Intelligent cabin scene self-adaption method and system, storage medium and electronic equipment

Abstract

The invention relates to an intelligent cabin scene self-adaption method and system, a storage medium and electronic equipment, and relates to the technical field of intelligent automobile cabins, and the method comprises the steps: determining target identity information corresponding to a target driver based on biological feature information, and according to the target identity information, determining the target driver according to the target identity information; matching initial sound effect preference parameters corresponding to the target identity information from a preset user archive library; obtaining an actual media type listened by the target driver in the vehicle, and adjusting and optimizing the initial sound effect preference parameter according to the actual media type to obtain a target sound effect preference parameter; and the actual vehicle speed of a target vehicle driven by the target driver is obtained, and if the actual vehicle speed exceeds a preset vehicle speed threshold value, equalizer parameters in the target sound effect preference parameters are adjusted and optimized to obtain final sound effect preference parameters. According to the method and the device, the individual requirements of a driver on the vehicle-mounted sound effect can be well met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent automobile cabin, in particular to an intelligent cabin adaptive scene method and system, a storage medium and an electronic device. BACKGROUND

[0002] Intelligent cabin aims to integrate various IT and artificial intelligence technologies to create a new integrated digital platform in the vehicle, providing intelligent experience for drivers and promoting vehicle safety. High-quality vehicle sound effect is a very important part of intelligent cabin, which can improve the overall quality and image of intelligent cabin. With the development of automobile intelligence, users have higher and higher requirements for intelligent cabin, not only limited to the realization of functions, but also including high-quality vehicle sound effect, which can show the high-tech content and humanized design of intelligent cabin, bringing users a better and high-end sound effect experience, and enhancing the competitiveness of intelligent cabin in the market. Therefore, the configuration of vehicle sound effect is of great significance to the experience of drivers in the intelligent cabin.

[0003] At present, the commonly used way for vehicle sound effect configuration is that fixed vehicle sound effect modes are preset in the vehicle, and the driver selects the vehicle sound effect mode to be configured from the fixed vehicle sound effect modes after getting into the vehicle. However, since the fixed vehicle sound effect modes preset in the vehicle are relatively single, and different drivers have different sound effect preferences, and the sound effect preferences of the driver may also change dynamically during driving, it is difficult to meet the individual needs of the driver for vehicle sound effect. SUMMARY

[0004] In order to better meet the individual needs of the driver for vehicle sound effect, the present application provides an intelligent cabin adaptive scene method, system, storage medium and electronic device.

[0005] In the first aspect of the present application, an intelligent cabin adaptive scene method is provided, which specifically comprises: obtaining biological feature information of a target driver getting into the vehicle; based on the biological feature information, determining target identity information corresponding to the target driver, and matching initial sound effect preference parameters corresponding to the target identity information from a preset user archive, the user archive including identity information and corresponding vehicle sound effect preference parameters of different drivers; obtaining the actual media type listened to by the target driver in the vehicle, and adjusting and optimizing the initial sound effect preference parameters according to the actual media type to obtain target sound effect preference parameters; acquire an actual vehicle speed of a target vehicle driven by the target driver, and if the actual vehicle speed exceeds a preset vehicle speed threshold, adjust and optimize an equalizer parameter in the target sound effect preference parameter to obtain a final sound effect preference parameter; if the actual vehicle speed does not exceed the vehicle speed threshold, adjust and optimize the target sound effect preference parameter to obtain a final sound effect preference parameter when it is detected that voice navigation prompting to the target driver is needed soon; if voice navigation prompting to the target driver is not needed, acquire congestion road condition information of a road section where the target vehicle is located, and adjust and optimize the target sound effect preference parameter according to the congestion road condition information to obtain a final sound effect preference parameter; configure in-vehicle sound effect in a cabin of the target vehicle according to the final sound effect preference parameter.

[0006] By using the above technical solution, the target driver is initially matched with corresponding individualized in-vehicle sound effect parameters, i.e., initial sound effect preference parameters, according to target identity information. Then, based on the initial sound effect preference parameters, the initial sound effect preference parameters are adjusted and optimized according to the actual media type currently listened to by the target driver and the sound effect preference of the target driver when listening to the actual media type, to obtain target sound effect preference parameters. Further, if the actual vehicle speed exceeds the vehicle speed threshold, it indicates that wind noise is large during driving, and accordingly, the equalizer parameter in the target sound effect preference parameter is adjusted to reduce the adverse effect of wind noise on in-vehicle sound effect, to obtain a final sound effect preference parameter. If the actual vehicle speed does not exceed the vehicle speed threshold, if voice navigation prompting to the target driver is needed soon, the target sound effect preference parameter is adjusted and optimized to obtain a final sound effect preference parameter, so as not to affect the target driver's listening to navigation broadcasting. If voice navigation prompting to the target driver is not needed, the target sound effect preference parameter is adjusted and optimized according to congestion road condition information, so as to relieve the target driver's frustrated and nervous emotions during congestion, and further better meet the individualized demand of the driver for in-vehicle sound effect.

[0007] In an embodiment, the adjusting and optimizing the target sound effect preference parameter to obtain a final sound effect preference parameter when it is detected that voice navigation prompting to the target driver is needed soon specifically includes: when it is detected that voice navigation prompting to the target driver is needed soon, acquiring an actual driving prompt operation to be performed by the target driver, and determining at least one target driving operation according to historical driving prompt operations that are performed in error under target vehicle navigation, the target driving operation being a driving prompt operation that is prone to be performed in error; According to the single target driving operation execution error case, the historical vehicle speed range before the navigation prompt voice is issued is determined, and at least one corresponding target vehicle speed range is determined, the target vehicle speed range being a vehicle speed range prone to causing the driver to make a corresponding target driving operation execution error; A first weight of each target driving operation is determined, and a second weight of the target vehicle speed range corresponding to each target driving operation is determined, the first weight representing the possibility of a target driving operation execution error, and the second weight representing the possibility of a target vehicle speed range causing a corresponding target driving operation execution error; According to the actual vehicle speed, the actual driving prompt operation, the first weight, and the second weight, the target sound effect preference parameter is adjusted and optimized to obtain a final sound effect preference parameter.

[0008] In an embodiment, the target sound effect preference parameter is adjusted and optimized according to the actual vehicle speed, the actual driving prompt operation, the first weight, and the second weight to obtain a final sound effect preference parameter, specifically including: When the actual driving prompt operation is a target driving operation, if the actual vehicle speed is included in the target vehicle speed range corresponding to the actual driving prompt operation, the corresponding target vehicle speed range is determined as a reference vehicle speed range; A first product of the first weight of the actual driving prompt operation and the second weight of the corresponding reference vehicle speed range is calculated, and the first product is compared with a preset first threshold value; If the first product exceeds the first threshold value, a risk coefficient of the target driver making an execution error of the actual driving prompt operation is determined according to the first product; According to the risk coefficient, a lead time and an increase amount of a navigation prompt volume are determined, the lead time being a time length by which a time node of media volume reduction in the target vehicle is advanced compared with a time node of the navigation prompt voice being issued, the greater the risk coefficient, the greater the lead time, and the greater the increase amount; According to the lead time and the increase amount, the media volume and the navigation prompt volume in the target sound effect preference parameter are adjusted and optimized to obtain a final sound effect preference parameter.

[0009] In an embodiment, the target driver making an execution error of the actual driving prompt operation is determined according to the first product, specifically including: A first remaining time length of the target driver driving the target vehicle to reach a destination in a case where the actual driving prompt operation is not executed incorrectly is determined, and a planned arrival time of the target driver reaching the destination is obtained; determining a second remaining time length for the target driver to drive the target vehicle to the destination in the case of the actual driving prompt operation execution error; determining a planned remaining time length according to the planned arrival time and the current time, and determining a correction coefficient according to a difference between the second remaining time length and the planned remaining time length if the second remaining time length is greater than the planned remaining time length; calculating a ratio of the second remaining time length and the first remaining time length, and determining a correction coefficient according to the ratio if the ratio is greater than 1 if the second remaining time length is not greater than the planned remaining time length; multiplying the correction coefficient and the first product to obtain a risk coefficient of the target driver to the actual driving prompt operation execution error, the correction coefficient being a positive number greater than 1.

[0010] In an embodiment, the method further comprises: calculating a second product of a first weight of each target driving operation and a second weight of a corresponding target speed range when the navigation voice prompt function of the target vehicle is in an off state and the actual driving prompt operation is a target driving operation; determining a key speed range as a target speed range containing the actual speed, and summing the second products corresponding to each key speed range to obtain a summation result; calculating a third product of the first weight of the actual driving prompt operation and the second weight of each corresponding target speed range if the summation result is greater than a preset second threshold, and selecting a minimum third product from each third product; determining a first speed range as a target speed range corresponding to the minimum third product, obtaining speed limit information of a road segment where the target vehicle is located, and determining a second speed range according to the speed limit information; performing an intersection operation on the first speed range and the second speed range to obtain a suitable speed range, and issuing a speed adjustment reminder according to the suitable speed range.

[0011] In an embodiment, the method further comprises: obtaining an actual electric quantity and an average electric consumption of the target vehicle in the case that the target vehicle is a range-extender vehicle and a range extender is not started; determining an estimated start time of the range extender in the target vehicle according to the actual electric quantity and the average electric consumption; determining a suitable listening media type for the target driver at the estimated start time; adjusting and optimizing an actual vehicle audio effect parameter of the target vehicle at a preset time length before the estimated start time according to the suitable listening media type to obtain an optimized audio effect parameter.

[0012] In an embodiment, the determining the suitable listening media type corresponding to the predicted starting time for the target driver specifically comprises: determining at least one target media type according to the historical media types listened by the target driver on the target vehicle, the target media type being a media type that the target driver is prone to listen to; determining at least one target time interval corresponding to each of the target media type according to the historical time intervals when the target driver listens to the target media type, the target time interval being a time interval when the target driver is prone to listen to the corresponding target media type; determining the suitable listening media type corresponding to the predicted starting time for the target driver according to each of the target media type and the corresponding target time interval.

[0013] In a second aspect of the present application, an intelligent cockpit adaptive scene system is provided, which specifically comprises: an information acquisition module configured to acquire biological feature information of a target driver getting into a vehicle; a first adjustment module configured to determine target identity information corresponding to the target driver based on the biological feature information, and match initial sound effect preference parameters corresponding to the target identity information from a preset user profile library, the user profile library comprising identity information of different drivers and corresponding vehicle-mounted sound effect preference parameters; a second adjustment module configured to acquire an actual media type listened to by the target driver in the vehicle, and adjust and optimize the initial sound effect preference parameters to obtain target sound effect preference parameters according to the actual media type; a third adjustment module configured to acquire an actual vehicle speed of a target vehicle driven by the target driver, and adjust and optimize equalizer parameters in the target sound effect preference parameters to obtain final sound effect preference parameters if the actual vehicle speed exceeds a preset vehicle speed threshold; a fourth adjustment module configured to adjust and optimize the target sound effect preference parameters to obtain final sound effect preference parameters if the actual vehicle speed does not exceed the vehicle speed threshold when it is detected that voice navigation prompting for the target driver is needed; a fifth adjustment module configured to acquire congestion road condition information of a road segment where the target vehicle is located, and adjust and optimize the target sound effect preference parameters to obtain final sound effect preference parameters according to the congestion road condition information if it is not detected that voice navigation prompting for the target driver is needed; a sound effect configuration module configured to configure vehicle-mounted sound effects in a cockpit of the target vehicle according to the final sound effect preference parameters.

[0014] By adopting the technical scheme, after the information acquisition module acquires the biometric information, the first adjustment module determines the initial sound effect preference parameter according to the target identity information, the second adjustment module determines the target sound effect preference parameter according to the actual media type, the third adjustment module adjusts and optimizes the target sound effect preference parameter when the actual vehicle speed exceeds the vehicle speed threshold, and obtains the final sound effect preference parameter; the fourth adjustment module adjusts and optimizes the target sound effect preference parameter when it is detected that voice navigation prompting for the target driver is needed, and obtains the final sound effect preference parameter; the fifth adjustment module adjusts and optimizes the target sound effect preference parameter according to the congestion road condition information, and obtains the final sound effect preference parameter. Finally, the sound effect configuration module configures the vehicle-mounted sound effect according to the final sound effect preference parameter.

[0015] In a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program. When the computer program is loaded and executed by a processor, the method steps of any one of the first aspect are performed.

[0016] In a fourth aspect of the present application, an electronic device is provided, specifically comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory, so that the electronic device performs the method of any one of the first aspect.

[0017] In summary, the present application includes at least one of the following beneficial technical effects: according to the target identity information, the target driver is initially matched with corresponding personalized vehicle-mounted sound effect parameters, i.e., initial sound effect preference parameters. Then, based on the initial sound effect preference parameters, the target sound effect preference parameters are obtained by adjusting and optimizing the initial sound effect preference parameters based on the actual media type currently listened to by the target driver and the sound effect preference of the target driver when listening to the actual media type. Further, if the actual vehicle speed exceeds the vehicle speed threshold, it indicates that the wind noise is large during driving, and accordingly, the equalizer parameter in the target sound effect preference parameter is adjusted to reduce the adverse effect of wind noise on the vehicle-mounted sound effect, and the final sound effect preference parameter is obtained. If the actual vehicle speed does not exceed the vehicle speed threshold, if voice navigation prompting for the target driver is needed, the final sound effect preference parameter is obtained by adjusting and optimizing the target sound effect preference parameter so as not to affect the listening of the target driver to the navigation broadcast. If language navigation prompting for the target driver is not needed, the final sound effect preference parameter is obtained by adjusting and optimizing the target sound effect preference parameter according to the congestion road condition information, thereby relieving the frustration and tension of the target driver in congestion, and further better meeting the individualized demand of the driver for the vehicle-mounted sound effect. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1is a flowchart of a method for an intelligent cockpit adaptive scenario provided by an embodiment of the present application; Figure 2 is a structural diagram of an intelligent cockpit adaptive scenario system provided by an embodiment of the present application; Figure 3 is a structural diagram of another intelligent cockpit adaptive scenario system provided by an embodiment of the present application.

[0019] Legend: 11, information acquisition module; 12, first adjustment module; 13, second adjustment module; 14, third adjustment module; 15, fourth adjustment module; 16, fifth adjustment module; 17, sound effect configuration module; 18, speed adjustment reminding module; 19, parameter adjustment module. DETAILED DESCRIPTION

[0020] In order to enable persons skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the present specification will be clearly and completely described below in conjunction with the drawings in the present specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0021] In the description of the embodiments of the present application, the words "exemplarily", "for example", or "for instance" are used to mean as an example, illustration, or description. Any embodiment or design scheme described as "exemplarily", "for example", or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplarily", "for example", or "for instance" are intended to present the relevant concept in a specific manner.

[0022] In the description of the embodiments of the present application, the term "and / or" is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, B alone, and A and B together. In addition, unless otherwise specified, the term "multiple" means two or more. For example, multiple systems mean two or more systems, and multiple screen terminals mean two or more screen terminals. In addition, the terms "first" and "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features limited by "first" and "second" can explicitly or implicitly include one or more features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0023] Reference Figure 1The embodiment of the application discloses a flowchart of an intelligent cabin adaptive scene method, which can be realized by relying on a computer program and can run on an intelligent cabin adaptive scene system based on a von Neumann system. The computer program can be integrated in an application or run as an independent tool application, and specifically includes the following steps. S101: Obtain biological feature information of a target driver who gets on the vehicle.

[0024] Specifically, in the embodiment of the application, the target driver is a driver who is currently preparing to drive a target vehicle and has also driven the target vehicle before, and the target vehicle is a vehicle that needs to be adaptively adjusted in terms of vehicle sound effect for the driver. The biological feature information is face information of the target driver, and in other embodiments, the biological feature information can also be voiceprint information of the target driver.

[0025] The execution subject of the intelligent cabin adaptive scene method disclosed in the application is the target vehicle itself, and a feasible way to obtain biological feature information is as follows: after the target driver gets on the main driver seat of the target vehicle, the face information or facial information of the target driver is obtained through a preset vehicle camera. In other embodiments, the voiceprint information of the target driver can also be collected through a preset microphone array. It should be noted that the biological feature information of the target driver is obtained on the premise that the consent or authorization of the target driver is obtained.

[0026] S102: Determine target identity information corresponding to the target driver based on the biological feature information, and match initial sound effect preference parameters corresponding to the target identity information from a preset user archive library. The user archive library includes identity information and corresponding vehicle sound effect preference parameters of different drivers.

[0027] Specifically, after the biometric information is acquired, the target driver is analyzed and identified based on the biometric information through a preset RetinaFace algorithm, target identity information of the target driver is determined, a preset user profile library is called from a database, and initial sound effect preference parameters corresponding to the target identity information are matched from the user profile library, where the user profile library includes identity information of different drivers and corresponding vehicle-mounted sound effect preference parameters. The setting idea of these data is as follows: according to the setting record of the driver who has driven the target vehicle for the vehicle-mounted sound effect of the vehicle, the setting times of different historical vehicle-mounted sound effect parameters set by each driver after getting on the vehicle are counted, and the historical vehicle-mounted sound effect parameter with the most setting times is determined as the vehicle-mounted sound effect preference parameter corresponding to the driver identity information. In addition, the vehicle-mounted sound effect parameter refers to various technical indicators and setting options for adjusting and optimizing the performance of the in-vehicle audio system. These parameters directly affect the sound quality, sound field effect and listening experience of the sound. In the embodiments of the present application, the vehicle-mounted sound effect parameter includes parameters such as equalizer (Equalizer, EQ), default volume and sound field configuration, where the default volume includes media volume and navigation prompt volume. The media volume refers to the playback volume of music, news and the like in the vehicle except the navigation prompt volume. The equalizer is used to adjust the gain of different frequency sound signals, and in the vehicle-mounted sound effect, it allows users to boost or attenuate each frequency band of the audio signal. The sound field configuration is mainly about the setting of the distribution and positioning of sound in the vehicle space. The vehicle-mounted sound effect preference parameter refers to the vehicle-mounted sound effect parameter that the driver tends to set based on personal sound effect preference when driving. S103: acquiring an actual media type listened to by the target driver in the vehicle, and adjusting and optimizing the initial sound effect preference parameter according to the actual media type to obtain a target sound effect preference parameter.

[0028] Specifically, after the initial sound effect preference parameter corresponding to the target driver is determined, the actual media type listened to by the target driver in the vehicle is acquired. A feasible acquisition method is to determine the currently active media process recorded continuously through the background process record of the vehicle system, and determine the media type corresponding to the media process as the actual media type listened to by the target driver currently. The media type includes but is not limited to music, broadcast, video, radio and the like. Further, the initial sound effect preference parameter is adjusted and optimized according to the actual media type to obtain the target sound effect preference parameter corresponding to the target driver. A realizable implementation manner is as follows: Based on the target driving target, the driver listens to the actual media type set by each vehicle sound effect parameter, and the set number of different media volume, different navigation prompt volume, different sound field configuration parameter and different equalizer parameter is counted. The most set number of preference media volume, the most set number of preference navigation prompt volume, the most set number of preference sound field configuration parameter and the most set number of preference equalizer parameter are selected respectively, and these selected parameters are used as reference. The media volume, navigation prompt volume, sound field configuration parameter and equalizer parameter in the initial sound effect preference parameter are adjusted and optimized, and the target driver's more preferred vehicle sound effect parameter when listening to the actual media type is obtained, that is, the target sound effect preference parameter.

[0029] S104: Obtain the actual speed of the target vehicle driven by the target driver. If the actual speed exceeds the preset speed threshold, adjust and optimize the equalizer parameter in the target sound effect preference parameter to obtain the final sound effect preference parameter.

[0030] Specifically, after the target sound effect preference parameter is determined, the actual speed of the target vehicle driven by the target driver is obtained through the preset speed sensor, and the actual speed is compared with the preset speed threshold. If the actual speed exceeds the speed threshold, it means that the speed of the target vehicle is fast, and the wind noise generated is also large, which makes it difficult to hear the voice in the played media content or navigation prompt in the vehicle. In order to make the target driver clearly hear the navigation prompt or the played media content, it is necessary to adjust and optimize the equalizer parameter in the target sound effect preference parameter to obtain the final sound effect preference parameter. One implementable embodiment is to increase the mid-frequency voice frequency band (1kHz~4kHz), which is the main frequency range of voice. Specifically, the equalizer setting in the vehicle audio system of the target vehicle is opened, the gain of the mid-frequency voice frequency band (1kHz~4kHz) in the equalizer setting is increased by 2dB or 3dB, and other frequency bands remain unchanged, so as to realize the adjustment and optimization of the equalizer parameter, so that the voice is clearer. In the embodiment of the application, the speed threshold is 100km / h, and in other embodiments, the speed threshold can also be 110km / h.

[0031] S105: If the actual speed does not exceed the speed threshold, when it is detected that the target driver needs to be prompted by voice navigation, the target sound effect preference parameter is adjusted and optimized to obtain the final sound effect preference parameter.

[0032] Specifically, if the actual vehicle speed does not exceed the vehicle speed threshold, it indicates that the current vehicle speed of the target vehicle is appropriate, and the wind noise generated is not too large. Then, it is detected whether the vehicle navigation will soon need to give a voice navigation prompt to the target driver. For example, if the vehicle screen pops up a predictive navigation prompt information of "300 meters ahead, turn right", it is determined that the target driver will soon need to be prompted for navigation, i.e., the vehicle navigation will issue a navigation prompt voice of turning right to remind the target driver to perform a right turn operation after the target vehicle travels 300 meters. Further, when it is detected that the target driver will soon need to be prompted for navigation, the actual driving prompt operation to be performed by the target driver is determined according to the navigation prompt information displayed on the vehicle screen, i.e., the correct driving operation of the target driver according to the navigation prompt information, such as turning right or U-turn, etc. Then, according to the vehicle navigation record of the target vehicle, the historical driving prompt operation that is executed incorrectly under the vehicle navigation is obtained, i.e., the driving operation that is not correctly performed by the person under the navigation prompt, for example, the historical driving prompt operation is a right turn, but the driver actually drives the target vehicle and does not normally turn right. The first frequency of repeated occurrence of a single historical driving prompt operation in all historical driving prompt operations is counted. If the first frequency exceeds the corresponding frequency threshold, the corresponding historical driving prompt operation is determined as the target driving operation, i.e., the driver is prone to make an error in the driving prompt operation. The vehicle navigation record includes, but is not limited to, the driving prompt operation that is executed incorrectly under the vehicle navigation and the vehicle speed range before the navigation prompt voice is issued, etc.

[0033] Further, based on the above vehicle navigation record, the historical vehicle speed range of the target vehicle before the navigation prompt voice is issued under the condition that a single target driving operation is executed incorrectly is obtained. The second frequency of repeated occurrence of a single historical vehicle speed range in all historical vehicle speed ranges is counted. If the second frequency exceeds the corresponding frequency threshold, the corresponding historical vehicle speed range is determined as the target vehicle speed range corresponding to the target driving operation, i.e., the vehicle speed range prone to cause the driver to make an error in the corresponding target driving operation. Then, the first weight of each target driving operation is determined, and the second weight of the target vehicle speed range corresponding to each target driving operation is determined. The first weight is the ratio of the first frequency of each target driving operation to the sum of the first frequencies of all target driving operations, and the first weight represents the possibility of making an error in the target driving operation in driving. The second weight is the ratio of the second frequency of a single target vehicle speed range corresponding to the target driving operation to the sum of the second frequencies of all target vehicle speed ranges corresponding to the target driving operation, and the second weight represents the possibility of causing an error in the corresponding target driving operation by the target vehicle speed range.

[0034] Further, the target sound effect preference parameter is adjusted and optimized according to the actual vehicle speed, the actual driving prompt operation, the first weight and the second weight, and a final sound effect preference parameter is obtained. A feasible adjustment and optimization manner is as follows: when the actual driving prompt operation is the target driving operation, if the actual vehicle speed is included in the target vehicle speed range corresponding to the actual driving prompt operation, the target vehicle speed range is determined as a reference vehicle speed range. A first product of the first weight of the actual driving prompt operation and the second weight of the corresponding reference vehicle speed range is calculated. The larger the first product is, the more likely it is that the target driver makes a mistake in performing the actual driving prompt operation. If the first product is greater than a preset first threshold value, it indicates that the target driver is more likely to make a driving operation mistake under the vehicle-mounted navigation prompt. Then, according to the first product, a risk coefficient of the target driver making a mistake in performing the actual driving prompt operation is determined. A feasible determination manner is as follows: according to the vehicle-mounted navigation, a first remaining time length of the target driver driving the target vehicle to reach the destination under the condition that the actual driving prompt operation is not performed incorrectly, i.e., the target vehicle normally travels according to the navigation route, is determined, and a planned arrival event of the target driver reaching the destination is received from a terminal of the target driver. Further, according to the vehicle-mounted navigation, a second remaining time length of the target driver driving the target vehicle to reach the destination under the condition that the actual driving prompt operation is performed incorrectly, i.e., the target vehicle does not normally travel according to the navigation route, is determined, and a new navigation route is planned. Then, the planned arrival time is subtracted from the current time to obtain a planned remaining time length. The second remaining time length is compared with the planned remaining time length. If the second remaining time length is greater than the planned remaining time length, it indicates that the target driver cannot reach the destination at the planned arrival time after the target vehicle does not travel according to the navigation, and then, according to a difference between the second remaining time length and the planned remaining time length, a correction coefficient is determined. The larger the difference is, the more the arrival time at the destination is later than the planned arrival time, and the larger the correction coefficient is. The correction coefficient is a positive number greater than 1. Specifically, according to the difference range in which the difference is located, a corresponding correction coefficient is determined through a first matching table stored in a database. The first matching table includes different difference ranges and corresponding correction coefficients. For example, the first matching table includes a difference range of 0-10 minutes and a corresponding correction coefficient of 1.1, a difference range of 10-20 minutes and a correction coefficient of 1.2, and so on. If the second remaining time length is not greater than the planned remaining time length, it indicates that the target driver can reach the destination at the planned arrival time after the target vehicle does not travel according to the navigation, and then, according to a ratio of the second remaining time length to the first remaining time length, a correction coefficient is determined. When the ratio is greater than 1, the larger the ratio is, the longer the new navigation route is than the original navigation route, and the larger the correction coefficient is. Specifically, according to the ratio range in which the ratio is located, a corresponding correction coefficient is determined through a second matching table stored in a database. The second matching table includes different ratio ranges and corresponding correction coefficients.

[0035] Further, the correction coefficient is multiplied with the first product to obtain a risk coefficient of the target driver to the actual driving prompt operation execution error, the greater the risk coefficient, the higher the risk degree caused by the execution error, the more need to reduce the media volume and increase the navigation prompt volume in advance to avoid the target driver being disturbed by the media volume in the vehicle and not hearing or not clearly hearing the navigation prompt voice, and the problem of driving route error occurs, and then according to the risk coefficient, the advance time and the increase amount of the navigation prompt volume are determined, the advance time is the time node of the media volume in the target vehicle being reduced compared with the time node of the navigation prompt voice being emitted, the greater the risk coefficient, the greater the advance time, and the greater the increase amount. Finally, according to the advance time, the media volume in the target sound effect preference parameter is reduced, and according to the determined increase amount, the navigation prompt volume in the target sound effect preference parameter is increased to obtain the final sound effect preference parameter. Exemplarily, the navigation prompt voice is a turn reminding, the advance time is 5 seconds, and the increase amount is 3 decibels. Therefore, 5 seconds before the turn reminding, the media volume in the vehicle is reduced and the navigation prompt volume is increased by 3 decibels. It should be noted that in the embodiments of the present application, the advance time and the increase amount of the navigation prompt volume can be determined according to a preset third matching table. The third matching table includes different risk coefficient ranges and corresponding advance times and increase amounts. Exemplarily, the third matching table includes a risk coefficient range of 0.5-1, and the corresponding advance time is 3 seconds and the increase amount is 2 decibels. If the risk coefficient is in the range of 0.5-1, the corresponding advance time is 3 seconds and the increase amount is 2 decibels.

[0036] In other embodiments, when the navigation voice prompt function of the target vehicle is in the off state and the actual driving prompt operation is the target driving operation, it means that the target driver may think that the navigation voice of the in-vehicle navigation is more interfering with driving, so he chooses to turn it off and only assists driving by viewing the navigation prompt information in the navigation interface. The second product of the first weight of each target driving operation and the second weight of the corresponding target speed range is calculated, and the target speed range including the actual speed is determined as the key speed range. The second products corresponding to each key speed range are summed to obtain the summed result. The larger the summed result, the greater the possibility that the target driver will incorrectly perform the driving prompt operation (driving operation prompted by the in-vehicle navigation) at the actual speed. If the summed result is greater than a preset second threshold, it indicates that the target driver is likely to have incorrectly executed the driving prompt operation at the actual vehicle speed. To reduce the risk of incorrect execution and prevent the target vehicle from deviating from the navigation, the third product of the first weight of the actual driving prompt operation and the second weight of each corresponding target speed range is calculated. The minimum third product is selected from these third products, and the target speed range corresponding to the minimum third product is determined as the first speed range. If the target vehicle's speed is within the first speed range, the likelihood of incorrect execution of the actual driving prompt operation is relatively low. Furthermore, if the navigation voice prompt function is disabled and the speed limit prompt cannot be announced, the speed limit information for the target vehicle's current road section is obtained based on the in-vehicle navigation system, and a second speed range is determined based on this speed limit information. Finally, an intersection operation is performed on the first speed range and the second speed range to obtain an appropriate speed range. Based on this appropriate speed range, a speed adjustment reminder is issued to the target driver. This not only reduces the risk of incorrect execution of the actual driving prompt operation but also prevents speeding.

[0037] In another embodiment, if the target vehicle is a range-extended vehicle and its range extender is not activated, the range extender in the target vehicle will activate when the remaining battery power falls below a preset battery power threshold, generating a loud range extender noise. The battery power threshold may be 20%. The target vehicle's on-board computer system then obtains the target vehicle's current actual battery power and average power consumption. The time it takes for the actual battery power to decrease to the battery power threshold is then determined based on the difference between the actual battery power and the battery power threshold, as well as the average power consumption. This time is then combined with the current time to determine the estimated activation time of the range extender in the target vehicle. Furthermore, based on the target driver's historical listening history of media types while driving the target vehicle, the target driver's historical media types are obtained. The number of recurrences of a single media type within all historical media types is then counted. If the number of recurrences exceeds a preset threshold, the corresponding media type is determined as the target media type, i.e., a media type that the target driver is likely to listen to.

[0038] Furthermore, based on the above historical listening records, historical time intervals during which the target driver listened to a single target media type are obtained. The recurrence frequency of a single historical time interval among all historical time intervals is counted. If the recurrence frequency exceeds a preset frequency threshold, the corresponding historical time interval is determined as a target time interval for the target media type, i.e., a time interval that is likely to be spent listening to the target media type. Then, a first weight is determined for each target media type, and a second weight is determined for each target time interval corresponding to the target media type. The first weight is the ratio of the number of recurrences of each target media type to the sum of the number of recurrences of all target media types, and the second weight is the ratio of the recurrence frequency of a single target time interval corresponding to the target media type to the sum of the recurrence frequencies of all target time intervals corresponding to the target media type.

[0039] The target time interval of the expected start time is determined as a key time interval. If a key time interval exists among the target time intervals corresponding to the target media type, the corresponding target media type is determined as the key media type. A fourth product is calculated for each key media type's first weight and the second weight of the corresponding key time interval. The larger the fourth product, the more likely the target driver is to listen to the corresponding key media type at the expected start time. If the fourth product exceeds a preset third threshold, the corresponding key media type is determined as a candidate media type. The preferred media volume corresponding to each candidate media type is then obtained. For details, see step S103 and will not be further described here. Then, the candidate media type with the highest preferred media volume is determined as the suitable listening media type. Finally, when the preset time is set before the expected start time, the actual in-vehicle sound effect parameters of the target vehicle are adjusted and optimized in advance based on the preferred media volume, preferred navigation prompt volume, preferred sound field configuration parameters and preferred equalizer parameters corresponding to the suitable listening media type, to obtain the optimized sound effect parameters. Under this in-vehicle sound effect adjustment and optimization, it can not only meet the personal sound effect needs of the target driver when listening to the media type, but also reduce the impact of the range extender noise on the target driver's emotions to a certain extent. It should be noted that the preset time length can be 3 seconds or 5 seconds. In addition, the historical listening record includes but is not limited to information such as the historical media types listened to by the target driver in the past and the corresponding historical listening time periods.

[0040] S106: When it is not detected that a voice navigation prompt is required for the target driver, congested traffic information of the road section where the target vehicle is located is obtained, and the target sound effect preference parameters are adjusted and optimized according to the congested traffic information to obtain final sound effect preference parameters.

[0041] S107: Configuring the in-vehicle sound effects in the cabin of the target vehicle according to the final sound effect preference parameters.

[0042] Specifically, if it is detected that the target driver does not need to be prompted for voice navigation, the congestion road condition information of the target road section is obtained through the in-vehicle navigation of the target vehicle, for example, the congestion road condition information is that the congestion is expected to be smooth in 20 minutes, the congestion is expected to be smooth in 10 minutes, etc. Since the driver in the car is prone to feel irritable and nervous during congestion, the sound field configuration parameter in the target sound preference parameter is adjusted according to the congestion road condition information, specifically, the rear sound volume ratio is increased and the output of the front row speaker is reduced to achieve sound channel balance, thereby relieving the target driver's irritable and nervous emotions. Finally, after the final sound preference parameter is determined, the in-vehicle sound effect of the target vehicle's cabin is configured according to the final sound preference parameter.

[0043] The implementation principle of the intelligent cabin adaptive scene method of the embodiments of the present application is: according to the target identity information, the target driver is initially matched with corresponding personalized in-vehicle sound effect parameters, i.e., initial sound preference parameters. Then, based on the initial sound preference parameters, the initial sound preference parameters are adjusted and optimized based on the actual media type currently listened to by the target driver, combined with the sound preference of the target driver when listening to the actual media type, to obtain the target sound preference parameters. Further, if the actual vehicle speed exceeds the vehicle speed threshold, it means that the wind noise is large during driving, and accordingly the equalizer parameter in the target sound preference parameter is adjusted to reduce the adverse effects of wind noise on the in-vehicle sound effect, to obtain the final sound preference parameter; if the actual vehicle speed does not exceed the vehicle speed threshold, if the target driver needs to be prompted for voice navigation, in order not to affect the target driver's listening to the navigation broadcast, the target sound preference parameter is adjusted and optimized to obtain the final sound preference parameter; if the target driver does not need to be prompted for voice navigation, the target sound preference parameter is adjusted and optimized according to the congestion road condition information, thereby relieving the target driver's irritable and nervous emotions during congestion, and further better meeting the driver's personalized demand for in-vehicle sound effect.

[0044] The following is an embodiment of the system of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the system embodiments of the present application, please refer to the method embodiments of the present application.

[0045] Please refer to Figure 2 The structure diagram of the intelligent cabin adaptive scene system provided by the embodiments of the present application. The intelligent cabin adaptive scene system can be realized by software, hardware or a combination of the two to become all or part of the system. The system includes an information acquisition module 11, a first adjustment module 12, a second adjustment module 13, a third adjustment module 14, a fourth adjustment module 15, a fifth adjustment module 16 and a sound effect configuration module 17.

[0046] The information acquisition module 11 is configured to obtain the biological feature information of the target driver who gets on the vehicle; The first adjusting module 12 is configured to determine target identity information corresponding to the target driver based on the biometric information, and match initial sound effect preference parameters corresponding to the target identity information from a preset user profile database, the user profile database including identity information of different drivers and corresponding vehicle sound effect preference parameters; The second adjusting module 13 is configured to obtain an actual media type listened to by the target driver in the vehicle, and adjust and optimize the initial sound effect preference parameters according to the actual media type to obtain target sound effect preference parameters; The third adjusting module 14 is configured to obtain an actual vehicle speed of the target vehicle driven by the target driver, and adjust and optimize equalizer parameters in the target sound effect preference parameters if the actual vehicle speed exceeds a preset vehicle speed threshold to obtain final sound effect preference parameters; The fourth adjusting module 15 is configured to adjust and optimize the target sound effect preference parameters to obtain final sound effect preference parameters if the actual vehicle speed does not exceed the vehicle speed threshold when it is detected that voice navigation prompting to the target driver is needed soon; The fifth adjusting module 16 is configured to obtain congestion road condition information of a road segment where the target vehicle is located, and adjust and optimize the target sound effect preference parameters according to the congestion road condition information to obtain final sound effect preference parameters if it is detected that voice navigation prompting to the target driver is not needed; The sound effect configuration module 17 is configured to configure vehicle sound effect in a cabin of the target vehicle according to the final sound effect preference parameters.

[0047] Optionally, the fourth adjusting module 15 is specifically configured to: obtain actual driving prompt operations to be performed by the target driver when it is detected that voice navigation prompting to the target driver is needed soon, and determine at least one target driving operation according to historical driving prompt operations that are performed in error under navigation of the target vehicle, the target driving operation being a driving prompt operation that is prone to be performed in error; determine at least one target vehicle speed range corresponding to the target driving operation according to a historical vehicle speed range before a navigation prompt voice is issued under a single target driving operation performed in error, the target vehicle speed range being a vehicle speed range that is prone to cause the target driving operation to be performed in error by the driver; determine a first weight of each target driving operation and a second weight of a target vehicle speed range corresponding to each target driving operation, the first weight representing a possibility of the target driving operation being performed in error, and the second weight representing a possibility of the target vehicle speed range causing the corresponding target driving operation to be performed in error; adjust and optimize the target sound effect preference parameters according to the actual vehicle speed, the actual driving prompt operation, the first weight and the second weight to obtain final sound effect preference parameters.

[0048] Optionally, the fourth adjustment module 15 is specifically configured to: When the actual driving prompt operation is the target driving operation, if the target speed range corresponding to the actual driving prompt operation includes the actual speed, the corresponding target speed range is determined as the reference speed range; Calculating a first product of a first weight of the actual driving prompt operation and a second weight of the corresponding reference vehicle speed range, and comparing the first product with a preset first threshold; If the first product exceeds a first threshold, determining a risk factor of the target driver performing an erroneous operation on the actual driving prompt based on the first product; Based on the risk factor, the lead time and the increase in the navigation prompt volume are determined. The lead time is the time in advance between the time when the media volume in the target vehicle is lowered and the time when the navigation prompt voice is issued. The greater the risk factor, the longer the lead time and the greater the increase. According to the advance time and the increase amount, the media volume and the navigation prompt volume in the target sound effect preference parameters are adjusted and optimized to obtain the final sound effect preference parameters.

[0049] Optionally, the fourth adjustment module 15 is specifically configured to: determining a first remaining time for the target driver to drive the target vehicle to the destination when the actual driving prompt operation is not executed incorrectly, and obtaining a planned arrival time of the target driver at the destination; determining a second remaining time for the target driver to drive the target vehicle to the destination in the event that an actual driving prompt operation is performed incorrectly; Determine the planned remaining time based on the planned arrival time and the current time. If the second remaining time is greater than the planned remaining time, determine the correction factor based on the difference between the second remaining time and the planned remaining time. If the second remaining time is not greater than the planned remaining time, the ratio of the second remaining time to the first remaining time is calculated, and when the ratio is greater than 1, a correction coefficient is determined based on the ratio; The correction coefficient is multiplied by the first product to obtain a risk coefficient of the target driver performing an erroneous operation on the actual driving prompt, where the correction coefficient is a positive number greater than 1.

[0050] Optional, such as Figure 3 As shown, the system also includes a speed adjustment reminder module 18, which is specifically used to: When the navigation voice prompt function of the target vehicle is in an off state and the actual driving prompt operation is a target driving operation, calculating a second product of the first weight of each target driving operation and the second weight of the corresponding target vehicle speed range; Determine a target vehicle speed range that includes the actual vehicle speed as a key vehicle speed range, and sum the second products corresponding to each key vehicle speed range to obtain a summation result; If the sum is greater than a preset second threshold, calculating a third product of the first weight of the actual driving prompt operation and the second weight of each corresponding target vehicle speed range, and selecting a minimum third product from each third product; Determine the target speed range corresponding to the minimum third product as the first speed range, obtain speed limit information of the road section where the target vehicle is located, and determine the second speed range based on the speed limit information; An intersection operation is performed on the first vehicle speed range and the second vehicle speed range to obtain a suitable vehicle speed range, and a speed adjustment reminder is issued according to the suitable vehicle speed range.

[0051] Optionally, the system further includes a parameter adjustment module 19, specifically configured to: When the target vehicle is a range-extended vehicle and the range extender is not started, obtain the current actual power and average power consumption of the target vehicle; Determine the expected start time of the range extender in the target vehicle based on the actual power and average power consumption; Determine the appropriate listening media type for the target driver at the expected start time; According to the type of media suitable for listening, the actual vehicle-mounted sound effect parameters of the target vehicle are adjusted and optimized at a preset time before the expected start time to obtain optimized sound effect parameters.

[0052] Optionally, the parameter adjustment module 19 is specifically configured to: Determining at least one target media type based on the historical media types that the target driver has listened to in the target vehicle in the past, where the target media type is a media type that the target driver is likely to listen to; Determining at least one corresponding target time interval based on a historical time interval during which the target driver listens to a single target media type, where the target time interval is a time interval during which the target driver is likely to listen to the corresponding target media type; According to each target media type and the corresponding target time interval, the appropriate listening media type for the target driver at the expected start time is determined.

[0053] It should be noted that the above-mentioned embodiments provide an intelligent cockpit adaptive scenario system, and only illustrate the division of the above-mentioned functional modules when executing the intelligent cockpit adaptive scenario method. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent cockpit adaptive scenario system provided in the above-mentioned embodiments and the intelligent cockpit adaptive scenario method embodiment are based on the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0054] The embodiment of the application further discloses a computer readable storage medium, and the computer readable storage medium stores a computer program.

[0055] The computer program can be stored in the computer readable medium, and the computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium includes any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the computer readable medium includes but is not limited to the above-mentioned components.

[0056] The computer readable storage medium stores the intelligent cabin adaptive scene method of the embodiment of the application, and is loaded and executed on the processor to facilitate the storage and application of the method.

[0057] The embodiment of the application further discloses an electronic device, and the computer readable storage medium stores a computer program, and the computer program is loaded and executed on the processor to realize the intelligent cabin adaptive scene method.

[0058] The electronic device can be a desktop computer, a notebook computer or a cloud server, and the electronic device includes but is not limited to a processor and a memory. For example, the electronic device can further include an input / output device, a network access device and a bus, etc.

[0059] The processor can be a central processing unit (CPU), and of course, according to the actual use, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), ready programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc. The application does not limit this.

[0060] Among them, the memory can be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device, or it can be an external storage device of the electronic device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD) or flash memory card (FC) equipped on the electronic device. In addition, the memory can also be a combination of an internal storage unit and an external storage device of the electronic device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or is to be output. This application does not impose any restrictions on this.

[0061] Among them, through this electronic device, an intelligent cockpit adaptive scene method of the above embodiment is stored in the memory of the electronic device, and is loaded and executed on the processor of the electronic device for easy use.

[0062] The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for intelligent cockpit self-adaptive scene, characterized in that: The method comprises: Obtain biometric information of the target driver who gets on the vehicle; Determining target identity information corresponding to the target driver based on the biometric information, and matching initial sound effect preference parameters corresponding to the target identity information from a preset user profile library, the user profile library including identity information of different drivers and corresponding in-vehicle sound effect preference parameters; Acquiring the actual media type listened to by the target driver in the car, and adjusting and optimizing the initial sound effect preference parameters according to the actual media type to obtain target sound effect preference parameters; obtaining an actual speed of a target vehicle driven by the target driver, and if the actual speed exceeds a preset speed threshold, adjusting and optimizing equalizer parameters in the target sound effect preference parameters to obtain final sound effect preference parameters; If the actual vehicle speed does not exceed the vehicle speed threshold, then when it is detected that a voice navigation prompt is about to be provided to the target driver, the target sound effect preference parameter is adjusted and optimized to obtain a final sound effect preference parameter; When no need for voice navigation prompting is detected for the target driver, obtaining traffic congestion information of the road section where the target vehicle is located, and adjusting and optimizing the target sound effect preference parameters according to the traffic congestion information to obtain final sound effect preference parameters; The vehicle-mounted sound effects in the cabin of the target vehicle are configured according to the final sound effect preference parameters.

2. The intelligent cockpit scene adaptation method according to claim 1, characterized in that: When it is detected that a voice navigation prompt is about to be provided to the target driver, the target sound effect preference parameters are adjusted and optimized to obtain final sound effect preference parameters, specifically including: When it is detected that a voice navigation prompt is about to be provided to the target driver, obtaining an actual driving prompt operation to be performed by the target driver and determining at least one target driving operation based on historical driving prompt operations that were performed incorrectly under the navigation of the target vehicle, the target driving operation being a driving prompt operation that is prone to being performed incorrectly; determining at least one corresponding target speed range based on a historical vehicle speed range before the navigation prompt voice is issued in the case of a single target driving operation being performed incorrectly, wherein the target speed range is a speed range that is likely to cause the driver to perform the corresponding target driving operation incorrectly; determining a first weight for each target driving operation and a second weight for a target vehicle speed range corresponding to each target driving operation, wherein the first weight represents a probability of an error in executing the target driving operation, and the second weight represents a probability that the target vehicle speed range will cause an error in executing the corresponding target driving operation; The target sound effect preference parameters are adjusted and optimized according to the actual vehicle speed, the actual driving prompt operation, the first weight, and the second weight to obtain final sound effect preference parameters.

3. The intelligent cockpit scene adaptation method according to claim 2, characterized in that: The adjusting and optimizing the target sound effect preference parameter according to the actual vehicle speed, the actual driving prompt operation, the first weight, and the second weight to obtain the final sound effect preference parameter specifically includes: When the actual driving prompt operation is a target driving operation, if the actual vehicle speed is included in the target vehicle speed range corresponding to the actual driving prompt operation, determining the corresponding target vehicle speed range as the reference vehicle speed range; Calculating a first product of a first weight of the actual driving prompt operation and a second weight of the corresponding reference vehicle speed range, and comparing the first product with a preset first threshold; If the first product exceeds the first threshold, determining, based on the first product, a risk coefficient of the target driver performing an erroneous operation on the actual driving prompt; Determining an advance time and an increase in the navigation prompt volume based on the risk factor, wherein the advance time is the time before the time when the media volume in the target vehicle is lowered compared to the time when the navigation prompt voice is emitted, and a greater risk factor leads to a greater advance time and a greater increase in the volume; According to the advance time and the increase amount, the media volume and the navigation prompt volume in the target sound effect preference parameters are adjusted and optimized to obtain final sound effect preference parameters.

4. The intelligent cockpit scene adaptation method according to claim 3, characterized in that: Determining, based on the first product, a risk coefficient of the target driver performing an erroneous operation on the actual driving prompt operation specifically includes: determining, when the actual driving prompt operation is not executed erroneously, a first remaining time for the target driver to drive the target vehicle to reach the destination, and obtaining a planned arrival time for the target driver to arrive at the destination; a second remaining time for the target driver to drive the target vehicle to the destination when it is determined that the actual driving prompt operation is performed incorrectly; Determining a planned remaining time based on the planned arrival time and the current time; if the second remaining time is greater than the planned remaining time, determining a correction coefficient based on the difference between the second remaining time and the planned remaining time; If the second remaining time is not greater than the planned remaining time, calculating a ratio of the second remaining time to the first remaining time, and when the ratio is greater than 1, determining a correction coefficient based on the ratio; The correction coefficient is multiplied by the first product to obtain a risk coefficient of the target driver performing an error in the actual driving prompt operation, where the correction coefficient is a positive number greater than 1.

5. The intelligent cockpit scene adaptation method according to claim 2, characterized in that: The method further comprises: When the navigation voice prompt function of the target vehicle is in an off state and the actual driving prompt operation is a target driving operation, calculating a second product of the first weight of each target driving operation and the second weight of the corresponding target vehicle speed range; determining a target vehicle speed range including the actual vehicle speed as a key vehicle speed range, and summing the second products corresponding to the key vehicle speed ranges to obtain a summation result; If the sum is greater than a preset second threshold, calculating a third product of the first weight of the actual driving prompt operation and the second weight of each corresponding target vehicle speed range, and selecting a minimum third product from each of the third products; Determining a target vehicle speed range corresponding to the minimum third product as a first vehicle speed range, obtaining speed limit information of a road section where the target vehicle is located, and determining a second vehicle speed range based on the speed limit information; An intersection operation is performed on the first vehicle speed range and the second vehicle speed range to obtain a suitable vehicle speed range, and a speed adjustment reminder is issued according to the suitable vehicle speed range.

6. The intelligent cockpit scene adaptation method according to claim 1, characterized in that: The method further comprises: When the target vehicle is a range-extended vehicle and the range extender is not started, obtaining the current actual power and average power consumption of the target vehicle; determining an estimated start time of a range extender in the target vehicle based on the actual power consumption and the average power consumption; Determining a media type suitable for the target driver to listen to at the expected start time; According to the suitable listening media type, the actual vehicle-mounted sound effect parameters of the target vehicle are adjusted and optimized at a preset time before the expected start time to obtain optimized sound effect parameters.

7. The intelligent cockpit scene adaptation method according to claim 6, characterized in that: Determining the media type suitable for the target driver to listen to at the expected start time specifically includes: Determining at least one target media type based on the historical media types that the target driver has listened to in the target vehicle in the past, wherein the target media type is a media type that the target driver is likely to listen to; Determining at least one corresponding target time interval based on a historical time interval during which the target driver listens to a single target media type, wherein the target time interval is a time interval during which the target driver is likely to listen to the corresponding target media type; According to each target media type and the corresponding target time interval, a media type suitable for the target driver to listen to at the expected start time is determined.

8. An intelligent cockpit adaptive scene system, characterized in that: include: An information acquisition module (11) is used to acquire biometric information of the target driver who gets on the vehicle; A first adjustment module (12) is configured to determine target identity information corresponding to the target driver based on the biometric information, and match initial sound effect preference parameters corresponding to the target identity information from a preset user archive, wherein the user archive includes identity information of different drivers and corresponding in-vehicle sound effect preference parameters; A second adjustment module (13) is used to obtain the actual media type listened to by the target driver in the car, and adjust and optimize the initial sound effect preference parameters according to the actual media type to obtain target sound effect preference parameters; a third adjustment module (14), configured to obtain an actual vehicle speed of the target vehicle driven by the target driver, and if the actual vehicle speed exceeds a preset vehicle speed threshold, to adjust and optimize the equalizer parameters in the target sound effect preference parameters to obtain final sound effect preference parameters; a fourth adjustment module (15), configured to adjust and optimize the target sound effect preference parameters to obtain final sound effect preference parameters when it is detected that a voice navigation prompt is about to be given to the target driver if the actual vehicle speed does not exceed the vehicle speed threshold; a fifth adjustment module (16), configured to obtain, when no voice navigation prompt is required for the target driver, traffic congestion information of the road section where the target vehicle is located, and adjust and optimize the target sound effect preference parameters according to the traffic congestion information to obtain final sound effect preference parameters; A sound effect configuration module (17) is used to configure the vehicle-mounted sound effects in the cabin of the target vehicle according to the final sound effect preference parameters.

9. A computer-readable storage medium storing a computer program, wherein: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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