An automobile intelligent driving safety warning system based on Internet of Things technology

Abstract

The present invention belongs to the technical field of automobile driving control, and specifically is an automobile intelligent driving safety warning system based on Internet of Things technology, including an environmental monitoring and warning module, a fatigue driving warning module, a safety hazard analysis module and an alarm reminder module; the present invention comprehensively monitors the environmental conditions around the automobile and issues timely warnings through the environmental monitoring and warning module, the fatigue driving warning module determines whether the driver is in a fatigue state by monitoring the driver's behavioral characteristics to avoid safety accidents caused by fatigue driving, and the safety hazard analysis module comprehensively analyzes the automobile driving safety hazards based on the automobile shaking condition, the driver's control performance and the in-car environment performance, which is conducive to the driver to take corresponding improvement measures in time, further ensure the safety of automobile driving, and effectively avoid the occurrence of driving safety accidents, with a high degree of intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile driving control, and in particular to an automobile intelligent driving safety warning system based on Internet of Things technology. Background Art

[0002] With the rapid development of information technology, intelligent and automatic control technologies have been widely popularized and applied in the automotive industry. As an important direction of future development, intelligent connected vehicles can achieve better control of vehicle operation status by equipping more sensors, control devices and information transmission equipment.

[0003] At present, when monitoring and warning of driving safety of automobiles, it is often difficult to conduct real-time and comprehensive monitoring of potential driving hazards and timely warnings, and it is impossible to comprehensively evaluate and accurately output the degree of driving hazards of automobiles within a certain period of time, which is not conducive to the driver to take corresponding improvement measures in time, and it is difficult to effectively avoid the occurrence of driving safety accidents and ensure the safety of automobile driving;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an automobile intelligent driving safety warning system based on Internet of Things technology, which solves the problem that the existing technology is difficult to conduct real-time and comprehensive monitoring of potential dangers in automobile driving and timely warning, and is unable to comprehensively evaluate and accurately output the degree of driving hazards of the automobile within a certain period of time, making it difficult to effectively avoid the occurrence of driving safety accidents and ensure the safety of automobile driving.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An automobile intelligent driving safety warning system based on Internet of Things technology, including an environmental monitoring warning module, a fatigue driving warning module, a safety hazard analysis module and an alarm reminder module;

[0008] The environmental monitoring and warning module obtains monitoring and analysis information around the car, and sends corresponding instructions to the alarm reminder module to sound an alarm when it obtains forward collision warning information, reverse collision warning information, lane departure warning information or blind spot risk warning information;

[0009] The fatigue driving warning module determines whether the driver is in a fatigue state by monitoring the driver's behavioral characteristics. When it detects signs of fatigue driving, it sends corresponding instructions to the alarm reminder module to make it sound an alarm;

[0010] The safety hazard analysis module conducts a comprehensive analysis of the safety hazards of automobile driving, obtains the automobile hazard detection value, the control behavior detection value and the in-vehicle environment detection value, and compares the automobile hazard detection value, the control behavior detection value and the in-vehicle environment detection value with the preset automobile hazard detection threshold, the preset control behavior detection threshold and the preset in-vehicle environment detection threshold respectively; if the automobile hazard detection value, the control behavior detection value or the in-vehicle environment detection value exceeds the corresponding preset threshold, a corresponding instruction is sent to the alarm reminder module to cause it to issue an alarm.

[0011] Furthermore, the environmental monitoring and warning module includes a forward collision warning module, a reverse collision warning module, a lane departure monitoring and warning module, and a blind spot monitoring and warning module; wherein the forward collision warning module uses a radar ring and a camera to monitor the distance and speed of the vehicle in front, analyzes the forward collision risk based on the information, and determines whether to generate a forward collision warning message, and sends the forward collision warning message to the environmental monitoring and warning module;

[0012] When reversing, the reversing collision warning module uses sensors to monitor obstacles behind the vehicle. When the distance to the obstacle behind the vehicle is less than the corresponding preset threshold, a reversing collision warning message is generated and sent to the environmental monitoring warning module.

[0013] The lane departure warning module uses a camera to identify the markings of the lane where the car is located. When the car deviates from the lane, it generates lane departure warning information and sends the lane departure warning information to the environmental monitoring and warning module; the blind spot monitoring and warning module detects the blind spot areas on both sides of the car. When other cars enter the blind spot area, it generates blind spot risk warning information and sends the blind spot risk warning information to the environmental monitoring and warning module.

[0014] Further, the safety hazard analysis module is communicatively connected to the automobile hazard detection module, the manipulation behavior analysis module and the in-vehicle environment impact module, the automobile hazard detection module detects the operation of the automobile and analyzes its safety to obtain an automobile hazard detection value, and sends the automobile hazard detection value to the safety hazard analysis module;

[0015] The control behavior analysis module monitors and analyzes the driver's driving control behavior to obtain the control behavior detection value, and sends the control behavior detection value to the safety hazard analysis module; the in-vehicle environment impact module monitors and analyzes the environmental conditions in the car's driving cab to obtain the in-vehicle environment detection value, and sends the in-vehicle environment detection value to the safety hazard analysis module.

[0016] Furthermore, the specific analysis process of the automobile hidden danger detection module is as follows:

[0017] The real-time shaking amplitude of the vehicle is collected, and the real-time shaking amplitude is compared with a preset real-time shaking amplitude threshold value. If the real-time shaking amplitude exceeds the preset real-time shaking amplitude threshold value, it is determined that the vehicle is in an unstable driving state;

[0018] The total duration of the car in an unstable driving state per unit time is obtained and marked as the driving stability abnormality time condition value, and the number of occurrences in which the single duration of the car in an unstable driving state exceeds the corresponding preset single duration threshold is marked as the driving stability and danger detection value, and the driving wave condition value is obtained by averaging all real-time shaking amplitudes of the car in unit time, and the car hidden danger detection value is obtained by numerically calculating the driving stability abnormality time condition value, the driving stability and danger detection value and the driving wave condition value.

[0019] Furthermore, the specific analysis process of the manipulation behavior analysis module is as follows:

[0020] The number of sudden braking and acceleration times of the driver per unit time is collected and the sum of the two is marked as the vehicle speed control abnormality value, the number of lane changes of the driver per unit time is collected and marked as the lane keeping risk value, and the movement speed of the car is compared with the preset movement speed threshold in real time, based on which the total time duration of the movement speed of the car exceeding the preset movement speed threshold per unit time is collected and marked as the speeding time table value; the control behavior detection value is obtained by numerically calculating the vehicle speed control abnormality value, the lane keeping risk value and the speeding time table value.

[0021] Furthermore, the specific analysis process of the in-vehicle environment impact module is as follows:

[0022] Conduct air quality testing in the car cab, collect the concentrations and values ​​of all hazardous gases that need to be monitored and mark them as gas hazard detection values, and collect the dust concentration and oxygen concentration in the car cab and mark them as dust detection values ​​and oxygen detection values ​​respectively;

[0023] The deviation value of the temperature in the car cab compared to the preset suitable driving temperature standard value is marked as the temperature detection value, and the deviation value of the humidity in the car cab compared to the preset suitable driving humidity standard value is marked as the humidity detection value. The in-vehicle environment detection value is obtained by numerically calculating the gas hazard detection value, dust detection value, oxygen detection value, temperature detection value and humidity detection value.

[0024] Furthermore, the alarm reminder module is communicatively connected to the alarm delay analysis module, and the alarm delay analysis module is communicatively connected to the user smart terminal. The alarm delay analysis module is used to set a detection period, analyze the alarm delay risk of the alarm reminder module within the detection period, generate a high-risk alarm delay signal or a low-risk alarm delay signal through analysis, and send the high-risk alarm delay signal to the user smart terminal.

[0025] Furthermore, the specific analysis process of the alarm delay analysis module is as follows:

[0026] Obtain all alarms issued by the alarm reminder module during the detection period, classify the instructions corresponding to all alarms, and calculate the time difference between the issuance time of the corresponding alarm and the generation time of the corresponding instruction to obtain the alarm delay value;

[0027] Compare the alarm delay value with the preset alarm delay threshold of the corresponding type of instruction. If the alarm delay value exceeds the corresponding preset alarm delay threshold, mark the corresponding alarm delay value as the alarm abnormal delay value. Collect the number of alarm abnormal delay values ​​corresponding to the corresponding type of instruction during the detection period and calculate the ratio of it to the number of alarm delay values ​​to obtain the alarm abnormal value. And mark the ratio of the mean value of the alarm delay value of the corresponding type of instruction during the detection period to the corresponding preset alarm delay threshold as the alarm delay value.

[0028] The alarm risk value of the corresponding type of instruction is obtained by numerically calculating the alarm anomaly value and the alarm delay value, and the alarm risk value is numerically compared with the preset alarm risk threshold. If the alarm risk value exceeds the preset alarm risk threshold, the corresponding type of instruction is marked as an abnormal reminder instruction; if there is no abnormal reminder instruction during the detection period, an alarm delay low risk signal is generated.

[0029] Furthermore, if there is an abnormal reminder instruction during the detection period, the alarm risk value of the abnormal reminder instruction is marked as a risk over-table value compared to the preset alarm risk threshold, and each type of instruction is set in advance to correspond to a set of preset weight values, and the risk over-table value of the corresponding abnormal reminder instruction is multiplied by the corresponding preset weight value to obtain the risk over-analysis value;

[0030] The delay risk coefficient is calculated by summing up the risk super-analysis values ​​of all abnormal warning instructions during the detection period, and the delay risk coefficient is numerically compared with the preset delay risk coefficient threshold. If the delay risk coefficient exceeds the preset delay risk coefficient threshold, an alarm delay high risk signal is generated; if the delay risk coefficient does not exceed the preset delay risk coefficient threshold, an alarm delay low risk signal is generated.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the present invention, the environment monitoring and early warning module comprehensively monitors the surrounding environment of the car and provides timely early warning. The fatigue driving early warning module determines whether the driver is in a fatigue state by monitoring the driver's behavioral characteristics. The potential safety hazard analysis module comprehensively analyzes the potential safety hazard of car driving based on the shaking condition of the car, the driver's control performance and the performance of the in-car environment, which is conducive to the driver to take corresponding improvement measures in time, significantly improve the safety of car driving, and effectively avoid the occurrence of driving safety accidents.

[0033] 2. In the present invention, all alarms issued within the detection period are sent to the alarm delay analysis module through the alarm reminder module. The alarm delay analysis module analyzes the alarm delay risk of the alarm reminder module to generate an alarm delay high risk signal or an alarm delay low risk signal. When the alarm delay high risk signal is generated, the cause investigation and analysis is carried out and corresponding inspections and repairs are performed to ensure the timeliness of the car's alarm, reduce the probability of driving accidents, and significantly reduce the difficulty of users' management of the car, with a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0035] Figure 1 This is an overall system block diagram of Embodiment 1 of the present invention;

[0036] Figure 2 It is a system block diagram of the potential safety hazard analysis module in the present invention;

[0037] Figure 3 This is a system block diagram of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1: Figure 1-2 As shown, the present invention proposes an automobile intelligent driving safety warning system based on Internet of Things technology, including an environmental monitoring warning module, a fatigue driving warning module, a safety hazard analysis module and an alarm reminder module;

[0040] The environmental monitoring and early warning module obtains monitoring and analysis information around the car, and sends corresponding instructions to the alarm reminder module to make it sound an alarm when it obtains forward collision warning information, reverse collision warning information, lane departure warning information or blind spot risk warning information, so as to achieve comprehensive monitoring of the environmental conditions around the car and timely warning, so as to remind the driver to take reasonable response measures in time, such as braking, deceleration, and adjusting the direction, which is conducive to ensuring the safety of car driving;

[0041] It should be noted that the environmental monitoring and warning module includes a forward collision warning module, a reverse collision warning module, a lane departure monitoring and warning module, and a blind spot monitoring and warning module;

[0042] The forward collision warning module uses radar rings and cameras to monitor the distance and speed of the car in front, analyzes the risk of forward collision and determines whether to generate forward collision warning information, and sends the forward collision warning information to the environmental monitoring and warning module; for example, if the vehicle in front suddenly slows down and the driver of this car does not respond in time, the driver will be reminded to brake through sound, images, etc.;

[0043] When reversing, the reversing collision warning module uses sensors to monitor obstacles behind the vehicle. When the distance to the obstacle behind the vehicle is less than the corresponding preset threshold, it generates a reversing collision warning message and sends the reversing collision warning message to the environmental monitoring and warning module to help the driver avoid reversing collision accidents.

[0044] The lane departure warning module uses a camera to identify the lane markings of the car. When the car deviates from the lane, it generates a lane departure warning message and sends the lane departure warning message to the environmental monitoring warning module (it is very effective in preventing lane departure accidents caused by driver fatigue, distraction, etc.);

[0045] The blind spot monitoring and warning module detects the blind spot areas on both sides of the car. When other cars enter the blind spot area, it generates blind spot risk warning information (especially important in situations such as changing lanes and overtaking), and sends the blind spot risk warning information to the environmental monitoring and warning module.

[0046] The fatigue driving warning module determines whether the driver is in a fatigued state by monitoring the driver's behavioral characteristics, such as the time the eyes are closed and the head posture. When it detects signs of fatigue driving, it sends corresponding instructions to the alarm reminder module to sound an alarm to remind the driver to rest, avoid safety accidents caused by fatigue driving, and further ensure the safety of car driving.

[0047] The safety hazard analysis module comprehensively analyzes the safety hazards of automobile driving, obtains the automobile hazard detection value HM, the control behavior detection value YW and the in-vehicle environment detection value LX, and compares the automobile hazard detection value HM, the control behavior detection value YW and the in-vehicle environment detection value LX with the preset automobile hazard detection threshold, the preset control behavior detection threshold and the preset in-vehicle environment detection threshold respectively;

[0048] If the automobile hidden danger detection value HM, the control behavior detection value YW or the in-vehicle environment detection value LX exceeds the corresponding preset threshold, indicating that the overall degree of the automobile driving hidden danger is relatively serious, which is not conducive to ensuring the driving safety of the automobile, then the corresponding instruction is sent to the alarm reminder module to make it sound an alarm, so as to achieve a comprehensive analysis and comprehensive evaluation of the automobile driving hidden dangers, which is conducive to the driver to take corresponding improvement measures in time, thereby ensuring safe driving of the car.

[0049] Furthermore, the safety hazard analysis module is communicatively connected to the automobile hazard detection module, the control behavior analysis module and the in-vehicle environment impact module. The automobile hazard detection module detects the operation of the automobile and analyzes its safety to obtain the automobile hazard detection value HM, and sends the automobile hazard detection value HM to the safety hazard analysis module, which can not only timely feedback the stability status of the automobile, but also provide data support for the analysis process of the safety hazard analysis module to ensure the accuracy of its analysis results; the specific analysis process of the automobile hazard detection module is as follows:

[0050] The real-time shaking amplitude of the vehicle is collected, and the real-time shaking amplitude is compared with a preset real-time shaking amplitude threshold value. If the real-time shaking amplitude exceeds the preset real-time shaking amplitude threshold value, it is determined that the vehicle is in an unstable driving state;

[0051] The total duration of the vehicle being in an unstable driving state within a unit time is obtained and marked as a driving stability abnormality time value, and the number of occurrences of the vehicle being in an unstable driving state for a single duration exceeding the corresponding preset single duration threshold is marked as a driving stability detection value, and all real-time shaking amplitudes of the vehicle within a unit time are averaged to obtain a driving wave condition value;

[0052] By formula The driving stability and abnormal condition value XP, the driving stability and danger detection value ZW and the driving wave condition value FY are numerically calculated to obtain the automobile hidden danger detection value HM; wherein wq1, wq2, and wq3 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the automobile hidden danger detection value HM is, the more unstable the automobile operation is, and the less conducive it is to ensuring driving safety.

[0053] The control behavior analysis module monitors and analyzes the driver's driving control behavior to obtain the control behavior detection value YW, and sends the control behavior detection value YW to the safety hazard analysis module, which can not only reasonably analyze and accurately feedback the driver's control performance, but also provide data support for the analysis process of the safety hazard analysis module to improve the accuracy of its analysis results; the specific analysis process of the control behavior analysis module is as follows:

[0054] The number of sudden braking and acceleration times of the driver in a unit time is collected and the sum of the two is marked as the vehicle speed control value, and the number of lane changes of the driver in a unit time is collected and marked as the lane keeping risk value, and the moving speed of the car is compared with the preset moving speed threshold in real time, and the total time that the moving speed of the car exceeds the preset moving speed threshold in a unit time is collected and marked as the speeding time table value;

[0055] By formula The vehicle speed control value SW, the lane keeping risk value NP and the speeding table value GY are numerically calculated to obtain the control behavior detection value YW; wherein, uy1, uy2, and uy3 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the control behavior detection value YW is, the greater the driver's control safety hazard is.

[0056] The in-vehicle environment impact module monitors and analyzes the environmental conditions in the car's driving room to obtain the in-vehicle environment detection value LX, and sends the in-vehicle environment detection value LX to the safety hazard analysis module, which can not only reasonably analyze and accurately feedback the environmental performance in the car's driving room, but also provide data support for the analysis process of the safety hazard analysis module, further improving the comprehensiveness of its analysis process and the accuracy of the analysis results; the specific analysis process of the in-vehicle environment impact module is as follows:

[0057] Conduct air quality testing in the car's cab, collect the concentrations and values ​​of all hazardous gases that need to be monitored (such as carbon monoxide, nitric oxide, nitrogen dioxide and other harmful gases) and mark them as gas hazard detection values, and collect the dust concentration and oxygen concentration in the car's cab and mark them as dust detection values ​​and oxygen detection values ​​respectively;

[0058] and collecting the temperature and humidity in the vehicle driving compartment, marking the deviation value of the temperature in the vehicle driving compartment compared to the preset suitable driving temperature standard value as the temperature detection value, and marking the deviation value of the humidity in the vehicle driving compartment compared to the preset suitable driving humidity standard value as the humidity detection value;

[0059] By formula The gas hazard detection value QW, dust detection value WN, oxygen detection value YS, temperature detection value TM and humidity detection value XS are numerically calculated to obtain the in-vehicle environment detection value LX; wherein b1, b2, b3, b4, b5 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the in-vehicle environment detection value LX, the worse the environmental conditions in the car's cab, and the less conducive it is to the driver's safe driving.

[0060] Embodiment 2: Figure 3 As shown, the difference between this embodiment and the first embodiment is that the alarm reminder module is connected to the alarm delay analysis module in communication, the alarm delay analysis module is connected to the user intelligent terminal in communication, the alarm delay analysis module is used to set the detection period, preferably, the detection period is seven days; the alarm reminder module sends all alarms issued during the detection period to the alarm delay analysis module;

[0061] The alarm delay analysis module analyzes the alarm delay risk of the alarm reminder module during the detection period, generates a high-risk alarm delay signal or a low-risk alarm delay signal through analysis, and sends the high-risk alarm delay signal to the user's smart terminal to remind the user to conduct cause investigation and analysis and perform corresponding inspections and repairs, thereby ensuring the timeliness of the car's alarm, reducing the probability of driving accidents, and significantly reducing the difficulty of user management of the car; the specific analysis process of the alarm delay analysis module is as follows:

[0062] Obtain all alarms issued by the alarm reminder module during the detection period, classify the instructions corresponding to all alarms, and calculate the time difference between the issuance time of the corresponding alarm and the generation time of the corresponding instruction to obtain the alarm delay value;

[0063] Compare the alarm delay value with the preset alarm delay threshold of the corresponding type of instruction. If the alarm delay value exceeds the corresponding preset alarm delay threshold, mark the corresponding alarm delay value as the alarm abnormal delay value. Collect the number of alarm abnormal delay values ​​corresponding to the corresponding type of instruction during the detection period and calculate the ratio of it to the number of alarm delay values ​​to obtain the alarm abnormal value. And mark the ratio of the mean value of the alarm delay value of the corresponding type of instruction during the detection period to the corresponding preset alarm delay threshold as the alarm delay value.

[0064] By formula The alarm abnormality value SP and the alarm delay value LM are numerically calculated to obtain the alarm risk value PM of the corresponding type of instruction; wherein ng1 and ng2 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the alarm risk value PM is, the less the alarm timeliness for the corresponding type of instruction during the detection period does not meet the requirements;

[0065] The alarm risk value PM is numerically compared with the preset alarm risk threshold. If the alarm risk value PM exceeds the preset alarm risk threshold, it indicates that the alarm timeliness for the corresponding type of instructions during the detection period does not meet the requirements, and the corresponding type of instructions are marked as abnormal reminder instructions; if there are no abnormal reminder instructions during the detection period, it indicates that the car alarm timeliness performance is extremely good during the detection period, and a low risk signal of alarm delay is generated.

[0066] Furthermore, if there is an abnormal reminder instruction during the detection period, the alarm risk value of the abnormal reminder instruction is marked as a risk over-table value compared to the preset alarm risk threshold, and each type of instruction is set in advance to correspond to a set of preset weight values, wherein the values ​​of the preset weight values ​​are all positive numbers, and the greater the adverse effect of the delay of the corresponding type of instruction on driving safety, the greater the value of the preset weight value matched therewith; the risk over-table value of the corresponding abnormal reminder instruction is multiplied by the corresponding preset weight value to obtain the risk over-analysis value;

[0067] The delay risk coefficient is calculated by summing up the risk super-analysis values ​​of all abnormal warning instructions during the detection period, and the delay risk coefficient is numerically compared with the preset delay risk coefficient threshold. If the delay risk coefficient exceeds the preset delay risk coefficient threshold, it indicates that the timeliness performance of the car alarm during the detection period is generally poor, and a high-risk alarm delay signal is generated; if the delay risk coefficient does not exceed the preset delay risk coefficient threshold, it indicates that the timeliness performance of the car alarm during the detection period is generally good, and a low-risk alarm delay signal is generated.

[0068] The working principle of the present invention is as follows: when in use, the environmental monitoring and early warning module is used to comprehensively monitor the environmental conditions around the car and issue early warnings in time to remind the driver to take reasonable countermeasures in time, and the fatigue driving early warning module is used to determine whether the driver is in a fatigue state by monitoring the driver's behavioral characteristics. When signs of fatigue driving are detected, the driver is reminded to rest to avoid safety accidents caused by fatigue driving, and the safety hazard analysis module is used to comprehensively analyze the safety hazards of car driving based on the car's shaking conditions, the driver's control performance and the in-car environment performance, which is conducive to the driver taking corresponding improvement measures in time, further ensuring the safety of car driving, with a high degree of intelligence, and effectively avoiding the occurrence of driving safety accidents.

[0069] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An automobile intelligent driving safety warning system based on Internet of Things technology, characterized in that: It includes environmental monitoring and early warning module, fatigue driving warning module, safety hazard analysis module and alarm reminder module; The environmental monitoring and warning module obtains monitoring and analysis information around the car, and sends corresponding instructions to the alarm reminder module to sound an alarm when it obtains forward collision warning information, reverse collision warning information, lane departure warning information or blind spot risk warning information; The fatigue driving warning module determines whether the driver is in a fatigue state by monitoring the driver's behavioral characteristics. When it detects signs of fatigue driving, it sends corresponding instructions to the alarm reminder module to make it sound an alarm; The safety hazard analysis module conducts a comprehensive analysis of the safety hazards of automobile driving, obtains the automobile hazard detection value, the control behavior detection value and the in-vehicle environment detection value, and sends a corresponding instruction to the alarm reminder module to make it sound an alarm if the automobile hazard detection value, the control behavior detection value or the in-vehicle environment detection value exceeds the corresponding preset threshold value; The safety hidden danger analysis module is communicatively connected to the automobile hidden danger detection module, the control behavior analysis module and the in-vehicle environment impact module. The automobile hidden danger detection module detects the operation of the automobile and analyzes its safety to obtain an automobile hidden danger detection value, and sends the automobile hidden danger detection value to the safety hidden danger analysis module; The control behavior analysis module monitors and analyzes the driver's driving control behavior to obtain a control behavior detection value, and sends the control behavior detection value to the safety hazard analysis module; The in-vehicle environment impact module monitors and analyzes the environmental conditions in the vehicle driving room to obtain the in-vehicle environment detection value, and sends the in-vehicle environment detection value to the safety hazard analysis module; The specific analysis process of the automobile hidden danger detection module is as follows: The real-time shaking amplitude of the car is collected. If the real-time shaking amplitude exceeds a preset real-time shaking amplitude threshold, it is determined that the car is in an unstable driving state; The total duration of the vehicle being in an unstable driving state within a unit time is obtained and marked as a driving stability abnormality time condition value, and the number of occurrences of the vehicle being in an unstable driving state that exceeds the corresponding preset single duration threshold is marked as a driving stability and danger detection value, and all real-time shaking amplitudes of the vehicle within a unit time are averaged to obtain a driving wave condition value, and the vehicle hidden danger detection value is obtained by numerically calculating the driving stability abnormality time condition value, the driving stability and danger detection value, and the driving wave condition value; The specific analysis process of the control behavior analysis module is as follows: the number of sudden braking and acceleration times of the driver in a unit time is collected and the sum of the two is marked as the vehicle speed control abnormality value, the number of lane changes of the driver in a unit time is collected and marked as the lane keeping risk value, and the total time when the vehicle's movement speed exceeds the preset movement speed threshold in a unit time is collected and marked as the speeding time table value; the control behavior detection value is obtained by numerically calculating the vehicle speed control abnormality value, the lane keeping risk value and the speeding time table value; The specific analysis process of the in-vehicle environment impact module is as follows: perform air quality detection in the car's cab, collect the concentrations and values ​​of all hazardous gases that need to be monitored and mark them as gas hazard detection values, and collect the dust concentration and oxygen concentration in the car's cab and mark them as dust detection values ​​and oxygen detection values ​​respectively; and marking the deviation value of the temperature in the vehicle driving cabin compared to the preset suitable driving temperature standard value as the temperature detection value, and marking the deviation value of the humidity in the vehicle driving cabin compared to the preset suitable driving humidity standard value as the humidity detection value, and obtaining the in-vehicle environment detection value by numerically calculating the gas hazard detection value, the dust detection value, the oxygen detection value, the temperature detection value and the humidity detection value; The alarm reminder module is communicatively connected to the alarm delay analysis module, which is communicatively connected to the user intelligent terminal. The alarm delay analysis module is used to set a detection period, analyze the alarm delay risk of the alarm reminder module within the detection period, and generate an alarm delay high risk signal or an alarm delay low risk signal through analysis; The specific analysis process of the alarm delay analysis module is as follows: obtain all alarms issued by the alarm reminder module during the detection period, classify the instructions corresponding to all alarms, calculate the time difference between the issuance time of the corresponding alarm and the generation time of the corresponding instruction to obtain the alarm delay value; Compare the alarm delay value with the preset alarm delay threshold of the corresponding type of instruction. If the alarm delay value exceeds the corresponding preset alarm delay threshold, mark the corresponding alarm delay value as the alarm abnormal delay value. Collect the number of alarm abnormal delay values ​​corresponding to the corresponding type of instruction during the detection period and calculate the ratio of it to the number of alarm delay values ​​to obtain the alarm abnormal value. And mark the ratio of the mean value of the alarm delay value of the corresponding type of instruction during the detection period to the corresponding preset alarm delay threshold as the alarm delay value. The alarm risk value of the corresponding type of instruction is obtained by numerically calculating the alarm abnormality value and the alarm delay value. If the alarm risk value exceeds the preset alarm risk threshold, the corresponding type of instruction is marked as an abnormal reminder instruction; If there is no abnormal reminder instruction during the detection period, an alarm delay low risk signal is generated; If there is an abnormal warning instruction during the detection period, the excess value of the alarm risk value of the abnormal warning instruction compared with the preset alarm risk threshold is marked as a risk over-table value, and each type of instruction is set in advance to correspond to a set of preset weight values, and the risk over-table value of the corresponding abnormal warning instruction is multiplied by the corresponding preset weight value to obtain the risk over-analysis value; The risk super-analysis values ​​of all abnormal warning instructions during the detection period are summed up to calculate the delay risk coefficient. If the delay risk coefficient exceeds the preset delay risk coefficient threshold, an alarm delay high risk signal is generated. If the delay risk coefficient does not exceed the preset delay risk coefficient threshold, an alarm delay low risk signal is generated.

2. According to the Internet of Things technology-based automobile intelligent driving safety warning system according to claim 1, it is characterized in that: The environmental monitoring and warning module includes a forward collision warning module, a reverse collision warning module, a lane departure monitoring and warning module, and a blind spot monitoring and warning module; the forward collision warning module uses a radar ring and a camera to monitor the distance and speed of the car in front, analyzes the forward collision risk and determines whether to generate a forward collision warning message, and sends the forward collision warning message to the environmental monitoring and warning module; When reversing, the reversing collision warning module uses sensors to monitor obstacles behind the vehicle. When the distance to the obstacle behind the vehicle is less than the corresponding preset threshold, a reversing collision warning message is generated and sent to the environmental monitoring warning module. The lane departure warning module uses a camera to identify the markings of the lane where the car is located. When the car deviates from the lane, it generates lane departure warning information and sends the lane departure warning information to the environmental monitoring and warning module; the blind spot monitoring and warning module detects the blind spot areas on both sides of the car. When other cars enter the blind spot area, it generates blind spot risk warning information and sends the blind spot risk warning information to the environmental monitoring and warning module.

Images