CN115964545A - Method for deducing pollution point location based on slag transport vehicle track point - Google Patents
Method for deducing pollution point location based on slag transport vehicle track point Download PDFInfo
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Abstract
The invention discloses a method for deducing pollution point positions based on trace points of a slag transport vehicle, and relates to the field of transport environment monitoring. Based on the characteristics of mining the space-time big data of the slag car, reasonable threshold values are set through a matrix method and a self-adaptive jump method to mine the stopping points of the slag car in the running process, the algorithm can identify the running and stopping rules according to the characteristics of the track of the car, and meanwhile, the problems of data loss and the like can be avoided. The algorithm adopts a simplified distance calculation mode, and the operation efficiency is greatly improved. The specific residence point information is combined, so that the building site, the dumping site, the muck parking lot and the like can be effectively identified, and scientific basis is provided for the management and control of the urban dust raising source. Therefore, government managers are helped to strengthen the supervision and control of slag transport vehicles and dust sources, and the pollution prevention and control are assisted.
Description
Technical Field
The invention relates to the field of traffic big data, in particular to a method for deducing point pollution positions based on track points of a slag car.
Background
The rapid development of urbanization makes the urban construction engineering increase for the sediment soil car operation volume of transporting construction waste promotes greatly, and the vehicle takes mud to go on the road, can cause road surface secondary raise dust pollution, and is overloaded, not airtight, spills on the way and leaks, and these phenomena all need follow source control. However, on the premise of lacking intelligent hardware identification and application, the slag transport vehicle has the disadvantages of large number of point positions, wide distribution, high centralized supervision difficulty, serious construction environmental pollution and difficulty in acquiring real-time effective data.
With the rapid development of technologies such as big data, cloud computing, artificial intelligence, internet of things and the like, in recent years, a GPS device is installed on a slag car to monitor the running condition of the slag car, and the recovery of vehicle track data and the coverage rate are more efficient.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for deducing the point position of pollution based on the track point of a slag transport vehicle.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a method for deducing pollution point positions based on track points of a slag transport vehicle comprises the following steps:
s1, acquiring original slag car data and preprocessing the data;
s2, sorting the preprocessed slag car data according to time, and traversing all track points of each slag car;
and S3, distinguishing whether the area surrounded by the plurality of track points is a parking point by using a self-adaptive method, matching the parking point to the divided grid and ending the process if the area surrounded by the plurality of track points is the parking point, and repeating the step S3 if the area surrounded by the plurality of track points is not the parking point.
Further, the step S1 specifically includes the following steps:
s11, reading original slag car data and carrying out format conversion;
s12, removing abnormal data of the original slag transport vehicle data;
and S13, identifying the inactive vehicle data of the slag transport vehicle after the abnormal data are removed by using a rectangle method, and reserving the mobile vehicle data in the selected time period.
Further, the abnormal data in S12 includes an abnormal value, a clear empty value, and data compensated within a geographic latitude and longitude range.
Further, in the step S13, the specific manner of identifying the inactive vehicle data of the slag car data from which the abnormal data is removed by using a rectangle method is as follows:
s131, finding out the maximum longitude and latitude and the minimum longitude and latitude in the running track of each slag car;
and S132, taking the obtained maximum longitude and latitude and the obtained minimum longitude and latitude as the maximum path distance of the vehicle track, judging whether the maximum path distance is smaller than a distance threshold value, and if so, judging that the slag transporting vehicle is an inactive vehicle.
Further, the step S3 specifically includes the following steps:
s31, carrying out slag conveying vehicle for each slag conveying vehiclekFromiStarting backward traversal of +1 track points, and finding the nearest track point with time greater than the minimum time threshold valuej；
S32, obtaining the current track point cluster by using a rectangle method (i，j-1) maximum path distanced max Judging the current track point cluster (i，jMaximum path distance of-1)d max If the track point cluster is smaller than the selected space threshold, judging the track point cluster as a parking point and adding all the track point clusters into a parking point set, and if the track point cluster is larger than the selected space threshold, entering the step S33;
s33, judging the current track point cluster (i，j-1) whether the point cluster is a sparse point cluster, if yes, the stopping point condition is not met, all track points within a future set time period are directly skipped, and track points after the future set time period are traversed; if not, the step S34 is carried out;
s34, judging the current track point cluster (i，j-1) whether the point cluster is a very sparse point cluster, if so, skipping directly all track points in the track point cluster and entering the track point clusterjIf the point of trackjIf the length of the track is larger than the length of the track, the loop is exited;
and S35, dividing the matching value of the parking point into grids, and ending the process.
Further, in the step S32, it is determined that the current trace point cluster (S)i，j-1) the condition whether it is a very sparse point cluster is:
(S33) determining the current trace point clusteri，j-1) the condition whether it is a sparse point cluster is:
wherein ,
is a spatial threshold.
Further, the determination conditions of the stopping point in S34 are:
wherein ,
in order to be the spatial threshold value,
The invention has the following beneficial effects:
the invention effectively screens the parking point location by utilizing the GPS data characteristics of the slag car track, so that the urban construction point location, the slag car parking point and the dumping field can be quickly identified, the behaviors of illegal handling and absorption of construction wastes can be found in time, the source and the destination of the slag car can be ensured to be checked and traced, the responsibility can be studied, and the law enforcement and investigation efficiency is obviously improved.
According to the method, reasonable threshold values are set through a matrix method and a self-adaptive jump method to dig out the stopping points of the slag car in the running process, the algorithm can identify the running and stopping rules according to the vehicle track characteristics, and meanwhile, the problems of data loss and the like can be avoided. The algorithm adopts a simplified distance calculation mode, and the operation efficiency is greatly improved.
Drawings
Fig. 1 is a schematic flow chart of a method for deducing pollution point positions based on track points of a slag transport vehicle.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
A method for deducing pollution point positions based on trace points of a slag transport vehicle is shown in figure 1 and comprises the following steps:
s1, acquiring original slag car data and preprocessing the data;
specifically, this example is realized by python3.7.3, and packages such as numpy, pandas, numba, and skearn are used. The operating system is Windows10, the hardware environment is i7-10700 8 cores CPU,16G memory. Reading original slag car data, converting the format into a Pandas DataFrame, and preprocessing the following data:
1. processing abnormal values of the data, clearing empty values and data which are not in the geographic longitude and latitude range;
2. removing inactive vehicle data from the original slag car data, and reserving moving vehicle data in a selected time period; the specific mode is the same as the rectangle method, the maximum longitude and latitude and the minimum longitude and latitude (rectangle) in the running track of each vehicle are found out to represent the maximum path distance of the vehicle running, whether the maximum path distance is smaller than a selected threshold (such as 3 kilometers) or not is judged, if the maximum path distance is smaller than the selected threshold, the vehicle is inactive to move, and the vehicle is rejected.
3. The time format is processed to enable computation of the time dimension.
S2, sorting the preprocessed slag car data according to time, and traversing all track points of each slag car;
and S3, distinguishing whether the area surrounded by the plurality of track points is a parking point by using a self-adaptive method, matching the parking point to the divided grid and ending the process if the area surrounded by the plurality of track points is the parking point, and repeating the step S3 if the area surrounded by the plurality of track points is not the parking point.
In this embodiment, the step S3 specifically includes the following steps:
s31, for each vehiclekFromiStarting backward traversal of +1 track points, and finding the nearest track point with time greater than the minimum time threshold valuej；
S32, obtaining the current track point cluster by using a rectangle methodi，j-1) maximum path distanced max Judging the current track point cluster (i，j-1) maximum path distanced max If the track point cluster is smaller than the selected space threshold, judging the track point cluster as a parking point and adding all the track point clusters into a parking point set, and if the track point cluster is larger than the selected space threshold, entering the step S33;
s33, judging the current track point cluster (i，j-1) whether the sparse point cluster is found, if yes, the stopping point condition is not met, all trace points in a future set time period are directly skipped, and trace points in the future set time period are traversed; if not, entering step S34;
s34, judging the current track point cluster (i，j-1) whether it is a very sparse point cluster, if so, skipping directly all trace points in the segment and entering the trace pointsjIf the point of trackjIf the length of the track is larger than the length of the track, the loop is exited;
and S35, dividing the matching value of the parking point into grids, and ending the process.
In the embodiment, for each vehicle k, all track points i are traversed, the condition that a plurality of points surround is recognized as a parking point, and the rectangular method is used for judging any given track point
Whether it is a "quasi-stationary point" method. The parameters include: time threshold value->
And a spatial threshold->
. The principle is to judge whether the track points adjacent to the track point are close in space, and the mathematical expression is as follows:
in the above formula, the first and second light sources are,
also the track point of the vehicle.
Candidate stagnation points (static points and loitering points) are screened from the moving vehicle track, and a matrix method and an adaptive jump method are adopted.
1) The time threshold is recorded as
And the spatial threshold value is recorded as->
The judgment criterion is ^ 5>
≤
Let σ = [. Sup. [>
]/
；
2) Traversing from the first track point;
3) Adaptive jumping;
if sigma is less than or equal to 1, then the point is determined to be
The point within the threshold is the dwell point cluster, the next @istraversed>
A point within a threshold;
if 1<If σ is less than or equal to 5, then directly skip the future
All trace points within/2 time;
if σ>5, directly skip the future
All trace points within time;
the self-adaptive method can quickly skip track points in the normal running process of the vehicle and carry out encrypted investigation near the stagnation point. Since most of the track points of the vehicle are in the normal running process, the method has an acceleration effect.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (7)
1. A method for deducing pollution point positions based on trace points of a slag transport vehicle is characterized by comprising the following steps:
s1, acquiring original slag car data and performing data preprocessing;
s2, sorting the preprocessed slag car data according to time, and traversing all track points of each slag car;
and S3, distinguishing whether the area surrounded by the plurality of track points is a parking point by using a self-adaptive method, matching the parking point to the divided grid and ending the process if the area surrounded by the plurality of track points is the parking point, and repeating the step S3 if the area surrounded by the plurality of track points is not the parking point.
2. The method for deducing the point location of the pollution based on the trace points of the slag transport vehicle according to claim 1, wherein the step S1 specifically comprises the following steps:
s11, reading original slag car data and carrying out format conversion;
s12, removing abnormal data of the original slag transport vehicle data;
and S13, identifying the inactive vehicle data of the slag transport vehicle after the abnormal data are removed by using a rectangle method, and reserving the mobile vehicle data in the selected time period.
3. The method for inferring pollution point locations based on slag car track points as claimed in claim 2, wherein the abnormal data in S12 includes abnormal values, clear empty values and data supplemented within geographical latitude and longitude ranges.
4. The method for deducing the point location of the pollution based on the track points of the slag transport vehicle according to claim 2, wherein the specific manner of identifying the inactive vehicle data of the slag transport vehicle after the abnormal data is removed by using a rectangle method in S13 is as follows:
s131, finding out the maximum longitude and latitude and the minimum longitude and latitude in the running track of each slag car;
and S132, taking the obtained maximum longitude and latitude and the obtained minimum longitude and latitude as the maximum path distance of the vehicle track, judging whether the maximum path distance is smaller than a distance threshold value, and if so, judging that the slag transport vehicle is an inactive vehicle.
5. The method for deducing the point location of the pollution based on the trace points of the slag transport vehicle according to claim 1, wherein the step S3 specifically comprises the following steps:
s31, carrying out slag conveying vehicle for each slag conveying vehiclekFromiStarting backward traversal of +1 track points, and finding the nearest track point with time greater than the minimum time threshold valuej；
S32, obtaining the current track point cluster by using a rectangle method (i，j-1) maximum path distanced max Judging the current track point cluster (i，j-1) maximum path distanced max If the track point cluster is smaller than the selected space threshold, judging the track point cluster as a parking point and adding all the track point clusters into a parking point set, and if the track point cluster is larger than the selected space threshold, entering the step S33;
s33, judging the current track point cluster (i，j-1) whether the point cluster is a sparse point cluster, if yes, the stopping point condition is not met, all track points within a future set time period are directly skipped, and track points after the future set time period are traversed; if not, the step S34 is carried out;
s34, judging the current track point cluster (i，j-1) whether the point cluster is a very sparse point cluster, if so, skipping directly all track points in the track point cluster and entering the track point clusterjIf the point of trackjIf the length of the track is larger than the length of the track, the loop is exited;
and S35, dividing the parking point matching value into grids, and ending the process.
6. The method for deducing point pollution based on trace points of slag transport vehicle according to claim 5, wherein said S32 is for judging current trace point cluster (S) (S)i，j-1) the condition whether it is a very sparse point cluster is:
in the step S33, the current track point cluster is determined (i，j-1) the condition whether it is a sparse point cluster is:
wherein ,
is a spatial threshold.
7. The method for deducing the point location of pollution based on the trace point of the slag transport vehicle according to claim 5, wherein the determination condition of the stopping point in S34 is as follows:
wherein ,
is a spatial threshold value that is set by the spatial threshold,
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