Safety system and method for preventing drunk driving of vehicle, vehicle and medium

Abstract

The invention discloses a safety system and method for preventing drunk driving of a vehicle, the vehicle and a medium, and relates to the technical field of safe driving. The safety system comprises a vehicle-mounted terminal and a cloud server which are in communication connection, the vehicle-mounted terminal comprises a detection module, a collection module, a processing module and a control module, and the processing module is used for verifying the identity of a driver through face data and voice data continuously collected by the collection module and sending the identity of the driver to the cloud server after verification is passed. The behavior state of a driver is determined according to voltage data continuously output by the comprehensive detection module, if the behavior state is drunk driving, a first control instruction is generated, early warning information is uploaded to the cloud server, and the control module is used for intelligently controlling the vehicle through the first control instruction or a second control instruction. Wherein the second control instruction is obtained by analyzing the early warning information through the cloud server to obtain the vehicle operation data and combining the traffic environment around the vehicle and the vehicle operation data until the vehicle stably stops, and safety is ensured.

Description

Technical Field

[0001] This application relates to the field of safe driving technology, and in particular to a safety system, method, vehicle, and medium for preventing drunk driving. Background Technology

[0002] As an indispensable means of transportation in modern life, automobiles have reshaped human travel habits and life experiences, offering great convenience for daily commutes and road trips. However, the safety issue of drivers driving under the influence of alcohol cannot be ignored. While related technologies allow for breathalyzer tests, these are susceptible to cheating and pose safety hazards. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the related technologies, it is desirable to provide a safety system, method, vehicle and medium for preventing drunk driving, which can effectively monitor driver behavior and ensure driving safety.

[0004] In a first aspect, this application provides a safety system for preventing drunk driving, the safety system including a vehicle-mounted terminal and a cloud server connected in communication; The vehicle-mounted terminal includes a detection module, a data acquisition module, a processing module, and a control module; the detection module is used to continuously detect the air exhaled by the driver during vehicle startup and driving, and output voltage data; the data acquisition module is used to continuously acquire the driver's facial data and voice data; The processing module is used to verify the driver's identity using the facial data and the voice data. After the verification is successful, it determines the driver's behavioral state based on the voltage data, the facial data, and the voice data. If the behavioral state is driving under the influence of alcohol, it sends a first control command to the control module and uploads a warning message to the cloud server. The cloud server parses the warning information to obtain vehicle operation data, and sends a second control command to the control module based on the surrounding traffic environment and the vehicle operation data; the control module is used to intelligently control the vehicle according to the first control command or the second control command until it stops.

[0005] Optionally, in some embodiments of this application, in determining the driver's behavioral state based on the voltage data, the facial data, and the voice data, the processing module is specifically used to calculate the alcohol concentration based on the voltage data, input the facial data into a first pre-trained neural network model to obtain a first probability of drunk driving corresponding to the facial data, and input the voice data into a second pre-trained neural network model to obtain a second probability of drunk driving corresponding to the voice data. The alcohol concentration, the first probability of drunk driving, and the second probability of drunk driving are weighted and fused together, and the behavioral state is determined based on the relationship between the fusion result and the alarm threshold.

[0006] Optionally, in some embodiments of this application, the processing module is specifically used to calculate the alcohol concentration based on the voltage data using the following formula: ; In the above formula, This indicates the alcohol concentration, expressed in mg / 100ml. Represents voltage data. Indicates the zero-concentration reference voltage. Indicates the reference voltage; This represents the sensor sensitivity coefficient.

[0007] Optionally, in some embodiments of this application, the processing module is further configured to perform temperature compensation on the alcohol concentration using the following formula: ; In the above formula, Indicates the compensated alcohol concentration; Indicates the temperature coefficient; Indicates ambient temperature. Indicates the calibration temperature.

[0008] Optionally, in some embodiments of this application, regarding the preset alarm threshold, the processing module is specifically used to obtain the driver's weight and alcohol metabolism rate, and calculate the alarm threshold based on the weight and the alcohol metabolism rate, wherein the weight is collected by a weight sensor installed on the vehicle seat.

[0009] Optionally, in some embodiments of this application, in verifying the identity of the driver using the facial data, the processing module is further specifically used to calculate the point cloud matching degree between the feature points corresponding to the facial data and the feature points corresponding to the pre-stored data, and to perform liveness detection based on the relationship between the point cloud matching degree and the preset matching degree threshold.

[0010] Optionally, in some embodiments of this application, in verifying the driver's identity using the voice data, the processing module is further specifically used to analyze the driver's blowing duration in the voice data and to perform anomaly detection based on the relationship between the blowing duration and a preset duration threshold.

[0011] Secondly, this application provides a safety method for preventing drunk driving, the safety method being used in the vehicle-mounted terminal described in any one of the first aspects, the safety method comprising: During vehicle startup and driving, the system continuously monitors the driver's exhaled air, outputs voltage data, and continuously collects the driver's facial and voice data. The driver's identity is verified using the facial data and the voice data. After verification, the driver's behavioral state is determined based on the voltage data, the facial data, and the voice data. If the behavioral state is driving under the influence of alcohol, a first control command is generated and a warning message is uploaded to the cloud server. The vehicle is intelligently controlled according to the first control command or the second control command issued by the cloud server until it stops. The second control command is obtained by the cloud server parsing the warning information to obtain vehicle operation data and based on the surrounding traffic environment and the vehicle operation data.

[0012] Thirdly, this application provides a vehicle that includes the vehicle-mounted terminal described in any one of the first aspects.

[0013] Fourthly, this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the security method described in the second aspect.

[0014] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: This application provides a safety system, method, vehicle, and medium for preventing drunk driving. Whether the vehicle is starting or in motion, it continuously detects the driver's exhaled air and collects the driver's facial and voice data without any gaps, ensuring no data is missed. The facial and voice data are then used to verify the driver's identity, preventing cheating. Upon successful verification, the system comprehensively analyzes multiple factors, including voltage data output by the detection module and facial and voice data collected by the acquisition module, to accurately determine the driver's behavioral state and effectively monitor their behavior. If the behavior indicates drunk driving, a first control command is generated and a warning message is uploaded to a cloud server. The vehicle can then be intelligently controlled using either the first or second control command. The second control command is derived from the cloud server's parsing of the warning message to obtain vehicle operation data, which is then combined with the surrounding traffic environment and vehicle operation data, taking public safety into account, until the vehicle comes to a stable stop. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the architecture of a safety system for preventing drunk driving is provided in an embodiment of this application; Figure 2 A flowchart illustrating a safety method for preventing drunk driving, provided as an embodiment of this application; Figure 3 This is a structural block diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The following examples illustrate this. Figures 1 to 3 This application provides a detailed description of the safety systems, methods, vehicles, and media provided in its embodiments for preventing drunk driving.

[0020] Please refer to Figure 1This is a schematic diagram of the architecture of a safety system for preventing drunk driving provided in an embodiment of this application. The safety system 100 includes an in-vehicle terminal 101 and a cloud server 102 connected by communication. The in-vehicle terminal 101 includes, but is not limited to, a detection module 1011, a data acquisition module 1012, a processing module 1013, and a control module 1014. The detection module 1011 can continuously detect the air exhaled by the driver during vehicle start-up and driving, and output voltage data. The data acquisition module 1012 can continuously acquire the driver's facial data and voice data, that is, there is no gap period, ensuring that no data is missed. For example, the detection module 1011 can integrate an alcohol sensor, which has a number of through holes on the housing of the vehicle terminal 101 for easy detection. The acquisition module 1012 can integrate a camera and a microphone. The camera has infrared function and can maintain clear imaging at night or in low light to avoid environmental interference. All modules are located in the vehicle terminal 101, which can be installed above the driver's seat of the vehicle. It has a standard interface for connecting to the vehicle to ensure compatibility and facilitate maintenance and replacement. It adopts a bottom shell and top cover snap-fit ​​encapsulation, and the internal circuit board is fixed by screws or soldering to prevent damage from non-tool disassembly.

[0021] The processing module 1013 can verify the driver's identity using facial and voice data. After successful verification, it determines the driver's behavioral state based on voltage, facial, and voice data. If the behavioral state is normal driving, no intervention is required. If the behavioral state is driving under the influence of alcohol, a first control command is sent to the control module 1014, and a warning message is uploaded to the cloud server 102. The first control command is used to roughly reduce the vehicle's speed, change its direction, and pull over. The first control command includes, but is not limited to, multiple parameters such as the vehicle's speed and direction. A voice prompt such as "Please do not drive under the influence of alcohol" is also issued inside the vehicle. The time of the incident and the alcohol concentration are recorded for traceability. If identity verification fails, it indicates that the driver may be a temporary visitor or a first-time user of the vehicle terminal 101. In this case, the acquisition module 1012 can collect and record the driver's facial and voice data, but the data still needs to be processed by the detection module 1011 to determine whether the driver is driving under the influence of alcohol.

[0022] Furthermore, the cloud server 102 can parse the warning information to obtain vehicle operation data, and based on the surrounding traffic environment and vehicle operation data, issue a second control command to the control module 1014. The second control command is used to comprehensively analyze the surrounding road conditions displayed in real time on the cloud map, and more precisely reduce the vehicle's speed, change its direction, and pull over. The second control command includes, but is not limited to, parameters such as the distribution of surrounding vehicles, no-stopping zones, and multiple speed and direction parameters of the vehicle. For example, the warning information is obtained by encrypting and encapsulating vehicle operation data according to message protocol standards. Vehicle operation data includes, but is not limited to, vehicle identification, vehicle speed, and vehicle location information. Furthermore, the cloud server 102 can also promptly notify traffic management departments and the driver's emergency contact. Thus, the control module 1014 can intelligently control the vehicle according to the first or second control command until it comes to a smooth stop. For example, intelligent control can involve automatically taking over the vehicle and adjusting its direction and speed. Even when the network is unstable or there is no network connection, it can still be operated via the first control command, thus ensuring public safety.

[0023] In some embodiments of this application, in determining the driver's behavioral state based on voltage data, facial data, and voice data, the processing module 1013 is specifically able to calculate the alcohol concentration based on the voltage data, input the facial data into a first pre-trained neural network model to obtain a first probability of drunk driving corresponding to the facial data, and input the voice data into a second pre-trained neural network model to obtain a second probability of drunk driving corresponding to the voice data. The dataset of the first pre-trained neural network model is obtained by collecting facial images of people who have been drinking and those who have not been drinking, and labeling these images as having been drinking or not drinking. For example, a person's complexion, eyes, eyebrows, and corners of the mouth will change after drinking. The dataset is then divided into a training set and a validation set. The first pre-trained neural network model is trained using the training set, and the parameters of the first pre-trained neural network model are adjusted using the validation set to make the detection results more accurate. The processing of the second pre-trained neural network model is the same in principle and will not be described in detail here. For example, a person's tone of voice and speech rate will change after drinking. Furthermore, the alcohol concentration, the probability of first drunk driving, and the probability of second drunk driving are weighted and fused, for example, with weights of 0.6, 0.3, and 0.1 respectively. Cross-validation is then performed to improve the accuracy of the judgment. The behavior status is determined based on the relationship between the fusion result and the alarm threshold. For example, if the fusion result is greater than or equal to the alarm threshold, the behavior status is determined to be drunk driving; otherwise, it is normal driving.

[0024] Furthermore, in calculating the alcohol concentration based on the voltage data, the processing module 1013 can specifically perform the calculation using equation (1), namely: (1) In equation (1), This indicates the alcohol concentration, expressed in mg / 100ml. Represents voltage data. Indicates the zero-concentration reference voltage. It can be 0.5 volts. Indicates the reference voltage. It can be 5 volts; This indicates the sensor sensitivity coefficient; the sensor can be an MQ-3 alcohol sensor. It can be 0.6.

[0025] In addition, the processing module 1013 can also specifically compensate for the alcohol concentration by temperature using equation (2). That is to say, the detection module 1011 can also integrate a temperature sensor. The advantage of this setting is that it can not only compensate for the influence of ambient temperature on the detection accuracy of the alcohol sensor, but also verify whether the breath sample comes from the human body. The normal range for the human body is 34℃ to 36℃, which ensures the accuracy and reliability of the measurement results and prevents cheating. That is: (2) In equation (2), Indicates the compensated alcohol concentration; Indicates the temperature coefficient. It can be 0.02 / ℃; Indicates ambient temperature. Indicates the calibration temperature. It can be 25℃.

[0026] Furthermore, regarding the preset alarm threshold, the processing module 1013 can specifically acquire the driver's weight and alcohol metabolism rate, and calculate the alarm threshold based on the weight and alcohol metabolism rate. The weight is collected by a weight sensor installed on the vehicle seat. That is, the weight sensor transmits the weight result to the processing module 1013 via a communication data line connected to the vehicle terminal 101. At the same time, the weight can also help verify the driver's identity, that is, compare the weight with the driver's weight stored in the historical records. For example, the alcohol metabolism rate ranges from 0.015 to 0.02. When calculating the alarm threshold using formula (3), the alcohol metabolism rate can be set to a maximum of 0.02. The advantage of this setting is that the obtained alarm threshold is the smallest, the drunk driving judgment standard is slightly stricter, which is beneficial to public safety. At the same time, it can also be dynamically adjusted according to the weight, which is more in line with reality. (3) In equation (3), Indicates the alarm threshold. represents weight, This indicates the alcohol metabolism rate.

[0027] In some embodiments of this application, in verifying the identity of the driver using facial data, the processing module 1013 can specifically calculate the point cloud matching degree between the feature points corresponding to the facial data and the feature points corresponding to the pre-stored data. For example, the pre-stored data is historically collected driver facial data, and liveness detection is performed according to the relationship between the point cloud matching degree and the preset matching degree threshold. For example, if the point cloud matching degree is greater than or equal to the preset matching degree threshold, it is a real liveness. Otherwise, there may be a situation where a photo is used to cheat, and a voice prompt such as "Please re-detect" needs to be issued in the car. For another example, the point cloud matching degree is calculated by formula (4), that is: (4) In equation (4), Indicates the point cloud matching degree. This represents the feature points corresponding to facial data. This indicates the feature points corresponding to the pre-stored data. This represents the total number of feature points.

[0028] In some embodiments of this application, in verifying the driver's identity using voice data, the processing module 1013 can specifically analyze the driver's blowing duration in the voice data and perform anomaly detection based on the relationship between the blowing duration and a preset duration threshold. For example, it can be calculated using equation (5). If the result is greater than or equal to the preset anomaly threshold, there may be a substitution behavior; otherwise, it is normal. (5) In equation (5), Indicates abnormal detection results. Indicates the duration of blowing air. This indicates the preset duration threshold, which is the average normal blowing time. It can be 3 seconds. It represents the standard deviation.

[0029] The safety system for preventing drunk driving provided in this application embodiment can continuously detect the driver's exhaled air and continuously collect the driver's facial and voice data, whether the vehicle is starting or in motion, without any gaps to ensure no data is missed. The facial and voice data are then used to verify the driver's identity to prevent cheating. If the verification is successful, the system comprehensively considers multiple factors, such as the voltage data output by the detection module and the facial and voice data collected by the acquisition module, to accurately determine the driver's behavioral state and effectively monitor the driver's behavior. If the behavioral state is drunk driving, a first control command is generated and a warning message is uploaded to the cloud server. The system can then use either the first or second control command to intelligently control the vehicle. The second control command is obtained by the cloud server parsing the warning message to obtain vehicle operation data and combining it with the surrounding traffic environment and vehicle operation data, taking public safety into account, until the vehicle comes to a stable stop.

[0030] Based on the foregoing embodiments, this application provides a safety method for preventing drunk driving, which can be used to Figure 1 The corresponding embodiment includes the vehicle-mounted terminal 101 of the safety system 100. Please refer to... Figure 2 This is a flowchart illustrating a safety method for preventing drunk driving provided in an embodiment of this application. The safety method specifically includes the following steps: S101 continuously monitors the driver's exhaled air, outputs voltage data, and continuously collects the driver's facial and voice data during vehicle startup and operation.

[0031] S102 verifies the driver's identity using facial and voice data. After successful verification, it determines the driver's behavioral status based on voltage, facial, and voice data. If the behavioral status indicates drunk driving, it generates the first control command and uploads a warning message to the cloud server.

[0032] In some embodiments of this application, when the behavior is determined to be driving under the influence of alcohol, a voice prompt such as "Please do not drive under the influence of alcohol" can be issued inside the vehicle. Simultaneously, the time of the incident and the alcohol concentration are recorded for traceability, and the alcohol concentration is classified, including but not limited to driving under the influence and drunk driving, and a corresponding first control command is generated based on the different levels. Conversely, if the behavior is normal driving, no intervention is required.

[0033] S103, intelligently controls the vehicle according to the first control command or the second control command issued by the cloud server until it stops. The second control command is obtained by the cloud server parsing the warning information to obtain vehicle operation data, and is based on the surrounding traffic environment and vehicle operation data.

[0034] In some embodiments of this application, vehicle operation data includes, but is not limited to, vehicle identification, vehicle speed, and vehicle location information. The cloud server can also promptly notify traffic management departments and the driver's emergency contact.

[0035] It should be noted that the descriptions of the same steps and contents as in other embodiments in this embodiment can be found in the descriptions in other embodiments, and will not be repeated here.

[0036] The safety method for preventing drunk driving provided in this application continuously detects the driver's exhaled air and collects the driver's facial and voice data, both during vehicle startup and operation, without any gaps to ensure no data is missed. The facial and voice data are then used to verify the driver's identity, preventing cheating. Upon successful verification, the method comprehensively considers multiple factors, including voltage data output by the detection module and facial and voice data collected by the acquisition module, to accurately determine the driver's behavioral state and effectively monitor their behavior. If the behavior indicates drunk driving, a first control command is generated and a warning message is uploaded to the cloud server. The vehicle can then be intelligently controlled using either the first or second control command. The second control command is derived from the cloud server's parsing of the warning message to obtain vehicle operation data, which is then combined with the surrounding traffic environment and vehicle operation data, taking public safety into account, until the vehicle comes to a stable stop.

[0037] In another aspect, this application provides a vehicle. Please refer to... Figure 3 The vehicle 200 may include Figure 1 The corresponding embodiment includes the vehicle terminal 101 of the safety system 100.

[0038] In another aspect, embodiments of this application provide a computer-readable storage medium for storing program code, which can be executed by one or more processors to implement the foregoing. Figure 2 The steps of the security method in the corresponding embodiment.

[0039] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0040] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other can be through some interfaces, indirect coupling or communication connection between devices or modules, and can be electrical, mechanical, or other forms. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0041] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more units can be integrated into one module. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0042] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the security methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0044] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A safety system for preventing drunk driving, characterized in that, The safety system (100) includes a vehicle-mounted terminal (101) and a cloud server (102) connected by communication. The vehicle terminal (101) includes a detection module (1011), a data acquisition module (1012), a processing module (1013), and a control module (1014); the detection module (1011) is used to continuously detect the air exhaled by the driver during vehicle startup and driving, and output voltage data; the data acquisition module (1012) is used to continuously acquire the driver's facial data and voice data; The processing module (1013) is used to verify the driver's identity using the facial data and the voice data, and after the verification is successful, to determine the driver's behavior status based on the voltage data, the facial data and the voice data. If the behavior status is drunk driving, the module sends a first control command to the control module (1014) and uploads a warning message to the cloud server (102). The cloud server (102) is used to parse the warning information to obtain vehicle operation data, and to issue a second control command to the control module (1014) based on the surrounding traffic environment and the vehicle operation data; the control module (1014) is used to intelligently control the vehicle according to the first control command or the second control command until it stops.

2. The security system according to claim 1, characterized in that, In determining the driver's behavioral state based on the voltage data, the facial data, and the voice data, the processing module (1013) is specifically used to calculate the alcohol concentration based on the voltage data, input the facial data into a first pre-trained neural network model to obtain a first drunk driving probability corresponding to the facial data, and input the voice data into a second pre-trained neural network model to obtain a second drunk driving probability corresponding to the voice data. The alcohol concentration, the first probability of drunk driving, and the second probability of drunk driving are weighted and fused together, and the behavioral state is determined based on the relationship between the fusion result and the alarm threshold.

3. The security system according to claim 2, characterized in that, In calculating the alcohol concentration based on the voltage data, the processing module (1013) is specifically used to perform the calculation using the following formula: ； In the above formula, This indicates the alcohol concentration, expressed in mg / 100ml. Represents voltage data. Indicates the zero-concentration reference voltage. Indicates the reference voltage; This represents the sensor sensitivity coefficient.

4. The security system according to claim 3, characterized in that, The processing module (1013) is also specifically used to perform temperature compensation on the alcohol concentration using the following formula: ； In the above formula, Indicates the compensated alcohol concentration; Indicates the temperature coefficient; Indicates ambient temperature. Indicates the calibration temperature.

5. The security system according to claim 2, characterized in that, Regarding the preset alarm threshold, the processing module (1013) is specifically used to acquire the driver's weight and alcohol metabolism rate, and calculate the alarm threshold based on the weight and alcohol metabolism rate. The weight is acquired by a weight sensor installed on the vehicle seat.

6. The security system according to any one of claims 1 to 5, characterized in that, In verifying the driver's identity using the facial data, the processing module (1013) is specifically used to calculate the point cloud matching degree between the feature points corresponding to the facial data and the feature points corresponding to the pre-stored data, and to perform liveness detection based on the relationship between the point cloud matching degree and the preset matching degree threshold.

7. The security system according to any one of claims 1 to 5, characterized in that, In verifying the driver's identity using the voice data, the processing module (1013) is specifically used to analyze the driver's blowing duration in the voice data and to perform anomaly detection based on the relationship between the blowing duration and a preset duration threshold.

8. A safety method for preventing drunk driving, characterized in that, The security method is used in the vehicle-mounted terminal (101) according to any one of claims 1 to 7, and the security method includes: During vehicle startup and driving, the system continuously monitors the driver's exhaled air, outputs voltage data, and continuously collects the driver's facial and voice data. The driver's identity is verified using the facial data and the voice data. After verification, the driver's behavioral state is determined based on the voltage data, the facial data, and the voice data. If the behavioral state is driving under the influence of alcohol, a first control command is generated and a warning message is uploaded to the cloud server. The vehicle is intelligently controlled according to the first control command or the second control command issued by the cloud server until it stops. The second control command is obtained by the cloud server parsing the warning information to obtain vehicle operation data and based on the surrounding traffic environment and the vehicle operation data.

9. A vehicle, characterized in that, The vehicle (200) includes the vehicle-mounted terminal (101) as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the security method of claim 8.

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