CN112699827A - Traffic police affair handling method and system based on block chain - Google Patents

Traffic police affair handling method and system based on block chain Download PDF

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CN112699827A
CN112699827A CN202110010043.8A CN202110010043A CN112699827A CN 112699827 A CN112699827 A CN 112699827A CN 202110010043 A CN202110010043 A CN 202110010043A CN 112699827 A CN112699827 A CN 112699827A
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林文国
黄河
王伟宗
戴文艳
张涛
潘德生
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Evecom Information Technology Development Co ltd
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Abstract

One aspect of the present invention provides a traffic police service handling method based on a block chain, which includes S1, acquiring a driving image of a vehicle and a face image of a person driving the vehicle; s2, inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors, and if so, generating violation behavior information according to the driving image and the face image; and S3, sending the violation information to an audit terminal for manual audit, and receiving an audit result fed back by the audit terminal S4, if the violation information is correct, inputting the violation information into a block chain node for storage, and if the violation information is incorrect, inputting the manual audit result into the block chain node for storage. The invention also provides a traffic police service disposal system based on the block chain, which is used for realizing the method.

Description

Traffic police affair handling method and system based on block chain
Technical Field
The invention relates to the field of traffic police service disposal, in particular to a traffic police service disposal method and system based on a block chain.
Background
In the prior art, for traffic violation, whether the traffic violation occurs is generally judged by taking a snapshot through an electronic camera, but the current image identification is easily affected by weather to cause misjudgment. And the misjudged owner needs to waste working day time for carrying out violation processing, and the method is not humanized. Therefore, we need to reduce the probability of false positives.
Disclosure of Invention
In view of the foregoing problems, an object of the present invention is to provide a traffic police service handling method and system based on a block chain.
The invention provides a traffic police service disposal method based on a block chain, which comprises the following steps:
s1, acquiring a driving image of a vehicle and a face image of a person driving the vehicle;
s2, inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors, and if so, generating violation behavior information according to the driving image and the face image;
s3, sending the violation information to an audit terminal for manual audit, and receiving an audit result fed back by the audit terminal, wherein the audit result comprises whether the violation information is correct and a manual audit result;
and S4, if the violation information is correct, inputting the violation information into a block chain node for storage, and if the violation information is incorrect, inputting the manual review result into the block chain node for storage.
Preferably, the manual audit result comprises the type of the violation and the occurrence time of the violation.
Preferably, the violation information includes a violation vehicle license plate number, a violation type, and the driving image.
Preferably, a high speed camera is used to acquire an image of the face of the person driving the vehicle.
Preferably, the inputting the driving image into a pre-trained neural network model for violation identification includes:
carrying out illumination adjustment processing on the driving image to obtain an illumination adjustment image;
carrying out graying processing on the illumination adjustment image to obtain a grayscale image;
carrying out noise reduction processing on the grayed image to obtain a noise reduction image;
and inputting the noise reduction image into a pre-trained neural network model for violation identification.
Preferably, the performing illumination adjustment processing on the driving image to obtain an illumination adjustment image includes:
s11, judging the processing type of the pixel point which is currently subjected to illumination adjustment;
s12, selecting a corresponding illumination adjusting function to perform illumination adjusting processing on the pixel points according to the processing type;
and S13, performing S11 and S12 processing on all pixel points in the driving image to obtain an illumination adjusting image.
The invention provides a traffic police service treatment system based on a block chain, which comprises a first processing module, a second processing module, a third processing module and a fourth processing module;
the first processing module is used for acquiring a running image of a vehicle and a face image of a person driving the vehicle;
the second processing module is used for inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors or not, and if so, generating violation behavior information according to the driving image and the face image;
the third processing module is used for sending the violation information to an auditing terminal for manual auditing and receiving an auditing result fed back by the auditing terminal, wherein the auditing result comprises whether the violation information is correct and a manual auditing result;
the fourth processing module is used for inputting the violation information into the block chain node for storage when the violation information is correct, and inputting the manual checking result into the block chain node for storage when the violation information is incorrect.
Compared with the prior art, the invention has the advantages that:
when the violation judgment is carried out, the driving image and the face image of the vehicle are obtained at the same time, so that the condition that the driver uses the driving license of other people to process the violation of the vehicle when the vehicle breaks rules can be avoided. Because of the existing image recognition, misjudgment is sometimes caused due to weather reasons. Therefore, after the neural network model is used for preliminarily judging the vehicle violation type, the vehicle violation type is sent to the auditor for auditing, the probability of misjudgment of the violation behavior can be greatly reduced, the phenomenon that an innocent owner wastes time to find out the traffic police to cancel misjudgment of the violation, and the auditor only needs to check whether the violation type is correct due to the fact that the violation behavior information is the type of the prior violation, and the auditor does not need to manually judge the violation type existing in the driving image is avoided, so that the auditing time can be greatly shortened, and the auditing efficiency is accelerated.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of a block chain-based traffic police handling method according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In one aspect, the present invention provides a method for handling traffic police service based on block chains, as shown in fig. 1, which includes:
the invention provides a traffic police service disposal method based on a block chain, which comprises the following steps:
s1, acquiring a driving image of a vehicle and a face image of a person driving the vehicle;
s2, inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors, and if so, generating violation behavior information according to the driving image and the face image;
s3, sending the violation information to an audit terminal for manual audit, and receiving an audit result fed back by the audit terminal, wherein the audit result comprises whether the violation information is correct and a manual audit result;
and S4, if the violation information is correct, inputting the violation information into a block chain node for storage, and if the violation information is incorrect, inputting the manual review result into the block chain node for storage.
Preferably, the manual audit result comprises the type of the violation and the occurrence time of the violation.
Preferably, the violation information includes a violation vehicle license plate number, a violation type, and the driving image.
Preferably, a high speed camera is used to acquire an image of the face of the person driving the vehicle.
Preferably, the inputting the driving image into a pre-trained neural network model for violation identification includes:
carrying out illumination adjustment processing on the driving image to obtain an illumination adjustment image;
carrying out graying processing on the illumination adjustment image to obtain a grayscale image;
carrying out noise reduction processing on the grayed image to obtain a noise reduction image;
and inputting the noise reduction image into a pre-trained neural network model for violation identification.
Preferably, the performing illumination adjustment processing on the driving image to obtain an illumination adjustment image includes:
s11, judging the processing type of the pixel point which is currently subjected to illumination adjustment;
s12, selecting corresponding illumination adjustment parameters to perform illumination adjustment processing on the pixel points according to the processing types;
and S13, performing S11 and S12 processing on all pixel points in the driving image to obtain an illumination adjusting image.
Preferably, the determining, for the pixel currently being subjected to illumination adjustment, the processing type to which the pixel belongs includes:
calculating the classification index of the pixel points;
if the classification index is larger than 0, the pixel point belongs to a first processing type;
if the classification index is less than or equal to 0, the pixel point belongs to a second processing type;
the classification index is calculated as follows:
Figure BDA0002884773200000031
wherein clusti represents a classification index of the pixel point, gbma represents a maximum value of an L component of the driving image in the Lab color space, gbmi represents a minimum value of the L component of the driving image in the Lab color space, gama and gami respectively represent a maximum value and a minimum value of an L component of a filtered image obtained by performing gaussian filtering on the driving image in the Lab color space, gpb represents a pixel value of the L component of the pixel point in the Lab color space corresponding to the driving image, and gpa represents a pixel value of the L component of the pixel point in the Lab color space corresponding to the filtered image; gw denotes an illumination adjustment coefficient, gw ∈ [0.34,0.7], and w denotes a median value of an L component of the travel image in the Lab color space.
In the above embodiment of the present invention, when performing illumination adjustment, the processing type of the pixel currently being processed is determined, and then the corresponding illumination adjustment function is selected according to the processing type for processing, so that the illumination adjustment processing for the pixel has higher pertinence, thereby avoiding the problem of overexposure easily occurring in the conventional global illumination adjustment. Specifically, when the classification index is calculated, parameters such as the maximum pixel value and the minimum pixel value of an image obtained by performing gaussian filtering on the driving image in the Lab color space, the maximum pixel value and the minimum pixel value of the driving image in the Lab color space, and the like are considered, so that the judgment dimension is reduced from the three-dimension of the RGB color space to one-dimension, and the judgment speed is effectively increased.
Preferably, the illumination adjustment parameters of the pixel points of the first processing type are as follows:
Figure BDA0002884773200000041
wherein oneg represents an illumination adjustment parameter of a pixel of the first processing type, j1 and j2 represent preset proportionality coefficients, the sum of j1 and j2 is 1, gpb (nei) represents the mean value of the pixel values of the L components of all pixels in the neighborhood of b × b size of the pixel in the Lab color space corresponding to the driving image, gpa (nei) represents the mean value of the pixel values of the L components of all pixels in the neighborhood of b × b size of the pixel in the Lab color space corresponding to the filtering image, onexs represents a correction parameter of the pixel of the first processing type, and a1 represents a preset weight parameter;
for the pixel points of the second processing type, the illumination adjusting parameters are as follows:
Figure BDA0002884773200000042
in the formula, j3 and j4 represent preset proportionality coefficients, the sum of j3 and j4 is 1, and twog represents the illumination adjustment parameter of the pixel point of the second processing type.
According to the embodiment of the invention, different illumination adjusting parameters are set for the pixel points of different processing types, so that the illumination adjustment of the pixel points is more targeted. Specifically, when the illumination adjustment parameters are set, not only are relevant parameters of the pixel points and neighborhood pixel points in a Lab color space corresponding to the driving image considered, but also relevant parameters of the pixel points and the neighborhood pixel points in the Lab color space corresponding to the filtering image considered, and parameters such as a correction coefficient, a proportionality coefficient and a weight coefficient are introduced, so that the illumination distribution of the processed image is more uniform, and the enhancement of the texture information of the image is facilitated.
Preferably, according to the processing type, selecting a corresponding illumination adjustment parameter to perform illumination adjustment processing on the pixel point, including:
recording the pixel value of the pixel point in the Lab color space corresponding to the driving image as f (t);
if the pixel point belongs to the first processing type, the illumination adjustment is as follows:
af(t)＝st[f(t)×oneg]
wherein, af (t) is the intermediate result of the illumination adjustment on f (t), st represents the limiting function, if f (t) Xoneg is greater than 100, the value of st [ f (t) Xoneg ] is 100, if f (t) Xoneg is less than or equal to 100, the value of st [ f (t) Xoneg ] is f (t) Xoneg;
converting af (t) from Lab color space to RGB color space, thereby obtaining a final processing result of the pixel point;
if the pixel point belongs to the second processing type, the illumination adjustment is as follows:
af(t)＝st[f(t)×twog]
wherein, af (t) is an intermediate result of the illumination adjustment on f (t), st represents a limiting function, if f (t) xxwog is greater than 100, the value of st [ f (t) xwog ] is 100, if f (t) xwog is less than or equal to 100, the value of st [ f (t) xwog ] is f (t) xoneg;
and converting the af (t) from the Lab color space to the RGB color space, thereby obtaining a final processing result of the pixel point.
Preferably, the performing noise reduction processing on the grayed image to obtain a noise-reduced image includes:
for pixel point q in the grayed image, the pixel points in the neighborhood of c × c size are stored in set neiq；
De-noising q using the following formula:
Figure BDA0002884773200000061
wherein af (q) represents the result of noise reduction processing on q, α1And alpha2As a weight parameter, α1+α21, f (r) denotes neiqThe pixel value of the pixel point r in the gray image, ph represents a Gaussian smoothing coefficient, and sigma represents neiqStandard deviation of pixel values of all elements in (1), gpqAnd gprRepresenting the gradient amplitude, gr, of q and r, respectivelyqRepresenting the pixel value of q in the greyscale image.
According to the embodiment of the invention, when noise is reduced, the corresponding weight parameter is obtained through calculation according to the relation between q and the neighborhood pixel point, and then nei is utilizedqThe weighted summation is carried out on the pixel points in the image, so that the result of carrying out noise reduction on the q is obtained, and the image can be subjected to weighted summation while the edge information of the image is effectively keptThe line noise reduction processing and the traditional processing modes such as mean value noise reduction are easy to cause loss of image edge information, and the loss degree of the edge information can be effectively reduced by the method, so that more effective information is reserved for the image. Specifically, when the weight is calculated, the relation of two pixel points on the pixel value is considered, the relation of the two pixel points on the gradient amplitude is also considered, and the gradient amplitude is closely related to the edge of the image, so that the edge information of the image can be better reserved.
The invention provides a traffic police service treatment system based on a block chain, which comprises a first processing module, a second processing module, a third processing module and a fourth processing module;
the first processing module is used for acquiring a running image of a vehicle and a face image of a person driving the vehicle;
the second processing module is used for inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors or not, and if so, generating violation behavior information according to the driving image and the face image;
the third processing module is used for sending the violation information to an auditing terminal for manual auditing and receiving an auditing result fed back by the auditing terminal, wherein the auditing result comprises whether the violation information is correct and a manual auditing result;
the fourth processing module is used for inputting the violation information into the block chain node for storage when the violation information is correct, and inputting the manual checking result into the block chain node for storage when the violation information is incorrect.
It should be noted that, the apparatus is used for implementing the functions of the method, and each module in the apparatus corresponds to the steps of the method, and can implement different embodiments of the method.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (8)

1. A traffic police treatment method based on a block chain is characterized by comprising the following steps:
s1, acquiring a driving image of a vehicle and a face image of a person driving the vehicle;
s2, inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors, and if so, generating violation behavior information according to the driving image and the face image;
s3, sending the violation information to an audit terminal for manual audit, and receiving an audit result fed back by the audit terminal, wherein the audit result comprises whether the violation information is correct and a manual audit result;
and S4, if the violation information is correct, inputting the violation information into a block chain node for storage, and if the violation information is incorrect, inputting the manual review result into the block chain node for storage.
2. The method of claim 1, wherein the manual review result includes a violation type and an occurrence time of the violation.
3. The blockchain-based traffic police treatment method of claim 1, wherein the violation information includes a violation vehicle license plate number, a violation type, and the driving image.
4. The method of claim 1, wherein a high speed camera is used to obtain the face image of the person driving the vehicle.
5. The traffic police handling method based on the block chain as claimed in claim 1, wherein the inputting the driving image into a pre-trained neural network model for violation identification comprises:
carrying out illumination adjustment processing on the driving image to obtain an illumination adjustment image;
carrying out graying processing on the illumination adjustment image to obtain a grayscale image;
carrying out noise reduction processing on the grayed image to obtain a noise reduction image;
and inputting the noise reduction image into a pre-trained neural network model for violation identification.
6. The method according to claim 5, wherein the performing illumination adjustment processing on the driving image to obtain an illumination adjustment image comprises:
s11, judging the processing type of the pixel point which is currently subjected to illumination adjustment;
s12, selecting a corresponding illumination adjusting function to perform illumination adjusting processing on the pixel points according to the processing type;
and S13, performing S11 and S12 processing on all pixel points in the driving image to obtain an illumination adjusting image.
7. The method as claimed in claim 6, wherein the determining the processing type of the pixel currently being subjected to illumination adjustment includes:
calculating the classification index of the pixel points;
if the classification index is larger than 0, the pixel point belongs to a first processing type;
if the classification index is less than or equal to 0, the pixel point belongs to a second processing type;
the classification index is calculated as follows:
Figure FDA0002884773190000021
wherein clusti represents a classification index of the pixel point, gbma represents a maximum value of an L component of the driving image in the Lab color space, gbmi represents a minimum value of the L component of the driving image in the Lab color space, gama and gami respectively represent a maximum value and a minimum value of an L component of a filtered image obtained by performing gaussian filtering on the driving image in the Lab color space, gpb represents a pixel value of the L component of the pixel point in the Lab color space corresponding to the driving image, and gpa represents a pixel value of the L component of the pixel point in the Lab color space corresponding to the filtered image; gw denotes an illumination adjustment coefficient, gw ∈ [0.34,0.7], and w denotes a median value of an L component of the travel image in the Lab color space.
8. A traffic police treatment system based on a block chain is characterized by comprising a first processing module, a second processing module, a third processing module and a fourth processing module;
the first processing module is used for acquiring a running image of a vehicle and a face image of a person driving the vehicle;
the second processing module is used for inputting the driving image into a pre-trained neural network model for carrying out violation identification, judging whether the vehicle has violation behaviors or not, and if so, generating violation behavior information according to the driving image and the face image;
the third processing module is used for sending the violation information to an auditing terminal for manual auditing and receiving an auditing result fed back by the auditing terminal, wherein the auditing result comprises whether the violation information is correct and a manual auditing result;
the fourth processing module is used for inputting the violation information into the block chain node for storage when the violation information is correct, and inputting the manual checking result into the block chain node for storage when the violation information is incorrect.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408396A (en) * 2021-06-15 2021-09-17 广西交科集团有限公司 Bridge intelligent sensing system based on cloud computing
CN113988991A (en) * 2021-11-16 2022-01-28 张雨钊 Digital e-commerce platform based on cloud computing

Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0329099A (en) * 1989-06-27 1991-02-07 Matsushita Electric Ind Co Ltd Automatic monitor system for illegal parking
CN101064065A (en) * 2007-03-29 2007-10-31 汤一平 Parking inducing system based on computer visual sense
CN101145200A (en) * 2007-10-26 2008-03-19 浙江工业大学 Inner river ship automatic identification system of multiple vision sensor information fusion
JP2013164803A (en) * 2012-02-13 2013-08-22 Sumitomo Electric Ind Ltd Violation vehicle detection device, violation vehicle detection method and violation vehicle detection program
DE102012012945A1 (en) * 2012-06-29 2014-01-16 Jenoptik Robot Gmbh Method for defacing e.g. face area of driver of violated motor car, in photographic image at traffic monitoring area, involves marking regions that are found by analysis process such that not found regions are defaced
CN106909923A (en) * 2017-02-20 2017-06-30 汪爱民 A kind of vehicles peccancy processing system
WO2017128776A1 (en) * 2016-01-27 2017-08-03 福建工程学院 Traffic violation evidence producing method and system thereof
CN107730904A (en) * 2017-06-13 2018-02-23 银江股份有限公司 Multitask vehicle driving in reverse vision detection system based on depth convolutional neural networks
CN108052929A (en) * 2017-12-29 2018-05-18 湖南乐泊科技有限公司 Parking space state detection method, system, readable storage medium storing program for executing and computer equipment
CN207895686U (en) * 2018-02-27 2018-09-21 上海市保安服务总公司 Non power driven vehicle violation snap-shooting system
US20180293684A1 (en) * 2016-09-07 2018-10-11 Southeast University Supervision and penalty method and system for expressway emergency lane occupancy
CN109118779A (en) * 2018-10-12 2019-01-01 东软集团股份有限公司 Break in traffic rules and regulations information identifying method, equipment and readable storage medium storing program for executing
CN109948418A (en) * 2018-12-31 2019-06-28 上海眼控科技股份有限公司 A kind of illegal automatic auditing method of violation guiding based on deep learning
CN110415529A (en) * 2019-09-04 2019-11-05 上海眼控科技股份有限公司 Automatic processing method, device, computer equipment and the storage medium of vehicle violation
CN110619750A (en) * 2019-08-15 2019-12-27 重庆特斯联智慧科技股份有限公司 Intelligent aerial photography identification method and system for illegal parking vehicle
CN111145551A (en) * 2020-01-03 2020-05-12 南京邮电大学 Intersection traffic planning system based on CNN detection follows chapter rate
WO2020173022A1 (en) * 2019-02-25 2020-09-03 平安科技（深圳）有限公司 Vehicle violation identifying method, server and storage medium
CN111860280A (en) * 2020-07-15 2020-10-30 南通大学 Deep learning-based driver violation behavior recognition system

Patent Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0329099A (en) * 1989-06-27 1991-02-07 Matsushita Electric Ind Co Ltd Automatic monitor system for illegal parking
CN101064065A (en) * 2007-03-29 2007-10-31 汤一平 Parking inducing system based on computer visual sense
CN101145200A (en) * 2007-10-26 2008-03-19 浙江工业大学 Inner river ship automatic identification system of multiple vision sensor information fusion
JP2013164803A (en) * 2012-02-13 2013-08-22 Sumitomo Electric Ind Ltd Violation vehicle detection device, violation vehicle detection method and violation vehicle detection program
DE102012012945A1 (en) * 2012-06-29 2014-01-16 Jenoptik Robot Gmbh Method for defacing e.g. face area of driver of violated motor car, in photographic image at traffic monitoring area, involves marking regions that are found by analysis process such that not found regions are defaced
WO2017128776A1 (en) * 2016-01-27 2017-08-03 福建工程学院 Traffic violation evidence producing method and system thereof
US20180293684A1 (en) * 2016-09-07 2018-10-11 Southeast University Supervision and penalty method and system for expressway emergency lane occupancy
CN106909923A (en) * 2017-02-20 2017-06-30 汪爱民 A kind of vehicles peccancy processing system
CN107730904A (en) * 2017-06-13 2018-02-23 银江股份有限公司 Multitask vehicle driving in reverse vision detection system based on depth convolutional neural networks
CN108052929A (en) * 2017-12-29 2018-05-18 湖南乐泊科技有限公司 Parking space state detection method, system, readable storage medium storing program for executing and computer equipment
CN207895686U (en) * 2018-02-27 2018-09-21 上海市保安服务总公司 Non power driven vehicle violation snap-shooting system
CN109118779A (en) * 2018-10-12 2019-01-01 东软集团股份有限公司 Break in traffic rules and regulations information identifying method, equipment and readable storage medium storing program for executing
CN109948418A (en) * 2018-12-31 2019-06-28 上海眼控科技股份有限公司 A kind of illegal automatic auditing method of violation guiding based on deep learning
WO2020173022A1 (en) * 2019-02-25 2020-09-03 平安科技（深圳）有限公司 Vehicle violation identifying method, server and storage medium
CN110619750A (en) * 2019-08-15 2019-12-27 重庆特斯联智慧科技股份有限公司 Intelligent aerial photography identification method and system for illegal parking vehicle
CN110415529A (en) * 2019-09-04 2019-11-05 上海眼控科技股份有限公司 Automatic processing method, device, computer equipment and the storage medium of vehicle violation
CN111145551A (en) * 2020-01-03 2020-05-12 南京邮电大学 Intersection traffic planning system based on CNN detection follows chapter rate
CN111860280A (en) * 2020-07-15 2020-10-30 南通大学 Deep learning-based driver violation behavior recognition system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113408396A (en) * 2021-06-15 2021-09-17 广西交科集团有限公司 Bridge intelligent sensing system based on cloud computing
CN113988991A (en) * 2021-11-16 2022-01-28 张雨钊 Digital e-commerce platform based on cloud computing

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