A continuous drive-through automatic inspection method, system and medium

Abstract

The present application provides a continuous driving through automatic inspection method, system and medium, belonging to the field of intelligent transportation and big data technology. The method includes: obtaining the detection information of the vehicles traveling within the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtaining the driver behavior information and the driver physiological reaction monitoring information under the authorization of the vehicle owner. Then extract the peak speed data and acceleration data to judge whether there is speeding and dangerous driving respectively; judge whether the driver has closed his eyes according to the driver behavior information; process the driver's physiological reaction monitoring information to obtain the driver's physiological fatigue index and judge the driver's fatigue level. The present application monitors parameters from three aspects: vehicle driving status, driver's physiological reaction and driving behavior, analyzes and judges whether the vehicle driving status is safe and whether the driver's driving status is normal, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation and big data technology, and more specifically, to a continuous drive-through automatic inspection method, system and medium. Background Art

[0002] Traditional road traffic safety monitoring methods are too simple. Currently, they mainly rely on speed measurement and photography functions in certain sections of the road for road safety monitoring. This method can improve road safety to a certain extent, but it also has obvious limitations. This single method is often unable to monitor road conditions comprehensively and in real time, especially when dealing with complex and changing traffic environments and emergencies.

[0003] There is a lack of a more scientific and comprehensive intelligent road safety monitoring method that can conduct a comprehensive and comprehensive posture safety analysis through vehicle status, driver behavior and driver physiological response, realize comprehensive, real-time and efficient monitoring and management of road safety, and provide strong protection for road traffic safety.

[0004] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0005] The purpose of this application is to provide a continuous driving through automatic inspection method, system and medium, which can obtain the detection information of the vehicles in the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, obtain the driver's behavior information and the driver's physiological reaction monitoring information under the authorization of the vehicle owner, and then extract the peak speed data and acceleration data to judge whether there is speeding and dangerous driving respectively; judge whether the driver has closed his eyes according to the driver's behavior information; process the driver's physiological reaction monitoring information to obtain the driver's physiological fatigue index and judge the driver's fatigue level. This application monitors parameters from three aspects: vehicle driving status, driver's physiological reaction and driving behavior, analyzes and judges whether the vehicle driving status is safe and whether the driver's driving status is normal, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

[0006] The present application provides a continuous drive-through automatic inspection method, comprising the following steps:

[0007] Obtaining vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information based on the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner;

[0008] Extracting peak speed data and acceleration data according to the vehicle running status monitoring information;

[0009] Determining whether the driver is speeding according to the peak speed data;

[0010] determining whether the driver is in a dangerous driving situation according to the acceleration data;

[0011] Extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes;

[0012] Processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index;

[0013] The driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged against a preset fatigue deviation threshold.

[0014] Among them, in the continuous driving through automatic inspection method described in the present application, it is characterized in that judging whether the driver is speeding according to the peak speed data is specifically:

[0015] Comparing the peak speed data with a preset first speed threshold and a second speed threshold;

[0016] The first speed threshold is less than the second speed threshold;

[0017] If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state;

[0018] If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent;

[0019] If the speed peak value data is greater than the second speed threshold, an overspeed warning message is sent.

[0020] Among them, in the continuous driving through type automatic inspection method described in the present application, it is characterized in that judging whether the driver has a dangerous driving situation according to the acceleration data is specifically:

[0021] Comparing the acceleration data with a preset acceleration threshold;

[0022] If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained;

[0023] Performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data;

[0024] Comparing the exceeding times data with a preset exceeding times threshold;

[0025] If the number of times exceeding the standard is greater than or equal to the preset number of times exceeding the standard threshold, the acceleration exceeding standard information is sent.

[0026] Among them, in the continuous driving through automatic inspection method described in the present application, it is characterized in that the facial image data is extracted according to the driver behavior information to determine whether the driver has closed eyes, specifically:

[0027] Extracting eye image features according to the face image data;

[0028] Comparing the eye image features with preset eye features to obtain eye feature similarity data;

[0029] Comparing the eye feature similarity data with a preset feature similarity threshold;

[0030] If the eye feature similarity data is greater than or equal to the preset feature similarity threshold, the driver is in a normal driving state;

[0031] If the eye feature similarity data is less than the preset feature similarity threshold, a timing process is performed to obtain timing time data;

[0032] If the timing time data is greater than or equal to the preset time threshold, the driver is in an eyes-closed state and an eyes-closed warning message is sent.

[0033] Among them, in the continuous driving automatic inspection method described in the present application, it is characterized in that the driver's physiological reaction monitoring information is processed by a preset physiological reaction evaluation model to obtain the driver's physiological fatigue index, specifically:

[0034] Extracting brain wave curve regularity data, heart wave curve stability data and eye wave curve transient change data according to the driver's physiological response monitoring information;

[0035] The driver's physiological fatigue index is obtained by processing the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data through a preset physiological response evaluation model;

[0036] The calculation formula of the physiological response evaluation model is:

[0037] in, is the driver's physiological fatigue index, They are the regularity data of brain wave curve, the stability data of heart wave curve and the transient change data of eye wave curve. is the preset characteristic coefficient.

[0038] Among them, in the continuous driving through type automatic inspection method described in the present application, it is characterized in that the driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged against a preset fatigue deviation threshold, specifically:

[0039] Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate;

[0040] Determining whether the fatigue deviation rate is greater than a preset fatigue deviation threshold;

[0041] If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal state information is sent;

[0042] If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's condition is normal.

[0043] In a second aspect, the present application provides a continuous drive-through automatic inspection system, the system comprising: a memory and a processor, the memory comprising a program of a continuous drive-through automatic inspection method, the program of the continuous drive-through automatic inspection method being executed by the processor to implement the following steps:

[0044] Obtaining vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information based on the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner;

[0045] Extracting peak speed data and acceleration data according to the vehicle running status monitoring information;

[0046] Determining whether the driver is speeding according to the peak speed data;

[0047] determining whether the driver is in a dangerous driving situation according to the acceleration data;

[0048] Extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes;

[0049] Processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index;

[0050] The driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged against a preset fatigue deviation threshold.

[0051] Among them, in the continuous driving through type automatic inspection system described in the present application, it is characterized in that judging whether the driver is speeding according to the peak speed data is specifically:

[0052] Comparing the peak speed data with a preset first speed threshold and a second speed threshold;

[0053] The first speed threshold is less than the second speed threshold;

[0054] If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state;

[0055] If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent;

[0056] If the speed peak value data is greater than the second speed threshold, an overspeed warning message is sent.

[0057] Among them, in the continuous driving through automatic inspection system described in the present application, it is characterized in that judging whether the driver has a dangerous driving situation according to the acceleration data is specifically:

[0058] Comparing the acceleration data with a preset acceleration threshold;

[0059] If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained;

[0060] Performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data;

[0061] Comparing the exceeding times data with a preset exceeding times threshold;

[0062] If the number of times exceeding the standard is greater than or equal to the preset number of times exceeding the standard threshold, the acceleration exceeding standard information is sent.

[0063] In a third aspect, the present application also provides a readable storage medium, which includes a continuous drive-through automatic inspection method program. When the continuous drive-through automatic inspection method program is executed by a processor, the steps of a continuous drive-through automatic inspection method as described in any one of the above items are implemented.

[0064] As can be seen from the above, the present application provides a method, system and medium for automatic inspection of continuous driving, which obtains the detection information of the vehicles traveling in the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtains the driver's behavior information and the driver's physiological reaction monitoring information under the authorization of the vehicle owner. Then extract the peak speed data and acceleration data to judge whether there is speeding and dangerous driving respectively; judge whether the driver has closed his eyes according to the driver's behavior information; process the driver's physiological reaction monitoring information to obtain the driver's physiological fatigue index and judge the driver's fatigue level. The present application monitors parameters from three aspects: the vehicle's driving state, the driver's physiological reaction and the driving behavior, analyzes and judges whether the vehicle's driving state is safe and whether the driver's driving state is normal, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

[0065] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 A flowchart of a continuous drive-through automatic inspection method provided in an embodiment of the present application;

[0068] Figure 2 A flow chart of a continuous driving through automatic inspection method for determining whether a driver is speeding provided in an embodiment of the present application;

[0069] Figure 3 A flowchart of a continuous driving through automatic inspection method for determining whether a driver is in a dangerous driving situation provided in an embodiment of the present application;

[0070] Figure 4 A flow chart of a continuous driving through automatic inspection method for determining whether a driver has closed eyes provided in an embodiment of the present application;

[0071] Figure 5 A flow chart of obtaining a driver's physiological fatigue index in a continuous driving-through automatic inspection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0073] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. 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 need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0074] Please refer to Figure 1 , Figure 1 1 is a flow chart of a continuous drive-through automatic inspection method in some embodiments of the present application. The continuous drive-through automatic inspection method is used in a terminal device, such as a computer, a mobile phone terminal, etc. The continuous drive-through automatic inspection method includes the following steps:

[0075] S101, obtaining driving vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information according to the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner;

[0076] S102, extracting peak speed data and acceleration data according to the vehicle running status monitoring information;

[0077] S103, judging whether the driver is speeding according to the peak speed data;

[0078] S104, judging whether the driver is in a dangerous driving situation according to the acceleration data;

[0079] S105, extracting facial image data according to the driver behavior information, and determining whether the driver has closed his eyes;

[0080] S106, processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index;

[0081] S107, comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and determining whether the fatigue deviation rate is equal to a preset fatigue deviation threshold.

[0082] Among them, the present application obtains the driving vehicle detection information in the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtains the driver behavior information and the driver physiological reaction monitoring information under the authorization of the vehicle owner. The driver behavior information refers to the driver's limb change behavior in the process of driving the vehicle, and the driver physiological reaction monitoring information refers to the changes in the driver's brain waves, heart waves and eye waves and other nervous system changes when the driver is driving the vehicle. Data monitoring and collection can be performed through various corresponding detection devices installed in the vehicle. According to the vehicle operation status monitoring information, the peak speed data and acceleration data are extracted to judge whether there is speeding and dangerous driving respectively; according to the driver behavior information, the image processing technology is used to judge whether the driver has closed his eyes; according to the driver's physiological reaction monitoring information, the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data are extracted and processed to obtain the driver's physiological fatigue index, which can judge the driver's fatigue level. The present application monitors parameters from three aspects: vehicle driving state, driver physiological reaction and driving behavior, analyzes and judges whether the vehicle driving state is safe, whether the driver's driving state is normal, improves driving safety, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

[0083] Please refer to Figure 2 , Figure 2 The flowchart of a continuous driving through automatic inspection method for determining whether the driver is speeding in some embodiments of the present application. According to an embodiment of the present invention, the method of determining whether the driver is speeding based on the peak speed data is specifically as follows:

[0084] S201, comparing the speed peak data with a preset first speed threshold and a second speed threshold;

[0085] S202: The first speed threshold is less than the second speed threshold;

[0086] S203: If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state;

[0087] S204: If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent;

[0088] S205: If the speed peak value data is greater than the second speed threshold, a speeding warning message is sent.

[0089] Among them, in order to improve driving safety, control the vehicle to travel within a safe speed range, compare the peak speed data with the preset first speed threshold and the second speed threshold, the first speed threshold is less than the second speed threshold, for example, the first speed threshold is 60km / h, the second speed threshold is 75km / h, if the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state, for example, the peak speed data is 55km / h, if the peak speed data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent, for example, the peak speed data is 69km / h, if the peak speed data is greater than the second speed threshold, an overspeed warning message is sent, for example, the peak speed data is 83km / h. Controlling and warning the speed of the moving vehicle can effectively reduce the occurrence of accidents and reduce the severity of accidents.

[0090] Please refer to Figure 3 , Figure 3 The flowchart of a continuous driving through automatic inspection method for determining whether the driver has a dangerous driving situation in some embodiments of the present application is as follows. According to an embodiment of the present invention, determining whether the driver has a dangerous driving situation based on the acceleration data is specifically:

[0091] S301, comparing the acceleration data with a preset acceleration threshold;

[0092] S302: if the acceleration data is greater than or equal to a preset acceleration threshold, then obtaining acceleration exceeding threshold data;

[0093] S303, performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data;

[0094] S304, comparing the exceeding times data with a preset exceeding times threshold;

[0095] S305: If the exceeding times data is greater than or equal to the preset exceeding times threshold, the acceleration exceeding times information is sent.

[0096] In order to further improve driving safety and vehicle handling, the acceleration data is compared with the preset acceleration threshold. If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained. For example, if the acceleration data is 8m / s 2 , the preset acceleration threshold is 6m / s 2, count the acceleration exceeding data, obtain the exceeding times data, compare the exceeding times data with the preset exceeding times threshold, if the exceeding times data is greater than or equal to the preset exceeding times threshold, then send the acceleration exceeding information, for example, the exceeding times data is 5, the preset exceeding times threshold is 3, monitor and count the instantaneous acceleration of the vehicle, and send the acceleration exceeding prompt to the driver when the warning times are reached, so as to help the driver develop safe driving habits.

[0097] Please refer to Figure 4 , Figure 4 The flowchart of a continuous driving through automatic inspection method for determining whether the driver has closed eyes in some embodiments of the present application is as follows. According to an embodiment of the present invention, extracting facial image data based on the driver behavior information to determine whether the driver has closed eyes is specifically as follows:

[0098] S401, extracting eye image features according to the face image data;

[0099] S402, comparing the eye image feature with a preset eye feature to obtain eye feature similarity data;

[0100] S403, comparing the eye feature similarity data with a preset feature similarity threshold;

[0101] S404: If the eye feature similarity data is greater than or equal to a preset feature similarity threshold, the driver is in a normal driving state;

[0102] S405: If the eye feature similarity data is less than a preset feature similarity threshold, a timing process is performed to obtain timing time data;

[0103] S406: If the timing time data is greater than or equal to the preset time threshold, the driver is in an eyes-closed state, and an eyes-closed warning message is sent.

[0104] Among them, in order to prevent the driver from engaging in dangerous behavior of driving with eyes closed, eye image features are extracted according to facial image data, the eye image features are compared with preset eye features to obtain eye feature similarity data, and the eye feature similarity data is compared with the preset feature similarity threshold. If the eye feature similarity data is greater than or equal to the preset feature similarity threshold, the driver is in a normal driving state. For example, the eye feature similarity data is 99%, and the preset feature similarity threshold is 96%. If the eye feature similarity data is less than the preset feature similarity threshold, timing processing is performed to obtain timing time data. For example, the eye feature similarity data is 94%, and the preset feature similarity threshold is 96%. If the timing time data is greater than or equal to the preset time threshold, the driver is in a closed-eye state, and an eye-closing warning message is sent. For example, the timing time data is 0.8s, and the preset time threshold is 0.6s.

[0105] Please refer to Figure 5 , Figure 5 The flowchart of a continuous driving through automatic inspection method for obtaining a driver's physiological fatigue index in some embodiments of the present application is as follows. According to an embodiment of the present invention, the driver's physiological reaction monitoring information is processed by a preset physiological reaction evaluation model to obtain the driver's physiological fatigue index, specifically:

[0106] S501, extracting brain wave curve regularity data, heart wave curve stability data and eye wave curve transient change data according to the driver's physiological response monitoring information;

[0107] S502, obtaining a driver's physiological fatigue index by processing the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data through a preset physiological response evaluation model;

[0108] The calculation formula of the physiological response evaluation model is:

[0109] in, is the driver's physiological fatigue index, They are the regularity data of brain wave curve, the stability data of heart wave curve and the transient change data of eye wave curve. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset traffic safety monitoring database).

[0110] Among them, in order to avoid the driver's fatigue driving or physical condition that makes it impossible to concentrate, the regularity data of the EEG curve, the smoothness data of the ECG curve and the transient change data of the EEG curve are extracted according to the driver's physiological response monitoring information. The EEG reflects the activity of nerve cells in the brain and has characteristics such as frequency, amplitude, waveform, and phase. The regularity data of the EEG curve mainly refers to the stability and predictability of the EEG waveform under normal or abnormal conditions. The ECG curve (usually refers to the electrocardiogram) is used to record the electrical activity of the heart, mainly including P waves, QRS complexes and T waves. The smoothness data of the ECG curve mainly reflects the stability and rhythmicity of the heart's electrical activity. The transient change data of the EEG curve refers to the rapid changes of signals such as the electroretinogram (ERG) and the electrooculogram (EOG) in a short period of time, which respectively reflect the stability and dynamic changes of the nervous system, cardiovascular system and eye function. The driver's physiological fatigue index is obtained by processing the regularity data of the brain wave curve, the smoothness data of the heart wave curve and the transient change data of the eye wave curve through a preset physiological response evaluation model, which mainly reflects the driver's physiological fatigue.

[0111] According to an embodiment of the present invention, the driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged to be different from a preset fatigue deviation threshold, specifically:

[0112] Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate;

[0113] Determining whether the fatigue deviation rate is greater than a preset fatigue deviation threshold;

[0114] If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal state information is sent;

[0115] If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's condition is normal.

[0116] Among them, in order to reduce dangerous driving and improve the mental state of the driver, the driver's physiological fatigue index is compared with the historical average physiological fatigue index of the driver in the preset section to obtain the fatigue deviation rate, the historical average physiological fatigue index of the driver in the preset section is obtained by querying the preset driver status monitoring database, and it is judged whether the fatigue deviation rate is greater than the preset fatigue deviation threshold. If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal status information is sent. For example, the fatigue deviation rate is 13%, and the preset fatigue deviation threshold is 8%. If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's status is normal, for example, the fatigue deviation rate is 5%, and the preset fatigue deviation threshold is 8%. It is worth mentioning that in the driving scene, excessive fatigue will seriously affect the driver's judgment, reaction speed and operation ability, thereby increasing the risk of traffic accidents. Therefore, in this case, reducing fatigue is crucial, but it does not mean pursuing absolute "zero fatigue". Appropriate fatigue can prompt people to rest and recover, thereby maintaining physical health and work efficiency. It is not "the lower the fatigue, the better", but it needs to be kept within a reasonable range, neither too tired nor too relaxed.

[0117] According to an embodiment of the present invention, it also includes:

[0118] Obtaining road surface pressure data within a preset road section;

[0119] Comparing the road surface pressure data with a preset first pressure threshold and a second pressure threshold;

[0120] The first pressure threshold is less than the second pressure threshold;

[0121] If the road surface pressure data is less than the first pressure threshold, the road surface is marked as safe;

[0122] If the road surface pressure data is greater than or equal to the first pressure threshold and less than the second pressure threshold, the road surface is marked as requiring control;

[0123] If the road surface pressure data is greater than or equal to the second pressure threshold, an overload message is sent.

[0124] Among them, in order to ensure that the driving vehicle is within the road load range, the road pressure data in the preset road section is obtained, and the road pressure data is compared with the preset first pressure threshold and the second pressure threshold. The first pressure threshold is less than the second pressure threshold, for example, the first pressure threshold is 200 tons, and the second pressure threshold is 250 tons. If the road pressure data is less than the first pressure threshold, the road is marked as safe, for example, the road pressure data is 170 tons. If the road pressure data is greater than or equal to the first pressure threshold and less than the second pressure threshold, the road is marked as a state requiring control, for example, the road pressure data is 230 tons. If the road pressure data is greater than or equal to the second pressure threshold, overload information is sent, for example, the road pressure data is 260 tons. It should be noted that the road pressure data refers to the monitoring width parameter is the body length of an ordinary car, and the length is the width of the preset road section.

[0125] The present invention also discloses a continuous drive-through automatic inspection system, comprising a memory and a processor, wherein the memory comprises a continuous drive-through automatic inspection method program, and when the continuous drive-through automatic inspection method program is executed by the processor, the following steps are implemented:

[0126] Obtaining vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information based on the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner;

[0127] Extracting peak speed data and acceleration data according to the vehicle running status monitoring information;

[0128] Determining whether the driver is speeding according to the peak speed data;

[0129] determining whether the driver is in a dangerous driving situation according to the acceleration data;

[0130] Extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes;

[0131] Processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index;

[0132] The driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged against a preset fatigue deviation threshold.

[0133] Among them, the present application obtains the driving vehicle detection information in the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtains the driver behavior information and the driver physiological reaction monitoring information under the authorization of the vehicle owner. The driver behavior information refers to the driver's limb change behavior in the process of driving the vehicle, and the driver physiological reaction monitoring information refers to the changes in the driver's brain waves, heart waves and eye waves and other nervous system changes when the driver is driving the vehicle. Data monitoring and collection can be performed through various corresponding detection devices installed in the vehicle. According to the vehicle operation status monitoring information, the peak speed data and acceleration data are extracted to judge whether there is speeding and dangerous driving respectively; according to the driver behavior information, the image processing technology is used to judge whether the driver has closed his eyes; according to the driver's physiological reaction monitoring information, the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data are extracted and processed to obtain the driver's physiological fatigue index, which can judge the driver's fatigue level. The present application monitors parameters from three aspects: vehicle driving state, driver physiological reaction and driving behavior, analyzes and judges whether the vehicle driving state is safe, whether the driver's driving state is normal, improves driving safety, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

[0134] According to an embodiment of the present invention, judging whether the driver is speeding according to the peak speed data is specifically as follows:

[0135] Comparing the peak speed data with a preset first speed threshold and a second speed threshold;

[0136] The first speed threshold is less than the second speed threshold;

[0137] If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state;

[0138] If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent;

[0139] If the speed peak value data is greater than the second speed threshold, an overspeed warning message is sent.

[0140] Among them, in order to improve driving safety, control the vehicle to travel within a safe speed range, compare the peak speed data with the preset first speed threshold and the second speed threshold, the first speed threshold is less than the second speed threshold, for example, the first speed threshold is 60km / h, the second speed threshold is 75km / h, if the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state, for example, the peak speed data is 55km / h, if the peak speed data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent, for example, the peak speed data is 69km / h, if the peak speed data is greater than the second speed threshold, an overspeed warning message is sent, for example, the peak speed data is 83km / h. Controlling and warning the speed of the moving vehicle can effectively reduce the occurrence of accidents and reduce the severity of accidents.

[0141] According to an embodiment of the present invention, judging whether the driver is in a dangerous driving situation according to the acceleration data is specifically as follows:

[0142] Comparing the acceleration data with a preset acceleration threshold;

[0143] If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained;

[0144] Performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data;

[0145] Comparing the exceeding times data with a preset exceeding times threshold;

[0146] If the number of times exceeding the standard is greater than or equal to the preset number of times exceeding the standard threshold, the acceleration exceeding standard information is sent.

[0147] In order to further improve driving safety and vehicle handling, the acceleration data is compared with the preset acceleration threshold. If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained. For example, if the acceleration data is 8m / s 2 , the preset acceleration threshold is 6m / s 2 , count the acceleration exceeding data, obtain the exceeding times data, compare the exceeding times data with the preset exceeding times threshold, if the exceeding times data is greater than or equal to the preset exceeding times threshold, then send the acceleration exceeding information, for example, the exceeding times data is 5, the preset exceeding times threshold is 3, monitor and count the instantaneous acceleration of the vehicle, and send the acceleration exceeding prompt to the driver when the warning times are reached, so as to help the driver develop safe driving habits.

[0148] According to an embodiment of the present invention, extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes is specifically as follows:

[0149] Extracting eye image features according to the face image data;

[0150] Comparing the eye image features with preset eye features to obtain eye feature similarity data;

[0151] Comparing the eye feature similarity data with a preset feature similarity threshold;

[0152] If the eye feature similarity data is greater than or equal to the preset feature similarity threshold, the driver is in a normal driving state;

[0153] If the eye feature similarity data is less than the preset feature similarity threshold, a timing process is performed to obtain timing time data;

[0154] If the timing time data is greater than or equal to the preset time threshold, the driver is in an eyes-closed state and an eyes-closed warning message is sent.

[0155] Among them, in order to prevent the driver from engaging in dangerous behavior of driving with eyes closed, eye image features are extracted according to facial image data, the eye image features are compared with preset eye features to obtain eye feature similarity data, and the eye feature similarity data is compared with the preset feature similarity threshold. If the eye feature similarity data is greater than or equal to the preset feature similarity threshold, the driver is in a normal driving state. For example, the eye feature similarity data is 99%, and the preset feature similarity threshold is 96%. If the eye feature similarity data is less than the preset feature similarity threshold, timing processing is performed to obtain timing time data. For example, the eye feature similarity data is 94%, and the preset feature similarity threshold is 96%. If the timing time data is greater than or equal to the preset time threshold, the driver is in a closed-eye state, and an eye-closing warning message is sent. For example, the timing time data is 0.8s, and the preset time threshold is 0.6s.

[0156] According to an embodiment of the present invention, the driver's physiological reaction monitoring information is processed by a preset physiological reaction evaluation model to obtain the driver's physiological fatigue index, specifically:

[0157] Extracting brain wave curve regularity data, heart wave curve stability data and eye wave curve transient change data according to the driver's physiological response monitoring information;

[0158] The driver's physiological fatigue index is obtained by processing the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data through a preset physiological response evaluation model;

[0159] The calculation formula of the physiological response evaluation model is:

[0160] in, is the driver's physiological fatigue index, They are the regularity data of brain wave curve, the stability data of heart wave curve and the transient change data of eye wave curve. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset traffic safety monitoring database).

[0161] Among them, in order to avoid the driver's fatigue driving or physical condition that makes it impossible to concentrate, the regularity data of the EEG curve, the smoothness data of the ECG curve and the transient change data of the EEG curve are extracted according to the driver's physiological response monitoring information. The EEG reflects the activity of nerve cells in the brain and has characteristics such as frequency, amplitude, waveform, and phase. The regularity data of the EEG curve mainly refers to the stability and predictability of the EEG waveform under normal or abnormal conditions. The ECG curve (usually refers to the electrocardiogram) is used to record the electrical activity of the heart, mainly including P waves, QRS complexes and T waves. The smoothness data of the ECG curve mainly reflects the stability and rhythmicity of the heart's electrical activity. The transient change data of the EEG curve refers to the rapid changes of signals such as the electroretinogram (ERG) and the electrooculogram (EOG) in a short period of time, which respectively reflect the stability and dynamic changes of the nervous system, cardiovascular system and eye function. The driver's physiological fatigue index is obtained by processing the regularity data of the brain wave curve, the smoothness data of the heart wave curve and the transient change data of the eye wave curve through a preset physiological response evaluation model, which mainly reflects the driver's physiological fatigue.

[0162] According to an embodiment of the present invention, the driver's physiological fatigue index is compared with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and the fatigue deviation rate is judged to be different from a preset fatigue deviation threshold, specifically:

[0163] Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate;

[0164] Determining whether the fatigue deviation rate is greater than a preset fatigue deviation threshold;

[0165] If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal state information is sent;

[0166] If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's condition is normal.

[0167] Among them, in order to reduce dangerous driving and improve the mental state of the driver, the driver's physiological fatigue index is compared with the historical average physiological fatigue index of the driver in the preset section to obtain the fatigue deviation rate, the historical average physiological fatigue index of the driver in the preset section is obtained by querying the preset driver status monitoring database, and it is judged whether the fatigue deviation rate is greater than the preset fatigue deviation threshold. If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal status information is sent. For example, the fatigue deviation rate is 13%, and the preset fatigue deviation threshold is 8%. If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's status is normal, for example, the fatigue deviation rate is 5%, and the preset fatigue deviation threshold is 8%. It is worth mentioning that in the driving scene, excessive fatigue will seriously affect the driver's judgment, reaction speed and operation ability, thereby increasing the risk of traffic accidents. Therefore, in this case, reducing fatigue is crucial, but it does not mean pursuing absolute "zero fatigue". Appropriate fatigue can prompt people to rest and recover, thereby maintaining physical health and work efficiency. It is not "the lower the fatigue, the better", but it needs to be kept within a reasonable range, neither too tired nor too relaxed.

[0168] According to an embodiment of the present invention, it also includes:

[0169] Obtaining road surface pressure data within a preset road section;

[0170] Comparing the road surface pressure data with a preset first pressure threshold and a second pressure threshold;

[0171] The first pressure threshold is less than the second pressure threshold;

[0172] If the road surface pressure data is less than the first pressure threshold, the road surface is marked as safe;

[0173] If the road surface pressure data is greater than or equal to the first pressure threshold and less than the second pressure threshold, the road surface is marked as requiring control;

[0174] If the road surface pressure data is greater than or equal to the second pressure threshold, an overload message is sent.

[0175] Among them, in order to ensure that the driving vehicle is within the road load range, the road pressure data in the preset road section is obtained, and the road pressure data is compared with the preset first pressure threshold and the second pressure threshold. The first pressure threshold is less than the second pressure threshold, for example, the first pressure threshold is 200 tons, and the second pressure threshold is 250 tons. If the road pressure data is less than the first pressure threshold, the road is marked as safe, for example, the road pressure data is 170 tons. If the road pressure data is greater than or equal to the first pressure threshold and less than the second pressure threshold, the road is marked as a state requiring control, for example, the road pressure data is 230 tons. If the road pressure data is greater than or equal to the second pressure threshold, overload information is sent, for example, the road pressure data is 260 tons. It should be noted that the road pressure data refers to the monitoring width parameter is the body length of an ordinary car, and the length is the width of the preset road section.

[0176] The third aspect of the present invention also provides a readable storage medium, which includes a continuous drive-through automatic inspection method program. When the continuous drive-through automatic inspection method program is executed by a processor, the steps of the continuous drive-through automatic inspection method as described in any one of the above items are implemented.

[0177] The present invention discloses a continuous driving automatic inspection method, system and medium, which obtains the detection information of the vehicles in the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtains the driver's behavior information and the driver's physiological reaction monitoring information under the authorization of the vehicle owner. The driver's behavior information refers to the driver's limb changes in the process of driving the vehicle, and the driver's physiological reaction monitoring information refers to the changes in the driver's nervous system such as brain waves, heart waves and eye waves when driving the vehicle. Data monitoring and collection can be performed through various corresponding detection devices installed in the vehicle, and the peak speed data and acceleration data are extracted according to the vehicle operation status monitoring information to judge whether there is speeding and dangerous driving respectively; according to the driver's behavior information, it is judged whether the driver has closed his eyes through image processing technology; according to the driver's physiological reaction monitoring information, the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data are extracted and processed to obtain the driver's physiological fatigue index, which can judge the driver's fatigue level. This application monitors parameters from three aspects: vehicle driving status, driver's physiological reaction and driving behavior, analyzes and determines whether the vehicle driving status is safe and whether the driver's driving status is normal, improves driving safety, avoids road vehicle accidents, and protects personal life safety.

[0178] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0179] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0180] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0181] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0182] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A continuous drive-through automatic inspection method, characterized in that: The following steps are involved: Obtaining vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information based on the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner; Extracting peak speed data and acceleration data according to the vehicle running status monitoring information; Determining whether the driver is speeding according to the peak speed data; determining whether the driver is in a dangerous driving situation according to the acceleration data; Extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes; Processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index; Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and determining the fatigue deviation rate and a preset fatigue deviation threshold; The driver's physiological reaction monitoring information is processed by a preset physiological reaction evaluation model to obtain the driver's physiological fatigue index, specifically: Extracting brain wave curve regularity data, heart wave curve stability data and eye wave curve transient change data according to the driver's physiological response monitoring information; The driver's physiological fatigue index is obtained by processing the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data through a preset physiological response evaluation model; The calculation formula of the physiological response evaluation model is: in, is the driver's physiological fatigue index, They are the regularity data of brain wave curve, the stability data of heart wave curve and the transient change data of eye wave curve. is the preset characteristic coefficient.

2. A continuous drive-through automatic inspection method according to claim 1, characterized in that: The step of judging whether the driver is speeding according to the peak speed data is as follows: Comparing the peak speed data with a preset first speed threshold and a second speed threshold; The first speed threshold is less than the second speed threshold; If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state; If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent; If the speed peak value data is greater than the second speed threshold, an overspeed warning message is sent.

3. A continuous drive-through automatic inspection method according to claim 2, characterized in that: The step of judging whether the driver is in a dangerous driving situation according to the acceleration data is specifically as follows: Comparing the acceleration data with a preset acceleration threshold; If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained; Performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data; Comparing the exceeding times data with a preset exceeding times threshold; If the number of times exceeding the standard is greater than or equal to the preset number of times exceeding the standard threshold, the acceleration exceeding standard information is sent.

4. A continuous drive-through automatic inspection method according to claim 3, characterized in that: The extracting of facial image data according to the driver behavior information to determine whether the driver has closed eyes is specifically as follows: Extracting eye image features according to the face image data; Comparing the eye image features with preset eye features to obtain eye feature similarity data; Comparing the eye feature similarity data with a preset feature similarity threshold; If the eye feature similarity data is greater than or equal to the preset feature similarity threshold, the driver is in a normal driving state; If the eye feature similarity data is less than the preset feature similarity threshold, a timing process is performed to obtain timing time data; If the timing time data is greater than or equal to the preset time threshold, the driver is in an eyes-closed state and an eyes-closed warning message is sent.

5. A continuous drive-through automatic inspection method according to claim 4, characterized in that: The step of comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and determining whether the fatigue deviation rate is equal to a preset fatigue deviation threshold, is specifically as follows: Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate; Determining whether the fatigue deviation rate is greater than a preset fatigue deviation threshold; If the fatigue deviation rate is greater than or equal to the preset fatigue deviation threshold, the driver's fatigue is abnormal, and the driver's abnormal state information is sent; If the fatigue deviation rate is less than the preset fatigue deviation threshold, the driver's condition is normal.

6. A continuous drive-through automatic inspection system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a continuous drive-through automatic inspection method program, and when the continuous drive-through automatic inspection method program is executed by the processor, the following steps are implemented: Obtaining vehicle detection information on a preset road section, including license plate recognition information and vehicle operation status monitoring information, obtaining vehicle registration information based on the license plate recognition information, and obtaining driver behavior information and driver physiological response monitoring information with the authorization of the vehicle owner; Extracting peak speed data and acceleration data according to the vehicle running status monitoring information; Determining whether the driver is speeding according to the peak speed data; determining whether the driver is in a dangerous driving situation according to the acceleration data; Extracting facial image data based on the driver behavior information to determine whether the driver has closed his eyes; Processing the driver's physiological reaction monitoring information through a preset physiological reaction evaluation model to obtain a driver's physiological fatigue index; Comparing the driver's physiological fatigue index with the historical average physiological fatigue index of drivers in a preset road section to obtain a fatigue deviation rate, and determining the fatigue deviation rate and a preset fatigue deviation threshold; The driver's physiological reaction monitoring information is processed by a preset physiological reaction evaluation model to obtain the driver's physiological fatigue index, specifically: Extracting brain wave curve regularity data, heart wave curve stability data and eye wave curve transient change data according to the driver's physiological response monitoring information; The driver's physiological fatigue index is obtained by processing the brain wave curve regularity data, the heart wave curve stability data and the eye wave curve transient change data through a preset physiological response evaluation model; The calculation formula of the physiological response evaluation model is: in, is the driver's physiological fatigue index, They are the regularity data of brain wave curve, the stability data of heart wave curve and the transient change data of eye wave curve. is the preset characteristic coefficient.

7. A continuous drive-through automatic inspection system according to claim 6, characterized in that: The step of judging whether the driver is speeding according to the peak speed data is as follows: Comparing the peak speed data with a preset first speed threshold and a second speed threshold; The first speed threshold is less than the second speed threshold; If the peak speed data is less than or equal to the first speed threshold, it is marked as a safe driving state; If the speed peak value data is greater than the first speed threshold and less than or equal to the second speed threshold, a speed warning reminder message is sent; If the speed peak value data is greater than the second speed threshold, an overspeed warning message is sent.

8. A continuous drive-through automatic inspection system according to claim 7, characterized in that: The step of judging whether the driver is in a dangerous driving situation according to the acceleration data is specifically as follows: Comparing the acceleration data with a preset acceleration threshold; If the acceleration data is greater than or equal to the preset acceleration threshold, the acceleration exceeding threshold data is obtained; Performing counting processing according to the acceleration exceeding standard data to obtain exceeding standard number data; Comparing the exceeding times data with a preset exceeding times threshold; If the number of times exceeding the standard is greater than or equal to the preset number of times exceeding the standard threshold, the acceleration exceeding standard information is sent.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a continuous drive-through automatic inspection method, system and medium program. When the continuous drive-through automatic inspection method, system and medium program are executed by a processor, the steps of a continuous drive-through automatic inspection method as described in any one of claims 1 to 5 are implemented.

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