CN117541035A - Road transportation driver post-adaptation portrait method integrating multi-source data - Google Patents
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Abstract
The invention discloses a road transportation driver post-fit portrait method integrating multisource data, which comprises the following steps of: multi-source data acquisition and construction of a suitable post image database; step 2: quantification of non-numerical class data in the post-adaptive image database; step 3: constructing a post-adaptive image tag set; step 4: calculating a second-level post-adaptive picture label value; step 5: standardization of the second-level post-adaptive picture label value; step 6: calculating a first-level adaptive image tag value; step 7: the road transport driver is adapted to the representation of the sentry representation. The invention can realize comprehensive evaluation on whether the driver accords with the post capability, and ensure social safety.
Description
Technical Field
The invention belongs to the technical field of driving safety risk management and control, and particularly relates to a road transportation driver post-adaptation portrait method integrating multisource data.
Background
The driver's suitability for the post refers to the degree to which the physical, psychological, skill and other quality of the driver meet the requirements of safe driving work, and has important influence on the safe driving of the driver and the road traffic safety. If a person who does not meet the proper post drives the vehicle to drive on the road, the potential hazard of burying the road traffic safety is greatly increased. The conventional evaluation method for the driver's suitability is to acquire the index values of the speed estimation, the selection reaction and the like of the driver by adopting corresponding detection equipment, and then determine whether the driver is qualified for driving work according to corresponding judgment standards. The evaluation method mainly evaluates the cognitive ability and the perception ability of the driver, and factors such as physical conditions, driving habits, living habits and the like of the driver have important influence on safe driving. Because the passengers or goods transported by the road transport driver for a single time are numerous, the accident of group death and group injury is easier to happen, and the safety of the road traffic is more important, a set of more comprehensive and scientific image method for the road transport driver for the post adaptation is necessary to be developed, and the post adaptation of the road transport driver is comprehensively evaluated, so that the road transport safety level is further improved.
Disclosure of Invention
Aiming at the defects and shortcomings of the prior art, the invention aims to provide a road transportation driver post-adaptation portrait method integrating multi-source data, which helps road transportation enterprises to purposefully screen and train drivers and improves road transportation safety.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a road transportation driver post-fit portrait method integrating multi-source data comprises the following steps:
step 1: multi-source data acquisition and construction of a suitable post image database;
step 2: quantification of non-numerical class data in the post-adaptive image database;
step 3: constructing a post-adaptive image tag set;
step 4: calculating a second-level post-adaptive picture label value;
step 5: standardization of the second-level post-adaptive picture label value;
step 6: calculating a first-level adaptive image tag value;
step 7: the road transport driver is adapted to the representation of the sentry representation.
Further, the multi-source data includes: pre-post evaluation data, off-post evaluation data and on-post evaluation data;
the pre-post evaluation data comprises height, weight, endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, chronic disease, mental health, stress resistance, expertise, driving style, speed estimation, depth perception, attention distribution, risk perception, selective response, emergency response, continuous emergency response, treatment judgment, vehicle type adaptation and road condition adaptation of a driver;
wherein:
the height and weight data are expressed as specific values;
the data of the chronic diseases are expressed as the names of specific chronic diseases;
the data of the driving style are expressed as 'discreet type, safe type, common type and aggressive type';
the data adapted to the vehicle type is expressed as an automobile train, a large/heavy vehicle, a medium-sized vehicle and a small-sized vehicle;
the data of road condition adaptation are expressed as 'simple, medium, complex and high and cold';
the data of endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, mental health, stress resistance, expertise, speed estimation, depth perception, attention distribution, risk perception, selective response, emergency response, continuous emergency response and treatment judgment are classified into four grades according to the capability level "A, B, C, D";
the off-Shift evaluation data comprise whether a driver has fever, cold emergency conditions, sleep insufficiency and alcohol test failure every time the driver goes off-Shift; the data of the emergency condition, the sleep deficiency condition and the alcohol test passing condition are expressed as yes and no;
the on-duty evaluation data comprise driving duration, fatigue driving times and risk behavior times of a driver during each on-duty; the driving duration, the fatigue driving times and the risk behavior times are all expressed as specific numerical values during on-duty.
Further, the method for quantifying the non-numerical class data in the post-fit image database comprises the following steps:
(1) Assigning the "A, B, C, D" class data as 4, 3, 2 and 1 respectively;
(2) Respectively assigning the 'yes' and 'no' class data as 1 and 0;
(3) Assigning each type of chronic disease to 1;
(4) The driving style data of 'cautious type, safe type, common type and aggressive type' are respectively assigned to 3, 4, 2 and 1;
(5) The model adaptation data of the 'car train, the large/heavy car, the medium-sized car and the small car' are respectively assigned with 4, 3, 2 and 1;
(6) And (3) respectively assigning the road condition adaptation data of 'simple, medium, complex and high and cold' as 1, 2, 3 and 4.
Further, the post-adaptive image tag set includes a first-stage post-adaptive image tag set and a second-stage post-adaptive image tag set, wherein:
the first-level post-adaptive picture tag set is as follows:
= { physical condition, psychological condition, driving skill, lifestyle, driving habit };
the second grade is fit for the sentry nature and is drawn the looks tag set and is:
= { body mass index, visual function, auditory function, endurance, chronic disease, emergency rate };
= { driving style, attention, stress, mental health };
= { expertise, perceptual ability, reactive ability, hand-eye coordination ability, job stability };
= { sleep health, drinking health };
= { fatigue driving, risk behavior, vehicle model adaptation, road condition adaptation }.
Further, the method for calculating the second-level adaptive image tag value comprises the following steps:
(1) Body weight index value:；
(2) Visual function value:* Quantitative value +.about.of static vision evaluation result>* Quantitative value +.about.f of dynamic vision evaluation result>* Quantized value +.about.of night vision evaluation result>* Quantized value +.about.of visual field evaluation result>* A quantization value of a dark adaptation evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(3) Listening function value: a quantized value of the hearing assessment result;
(4) Endurance value: a quantized value of the endurance evaluation result;
(5) Chronic disease value:；
(6) Emergency rate:；
(7) Driving style value: a quantized value of the driving style evaluation result;
(8) Attention capability value: a quantized value of the attention allocation evaluation result;
(9) Compression resistance value: a quantized value of the anti-stress evaluation result;
(10) Mental health value: a quantitative value of the psychological health assessment result;
(11) Expertise value: a quantized value of the expert knowledge evaluation result;
(12) Perceptual ability value:* Quantized value +.about.of speed estimation evaluation result>* Quantized value +.about.of depth perception evaluation result>* A quantized value of a risk perception evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(13) Reaction capability value:* Selecting a quantized value +.about.of the reaction evaluation result>* A quantized value of an emergency response evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(14) Hand-eye coordination ability value: disposing a quantized value of the judgment evaluation result;
(15) Job stability value: a quantized value of the continuous emergency response evaluation result;
(16) Sleep health value:；
(17) Drinking health value:；
(18) Fatigue driving value:；
(19) Risk behavior value:；
(20) Vehicle model adaptation value: the vehicle model adapts to the quantized value of the evaluation result;
(21) Road condition adaptation value: and the road condition is adapted to the quantized value of the evaluation result.
Further, the method for normalizing the second-level adaptive image tag value comprises the following steps:
and (3) standardizing all the secondary adaptive image tag values according to 4 minutes, and setting an upper limit and a lower limit for each minute.
Further, the method for calculating the first-level adaptive image tag value comprises the following steps:
and collecting each index in the first-level adaptive image label set according to the standardized value of the corresponding second-level adaptive image label.
Further, the road transport driver is adapted to the sentry portrait in the following expression mode: the radar chart is adopted, and the first-level adaptive image label value is used for marking.
The beneficial effects of the invention are as follows: the road transportation driver post-adapting portrait method integrating the multi-source data can evaluate whether the road transportation driver has safe driving capability meeting post requirements, help road transportation enterprises to purposefully screen and train drivers, realize source control of road transportation safety, prevent serious public safety accidents caused by group death and group injury, and ensure stable social operation.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a road transport driver post-fit representation method incorporating multi-source data in accordance with the present invention.
FIG. 2 is a schematic representation of a road transport driver's post-job-adapted representation of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, but it should be understood by those skilled in the art that the following examples are not to be construed as limiting the technical scope of the present invention, and any equivalent transformation or modification made within the spirit of the technical scope of the present invention should be considered as falling within the scope of the present invention.
The invention provides a method for fusing multisource data on road transport driver post-fit portraits, and a specific implementation method flow is shown in figure 1.
Step 1: multisource data acquisition and construction of sentry-fit image database
In order to realize the construction of the suitable post image of the driver, the invention collects the following multi-source data as an evaluation basis, wherein the data sources comprise: pre-post, off-post and on-post assessment data.
(1) The pre-post evaluation data is derived from a pre-post evaluation report, and the pre-post evaluation report comprises relevant evaluation data such as height, weight, endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, chronic disease, mental health, stress resistance, expertise, driving style, speed estimation, depth perception, attention allocation, risk perception, selective response, emergency response, continuous emergency response, treatment judgment, vehicle type adaptation, road condition adaptation and the like of each road transportation driver.
Wherein:
the height and weight data are expressed as specific numerical values, and the corresponding units are respectively meter and kilogram;
the chronic disease data are expressed as names of specific chronic diseases, such as diabetes and hypertension;
the data of the driving style are expressed as 'discreet type, safe type, common type and aggressive type';
the data adapted to the vehicle type is expressed as an automobile train, a large/heavy vehicle, a medium-sized vehicle and a small-sized vehicle;
the data of road condition adaptation are expressed as 'simple, medium, complex and high and cold';
the data of endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, mental health, stress resistance, expertise, speed estimation, depth perception, attention distribution, risk perception, selective response, emergency response, continuous emergency response and treatment judgment are classified into four grades of A, B, C, D according to the capability.
(2) The off-Shift evaluation data is derived from an off-Shift evaluation report and comprises relevant evaluation data such as whether emergency situations such as fever and cold exist when each road transport driver goes off-Shift, whether sleep is insufficient or whether alcohol test fails.
The data of the emergency condition, the sleep deficiency condition and the alcohol test passing condition are judging data, and are expressed as yes and no.
(3) The on-duty evaluation data is derived from an on-duty evaluation report and comprises driving duration, fatigue driving times and risk behavior times of each road transportation driver during each on-duty.
The driving duration at the time of on duty is expressed as a specific value, and the corresponding unit is hour; the fatigue driving times and the risk behavior times are also expressed as specific numerical values, and the corresponding units are times.
The front-post evaluation report, the off-post evaluation report and the on-post evaluation report described above can be transmitted to the evaluation management end through a storage medium, a wired network, a wireless network or a 5G network. The management end adopts an image character recognition mode to recognize data in the report and store the data in a corresponding field of the post-fit portrait database if the report is a paper document; if the file is an electronic document, the management end adopts a keyword recognition mode to recognize the data in the report and store the data in the corresponding field of the post-fit portrait database.
The portrait system is provided with a management end and a database, wherein the management end is used for identifying the front-post evaluation report, the off-post evaluation report and the data on the on-post evaluation report, storing the data in a suitable post portrait database, calculating the data in the suitable post portrait database, finally obtaining the suitable post portrait of the road transportation driver and taking the suitable post portrait as a visual carrier of the suitable post portrait.
Step 2: non-numerical class data quantization in post-fit image database
The method for quantifying the non-numerical class data in the post-fit image database by the management end comprises the following steps:
(1) Assigning the "A, B, C, D" class data as 4, 3, 2 and 1 respectively;
(2) Respectively assigning the 'yes' and 'no' class data as 1 and 0;
(3) Assigning each type of chronic disease to 1;
(4) The driving style data of 'cautious type, safe type, common type and aggressive type' are respectively assigned to 3, 4, 2 and 1;
(5) The model adaptation data of the 'car train, the large/heavy car, the medium-sized car and the small car' are respectively assigned with 4, 3, 2 and 1;
(6) And (3) respectively assigning the road condition adaptation data of 'simple, medium, complex and high and cold' as 1, 2, 3 and 4.
Step 3: post-adaptive image tag set construction
The post-adaptive picture tag set is characterized by comprising a first-stage post-adaptive picture tag set and a second-stage post-adaptive picture tag set.
The first-level post-adaptive picture tag set is as follows:
= { physical condition, psychological condition, driving skill, lifestyle, driving habit };
the second grade is fit for the sentry nature and is drawn the looks tag set and is:
= { body mass index, visual function, auditory function, endurance, chronic disease, emergency rate };
= { driving style, attention, stress, mental health };
= { expertise, perceptual ability, reactive ability, hand-eye coordination ability, job stability };
= { sleep health, drinking health };
= { fatigue driving, risk behavior, vehicle model adaptation, road condition adaptation }.
Step 4: second-level post-adaptive portrait tag value calculation
The management end processes the data in the post-adaption image database and calculates the numerical value corresponding to the secondary post-adaption image label.
The specific implementation method for the management end to calculate the label value of the second-level post-adaptive picture is as follows:
(1) Body weight index value:；
(2) Visual function value:* Quantitative value +.about.of static vision evaluation result>* Quantitative value +.about.f of dynamic vision evaluation result>* Quantized value +.about.of night vision evaluation result>* Quantized value +.about.of visual field evaluation result>* A quantization value of a dark adaptation evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(3) Listening function value: a quantized value of the hearing assessment result;
(4) Endurance value: a quantized value of the endurance evaluation result;
(5) Chronic disease value:；
(6) Emergency rate:；
(7) Driving style value: a quantized value of the driving style evaluation result;
(8) Attention capability value: a quantized value of the attention allocation evaluation result;
(9) Compression resistance value: a quantized value of the anti-stress evaluation result;
(10) Mental health value: a quantitative value of the psychological health assessment result;
(11) Expertise value: a quantized value of the expert knowledge evaluation result;
(12) Perceptual ability value:* Quantized value +.about.of speed estimation evaluation result>* Quantized value +.about.of depth perception evaluation result>* A quantized value of a risk perception evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(13) Reaction capability value:* Selecting a quantized value +.about.of the reaction evaluation result>* A quantized value of an emergency response evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(14) Hand-eye coordination ability value: disposing a quantized value of the judgment evaluation result;
(15) Job stability value: a quantized value of the continuous emergency response evaluation result;
(16) Sleep health value:；
(17) Drinking health value:；
(18) Fatigue driving value:；
(19) Risk behavior value:；
(20) Vehicle model adaptation value: the vehicle model adapts to the quantized value of the evaluation result;
(21) Road condition adaptation value: and the road condition is adapted to the quantized value of the evaluation result.
Step 5: second grade image label value standardization
The management end performs 4-division standardization on the label value of the second-level post-adaptation image, and sets an upper limit value and a lower limit value of each division, for example, the specific implementation method of standardization is as follows:
(1) Body mass index normalization:
(2) Vision function value, hearing function value, endurance value, driving style value, attention ability value, stress resistance value, mental health value, expertise value, perception ability value, reaction ability value, hand-eye coordination ability value, action stability value, vehicle model adaptation value, road condition adaptation value standardization:
(3) Chronic disease value normalization:
(4) Emergency rate normalization:
(5) Sleep health value, drinking health value standardization:
(6) Fatigue driving value and risk behavior value are standardized:
step 6: first-level post-adaptive portrait tag value calculation
And expressing the first-level post-adaptive portrait tag value according to a standardized value.
(1) The label value of the first-level post-adaptation picture label is as follows:
physical condition value= { body weight index value, visual function value, auditory function value, endurance value, chronic disease value, emergency rate value };
(2) The label value of the first-level sentry-fit picture label 'psychological condition' is as follows:
psychological condition value= { driving style value, attention ability value, stress resistance value, psychological health value };
(3) The label value of the first-level post-adaptive picture label driving skill is as follows:
driving skill value= { expertise value, perceptual ability value, reaction ability value, hand-eye coordination ability value, job stability value };
(4) The label value of the first-level post-adaptation sexual image label 'life habit' is as follows:
lifestyle value= { sleep health value, drinking health value };
(5) The label value of the first-level post-adaptive picture label driving habit is as follows:
driving habit value= { fatigue driving value, risk behavior value, vehicle model adaptation value, road condition adaptation value }.
Step 7: road transport driver sentry-fit pictographic avatar representation
And integrating the first-level post-adaptive portrait tag value, and carrying out the portrait expression on the post-adaptive portrait of the road transportation driver by adopting a radar chart. The method for expressing the image with the suitability for post is shown in figure 2.
Examples
The following describes a road transport driver post-fit portrait method integrating multi-source data by adopting an embodiment of the invention so as to verify the effectiveness of the method.
Step 1: acquiring evaluation data before the road transportation driver is on duty, evaluation data on duty and evaluation data on duty, and storing the evaluation data in a suitable-duty image database.
It is assumed that the pre-post evaluation data obtained from the pre-post evaluation report is:
the off Shift evaluation data obtained from the off Shift evaluation report is:
on Shift evaluation data obtained from the on Shift evaluation report is:
step 2: quantization of non-numeric class data in post-appropriate image database
The quantized pre-post evaluation data are:
the quantized off-Shift evaluation data are:
the quantized on-Shift evaluation data are:
step 3: calculating the label value of the second grade sentry-fit picture
(1) Body weight index value =；
(2) Visual function value =* Quantitative value +.about.of static vision evaluation result>* Quantitative value +.about.f of dynamic vision evaluation result>* Quantized value +.about.of night vision evaluation result>* Quantized value +.about.of visual field evaluation result>* A quantization value of a dark adaptation evaluation result; wherein->Determining weights by expert scoring;
assumption is determined by expert scoringWeights of 0.25, 0.2, 0.15, 0.2, respectively;
the visual function value =；
(3) Hearing function value = quantized value of hearing assessment result = 4;
(4) Endurance value = quantized value of endurance assessment result = 3;
(5) Chronic disease value = ；
(6) Emergency rate =；
(7) Driving style value = quantized value of driving style evaluation result = 3;
(8) Attention value = quantized value of attention allocation assessment result = 2;
(9) Resistance value = quantized value of resistance evaluation result = 4;
(10) Mental health value = quantized value of mental health assessment result = 3;
(11) Expertise value = quantized value of expertise assessment result = 4;
(12) Perceptual ability value =* Quantized value +.about.of speed estimation evaluation result>* Quantized value +.about.of depth perception evaluation result>* A quantized value of a risk perception evaluation result;
assumption is determined by expert scoringWeights of (2) are 0.25, 0.5, respectively, the perceptual ability value=0.25×3+0.25×2+0.5×1=1.75;
(13) Reaction ability value =* Selecting a quantized value +.about.of the reaction evaluation result>* A quantized value of an emergency response evaluation result; />
Assumption is determined by expert scoringThe weight of (2) is 0.4, 0.6, respectively, the reaction capacity value=0.4×4+0.6×2=2.8;
(14) Hand-eye coordination ability value=quantized value of treatment judgment evaluation result=2;
(15) Action stability value = quantized value of continuous emergency response assessment result = 3;
(16) Sleep health value =；
(17) Drinking health value =；
(18) Fatigue driving value =；
(19) Risk behavior value =；
(20) Vehicle model adaptation value = quantized value of vehicle model adaptation assessment result = 3;
(21) Road condition adaptation value=quantized value of road condition adaptation evaluation result=3.
Step 4: second grade image label value standardization
The standardized second-level post-adaption image tag value is as follows:
step 5: first-level post-adaptive portrait tag value calculation
The physical status value = {4, 3.3, 4, 3, 2, 4};
the mental state value = {3, 2, 4, 3};
the driving skill value= {4, 1.75, 2.8, 2, 3};
the lifestyle value= {2, 3};
the driving habit value= {3, 4, 3}.
Step 6: road transport driver sentry-fit pictographic avatar representation
And integrating the first-level post-adaptive portrait tag values, and carrying out the representation of the post-adaptive portrait of the road transport driver by adopting a radar chart, as shown in figure 2.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above description will be apparent to those of ordinary skill in the art, and it is not necessary or intended to be exhaustive of all embodiments, and obvious variations or modifications are contemplated as falling within the scope of the invention.
Claims (8)
1. A road transport driver on-duty image-taking method integrating multisource data is characterized by comprising the following steps:
step 1: multi-source data acquisition and construction of a suitable post image database;
step 2: quantification of non-numerical class data in the post-adaptive image database;
step 3: constructing a post-adaptive image tag set;
step 4: calculating a second-level post-adaptive picture label value;
step 5: standardization of the second-level post-adaptive picture label value;
step 6: calculating a first-level adaptive image tag value;
step 7: the road transport driver is adapted to the representation of the sentry representation.
2. The method of claim 1, wherein the multi-source data comprises: pre-post evaluation data, off-post evaluation data and on-post evaluation data;
the pre-post evaluation data comprises height, weight, endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, chronic disease, mental health, stress resistance, expertise, driving style, speed estimation, depth perception, attention distribution, risk perception, selective response, emergency response, continuous emergency response, treatment judgment, vehicle type adaptation and road condition adaptation of a driver;
wherein:
the height and weight data are expressed as specific values;
the data of the chronic diseases are expressed as the names of specific chronic diseases;
the data of the driving style are expressed as 'discreet type, safe type, common type and aggressive type';
the data adapted to the vehicle type is expressed as an automobile train, a large/heavy vehicle, a medium-sized vehicle and a small-sized vehicle;
the data of road condition adaptation are expressed as 'simple, medium, complex and high and cold';
the data of endurance, hearing, static vision, dynamic vision, night vision, visual field, dark adaptation, mental health, stress resistance, expertise, speed estimation, depth perception, attention distribution, risk perception, selective response, emergency response, continuous emergency response and treatment judgment are classified into four grades according to the capability level "A, B, C, D";
the off-Shift evaluation data comprise whether a driver has fever, cold emergency conditions, sleep insufficiency and alcohol test failure every time the driver goes off-Shift; the data of the emergency condition, the sleep deficiency condition and the alcohol test passing condition are expressed as yes and no;
the on-duty evaluation data comprise driving duration, fatigue driving times and risk behavior times of a driver during each on-duty; the driving duration, the fatigue driving times and the risk behavior times are all expressed as specific numerical values during on-duty.
3. The method of claim 2, wherein the method for quantifying non-numeric class data in the adaptive image database is:
(1) Assigning the "A, B, C, D" class data as 4, 3, 2 and 1 respectively;
(2) Respectively assigning the 'yes' and 'no' class data as 1 and 0;
(3) Assigning each type of chronic disease to 1;
(4) The driving style data of 'cautious type, safe type, common type and aggressive type' are respectively assigned to 3, 4, 2 and 1;
(5) The model adaptation data of the 'car train, the large/heavy car, the medium-sized car and the small car' are respectively assigned with 4, 3, 2 and 1;
(6) And (3) respectively assigning the road condition adaptation data of 'simple, medium, complex and high and cold' as 1, 2, 3 and 4.
4. A method according to claim 1, 2 or 3, characterized in that,
the post-adaptive image tag set comprises a first-stage post-adaptive image tag set and a second-stage post-adaptive image tag set, wherein:
the first-level post-adaptive picture tag set is as follows:
= { physical condition, psychological condition, driving skill, lifestyle, driving habit };
the second grade is fit for the sentry nature and is drawn the looks tag set and is:
= { body mass index, visual function, auditory function, endurance, chronic disease, emergency rate };
= { driving style, attention, stress, mental health };
-expertise, perceptual ability, reactive ability, hand-eye coordination ability, job stability };
= { sleep health, drinking health };
= { fatigue driving, risk behavior, vehicle model adaptation, road condition adaptation }.
5. The method of claim 4, wherein the secondary adaptive image tag value is calculated by:
(1) Body weight index value:；
(2) Visual function value:* Quantitative value +.about.of static vision evaluation result>* Quantitative value +.about.f of dynamic vision evaluation result>* Quantized value +.about.of night vision evaluation result>* Quantized value +.about.of visual field evaluation result>* A quantization value of a dark adaptation evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(3) Listening function value: a quantized value of the hearing assessment result;
(4) Endurance value: a quantized value of the endurance evaluation result;
(5) Chronic disease value:；
(6) Emergency rate:；
(7) Driving style value: a quantized value of the driving style evaluation result;
(8) Attention capability value: a quantized value of the attention allocation evaluation result;
(9) Compression resistance value: a quantized value of the anti-stress evaluation result;
(10) Mental health value: a quantitative value of the psychological health assessment result;
(11) Expertise value: a quantized value of the expert knowledge evaluation result;
(12) Perceptual ability value:* Quantized value +.about.of speed estimation evaluation result>* Quantized value of depth perception evaluation result +* A quantized value of a risk perception evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(13) Reaction capability value:* Selecting a quantized value +.about.of the reaction evaluation result>* A quantized value of an emergency response evaluation result;
wherein the method comprises the steps ofDetermining weights by expert scoring;
(14) Hand-eye coordination ability value: disposing a quantized value of the judgment evaluation result;
(15) Job stability value: a quantized value of the continuous emergency response evaluation result;
(16) Sleep health value:；
(17) Drinking health value:；
(18) Fatigue driving value:；
(19) Risk behavior value:；
(20) Vehicle model adaptation value: the vehicle model adapts to the quantized value of the evaluation result;
(21) Road condition adaptation value: and the road condition is adapted to the quantized value of the evaluation result.
6. The method of claim 5, wherein the method for normalizing the secondary post-adaptive image tag value is as follows:
and (3) standardizing all the secondary adaptive image tag values according to 4 minutes, and setting an upper limit and a lower limit for each minute.
7. The method of claim 6, wherein the first-order adaptive image tag value is calculated by:
and collecting each index in the first-level adaptive image label set according to the standardized value of the corresponding second-level adaptive image label.
8. The method of claim 7, wherein the road transport driver's post-job-adapted representation is expressed in the form of: the radar chart is adopted, and the first-level adaptive image label value is used for marking.
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