Vehicle driver state monitoring method and device, vehicle machine equipment and storage medium

Abstract

The invention relates to a state monitoring method and device for a vehicle driver, vehicle equipment and a storage medium. The method comprises the steps of obtaining current multi-dimensional monitoring data of a vehicle driver through a wearable vehicle key device, and determining historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data comprises alcohol monitoring data obtained through monitoring of an alcohol monitoring module; acquiring current environment data corresponding to the vehicle under the condition that the alcohol monitoring data does not meet the preset drinking condition; determining a physiological risk assessment result of the vehicle driver according to the historical multi-dimensional monitoring data and the current multi-dimensional monitoring data; obtaining a travel risk assessment result of the vehicle driver according to the current environment data; and obtaining a comprehensive risk assessment result according to the physiological risk assessment result and the travel risk assessment result, so that the vehicle driving state can be dynamically adjusted, and the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method, device, vehicle-mounted equipment, and storage medium for monitoring the status of a vehicle driver. Background Art

[0002] With the rapid development of intelligent transportation systems, driver health monitoring has become a critical component in improving road safety. Existing vehicle control systems primarily rely on passive safety devices (such as seatbelts and airbags) to provide protection after an accident, but lack active monitoring and early warning mechanisms for the driver's physiological state.

[0003] Some related technologies have been developed for health monitoring. However, during implementation, the applicant discovered that these technologies at least have the problem of not being able to integrate with vehicle control of driving status. Summary of the Invention

[0004] Based on this, the purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect of the lack of adjustment of the vehicle driving status in the existing technology. This application provides a vehicle driver status monitoring method, device, vehicle-mounted equipment and storage medium.

[0005] In a first aspect, the present application provides a vehicle driver status monitoring method, which is applied to a wearable car key device. The wearable car key device includes a multi-dimensional physiological monitoring module, which includes an alcohol monitoring module. The method includes:

[0006] Obtain the current multi-dimensional monitoring data of the vehicle driver through the wearable car key device, and determine the historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data includes the alcohol monitoring data obtained through the alcohol monitoring module;

[0007] When the alcohol monitoring data does not meet the preset drinking conditions, obtain the current environment data corresponding to the vehicle;

[0008] Determine the physiological risk assessment results of the vehicle driver based on historical multi-dimensional monitoring data and current multi-dimensional monitoring data;

[0009] Based on the current environmental data, the driver's travel risk assessment results are obtained;

[0010] Based on the physiological risk assessment results and the travel risk assessment results, a comprehensive risk assessment result is obtained.

[0011] In one embodiment, a comprehensive risk assessment result is obtained based on the physiological risk assessment result and the travel risk assessment result, including:

[0012] Determine the physiological risk assessment weight corresponding to the current multi-dimensional monitoring data based on the current multi-dimensional monitoring data;

[0013] Based on the current environmental data, the environmental risk assessment weight corresponding to the previous environmental data is obtained;

[0014] Based on the physiological risk assessment weights and environmental risk assessment weights, the physiological risk assessment results and travel risk assessment results are weighted and fused to obtain a comprehensive risk assessment result.

[0015] In one embodiment, the method further comprises:

[0016] If the current multi-dimensional monitoring data meets the preset physiological abnormal fluctuation conditions, the physiological risk assessment weight is increased;

[0017] If the current environmental data meets the preset environmental mutation conditions, the environmental risk assessment weight will be increased.

[0018] In one embodiment, determining a physiological risk assessment result of a vehicle driver based on historical multi-dimensional monitoring data and current multi-dimensional monitoring data includes:

[0019] Update pre-built physiological trend analysis models based on historical multi-dimensional monitoring data;

[0020] Input the current multi-dimensional monitoring data into the physiological trend analysis model to determine the physiological status assessment results of the vehicle driver;

[0021] According to the physiological status assessment results, the physiological risk assessment results of the vehicle driver are obtained.

[0022] In one embodiment, the comprehensive risk assessment result includes a high risk assessment result, a low risk assessment result, and a no risk assessment result; and the method further includes:

[0023] If the alcohol monitoring data meets the preset drinking conditions, the comprehensive risk assessment result will be determined as a high-risk assessment result.

[0024] In one embodiment, the method further comprises:

[0025] If the comprehensive risk assessment result is a high risk assessment result, the vehicle to be driven by the driver is placed in an unstartable state;

[0026] If the comprehensive risk assessment result is a low risk assessment result, the vehicle is controlled to be in a safe driving state; wherein the safe driving state means that a safe driving prompt is given after the vehicle is started;

[0027] If the comprehensive risk assessment result is no risk assessment result, the current multi-dimensional monitoring data and current environmental data will be continuously monitored.

[0028] In a second aspect, the present application provides a vehicle driver status monitoring device, which is applied to a wearable car key device. The wearable car key device includes a multi-dimensional physiological monitoring module, which includes an alcohol monitoring module. The device includes:

[0029] The physiological monitoring module uses a wearable car key device to obtain the current multi-dimensional monitoring data of the vehicle driver and determine the historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data includes the alcohol monitoring data obtained by the alcohol monitoring module;

[0030] The environment monitoring module is used to obtain the current environment data corresponding to the vehicle when the alcohol monitoring data does not meet the preset drinking conditions;

[0031] A physiological risk module is used to determine the physiological risk assessment results of the vehicle driver based on historical multi-dimensional monitoring data and current multi-dimensional monitoring data;

[0032] The trip risk module is used to obtain the trip risk assessment result of the vehicle driver based on the current environmental data;

[0033] The comprehensive risk module is used to obtain a comprehensive risk assessment result based on the physiological risk assessment result and the travel risk assessment result.

[0034] In a third aspect, the present application provides a vehicle-mounted device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0036] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0037] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0038] The vehicle driver status monitoring method, device, vehicle-mounted equipment and storage medium provided in this application can realize dynamic weight adjustment and comprehensive risk classification control by integrating a multi-dimensional physiological monitoring module into a wearable device and combining historical data trend analysis with real-time environmental risk assessment. Accurate risk warning and improved driving safety can be achieved through the fusion assessment of multi-dimensional physiological monitoring and environmental data. In this way, this application can combine the driver's physiological status and environmental data to obtain a comprehensive risk assessment result, so that the vehicle driving status can be dynamically adjusted to improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic structural diagram of a wearable car key device provided in an embodiment of the present application;

[0041] Figure 2 A flow chart of a method for monitoring the status of a vehicle driver provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a process for determining a comprehensive risk assessment result provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of a process for determining a comprehensive risk assessment result provided in an embodiment of the present application;

[0044] Figure 5 A flowchart of a specific implementation method of a vehicle driver status monitoring method provided in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of a vehicle driver status monitoring device provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of the internal structure of a vehicle-mounted device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] With the rapid development of intelligent transportation systems, driver health monitoring has become a critical component in improving road safety. Existing technologies suffer from three significant drawbacks: First, traditional monitoring equipment uses a single-indicator detection model, failing to comprehensively assess a driver's overall physiological state. In particular, it lacks the ability to collaboratively analyze alcohol concentration and other physiological parameters. Second, the monitoring system is independent of the vehicle control unit, resulting in a disconnect between risk warnings and vehicle operation authority management, making it difficult to promptly prevent high-risk driving behavior. Third, existing solutions require drivers to actively use additional equipment, which not only affects the driving experience but also reduces the continuity of monitoring data.

[0049] More specifically, current technical solutions face multiple implementation challenges: Fixed on-board sensors struggle to continuously capture accurate driver physiological parameters; data analysis lacks a multi-dimensional risk assessment model that integrates historical data with real-time environmental factors; and system integration lacks a deep connection between wearable devices and vehicle-mounted systems. These challenges render existing systems incapable of effectively identifying dangerous conditions like drunk driving and sudden illness, and even more so, unable to dynamically adjust vehicle control strategies based on risk levels.

[0050] Based on this, the present application provides a vehicle driver status monitoring method, device, vehicle-mounted equipment and storage medium, which can combine the driver's physiological state and environmental data to obtain a comprehensive risk assessment result, so as to dynamically adjust the vehicle driving status and improve driving safety.

[0051] In an exemplary embodiment, Figure 1 A structural diagram of a wearable car key device provided in an embodiment of the present application; Figure 1 As shown, the wearable car key device includes a dial 1, a strap 2, and a sensor 3. The wearable car key device may also include a multi-dimensional physiological monitoring module, which includes an alcohol monitoring module.

[0052] In an exemplary embodiment, Figure 2 A flow chart of a vehicle driver status monitoring method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, a method for monitoring the state of a vehicle driver is provided, and the method is applied to Figure 1The wearable car key device shown is illustrated using a vehicle terminal as an execution terminal. It is understood that the method can also be applied to a server as the execution terminal, and the execution subject can also include a system of terminals and servers, and be implemented through the interaction between the terminals and servers. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following S201 to S205. Among them:

[0053] S201. Obtain the current multi-dimensional monitoring data of the vehicle driver through the wearable car key device, and determine the historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data includes the alcohol monitoring data obtained by the alcohol monitoring module.

[0054] S202: When the alcohol monitoring data does not meet the preset drinking conditions, obtain current environment data corresponding to the vehicle.

[0055] S203. Determine a physiological risk assessment result of the vehicle driver based on historical multi-dimensional monitoring data and current multi-dimensional monitoring data.

[0056] S204: Obtain a travel risk assessment result of the vehicle driver based on the current environmental data.

[0057] S205. Obtain a comprehensive risk assessment result based on the physiological risk assessment result and the travel risk assessment result.

[0058] Among them, the wearable car key device refers to a portable device with integrated physiological monitoring function, which can be worn in the form of a wristband or pendant. Specifically, it can use Bluetooth or NFC communication modules to interact with the vehicle to achieve the dual functions of identity authentication and data transmission, and can connect to the vehicle to unlock it.

[0059] Multi-dimensional monitoring data refers to a set of complex physiological indicators including heart rate, blood pressure, blood oxygen and alcohol concentration. Specifically, it can be continuously collected using a biosensor array to build a baseline for the driver's physiological status.

[0060] The preset drinking condition refers to a pre-set blood alcohol concentration threshold, which can be obtained by converting the concentration of volatile alcohol on the skin surface detected by an electrochemical sensor, and is used to preliminarily determine whether one is qualified to drive safely.

[0061] Environmental data refers to real-time traffic and weather information at the vehicle's location, which can be obtained through the vehicle's GPS and cloud data interface. It includes parameters such as road condition complexity, visibility and precipitation intensity, the driver's preset driving destination, expected driving distance, predicted weather conditions, and vehicle condition (tire pressure, engine oil, brakes, gasoline).

[0062] For example, a wearable device can continuously collect data on the driver's heart rate, blood pressure, and alcohol concentration. When the alcohol concentration falls below a preset threshold, the vehicle's system can automatically retrieve real-time weather and traffic congestion information for the vehicle's current location. By comparing the deviation of current physiological data with a historical health baseline, the driver's physical fitness for driving is assessed. Environmental data is also analyzed for risk factors, such as the impact of rainy and foggy conditions on driving performance. The physiological and environmental risk levels are input into a weighted assessment model, which outputs a comprehensive risk index that serves as the basis for vehicle launch control decisions.

[0063] Compared to existing technologies, existing solutions typically use single alcohol detection or independent environmental warning systems, failing to achieve a coordinated analysis of physiological and environmental risks. This embodiment integrates multi-dimensional sensors into wearable devices to establish a dynamic risk assessment model, combining the driver's real-time status with environmental changes to form a more comprehensive basis for safety judgment. Furthermore, by seamlessly connecting the wearable device to the vehicle, it directly participates in vehicle control decisions, resolving the issue of fragmented device functionality.

[0064] This embodiment monitors the driver's key physiological indicators in real time and assesses health risks. This, combined with the impact of external environmental changes on driving safety, provides a dual-dimensional risk warning. When the comprehensive assessment indicates a high risk, the system can promptly restrict vehicle start-up or trigger safety protection mechanisms, effectively preventing traffic accidents caused by abnormal driver status. Through the deep integration of wearable devices and vehicle systems, user convenience is enhanced while ensuring monitoring accuracy.

[0065] In an exemplary embodiment, Figure 3 A schematic flow chart of the steps for determining the comprehensive risk assessment result provided in an embodiment of the present application is as follows: Figure 3 As shown, in Figure 2 Based on the above, the steps of the vehicle driver status monitoring method are exemplified. In step S205, a comprehensive risk assessment result is obtained based on the physiological risk assessment result and the travel risk assessment result, including:

[0066] S301. Determine a physiological risk assessment weight corresponding to the current multi-dimensional monitoring data based on the current multi-dimensional monitoring data;

[0067] S302. Obtaining an environmental risk assessment weight corresponding to previous environmental data based on current environmental data;

[0068] S303: Based on the physiological risk assessment weight and the environmental risk assessment weight, a weighted fusion assessment process is performed on the physiological risk assessment result and the travel risk assessment result to obtain a comprehensive risk assessment result.

[0069] Among them, the physiological risk assessment weight may refer to a quantitative indicator that reflects the degree of influence of the driver's physiological state on the comprehensive risk. For example, it can be implemented by a classification model trained based on historical data or a dynamic threshold algorithm, and is used to adjust the proportion of physiological risk in the comprehensive assessment according to the fluctuation range of real-time physiological data. The environmental risk assessment weight may refer to a dynamic parameter that reflects the degree of influence of external environmental factors on driving safety. For example, it can be calculated by an environmental parameter analysis algorithm combined with real-time weather and road condition data, and is used to quantify the weight distribution of travel risks under different environmental conditions. Weighted fusion assessment processing may refer to the operation process of integrating risk assessment results of different dimensions. For example, it can be implemented by a linear weighted model or a fuzzy logic algorithm, and collaborative analysis of multi-source data can be achieved by dynamically adjusting the weight coefficient.

[0070] For example, if alcohol monitoring data is within the permitted range, the vehicle-mounted system first calculates the degree of deviation of physiological indicators such as heart rate and blood pressure through a data analysis algorithm, generating corresponding physiological risk assessment weights. Simultaneously, the environmental monitoring module processes weather, lighting, and traffic flow data to generate environmental risk assessment weights. After these two weighting parameters are input into the risk assessment model, the physiological risk score and the trip risk score are weighted and summed according to the dynamic weighting coefficients, ultimately outputting a comprehensive risk assessment result. For example, in heavy rain, the environmental weight is automatically increased, giving trip risk a higher weight in the comprehensive assessment. If the driver experiences a sudden abnormal heart rate, the physiological weight is increased to strengthen the impact of physiological risk on the comprehensive result.

[0071] This embodiment uses a dynamic weight allocation mechanism to automatically adjust the contribution of different risk factors according to real-time data changes, effectively solving the problem of static and single risk assessment in traditional methods.

[0072] In this embodiment, a dynamic weighting calculation model is established to quantitatively integrate physiological and travel risks, enabling the vehicle control system to accurately identify complex driving risks. For example, if the driver is slightly fatigued but road conditions are complex, the system can promptly identify potential hazards by increasing the environmental weighting, avoiding misjudgments caused by relying solely on physiological data.

[0073] In an exemplary embodiment, the method further comprises:

[0074] If the current multi-dimensional monitoring data meets the preset physiological abnormal fluctuation conditions, the physiological risk assessment weight is increased;

[0075] If the current environmental data meets the preset environmental mutation conditions, the environmental risk assessment weight will be increased.

[0076] The abnormal physiological fluctuation condition can refer to the criteria for determining whether a driver's physiological parameters deviate significantly from historical data. For example, this can be achieved by using a dynamic threshold algorithm combined with a sliding window statistical method, and the determination is made by calculating the multiple of the standard deviation between the current monitored value and the historical average value. The sudden environmental change condition can refer to the criteria for identifying a sharp change in the vehicle's environmental parameters. For example, this can be achieved through multi-sensor data fusion technology, such as combining GPS positioning data, meteorological sensor data, and traffic flow data for sudden change detection.

[0077] For example, if the wearable car key device detects that the driver's heart rate variability coefficient exceeds two standard deviations of the historical baseline value for three consecutive monitoring cycles, the system can automatically increase the physiological risk assessment weight from the baseline value of 0.6 to 0.8. Simultaneously, if the environmental monitoring module detects that the rainfall intensity in the vehicle's area increases from 0 mm to 20 mm within ten minutes, the system adjusts the environmental risk assessment weight from 0.4 to 0.6. This dual weighting adjustment mechanism enables the comprehensive risk assessment model to more sensitively reflect the impact of unexpected situations on driving safety.

[0078] This embodiment achieves a rapid response to abnormal events by establishing a dynamic weight adjustment mechanism, and solves the technical defect of the fixed weight model in lagging risk assessment in emergency scenarios.

[0079] In this embodiment, the evaluation model parameters can be dynamically adjusted according to real-time monitoring data, effectively improving the accuracy and timeliness of driving risk assessment in complex traffic environments, and providing a more reliable basis for safety decision-making of the vehicle control system.

[0080] In an exemplary embodiment, Figure 4 A schematic flow chart of the steps for determining the comprehensive risk assessment result provided in an embodiment of the present application is as follows: Figure 4 As shown, in Figure 2 Based on the above, the steps of the vehicle driver status monitoring method can be exemplified. In step S203, based on the historical multi-dimensional monitoring data and the current multi-dimensional monitoring data, the physiological risk assessment result of the vehicle driver is determined, including:

[0081] S401. Update a pre-built physiological trend analysis model based on historical multi-dimensional monitoring data;

[0082] S402: Input the current multi-dimensional monitoring data into a physiological trend analysis model to determine a physiological status assessment result of the vehicle driver;

[0083] S403: Obtain a physiological risk assessment result of the vehicle driver based on the physiological status assessment result.

[0084] Among them, historical multi-dimensional monitoring data may refer to a set of long-term physiological indicators of the driver that is continuously collected and stored by a wearable car key device. For example, a time series database may be used for storage and management to establish an individualized physiological baseline curve. The physiological trend analysis model may refer to a driver physiological state prediction model built based on a machine learning algorithm. For example, it may be implemented using a long-short-term memory neural network, and a prediction benchmark is established by analyzing the changing patterns of physiological indicators in historical data. The physiological state assessment result may refer to a quantitative indicator generated by comparing the degree of deviation between the current monitoring data and the model prediction value. For example, a dynamic time warping algorithm may be used to calculate data similarity to identify sudden physiological abnormalities.

[0085] For example, during the operation of the wearable car key device, historical monitoring data can be periodically input into the model training module, and the model parameters can be updated through incremental learning to track the long-term changes in the driver's physiological characteristics. When the current monitoring data is acquired, the feature vector is extracted through a sliding time window and input into the updated model. The model output includes a predicted value range for parameters such as the heart rate variability coefficient and the blood pressure fluctuation index. By calculating the match between the current measured data and the predicted range, an assessment result can be generated, including three levels of normal fluctuation, mild anomaly, and severe anomaly. Ultimately, the anomaly level is mapped to the corresponding risk quantification value.

[0086] This embodiment establishes a dynamically updated personalized analysis model to effectively filter out short-term data fluctuations caused by non-risk factors such as exercise and emotions, while accurately capturing persistent abnormal indicator changes with clinical significance, significantly improving the accuracy of identifying potential health risks.

[0087] In this embodiment, personalized dynamic assessment of the driver's physiological state can be achieved, misjudgment problems caused by individual physiological differences can be avoided, the risk of reduced driving ability caused by the development of chronic diseases or sudden physical conditions can be discovered in a timely manner, and a more accurate decision-making basis can be provided for the vehicle control system.

[0088] In an exemplary embodiment, the comprehensive risk assessment result includes a high risk assessment result, a low risk assessment result, and a no risk assessment result; and the method further includes:

[0089] If the alcohol monitoring data meets the preset drinking conditions, the comprehensive risk assessment result will be determined as a high-risk assessment result.

[0090] Among them, alcohol monitoring data may refer to the alcohol concentration information in the driver's body collected by the alcohol monitoring module. For example, it can be implemented by using electrochemical sensors or infrared spectroscopy analysis technology to quantitatively assess whether the driver is in a state of drinking. The preset drinking condition may refer to a pre-set alcohol concentration threshold. For example, it can be implemented by using the standard value for drunk driving stipulated in national road traffic safety regulations, such as a blood alcohol concentration of 20 mg / 100 ml or more. The high-risk assessment result may refer to the system's highest risk level judgment of the driving safety status. For example, it can be implemented by outputting a control signal through a logic judgment module, which is directly related to the management and control of vehicle startup authority.

[0091] For example, if the alcohol monitoring module detects that the driver's blood alcohol concentration reaches a preset drinking threshold, the system will skip other risk assessment processes and directly trigger the high-risk assessment result judgment mechanism. At this point, the vehicle control system will immediately implement a preset safety strategy, such as locking the vehicle start function. This judgment logic takes precedence over the comprehensive analysis of other physiological indicators and environmental data, ensuring a rapid response when clear drinking behavior is detected.

[0092] In this embodiment, a direct determination mechanism based on pre-set drinking conditions enables immediate identification of drunk driving risks. This allows the vehicle's start restriction function to be immediately triggered upon detecting an excessive alcohol concentration in the driver's blood, effectively preventing drunk driving. By establishing clear determination thresholds and a prioritized response mechanism, the timeliness and reliability of drunk driving prevention are significantly improved.

[0093] In an exemplary embodiment, the method further comprises:

[0094] If the comprehensive risk assessment result is a high risk assessment result, the vehicle to be driven by the driver is placed in an unstartable state;

[0095] If the comprehensive risk assessment result is a low risk assessment result, the vehicle is controlled to be in a safe driving state; wherein the safe driving state means that a safe driving prompt is given after the vehicle is started;

[0096] If the comprehensive risk assessment result is no risk assessment result, the current multi-dimensional monitoring data and current environmental data will be continuously monitored.

[0097] A high-risk assessment result may refer to a driving risk exceeding a safety threshold, as determined by analyzing multi-dimensional physiological monitoring data and environmental data. For example, this can be achieved by using a risk classification algorithm combined with historical data modeling to trigger a vehicle startup restriction mechanism. A low-risk assessment result may refer to a situation where potential risk exists but has not reached the danger threshold. For example, this can be achieved through an onboard human-computer interaction system, activating a safety reminder function simultaneously when the vehicle is started. A no-risk assessment result may refer to monitoring data falling within a normal fluctuation range. For example, this can be achieved by using a continuous data acquisition module combined with a real-time analysis algorithm to maintain dynamic tracking of the driver's status.

[0098] For example, when alcohol monitoring data and physiological indicator analysis indicate the driver is intoxicated, the vehicle's electronic control unit immediately disconnects the start circuit upon receiving a risk assessment signal. If the driver is slightly fatigued but not at a dangerous level, the instrument panel warning lights and voice prompt system are automatically activated after the vehicle is started. When all monitored parameters are within normal ranges, the system maintains periodic data collection mode, continuously acquiring physiological indicators such as heart rate and blood pressure through wearable devices. This processing logic is implemented through an embedded control system and is deeply integrated with the vehicle bus system to ensure real-time response to control commands.

[0099] For example, in high-risk scenarios (such as drinking or abnormal physiological indicators): the vehicle ECU can be used to forcibly lock the starting system and trigger an audible and visual alarm to prompt the driver that the blood alcohol concentration exceeds the standard / the physiological state is not suitable for driving, and the on-board system is linked to generate a temporary disable report; in low-risk scenarios: the vehicle is allowed to start, and the risk adaptation function can be activated simultaneously to dynamically adjust vehicle parameters according to environmental data (such as automatically turning on ABS+ESP in rainy days, increasing the adaptive cruise following distance when fatigued), and push physiological status prompts and travel time suggestions in real time through the HUD head-up system (such as the current fatigue index rises, it is recommended to take a break every 45 minutes); in risk-free scenarios: only basic safety monitoring can be retained, and vehicle energy consumption can be optimized through energy-saving algorithms.

[0100] In this embodiment, a dynamic risk assessment mechanism enables intelligent management of driving permissions, ensuring safety while avoiding excessive interference with normal driving. This creates a hierarchical safety control mechanism. When a high-risk condition is detected, the vehicle is directly blocked from starting, eliminating dangerous driving behaviors such as drunk driving at the source. Active warnings are activated for potential risk situations, ensuring driving safety while preventing misjudgments that could impact normal travel. Monitoring continuity is maintained in risk-free states, ensuring that abnormal conditions are promptly detected. This solution optimizes the human-vehicle interaction logic through system-level integration, achieving a balance between safety protection and ease of use.

[0101] In an exemplary embodiment, Figure 5A flow chart of a specific implementation method of a vehicle driver status monitoring method provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the vehicle driver status monitoring method may include, for example:

[0102] S501, sensor physiological data collection;

[0103] S502, the processor preliminarily analyzes the data;

[0104] S503: Determine whether the person has been drinking alcohol. If so, proceed directly to S512. If not, proceed to S504.

[0105] S504, physiological status analysis;

[0106] S505: Combine historical data analysis (long-term collection of driver physiological information) with a machine learning module (to determine the driver's physical condition);

[0107] S506, weather information collection;

[0108] S507: The driver presets a destination.

[0109] S508. Synchronize starting location environmental data (weather, temperature, visibility, etc.), road condition data (recommend optimal driving routes), and predict driving time based on driving distance;

[0110] S509, processor dynamic weight calculation;

[0111] S510: When connected to a vehicle, continuously upload and update vehicle status information (tire pressure, engine oil, fuel, etc.);

[0112] S511, risk assessment;

[0113] S512, high-risk output;

[0114] S513, low-risk output;

[0115] S514, risk-free output.

[0116] Among them, steps S501-S514 can be implemented through the above-mentioned embodiments or a combination of multiple embodiments. The specific limitations in this embodiment can refer to the limitations of the various embodiments of the vehicle driver status monitoring method above. It can be understood that S501 and sensor physiological data acquisition can be equivalent to step S201; steps S506-S508 can be equivalent to step S202; step S509 can be equivalent to steps S202-S205.

[0117] In this embodiment, the safety of driving a vehicle can be improved through multimodal physiological parameter precision monitoring technology, low-power real-time computing and edge AI processing, vehicle-machine interconnection and keyless control system integration, interaction optimization under driving status, regulatory compliance and data privacy protection.

[0118] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0119] The following describes the vehicle driver's status monitoring device provided in an embodiment of the present application. The vehicle driver's status monitoring device and the above-mentioned vehicle driver's status monitoring method have the same inventive concept, and the implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method. Therefore, the specific limitations in the embodiments of one or more vehicle driver's status monitoring devices provided below can refer to the limitations on the vehicle driver's status monitoring method above. The vehicle driver's status monitoring device described below and the vehicle driver's status monitoring method described above can be referenced to each other and will not be repeated here.

[0120] In an exemplary embodiment, Figure 6 A schematic diagram of a vehicle driver status monitoring device provided in an embodiment of the present application is applied to a wearable car key device. The wearable car key device includes a multi-dimensional physiological monitoring module, and the physiological monitoring module includes an alcohol monitoring module, such as Figure 6 As shown, the vehicle driver status monitoring device 60 includes: a physiological monitoring module 610, an environmental monitoring module 620, a physiological risk module 630, a travel risk module 640, and a comprehensive risk module 650, wherein:

[0121] The physiological monitoring module 610 obtains the current multi-dimensional monitoring data of the vehicle driver through the wearable car key device and determines the historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data includes the alcohol monitoring data obtained by the alcohol monitoring module;

[0122] The environment monitoring module 620 is used to obtain the current environment data corresponding to the vehicle when the alcohol monitoring data does not meet the preset drinking conditions;

[0123] A physiological risk module 630 is used to determine a physiological risk assessment result of the vehicle driver based on historical multi-dimensional monitoring data and current multi-dimensional monitoring data;

[0124] The trip risk module 640 is used to obtain a trip risk assessment result of the vehicle driver based on the current environmental data;

[0125] The comprehensive risk module 650 is used to obtain a comprehensive risk assessment result based on the physiological risk assessment result and the travel risk assessment result.

[0126] In an exemplary embodiment, the comprehensive risk module is used to determine the physiological risk assessment weight corresponding to the current multi-dimensional monitoring data based on the current multi-dimensional monitoring data; obtain the environmental risk assessment weight corresponding to the previous environmental data based on the current environmental data; and perform weighted fusion assessment processing on the physiological risk assessment results and the travel risk assessment results based on the physiological risk assessment weight and the environmental risk assessment weight to obtain a comprehensive risk assessment result.

[0127] In an exemplary embodiment, the comprehensive risk module is used to increase the physiological risk assessment weight if the current multi-dimensional monitoring data meets the preset physiological abnormal fluctuation conditions; if the current environmental data meets the preset environmental mutation conditions, then increase the environmental risk assessment weight.

[0128] In an exemplary embodiment, the physiological risk module is used to update a pre-built physiological trend analysis model based on historical multi-dimensional monitoring data; input the current multi-dimensional monitoring data into the physiological trend analysis model to determine the physiological status assessment result of the vehicle driver; and obtain the physiological risk assessment result of the vehicle driver based on the physiological status assessment result.

[0129] In an exemplary embodiment, the comprehensive risk assessment result includes a high risk assessment result, a low risk assessment result, and a no risk assessment result. The comprehensive risk module is configured to determine the comprehensive risk assessment result as a high risk assessment result if the alcohol monitoring data meets a preset drinking condition.

[0130] In an exemplary embodiment, the comprehensive risk module is used to control the vehicle driver to put the corresponding vehicle to be driven into an unstartable state if the comprehensive risk assessment result is a high risk assessment result; if the comprehensive risk assessment result is a low risk assessment result, then control the vehicle to be in a safe driving state; wherein the safe driving state represents a safe driving prompt after the vehicle is started; if the comprehensive risk assessment result is a no risk assessment result, then maintain a continuous monitoring state of the current multi-dimensional monitoring data and the current environmental data.

[0131] In an exemplary embodiment, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors execute the steps of the vehicle driver status monitoring method as described in any of the above embodiments.

[0132] In an exemplary embodiment, the present application also provides a vehicle-mounted device, in which a computer program is stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the vehicle driver status monitoring method as described in any of the above embodiments.

[0133] In an exemplary embodiment, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the vehicle driver status monitoring method as described in any one of the above embodiments.

[0134] Schematically, as Figure 7 As shown, Figure 7 This is a schematic diagram of the internal structure of a vehicle-mounted device provided in an embodiment of the present application. The vehicle-mounted device 700 can be provided as a server. Figure 7 The vehicle-mounted device 700 includes a processing component 702, which further includes one or more processors, and a memory resource represented by a memory 701 for storing instructions executable by the processing component 702, such as an application. The application stored in the memory 701 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 702 is configured to execute the instructions to perform the text recognition method of any of the above-described embodiments.

[0135] The vehicle-mounted device 700 may further include a power supply component 703 configured to perform power management of the vehicle-mounted device 700, a wired or wireless network interface 704 configured to connect the vehicle-mounted device 700 to a network, and an input / output (I / O) interface 705. The vehicle-mounted device 700 may operate based on an operating system stored in the memory 701, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0136] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the vehicle-mounted equipment to which the scheme of the present application is applied. The specific vehicle-mounted equipment may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0138] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0139] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0140] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the state of a vehicle driver, characterized in that: Applied to a wearable car key device, the wearable car key device includes a multi-dimensional physiological monitoring module, the physiological monitoring module includes an alcohol monitoring module, and the method includes: Obtaining, through the wearable car key device, current multi-dimensional monitoring data of the vehicle driver and determining historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data includes alcohol monitoring data obtained through monitoring by the alcohol monitoring module; When the alcohol monitoring data does not meet the preset drinking conditions, obtaining current environment data corresponding to the vehicle; determining a physiological risk assessment result of the vehicle driver based on the historical multi-dimensional monitoring data and the current multi-dimensional monitoring data; Obtaining a travel risk assessment result of the vehicle driver based on the current environmental data; A comprehensive risk assessment result is obtained based on the physiological risk assessment result and the travel risk assessment result.

2. The method according to claim 1, characterized in that The comprehensive risk assessment result is obtained based on the physiological risk assessment result and the travel risk assessment result, including: Determining a physiological risk assessment weight corresponding to the current multi-dimensional monitoring data based on the current multi-dimensional monitoring data; Obtaining, based on the current environmental data, an environmental risk assessment weight corresponding to the previous environmental data; Based on the physiological risk assessment weight and the environmental risk assessment weight, the physiological risk assessment result and the travel risk assessment result are subjected to weighted fusion assessment processing to obtain the comprehensive risk assessment result.

3. The method according to claim 2, characterized in that The method further comprises: If the current multi-dimensional monitoring data meets the preset physiological abnormal fluctuation condition, then increase the physiological risk assessment weight; If the current environmental data meets the preset environmental mutation conditions, the environmental risk assessment weight is increased.

4. The method according to claim 1, wherein The determining of the physiological risk assessment result of the vehicle driver based on the historical multi-dimensional monitoring data and the current multi-dimensional monitoring data includes: Updating a pre-built physiological trend analysis model based on the historical multi-dimensional monitoring data; Inputting the current multi-dimensional monitoring data into the physiological trend analysis model to determine a physiological status assessment result of the vehicle driver; A physiological risk assessment result of the vehicle driver is obtained based on the physiological status assessment result.

5. The method according to claim 1, characterized in that The comprehensive risk assessment result includes a high risk assessment result, a low risk assessment result, and a no risk assessment result; the method further includes: If the alcohol monitoring data meets the preset drinking conditions, the comprehensive risk assessment result is determined to be a high-risk assessment result.

6. The method according to claim 5, characterized in that The method further comprises: If the comprehensive risk assessment result is a high risk assessment result, controlling the vehicle to be driven by the vehicle driver to be in an unstartable state; If the comprehensive risk assessment result is a low risk assessment result, controlling the vehicle to be in a safe driving state; wherein the safe driving state indicates that a safe driving prompt is given after the vehicle is started; If the comprehensive risk assessment result is a no-risk assessment result, the continuous monitoring state of the current multi-dimensional monitoring data and the current environmental data is maintained.

7. A vehicle driver status monitoring device, characterized in that: Applied to a wearable car key device, the wearable car key device includes a multi-dimensional physiological monitoring module, the physiological monitoring module includes an alcohol monitoring module, and the device includes: a physiological monitoring module, configured to obtain, through the wearable vehicle key device, current multi-dimensional monitoring data of the vehicle driver and determine historical multi-dimensional monitoring data of the vehicle driver; the current multi-dimensional monitoring data including alcohol monitoring data obtained through monitoring by the alcohol monitoring module; An environment monitoring module, configured to obtain current environment data corresponding to the vehicle if the alcohol monitoring data does not meet the preset drinking conditions; a physiological risk module, configured to determine a physiological risk assessment result of the vehicle driver based on the historical multi-dimensional monitoring data and the current multi-dimensional monitoring data; A trip risk module, configured to obtain a trip risk assessment result of the vehicle driver based on the current environmental data; The comprehensive risk module is used to obtain a comprehensive risk assessment result based on the physiological risk assessment result and the travel risk assessment result.

8. A vehicle-mounted device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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