CN117711185A - Multi-source data-based early warning and monitoring system and method for highway construction - Google Patents
Multi-source data-based early warning and monitoring system and method for highway construction Download PDFInfo
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Abstract
The invention discloses a highway construction early warning monitoring system and method based on multi-source data, which belong to the field of data processing systems specially used for management.
Description
Technical Field
The invention belongs to the field of data processing systems specially used for management, and particularly relates to a highway construction early warning and monitoring system and method based on multi-source data.
Background
The road transportation is a transportation mode on land and is usually used for transporting goods in a longer distance, has the advantages of flexible maneuvering, high turnover speed, large transportation capacity, wide applicable goods and the like, and when road construction is carried out, vehicles need to be monitored and early-warned to avoid damage to the vehicles or roads caused by the vehicles running on construction roads, and the matching degree of the vehicles and the roads cannot be judged through joint evaluation and calculation of road data and vehicle data when the road early warning is carried out in the prior art, so that the damage to the vehicles or/and the roads caused by the vehicles running on unmatched roads is caused, and the problems exist in the prior art;
for example, in chinese patent with application publication number CN116645778A, an early warning monitoring system for highway construction is disclosed, which includes a central monitoring center, where the central monitoring center is communicatively connected with a shooting module, a speed measuring module, a ranging module and an alarm unit; the shooting module is used for shooting and monitoring the environment around the construction in real time and displaying the shot video picture on a display screen of the central monitoring center in real time; the speed measuring module and the distance measuring module are electrically connected with a display module; the speed measuring module is used for detecting the real-time speed of a vehicle travelling towards a construction area in real time; the distance measuring module is used for detecting the distance between a vehicle traveling towards a construction area and a construction site. Through the arrangement of the alarm meter ring, constructors can be reminded to escape from the site in time through the vibration alarm module, so that the constructors are prevented from being damaged when a non-construction vehicle enters the construction site, the function of doubly reminding the constructors is achieved, and the safety of the constructors is greatly improved;
the problems proposed in the background art exist in the above patents: in order to solve the problems, the application designs a multi-source data-based early warning and monitoring system and a multi-source data-based early warning and monitoring method for road construction.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a highway construction early warning monitoring system and a highway construction early warning monitoring method based on multi-source data.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a highway construction early warning monitoring method based on multi-source data comprises the following specific steps:
s1, acquiring road data of a road construction section through an unmanned aerial vehicle, acquiring vehicle data in the running process of a vehicle through a vehicle data sensor, and simultaneously storing the acquired data in a storage module;
s2, acquiring road data of a road construction section from the storage module, and importing the road data of the real-time road construction section into a constructed road hazard value calculation model to derive a road hazard value;
s3, acquiring vehicle data in the vehicle running process from the storage module, and importing the vehicle data in the vehicle running process into the constructed vehicle hazard value calculation model to derive a vehicle hazard value;
s4, importing the calculated real-time road hazard value and the calculated vehicle hazard value into a whole threat degree judgment strategy to judge the whole threat degree;
s5, comparing the real-time overall threat level with a set threat level threshold, if the overall threat level is greater than or equal to the set threat level threshold, issuing a vehicle diversion instruction to the vehicle end and the maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end.
Specifically, the step S1 includes the following specific steps:
s11, carrying out scanning imaging on a road three-dimensional image of a road construction section by using an unmanned aerial vehicle carrying scanning equipment, acquiring the road three-dimensional image of the road construction section, acquiring the average height of a road from the road three-dimensional image of the road construction section, and dividing the road into n x m blocks, wherein n is the number of transversely divided areas on a horizontal plane, and m is the number of longitudinally divided areas on the horizontal plane, acquiring the average level height of each area, acquiring the road surface crack length and depth data of the road three-dimensional image, and storing the acquired average height of the road, the average level height of each area, the road surface crack length and depth data of the road three-dimensional image in a first storage module;
s12, acquiring overall weight data, vehicle running left-right shaking data and vehicle volume data of the vehicle in the running process through a vehicle data sensor, and storing the acquired overall weight data, the vehicle running left-right shaking data and the vehicle volume data in a second storage module;
specifically, the road hazard value calculation model in S2 includes the following specific contents:
s21, obtaining road data of a road construction section from a storage module, wherein the road data comprise average height of a road, average level height of each area, and road surface crack length and depth data of a road three-dimensional image;
s22, extracting the average height of the road and the areasSubstituting the average height of the road and the average height of each area into a road level dangerous value calculation formula to calculate a road level dangerous safety value, wherein the road level dangerous value calculation formula is as follows:wherein->Average height of the divided areas for the ith row and jth column +.>Is the average height of the road;
s23, acquiring the length and depth data of the road surface crack of the road three-dimensional image, and substituting the acquired length and depth data of the road surface crack into a road crack risk value calculation formula to calculate the road crack risk value, wherein the road crack risk value calculation formula is as follows:wherein->The number of cracks is->Length of the z-th crack, +.>For the set standard value of the crack length, < > for>Maximum width of the z-th crack, +.>The set standard value of the crack width;
s24, obtaining the calculated road level height dangerous value and the road crack dangerous value, substituting the obtained road level height dangerous value and the road crack dangerous value into a road dangerous value calculation formula to calculate the road dangerous value, wherein the road dangerous value calculation formula is as follows:wherein->For the road level height risk value duty factor, < +.>The ratio coefficient of the dangerous value of the road crack is +.>。
Specifically, the vehicle risk value calculation model of S3 includes the following specific steps:
s31, acquiring overall weight data of the vehicle in the running process, left and right shaking data of the vehicle in the set time and vehicle volume data;
s32, importing the obtained overall weight data of the vehicle in the running process, the vehicle running left-right shaking data and the vehicle volume data in the set time into a vehicle hazard value calculation formula to calculate a vehicle hazard value, wherein the vehicle hazard value calculation formula is as follows:wherein T is the overall weight data, +.>Exp () is the power of e, V is the vehicle bulk volume data, ++>Maximum volume of vehicle allowed to travel for road, +.>For setting time duration, +.>The vehicle driving left-right shaking data at the moment t is M, the maximum value of the safety range of the vehicle driving left-right shaking data is M, dt is a time integral, and +.>Is a weight ratio coefficient->Is the volume ratio coefficient +.>Is a duty ratio of left and right shaking, wherein +.>。
Specifically, the overall threat level judgment strategy of S4 further includes the following specific steps:
s41, extracting and calculating to obtain a real-time road hazard value of a maintenance road and a vehicle hazard value of each vehicle;
s42, substituting the obtained real-time road hazard value of the maintenance road and the vehicle hazard value of each vehicle into a whole threat degree calculation formula to calculate the whole threat degree, wherein the whole threat degree calculation formula is as follows:wherein->For the road hazard value duty factor, +.>For the vehicle hazard value duty factor, +.>；
Specifically, the specific steps of S5 are as follows:
s51, extracting the calculated overall threat level, and comparing the real-time overall threat level with a set threat level threshold;
s52, if the overall threat level is greater than or equal to a set threat level threshold, issuing a vehicle diversion instruction to the vehicle end and the maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end.
The weight ratio coefficient, the volume ratio coefficient, the left-right shake ratio coefficient, the road hazard value ratio coefficient, the vehicle hazard value ratio coefficient and the threat degree threshold are as follows: obtaining 5000 groups of road data and vehicle running data for vehicle running experiments to obtain the times of faults generated by the road and the vehicle after the vehicle runs on the corresponding road, substituting the obtained road data and vehicle running data into a whole threat degree calculation formula to calculate the whole threat degree, substituting the calculated whole threat degree and fault judgment result into fitting software to output optimal weight ratio coefficient, volume ratio coefficient, left and right shaking ratio coefficient, road danger value ratio coefficient, vehicle danger value ratio coefficient and threat degree threshold value which accord with the fault judgment accuracy;
the utility model provides a highway construction is with early warning monitored control system based on multisource data, its is realized based on the highway construction is with early warning monitored control method based on multisource data, and it includes data acquisition module, vehicle danger value calculation module, road danger value calculation module, whole threat degree calculation module, data comparison module, instruction issue module and control module, data acquisition module is used for gathering the road data of road construction section through unmanned vehicles, gathers the vehicle data in the vehicle driving process through vehicle data sensor, simultaneously stores the data of gathering in the storage module, vehicle danger value calculation module is used for obtaining vehicle data in the vehicle driving process from the storage module, exports vehicle danger value in the vehicle danger value calculation model of leading-in the vehicle data in the vehicle driving process of construction.
Specifically, the road hazard value calculation module is used for acquiring road data of a road construction section from the storage module, guiding the road data of the real-time road construction section into the constructed road hazard value calculation model to derive a road hazard value, the overall threat degree calculation module is used for guiding the calculated real-time road hazard value and the calculated vehicle hazard value into the overall threat degree judgment strategy to judge the overall threat degree, the data comparison module is used for comparing the real-time overall threat degree with a set threat degree threshold value, and the instruction issuing module is used for issuing an instruction for vehicle diversion or normal passing to a vehicle end and a maintenance end.
Specifically, the control module is used for controlling the operation of the data acquisition module, the vehicle danger value calculation module, the road danger value calculation module, the overall threat degree calculation module, the data comparison module and the instruction issuing module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the early warning monitoring method for highway construction based on the multi-source data by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a multi-source data based highway construction pre-warning monitoring method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention collects the road data of the road construction section through the unmanned aerial vehicle, collects the vehicle data in the vehicle running process through the vehicle data sensor, stores the collected data in the storage module, acquires the road data of the road construction section from the storage module, imports the road data of the real-time road construction section into the constructed road hazard value calculation model to derive the road hazard value, acquires the vehicle data in the vehicle running process from the storage module, imports the vehicle data in the vehicle running process into the constructed vehicle hazard value calculation model to derive the vehicle hazard value, imports the calculated real-time road hazard value and the vehicle hazard value into the overall threat degree judgment strategy to judge the overall threat degree, compares the real-time overall threat degree with the set threat degree threshold, issues the vehicle diversion or normal passing instruction to the vehicle end and the maintenance end according to the comparison result, calculates and judges the matching degree of the vehicle and the road through the joint evaluation of the road data and the vehicle data, and avoids the damage to the vehicle or/and the road caused by the vehicle running on the unmatched road.
Drawings
FIG. 1 is a schematic flow diagram of a multi-source data-based early warning and monitoring method for highway construction;
fig. 2 is a schematic diagram of an overall framework of the early warning and monitoring system for highway construction based on multi-source data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: a highway construction early warning monitoring method based on multi-source data comprises the following specific steps:
s1, acquiring road data of a road construction section through an unmanned aerial vehicle, acquiring vehicle data in the running process of a vehicle through a vehicle data sensor, and simultaneously storing the acquired data in a storage module;
it should be specifically noted here that S1 includes the following specific steps:
s11, carrying out scanning imaging on a road three-dimensional image of a road construction section by using an unmanned aerial vehicle carrying scanning equipment to obtain a road three-dimensional image of the road construction section, obtaining the average height of a road from the road three-dimensional image of the road construction section, and carrying out regional division on the road to divide the road into n x m blocks, wherein n is the number of transverse division regions on a horizontal plane, and m is the number of longitudinal division regions on the horizontal plane, obtaining the average horizontal height of each region, simultaneously obtaining the road surface crack length and depth data of the road three-dimensional image, and storing the obtained average height of the road, the average horizontal height of each region, the road surface crack length and depth data of the road three-dimensional image in a first storage module, wherein the description needs to be made that the unmanned aerial vehicle carrying the scanning equipment carries out scanning imaging on the road three-dimensional image of the road construction section in a conventional technical means in the prior art;
s12, acquiring overall weight data, vehicle running left-right shaking data and vehicle volume data of the vehicle in the running process through a vehicle data sensor, and storing the acquired overall weight data, the vehicle running left-right shaking data and the vehicle volume data in a second storage module;
s2, acquiring road data of a road construction section from the storage module, and importing the road data of the real-time road construction section into a constructed road hazard value calculation model to derive a road hazard value;
it should be specifically noted that the road hazard value calculation model in S2 includes the following specific contents:
s21, obtaining road data of a road construction section from a storage module, wherein the road data comprise average height of a road, average level height of each area, and road surface crack length and depth data of a road three-dimensional image;
s22, extracting the average height of the road and the average height of each area, substituting the average height of the road and the average height of each area into a road level dangerous value calculation formula to calculate a road level dangerous safety value, wherein the road level dangerous value calculation formula is as follows:wherein->Average height of the divided areas for the ith row and jth column +.>Is the average height of the road;
the following is an example code written in C language to extract the average height of the road and the average height of each area, and calculate the road level risk safety value:
#include<stdio.h>
number of divided areas on # definition N10// horizontal plane
float calculateDangerValue(float xij[], float xc, int n, int m);
int main() {
int i, j;
int m=5;// number of longitudinally divided areas in horizontal plane
float roadheight=10.0;// average road height
float xij [ m ] [ N ];// average level of each region
Extracting the average level of each region from the data source into the xij array
The// code is omitted.
Calculating road level hazard safety values
float dangerValue = calculateDangerValue(xij, roadHeight, N, m);
printf ("road level hazard safety value:% f\n", dangerValue);
return 0;
}
float calculateDangerValue(float xij[], float xc, int n, int m) {
float sum = 0.0;
int i, j;
int mn = m * n;
for (i = 0; i<m; i++) {
for (j = 0; j<n; j++) {
sum += fabs((xij[i][j]- xc) / xc);
}
}
return sum / mn;
}
note that this is just one example code, and error handling and other logic may need to be added to the code as required. Please make corresponding modification and perfection according to your actual situation;
s23, acquiring the length and depth data of the road surface crack of the road three-dimensional image, and substituting the acquired length and depth data of the road surface crack into a road crack risk value calculation formula to calculate the road crack risk value, wherein the road crack risk value calculation formula is as follows:wherein->The number of cracks is->Length of the z-th crack, +.>For the set standard value of the crack length, < > for>Maximum width of the z-th crack, +.>The set standard value of the crack width;
s24, obtaining the calculated road level height dangerous value and the road crack dangerous value, substituting the obtained road level height dangerous value and the road crack dangerous value into a road dangerous value calculation formula to calculate the road dangerous value, wherein the road dangerous value calculation formula is as follows:wherein->For the road level height risk value duty factor, < +.>The ratio coefficient of the dangerous value of the road crack is +.>；
S3, acquiring vehicle data in the vehicle running process from the storage module, and importing the vehicle data in the vehicle running process into the constructed vehicle hazard value calculation model to derive a vehicle hazard value;
it should be specifically noted that the vehicle risk value calculation model of S3 includes the following specific steps:
s31, acquiring overall weight data of the vehicle in the running process, left and right shaking data of the vehicle in the set time and vehicle volume data;
s32, importing the obtained overall weight data of the vehicle in the running process, the vehicle running left-right shaking data and the vehicle volume data in the set time into a vehicle hazard value calculation formula to calculate a vehicle hazard value, wherein the vehicle hazard value calculation formula is as follows:
wherein T is the overall weight data, +.>Exp () is the power of e, V is the vehicle bulk volume data, ++>Maximum volume of vehicle allowed to travel for road, +.>For setting time duration, +.>The vehicle driving left-right shaking data at the moment t is M, the maximum value of the safety range of the vehicle driving left-right shaking data is M, dt is a time integral, and +.>Is a weight ratio coefficient->Is the volume ratio coefficient +.>Is a duty ratio of left and right shaking, wherein +.>；
S4, importing the calculated real-time road hazard value and the calculated vehicle hazard value into a whole threat degree judgment strategy to judge the whole threat degree;
the specific explanation here is that the overall threat level judgment strategy of S4 further includes the following specific steps:
s41, extracting and calculating to obtain a real-time road hazard value of a maintenance road and a vehicle hazard value of each vehicle;
s42, substituting the obtained real-time road hazard value of the maintenance road and the vehicle hazard value of each vehicle into a whole threat degree calculation formula to calculate the whole threat degree, wherein the whole threat degree calculation formula is as follows:wherein->For the road hazard value duty factor, +.>For the vehicle hazard value duty factor, +.>；
S5, comparing the real-time overall threat level with a set threat level threshold, if the overall threat level is greater than or equal to the set threat level threshold, issuing a vehicle diversion instruction to the vehicle end and the maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end.
The specific steps of S5 are as follows:
s51, extracting the calculated overall threat level, and comparing the real-time overall threat level with a set threat level threshold;
s52, if the overall threat level is greater than or equal to a set threat level threshold, issuing a vehicle diversion instruction to a vehicle end and a maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end;
the weight ratio coefficient, the volume ratio coefficient, the left-right shake ratio coefficient, the road hazard value ratio coefficient, the vehicle hazard value ratio coefficient and the threat degree threshold are as follows: and acquiring 5000 groups of road data and vehicle driving data for vehicle driving experiments, obtaining the times of faults of the road and the vehicle after the vehicle drives on the corresponding road, substituting the acquired road data and vehicle driving data into a whole threat degree calculation formula to calculate the whole threat degree, substituting the calculated whole threat degree and fault judgment result into fitting software to output the optimal weight ratio coefficient, volume ratio coefficient, left and right shaking ratio coefficient, road danger value ratio coefficient, vehicle danger value ratio coefficient and threat degree threshold value which accord with the fault judgment accuracy.
Therefore, the advantages of this embodiment over the prior art are: collecting road data of a road construction section through an unmanned aerial vehicle, collecting vehicle data in the vehicle driving process through a vehicle data sensor, storing the collected data in a storage module, acquiring the road data of the road construction section from the storage module, guiding the road data of the real-time road construction section into a constructed road hazard value calculation model to derive a road hazard value, acquiring the vehicle data in the vehicle driving process from the storage module, guiding the vehicle data in the vehicle driving process into the constructed vehicle hazard value calculation model to derive a vehicle hazard value, guiding the calculated real-time road hazard value and the vehicle hazard value into an overall threat degree judgment strategy to judge the overall threat degree, comparing the real-time overall threat degree with a set threat degree threshold, issuing a vehicle diversion or normal passing instruction to a vehicle end and a maintenance end according to a comparison result, calculating and judging the matching degree of the vehicle and the road through joint evaluation of the road data, and avoiding damage to the vehicle or/and the road caused by the vehicle driving on an unmatched road.
Example 2
As shown in fig. 2, the early warning monitoring system for highway construction based on multi-source data is realized based on the early warning monitoring method for highway construction based on multi-source data, and the early warning monitoring system comprises a data acquisition module, a vehicle hazard value calculation module, a road hazard value calculation module, an overall threat degree calculation module, a data comparison module, an instruction issuing module and a control module, wherein the data acquisition module is used for acquiring road data of a road construction section through an unmanned aerial vehicle, acquiring vehicle data in the vehicle driving process through a vehicle data sensor, storing the acquired data in a storage module, and the vehicle hazard value calculation module is used for acquiring the vehicle data in the vehicle driving process from the storage module, and importing the vehicle data in the vehicle driving process into a constructed vehicle hazard value calculation model to derive a vehicle hazard value; the road hazard value calculation module is used for acquiring road data of a road construction section from the storage module, importing the road data of the real-time road construction section into the constructed road hazard value calculation model to derive a road hazard value, the overall threat degree calculation module is used for importing the real-time road hazard value and the vehicle hazard value obtained through calculation into the overall threat degree judgment strategy to judge the overall threat degree, the data comparison module is used for comparing the real-time overall threat degree with a set threat degree threshold value, and the instruction issuing module is used for issuing a vehicle diversion or normal passing instruction to a vehicle end and a maintenance end.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the early warning monitoring method for highway construction based on the multi-source data by calling the computer program stored in the memory.
The electronic device can generate larger difference due to different configurations or performances, and can comprise one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to realize the early warning monitoring method for highway construction based on the multi-source data. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the early warning monitoring method for highway construction based on the multi-source data.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one way of partitioning, and there may be additional ways of partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The highway construction early warning and monitoring method based on the multi-source data is characterized by comprising the following specific steps of:
s1, acquiring road data of a road construction section through an unmanned aerial vehicle, acquiring vehicle data in the running process of a vehicle through a vehicle data sensor, and simultaneously storing the acquired data in a storage module;
s2, acquiring road data of a road construction section from the storage module, and importing the road data of the real-time road construction section into a constructed road hazard value calculation model to derive a road hazard value;
s3, acquiring vehicle data in the vehicle running process from the storage module, and importing the vehicle data in the vehicle running process into the constructed vehicle hazard value calculation model to derive a vehicle hazard value;
s4, importing the calculated real-time road hazard value and the calculated vehicle hazard value into a whole threat degree judgment strategy to judge the whole threat degree;
s5, comparing the real-time overall threat level with a set threat level threshold, if the overall threat level is greater than or equal to the set threat level threshold, issuing a vehicle diversion instruction to the vehicle end and the maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end.
2. The method for monitoring and early warning for highway construction based on multi-source data according to claim 1, wherein the step S1 comprises the following specific steps:
s11, carrying out scanning imaging on a road three-dimensional image of a road construction section by using an unmanned aerial vehicle carrying scanning equipment, acquiring the road three-dimensional image of the road construction section, acquiring the average height of a road from the road three-dimensional image of the road construction section, and dividing the road into n x m blocks, wherein n is the number of transversely divided areas on a horizontal plane, and m is the number of longitudinally divided areas on the horizontal plane, acquiring the average level height of each area, acquiring the road surface crack length and depth data of the road three-dimensional image, and storing the acquired average height of the road, the average level height of each area, the road surface crack length and depth data of the road three-dimensional image in a first storage module;
s12, acquiring overall weight data, vehicle driving left-right shaking data and vehicle volume data of the vehicle in the driving process through a vehicle data sensor, and storing the acquired overall weight data, the vehicle driving left-right shaking data and the vehicle volume data in a second storage module.
3. The method for monitoring and early warning for highway construction based on multi-source data according to claim 2, wherein the road hazard value calculation model in S2 comprises the following specific contents:
s21, obtaining road data of a road construction section from a storage module, wherein the road data comprise average height of a road, average level height of each area, and road surface crack length and depth data of a road three-dimensional image;
s22, extracting the average height of the road and the average height of each area, substituting the average height of the road and the average height of each area into a road level dangerous value calculation formula to calculate a road level dangerous safety value, wherein the road level dangerous value calculation formula is as follows:wherein->The average height of the divided areas for the ith row and jth column,is the average height of the road;
s23, acquiring the length and depth data of the road surface crack of the road three-dimensional image, and substituting the acquired length and depth data of the road surface crack into a road crack risk value calculation formula to calculate the road crack risk value, wherein the road crack risk value calculation formula is as follows:wherein->The number of cracks is->For the length of the z-th slit,for the set standard value of the crack length, < > for>Maximum width of the z-th crack, +.>The set standard value of the crack width;
s24, obtaining the calculated road level height dangerous value and the road crack dangerous value, substituting the obtained road level height dangerous value and the road crack dangerous value into a road dangerous value calculation formula to calculate the road dangerous value, wherein the road dangerous value calculation formula is as follows:wherein->For the road level height risk value duty factor, < +.>Is the ratio coefficient of the road crack danger value, wherein。
4. The method for monitoring and early warning for highway construction based on multi-source data according to claim 3, wherein the vehicle risk value calculation model of S3 comprises the following specific steps:
s31, acquiring overall weight data of the vehicle in the running process, left and right shaking data of the vehicle in the set time and vehicle volume data;
s32, importing the obtained overall weight data of the vehicle in the running process, the vehicle running left-right shaking data and the vehicle volume data in the set time into a vehicle hazard value calculation formula to calculate a vehicle hazard value, wherein the vehicle hazard value calculation formula is as follows:
wherein T is the overall weight data, +.>Exp () is the power of e, V is the vehicle bulk volume data, ++>Maximum volume of vehicle allowed to travel for road, +.>For setting time duration, +.>The vehicle driving left-right shaking data at the moment t is M, the maximum value of the safety range of the vehicle driving left-right shaking data is M, dt is a time integral, and +.>Is a weight ratio coefficient->Is the volume ratio coefficient +.>Is a duty ratio of left and right shaking, wherein +.>。
5. The method for monitoring and early warning for highway construction based on multi-source data according to claim 4, wherein the overall threat level judgment strategy of S4 further comprises the following specific steps:
s41, extracting and calculating to obtain a real-time road hazard value of a maintenance road and a vehicle hazard value of each vehicle;
s42, substituting the obtained real-time road hazard value of the maintenance road and the vehicle hazard value of each vehicle into a whole threat degree calculation formula to calculate the whole threat degree, wherein the whole threat degree calculation formula is as follows:wherein->For the road hazard value duty factor, +.>For the vehicle hazard value duty factor, +.>。
6. The method for monitoring and early warning for highway construction based on multi-source data according to claim 5, wherein the specific steps of S5 are as follows:
s51, extracting the calculated overall threat level, and comparing the real-time overall threat level with a set threat level threshold;
s52, if the overall threat level is greater than or equal to a set threat level threshold, issuing a vehicle diversion instruction to the vehicle end and the maintenance end, and if the overall threat level is less than the set threat level threshold, issuing a normal passing instruction to the vehicle end and the maintenance end.
7. The multi-source data-based early warning and monitoring system for highway construction is realized based on the multi-source data-based early warning and monitoring method for highway construction according to any one of claims 1-6, and is characterized by comprising a data acquisition module, a vehicle hazard value calculation module, a road hazard value calculation module, an overall threat degree calculation module, a data comparison module, an instruction issuing module and a control module, wherein the data acquisition module is used for acquiring road data of a road construction section through an unmanned aerial vehicle, acquiring vehicle data in the vehicle driving process through a vehicle data sensor, storing the acquired data in a storage module, and the vehicle hazard value calculation module is used for acquiring vehicle data in the vehicle driving process from the storage module, and importing the vehicle data in the vehicle driving process into a constructed vehicle hazard value calculation model to derive a vehicle hazard value.
8. The early warning monitoring system for road construction based on multi-source data according to claim 7, wherein the road hazard value calculation module is used for obtaining road data of a road construction section from the storage module, guiding the road data of the real-time road construction section into the constructed road hazard value calculation model to derive the road hazard value, the overall threat level calculation module is used for guiding the calculated real-time road hazard value and the vehicle hazard value into the overall threat level judgment strategy to judge the overall threat level, the data comparison module is used for comparing the real-time overall threat level with a set threat level threshold, the instruction issuing module is used for issuing a vehicle diversion or normal passing instruction to a vehicle end and a maintenance end, and the control module is used for controlling the operation of the data acquisition module, the vehicle hazard value calculation module, the road hazard value calculation module, the overall threat level calculation module, the data comparison module and the instruction issuing module.
9. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the method is characterized in that the processor executes the multi-source data-based highway construction early warning and monitoring method according to any one of claims 1 to 6 by calling the computer program stored in the memory.
10. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform a multi-source data based highway construction warning and monitoring method according to any one of claims 1 to 6.
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