CN115762155A - Highway pavement abnormity monitoring method and system - Google Patents
Highway pavement abnormity monitoring method and system Download PDFInfo
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Abstract
The invention discloses a highway pavement abnormity monitoring method and system, which adopts a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar to simultaneously acquire highway pavement data of an appointed area to obtain highway road condition basic data, acquires real-time highway pavement data of a special natural environment and special natural environment prediction data to obtain data to be processed of the special natural environment, acquires highway data of vehicles running in real time in a preset area range to obtain a prediction pavement safety result, acquires an appointed highway pavement area to be detected according to the prediction pavement safety result, and retests the appointed highway pavement area to be detected to obtain highway pavement abnormity monitoring data. The method can solve the problems that when the natural environment weather suddenly rains and snows, data cannot be accurately acquired by using a geological radar, and the modeling result has large error, so that the condition of the high-speed road surface cannot be accurately detected.
Description
Technical Field
The invention belongs to the technical field of roads, and particularly relates to a method and a system for monitoring the road surface abnormity of an expressway.
Background
The safety quality of the highway pavement is always concerned by the public, the traffic volume of the highway is increased year by year, the highway pavement can crack, rut, pit and groove on the highway pavement after several years of time on the basis of continuous traffic of vehicles in part of the highway pavement under the background of sudden expansion of the traffic volume and expansion of traffic section, and the highway pavement has great threat to the stability of the service performance of the highway pavement at present.
In the prior art, CN202111031798.2 is a method, device and system for detecting the condition of an asphalt pavement of an expressway, and is used for improving the detection accuracy of the condition of the asphalt pavement of the expressway. The method comprises the following steps: the detection system transmits a first electromagnetic wave to the expressway asphalt pavement to be detected through a transmitting antenna of the geological radar, and receives a second electromagnetic wave reflected by the expressway asphalt pavement through a receiving antenna of the geological radar; the detection system generates an internal structure model on the basis of the spatial characteristics of the reaction of the first electromagnetic wave and the second electromagnetic wave; the detection system determines the internal disease condition of the asphalt pavement of the expressway through the internal structure model; the detection system acquires a surface image of the asphalt pavement of the expressway through a machine vision module; the detection system determines the surface disease condition of the asphalt pavement of the expressway through image recognition processing on the basis of the surface image; the detection system integrates the internal disease condition and the surface disease condition to determine the condition of the asphalt pavement of the highway.
The problem is not found after the detection of the expressway, but the expressway is subjected to the change of the natural environment, the image of the expressway can appear, the expressway is required to be detected in time under the condition of the change of the natural environment, when the natural environment weather suddenly changes rain and snow, the geological radar cannot accurately acquire data, the error of a modeling result is large, and therefore the expressway condition cannot be accurately detected.
Disclosure of Invention
The technical problem to be solved is as follows: the invention provides a highway pavement abnormity monitoring method and system, which can solve the problems that data cannot be accurately acquired by using a geological radar when natural environment weather suddenly rains and snows, modeling result errors are large, and the highway pavement condition cannot be accurately detected.
The technical scheme is as follows:
a highway pavement abnormity monitoring method comprises the following steps:
s1, simultaneously collecting highway pavement data in a specified area by adopting a plurality of highway cameras, a vehicle video collecting terminal and a geological radar to obtain highway road condition basic data;
s2, collecting real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment to-be-processed data;
s3, modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
s4, collecting real-time running highway data of vehicles in a preset area range, substituting the real-time running highway data of the vehicles into the highway surface model in the special natural environment of the highway surface, and obtaining a predicted road surface safety result;
and S5, obtaining an appointed to-be-tested express way road surface area according to the predicted road surface safety result, and retesting the appointed to-be-tested express way road surface area to obtain the abnormal monitoring data of the express way road surface.
Further, in step S1, the process of obtaining the highway road condition basic data includes the following substeps:
the highway camera, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring highway pavement data in a specified area and storing the highway pavement data in a block chain in a grouping manner to obtain a hash value corresponding to the grouped storage data and generate a grouped data hash value label;
the grouped data hash value labels and the expressway cameras, the vehicle video acquisition terminals and the geological radar which are stored in groups in a block chain are used for simultaneously acquiring expressway pavement data in a specified area to establish a basic database;
and matching the query request received by the basic database with the generated packet data hash value label.
Further, in the step S2, the process of acquiring real-time data of the highway pavement in the special natural environment and the prediction data of the special natural environment to obtain the data to be processed in the special natural environment includes the following substeps:
collecting future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
performing data grouping classification on the future natural environment data according to time intervals, and establishing special natural environment data clustering analysis;
and continuously substituting the future real-time updated data of the natural environment into the special natural environment data cluster analysis model to obtain the data to be processed of the special natural environment.
Further, in step S4, the process of obtaining the predicted road surface safety result includes the following sub-steps:
carrying out regional division on the highway section to obtain preset regional range data;
calling historical data in the basic data of the highway road conditions in the preset area range data, and comparing the historical data with the collected data of the highway driven by vehicles in the preset area range in real time to obtain a basic data difference value;
and selecting a preset special natural environment road surface model according to the basic data difference value.
Further, in step S5, obtaining an appointed highway surface area to be tested according to the predicted road surface safety result, and retesting the appointed highway surface area to be tested, so as to obtain highway surface anomaly monitoring data, the process includes the following substeps:
analyzing a predicted road surface safety result, wherein the predicted road surface safety result comprises road surface damage data and damage position coordinate data;
carrying out coordinate positioning according to the damaged position data to obtain the designated high-speed pavement area to be detected;
acquiring basic data of the designated highway pavement area to be tested to obtain retest basic data, acquiring natural environment parameters, bringing the natural environment parameters and the retest basic data into a preset retest model, and if the retest result is consistent with the predicted pavement safety result, acquiring abnormal monitoring data of the highway pavement
The invention also provides an expressway road surface abnormity monitoring system, which comprises:
the system comprises an acquisition unit, a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar, wherein the highway cameras, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring highway pavement data in a designated area to obtain highway road condition basic data;
the system comprises a collecting unit, a processing unit and a processing unit, wherein the collecting unit is used for collecting real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment data to be processed;
the data generation unit is used for modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
the data analysis unit is used for acquiring the real-time running highway data of the vehicle in a preset area range, and substituting the real-time running highway data of the vehicle into the highway surface model in the special natural environment of the highway surface to obtain a predicted road surface safety result;
and the result detection unit is used for obtaining an appointed to-be-detected highway surface area according to the predicted road surface safety result and retesting the appointed to-be-detected highway surface area to obtain highway surface abnormity monitoring data.
Further, the acquisition unit includes:
the data grouping unit is used for simultaneously collecting and storing highway pavement data in a specified area into a block chain by the highway camera, the vehicle video collecting terminal and the geological radar in a grouping manner to obtain a hash value corresponding to the grouping storage data and generate a grouping data hash value label;
the database construction unit is used for storing the grouped data hash value labels and the grouped data hash value labels to the expressway camera, the vehicle video acquisition terminal and the geological radar of the block chain and simultaneously acquiring expressway pavement data of the specified area to establish a basic database;
and the data matching unit is used for matching the query request received by the basic database with the generated packet data hash value label.
Further, the acquisition unit includes:
the environment data acquisition unit is used for acquiring future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
the cluster analysis unit is used for grouping and classifying the future natural environment data according to time intervals and establishing special natural environment data cluster analysis;
and the data updating unit is used for continuously substituting the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the special natural environment data to be processed.
Further, the data analysis unit includes:
the detection area dividing unit is used for carrying out area division on the highway section to obtain preset area range data;
the data comparison unit is used for calling historical data in the basic data of the highway road conditions in the preset area range data and comparing the historical data with the collected real-time highway driving data in the preset area range to obtain a basic data difference value;
and the model selection unit is used for selecting a preset special natural environment road surface model according to the basic data difference value.
Further, the result detection unit includes:
the prediction data updating unit is used for updating the road surface damage data and the damaged position coordinate data in real time, wherein the predicted road surface safety result comprises the road surface damage data and the damaged position coordinate data;
the damaged position determining unit is used for carrying out coordinate positioning according to the damaged position data to obtain the specified high-speed pavement area to be detected;
and the damaged position retest unit is used for acquiring basic data of the specified to-be-tested expressway surface area to obtain retest basic data, acquiring natural environment parameters, bringing the natural environment parameters and the retest basic data into a preset retest model, and if the retest result is consistent with the predicted road surface safety result, obtaining expressway road surface abnormity monitoring data.
Has the advantages that:
the invention provides a highway pavement abnormity monitoring method and system, wherein a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar simultaneously acquire highway pavement data of a designated area to obtain highway road condition basic data, acquire real-time highway pavement data of a special natural environment and special natural environment prediction data to obtain special natural environment to-be-processed data, model the special natural environment to-be-processed data with the highway road condition basic data to generate a highway pavement special natural environment pavement model, acquire real-time highway driving data of vehicles in a preset area range, substitute the highway real-time highway driving data into the highway pavement special natural environment pavement model to obtain a predicted pavement safety result, obtain a designated highway pavement area to be detected according to the predicted pavement safety result, and retest the designated highway pavement area to be detected to obtain the highway pavement abnormity monitoring data. The highway is segmented, the road surface data of each segment of the highway under the normal condition is collected, the natural environment data, such as environmental parameters of rain, snow and the like, and the road surface data under the normal condition are jointly modeled to generate a highway surface special natural environment road surface model, a predicted road surface safety result is obtained through the highway surface special natural environment road surface model, the road surface is retested to obtain highway surface abnormity monitoring data, and the problems that data cannot be accurately obtained by using a geological radar when the natural environment weather suddenly changes rain and snow, the modeling result has large error, and the highway surface condition cannot be accurately detected are solved.
Drawings
Fig. 1 is a schematic view illustrating a method for monitoring an abnormal road surface of an expressway according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a highway pavement abnormality monitoring method S101 according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a step S102 of a highway pavement abnormality monitoring method according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a step S104 of a highway pavement abnormality monitoring method according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a step S105 of a highway pavement abnormality monitoring method according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an expressway road surface abnormality monitoring system according to an embodiment of the present invention.
Detailed Description
The following examples are presented to enable one of ordinary skill in the art to more fully understand the present invention and are not intended to limit the invention in any way.
In a first aspect, referring to fig. 1, an embodiment of the invention provides a method for monitoring an abnormal road surface of an expressway, including;
s101, simultaneously collecting highway pavement data in a designated area by a plurality of highway cameras, a vehicle video collecting terminal and a geological radar to obtain highway road condition basic data;
the highway sections are segmented, for example, 500 meters are divided into one section, the section from A to B is divided into 100 ends, each section is labeled, and the server acquires highway cameras and vehicle video acquisition terminals beside each section of highway and acquires road condition data on each section of highway by vehicle-mounted geological radar.
S102, collecting real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment data to be processed;
the special natural environment comprises rainy weather, snowy weather, haze weather and the like, water can be accumulated on the road surface in the rainy weather, and the accuracy of acquiring road condition data by the video acquisition equipment and the geological radar can be influenced, so that the actual environmental parameters such as rainwater in the acquisition time are required, and the data are processed and analyzed as reference data for subsequent road condition data.
S103, modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
the collected data of the rainy weather, the snowy weather, the haze weather and the like of the road surface are integrated with the data of the highway under the normal condition, the collected data to be processed of the special natural environment and the basic data of the highway road condition are modeled, and the accuracy of generating the road surface model of the special natural environment of the highway surface can be effectively improved.
S104, collecting real-time running highway data of vehicles in a preset area range, substituting the real-time running highway data of the vehicles into the highway surface model in the special natural environment of the highway surface, and obtaining a predicted road surface safety result;
when the specified road condition needs to be detected, the road section needing to be detected is determined, data are collected according to the road section, the collected data comprise real-time driving highway data of vehicles in a preset area range, weather and other data are brought into the highway surface model with the special natural environment of the highway surface, a predicted road surface safety result is obtained, the predicted road surface safety result is fed back to the background control end and the user operation end in real time, and reference data are provided for a user to monitor the actual road condition.
And S105, obtaining an appointed to-be-tested express way road surface area according to the predicted road surface safety result, and retesting the appointed to-be-tested express way road surface area to obtain the abnormal monitoring data of the express way road surface.
The highway is segmented, the road surface data of each segment of the highway under the normal condition is collected, the natural environment data, such as environmental parameters of rain, snow and the like, and the road surface data under the normal condition are jointly modeled to generate a highway surface special natural environment road surface model, a predicted road surface safety result is obtained through the highway surface special natural environment road surface model, the road surface is retested to obtain highway surface abnormity monitoring data, and the problems that data cannot be accurately obtained by using a geological radar when the natural environment weather suddenly changes rain and snow, the modeling result has large error, and the highway surface condition cannot be accurately detected are solved.
Further, referring to fig. 2, a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar simultaneously acquire highway pavement data in a designated area to obtain highway road condition basic data, including;
s201, the expressway camera, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring expressway pavement data of the appointed area and storing the expressway pavement data into a block chain in a grouping mode to obtain a hash value corresponding to the grouping storage data and generate a grouping data hash value label;
the method comprises the steps that after the acquired expressway camera, the vehicle video acquisition terminal and the geological radar acquire and store expressway pavement data of the designated area to a block chain in a grouped manner, the truth of subsequent data processing can be improved, false data can cause road condition evaluation errors if the authenticity of the data cannot be guaranteed, the data stored to the block chain is based on the block chain principle, and the stored data can only be called and read and cannot be modified.
S202, storing the grouped data Hash value labels and the groups to the expressway camera, the vehicle video acquisition terminal and the geological radar of the block chain, and simultaneously acquiring expressway pavement data of a specified area to establish a basic database;
in order to facilitate subsequent data of the data, the acquired data is established into an independent storage basic database, so that subsequent data processing is facilitated.
S203, matching the query request received by the basic database with the generated packet data hash value label.
Matching the label with the hash value can realize the authenticity of the data verified by the hash value.
The highway sections are segmented, for example, 500 meters are divided into one section, the section from A to B is divided into 100 ends, each section is labeled, and the server acquires highway cameras and vehicle video acquisition terminals beside each section of highway and acquires road condition data on each section of highway by vehicle-mounted geological radar.
Further, referring to fig. 3, acquiring real-time data of the highway pavement in the special natural environment and predicted data of the special natural environment to obtain data to be processed in the special natural environment includes:
s301, collecting future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
s302, performing data grouping classification on the future natural environment data according to time intervals, and establishing special natural environment data clustering analysis;
and S303, continuously substituting the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the special natural environment data to be processed.
The special natural environment comprises rainy weather, snowy weather, haze weather and the like, water can be accumulated on the road surface in the rainy weather, and the accuracy of acquiring road condition data by the video acquisition equipment and the geological radar can be influenced, so that the actual environmental parameters such as rainwater in the acquisition time are required, and the data are processed and analyzed as reference data for subsequent road condition data.
Further, referring to fig. 4, acquiring real-time highway data of a vehicle traveling in a preset area range, and substituting the real-time highway data of the vehicle traveling in the highway data into the highway model in the special natural environment of the highway to obtain a predicted safety result of the road, including:
s401, performing region division on the highway sections to obtain preset region range data;
s402, calling historical data in the preset area range data in the highway road condition basic data, and comparing the historical data with the collected highway data of vehicles running in real time in the preset area range to obtain a basic data difference value;
and S403, selecting a preset special natural environment road surface model according to the basic data difference value.
When the specified road condition needs to be detected, the road section needing to be detected is determined, data are collected according to the road section, the collected data comprise real-time driving highway data of vehicles in a preset area range, weather and other data are brought into the highway surface model with the special natural environment of the highway surface, a predicted road surface safety result is obtained, the predicted road surface safety result is fed back to the background control end and the user operation end in real time, and reference data are provided for a user to monitor the actual road condition.
Further, referring to fig. 5, obtaining an appointed highway surface area to be tested according to the predicted road surface safety result, and retesting the appointed highway surface area to be tested to obtain highway surface anomaly monitoring data, including:
s501, the road surface safety prediction result comprises road surface damage data and damage position coordinate data;
s502, carrying out coordinate positioning according to the damaged position data to obtain the designated high-speed pavement area to be detected;
s503, basic data acquisition is carried out on the designated to-be-detected highway surface area to obtain retest basic data, natural environment parameters are acquired, the natural environment parameters and the retest basic data are brought into a preset retest model, and if the retest result is consistent with the predicted road surface safety result, highway surface abnormity monitoring data are obtained.
The highway is segmented, the pavement data of each segment of the highway under normal conditions are collected, natural environment data, such as environmental parameters of rain, snow and the like, are jointly modeled with the pavement data under normal conditions to generate a highway special natural environment pavement model, a predicted pavement safety result is obtained through the highway special natural environment pavement model, the pavement is retested to obtain highway pavement abnormity monitoring data, and the problem that the highway pavement abnormity monitoring data cannot be accurately obtained by using a geological radar when the natural environment weather suddenly changes rain and snow, the modeling result has large error, and the highway pavement condition cannot be accurately detected is solved.
In a second aspect, referring to fig. 6, an embodiment of the present invention further provides a system for monitoring an abnormal road surface of an expressway, including;
the system comprises an acquisition unit 601, an acquisition unit, a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar, wherein the highway cameras, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring highway pavement data in a designated area to obtain highway road condition basic data;
the acquisition unit 602 is used for acquiring real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment to-be-processed data;
the data generation unit 603 is used for modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
the data analysis unit 604 is used for acquiring the real-time running highway data of the vehicle in a preset area range, and substituting the real-time running highway data of the vehicle into the highway surface model in the special natural environment of the highway surface to obtain a predicted road surface safety result;
and the result detection unit 605 obtains the specified expressway surface area to be detected according to the predicted road surface safety result, and performs retesting on the specified expressway surface area to be detected to obtain expressway surface abnormality monitoring data.
Further, the acquiring unit includes;
the data grouping unit is used for simultaneously collecting and storing highway pavement data in a specified area into a block chain by the highway camera, the vehicle video collecting terminal and the geological radar in a grouping manner to obtain a hash value corresponding to the grouping storage data and generate a grouping data hash value label;
the database construction unit is used for storing the grouped data hash value labels and the grouped data hash value labels to the expressway camera, the vehicle video acquisition terminal and the geological radar of the block chain and simultaneously acquiring expressway pavement data of the specified area to establish a basic database;
and the data matching unit is used for matching the query request received by the basic database with the generated packet data hash value label.
Further, the acquisition unit comprises;
the environment data acquisition unit is used for acquiring future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
the cluster analysis unit is used for grouping and classifying the future natural environment data according to time intervals and establishing special natural environment data cluster analysis;
and the data updating unit is used for continuously substituting the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the special natural environment data to be processed.
Further, the data analysis unit includes;
the detection area dividing unit is used for carrying out area division on the highway section to obtain preset area range data;
the data comparison unit is used for calling historical data in the basic data of the highway road conditions in the preset area range data and comparing the historical data with the collected real-time highway driving data in the preset area range to obtain a basic data difference value;
and the model selection unit is used for selecting a preset special natural environment road surface model according to the basic data difference value.
Further, the result detection unit includes;
the prediction data updating unit is used for updating the road surface damage data and the damaged position coordinate data in real time, wherein the road surface safety prediction result comprises the road surface damage data and the damaged position coordinate data;
the damaged position determining unit is used for carrying out coordinate positioning according to the damaged position data to obtain the specified high-speed pavement area to be detected;
and the damaged position retest unit is used for acquiring basic data of the specified to-be-tested expressway surface area to obtain retest basic data, acquiring natural environment parameters, bringing the natural environment parameters and the retest basic data into a preset retest model, and if the retest result is consistent with the predicted road surface safety result, obtaining expressway road surface abnormity monitoring data.
According to the embodiment, the highway road surface abnormity monitoring method and device provided by the invention are characterized in that a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar are used for simultaneously acquiring highway road surface data in a specified area to obtain highway road condition basic data, acquiring real-time highway road surface data in a special natural environment and special natural environment prediction data to obtain special natural environment to-be-processed data, modeling the special natural environment to-be-processed data and the highway road condition basic data to generate a highway road surface special natural environment road surface model, acquiring real-time highway driving data of vehicles in a preset area range, substituting the real-time highway driving data of the vehicles into the highway road surface special natural environment road surface model to obtain a predicted road surface safety result, obtaining a specified highway surface area to be detected according to the predicted road surface safety result, and retesting the specified highway surface area to be detected to obtain highway road surface abnormity monitoring data.
The highway is segmented, the road surface data of each segment of the highway under the normal condition is collected, the natural environment data, such as environmental parameters of rain, snow and the like, and the road surface data under the normal condition are jointly modeled to generate a highway surface special natural environment road surface model, a predicted road surface safety result is obtained through the highway surface special natural environment road surface model, the road surface is retested to obtain highway surface abnormity monitoring data, and the problems that data cannot be accurately obtained by using a geological radar when the natural environment weather suddenly changes rain and snow, the modeling result has large error, and the highway surface condition cannot be accurately detected are solved.
An embodiment of the present invention further provides a storage medium, and a storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a processor, the computer program implements part or all of the steps of the embodiments of the big data-based public ecosystem provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention.
Claims (10)
1. A highway pavement abnormity monitoring method is characterized by comprising the following steps:
s1, simultaneously acquiring highway pavement data in a designated area by adopting a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar to obtain highway road condition basic data;
s2, collecting real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment data to be processed;
s3, modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
s4, acquiring real-time running highway data of vehicles in a preset area range, substituting the real-time running highway data of the vehicles into the highway special natural environment road surface model to obtain a predicted road surface safety result;
and S5, obtaining an appointed to-be-tested express way road surface area according to the predicted road surface safety result, and retesting the appointed to-be-tested express way road surface area to obtain the abnormal monitoring data of the express way road surface.
2. The method for monitoring the road surface abnormality of the expressway according to claim 1, wherein the step S1 of obtaining the basic data of the expressway road conditions comprises the following substeps:
the highway camera, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring highway pavement data in a specified area and storing the highway pavement data in a block chain in a grouping manner to obtain a hash value corresponding to the grouped storage data and generate a grouped data hash value label;
the grouped data hash value labels and the grouped data hash value labels are stored to the expressway cameras, the vehicle video acquisition terminals and the geological radar of the block chain in a grouping mode, and expressway pavement data in a specified area are acquired at the same time to establish a basic database;
and matching the query request received by the basic database with the generated packet data hash value label.
3. The method for monitoring the highway pavement abnormality according to claim 1, wherein in step S2, the process of acquiring real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment data to be processed comprises the following substeps:
collecting future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
performing data grouping classification on the future natural environment data according to time intervals, and establishing special natural environment data clustering analysis;
and continuously substituting the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the special natural environment data to be processed.
4. The method for monitoring the road surface abnormality of the expressway according to claim 1, wherein the step S4 of obtaining the predicted road surface safety result comprises the substeps of:
carrying out regional division on the highway section to obtain preset regional range data;
calling historical data in the basic data of the highway road conditions in the preset area range data, and comparing the historical data with the collected data of the highway driven by vehicles in the preset area range in real time to obtain a basic data difference value;
and selecting a preset special natural environment road surface model according to the basic data difference value.
5. The method for monitoring the road surface abnormality of the expressway according to claim 1, wherein in step S5, a specified expressway area to be tested is obtained according to the predicted road surface safety result, and retesting is performed on the specified expressway area to be tested, so that the process of obtaining the monitoring data of the expressway road surface abnormality comprises the following substeps:
analyzing a predicted road surface safety result, wherein the predicted road surface safety result comprises road surface damage data and damage position coordinate data;
carrying out coordinate positioning according to the damaged position data to obtain the designated high-speed pavement area to be detected;
and acquiring basic data of the designated highway pavement area to be tested to obtain retest basic data, acquiring natural environment parameters, bringing the natural environment parameters and the retest basic data into a preset retest model, and acquiring abnormal highway pavement monitoring data if retest results are consistent with the predicted pavement safety results.
6. An expressway road surface abnormality monitoring system, comprising:
the system comprises an acquisition unit, a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar, wherein the highway cameras, the vehicle video acquisition terminal and the geological radar are used for simultaneously acquiring highway pavement data in a designated area to obtain highway road condition basic data;
the system comprises a collecting unit, a processing unit and a processing unit, wherein the collecting unit is used for collecting real-time special natural environment highway pavement data and special natural environment prediction data to obtain special natural environment data to be processed;
the data generation unit is used for modeling the data to be processed in the special natural environment and the highway condition basic data to generate a highway surface model in the special natural environment of the highway surface;
the data analysis unit is used for acquiring the real-time running highway data of the vehicle in a preset area range, and substituting the real-time running highway data of the vehicle into the highway surface model in the special natural environment of the highway surface to obtain a predicted road surface safety result;
and the result detection unit is used for obtaining an appointed to-be-detected highway surface area according to the predicted road surface safety result and retesting the appointed to-be-detected highway surface area to obtain highway surface abnormity monitoring data.
7. The system for monitoring abnormality in highway pavement according to claim 6, wherein said acquisition unit comprises:
the data grouping unit is used for simultaneously collecting and storing highway pavement data in a specified area into a block chain by the highway camera, the vehicle video collecting terminal and the geological radar in a grouping manner to obtain a hash value corresponding to the grouping storage data and generate a grouping data hash value label;
the database construction unit is used for storing the grouped data hash value labels and the grouped data hash value labels to the expressway camera, the vehicle video acquisition terminal and the geological radar of the block chain and simultaneously acquiring expressway pavement data of the specified area to establish a basic database;
and the data matching unit is used for matching the query request received by the basic database with the generated packet data hash value label.
8. The system for monitoring the abnormality of the road surface for the expressway according to claim 6, wherein the collection unit comprises:
the environment data acquisition unit is used for acquiring future natural environment data, wherein the future natural environment data comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
the cluster analysis unit is used for grouping and classifying the future natural environment data according to time intervals and establishing special natural environment data cluster analysis;
and the data updating unit is used for continuously substituting the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the special natural environment data to be processed.
9. The system for monitoring abnormality in highway pavement according to claim 6, wherein said data analysis unit comprises:
the detection area dividing unit is used for carrying out area division on the highway section to obtain preset area range data;
the data comparison unit is used for calling historical data in the basic data of the highway road conditions in the preset area range data and comparing the historical data with the collected real-time highway driving data in the preset area range to obtain a basic data difference value;
and the model selection unit is used for selecting a preset special natural environment road surface model according to the basic data difference value.
10. The system for monitoring abnormality in road surface for expressway according to claim 6, wherein the result detecting unit includes:
the prediction data updating unit is used for updating the road surface damage data and the damaged position coordinate data in real time, wherein the road surface safety prediction result comprises the road surface damage data and the damaged position coordinate data;
the damaged position determining unit is used for carrying out coordinate positioning according to the damaged position data to obtain the specified high-speed pavement area to be detected;
and the damaged position retest unit is used for acquiring basic data of the specified to-be-tested expressway surface area to obtain retest basic data, acquiring natural environment parameters, bringing the natural environment parameters and the retest basic data into a preset retest model, and if the retest result is consistent with the predicted road surface safety result, obtaining expressway road surface abnormity monitoring data.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408936A (en) * | 2016-07-28 | 2017-02-15 | 东南大学 | Mobile-phone-data-based real-time detection method for abnormal event of highway |
| CN109716108A (en) * | 2016-12-30 | 2019-05-03 | 同济大学 | A kind of Asphalt Pavement Damage detection system based on binocular image analysis |
| CN111968366A (en) * | 2020-07-27 | 2020-11-20 | 江苏量动信息科技有限公司 | Expressway path fitting method and device based on block chain |
| CN112149763A (en) * | 2020-11-25 | 2020-12-29 | 江苏量动信息科技有限公司 | Method and device for improving road surface abnormity detection by using crowdsourcing concept |
| CN112800913A (en) * | 2021-01-20 | 2021-05-14 | 同济大学 | Pavement damage data space-time analysis method based on multi-source feature fusion |
| CN113215940A (en) * | 2021-04-30 | 2021-08-06 | 浙江数智交院科技股份有限公司 | Pavement detection display method, server and system |
| CN113724503A (en) * | 2021-08-31 | 2021-11-30 | 山东交通学院 | Automatic highway state inspection system and method based on cloud platform |
| CN114202908A (en) * | 2021-12-13 | 2022-03-18 | 中国平安财产保险股份有限公司 | Vehicle early warning method, device, equipment and storage medium based on disaster weather |
| CN114855570A (en) * | 2022-05-24 | 2022-08-05 | 湖南东数交通科技有限公司 | Municipal road maintenance strategy processing method and device and computer equipment |
| CN115188204A (en) * | 2022-06-29 | 2022-10-14 | 东南大学 | Expressway lane-level variable speed limit control method under abnormal weather condition |
| CN115206134A (en) * | 2022-09-15 | 2022-10-18 | 江苏路必达物联网技术有限公司 | Vehicle tire burst early warning system and method based on Internet of things |
| CN115271114A (en) * | 2022-07-11 | 2022-11-01 | 山东金宇信息科技集团有限公司 | Method and equipment for maintaining road surface in tunnel |
-
2022
- 2022-11-14 CN CN202211421089.XA patent/CN115762155B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408936A (en) * | 2016-07-28 | 2017-02-15 | 东南大学 | Mobile-phone-data-based real-time detection method for abnormal event of highway |
| CN109716108A (en) * | 2016-12-30 | 2019-05-03 | 同济大学 | A kind of Asphalt Pavement Damage detection system based on binocular image analysis |
| CN111968366A (en) * | 2020-07-27 | 2020-11-20 | 江苏量动信息科技有限公司 | Expressway path fitting method and device based on block chain |
| CN112149763A (en) * | 2020-11-25 | 2020-12-29 | 江苏量动信息科技有限公司 | Method and device for improving road surface abnormity detection by using crowdsourcing concept |
| CN112800913A (en) * | 2021-01-20 | 2021-05-14 | 同济大学 | Pavement damage data space-time analysis method based on multi-source feature fusion |
| CN113215940A (en) * | 2021-04-30 | 2021-08-06 | 浙江数智交院科技股份有限公司 | Pavement detection display method, server and system |
| CN113724503A (en) * | 2021-08-31 | 2021-11-30 | 山东交通学院 | Automatic highway state inspection system and method based on cloud platform |
| CN114202908A (en) * | 2021-12-13 | 2022-03-18 | 中国平安财产保险股份有限公司 | Vehicle early warning method, device, equipment and storage medium based on disaster weather |
| CN114855570A (en) * | 2022-05-24 | 2022-08-05 | 湖南东数交通科技有限公司 | Municipal road maintenance strategy processing method and device and computer equipment |
| CN115188204A (en) * | 2022-06-29 | 2022-10-14 | 东南大学 | Expressway lane-level variable speed limit control method under abnormal weather condition |
| CN115271114A (en) * | 2022-07-11 | 2022-11-01 | 山东金宇信息科技集团有限公司 | Method and equipment for maintaining road surface in tunnel |
| CN115206134A (en) * | 2022-09-15 | 2022-10-18 | 江苏路必达物联网技术有限公司 | Vehicle tire burst early warning system and method based on Internet of things |
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|---|---|
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