CN115269653B - Automatic vision-identity judging method for safety standard-reaching vehicle type detection project - Google Patents

Abstract

The application belongs to the technical field of automatic checking enterprise schemes, and particularly relates to an automatic vision-co-judgment method for a safety standard-reaching vehicle type detection project, which comprises the following steps of 1, acquiring record information of a certain declared vehicle type in a road transport vehicle technical service system; step 2, establishing a database based on MySQL to store parameters of a sample condition registry of the existing qualified report; step 3, traversing an enterprise declaration detection scheme created based on Excel according to items by Python; step 4, python judges whether the obtained apparent reports in step 3 all exist in the database of the step 2 MySQL; step 5, according to the judging results of the step 3 and the step 4, python compares the reporting parameters of the step 1 with the database parameters of the step 4 to give a reasonable scheme; the automatic vision and identity judging method replaces a manual mode, reduces the workload of detection mechanism personnel, reduces the error rate, and can give corresponding rationality suggestions when the unreasonable condition of the inspection project appears.

Description

Automatic vision-identity judging method for safety standard-reaching vehicle type detection project

Technical Field

The application belongs to the technical field of automatic checking enterprise schemes, and particularly relates to an automatic vision-based judging method for a safety standard-reaching vehicle type detection project.

Background

In recent years, multiple crane traffic accidents happen in succession all over the country, so that important losses are caused to lives and properties of people and bad social influence is generated. The overall safety performance of the operation vehicle is not high, and the operation vehicle becomes an outstanding short plate and a weak link for restricting the people to travel safely and reliably. For deep drawing of accident training, the problems of safety and the like of operating vehicles are practically solved, and a plurality of standards for operating requirements are established by the transportation department organization.

JT/T1094-2016 technical conditions for safety of commercial buses are formally implemented by the department of transportation and transportation, 4.1.2017; JT/T1178.1-2018, part 1 of safety technical Condition for commercial trucks: the formal requirements of trucks; JT/T1178.2-2019, part 2 of commercial truck safety technical Condition, 7.1.2019: the hauling vehicle and the trailer carry out schedule; JT/T1285-2020, safety technical Condition for road transportation of dangerous goods, operating vehicle, is formally executed on 1 month 4 of 2020. The transportation department has many requirements for operating vehicles, and each model of vehicle must meet the requirements of all the projects to handle the operation license, and the projects include: electronic stability control system performance, lane departure warning system performance, vehicle forward collision warning system performance, steering force and steering stability, curve braking stability, and the like; the above are all whole car test projects, and besides corresponding whole car projects, the traffic department also makes requirements on some spare part projects, such as: pressure test connectors, brake pad performance, ABS electromagnetic compatibility, and the like. The corresponding items must have corresponding reports as support, which can be classified according to category: the actual measurement report is a report issued by the reporting vehicle after the test, and the apparent report is an actual measurement report issued by other types of vehicles, but the report cannot be directly cited and must meet the apparent condition requirements specified by the traffic department.

When an enterprise performs operation and declaration, all detection schemes of the vehicle type are required to be provided, namely, a detection mechanism is informed of which projects need actual measurement and which projects need to be considered as the same; meanwhile, the detection mechanism must inspect the corresponding scheme, and needs to judge whether the vision report in the car scheme meets the vision condition or not, and whether the vision report can be truly cited or not. In addition, when some parameters (such as wheelbase, whole vehicle size, tire model, etc.) of the enterprise declare a plurality of configurations, whether the actual measurement times provided by the enterprise are enough or not needs to be judged, and whether the number of the sample vehicles is correct or not needs to be very familiar to detection mechanism personnel.

In the traditional detection process, the scheme checking process is that detection mechanism personnel are compared item by item according to the same conditions, meanwhile, the detection mechanism personnel are recorded in a volume, different vehicle enterprises are different in operation reporting time due to different service development, the operation reporting work is generally stopped at month 5, the enterprise bundling reporting condition usually occurs before the operation reporting work is stopped, certain trouble is caused to the service development of the detection mechanism personnel, the scheme checking quality is not guaranteed, in addition, the same vision and reporting can be cited by a plurality of vehicle types in the vision and judging process, the vision and reporting parameter information is required to be extracted for a plurality of times when the comparison is carried out, certain repeated labor exists, and the efficiency is not high.

Disclosure of Invention

Aiming at the defects of the prior art, the application provides an automatic vision and identity judging method aiming at a safety standard-reaching vehicle type detection project, wherein the automatic vision and identity judging method replaces a manual mode, and the judgment of vision and identity conditions is realized through a logic statement, so that whether a vision and identity report meets requirements is judged, whether the actual measurement times are enough or not is determined, the workload of detection mechanism personnel is reduced, the error rate is reduced, and meanwhile, when the inspection project is unreasonable, the automatic vision and identity judging method can give corresponding rationality suggestions.

In order to achieve the above purpose, the technical scheme adopted in the application provides an automatic vision and identity judging method for a safety standard-reaching vehicle type detection project, the automatic vision and identity judging method is suitable for the vision and identity judgment of detection project by detection mechanism personnel when a vehicle is in operation and declaration, and the automatic vision and identity judging method comprises the following steps:

step 1, acquiring record information of a certain declared model in a road transport vehicle technical service system;

step 2, dividing according to the types and detection projects of the vehicle types, establishing a database based on MySQL for storing parameters of a sample condition registry of the existing qualified report, and establishing interaction between MySQL and Python;

step 3, a detection scheme of the vehicle is established based on Excel, traversal is completed on the detection scheme according to items based on Python, and visual identity information of corresponding items is obtained, wherein the visual identity information comprises visual identity vehicle types and visual identity reports;

step 4, the Python judges whether the apparent reports obtained in the step 3 exist in the database established by the MySQL in the step 2, and if so, the next step is directly carried out; if the obtained looking at report is not received by the database built by MySQL, inputting the corresponding looking at information and corresponding looking at parameters of the vehicle in Python, storing the looking at information and the corresponding looking at parameters into the MySQL database, and then entering the next step;

step 5, according to the detection category determined in the step 3, according to the determination result in the step 4, converting the vision-identical condition determination criterion formulated by the traffic department into a logic statement executable by a program in Python, automatically checking and comparing the record parameters acquired in the step 1 with the vision-identical parameters in the MySQL database determined in the step 4 based on Python, determining whether the project meets the vision-identical, recording the checking and comparing result, storing the checking and comparing result in Word documents, and finding out the minimum actual measurement times and the corresponding actual measurement configuration information required by vehicle inspection aiming at the project with actual measurement;

the step 4 specifically includes:

step 4.1, judging whether the looking at the same report exists in the MySQL database of the step 2 or not based on the looking at the same report number obtained in the step 3, and calling corresponding looking at parameters by Python if the looking at the same report number exists;

step 4.2, if the apparent identity report number is not recorded in the MySQL database, creating a Web application program by Python, entering apparent identity report parameters in the form of a webpage, entering, completing the interactive connection established by the step 2, storing information of the apparent identity report in the MySQL database, and calling the corresponding apparent identity parameters for subsequent use;

the step 5 specifically includes:

step 5.1, if the same inspection category appears in the step 3, comparing according to the same condition judgment criterion of the step 5; if the actually measured inspection category appears in the step 3, only a declaration value exists at the moment; if the actually measured and the same inspection category simultaneously appear in the step 3, firstly judging the same condition, removing all configurations meeting the same condition after judging the same condition, and then carrying out actually measured judgment on the rest configurations to finally obtain a complete scheme;

and 5.2, synchronously recording the result of checking the detection scheme of the step 5.1 in a Word file in the Python, and naming the Word file with the application number and the vehicle model.

Further, the step 1 of obtaining the record information of the enterprise declaration operation vehicle is implemented by using a network packet capturing tool, and specifically includes:

step 1.1, according to an application number provided by an enterprise, opening a recording information webpage corresponding to the application number, inputting a webpage analysis code in a network packet capturing tool, and obtaining recording information corresponding to a reporting vehicle type;

and 1.2, storing the record information corresponding to the declared model in a Json format, and providing a Python program for calling.

Further, the recording information in step 1.1 includes: all parameters related to the condition-based decisions such as vehicle size information, chassis information, mass information, brake information, suspension information, centroid information, and component information.

Further, in the step 2, interaction between MySQL and Python is established to implement real-time calling of the apparent parameters of the MySQL database by Python, and static input of apparent information lacking in the MySQL database by Python is implemented.

Further, in the step 3, traversing the Excel solution information according to items based on the Python, and obtaining a report type corresponding to each detection item, where the report type includes three types of actual measurement, visual inspection and visual inspection, actual measurement times corresponding to actual measurement, visual inspection corresponding to visual inspection vehicle model number and visual inspection report number.

Further, the execution of the logic statement converted into the executable program in the step 5 is according to the specific parameters of the simultaneous report in the step 3, and the specific parameters of the simultaneous report in the step 3 correspond to the parameters in the record information corresponding to the vehicle type declared in the step 1;

and (3) the information parameter of the enterprise reporting record in the step (1) is a reporting value, and the information of the MySQL database judged in the step (4) is a vision value.

Further, the apparent condition judgment criteria in the step 5 include three types:

the first category is that the requirement declaration value is the same as or equal to the apparent value; the second type is to require the declared value to be increased or decreased relative to the apparent value; the third class is to require that the declared value be within a certain range of variation of the apparent value.

Further, when a plurality of configurations exist in a certain declaration parameter, which may cause the test number of the detection item to be more than 1, traversing the plurality of configured parameters by Python;

when traversing to a certain configuration, taking the configuration as a declaration value, taking the rest configurations as apparent values, judging according to the step 5.1, and when all configuration traversal is finished, finding out the test scheme with the least comprehensive actual measurement times as a final actual measurement scheme based on a judging result.

The beneficial effects of this application are:

firstly, according to the technical scheme, vehicle record information webpages in a road transport vehicle technical service system are analyzed to obtain record information of vehicles, a database of a visual co-project is established through MySQL, interaction between MySQL and Python is established, a detection scheme of an Excel format provided for detection mechanism personnel by enterprises is traversed according to the project through Python, whether all visual co-reports in the Excel exist in the database of the MySQL is judged, if not, the detection mechanism personnel input the visual co-reports of the corresponding project and corresponding visual co-parameters in an interaction interface, and meanwhile the information is stored in the database; finally, judging the same-vision condition through a logic statement of Python, so as to judge whether the same-vision report meets the requirement or not, and simultaneously giving a reasonable vehicle test scheme;

secondly, traversing the detection item parameters of Excel through Python to obtain a report type corresponding to each detection item, wherein the report type comprises actual measurement, same view and same view, and comparing the record parameters of an enterprise with the same view parameters of a MySQL database according to same view condition judgment criteria, when a certain reporting parameter has a plurality of configurations, the test times of the detection item are possibly more than 1 time, the parameters of the configurations can be traversed through Python, and when an unreasonable condition occurs to a detection item, a corresponding reasonable actual measurement scheme can be given;

third, the record information collected by the network packet capturing tool in the technical scheme of the application comprises all parameters related to the judgment of the same conditions, such as vehicle size information, chassis information, quality information, braking information, suspension information, mass center information and part information; the MySQL building database is divided into a trailer, a traction vehicle, a cargo vehicle, a passenger car and the like according to the type of the vehicle, and is divided into a whole vehicle test item and a part test item according to the same viewing item; the operating vehicle type detection project scheme filled in by Excel can cover scheme information of a truck, a traction vehicle, a semitrailer and a passenger car, meanwhile, the inspection category, the actual measurement times, the same vehicle type and the same report number corresponding to each inspection project are clearly displayed, the completeness verification of a reporting project of a certain vehicle type and the normalization verification of a filling format can be realized, and the result of checking the detection scheme is synchronously recorded in a Word file through Python, so that an enterprise is guided to carry out sample vehicle preparation work.

Drawings

Fig. 1 is a flow chart of an automatic vision-based determination method for a safety standard-reaching vehicle type detection project according to an embodiment of the present application.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced otherwise than as described herein, and thus the scope of the present application is not limited to the specific embodiments disclosed below.

As shown in fig. 1, the embodiment provides an automatic vision-identity determination method for a safety standard-reaching vehicle type detection item, which is suitable for the vision-identity determination of detection item by detection mechanism personnel during vehicle operation and declaration, and the determination method comprises the following steps:

step 1, acquiring record information of a certain declared model in a road transport vehicle technical service system.

And 2, dividing according to the types and detection projects of the vehicle types, establishing a database for storing sample condition registry parameters of the existing qualified report based on MySQL, and establishing interaction between MySQL and Python.

And 3, creating a detection scheme based on the Excel, completing traversal of scheme information in the Excel according to items by using the Python, and acquiring vision information required by the vehicle, wherein the vision information comprises a vision same vehicle type and a vision same report.

Step 4, the Python judges whether the apparent reports obtained in the step 3 all exist in the database established by the MySQL in the step 2, and if so, the next step is directly carried out; if the obtained apparent report is not received by the database built by MySQL, the corresponding apparent information and the corresponding apparent parameters of the vehicle are input in Python and stored in the MySQL database to enter the next step.

And 5, according to the detection category determined in the step 3, converting a viewing condition determination criterion formulated by a traffic department into a logic statement executable by a program in Python according to the determination result in the step 4, automatically checking and comparing the recorded parameters acquired in the step 1 and the viewing parameters in the MySQL database determined in the step 4 based on the Python, determining whether the items meet the viewing condition, recording the checking and comparing result, storing the checking and comparing result in a Word document, and finding out the minimum actual measurement times and the corresponding actual measurement configuration information required by vehicle inspection aiming at the items with actual measurement.

Further, the record information of a certain declared model in the step 1 is realized by using a network packet capturing tool, and specifically includes:

step 1.1, according to the application number provided by an enterprise, opening a recording information webpage corresponding to the application number, inputting a webpage analysis code in a network packet capturing tool, and obtaining recording information corresponding to a reporting vehicle type, wherein the recording information comprises all parameters related to the judgment of the same condition, such as vehicle size information, chassis information, quality information, braking information, suspension information, mass center information and part information.

And 1.2, storing the record information corresponding to the declared model in a Json format, and providing a Python program for calling.

Further, step 2 is configured to build a database storing the visual statement through MySQL, and specifically includes:

the database for establishing and storing the sample condition registry parameters of the existing qualified report based on MySQL is required to be divided according to the type of the vehicle and the detection project, because the different vehicle types have different vision requirements in different projects.

According to the type of the vehicle, the trailer, the traction vehicle, the truck, the passenger car and the like can be divided; the method can be divided into whole vehicle test items and part test items according to the same-vision item, specific parameters corresponding to each item are set according to the same-vision condition, and parameters appearing in the same-vision condition are all reflected in a database established by MySQL.

It should be noted that, the interaction between MySQL and Python is established, so that Python can call the database of MySQL in real time, and Python can realize static input of apparent information lacking in the database of MySQL.

Further, step 3 is used for the detection mechanism to obtain a detection project scheme of the declared commercial vehicle type of the enterprise through the Excel template, and the Excel filled commercial vehicle type detection project scheme can cover scheme information of a truck, a traction vehicle, a semitrailer and a passenger car, and clearly shows the inspection category, the actual measurement times, the same vehicle type and the same report number corresponding to each inspection project at the same time, and specifically comprises the following steps:

performing parameter traversal of detection items on Excel submitted by an enterprise based on a Pandas library in Python, and obtaining report types corresponding to each detection item, wherein the report types comprise actual measurement, visual inspection and actual measurement and visual inspection, the actual measurement is represented by delta, the visual inspection and visual inspection are represented by delta, and the report comprises actual measurement times corresponding to the detection items, corresponding visual inspection car models and visual inspection report numbers.

Further, step 4 is configured to determine whether the visibility reports provided by the enterprise are all recorded in the database, and specifically includes:

and 4.1, judging whether the apparent report exists in the MySQL database in the step 2 according to the apparent report number obtained in the step 3, and calling apparent parameters corresponding to the detection items by a Pymysql library in Python if the apparent report number exists.

And 4.2, if the apparent report number is not recorded in the MySQL database, creating 1 Web application program through a flash library in the Python, inputting apparent report parameters required to be recorded by detection mechanism personnel in the form of a webpage, storing information of the apparent report in the database through the interactive connection established between the Python and the MySQL in the step 2 after the input is completed, and calling the corresponding apparent parameters for subsequent use.

Further, step 5 is used for converting the condition-based judgment criteria into logic statements executable by the program, and checking and comparing to find out the least practical project scheme required by vehicle inspection.

It should be noted that, the conversion into the logic statement executable by the program is executed for a specific parameter in the visual identity report, and the specific parameter has a corresponding parameter in the record information corresponding to the reporting vehicle type in step 1.2; the checking and comparing process is to compare the enterprise reporting record information parameter (hereinafter referred to as reporting value) in step 1.2 with the information (hereinafter referred to as viewing value) in the MySQL database judged in step 4.

In this embodiment, the checking and comparing process of the vehicle inspection scheme in step 5 is performed simultaneously with step 3 and step 4, when the test scheme of each item is traversed in step 3, it is determined whether the category of the item belongs to vision or actual measurement, or both, and meanwhile, it is determined whether the vision report provided by the enterprise exists in the database established in step 2MySQL, if not, the parameter input work in step 4 is performed, after the vision report is received in the warehouse, python executes a call logic statement to obtain the vision report parameter value of the corresponding item, and meanwhile, the checking and comparing work of the scheme is performed based on the declaration value obtained in step 1.

The apparent condition judgment criteria include three categories: the first category is that the requirement declaration value is the same as or equal to the apparent value; the second type is to require the declared value to be increased or decreased relative to the apparent value; the third class is to require that the declared value be within a certain range of variation of the apparent value. Since the declared value obtained in step 1 and the apparent value obtained in step 4 have a difference in data type, it is required to convert them into corresponding data types in a specific determination.

The step 5 specifically comprises the following steps:

step 5.1, when the same inspection category appears in the step 3, comparing according to the same condition judgment criterion; when the actually measured inspection category appears in the step 3, only a declaration value exists at the moment; and 3, when the actually measured and the same inspection category simultaneously appear in the step, firstly judging the same condition, removing all configurations meeting the same condition after judging the same condition, and then carrying out actually measured judgment on the rest configurations to finally obtain a complete scheme.

In the Python program, selection can be made based on the conditional judgment statement, and then judgment can be made based on the logical judgment statement. The first category of the same-looking condition judgment criterion can directly judge whether the declared value is the same as the text value of the same-looking value; the second class and the third class look at the condition judgment criterion, and the corresponding declaration value and look at the same value to be converted into the corresponding numerical data for judgment, for example, the int type data or the float type data.

It should be noted that, when there are multiple configurations in a certain reporting parameter, the number of tests of the test item may be greater than 1, at this time, the Pandas library in Python needs to traverse the parameters of the multiple configurations, when traversing to a certain configuration, the configuration is used as a reporting value, the remaining configurations are used as an apparent value, then the determination is performed according to step 5, and when all configurations are traversed, through the determination of the result, the test scheme with the least comprehensive actual measurement number is found out as the final actual measurement scheme.

And 5.2, synchronously recording the result of checking the detection scheme in the step 5.1 in a Word file by the Docx library in the Python, naming the Word file by application number and vehicle model, clearly showing specific comparison results of each project by the Word file, showing which configurations in the report-following reporting value correspond to each apparent report, and simultaneously showing the actual measurement scheme with the least actual measurement times, and guiding enterprises to prepare sample cars.

The steps in the present application may be sequentially adjusted, combined, and pruned according to actual requirements.

Although the present application is disclosed in detail with reference to the accompanying drawings, it is to be understood that such descriptions are merely illustrative and are not intended to limit the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, alterations, and equivalents to the invention without departing from the scope and spirit of the application.

Claims (8)

1. An automatic vision-co-identification method for a safety standard vehicle type detection project is characterized in that the automatic vision-co-identification method is suitable for the vision-co-identification of detection project by detection mechanism personnel during vehicle operation and declaration, and comprises the following steps:

step 1, acquiring record information of a certain declared model in a road transport vehicle technical service system;

step 2, dividing according to the types and detection projects of the vehicle types, establishing a database based on MySQL for storing parameters of a sample condition registry of the existing qualified report, and establishing interaction between MySQL and Python;

step 3, a detection scheme of the vehicle is established based on Excel, traversal is completed on the detection scheme according to items based on Python, and visual identity information of corresponding items is obtained, wherein the visual identity information comprises visual identity vehicle types and visual identity reports;

step 4, the Python judges whether the apparent reports obtained in the step 3 exist in the database established by the MySQL in the step 2, and if so, the next step is directly carried out; if the obtained looking at report is not received by the database built by MySQL, inputting the corresponding looking at information and corresponding looking at parameters of the vehicle in Python, storing the looking at information and the corresponding looking at parameters into the MySQL database, and then entering the next step;

step 5, according to the detection category determined in the step 3, according to the determination result in the step 4, converting the vision-identical condition determination criterion formulated by the traffic department into a logic statement executable by a program in Python, automatically checking and comparing the record parameters acquired in the step 1 with the vision-identical parameters in the MySQL database determined in the step 4 based on Python, determining whether the project meets the vision-identical, recording the checking and comparing result, storing the checking and comparing result in Word documents, and finding out the minimum actual measurement times and the corresponding actual measurement configuration information required by vehicle inspection aiming at the project with actual measurement;

the step 4 specifically includes:

step 4.1, judging whether the looking at the same report exists in the MySQL database of the step 2 or not based on the looking at the same report number obtained in the step 3, and calling corresponding looking at parameters by Python if the looking at the same report number exists;

step 4.2, if the apparent identity report number is not recorded in the MySQL database, creating a Web application program by Python, entering apparent identity report parameters in the form of a webpage, entering, completing the interactive connection established by the step 2, storing information of the apparent identity report in the MySQL database, and calling the corresponding apparent identity parameters for subsequent use;

the step 5 specifically includes:

step 5.1, if the same inspection category appears in the step 3, comparing according to the same condition judgment criterion of the step 5; if the actually measured inspection category appears in the step 3, only a declaration value exists at the moment; if the actually measured and the same inspection category simultaneously appear in the step 3, firstly judging the same condition, removing all configurations meeting the same condition after judging the same condition, and then carrying out actually measured judgment on the rest configurations to finally obtain a complete scheme;

and 5.2, synchronously recording the result of checking the detection scheme of the step 5.1 in a Word file in the Python, and naming the Word file with the application number and the vehicle model.

2. The method for automatically identifying and judging the safety standard vehicle type detection project according to claim 1, wherein the step 1 of obtaining the record information of the enterprise declaration operation vehicle is realized by using a network packet capturing tool, and specifically comprises the following steps:

step 1.1, according to an application number provided by an enterprise, opening a recording information webpage corresponding to the application number, inputting a webpage analysis code in a network packet capturing tool, and obtaining recording information corresponding to a reporting vehicle type;

and 1.2, storing the record information corresponding to the declared model in a Json format, and providing a Python program for calling.

3. The automatic vision-co-identification method for a safety-compliant vehicle type detection item according to claim 2, wherein the record information of step 1.1 includes: all parameters related to the condition-based decisions such as vehicle size information, chassis information, mass information, brake information, suspension information, centroid information, and component information.

4. The automatic vision identity determination method for the safety standard vehicle type detection project according to claim 1, wherein in the step 2, interaction between MySQL and Python is established for realizing real-time calling of vision identity parameters of a MySQL database by Python, and static input of vision identity information lacking in the MySQL database by Python is realized.

5. The method for automatically identifying and judging the safety standard vehicle type detection project according to claim 1, wherein in the step 3, the Excel scheme information is traversed according to the project based on the Python, and the report type corresponding to each detection project is obtained, wherein the report type comprises three types of actual measurement, vision and vision, namely actual measurement times, vision and vision corresponding to actual measurement, vision and vision corresponding to vision and vision type and vision report number.

6. The automatic vision-co-judging method for safety standard reaching vehicle type detection projects according to claim 1, wherein the execution of the logic statement converted into program executable in the step 5 is based on the specific parameters of the vision-co-report in the step 3, and the specific parameters of the vision-co-report in the step 3 correspond to the parameters in the record information corresponding to the vehicle type declared in the step 1;

and (3) the information parameter of the enterprise reporting record in the step (1) is a reporting value, and the information of the MySQL database judged in the step (4) is a vision value.

7. The automatic vision-co-identification method for safety standard vehicle type detection items according to claim 1, wherein the vision-co-identification condition judgment criteria in step 5 include three types:

the first category is that the requirement declaration value is the same as or equal to the apparent value; the second type is to require the declared value to be increased or decreased relative to the apparent value; the third class is to require that the declared value be within a certain range of variation of the apparent value.

8. The automatic vision-co-determination method for a safety standard vehicle type detection item according to claim 1, wherein when a plurality of configurations exist in a certain reporting parameter, the number of tests of the detection item may be greater than 1, python traverses the plurality of configured parameters;

when traversing to a certain configuration, taking the configuration as a declaration value, taking the rest configurations as apparent values, judging according to the step 5.1, and when all configuration traversal is finished, finding out the test scheme with the least comprehensive actual measurement times as a final actual measurement scheme based on a judging result.

Images