes include a sports mode, a racing mode, and a supercar mode, and the behavior states of the user include talking, listening to music, resting, and driving; S5, combining the information acquired by the information acquisition module in steps S1 to S4 with the map navigation route information, the vehicle's current location information, and the vehicle's surrounding area information actively provided by the user to form an independent database for information sharing; S6. According to the information in the database, when the noise standard is about to be reached in the vehicle or the vehicle is about to pass through a noise area or a vehicle that generates noise is about to pass by, the strategy matching module performs strategy matching according to the current state of the vehicle and the current state of the user, and triggers the strategy instruction before the noise occurs to realize noise prediction and recognition; S7. After receiving the strategy instruction, the strategy execution module executes the software execution strategy and / or the hardware execution strategy. The software execution strategy includes calling the vehicle noise reduction function, and the interface display prompts and voice prompts when the noise is about to occur or has occurred. The hardware execution strategy includes opening and closing the windows, adjusting the size of the sealing strip, unfolding the sound-absorbing cloth, adjusting the vehicle suspension, adjusting the tire pressure and switching the hybrid engine, so as to enable the noise reduction function before the noise occurs.

10. The vehicle noise prediction, identification and noise reduction method according to claim 9, characterized in that: In step S6, when the noise level in the vehicle is about to reach the noise standard or the vehicle is about to pass through a noise area or is about to pass by a vehicle that generates noise, the strategy matching module has the following matching strategies: When the vehicle is in racing mode or supercar mode, the policy command is not triggered. When the vehicle is in sports mode, the policy command is matched with the behavior of the user in the vehicle. When the user in the vehicle is currently listening to music, the strategy instruction will not be triggered. When the user in the vehicle is currently talking, resting, or driving, it matches the current state of the vehicle in sports mode and triggers the strategy instruction before noise occurs.

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