CN116912975A - Automobile data recorder lens utilization efficiency evaluation system based on artificial intelligence - Google Patents
Automobile data recorder lens utilization efficiency evaluation system based on artificial intelligence Download PDFInfo
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Abstract
The application belongs to the field of lens use efficiency evaluation, relates to a data analysis technology, and aims to solve the problem that an existing lens use efficiency evaluation system adopts the same standard for efficiency evaluation aiming at different types of faults, so that the accuracy of an evaluation result is low; the lens detection module is used for detecting and analyzing the lens use state of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; the application can detect and analyze the use state of the lens of the automobile data recorder, and can timely perform early warning and feedback when the lens is abnormal in operation after decomposing the automobile data recorder.
Description
Technical Field
The application belongs to the field of lens use efficiency evaluation, relates to a data analysis technology, and particularly relates to an artificial intelligence-based automobile data recorder lens use efficiency evaluation system.
Background
The automobile data recorder is an instrument for recording related information such as images, sounds and the like during the running of the automobile; after the automobile data recorder is installed, video images and sound of the whole running process of the automobile can be recorded, and evidence can be provided for traffic accidents; people who like self-driving tour can also record the process of conquering difficult dangerous resistance, record the video while walking when driving, and record the time, speed and position in the video.
The existing automobile data recorder lens use efficiency evaluation system can evaluate the use efficiency of the lens only by combining the use time when the lens is abnormal, but a plurality of types of faults exist in abnormal operation of the lens, and the efficiency evaluation is performed by adopting the same standard aiming at different types of faults, so that the accuracy of an evaluation result is low, and the evaluation result is not persuasive.
The application provides a solution to the technical problem.
Disclosure of Invention
The application aims to provide an artificial intelligence-based automobile data recorder lens use efficiency evaluation system, which is used for solving the problem that the accuracy of an evaluation result is low because the existing lens use efficiency evaluation system adopts the same standard for efficiency evaluation aiming at different types of faults;
the technical problems to be solved by the application are as follows: how to provide an artificial intelligence based automobile data recorder lens use efficiency evaluation system which can evaluate the efficiency of different types of faults by adopting corresponding standards.
The aim of the application can be achieved by the following technical scheme:
the automobile data recorder lens use efficiency evaluation system based on artificial intelligence comprises an efficiency evaluation platform which is in communication connection with a lens detection module, a feature analysis module, an efficiency evaluation module and a storage module;
the lens detection module is used for detecting and analyzing the use state of the lens of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; amplifying an analysis image into a pixel grid image, carrying out gray level transformation, dividing the analysis image into a plurality of analysis areas, numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas, and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient; when the number of the analysis areas is not zero, judging that the using state of the lens of the automobile data recorder does not meet the requirement, generating a characteristic analysis signal and sending the characteristic analysis signal to an efficiency evaluation platform, and sending the characteristic analysis signal to a characteristic analysis module after the efficiency evaluation platform receives the characteristic analysis signal;
the characteristic analysis module is used for detecting and analyzing the abnormal characteristics of the lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, sending the abnormal characteristics of the lens of the automobile data recorder to the efficiency evaluation platform, and sending the abnormal characteristics of the lens to the efficiency evaluation module after the abnormal characteristics of the lens are received by the efficiency evaluation platform;
the efficiency evaluation module is used for evaluating and analyzing the lens use efficiency of the automobile data recorder.
As a preferred embodiment of the present application, the process of acquiring the coincidence coefficient of the analysis region includes: the gray values of the pixel grids in the analysis area are summed and averaged to obtain the gray average value of the analysis area, a gray average set is formed by all the gray average values of all the analysis images in the same analysis area, and variance calculation is carried out on the gray average set to obtain the coincidence coefficient of the analysis area.
As a preferred embodiment of the present application, the specific process of marking the analysis region as a normal region or an abnormal region includes: acquiring a coincidence threshold value through a storage module, and comparing the coincidence coefficient with the coincidence threshold value: if the coincidence coefficient is smaller than the coincidence threshold, judging that the lens state of the analysis area does not meet the requirement, and marking the corresponding analysis area as an abnormal area; if the coincidence coefficient is larger than or equal to the coincidence threshold, judging that the lens state of the analysis area meets the requirement, and marking the corresponding analysis area as a normal area; and when the number of the analysis areas is zero, judging that the using state of the lens of the automobile data recorder meets the requirement.
As a preferred embodiment of the application, the specific process of the feature analysis module for detecting and analyzing the abnormal features of the lens of the automobile data recorder comprises the following steps: marking the number of abnormal areas in the detection period as an abnormal sum value, marking the number of abnormal areas in the detection period, which are in contact with each other, as a connection value, marking the ratio of the connection value to the abnormal sum value as an abnormal value, acquiring an abnormal threshold value through a storage module, and comparing the abnormal value with the abnormal threshold value: if the abnormal value is smaller than the abnormal threshold value, marking the abnormal characteristic of the lens of the automobile data recorder as abrasion; and if the abnormal value is greater than or equal to the abnormal threshold value, performing mildew analysis.
As a preferred embodiment of the application, the specific process of mildew analysis comprises: gray values of the same pixel grid in all analysis images in the abnormal region form a gray set of the pixel grid, variance calculation is carried out on the gray set to obtain gray difference values of the pixel grid, a gray difference threshold value is obtained through a storage module, and the gray difference values are compared with the gray difference threshold value: if the gray difference value is smaller than the gray difference threshold value, marking the corresponding pixel grid as an abnormal grid; if the gray difference value is greater than or equal to the gray difference threshold value, marking the corresponding pixel grid as a normal grid; marking the ratio of the number and the value of the abnormal cells to the number and the value of the pixel cells in all abnormal areas as mildew coefficients, acquiring mildew thresholds through a storage module, and comparing the mildew coefficients with the mildew thresholds: if the mildew coefficient is smaller than the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as a crack; if the mildew coefficient is greater than or equal to the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as mildew; the method comprises the steps that abnormal lens characteristics of the automobile data recorder are sent to an efficiency evaluation platform, and the efficiency evaluation platform sends the abnormal lens characteristics to an efficiency evaluation module after receiving the abnormal lens characteristics.
As a preferred embodiment of the present application, the specific process of the efficiency evaluation module for evaluating and analyzing the lens usage efficiency of the automobile data recorder includes: marking the difference value between the moment when the efficiency evaluation module receives the abnormal characteristics of the lens and the moment when the lens comes out of the field as a using time length SS, marking the accumulated working time length of the automobile data recorder as an accumulated time length LS, and obtaining an efficiency coefficient XL of the lens of the automobile data recorder through a formula XL= (alpha 1 x SS+alpha 2 x LS)/(alpha 3 x TZ), wherein alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; TZ is a characteristic value which is called according to abnormal characteristics of the lens, and the magnitude relation of the characteristic value is as follows: mildew > wear > crack; the method comprises the steps of obtaining an efficiency threshold XLmin through a storage module, comparing an efficiency coefficient XL of a lens of the automobile data recorder with the efficiency threshold XLmin, and judging whether the use efficiency of the lens of the automobile data recorder meets the requirement or not according to a comparison result.
As a preferred embodiment of the present application, the specific process of comparing the efficiency coefficient XL of the vehicle recorder lens with the efficiency threshold XLmin includes: if the efficiency coefficient XL is smaller than the efficiency threshold XLmin, judging that the lens use efficiency of the automobile data recorder does not meet the requirement, generating a lens efficiency abnormal signal and sending the lens efficiency abnormal signal to a mobile phone terminal of a manager through an efficiency evaluation platform; if the efficiency coefficient XL is larger than or equal to the efficiency threshold XLmin, judging that the lens use efficiency of the automobile data recorder meets the requirement, generating a lens replacement signal and sending the lens replacement signal to a mobile phone terminal of a manager through an efficiency evaluation platform.
As a preferred embodiment of the present application, the working method of the artificial intelligence-based vehicle recorder lens use efficiency evaluation system includes the following steps:
step one: detecting and analyzing the using state of the lens of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; dividing an analysis image into a plurality of analysis areas and numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient;
step two: detecting and analyzing abnormal characteristics of a lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, and sending the abnormal characteristics of the lens to an efficiency evaluation module through an efficiency evaluation platform;
step three: and (3) evaluating and analyzing the lens use efficiency of the automobile data recorder, acquiring a use time length SS and an accumulated time length LS, acquiring a characteristic value TZ according to abnormal characteristics of the lens, calculating the use time length SS, the accumulated time length LS and the characteristic value TZ to obtain an efficiency coefficient XL, and judging whether the lens use efficiency of the automobile data recorder meets the requirement or not through the efficiency coefficient XL.
The application has the following beneficial effects:
1. the lens use state of the automobile data recorder can be detected and analyzed through the lens detection module, and after the automobile data record is decomposed, the use state of the lens is estimated by carrying out area comparison on a plurality of analysis images which are randomly extracted, so that early warning and feedback are timely carried out when the lens is abnormal in operation;
2. the abnormal characteristics of the lens of the automobile data recorder can be detected and analyzed through the characteristic analysis module, the abnormal characteristics of the lens are marked through the analysis of the data characteristics of the abnormal region, the lens use efficiency is estimated through the marking result by adopting the corresponding estimation standard, and the accuracy of the efficiency estimation result is improved;
3. the efficiency evaluation module can evaluate and analyze the lens use efficiency of the automobile data recorder, comprehensively analyze and calculate the lens use duration and abnormal characteristics to obtain an efficiency coefficient, and feed back the lens use efficiency through the efficiency coefficient.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present application;
fig. 2 is a flowchart of a method according to a second embodiment of the application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in fig. 1, the system for evaluating the usage efficiency of the lens of the automobile data recorder based on artificial intelligence comprises an efficiency evaluation platform, wherein the efficiency evaluation platform is in communication connection with a lens detection module, a feature analysis module, an efficiency evaluation module and a storage module.
The lens detection module is used for detecting and analyzing the lens use state of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; amplifying an analysis image into a pixel grid image, carrying out gray level conversion, dividing the analysis image into a plurality of analysis areas, numbering, summing gray level values of the pixel grids in the analysis areas, averaging to obtain a gray level average value of the analysis areas, forming a gray level average set by all gray level average values of all the analysis images in the same analysis area, carrying out variance calculation on the gray level average set to obtain a superposition coefficient of the analysis areas, obtaining a superposition threshold value through a storage module, and comparing the superposition coefficient with the superposition threshold value: if the coincidence coefficient is smaller than the coincidence threshold, judging that the lens state of the analysis area does not meet the requirement, and marking the corresponding analysis area as an abnormal area; if the coincidence coefficient is larger than or equal to the coincidence threshold, judging that the lens state of the analysis area meets the requirement, and marking the corresponding analysis area as a normal area; when the number of the analysis areas is zero, judging that the using state of the lens of the automobile data recorder meets the requirement; when the number of the analysis areas is not zero, judging that the using state of the lens of the automobile data recorder does not meet the requirement, generating a characteristic analysis signal and sending the characteristic analysis signal to an efficiency evaluation platform, and sending the characteristic analysis signal to a characteristic analysis module after the efficiency evaluation platform receives the characteristic analysis signal; the method comprises the steps of detecting and analyzing the using state of a lens of a vehicle recorder, and comparing areas of a plurality of analysis images extracted randomly after decomposing the vehicle video to evaluate the using state of the lens, so that early warning and feedback are timely carried out when the lens is abnormal in operation.
The characteristic analysis module is used for detecting and analyzing abnormal characteristics of a lens of the automobile data recorder: marking the number of abnormal areas in the detection period as an abnormal sum value, marking the number of abnormal areas in the detection period, which are in contact with each other, as a connection value, marking the ratio of the connection value to the abnormal sum value as an abnormal value, acquiring an abnormal threshold value through a storage module, and comparing the abnormal value with the abnormal threshold value: if the abnormal value is smaller than the abnormal threshold value, marking the abnormal characteristic of the lens of the automobile data recorder as abrasion; if the abnormal value is greater than or equal to the abnormal threshold value, performing mildew analysis: gray values of the same pixel grid in all analysis images in the abnormal region form a gray set of the pixel grid, variance calculation is carried out on the gray set to obtain gray difference values of the pixel grid, a gray difference threshold value is obtained through a storage module, and the gray difference values are compared with the gray difference threshold value: if the gray difference value is smaller than the gray difference threshold value, marking the corresponding pixel grid as an abnormal grid; if the gray difference value is greater than or equal to the gray difference threshold value, marking the corresponding pixel grid as a normal grid; marking the ratio of the number and the value of the abnormal cells to the number and the value of the pixel cells in all abnormal areas as mildew coefficients, acquiring mildew thresholds through a storage module, and comparing the mildew coefficients with the mildew thresholds: if the mildew coefficient is smaller than the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as a crack; if the mildew coefficient is greater than or equal to the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as mildew; the method comprises the steps that lens abnormal characteristics of an automobile data recorder are sent to an efficiency evaluation platform, and the efficiency evaluation platform sends the lens abnormal characteristics to an efficiency evaluation module after receiving the lens abnormal characteristics; detecting and analyzing abnormal characteristics of a lens of the automobile data recorder, marking the abnormal characteristics of the lens by analyzing the data characteristics of the abnormal region, and evaluating the use efficiency of the lens by adopting corresponding evaluation standards according to marking results, so that the accuracy of the efficiency evaluation results is improved.
The efficiency evaluation module is used for evaluating and analyzing the lens use efficiency of the automobile data recorder: marking the difference value between the moment when the efficiency evaluation module receives the abnormal characteristics of the lens and the moment when the lens comes out of the field as a using time length SS, marking the accumulated working time length of the automobile data recorder as an accumulated time length LS, and obtaining an efficiency coefficient XL of the lens of the automobile data recorder through a formula XL= (alpha 1 x SS+alpha 2 x LS)/(alpha 3 x TZ), wherein the efficiency coefficient is a numerical value reflecting the using efficiency degree of the lens of the automobile data recorder, and the larger the numerical value of the efficiency coefficient is, the higher the using efficiency of the lens of the automobile data recorder is; wherein, alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 > alpha 2 > alpha 3 > 1; TZ is a characteristic value which is called according to abnormal characteristics of the lens, and the magnitude relation of the characteristic value is as follows: mildew > wear > crack; the method comprises the steps that an efficiency threshold XLmin is obtained through a storage module, and an efficiency coefficient XL of a vehicle event data recorder lens is compared with the efficiency threshold XLmin: if the efficiency coefficient XL is smaller than the efficiency threshold XLmin, judging that the lens use efficiency of the automobile data recorder does not meet the requirement, generating a lens efficiency abnormal signal and sending the lens efficiency abnormal signal to a mobile phone terminal of a manager through an efficiency evaluation platform; if the efficiency coefficient XL is larger than or equal to an efficiency threshold XLmin, judging that the using efficiency of the lens of the automobile data recorder meets the requirement, generating a lens replacement signal and sending the lens replacement signal to a mobile phone terminal of a manager through an efficiency evaluation platform; and (3) evaluating and analyzing the lens use efficiency of the automobile data recorder, comprehensively analyzing and calculating by combining the lens use duration and the abnormal characteristics to obtain an efficiency coefficient, and feeding back the lens use efficiency through the efficiency coefficient.
Example two
As shown in fig. 2, the method for evaluating the usage efficiency of the automobile data recorder lens based on artificial intelligence comprises the following steps:
step one: detecting and analyzing the using state of the lens of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; dividing an analysis image into a plurality of analysis areas and numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient;
step two: detecting and analyzing abnormal characteristics of a lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, and sending the abnormal characteristics of the lens to an efficiency evaluation module through an efficiency evaluation platform;
step three: and (3) evaluating and analyzing the lens use efficiency of the automobile data recorder, acquiring a use time length SS and an accumulated time length LS, acquiring a characteristic value TZ according to abnormal characteristics of the lens, calculating the use time length SS, the accumulated time length LS and the characteristic value TZ to obtain an efficiency coefficient XL, and judging whether the lens use efficiency of the automobile data recorder meets the requirement or not through the efficiency coefficient XL.
The system for evaluating the service efficiency of the lens of the automobile data recorder based on artificial intelligence generates a detection period when in operation, decomposes an automobile data record shot by the automobile data recorder in the detection period into automobile data images and randomly extracts a plurality of automobile data image marks as analysis images; dividing an analysis image into a plurality of analysis areas and numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient; detecting and analyzing abnormal characteristics of a lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, and sending the abnormal characteristics of the lens to an efficiency evaluation module through an efficiency evaluation platform; and (3) evaluating and analyzing the lens use efficiency of the automobile data recorder, acquiring a use time length SS and an accumulated time length LS, acquiring a characteristic value TZ according to abnormal characteristics of the lens, calculating the use time length SS, the accumulated time length LS and the characteristic value TZ to obtain an efficiency coefficient XL, and judging whether the lens use efficiency of the automobile data recorder meets the requirement or not through the efficiency coefficient XL.
The foregoing is merely illustrative of the structures of this application and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the application or from the scope of the application as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula xl= (α1ss+α2ls)/(α3tz); collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding efficiency coefficient for each group of sample data; substituting the set efficiency coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are 3.65, 2.84 and 2.23 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding efficiency coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the efficiency coefficient is proportional to the value of the time length.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The automobile data recorder lens use efficiency evaluation system based on artificial intelligence is characterized by comprising an efficiency evaluation platform, wherein the efficiency evaluation platform is in communication connection with a lens detection module, a feature analysis module, an efficiency evaluation module and a storage module;
the lens detection module is used for detecting and analyzing the use state of the lens of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; amplifying an analysis image into a pixel grid image, carrying out gray level transformation, dividing the analysis image into a plurality of analysis areas, numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas, and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient; when the number of the analysis areas is not zero, judging that the using state of the lens of the automobile data recorder does not meet the requirement, generating a characteristic analysis signal and sending the characteristic analysis signal to an efficiency evaluation platform, and sending the characteristic analysis signal to a characteristic analysis module after the efficiency evaluation platform receives the characteristic analysis signal;
the characteristic analysis module is used for detecting and analyzing the abnormal characteristics of the lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, sending the abnormal characteristics of the lens of the automobile data recorder to the efficiency evaluation platform, and sending the abnormal characteristics of the lens to the efficiency evaluation module after the abnormal characteristics of the lens are received by the efficiency evaluation platform;
the efficiency evaluation module is used for evaluating and analyzing the lens use efficiency of the automobile data recorder.
2. The system for evaluating the usage efficiency of an artificial intelligence based vehicle recorder lens according to claim 1, wherein the process of obtaining the coincidence factor of the analysis area comprises: the gray values of the pixel grids in the analysis area are summed and averaged to obtain the gray average value of the analysis area, a gray average set is formed by all the gray average values of all the analysis images in the same analysis area, and variance calculation is carried out on the gray average set to obtain the coincidence coefficient of the analysis area.
3. The system for evaluating the usage efficiency of an artificial intelligence based vehicle recorder lens according to claim 2, wherein the specific process of marking the analysis area as a normal area or an abnormal area comprises: acquiring a coincidence threshold value through a storage module, and comparing the coincidence coefficient with the coincidence threshold value: if the coincidence coefficient is smaller than the coincidence threshold, judging that the lens state of the analysis area does not meet the requirement, and marking the corresponding analysis area as an abnormal area; if the coincidence coefficient is larger than or equal to the coincidence threshold, judging that the lens state of the analysis area meets the requirement, and marking the corresponding analysis area as a normal area; and when the number of the analysis areas is zero, judging that the using state of the lens of the automobile data recorder meets the requirement.
4. The system for evaluating the lens use efficiency of the automobile data recorder based on artificial intelligence according to claim 3, wherein the specific process of detecting and analyzing the abnormal characteristics of the lens of the automobile data recorder by the characteristic analysis module comprises the following steps: marking the number of abnormal areas in the detection period as an abnormal sum value, marking the number of abnormal areas in the detection period, which are in contact with each other, as a connection value, marking the ratio of the connection value to the abnormal sum value as an abnormal value, acquiring an abnormal threshold value through a storage module, and comparing the abnormal value with the abnormal threshold value: if the abnormal value is smaller than the abnormal threshold value, marking the abnormal characteristic of the lens of the automobile data recorder as abrasion; and if the abnormal value is greater than or equal to the abnormal threshold value, performing mildew analysis.
5. The system for evaluating the use efficiency of the automobile data recorder lens based on the artificial intelligence according to claim 4, wherein the specific process of mildew analysis comprises the following steps: gray values of the same pixel grid in all analysis images in the abnormal region form a gray set of the pixel grid, variance calculation is carried out on the gray set to obtain gray difference values of the pixel grid, a gray difference threshold value is obtained through a storage module, and the gray difference values are compared with the gray difference threshold value:
if the gray difference value is smaller than the gray difference threshold value, marking the corresponding pixel grid as an abnormal grid;
if the gray difference value is greater than or equal to the gray difference threshold value, marking the corresponding pixel grid as a normal grid; marking the ratio of the number and the value of the abnormal cells to the number and the value of the pixel cells in all abnormal areas as mildew coefficients, acquiring mildew thresholds through a storage module, and comparing the mildew coefficients with the mildew thresholds:
if the mildew coefficient is smaller than the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as a crack;
if the mildew coefficient is greater than or equal to the mildew threshold, marking the abnormal lens characteristic of the automobile data recorder as mildew; the method comprises the steps that abnormal lens characteristics of the automobile data recorder are sent to an efficiency evaluation platform, and the efficiency evaluation platform sends the abnormal lens characteristics to an efficiency evaluation module after receiving the abnormal lens characteristics.
6. The system for evaluating the lens use efficiency of the automobile data recorder based on artificial intelligence according to claim 5, wherein the specific process of evaluating and analyzing the lens use efficiency of the automobile data recorder by the efficiency evaluation module comprises: marking the difference value between the moment when the efficiency evaluation module receives the abnormal characteristics of the lens and the moment when the lens comes out of the field as a using time length SS, marking the accumulated working time length of the automobile data recorder as an accumulated time length LS, and obtaining an efficiency coefficient XL of the lens of the automobile data recorder through a formula XL= (alpha 1 x SS+alpha 2 x LS)/(alpha 3 x TZ), wherein alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; TZ is a characteristic value which is called according to abnormal characteristics of the lens, and the magnitude relation of the characteristic value is as follows: mildew > wear > crack; the method comprises the steps of obtaining an efficiency threshold XLmin through a storage module, comparing an efficiency coefficient XL of a lens of the automobile data recorder with the efficiency threshold XLmin, and judging whether the use efficiency of the lens of the automobile data recorder meets the requirement or not according to a comparison result.
7. The system for evaluating the usage efficiency of a vehicle recorder lens based on artificial intelligence according to claim 6, wherein the specific process of comparing the efficiency coefficient XL of the vehicle recorder lens with the efficiency threshold XLmin comprises:
if the efficiency coefficient XL is smaller than the efficiency threshold XLmin, judging that the lens use efficiency of the automobile data recorder does not meet the requirement, generating a lens efficiency abnormal signal and sending the lens efficiency abnormal signal to a mobile phone terminal of a manager through an efficiency evaluation platform;
if the efficiency coefficient XL is larger than or equal to the efficiency threshold XLmin, judging that the lens use efficiency of the automobile data recorder meets the requirement, generating a lens replacement signal and sending the lens replacement signal to a mobile phone terminal of a manager through an efficiency evaluation platform.
8. The system for evaluating the usage efficiency of an artificial intelligence-based vehicle recorder lens according to any one of claims 1 to 7, wherein the working method of the system for evaluating the usage efficiency of an artificial intelligence-based vehicle recorder lens comprises the steps of:
step one: detecting and analyzing the using state of the lens of the automobile data recorder: generating a detection period, decomposing a driving video shot by a driving recorder in the detection period into driving images, and randomly extracting a plurality of driving image marks as analysis images; dividing an analysis image into a plurality of analysis areas and numbering the analysis areas, obtaining the coincidence coefficient of the analysis areas and marking the analysis areas as normal areas or abnormal areas through the coincidence coefficient;
step two: detecting and analyzing abnormal characteristics of a lens of the automobile data recorder, marking the abnormal characteristics of the lens as mildewing, wearing or cracking, and sending the abnormal characteristics of the lens to an efficiency evaluation module through an efficiency evaluation platform;
step three: and (3) evaluating and analyzing the lens use efficiency of the automobile data recorder, acquiring a use time length SS and an accumulated time length LS, acquiring a characteristic value TZ according to abnormal characteristics of the lens, calculating the use time length SS, the accumulated time length LS and the characteristic value TZ to obtain an efficiency coefficient XL, and judging whether the lens use efficiency of the automobile data recorder meets the requirement or not through the efficiency coefficient XL.
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| CN202311041358.4A Pending CN116912975A (en) | 2023-09-12 | 2023-09-12 | Automobile data recorder lens utilization efficiency evaluation system based on artificial intelligence |
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| CN (1) | CN116912975A (en) |
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