Driver physical examination equipment, anti-cheating method and system and medium

Abstract

The invention provides driver physical examination equipment, an anti-cheating method, an anti-cheating system and a medium. The method comprises the steps that face recognition information of a target driver through a preset physical examination equipment recognition system is collected, identity verification judgment is conducted on the target driver, if identity verification of the target driver succeeds, vision detection is conducted on the target driver according to the preset physical examination equipment, processing is conducted according to head position data, and the target driver is determined; head position deviation data is obtained, position cheating judgment processing is carried out, recognition cheating judgment processing is carried out according to sighting mark recognition data, processing is carried out according to eye movement data, an eye movement abnormal index is obtained, eye movement cheating judgment processing is carried out, processing is carried out according to vision data, and a vision comparison deviation coefficient is obtained; and vision data cheating judgment processing is carried out, and a physical examination report is correspondingly generated and uploaded to a background, so that the driver physical examination equipment and an anti-cheating technology are realized.

Description

Technical Field

[0001] The present application relates to the technical field of driver physical examination, and in particular to driver physical examination equipment and anti-cheating methods, systems and media. Background Art

[0002] In driver physical examinations, accurate detection of the driver's vision is crucial to ensuring road traffic safety. However, there are currently some people who use various cheating methods to obtain physical examination results that do not match their actual vision conditions, which poses a huge hidden danger to road traffic safety. Existing vision examination methods are difficult to effectively identify and prevent these cheating behaviors.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of the present application is to provide a driver physical examination device and an anti-cheating method, system and medium, which can perform vision detection on the target driver according to a preset physical examination device, process the head position data to obtain the head position deviation data, and perform position cheating judgment processing, perform identification cheating judgment processing according to the sight mark recognition data, process the eye movement data to obtain the eye movement abnormality index, and perform eye movement cheating judgment processing, process the vision data to obtain the vision comparison deviation coefficient, and perform vision data cheating judgment processing, generate a physical examination report accordingly and upload it to the background, so as to realize the driver physical examination device and anti-cheating technology.

[0005] The present application also provides a driver physical examination device and an anti-cheating method, comprising the following steps: Collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; If the identity verification of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination device, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time; Processing is performed according to the head position data to obtain head position deviation data, and position cheating determination processing is performed; Performing cheating identification and judgment processing according to the visual mark recognition data; Processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; Processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; A corresponding physical examination report is generated and uploaded to the backend.

[0006] Optionally, in the driver physical examination device and anti-cheating method described in the present application, the processing according to the head position data to obtain the head position deviation data and perform position cheating judgment processing includes: The head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data, and head left and right displacement data; Obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front-back displacement data, and standard head left-right displacement data; Performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; The head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data and the head left-right displacement deviation data are processed by a preset head deviation recognition model to obtain head position deviation data; Comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; Determining whether the deviation of the target driver's head position exceeds the standard according to the first threshold comparison result; If the deviation exceeds the limit, it will be judged as position cheating.

[0007] Optionally, in the driver physical examination device and anti-cheating method described in the present application, the step of performing cheating identification and judgment processing according to the sight mark recognition data includes: The sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; Obtaining standard recognition time data, and performing comparison processing in combination with the maximum recognition time data to obtain recognition time deviation data; Obtaining mean data of the sight mark recognition error rate, and performing comparative processing on the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; Obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; Performing a threshold comparison based on the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

[0008] Optionally, in the driver physical examination device and anti-cheating method described in the present application, the processing according to the eye movement data to obtain the eye movement abnormality index and perform eye movement cheating judgment processing include: The eye movement data includes blink frequency data, left-right eyeball rotation angle data, and up-down eyeball rotation angle data; Acquire blink frequency standard data, and perform comparison processing on the blink frequency data to obtain blink frequency deviation data; Obtaining standard data of eyeball left-right rotation angle and standard data of eyeball up-down rotation angle, combining the eyeball left-right rotation angle data and the eyeball up-down rotation angle data for comparison and processing, respectively, to obtain corresponding eyeball left-right rotation angle deviation data and eyeball up-down rotation angle deviation data; Performing weighted processing on the blink frequency deviation data, the eyeball left-right rotation angle deviation data, and the eyeball up-down rotation angle deviation data to obtain an eye movement abnormality index; Comparing the eye movement abnormality index with a preset eye movement abnormality threshold to obtain a second threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the second threshold value; If the second threshold comparison result is greater than a preset threshold, then there is an abnormality in the eye movement of the target driver, and it is determined as eye movement cheating.

[0009] Optionally, in the driver physical examination device and anti-cheating method described in the present application, the processing according to the vision data to obtain the vision comparison deviation coefficient and perform vision data cheating judgment processing include: Extracting vision data of this time according to the vision test result of the target driver; Acquiring historical vision test data of the target driver; Performing comparison processing based on the current vision data and the historical vision test data to obtain a vision comparison deviation coefficient; Comparing the vision comparison deviation coefficient with a preset vision comparison deviation threshold to obtain a third threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the third threshold value; If the comparison result of the third threshold is greater than the preset threshold, then there is an abnormality in the sixteen data of the target driver, and it is determined that the vision data is cheated.

[0010] Optionally, in the driver physical examination device and anti-cheating method described in the present application, the corresponding generation of a physical examination report and uploading it to the background includes: generating a physical examination report according to the vision test result; If the results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment are all that there is no cheating, then the physical examination report is a normal report; If any of the judgment results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment contain cheating, the physical examination report will be marked as an abnormal report and further review and processing is required; The physical examination report is uploaded to the backend.

[0011] In a second aspect, the present application provides a driver physical examination device and an anti-cheating system, the system comprising: a memory and a processor, the memory comprising a program of a driver physical examination device and an anti-cheating method, the program of the driver physical examination device and the anti-cheating method being executed by the processor to implement the following steps: Collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; If the identity verification of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination device, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time; Processing is performed according to the head position data to obtain head position deviation data, and position cheating determination processing is performed; Performing cheating identification and judgment processing according to the visual mark recognition data; Processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; Processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; A corresponding physical examination report is generated and uploaded to the backend.

[0012] Optionally, in the driver physical examination device and anti-cheating system described in the present application, the processing according to the head position data to obtain the head position deviation data and perform position cheating judgment processing includes: The head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data, and head left and right displacement data; Obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front-back displacement data, and standard head left-right displacement data; Performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; The head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data and the head left-right displacement deviation data are processed by a preset head deviation recognition model to obtain head position deviation data; Comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; Determining whether the deviation of the target driver's head position exceeds the standard according to the first threshold comparison result; If the deviation exceeds the limit, it will be judged as position cheating.

[0013] Optionally, in the driver physical examination device and anti-cheating system described in the present application, the step of performing cheating identification and judgment processing according to the sight mark recognition data includes: The sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; Obtaining standard recognition time data, and performing comparison processing in combination with the maximum recognition time data to obtain recognition time deviation data; Obtaining mean data of the sight mark recognition error rate, and performing comparative processing on the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; Obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; Performing a threshold comparison based on the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, which stores a driver physical examination device and an anti-cheating method program. When the driver physical examination device and an anti-cheating method program are executed by a processor, the steps of the driver physical examination device and an anti-cheating method as described in any one of the above items are implemented.

[0015] As can be seen from the above, the driver physical examination equipment and anti-cheating method, system and medium disclosed in the present invention collect the face recognition information of the target driver through the preset physical examination equipment recognition system, and perform identity authentication judgment on the target driver. If the identity authentication of the target driver is successful, the target driver is subjected to a vision test according to the preset physical examination equipment, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time. The head position data is processed to obtain the head position deviation data, and position cheating judgment processing is performed, the sight mark recognition data is processed to perform identification cheating judgment processing, the eye movement data is processed to obtain the eye movement abnormality index, and eye movement cheating judgment processing is performed, the vision data is processed to obtain the vision comparison deviation coefficient, and the vision data cheating judgment processing is performed, and a physical examination report is generated accordingly and uploaded to the background, thereby realizing the driver physical examination equipment and anti-cheating technology.

[0016] 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

[0017] 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.

[0018] Figure 1 A flowchart of the driver physical examination device and anti-cheating method provided in the embodiment of the present application; Figure 2 A flowchart of the location cheating judgment process of the driver physical examination device and anti-cheating method provided in the embodiment of the present application; Figure 3 A flowchart of the driver physical examination device and anti-cheating method provided in the embodiment of the present application for identifying and judging cheating; Figure 4 A flowchart of the eye movement cheating judgment process of the driver physical examination equipment and anti-cheating method provided in the embodiments of the present application. DETAILED DESCRIPTION

[0019] 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.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. 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.

[0021] Please refer to Figure 1 , Figure 1 1 is a flowchart of a driver physical examination device and an anti-cheating method in some embodiments of the present application. The driver physical examination device and the anti-cheating method are used in a terminal device, such as a computer, a mobile phone terminal, etc. The driver physical examination device and the anti-cheating method include the following steps: S11, collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; S12, if the identity verification of the target driver is successful, performing vision testing on the target driver according to the preset physical examination equipment, and obtaining the head position data, sight mark recognition data, vision data and eye movement data of the target driver in real time; S13, processing the head position data to obtain head position deviation data, and performing position cheating judgment processing; S14, performing cheating identification and judgment processing according to the visual mark recognition data; S15, processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; S16, processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; S17. Generate a corresponding physical examination report and upload it to the backend.

[0022] It should be noted that, during the current physical examination process using the driver's self-service physical examination equipment, users often cheat in the vision test. The existing vision physical examination methods are difficult to effectively identify and prevent these cheating behaviors. Therefore, a new method is needed to effectively and accurately prevent cheating in the driver's vision physical examination. In this embodiment, the face recognition information of the target driver is collected through the preset physical examination equipment recognition system, and the identity authentication is judged. If the identity authentication is successful, the target driver is tested for vision according to the preset physical examination equipment, and data is collected, including head position data, sight mark recognition data, vision data, and eye movement data. The head position data is processed to obtain head position deviation data, and position cheating judgment processing is performed. The sight mark recognition data is used to perform identification cheating judgment processing. The eye movement data is processed to obtain an eye movement abnormality index, and an eye movement cheating judgment processing is performed. The vision comparison deviation coefficient is obtained, and the vision data cheating judgment processing is performed. A physical examination report is generated accordingly and uploaded to the background, thereby realizing the driver's physical examination equipment and anti-cheating technology.

[0023] Please refer to Figure 2 , Figure 2 The flowchart of the position cheating judgment process of the driver physical examination device and the anti-cheating method in some embodiments of the present application. According to the embodiment of the present invention, the processing according to the head position data, obtaining the head position deviation data, and performing the position cheating judgment process include: S21, the head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data and head left and right displacement data; S22, obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front and back displacement data, and standard head left and right displacement data; S23, performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; S24, processing the head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data, and the head left-right displacement deviation data through a preset head deviation recognition model to obtain head position deviation data; S25, comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; S26, judging whether the deviation of the head position of the target driver exceeds the standard according to the comparison result of the first threshold value; S27. If the deviation exceeds the standard, it is determined to be position cheating.

[0024] It should be noted that during the driver's physical examination, the allowable angle deviation range of the head in the horizontal and vertical directions needs to be specified. For example, the allowable deviation angle in the horizontal direction (turning the head left and right) is generally ±5, and the allowable deviation angle in the vertical direction (raising or lowering the head) is also ±5. If it exceeds this range, it may be judged as suspected of cheating, and the displacement of the head forward, backward, left and right must also be restricted. During normal testing, the distance between the head and the vision testing equipment should remain relatively stable, and the front and back displacement generally does not exceed ±3 cm, and the left and right displacement does not exceed ±2 cm. If the displacement range is exceeded, the anti-cheating mechanism will be triggered. Therefore, the driver's head position data, including the head horizontal angle, the head vertical angle, the head front and rear displacement, and the head left and right displacement data, are obtained, and then compared with the corresponding standard values ​​to obtain the corresponding head position deviation data, including the head horizontal angle deviation data, the head vertical angle deviation data, the head front and rear displacement deviation data, and the head left and right displacement deviation data. Finally, the data is processed by a preset head deviation recognition model to obtain the head position deviation data, wherein the head deviation recognition model belongs to a neural network model, which is based on a large amount of historical head horizontal angle deviation data, head vertical angle deviation data, head front and rear displacement deviation data, and head left and right displacement deviation data. The initialized head deviation recognition model is trained to obtain the trained head deviation recognition model, and then the head position deviation data is compared with the preset head position deviation threshold. According to the comparison result, it is determined whether the deviation of the target driver's head position exceeds the standard. If so, it is determined to be position cheating. For example, the preset head position deviation threshold is 0.5, and the head position deviation data is 0.7. At this time, the deviation exceeds the standard, and the target driver has cheated in the head position.

[0025] Please refer to Figure 3 , Figure 3 The flowchart of the driver physical examination device and the anti-cheating method in some embodiments of the present application is to identify and judge the cheating. According to the embodiment of the present invention, the identification and judgment of cheating based on the visual mark recognition data includes: S31, the sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; S32, obtaining standard recognition time data, and performing comparison processing with the maximum recognition time data to obtain recognition time deviation data; S33, obtaining mean data of the sight mark recognition error rate, and performing comparison processing with the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; S34, obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; S35, performing threshold comparison according to the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; S36. If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

[0026] It should be noted that a standard recognition time is set for each sight sign, usually between 3 and 5 seconds. If the driver takes more time than this limit, the system will regard the recognition as suspicious. For example, for common E or C sight signs, if the time from the appearance of the sight sign to the driver's reaction exceeds 5 seconds, there may be a risk of cheating. In addition, the accuracy of the driver's recognition of the sight sign is compared with the expected accuracy of people with normal vision. If the driver's recognition error rate for a certain size or type of sight sign is significantly higher than normal (for example, the recognition error rate of people with normal vision for a row of sight signs is less than 10%, while the driver's error rate is more than 30%), or the recognition error rate for simple sight signs is higher than normal, the system will consider the recognition time as suspicious. If there are abnormal errors (such as frequent errors with larger sight marks), this may be a sign of cheating. In this regard, the maximum recognition time data and sight mark recognition error rate data of the target driver during the vision examination are obtained, and compared with the standard recognition time data and the mean sight mark recognition error rate data, respectively, to obtain the corresponding recognition time deviation data and sight mark recognition error rate deviation data, where the mean sight mark recognition error rate data refers to the average value of the sight mark recognition error rate of people with normal vision. Then, based on the above data and the preset sight mark recognition deviation limit threshold set, a threshold comparison is performed. If any data is greater than or equal to the threshold, it is determined that cheating has occurred.

[0027] Please refer to Figure 4 , Figure 4 The flowchart of the driver physical examination device and the anti-cheating method in some embodiments of the present application is to perform eye movement cheating judgment processing. According to the embodiment of the present invention, the eye movement data is processed to obtain the eye movement abnormality index and perform eye movement cheating judgment processing, including: S41, the eye movement data includes blink frequency data, left and right eye rotation angle data, and up and down eye rotation angle data; S42, acquiring blink frequency standard data, and performing comparison processing on the blink frequency data to obtain blink frequency deviation data; S43, obtaining standard data of eyeball left-right rotation angle and standard data of eyeball up-down rotation angle, combining the eyeball left-right rotation angle data and the eyeball up-down rotation angle data for comparison and processing, respectively, to obtain corresponding eyeball left-right rotation angle deviation data and eyeball up-down rotation angle deviation data; S44, performing weighted processing according to the blink frequency deviation data, the left-right eyeball rotation angle deviation data, and the up-down eyeball rotation angle deviation data to obtain an eye movement abnormality index; S45, comparing the eye movement abnormality index with a preset eye movement abnormality threshold to obtain a second threshold comparison result; S46, judging whether the eye movement of the target driver is abnormal according to the second threshold comparison result; S47: If the comparison result of the second threshold value is greater than a preset threshold value, then there is an abnormality in the eye movement of the target driver, and it is determined as cheating in eye movement.

[0028] It should be noted that during normal vision testing, the blinking frequency is usually between 10 and 20 times per minute. If the blinking frequency is lower than 5 times per minute or higher than 30 times per minute during the display of the key sight mark, there may be a risk of cheating. In addition, when observing the sight mark normally, the eye movement angle is mainly concentrated in the direction and range of the sight mark. Generally, the left and right eye movement angle does not exceed ±30°, and the up and down eye movement angle does not exceed ±20°. If the eye movement angle exceeds this range, it will be regarded as a suspicious eye movement. Therefore, weighted processing is performed based on the blinking frequency deviation data, the left and right eye movement angle deviation data, and the up and down eye movement angle deviation data to obtain the eye movement abnormality index, which is then compared with the preset eye movement abnormality threshold. Based on the comparison results, it is determined whether there is cheating. For example, the preset eye movement abnormality threshold is 2.0. When the obtained eye movement abnormality index is 2.5, it is greater than the preset threshold, which means that there is eye movement cheating in this physical examination.

[0029] According to an embodiment of the present invention, the processing according to the vision data to obtain the vision comparison deviation coefficient and perform vision data cheating judgment processing includes: Extracting vision data of this time according to the vision test result of the target driver; Acquiring historical vision test data of the target driver; Performing comparison processing based on the current vision data and the historical vision test data to obtain a vision comparison deviation coefficient; Comparing the vision comparison deviation coefficient with a preset vision comparison deviation threshold to obtain a third threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the third threshold value; If the comparison result of the third threshold is greater than the preset threshold, then there is an abnormality in the sixteen data of the target driver, and it is determined that the vision data is cheated.

[0030] It should be noted that the vision test result will be compared with the driver's previous vision test records. If the vision fluctuates greatly in a short period of time (such as the interval between two physical examinations is less than one year), and the fluctuation range exceeds 0.3 (taking the decimal vision recording method as an example), and there is no reasonable proof of vision correction surgery or other medical intervention, it will be judged as suspicious. For example, if the vision was 4.8 last time and it suddenly changed to 5.1 this time, further verification is required. Therefore, the vision comparison deviation coefficient is obtained based on the current vision data combined with the historical vision test data. Among them, the historical vision test data refers to the vision result data corresponding to the vision test that is closest to the current vision test, and there is no vision correction surgery or other medical intervention between the two tests.

[0031] According to an embodiment of the present invention, the corresponding generation of a physical examination report and uploading it to the background includes: generating a physical examination report according to the vision test result; If the results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment are all that there is no cheating, then the physical examination report is a normal report; If any of the judgment results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment contain cheating, the physical examination report will be marked as an abnormal report and further review and processing is required; The physical examination report is uploaded to the backend.

[0032] It should be noted that after the entire vision test is completed, the system automatically generates a corresponding physical examination report, and divides the report into normal report and abnormal report based on the previous four cheating judgment results. If one or more of the previous four cheating judgment results are cheating, it is an abnormal report. The abnormal report will be specially marked and uploaded to the background for further review.

[0033] In a second aspect, the present invention further discloses a driver physical examination device and an anti-cheating system, comprising a memory and a processor, wherein the memory comprises a driver physical examination device and an anti-cheating method program, and when the driver physical examination device and the anti-cheating method program are executed by the processor, the following steps are implemented: Collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; If the identity verification of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination equipment, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time; Processing is performed according to the head position data to obtain head position deviation data, and position cheating determination processing is performed; Performing cheating identification and judgment processing according to the visual mark recognition data; Processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; Processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; A corresponding physical examination report is generated and uploaded to the backend.

[0034] It should be noted that, during the current physical examination process using the driver's self-service physical examination equipment, users often cheat in the vision test. The existing vision physical examination methods are difficult to effectively identify and prevent these cheating behaviors. Therefore, a new method is needed to effectively and accurately prevent cheating in the driver's vision physical examination. In this embodiment, the face recognition information of the target driver is collected through the preset physical examination equipment recognition system, and the identity authentication is judged. If the identity authentication is successful, the target driver is tested for vision according to the preset physical examination equipment, and data is collected, including head position data, sight mark recognition data, vision data, and eye movement data. The head position data is processed to obtain head position deviation data, and position cheating judgment processing is performed. The sight mark recognition data is used to perform identification cheating judgment processing. The eye movement data is processed to obtain an eye movement abnormality index, and an eye movement cheating judgment processing is performed. The vision comparison deviation coefficient is obtained, and the vision data cheating judgment processing is performed. A physical examination report is generated accordingly and uploaded to the background, thereby realizing the driver's physical examination equipment and anti-cheating technology.

[0035] According to an embodiment of the present invention, the processing according to the head position data to obtain the head position deviation data and perform position cheating judgment processing includes: The head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data, and head left and right displacement data; Obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front-back displacement data, and standard head left-right displacement data; Performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; The head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data and the head left-right displacement deviation data are processed by a preset head deviation recognition model to obtain head position deviation data; Comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; Determining whether the deviation of the target driver's head position exceeds the standard according to the first threshold comparison result; If the deviation exceeds the limit, it will be judged as position cheating.

[0036] It should be noted that during the driver's physical examination, the allowable angle deviation range of the head in the horizontal and vertical directions needs to be specified. For example, the allowable deviation angle in the horizontal direction (turning the head left and right) is generally ±5, and the allowable deviation angle in the vertical direction (raising or lowering the head) is also ±5. If it exceeds this range, it may be judged as suspected of cheating, and the displacement of the head forward, backward, left and right must also be restricted. During normal testing, the distance between the head and the vision testing equipment should remain relatively stable, and the front and back displacement generally does not exceed ±3 cm, and the left and right displacement does not exceed ±2 cm. If the displacement range is exceeded, the anti-cheating mechanism will be triggered. Therefore, the driver's head position data, including the head horizontal angle, the head vertical angle, the head front and rear displacement, and the head left and right displacement data, are obtained, and then compared with the corresponding standard values ​​to obtain the corresponding head position deviation data, including the head horizontal angle deviation data, the head vertical angle deviation data, the head front and rear displacement deviation data, and the head left and right displacement deviation data. Finally, the data is processed by a preset head deviation recognition model to obtain the head position deviation data, wherein the head deviation recognition model belongs to a neural network model, which is based on a large amount of historical head horizontal angle deviation data, head vertical angle deviation data, head front and rear displacement deviation data, and head left and right displacement deviation data. The initialized head deviation recognition model is trained to obtain the trained head deviation recognition model, and then the head position deviation data is compared with the preset head position deviation threshold. According to the comparison result, it is determined whether the deviation of the target driver's head position exceeds the standard. If so, it is determined to be position cheating. For example, the preset head position deviation threshold is 0.5, and the head position deviation data is 0.7. At this time, the deviation exceeds the standard, and the target driver has cheated in the head position.

[0037] According to an embodiment of the present invention, the step of performing cheating identification and judgment processing according to the sight mark recognition data includes: The sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; Obtaining standard recognition time data, and performing comparison processing in combination with the maximum recognition time data to obtain recognition time deviation data; Obtaining mean data of the sight mark recognition error rate, and performing comparative processing on the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; Obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; Performing a threshold comparison based on the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

[0038] It should be noted that a standard recognition time is set for each sight sign, usually between 3 and 5 seconds. If the driver takes more time than this limit, the system will regard the recognition as suspicious. For example, for common E or C sight signs, if the time from the appearance of the sight sign to the driver's reaction exceeds 5 seconds, there may be a risk of cheating. In addition, the accuracy of the driver's recognition of the sight sign is compared with the expected accuracy of people with normal vision. If the driver's recognition error rate for a certain size or type of sight sign is significantly higher than normal (for example, the recognition error rate of people with normal vision for a row of sight signs is less than 10%, while the driver's error rate is more than 30%), or the recognition error rate for simple sight signs is higher than normal, the system will consider the recognition time as suspicious. If there are abnormal errors (such as frequent errors with larger sight marks), this may be a sign of cheating. In this regard, the maximum recognition time data and sight mark recognition error rate data of the target driver during the vision examination are obtained, and compared with the standard recognition time data and the mean sight mark recognition error rate data, respectively, to obtain the corresponding recognition time deviation data and sight mark recognition error rate deviation data, where the mean sight mark recognition error rate data refers to the average value of the sight mark recognition error rate of people with normal vision. Then, based on the above data and the preset sight mark recognition deviation limit threshold set, a threshold comparison is performed. If any data is greater than or equal to the threshold, it is determined that cheating has occurred.

[0039] According to an embodiment of the present invention, the processing according to the eye movement data to obtain an eye movement abnormality index and perform eye movement cheating judgment processing includes: The eye movement data includes blink frequency data, left-right eyeball rotation angle data, and up-down eyeball rotation angle data; Acquire blink frequency standard data, and perform comparison processing on the blink frequency data to obtain blink frequency deviation data; Obtaining standard data of eyeball left-right rotation angle and standard data of eyeball up-down rotation angle, combining the eyeball left-right rotation angle data and the eyeball up-down rotation angle data for comparison and processing, respectively, to obtain corresponding eyeball left-right rotation angle deviation data and eyeball up-down rotation angle deviation data; Performing weighted processing on the blink frequency deviation data, the eyeball left-right rotation angle deviation data, and the eyeball up-down rotation angle deviation data to obtain an eye movement abnormality index; Comparing the eye movement abnormality index with a preset eye movement abnormality threshold to obtain a second threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the second threshold value; If the second threshold comparison result is greater than a preset threshold, then there is an abnormality in the eye movement of the target driver, and it is determined as eye movement cheating.

[0040] It should be noted that during normal vision testing, the blinking frequency is usually between 10 and 20 times per minute. If the blinking frequency is lower than 5 times per minute or higher than 30 times per minute during the display of the key sight mark, there may be a risk of cheating. In addition, when observing the sight mark normally, the eye movement angle is mainly concentrated in the direction and range of the sight mark. Generally, the left and right eye movement angle does not exceed ±30°, and the up and down eye movement angle does not exceed ±20°. If the eye movement angle exceeds this range, it will be regarded as a suspicious eye movement. Therefore, weighted processing is performed based on the blinking frequency deviation data, the left and right eye movement angle deviation data, and the up and down eye movement angle deviation data to obtain the eye movement abnormality index, which is then compared with the preset eye movement abnormality threshold. Based on the comparison results, it is determined whether there is cheating. For example, the preset eye movement abnormality threshold is 2.0. When the obtained eye movement abnormality index is 2.5, it is greater than the preset threshold, which means that there is eye movement cheating in this physical examination.

[0041] According to an embodiment of the present invention, the processing according to the vision data to obtain the vision comparison deviation coefficient and perform vision data cheating judgment processing includes: Extracting vision data of this time according to the vision test result of the target driver; Acquiring historical vision test data of the target driver; Performing comparison processing based on the current vision data and the historical vision test data to obtain a vision comparison deviation coefficient; Comparing the vision comparison deviation coefficient with a preset vision comparison deviation threshold to obtain a third threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the third threshold value; If the comparison result of the third threshold is greater than the preset threshold, then there is an abnormality in the sixteen data of the target driver, and it is determined that the vision data is cheated.

[0042] It should be noted that the vision test result will be compared with the driver's previous vision test records. If the vision fluctuates greatly in a short period of time (such as the interval between two physical examinations is less than one year), and the fluctuation range exceeds 0.3 (taking the decimal vision recording method as an example), and there is no reasonable proof of vision correction surgery or other medical intervention, it will be judged as suspicious. For example, if the vision was 4.8 last time and it suddenly changed to 5.1 this time, further verification is required. Therefore, the vision comparison deviation coefficient is obtained based on the current vision data combined with the historical vision test data. Among them, the historical vision test data refers to the vision result data corresponding to the vision test that is closest to the current vision test, and there is no vision correction surgery or other medical intervention between the two tests.

[0043] According to an embodiment of the present invention, the corresponding generation of a physical examination report and uploading it to the background includes: generating a physical examination report according to the vision test result; If the results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment are all that there is no cheating, then the physical examination report is a normal report; If any of the judgment results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment contain cheating, the physical examination report will be marked as an abnormal report and further review and processing is required; The physical examination report is uploaded to the backend.

[0044] It should be noted that after the entire vision test is completed, the system automatically generates a corresponding physical examination report, and divides the report into normal report and abnormal report based on the previous four cheating judgment results. If one or more of the previous four cheating judgment results are cheating, it is an abnormal report. The abnormal report will be specially marked and uploaded to the background for further review.

[0045] A third aspect of the present invention provides a readable storage medium, which stores a driver physical examination device and an anti-cheating method program. When the driver physical examination device and the anti-cheating method program are executed by a processor, the steps of the driver physical examination device and the anti-cheating method as described in any one of the above items are implemented.

[0046] The driver physical examination equipment and anti-cheating method, system and medium disclosed in the present invention collect face recognition information of the target driver through a preset physical examination equipment recognition system, and perform identity authentication judgment on the target driver. If the identity authentication of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination equipment, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time. The head position data is processed to obtain the head position deviation data, and position cheating judgment processing is performed; the sight mark recognition data is processed to perform identification cheating judgment processing; the eye movement data is processed to obtain the eye movement abnormality index, and eye movement cheating judgment processing is performed; the vision data is processed to obtain the vision comparison deviation coefficient, and the vision data cheating judgment processing is performed; a physical examination report is generated accordingly and uploaded to the background, thereby realizing the driver physical examination equipment and anti-cheating technology.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0051] 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 driver physical examination device and an anti-cheating method, characterized in that: The following steps are involved: Collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; If the identity verification of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination device, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time; Processing is performed according to the head position data to obtain head position deviation data, and position cheating determination processing is performed; Performing cheating identification and judgment processing according to the visual mark recognition data; Processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; Processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; A corresponding physical examination report is generated and uploaded to the backend.

2. The driver physical examination device and anti-cheating method according to claim 1, characterized in that: The processing according to the head position data to obtain the head position deviation data and perform position cheating judgment processing includes: The head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data, and head left and right displacement data; Obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front-back displacement data, and standard head left-right displacement data; Performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; The head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data and the head left-right displacement deviation data are processed by a preset head deviation recognition model to obtain head position deviation data; Comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; Determining whether the deviation of the target driver's head position exceeds the standard according to the first threshold comparison result; If the deviation exceeds the limit, it will be judged as position cheating.

3. The driver physical examination device and anti-cheating method according to claim 2, characterized in that: The cheating identification and judgment processing according to the visual mark recognition data includes: The sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; Obtaining standard recognition time data, and performing comparison processing in combination with the maximum recognition time data to obtain recognition time deviation data; Obtaining mean data of the sight mark recognition error rate, and performing comparative processing on the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; Obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; Performing a threshold comparison based on the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

4. The driver physical examination device and anti-cheating method according to claim 3, characterized in that: The processing according to the eye movement data to obtain an eye movement abnormality index and perform eye movement cheating judgment processing includes: The eye movement data includes blink frequency data, left-right eyeball rotation angle data, and up-down eyeball rotation angle data; Acquire blink frequency standard data, and perform comparison processing on the blink frequency data to obtain blink frequency deviation data; Obtaining standard data of eyeball left-right rotation angle and standard data of eyeball up-down rotation angle, combining the eyeball left-right rotation angle data and the eyeball up-down rotation angle data for comparison and processing, respectively, to obtain corresponding eyeball left-right rotation angle deviation data and eyeball up-down rotation angle deviation data; Performing weighted processing on the blink frequency deviation data, the eyeball left-right rotation angle deviation data, and the eyeball up-down rotation angle deviation data to obtain an eye movement abnormality index; Comparing the eye movement abnormality index with a preset eye movement abnormality threshold to obtain a second threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the second threshold value; If the second threshold comparison result is greater than a preset threshold, then there is an abnormality in the eye movement of the target driver, and it is determined as eye movement cheating.

5. The driver physical examination device and anti-cheating method according to claim 4, characterized in that: The processing according to the vision data to obtain the vision comparison deviation coefficient and perform vision data cheating judgment processing includes: Extracting vision data of this time according to the vision test result of the target driver; Acquiring historical vision test data of the target driver; Performing comparison processing based on the current vision data and the historical vision test data to obtain a vision comparison deviation coefficient; Comparing the vision comparison deviation coefficient with a preset vision comparison deviation threshold to obtain a third threshold comparison result; Determining whether the eye movement of the target driver is abnormal according to the comparison result of the third threshold value; If the comparison result of the third threshold is greater than the preset threshold, then there is an abnormality in the sixteen data of the target driver, and it is determined that the vision data is cheated.

6. The driver physical examination device and anti-cheating method according to claim 5, characterized in that: The corresponding physical examination report is generated and uploaded to the backend, including: generating a physical examination report according to the vision test result; If the results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment are all that there is no cheating, then the physical examination report is a normal report; If any of the judgment results of the position cheating judgment, identification cheating judgment, eye movement cheating judgment and vision data judgment contain cheating, the physical examination report will be marked as an abnormal report and further review and processing is required; The physical examination report is uploaded to the backend.

7. A driver physical examination device and an anti-cheating system, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a driver physical examination device and an anti-cheating method, and when the program of the driver physical examination device and the anti-cheating method is executed by the processor, the following steps are implemented: Collecting face recognition information of the target driver through a preset physical examination equipment recognition system, and performing identity verification judgment on the target driver; If the identity verification of the target driver is successful, a vision test is performed on the target driver according to the preset physical examination device, and the head position data, sight mark recognition data, vision data and eye movement data of the target driver are obtained in real time; Processing is performed according to the head position data to obtain head position deviation data, and position cheating determination processing is performed; Performing cheating identification and judgment processing according to the visual mark recognition data; Processing the eye movement data to obtain an eye movement abnormality index, and performing eye movement cheating judgment processing; Processing the vision data to obtain a vision comparison deviation coefficient, and performing a vision data cheating judgment process; A corresponding physical examination report is generated and uploaded to the backend.

8. The driver physical examination equipment and anti-cheating system according to claim 7, characterized in that: The processing according to the head position data to obtain the head position deviation data and perform position cheating judgment processing includes: The head position data includes head horizontal angle data, head vertical angle data, head front and back displacement data, and head left and right displacement data; Obtaining standard head position data, including standard head horizontal angle data, standard head vertical angle data, standard head front-back displacement data, and standard head left-right displacement data; Performing statistical processing on the head position data in combination with the head standard position data to obtain corresponding head position deviation data, including head horizontal angle deviation data, head vertical angle deviation data, head front-to-back displacement deviation data, and head left-to-right displacement deviation data; The head horizontal angle deviation data, the head vertical angle deviation data, the head front-back displacement deviation data and the head left-right displacement deviation data are processed by a preset head deviation recognition model to obtain head position deviation data; Comparing the head position deviation data with a preset head position deviation threshold to obtain a first threshold comparison result; Determining whether the deviation of the target driver's head position exceeds the standard according to the first threshold comparison result; If the deviation exceeds the limit, it will be judged as position cheating.

9. The driver physical examination equipment and anti-cheating system according to claim 8, characterized in that: The cheating identification and judgment processing according to the visual mark recognition data includes: The sight mark recognition data includes maximum recognition time data and sight mark recognition error rate data; Obtaining standard recognition time data, and performing comparison processing in combination with the maximum recognition time data to obtain recognition time deviation data; Obtaining mean data of the sight mark recognition error rate, and performing comparative processing on the sight mark recognition error rate data to obtain sight mark recognition error rate deviation data; Obtaining a preset sight mark recognition deviation limit threshold set, including a recognition time deviation limit threshold and a recognition error rate deviation limit threshold; Performing threshold comparison according to the recognition time deviation data and the sight mark recognition error rate deviation data and two thresholds corresponding to the preset sight mark recognition deviation limit threshold set; If the comparison results of the two threshold values ​​are not both smaller than the corresponding threshold values ​​of the preset sight mark recognition deviation limit threshold value set, cheating exists in sight mark recognition.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a driver physical examination device and an anti-cheating method program. When the driver physical examination device and the anti-cheating method program are executed by a processor, the steps of the driver physical examination device and the anti-cheating method as described in any one of claims 1 to 6 are implemented.

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