Alarm method, system and device of intelligent cabin and storage medium

Abstract

The invention relates to an alarm method, system and device for an intelligent cabin and a storage medium, and the method comprises the steps: obtaining alarm information, carrying out the feature extraction of the alarm information through a feature extraction algorithm, and obtaining a feature set of the alarm information; the feature set of the alarm information is analyzed, an analysis result of the alarm information is obtained, and the analysis result comprises the priority of the alarm information and an alarm popup window format; the current audio state of the vehicle is obtained, analysis is carried out in combination with the alarm information priority and the current audio state of the vehicle, and an audio adjustment scheme is obtained; and according to the audio adjustment scheme, the output audio of the vehicle is adjusted, and the alarm pop-up window format is transmitted to the central control large screen. The method has the effect of enhancing the user experience.

Description

Technical Field

[0001] The present application relates to the field of automotive electronic technology, and in particular to an alarm method, system, device and storage medium for a smart cockpit. Background Art

[0002] With the improvement of automobile intelligence, the in-vehicle information system has gradually become one of the important indicators of automobile modernization. At present, most high-end models are equipped with relatively complete in-vehicle information systems, including navigation, entertainment, communication and other functions. Vehicle alarm information mainly relies on the indicator lights on the dashboard and independently set buzzers or small speakers to issue warning sounds. Although this method is simple and effective, it is easy to cause information overload to the driver when multiple information is concurrent, affecting driving safety. In addition, due to hardware limitations, the sound quality of such alarms is limited, which is difficult to meet the needs of modern cars for high-quality sound effects, and lacks an effective coordination mechanism with other audio sources in the current car.

[0003] The above-mentioned existing technical solutions have the following defects: the traditional single-channel alarm sound cannot be integrated with the complex and diverse sound system in the car, resulting in a decrease in passenger experience, so there is room for improvement. Summary of the invention

[0004] In order to enhance the user experience, the present application provides an alarm method, system, device and storage medium for a smart cockpit.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: An alarm method for a smart cockpit, the alarm method for a smart cockpit comprising: Acquire alarm information, and extract features from the alarm information using a feature extraction algorithm to obtain a feature set of the alarm information; Analyze the feature set of the alarm information to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window; Acquire the current audio state of the vehicle, analyze the alarm information priority and the current audio state of the vehicle, and obtain an audio adjustment plan; According to the audio adjustment scheme, the output audio of the vehicle is adjusted, and the alarm pop-up window format is transmitted to the central control large screen.

[0006] By adopting the above technical solution, by obtaining alarm information and extracting its features using a feature extraction algorithm, a feature set of the alarm information is obtained, which can extract important features from complex alarm information, help identify the key attributes of the alarm, and improve the accuracy and efficiency of subsequent analysis; by analyzing the feature set of the alarm information, the system can assign priority and display format to each alarm information according to specific rules or algorithms, ensure that the system handles the alarm according to importance, and give priority to notifying the driver in an emergency, and design a suitable pop-up window format to ensure that the information is clearly and intuitively conveyed; by obtaining the current audio status of the vehicle and combining the priority analysis of the alarm information, a reasonable audio adjustment plan can be formulated. In this way, the system can ensure that the alarm sound is played at an appropriate volume and timing to avoid disturbing the driver when it is not necessary, and ensure that the alarm can be heard in an emergency; according to the formulated audio adjustment plan, the audio output of the vehicle is adjusted to ensure that the alarm sound can be played at an appropriate volume and frequency, and the designed alarm pop-up window format is transmitted to the central control large screen, which can ensure that the driver can receive the alarm information in a timely manner both visually and auditorily, and improve the driving safety and experience.

[0007] In a preferred example, the present application may be further configured as follows: the alarm information is obtained, and a feature extraction algorithm is used to extract features from the alarm information, and the feature set of the alarm information obtained includes: Analyze the alarm information by using natural language processing technology combined with TF-IDF algorithm to obtain key feature points of the alarm information; The principal component analysis method is applied to perform dimension reduction processing on the key feature points of the alarm information to obtain the feature set of the alarm information.

[0008] By adopting the above technical solution, the alarm information is parsed through natural language processing technology combined with the TF-IDF algorithm, which can effectively extract key feature points in documents or texts to help the system mine the most relevant and important features from a large amount of text, and help extract the most representative and relevant content from complex texts, thereby improving the system's understanding and processing capabilities of alarm information; by using the principal component analysis method to reduce the dimensionality of the key feature points of the alarm information, the data dimension can be reduced while maintaining important information in the data. The final feature set can be processed more efficiently in subsequent analysis without affecting the overall performance and prediction accuracy of the system.

[0009] In a preferred example, the present application may be further configured as follows: the feature set of the alarm information is analyzed to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window: By analyzing the feature set of the alarm information in combination with predefined rules and historical data patterns, the alarm information is prioritized to determine the priority of the alarm information; The corresponding alarm pop-up window format is generated according to the priority of the alarm information, and the alarm pop-up window format includes color, size, and display position.

[0010] By adopting the above technical solution, the feature set of the alarm information is analyzed by combining pre-defined rules and historical data patterns. Through such analysis, the alarm information can be prioritized to ensure that the most urgent or important alarm information can be processed first; according to the priority of the alarm information, the system will generate a corresponding alarm pop-up window format for each alarm information. By adjusting the color, size and display position of the pop-up window, the urgency of the alarm information can be conveyed intuitively.

[0011] In a preferred example, the present application may be further configured as follows: the obtaining of the current audio status of the vehicle includes: Acquiring an audio signal inside the vehicle using a microphone array, and filtering the audio signal inside the vehicle using an adaptive filtering technique to obtain an internal audio signal; Positioning and analyzing the internal audio signal by a sound source localization algorithm to obtain the position and characteristics of the internal audio signal; Performing pattern recognition on the internal audio signal through a convolutional neural network to obtain an audio pattern of the internal audio signal; The position and characteristics of the internal audio signal and the audio mode of the internal audio signal are statistically integrated to obtain the current audio state of the vehicle.

[0012] By adopting the above technical solution, the audio signals inside the vehicle can be effectively collected by using a microphone array. Then, the adaptive filtering technology can dynamically adjust the filter coefficients according to environmental changes, effectively reduce noise and interference, and highlight useful audio signals to ensure that the audio signal quality is higher and the error is less during subsequent processing; the sound source localization algorithm is used to locate and analyze the audio signals inside the vehicle, and the specific position and sound characteristics of each sound source are obtained to help the system accurately identify the sound source and provide accurate context information for subsequent processing; the convolutional neural network is used to perform pattern recognition on the internal audio signals, and the types and patterns of different audio signals are automatically identified and classified, thereby helping the system to accurately classify and analyze complex audio events; by integrating and statistically analyzing the position, characteristics and audio patterns of the internal audio signals, the system can generate the current audio status of the vehicle, helping the system to fully understand the acoustic environment inside the vehicle, thereby providing data support for further decision-making and control.

[0013] In a preferred example, the present application may be further configured as follows: the analysis is performed in combination with the alarm information priority and the current audio state of the vehicle to obtain an audio adjustment solution including: Determining the urgency of adjusting the audio output according to the priority of the alarm information; Performing a weight analysis on the current audio state of the vehicle to obtain a weight ratio of the current audio state of the vehicle; The audio adjustment plan is generated according to the urgency of adjusting the audio output and the weight ratio of the current audio state of the vehicle.

[0014] By adopting the above technical solution, by determining the urgency of adjusting the audio output according to the priority of the alarm information, the system can give priority to important alarm information in an emergency, ensure that key information can be conveyed to the user in a timely manner, and thus improve vehicle safety; by performing a weighted analysis on the current audio status of the vehicle, the system can assign weight ratios according to the importance of different sound sources, thereby providing a basis for subsequent audio adjustments and ensuring that high-priority audio signals are given priority; based on the weight ratio of the urgency and the audio status, the system will generate an appropriate audio adjustment plan, and the system will comprehensively analyze the urgency of the alarm information and the weight ratio of the audio status to decide how to adjust the audio output.

[0015] In a preferred example, the present application may be further configured as follows: the alarm method of the smart cockpit further includes: Recording the user's setting preferences and usage time points within a preset time period, and integrating the user's setting preferences and activation time points to obtain user preference data; The priority of the alarm information is dynamically adjusted according to the user preference data.

[0016] By adopting the above technical solution, by recording the user's setting preferences and usage time points within a preset time period and integrating the data, the system can effectively capture and analyze the user's usage habits in different time periods, generate personalized user preference data, and provide a basis for subsequent dynamic adjustments; by dynamically adjusting the alarm information priority based on user preference data, the system can intelligently adjust the prompt method and urgency of the alarm information according to the user's usage habits and time points, optimize the user's alarm experience, and ensure that the most appropriate prompt is provided at the right time.

[0017] The second object of the invention is achieved by the following technical solutions: An alarm system for a smart cockpit, the alarm system for the smart cockpit comprising: A feature extraction module is used to obtain alarm information, and extract features from the alarm information using a feature extraction algorithm to obtain a feature set of the alarm information; An information analysis module, used to analyze the feature set of the alarm information to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window; An adjustment scheme determination module is used to obtain the current audio state of the vehicle, analyze the alarm information priority and the current audio state of the vehicle, and obtain an audio adjustment scheme; The adjustment display module is used to adjust the output audio of the vehicle according to the audio adjustment scheme, and transmit the alarm pop-up window format to the central control large screen.

[0018] By adopting the above technical solution, by obtaining alarm information and extracting its features using a feature extraction algorithm, a feature set of the alarm information is obtained, which can extract important features from complex alarm information, help identify the key attributes of the alarm, and improve the accuracy and efficiency of subsequent analysis; by analyzing the feature set of the alarm information, the system can assign priority and display format to each alarm information according to specific rules or algorithms, ensure that the system handles the alarm according to importance, and give priority to notifying the driver in an emergency, and design a suitable pop-up window format to ensure that the information is clearly and intuitively conveyed; by obtaining the current audio status of the vehicle and combining the priority analysis of the alarm information, a reasonable audio adjustment plan can be formulated. In this way, the system can ensure that the alarm sound is played at an appropriate volume and timing to avoid disturbing the driver when it is not necessary, and ensure that the alarm can be heard in an emergency; according to the formulated audio adjustment plan, the audio output of the vehicle is adjusted to ensure that the alarm sound can be played at an appropriate volume and frequency, and the designed alarm pop-up window format is transmitted to the central control large screen, which can ensure that the driver can receive the alarm information in a timely manner both visually and auditorily, and improve the driving safety and experience.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent cockpit alarm method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent cockpit alarm method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring alarm information and extracting its features using feature extraction algorithms, the feature set of alarm information can be obtained. Important features can be extracted from complex alarm information, which helps to identify the key attributes of the alarm and improve the accuracy and efficiency of subsequent analysis. By analyzing the feature set of alarm information, the system can assign priority and display format to each alarm information according to specific rules or algorithms, ensuring that the system handles alarms according to importance, notifying the driver first in an emergency, and designing a suitable pop-up window format to ensure that information is conveyed clearly and intuitively. 2. By obtaining the current audio status of the vehicle and combining it with the priority analysis of the alarm information, a reasonable audio adjustment plan can be formulated. In this way, the system can ensure that the alarm sound is played at an appropriate volume and timing to avoid disturbing the driver when it is not necessary, while ensuring that the alarm can be heard in an emergency; according to the formulated audio adjustment plan, the vehicle's audio output is adjusted to ensure that the alarm sound can be played at an appropriate volume and frequency, and the designed alarm pop-up format is transmitted to the central control screen. This ensures that the driver can receive the alarm information in a timely manner both visually and aurally, thereby improving driving safety and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of an alarm method for a smart cockpit in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in an alarm method for a smart cockpit in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S20 in an alarm method for a smart cockpit in one embodiment of the present application; Figure 4 is a flowchart for implementing step S30 in an alarm method for a smart cockpit in one embodiment of the present application; Figure 5 This is another implementation flow chart of step S30 in an alarm method for a smart cockpit in one embodiment of the present application; Figure 6 is another implementation flow chart of an alarm method for a smart cockpit in one embodiment of the present application; Figure 7 It is a principle block diagram of an alarm system of a smart cockpit in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1As shown, the present application discloses an alarm method for a smart cockpit, which specifically includes the following steps: S10: Acquire alarm information, and use a feature extraction algorithm to extract features from the alarm information to obtain a feature set of the alarm information.

[0025] Specifically, alarm information is collected in real time from the vehicle's sensor system or control module. The information includes basic attributes such as alarm type, time, and location. Natural language processing technology, signal processing technology, or image processing technology are used to extract features based on the nature of the alarm information to generate a feature set that includes multiple dimensional features of the alarm information, such as type, frequency, severity, etc.

[0026] S20: Analyze the feature set of the alarm information to obtain the analysis result of the alarm information, where the analysis result includes the priority of the alarm information and the format of the alarm pop-up window.

[0027] Specifically, machine learning algorithms such as decision trees, support vector machines or rule engines are used to analyze feature sets and identify key factors that affect priority. Based on the analysis results, a priority is assigned to each alarm message, which is usually divided into high, medium and low. The visual attributes of the pop-up window are determined according to the priority, such as high priority: use red, larger size, and center position; medium priority: use yellow, medium size, and side position; low priority: use green, small size, and bottom position.

[0028] S30: Acquire the current audio state of the vehicle, analyze the alarm information priority and the current audio state of the vehicle, and obtain an audio adjustment plan.

[0029] Specifically, the current audio status is obtained from the vehicle entertainment system, including volume and audio source, such as radio, music player, etc. The relationship between the current audio status and the alarm priority is analyzed, and an audio adjustment strategy is designed. For example, a high-priority alarm may require interrupting the current audio and playing an alarm sound. A medium-priority alarm may require lowering the volume and playing a notification sound. Finally, specific audio adjustment measures are determined, such as volume adjustment, pausing the current audio source, inserting an alarm sound, etc.

[0030] S40: Adjust the vehicle's output audio according to the audio adjustment plan, and transmit the alarm pop-up format to the central control screen.

[0031] Specifically, according to the audio adjustment plan, the volume is adjusted, the sound source is switched or the alarm is played through the vehicle audio system interface, and the generated alarm pop-up format data is transmitted to the central control screen using the vehicle bus (such as CAN bus) or a dedicated interface, and ensure that the large screen correctly receives and displays the designed pop-up format.

[0032] By adopting the above technical solution, by obtaining alarm information and extracting its features using a feature extraction algorithm, a feature set of the alarm information is obtained, which can extract important features from complex alarm information, help identify the key attributes of the alarm, and improve the accuracy and efficiency of subsequent analysis; by analyzing the feature set of the alarm information, the system can assign priority and display format to each alarm information according to specific rules or algorithms, ensure that the system handles the alarm according to importance, and give priority to notifying the driver in an emergency, and design a suitable pop-up window format to ensure that the information is clearly and intuitively conveyed; by obtaining the current audio status of the vehicle and combining the priority analysis of the alarm information, a reasonable audio adjustment plan can be formulated. In this way, the system can ensure that the alarm sound is played at an appropriate volume and timing to avoid disturbing the driver when it is not necessary, and ensure that the alarm can be heard in an emergency; according to the formulated audio adjustment plan, the audio output of the vehicle is adjusted to ensure that the alarm sound can be played at an appropriate volume and frequency, and the designed alarm pop-up window format is transmitted to the central control large screen, which can ensure that the driver can receive the alarm information in a timely manner both visually and auditorily, and improve the driving safety and experience.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the alarm information is obtained, and the feature extraction algorithm is used to extract the features of the alarm information to obtain the feature set of the alarm information, which specifically includes: S11: Analyze the alarm information using natural language processing technology combined with the TF-IDF algorithm to obtain key feature points of the alarm information.

[0034] Specifically, the alarm information text is subjected to standard natural language processing preprocessing steps, including dividing the alarm information text into separate words, removing common words that are not important in feature extraction, and restoring the words to their basic form. According to the TF-IDF algorithm, the frequency of each word in the alarm information text is calculated, and words with high importance are selected according to the TF-IDF value as the key feature points of the alarm information.

[0035] S12: Apply the principal component analysis method to perform dimensionality reduction processing on the key feature points of the alarm information to obtain a feature set of the alarm information.

[0036] Specifically, the extracted key feature points and their TF-IDF values ​​constitute a feature matrix, with the feature points of each alarm information as a row and each feature point as a column. The feature matrix is ​​standardized so that the value of each feature point has the same scale. The standardized data is processed by principal component analysis. After PCA processing, the generated low-dimensional feature set can effectively represent the key information of the original alarm information for subsequent analysis and processing.

[0037] In one embodiment, if Figure 3 As shown, in step S20, the feature set of the alarm information is analyzed to obtain the analysis result of the alarm information, which includes the priority of the alarm information and the format of the alarm pop-up window, specifically including: S21: Analyze the feature set of the alarm information by combining pre-defined rules and historical data patterns, prioritize the alarm information, and determine the priority of the alarm information.

[0038] Specifically, a set of rules is created to evaluate the importance of alarm information, such as a certain type of alarm has a higher priority or the priority of certain alarms increases in certain situations (such as when driving at high speed). The feature set of the alarm information is analyzed by combining pre-defined rules and historical data patterns. For example, if a certain alarm has often led to serious consequences in the past, its priority can be increased. According to the rules and historical data patterns, the feature set of each alarm message is scored, and the alarm information is sorted according to the score. Alarm information with a higher priority will be ranked first to determine the priority of the alarm information.

[0039] S22: Generate a corresponding alarm pop-up window format according to the priority of the alarm information, and the alarm pop-up window format includes color, size, and display position.

[0040] Specifically, different pop-up formats are designed for alarm information of different priorities. According to the priority of the alarm information, the corresponding pop-up format is generated and displayed on the user interface. The alarm pop-up format includes color, size, and display position, such as eye-catching colors for high priority and soft colors for low priority.

[0041] In one embodiment, if Figure 4 As shown, in step S30, the current audio state of the vehicle is obtained, which specifically includes: S31: using a microphone array to obtain an audio signal inside the vehicle, and filtering the audio signal inside the vehicle through an adaptive filtering technology to obtain an internal audio signal.

[0042] Specifically, one or more microphone arrays are installed inside the vehicle to capture the audio signals inside the vehicle. These microphones need to be evenly distributed inside the vehicle to fully capture the sound. The microphone array collects the audio signals inside the vehicle in real time, and uses an adaptive filter (such as the LMS algorithm) to process the audio signals, filter out background noise and interference, extract effective signals, and finally obtain internal audio signals.

[0043] S32: Positioning and analyzing the internal audio signal through a sound source localization algorithm to obtain the position and characteristics of the internal audio signal.

[0044] Specifically, commonly used sound source localization algorithms, such as TDOA and beamforming, are used to determine the location of the sound source. The specific position and direction of the internal audio signal, such as X, Y, and Z coordinates, are calculated through the algorithm, and the characteristics of the sound, such as frequency and amplitude, are analyzed to identify the nature of the sound, such as speech, music, alarms, etc., and finally the position and characteristics of the internal audio signal are obtained.

[0045] S33: Perform pattern recognition on the internal audio signal through a convolutional neural network to obtain an audio pattern of the internal audio signal.

[0046] Specifically, CNN receives the filtered audio signal and performs feature extraction, and identifies the pattern of the audio signal, such as speech, music, noise, alarm, etc., through hierarchical calculation, and identifies the audio pattern of the internal audio signal.

[0047] S34: Statistically integrating the position and characteristics of the internal audio signal and the audio mode of the internal audio signal to obtain a current audio state of the vehicle.

[0048] Specifically, the position and characteristic information obtained by locating the sound source are integrated with the audio pattern recognized by the convolutional neural network. By performing statistical analysis on the integrated data, a comprehensive description of the current audio state is formed. This state may include the type of audio currently being played, the source location, and its characteristics, that is, the current audio state of the vehicle.

[0049] In one embodiment, if Figure 5 As shown, in step S30, the priority of the alarm information and the current audio state of the vehicle are analyzed to obtain an audio adjustment solution, which specifically includes: S35: Determine the urgency of adjusting the audio output according to the priority of the alarm information.

[0050] Specifically, the priority of the alarm information that has been evaluated is obtained from the previous system, and the mapping rules from priority to urgency are defined. For example, high-priority alarm information is mapped to high urgency, medium priority is mapped to medium urgency, and low priority is mapped to low urgency. According to the priority and mapping rules, the urgency of the audio output adjustment required for each alarm message is determined.

[0051] S36: Perform a weight analysis on the current audio state of the vehicle to obtain a weight ratio of the current audio state of the vehicle.

[0052] Specifically, a weight analysis is performed on the vehicle's current audio status, including volume, source type (such as radio, music player), mute status, etc., and corresponding weights are set according to the current volume. A higher volume may require a larger adjustment, and different sound sources may have different importances. Different weights can be set for each sound source. For example, a navigation prompt may be more important than music. If the audio system is in mute mode, special processing may be required, such as temporarily unmuting the system to play an emergency alarm. Based on the above weight factors, the total weight ratio of the current audio status is calculated. For example, the weighted sum of the volume weight, sound source weight, and mute status weight can form a comprehensive weight ratio that reflects the complexity of the current audio status and the need for adjustment.

[0053] S37: Generate an audio adjustment plan according to the urgency of adjusting the audio output and the weight ratio of the current audio state of the vehicle.

[0054] Specifically, based on the urgency of the audio output and the weight ratio of the current audio status, the required adjustment strength and method are comprehensively evaluated, and different adjustment strategies are selected for different urgency levels and weight ratios. For example, high urgency and high weight ratios may require immediate volume reduction or audio pause, and a specific audio adjustment plan is formulated, including the magnitude of the adjustment (such as how much to reduce the volume), the rate of adjustment (such as immediate or gradual), and the duration of the adjustment (such as how long to maintain the adjustment state).

[0055] In one embodiment, if Figure 6 As shown, the alarm method of the smart cockpit also includes: S50: Recording the user's setting preferences and usage time points within a preset time period, and integrating the user's setting preferences and the activation time points to obtain user preference data.

[0056] Specifically, determine the time period for recording user setting preferences, such as daily, weekly or monthly. According to the length of the time period, set an appropriate recording frequency to ensure the continuity and integrity of the data. Record the user's setting preferences and usage time points within the preset time period, such as volume settings, prompt sound types, priority filtering, etc., and add timestamps to identify specific usage time points when recording user operations. By analyzing the usage time points, identify the user's accustomed usage time periods, such as commuting time, night driving, etc., and correlate the user's setting preferences with the usage time points to form a complete user preference data set.

[0057] S60: Dynamically adjust the priority of the alarm information according to the user preference data.

[0058] Specifically, based on user preference data, define adjustment rules for alarm information priority. For example, increase the priority of certain alarm information during time periods frequently used by users, monitor changes in user preference data in real time, dynamically adjust the priority of alarm information, and ensure that alarm information prompts meet user preferences.

[0059] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] In one embodiment, an alarm system for a smart cockpit is provided, and the alarm system for the smart cockpit corresponds one-to-one to the alarm method for the smart cockpit in the above embodiment. Figure 7 As shown, the alarm system of the intelligent cockpit includes a feature extraction module, an information analysis module, an adjustment scheme determination module and an adjustment display module. The detailed description of each functional module is as follows: The feature extraction module is used to obtain alarm information, extract features from the alarm information using a feature extraction algorithm, and obtain a feature set of the alarm information; The information analysis module is used to analyze the feature set of the alarm information and obtain the analysis results of the alarm information, including the priority of the alarm information and the format of the alarm pop-up window; The adjustment scheme determination module is used to obtain the current audio status of the vehicle, analyze the alarm information priority and the current audio status of the vehicle, and obtain the audio adjustment scheme; The adjustment display module is used to adjust the output audio of the vehicle according to the audio adjustment plan, and transmit the alarm pop-up window format to the central control screen.

[0061] Optionally, the alarm system of the smart cockpit further includes: A preference collection module is used to record the user's setting preferences and usage time points within a preset time period, and integrate the user's setting preferences and activation time points to obtain user preference data; The priority adjustment module is used to dynamically adjust the priority of alarm information based on user preference data.

[0062] Optionally, the feature extraction module includes: The parsing submodule is used to parse the alarm information using natural language processing technology combined with the TF-IDF algorithm to obtain the key feature points of the alarm information; The dimension reduction submodule is used to apply the principal component analysis method to reduce the dimension of the key feature points of the alarm information to obtain the feature set of the alarm information.

[0063] Optionally, the information analysis module includes: The feature analysis submodule is used to analyze the feature set of the alarm information by combining pre-defined rules and historical data patterns, prioritize the alarm information, and determine the priority of the alarm information.

[0064] The pop-up window format submodule is used to generate the corresponding alarm pop-up window format according to the priority of the alarm information. The alarm pop-up window format includes color, size, and display position.

[0065] Optionally, the module for determining an adjustment plan includes: The audio acquisition submodule is used to acquire the audio signal inside the vehicle using the microphone array, and filter the audio signal inside the vehicle using the adaptive filtering technology to obtain the internal audio signal; The positioning and analysis submodule is used to locate and analyze the internal audio signal through the sound source localization algorithm to obtain the position and characteristics of the internal audio signal; A pattern recognition submodule, used for performing pattern recognition on the internal audio signal through a convolutional neural network to obtain an audio pattern of the internal audio signal; The integrated data submodule is used to statistically integrate the position and characteristics of the internal audio signal and the audio mode of the internal audio signal to obtain the current audio status of the vehicle.

[0066] The urgency determination submodule is used to determine the urgency of adjusting the audio output according to the priority of the alarm information; The weight analysis submodule is used to perform weight analysis on the current audio state of the vehicle to obtain the weight ratio of the current audio state of the vehicle; The adjustment scheme generation submodule is used to generate an audio adjustment scheme according to the urgency of adjusting the audio output and the weight ratio of the current audio state of the vehicle.

[0067] For the specific definition of an alarm system for a smart cockpit, please refer to the definition of an alarm method for a smart cockpit above, which will not be repeated here. Each module in the above-mentioned alarm system for a smart cockpit can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0068] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an alarm method for an intelligent cockpit is implemented.

[0069] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire alarm information, extract features from the alarm information using a feature extraction algorithm, and obtain a feature set of the alarm information; Analyze the feature set of the alarm information to obtain the analysis result of the alarm information, which includes the priority of the alarm information and the format of the alarm pop-up window; Obtain the current audio status of the vehicle, analyze the alarm information priority and the current audio status of the vehicle, and obtain an audio adjustment plan; According to the audio adjustment plan, adjust the vehicle's output audio and transmit the alarm pop-up format to the central control screen.

[0070] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire alarm information, extract features from the alarm information using a feature extraction algorithm, and obtain a feature set of the alarm information; Analyze the feature set of the alarm information to obtain the analysis result of the alarm information, which includes the priority of the alarm information and the format of the alarm pop-up window; Obtain the current audio status of the vehicle, analyze the alarm information priority and the current audio status of the vehicle, and obtain an audio adjustment plan; According to the audio adjustment plan, adjust the vehicle's output audio and transmit the alarm pop-up format to the central control screen.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0072] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0073] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An alarm method for a smart cockpit, characterized in that: The alarm method of the smart cockpit includes: Acquire alarm information, and extract features from the alarm information using a feature extraction algorithm to obtain a feature set of the alarm information; Analyze the feature set of the alarm information to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window; Acquire the current audio state of the vehicle, analyze the alarm information priority and the current audio state of the vehicle, and obtain an audio adjustment plan; According to the audio adjustment scheme, the output audio of the vehicle is adjusted, and the alarm pop-up window format is transmitted to the central control large screen.

2. The alarm method of a smart cockpit according to claim 1, characterized in that: The obtaining of alarm information, extracting features from the alarm information using a feature extraction algorithm, and obtaining a feature set of the alarm information includes: Analyze the alarm information by using natural language processing technology combined with TF-IDF algorithm to obtain key feature points of the alarm information; The principal component analysis method is applied to perform dimension reduction processing on the key feature points of the alarm information to obtain the feature set of the alarm information.

3. The alarm method of a smart cockpit according to claim 1, characterized in that: The feature set of the alarm information is analyzed to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window: By analyzing the feature set of the alarm information in combination with predefined rules and historical data patterns, the alarm information is prioritized to determine the priority of the alarm information; The corresponding alarm pop-up window format is generated according to the priority of the alarm information, and the alarm pop-up window format includes color, size, and display position.

4. The intelligent cockpit alarm method according to claim 1, characterized in that: The obtaining of the current audio status of the vehicle includes: Acquiring an audio signal inside the vehicle using a microphone array, and filtering the audio signal inside the vehicle using an adaptive filtering technique to obtain an internal audio signal; Positioning and analyzing the internal audio signal by a sound source localization algorithm to obtain the position and characteristics of the internal audio signal; Performing pattern recognition on the internal audio signal through a convolutional neural network to obtain an audio pattern of the internal audio signal; The position and characteristics of the internal audio signal and the audio mode of the internal audio signal are statistically integrated to obtain the current audio state of the vehicle.

5. The intelligent cockpit alarm method according to claim 1, characterized in that: The analysis of the alarm information priority and the current audio state of the vehicle to obtain the audio adjustment solution includes: Determining the urgency of adjusting the audio output according to the priority of the alarm information; Performing a weight analysis on the current audio state of the vehicle to obtain a weight ratio of the current audio state of the vehicle; The audio adjustment plan is generated according to the urgency of adjusting the audio output and the weight ratio of the current audio state of the vehicle.

6. The intelligent cockpit alarm method according to claim 1, characterized in that: The alarm method of the smart cockpit also includes: Recording the user's setting preferences and usage time points within a preset time period, and integrating the user's setting preferences and activation time points to obtain user preference data; The priority of the alarm information is dynamically adjusted according to the user preference data.

7. An intelligent cockpit alarm system, characterized in that: The alarm system of the intelligent cockpit comprises: A feature extraction module is used to obtain alarm information, and extract features from the alarm information using a feature extraction algorithm to obtain a feature set of the alarm information; An information analysis module, used to analyze the feature set of the alarm information to obtain the analysis result of the alarm information, wherein the analysis result includes the priority of the alarm information and the format of the alarm pop-up window; An adjustment scheme determination module is used to obtain the current audio state of the vehicle, analyze the alarm information priority and the current audio state of the vehicle, and obtain an audio adjustment scheme; The adjustment display module is used to adjust the output audio of the vehicle according to the audio adjustment scheme, and transmit the alarm pop-up window format to the central control large screen.

8. The intelligent cockpit alarm system according to claim 7, characterized in that: The alarm system of the smart cockpit also includes: A preference collection module is used to record the user's setting preferences and usage time points within a preset time period, and integrate the user's setting preferences and activation time points to obtain user preference data; The priority adjustment module is used to dynamically adjust the priority of the alarm information according to the user preference data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the alarm method for the smart cockpit as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the alarm method for a smart cockpit as described in any one of claims 1 to 6 are implemented.

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