CN115964545B - Method for deducing pollution point location based on slag transport vehicle track point - Google Patents
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Abstract
The invention discloses a method for deducing pollution points based on a slag car track point, and relates to the field of monitoring of transportation environment. Based on the space-time big data characteristics of the slag car, reasonable thresholds are set through a matrix method and a self-adaptive jump method to excavate the stop standing points of the slag car in the running process, and the algorithm can distinguish the running and stop rules according to the track characteristics of the car and can avoid the problems of data loss and the like. The algorithm adopts a simplified distance calculation mode, and the operation efficiency is greatly improved. By combining specific residence point information, the method can effectively help identify construction sites, dumping fields, slag parking lots and the like, and provides scientific basis for management and control of urban dust sources. Thereby helping government managers strengthen the control of slag transport vehicles and the supervision of dust sources, and assisting in pollution prevention and control.
Description
Technical Field
The invention relates to the field of traffic big data, in particular to a method for deducing pollution points based on slag car track points.
Background
The rapid development of urban construction increases urban construction engineering, the camping and transportation amount of the dregs car for transporting construction waste is greatly improved, the car is carried with mud to get on the road, secondary dust pollution on the road surface can be caused, overload and unsealing are carried out, and the phenomena of scattering and leakage along the road need to be controlled from the source. However, on the premise of lacking intelligent hardware identification and application, the slag transport vehicle has the advantages of large number of points, wide distribution, high centralized supervision difficulty, serious construction environment pollution and difficulty in acquiring real-time effective data.
With the high-speed development of technologies such as big data, cloud computing, artificial intelligence, the Internet of things and the like, a GPS device is installed on a slag transport vehicle in recent years to monitor the running condition of the slag transport vehicle, so that the recovery and coverage rate of vehicle track data are ensured to be more efficient.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for deducing pollution points based on the track points of a slag car.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
a method for deducing pollution points based on slag car track points 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, judging whether the area surrounded by the plurality of track points is a parking point by using an adaptive method, if so, matching the parking point to the divided grid, ending the flow, and if not, repeating the step S3.
Further, the step S1 specifically includes the following steps:
s11, reading original slag car data and performing format conversion;
s12, eliminating abnormal data of the original slag car data;
s13, identifying non-active vehicle data of the slag car after abnormal data are removed by using a rectangular method, and reserving moving vehicle data in a selected period.
Further, the abnormal data in S12 includes abnormal values, clear null values, and data complemented in the geographical latitude and longitude range.
Further, in the step S13, the specific method for identifying the non-active vehicle data of the slag car data after the abnormal data is removed by using the rectangular 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 carrier;
s132, judging whether the maximum path distance is smaller than a distance threshold value or not by taking the obtained maximum longitude and latitude and the obtained minimum longitude and latitude as the maximum path distance of the vehicle track, and if so, judging that the slag transport vehicle is an inactive vehicle.
Further, the step S3 specifically includes the following steps:
s31, for each slag carrierkFrom the slaveiStarting backward traversal of +1 track points, finding the nearest track point with time greater than the minimum time thresholdj；
S32, obtaining the current track point cluster by using a rectangular methodi，jMaximum path distance of-1)d max Judging the current track point clusteri，jMaximum path distance of-1)Separation ofd max If the track point cluster is smaller than the selected space threshold value, 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 value, entering step S33;
s33, judging the current track point clusteri，j-1) if the track points are sparse point clusters, if so, not meeting the parking point condition and directly skipping all track points within a future set time period, and traversing the track points after the future set time period; if not, go to step S34;
s34, judging the current track point clusteri，j-1) whether it is a very sparse cluster, if so, directly skipping all the track points in the cluster, entering the track pointjIf the track pointsjIf the track length is greater than the track length, the loop is exited;
s35, dividing the parking point matching values into grids, and ending the flow.
Further, in the step S32, the current track point cluster is judgedi，j-1) whether it is a very sparse cluster of points is conditioned by:
judging the current track point cluster in the S33i，j-1) if it is a sparse cluster of points, the condition is:
wherein ,
is a spatial threshold.
Further, the judging condition of the stopping and standing point in S34 is as follows:
wherein ,
as a spatial threshold value,
The invention has the following beneficial effects:
the invention utilizes the GPS data characteristics of the slag car track to effectively screen the parking points, so that the urban construction points, slag car parking points and dumping sites can be quickly identified, the illegal disposal and the digestion of the construction wastes can be timely found, the source of the slag car can be ensured to be found, the trace and the responsibility can be ensured to be correct, and the efficiency of law enforcement and inspection is obviously improved.
According to the method, the stopping standing point of the slag car in the running process is excavated by setting reasonable thresholds through the matrix method and the self-adaptive jump method, the algorithm can distinguish the running and stopping rules according to the track characteristics 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.
Drawings
FIG. 1 is a flow chart of a method for deducing pollution points based on track points of a slag car.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate 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 all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
A method for deducing pollution points based on slag car track points is shown in figure 1, and comprises the following steps:
s1, acquiring original slag car data and preprocessing the data;
specifically, this embodiment is implemented by python3.7.3, and uses a packet such as numpy, pandas, numba, sklearn. The operating system is Windows10, the hardware environment is i7-10700 8 core CPU,16G memory. The original slag car data is read, the format is converted into Pandas DataFrame, and the following data preprocessing is carried out:
1. processing abnormal values of the data, clearing null values and data which are not in the geographical longitude and latitude range;
2. removing non-active vehicle data from the original slag car data, and reserving the vehicle data moving in the selected period; the specific method is the same as the rectangle method, the maximum longitude and latitude and the minimum longitude and latitude (rectangle) in each vehicle running track are found out, the maximum path distance of the vehicle running is represented, whether the maximum path distance is smaller than a selected threshold value (such as 3 km) is judged, if the maximum path distance is smaller than the selected threshold value, the vehicle is not active, and the vehicle is removed.
3. The time format is processed so that it can be calculated in the time dimension.
S2, sorting the preprocessed slag car data according to time, and traversing all track points of each slag car;
and S3, judging whether the area surrounded by the plurality of track points is a parking point by using an adaptive method, if so, matching the parking point to the divided grid, ending the flow, and if not, repeating the step S3.
In this embodiment, the step S3 specifically includes the following steps:
s31, for each vehiclekFrom the slaveiStarting backward traversal of +1 track points, finding the nearest track point with time greater than the minimum time thresholdj；
S32, obtaining the current track point cluster by using a rectangular methodi，jMaximum path distance of-1)d max Judging the current track point clusteri，jMaximum path distance of-1)d max Whether or not to be less than the selected spatial thresholdIf the value is smaller than the value, the track point cluster is judged to be the parking point and all the parking points are added into the parking point set, and if the value is larger than the value, the step S33 is carried out;
s33, judging the current track point clusteri，j-1) whether the track points are sparse clusters, if so, not meeting the parking point condition and directly skipping all track points for which the future set time period is set, and traversing the track points after the future set time period; if not, go to step S34;
s34, judging the current track point clusteri，j-1) whether it is a very sparse cluster of points, if so, directly skipping all the track points in the segment, entering the track pointsjIf the track pointsjIf the track length is greater than the track length, the loop is exited;
s35, dividing the parking point matching values into grids, and ending the flow.
In the present embodiment, for each vehicle k, all the track points i are traversed, the condition of surrounding a plurality of points is recognized as a parking point, and a rectangular method is used for determining any given track point
Whether a "quasi-standing point" approach. The parameters include: time threshold->
And spatial threshold->
. The principle is to judge whether the adjacent track points are similar in space or not, and the mathematical expression is as follows:
in the above formula, the water content of the water-soluble polymer,
also the locus point of the vehicle.
Candidate stay points (stationary points and wander 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
The spatial threshold is marked->
The judgment standard is->
≤/>
Let σ= [ -jersey ]>
]//>
；
2) Traversing from the first trace point;
3) Adaptive jumping;
if sigma is less than or equal to 1, the point is determined
Points within the threshold are clusters of parking points, traversing the next +.>
Points within the threshold;
if 1<Sigma is less than or equal to 5, the future is skipped directly
All track points within/2 time;
if sigma>5, skip the future directly
All track points within the time;
the self-adaptive method can quickly skip track points in the normal running process of the vehicle, and the track points are closely checked near the standing points. Since most of 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (4)
1. The method for deducing the pollution point location based on the track point of the slag car is characterized by comprising 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;
s3, distinguishing whether the area surrounded by the plurality of track points is a parking point or not by using an adaptive method, if so, matching the parking point to the divided grid and ending the flow, and if not, repeating the step S3, wherein the method specifically comprises the following steps:
s31, for each slag carrierkFrom the slaveiStarting backward traversal of +1 track points, finding the nearest track point with time greater than the minimum time thresholdj；
S32, obtaining the current point cluster by using a rectangle method（i，jMaximum path distance of-1)d max Judging the current point clusteri，jMaximum path distance of-1)d max If the track point cluster is smaller than the selected space threshold value, the track point cluster is judged to be the parking point and all the track point clusters are added into the parking point set, and if the track point cluster is larger than the selected space threshold value, the step S33 is entered, wherein the judging condition of the parking point is as follows:
wherein ,
as a spatial threshold value,
s33, judging the current point clusteri，j-1) if the track points are sparse point clusters, if so, not meeting the parking point condition and directly skipping all track points within a future set time period, and traversing the track points after the future set time period; if not, step S34 is entered, wherein the current point cluster is judgedi，j-1) if it is a sparse cluster of points, the condition is:
s34, judging the current point clusteri，j-1) if it is a very sparse cluster of points, if so, directly skipping all the track points in the future set period of time, entering a track pointjIf the track pointsjIf the current point cluster is larger than the track length, the cycle is exited, wherein the current point cluster is judgedi，j-1) whether it is a very sparse cluster of points is conditioned by:
s35, matching the parking points into the divided grids, and ending the flow.
2. The method for deducing pollution points based on the track points of the slag car according to claim 1, wherein the step S1 specifically comprises the following steps:
s11, reading original slag car data and performing format conversion;
s12, eliminating abnormal data of the original slag car data;
s13, identifying non-active vehicle data of the slag car after abnormal data are removed by using a rectangular method, and reserving moving vehicle data in a selected period.
3. The method for deducing pollution points based on the trajectory points of the slag car according to claim 2, wherein the abnormal data in S12 includes abnormal values, clear null values and data complemented in the geographical latitude and longitude range.
4. The method for deducing pollution points based on slag car track points according to claim 2, wherein the specific method for identifying the non-active vehicle data of the slag car data after eliminating the abnormal data by using the rectangular 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 carrier;
s132, judging whether the road short distance is smaller than a distance threshold value or not by taking the obtained maximum longitude and latitude and the obtained minimum longitude and latitude as the maximum path distance of the vehicle track, and if so, judging that the slag transport vehicle is an inactive vehicle.
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| CN117131149B (en) * | 2023-10-26 | 2024-01-23 | 四川国蓝中天环境科技集团有限公司 | Earth and rock point location and transportation network identification method based on GPS track of slag transport vehicle |
| CN117473398B (en) * | 2023-12-26 | 2024-03-19 | 四川国蓝中天环境科技集团有限公司 | Urban dust pollution source classification method based on slag transport vehicle activity |
| CN117494011B (en) * | 2023-12-29 | 2024-03-12 | 四川国蓝中天环境科技集团有限公司 | Dust raising point position type discriminating method based on earth and stone transport characteristics of slag transport vehicle |
| CN118211853B (en) * | 2024-05-17 | 2024-07-19 | 四川国蓝中天环境科技集团有限公司 | Slag car active point position calculation method based on cross-space-time clustering |
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