CN115762155B - Expressway pavement abnormality monitoring method and system - Google Patents
Expressway pavement abnormality monitoring method and system Download PDFInfo
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Abstract
The invention discloses a highway pavement anomaly monitoring method and system, which are characterized in that a plurality of highway cameras, a vehicle video acquisition terminal and geological radars are adopted to simultaneously acquire highway pavement data of an appointed area to obtain highway road condition basic data, real-time highway pavement data of special natural environment and special natural environment prediction data are acquired to obtain data to be processed of the special natural environment, vehicle real-time highway data in a preset area are acquired to obtain a predicted pavement safety result, an appointed highway pavement area to be detected is obtained according to the predicted pavement safety result, and retest is carried out on the appointed highway pavement area to be detected to obtain highway pavement anomaly monitoring data. The method can solve the problems that the geological radar cannot accurately acquire data when the weather in the natural environment suddenly changes into rain and snow, and the modeling result has larger 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 pavement abnormality of an expressway.
Background
The safety quality of the expressway pavement is always focused by the public, the traffic volume of the expressway is increased year by year, under the background of the storm of the alternating current volume and the expansion of traffic shaft section, the pavement of the expressway pavement is cracked after a plurality of years on the basis of continuous passing of vehicles, ruts, pavement pits and the like can be caused on the expressway pavement, and the use performance of the expressway pavement is stable at present, so that the serious threat is caused.
The prior art CN202111031798.2 is a method, a device and a system for detecting the condition of the expressway asphalt pavement, and is used for improving the detection precision of the condition of the expressway asphalt pavement. The method comprises the following steps: the detection system transmits first electromagnetic waves to the expressway asphalt pavement to be detected through a transmitting antenna of the geological radar, and receives second electromagnetic waves reflected by the expressway asphalt pavement through a receiving antenna of the geological radar; the detection system generates an internal structure model based on the spatial characteristics of the first electromagnetic wave and the second electromagnetic wave; the detection system determines the internal disease condition of the expressway asphalt pavement through an internal structure model; the detection system acquires a surface image of the expressway asphalt pavement through the machine vision module; the detection system determines the surface disease condition of the expressway asphalt pavement through image recognition processing on the basis of the surface image; the detection system fuses the internal disease condition and the surface disease condition and determines the condition of the highway asphalt pavement.
The expressway has no problem after detection, but the expressway has images after being subjected to natural environment change, the expressway needs to be detected in time under the condition of natural environment change, and when the natural environment is suddenly changed in weather, rains and snows, data cannot be accurately acquired by using a geological radar, and modeling result errors are large, so that the expressway condition cannot be accurately detected.
Disclosure of Invention
The technical problems to be solved are as follows: the invention provides a highway pavement anomaly monitoring method and system, which can solve the problems that data cannot be accurately obtained by using a geological radar when natural environment weather is suddenly changed into rain or snow, and the condition of a highway pavement cannot be accurately detected due to large modeling result error and the like.
The technical scheme is as follows:
a highway pavement anomaly monitoring method, comprising the steps of:
s1, simultaneously acquiring highway pavement data of 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 expressway pavement data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
s3, modeling the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
s4, collecting real-time driving expressway data of the vehicle in a preset area range, substituting the real-time driving expressway data of the vehicle into the expressway special natural environment pavement model to obtain a predicted pavement safety result;
and S5, obtaining an appointed to-be-detected high-speed road surface area according to the predicted road surface safety result, and retesting the appointed to-be-detected high-speed road surface area to obtain the abnormal monitoring data of the expressway road surface.
Further, in step S1, the process of obtaining the highway condition basic data includes the following substeps:
the expressway camera, the vehicle video acquisition terminal and the geological radar acquire expressway pavement data packets of a designated area at the same time and store the expressway pavement data packets into a blockchain to obtain hash values corresponding to the packet storage data, and a packet data hash value tag is generated;
the packet data hash value tag and the expressway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping mode are used for acquiring expressway pavement data of a designated area 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 tag.
Further, in step S2, the process of acquiring real-time highway pavement data with special natural environment and prediction data with special natural environment to obtain the data to be processed with special natural environment includes the following sub-steps:
collecting future natural environment data, wherein the future natural environment comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
classifying the future natural environment data according to the data grouping according to the time period, and establishing special natural environment data clustering analysis;
and continuously substituting the future natural environment real-time updated data into the special natural environment data cluster analysis model to obtain the data to be processed in the special natural environment.
Further, in step S4, the process of obtaining the predicted road surface safety result includes the following sub-steps:
dividing the expressway section into areas to obtain preset area range data;
retrieving historical data in the highway road condition basic data in the preset area range data, and comparing the historical data with the collected highway data of the vehicles in the preset area range for real-time driving to obtain a basic data difference value;
and selecting a preset special natural environment pavement model according to the basic data difference value.
Further, in step S5, according to the predicted road surface safety result, a specified area of the to-be-detected road surface is obtained, and the specified area of the to-be-detected road surface is retested, and the process of obtaining the abnormal monitoring data of the expressway road surface includes the following sub-steps:
analyzing a predicted road surface safety result, wherein the predicted road surface safety result comprises road surface damage data and damage position coordinate data;
coordinate positioning is carried out according to the damaged position data, and the designated area of the high-speed pavement to be detected is obtained;
collecting basic data of the appointed to-be-detected high-speed pavement area to obtain retest basic data, collecting natural environment parameters, and introducing the natural environment parameters and the retest basic data into a preset retest model, and obtaining the abnormal monitoring data of the expressway pavement if the retest result is consistent with the predicted pavement safety result
The invention also relates to a highway pavement anomaly monitoring system, which comprises:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for simultaneously acquiring expressway road surface data of a designated area by a plurality of expressway cameras, a vehicle video acquisition terminal and a geological radar to obtain expressway road condition basic data;
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit acquires real-time expressway pavement data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
the data generating unit models the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
the data analysis unit is used for collecting real-time driving expressway data of the vehicle in a preset area range, substituting the real-time driving expressway data of the vehicle into the expressway special natural environment pavement model to obtain a predicted pavement safety result;
and the result detection unit is used for obtaining an appointed to-be-detected high-speed road surface area according to the predicted road surface safety result, and retesting the appointed to-be-detected high-speed road surface area to obtain the abnormal monitoring data of the expressway road surface.
Further, the acquisition unit includes:
the data grouping unit is used for simultaneously collecting and storing the expressway pavement data grouping of the appointed area to the blockchain through the expressway camera, the vehicle video collecting terminal and the geological radar, obtaining a hash value corresponding to grouping storage data and generating a grouping data hash value label;
the database construction unit is used for simultaneously acquiring the highway pavement data of the appointed area by the packet data hash value tag and the highway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping way 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 tag.
Further, the acquisition unit comprises:
the environment data acquisition unit acquires 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 clustering analysis unit is used for grouping and classifying the future natural environment data according to the time period and establishing special natural environment data clustering analysis;
and the data updating unit continuously substitutes the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the data to be processed in the special natural environment.
Further, the data analysis unit includes:
the detection area dividing unit is used for dividing the expressway section into areas to obtain preset area range data;
the data comparison unit is used for retrieving 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 the vehicles in the preset area range in real time to obtain a basic data difference value;
and the model selection unit is used for selecting a preset special natural environment pavement model according to the basic data difference value.
Further, the result detection unit includes:
the predicted pavement safety result comprises pavement damage data and damage position coordinate data, and the pavement damage data and the damage position coordinate data are updated in real time;
the damage position determining unit is used for carrying out coordinate positioning according to the damage position data to obtain the designated to-be-detected high-speed pavement area;
and the damaged position retest unit is used for acquiring basic data of the designated area of the highway surface to be tested to obtain retest basic data, acquiring natural environment parameters, and taking 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, obtaining the abnormal monitoring data of the highway pavement.
The beneficial effects are that:
according to the expressway road surface abnormality monitoring method and system, a plurality of expressway cameras, a vehicle video acquisition terminal and geological radars acquire expressway road surface data of a specified area at the same time to obtain expressway road condition basic data, acquire expressway road surface data of real-time 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 and the expressway road condition basic data to generate an expressway special natural environment road surface model, acquire expressway data of vehicles running in a preset area in real time, substitute the vehicle real-time to-be-driven expressway data into the expressway special natural environment road surface model to obtain a predicted road surface safety result, obtain a specified expressway road surface area to be detected according to the predicted road surface safety result, and retest the specified expressway road surface area to be detected to obtain expressway road surface abnormality monitoring data. The highway is segmented, road surface data of each segment of the highway under normal conditions are collected, natural environment data, such as raining, snowing and other environment parameters, are modeled together with the road surface data under normal conditions to generate a special natural environment road surface model of the highway, a predicted road surface safety result is obtained through the special natural environment road surface model of the highway, and then the road surface is retested to obtain abnormal monitoring data of the highway, so that the problems that the data cannot be accurately obtained by using geological radars when the natural environment weather is suddenly rained and snowed, and the modeling result error is large, and the condition of the highway cannot be accurately detected are solved.
Drawings
FIG. 1 is a schematic diagram of a method for monitoring anomalies on a highway pavement according to an embodiment of the present invention;
fig. 2 is a schematic diagram of steps of a method S101 for monitoring an abnormal road surface of an expressway according to an embodiment of the present invention;
fig. 3 is a schematic diagram of steps of an embodiment of a method for monitoring an abnormal road surface on an expressway S102;
fig. 4 is a schematic diagram of steps of an expressway pavement anomaly monitoring method S104 according to an embodiment of the present invention;
fig. 5 is a schematic diagram of step S105 of a method for monitoring abnormal pavement of expressway according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an abnormal highway pavement monitoring system according to an embodiment of the present invention.
Detailed Description
The following examples will provide those skilled in the art with a more complete understanding of the invention, but are not intended to limit the invention in any way.
In a first aspect, referring to fig. 1, an embodiment of the present invention provides a method for monitoring an abnormal road surface of an expressway, including;
s101, a plurality of expressway cameras, a vehicle video acquisition terminal and a geological radar acquire expressway road surface data of a designated area at the same time to obtain expressway road condition basic data;
the highway section is segmented, for example, 500 meters is used as one section, the sections A to B are divided into 100 ends, each section is marked, and a server acquires a highway camera, a vehicle video acquisition terminal and a vehicle geological radar on each section of the highway, and acquires road condition data on each section of the highway.
S102, collecting real-time expressway pavement data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
the special natural environment comprises rainy weather, snowy weather, haze weather and the like, water can accumulate on the air path surface in rainy days, and the accuracy of the video acquisition equipment and the geological radar for acquiring road condition data can be influenced, so that the acquisition time is required to acquire the actual environment parameters such as rainwater and the like, and the environment parameters are used as reference data for processing and analyzing the follow-up road condition data.
S103, modeling the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
the collected data such as rainy weather, snowy weather and haze weather of the road surface are integrated with the data under the normal condition of the expressway, and the collected data to be processed in the special natural environment and the road condition basic data of the expressway are modeled, so that the accuracy of generating the road surface model of the special natural environment of the expressway can be effectively improved.
S104, collecting real-time driving expressway data of a vehicle in a preset area range, substituting the real-time driving expressway data of the vehicle into a special natural environment pavement model of the expressway to obtain a predicted pavement safety result;
when the specified road condition is required to be detected, the road section required to be detected is determined firstly, data acquisition is carried out according to the road section, the acquired data comprise real-time expressway data of vehicles running in a preset area range, and when data such as weather and the like are brought into the road surface model of the special natural environment of the expressway, a predicted road surface safety result is obtained and fed back to a background control end and a user operation end in real time, so that reference data are provided for the user to monitor the actual condition of the road condition.
S105, obtaining an appointed to-be-detected high-speed road surface area according to the predicted road surface safety result, and retesting the appointed to-be-detected high-speed road surface area to obtain the abnormal monitoring data of the expressway road surface.
The highway is segmented, road surface data of each segment of the highway under normal conditions are collected, natural environment data, such as raining, snowing and other environment parameters, are modeled together with the road surface data under normal conditions to generate a special natural environment road surface model of the highway, a predicted road surface safety result is obtained through the special natural environment road surface model of the highway, and then the road surface is retested to obtain abnormal monitoring data of the highway, so that the problems that the data cannot be accurately obtained by using geological radars when the natural environment weather is suddenly rained and snowed, and the modeling result error is large, and the condition of the highway cannot be accurately detected are solved.
Further, referring to fig. 2, a plurality of expressway cameras, a vehicle video acquisition terminal and a geological radar acquire expressway road surface data of a designated area simultaneously to obtain expressway road condition basic data, including;
s201, simultaneously acquiring and storing the expressway pavement data packets of the appointed area by the expressway camera, the vehicle video acquisition terminal and the geological radar to a blockchain to obtain hash values corresponding to the packet storage data, and generating a packet data hash value tag;
after the obtained expressway cameras, the vehicle video acquisition terminal and the geological radar acquire the expressway pavement data packets of the appointed area at the same time and store the data packets to the blockchain, the data reality of subsequent data processing can be improved, if the data reality cannot be ensured, false data can cause road condition evaluation errors, the data stored to the blockchain can only be called and read based on the blockchain principle, and the stored data cannot be modified.
S202, the packet data hash value tag and the expressway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping mode are used for acquiring expressway pavement data of a designated area at the same time to establish a basic database;
in order to facilitate the subsequent data of the data, a separate storage basic database is established for the collected data, so that the subsequent data processing is facilitated.
S203, matching the query request received by the basic database with the generated packet data hash value tag.
The tag is matched with the hash value, so that the authenticity of the data can be verified through the hash value.
The highway section is segmented, for example, 500 meters is used as one section, the sections A to B are divided into 100 ends, each section is marked, and a server acquires a highway camera, a vehicle video acquisition terminal and a vehicle geological radar on each section of the highway, and acquires road condition data on each section of the highway.
Further, referring to fig. 3, collecting real-time expressway road surface data with special natural environment and special natural environment prediction data to obtain data to be processed with special natural environment includes:
s301, 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;
s302, data grouping and classifying are carried out on the future natural environment data according to time periods, and special natural environment data clustering analysis is established;
s303, continuously substituting the future natural environment real-time updated data into the special natural environment data clustering analysis model to obtain the data to be processed in the special natural environment.
The special natural environment comprises rainy weather, snowy weather, haze weather and the like, water can accumulate on the air path surface in rainy days, and the accuracy of the video acquisition equipment and the geological radar for acquiring road condition data can be influenced, so that the acquisition time is required to acquire the actual environment parameters such as rainwater and the like, and the environment parameters are used as reference data for processing and analyzing the follow-up road condition data.
Further, referring to fig. 4, collecting real-time driving highway data of a vehicle in a preset area, substituting the real-time driving highway data of the vehicle into the special natural environment road surface model of the highway to obtain a predicted road surface safety result, including:
s401, dividing the expressway section into areas to obtain preset area range data;
s402, historical data in the highway road condition basic data in the preset area range data are retrieved and compared with the collected highway data in the preset area range, wherein the highway data run in real time by the vehicle, so that a basic data difference value is obtained;
s403, selecting a preset special natural environment pavement model according to the basic data difference value.
When the specified road condition is required to be detected, the road section required to be detected is determined firstly, data acquisition is carried out according to the road section, the acquired data comprise real-time expressway data of vehicles running in a preset area range, and when data such as weather and the like are brought into the road surface model of the special natural environment of the expressway, a predicted road surface safety result is obtained and fed back to a background control end and a user operation end in real time, so that reference data are provided for the user to monitor the actual condition of the road condition.
Further, referring to fig. 5, according to the predicted road safety result, a specified area of the to-be-detected road surface is obtained, and the specified area of the to-be-detected road surface is retested to obtain abnormal monitoring data of the road surface of the expressway, including:
s501, the predicted road surface safety 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 to-be-detected high-speed pavement area;
and S503, acquiring basic data of the designated area of the highway to be detected to obtain retest basic data, acquiring natural environment parameters, and introducing 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, obtaining the abnormal highway pavement monitoring data.
The highway is segmented, road surface data of each segment of the highway under normal conditions are collected, natural environment data, such as raining, snowing and other environment parameters, are modeled together with the road surface data under normal conditions to generate a special natural environment road surface model of the highway, a predicted road surface safety result is obtained through the special natural environment road surface model of the highway, and then the road surface is retested to obtain abnormal monitoring data of the highway, so that the problems that the data cannot be accurately obtained by using geological radars when the natural environment weather is suddenly rained and snowed, and the modeling result error is large, and the condition of the highway cannot be accurately detected are solved.
In a second aspect, referring to fig. 6, an embodiment of the present invention further provides a highway pavement anomaly monitoring system, including;
the acquisition unit 601 acquires highway pavement data of a designated area simultaneously by a plurality of highway cameras, a vehicle video acquisition terminal and a geological radar to obtain highway road condition basic data;
the acquisition unit 602 acquires real-time expressway road surface data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
the data generating unit 603 models the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
the data analysis unit 604 collects real-time driving expressway data of the vehicle in a preset area range, and substitutes the real-time driving expressway data of the vehicle into the expressway special natural environment pavement model to obtain a predicted pavement safety result;
and the result detection unit 605 obtains a specified area of the highway surface to be detected according to the predicted road surface safety result, and retests the specified area of the highway surface to be detected to obtain abnormal monitoring data of the highway surface.
Further, the acquisition unit includes;
the data grouping unit is used for simultaneously collecting and storing the expressway pavement data grouping of the appointed area to the blockchain through the expressway camera, the vehicle video collecting terminal and the geological radar, obtaining a hash value corresponding to grouping storage data and generating a grouping data hash value label;
the database construction unit is used for simultaneously acquiring the highway pavement data of the appointed area by the packet data hash value tag and the highway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping way 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 tag.
Further, the acquisition unit comprises;
the environment data acquisition unit acquires 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 clustering analysis unit is used for grouping and classifying the future natural environment data according to the time period and establishing special natural environment data clustering analysis;
and the data updating unit continuously substitutes the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the data to be processed in the special natural environment.
Further, the data analysis unit includes;
the detection area dividing unit is used for dividing the expressway section into areas to obtain preset area range data;
the data comparison unit is used for retrieving 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 the vehicles in the preset area range in real time to obtain a basic data difference value;
and the model selection unit is used for selecting a preset special natural environment pavement model according to the basic data difference value.
Further, the result detection unit includes;
the predicted pavement safety result comprises pavement damage data and damage position coordinate data, and the pavement damage data and the damage position coordinate data are updated in real time;
the damage position determining unit is used for carrying out coordinate positioning according to the damage position data to obtain the designated to-be-detected high-speed pavement area;
and the damaged position retest unit is used for acquiring basic data of the designated area of the highway surface to be tested to obtain retest basic data, acquiring natural environment parameters, and taking 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, obtaining the abnormal monitoring data of the highway pavement.
According to the expressway road surface abnormality monitoring method and device provided by the embodiment of the invention, the expressway cameras, the vehicle video acquisition terminals and the geological radar acquire expressway road surface data of a specified area at the same time to obtain expressway road condition basic data, acquire expressway road surface data of real-time 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 and the expressway road condition basic data to generate an expressway special natural environment road surface model, acquire expressway data of vehicles in a preset area range and substituting the vehicle real-time to-be-driven expressway data into the expressway special natural environment road surface model to obtain a predicted road surface safety result, obtain a specified expressway surface area to be detected according to the predicted road surface safety result, and retest the specified expressway road surface area to be detected to obtain expressway road surface abnormality monitoring data.
The highway is segmented, road surface data of each segment of the highway under normal conditions are collected, natural environment data, such as raining, snowing and other environment parameters, are modeled together with the road surface data under normal conditions to generate a special natural environment road surface model of the highway, a predicted road surface safety result is obtained through the special natural environment road surface model of the highway, and then the road surface is retested to obtain abnormal monitoring data of the highway, so that the problems that the data cannot be accurately obtained by using geological radars when the natural environment weather is suddenly rained and snowed, and the modeling result error is large, and the condition of the highway cannot be accurately detected are solved.
The embodiment of the invention also provides a storage medium, and a computer program is stored in the storage medium, and when the computer program is executed by a processor, part or all of the steps in the embodiments of the public ecological system based on big data provided by the invention are realized. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a Random Access Memory (RAM), or the like.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
The embodiments of the present invention described above do not limit the scope of the present invention.
Claims (4)
1. The method for monitoring the abnormal expressway pavement is characterized by comprising the following steps of:
s1, simultaneously acquiring highway pavement data of 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 expressway pavement data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
s3, modeling the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
s4, collecting real-time driving expressway data of the vehicle in a preset area range, substituting the real-time driving expressway data of the vehicle into the expressway special natural environment pavement model to obtain a predicted pavement safety result;
s5, obtaining an appointed to-be-detected high-speed road surface area according to the predicted road surface safety result, and retesting the appointed to-be-detected high-speed road surface area to obtain expressway road surface abnormal monitoring data;
in step S2, the process of acquiring real-time highway pavement data with special natural environment and prediction data of special natural environment to obtain the data to be processed in the special natural environment includes the following sub-steps:
collecting future natural environment data, wherein the future natural environment comprises temperature data, humidity data, rain and snow data, visibility data and wind power data;
classifying the future natural environment data according to the data grouping according to the time period, and establishing special natural environment data clustering analysis;
continuously substituting the future natural environment real-time updated data into the special natural environment data cluster analysis model to obtain to-be-processed data of the special natural environment;
in step S4, the process of obtaining the predicted road surface safety result includes the following sub-steps:
dividing the expressway section into areas to obtain preset area range data;
retrieving historical data in the highway road condition basic data in the preset area range data, and comparing the historical data with the collected highway data of the vehicles in the preset area range for real-time driving to obtain a basic data difference value;
selecting a preset special natural environment pavement model according to the basic data difference value;
in step S5, according to the predicted road surface safety result, a specified area of the to-be-detected road surface is obtained, and the specified area of the to-be-detected road surface is retested, and the process of obtaining the abnormal monitoring data of the road surface of the expressway includes the following sub-steps:
analyzing a predicted road surface safety result, wherein the predicted road surface safety result comprises road surface damage data and damage position coordinate data;
coordinate positioning is carried out according to the damaged position data, and the designated area of the high-speed pavement to be detected is obtained;
and acquiring basic data of the appointed to-be-detected high-speed pavement area to obtain retest basic data, acquiring natural environment parameters, and introducing 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, obtaining the abnormal monitoring data of the expressway pavement.
2. The method for monitoring the abnormal condition of the expressway road surface according to claim 1, wherein the process of obtaining the basic data of the road condition of the expressway in the step S1 comprises the following sub-steps:
the expressway camera, the vehicle video acquisition terminal and the geological radar acquire expressway pavement data packets of a designated area at the same time and store the expressway pavement data packets into a blockchain to obtain hash values corresponding to the packet storage data, and a packet data hash value tag is generated;
the packet data hash value tag and the expressway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping mode are used for acquiring expressway pavement data of a designated area 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 tag.
3. An expressway road surface abnormality monitoring system, characterized by comprising:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for simultaneously acquiring expressway road surface data of a designated area by a plurality of expressway cameras, a vehicle video acquisition terminal and a geological radar to obtain expressway road condition basic data;
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit acquires real-time expressway pavement data with special natural environment and special natural environment prediction data to obtain data to be processed in the special natural environment;
the data generating unit models the data to be processed in the special natural environment and the highway road condition basic data to generate a highway road surface model in the special natural environment;
the data analysis unit is used for collecting real-time driving expressway data of the vehicle in a preset area range, substituting the real-time driving expressway data of the vehicle into the expressway special natural environment pavement model to obtain a predicted pavement safety result;
the result detection unit is used for obtaining an appointed to-be-detected high-speed road surface area according to the predicted road surface safety result, and retesting the appointed to-be-detected high-speed road surface area to obtain expressway road surface abnormal monitoring data;
the acquisition unit comprises:
the environment data acquisition unit acquires 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 clustering analysis unit is used for grouping and classifying the future natural environment data according to the time period and establishing special natural environment data clustering analysis;
the data updating unit continuously substitutes the future natural environment real-time updating data into the special natural environment data clustering analysis model to obtain the data to be processed of the special natural environment;
the data analysis unit includes:
the detection area dividing unit is used for dividing the expressway section into areas to obtain preset area range data;
the data comparison unit is used for retrieving 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 the vehicles in the preset area range in real time to obtain a basic data difference value;
the model selection unit is used for selecting a preset special natural environment pavement model according to the basic data difference value;
the result detection unit includes:
the predicted pavement safety result comprises pavement damage data and damage position coordinate data, and the pavement damage data and the damage position coordinate data are updated in real time;
the damage position determining unit is used for carrying out coordinate positioning according to the damage position data to obtain the designated to-be-detected high-speed pavement area;
and the damaged position retest unit is used for acquiring basic data of the designated area of the highway surface to be tested to obtain retest basic data, acquiring natural environment parameters, and taking 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, obtaining the abnormal monitoring data of the highway pavement.
4. The expressway surface abnormality monitoring system according to claim 3, wherein said acquisition unit includes:
the data grouping unit is used for simultaneously collecting and storing the expressway pavement data grouping of the appointed area to the blockchain through the expressway camera, the vehicle video collecting terminal and the geological radar, obtaining a hash value corresponding to grouping storage data and generating a grouping data hash value label;
the database construction unit is used for simultaneously acquiring the highway pavement data of the appointed area by the packet data hash value tag and the highway camera, the vehicle video acquisition terminal and the geological radar which are stored in the block chain in a grouping way 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 tag.
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