CN115143925B - Locomotive operation information processing method based on satellite positioning and running track analysis - Google Patents
Locomotive operation information processing method based on satellite positioning and running track analysis Download PDFInfo
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Abstract
The invention discloses a locomotive operation information processing method based on satellite positioning and running track analysis, which relates to the technical field of agricultural machinery and comprises the following steps: A. acquiring data uploaded by an agricultural machine terminal; B. generating a working line according to the track points in the data; C. dividing land according to the working line; D. extracting the land parcel boundary; E. and (5) repeatedly judging the land parcels. The beneficial technical effects of the invention are as follows: 1. the scale of the platform for storing the original data and some process data is greatly reduced; 2. the dividing result of the operation line is more similar to the original track; 3. the land block is accurately divided, and the invalid area generated by road running is removed; 4. the repeated judgment of the operation area ensures the execution efficiency on the premise of accuracy.
Description
Technical Field
The invention relates to the technical field of agricultural machinery, in particular to a locomotive operation information processing method based on satellite positioning and running track analysis.
Background
The intelligent monitoring system for agricultural machinery operation is characterized in that a sensor and a positioning device are arranged on the agricultural machinery, agricultural machinery operation, position data and state data are reported to a background, remote monitoring and management requirements of various operations such as deep scarification, deep turning, rotary tillage, combine harvesting, straw returning and agricultural machinery plant protection of the agricultural machinery are met, and agricultural machinery operation monitoring with high precision, high reliability, real-time convenience and convenience is realized for agricultural production cultivation and harvesting.
In agricultural work, an agricultural work area cannot be directly obtained. Moreover, due to the ubiquitous presence of social services of agricultural machinery, the land ownership, the agricultural machinery ownership and rights of agricultural machinery operation services are not uniform, so that the problems of material reduction by steal and labor reduction and the like are ubiquitous, and the problems occur when the national operation patch is taken out. How to monitor the operation area and the operation quality of the agricultural machinery becomes the rigidity requirement of the current agricultural institution management and the rigidity requirement of the agricultural machinery operation popularization.
The existing platform processing flow is roughly divided into the following steps.
(1) And (3) data acquisition: the agricultural machine terminal collects the track points in real time and uploads the track points to the platform at a set frequency, and the platform is ordered according to the ascending order of time. The data of each track point comprises fields such as longitude and latitude coordinates, acquisition time, operation state marks, heading angles, speed and the like.
(2) Track point generation working line: a line is generated based on the relationship between the working state, the angle, and the like.
(3) Generating a land block by a working line: dividing the land according to the time-distance relation of the adjacent lines.
(4) Extracting land parcel boundaries: and acquiring an enclosed track point group of each divided land, sequentially connecting lines to generate a land boundary, solving an average value of all boundary points to serve as a center point of the land, carrying out GEOHASH coding on the center point of the land, and storing the boundary point group, the center point and GEOHASH coding to serve as identification information of the land.
(5) And (5) repeatedly judging the land parcels: and searching other land block center points (generally, center points with the same GEOHASH codes are searched) within a certain query range from the land block in a database by taking the center point of the current land block as an index, indexing out the corresponding land block, sequentially judging whether the current land block and the searched other land blocks have a common operation area by using a geometric polygon judgment intersection method, and subtracting the repeated operation area from the current operation area to obtain the real operation area of the agricultural machinery.
At present, the agricultural platform has at least the following technical problems in the prior art in the treatment process: the volume of the uploading data and the process data stored by the platform is large, so that the storage and calculation performance of the uploading data and the process data are affected; the operation line and the land block are not accurately divided, so that the accuracy of operation area calculation and land block repeated judgment is affected; in addition, as the operation mode of the agricultural machinery is very random, the types of the land parcels are very many, the situation is very complex, and if the hollow part appears in the land parcels, the inclusion relationship exists between the land parcels, the two cases exist together, and the like, the geometric intersection method is easy to have loopholes, and misjudgment is caused.
As shown in fig. 1, both plots 1 and 2 do not intersect, there is no repeated area, but since plot 2 has a hollow portion, and plot 1 is just in its hollow portion, when judging by geometric intersection, both plots will be treated as solid plane patterns, and erroneous judgment will occur.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a locomotive operation information processing method based on satellite positioning and running track analysis, which aims at special situations and improves judgment accuracy.
In order to solve the technical problems, the invention adopts the following technical scheme: a locomotive operation information processing method based on satellite positioning and running track analysis comprises the following steps: A. acquiring data uploaded by an agricultural machine terminal; B. generating a working line according to the track points in the data; C. dividing land according to the working line; D. extracting the land parcel boundary; E. repeatedly judging the land parcels;
The repeated judgment of the land block comprises the following steps:
E.1, judging whether agricultural tool information of the agricultural machinery is missing or whether the land block meets the operation standard, if the information is missing or the operation does not meet the operation standard, not calculating repetition of the current land block and setting the repetition area to be 0, otherwise, executing the step E.2;
E.2, traversing suspected plots with the same GEOHASH codes as the current plots to obtain a list of suspected repeated plots Bi with the same GEOHASH codes as the current plots, wherein i=1, n, n is the number of list elements;
E.3, traversing the list, generating an external rectangle Si of the suspected repeated land block Bi and an external rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each external rectangle Si is intersected with the external rectangle S, if so, reserving, otherwise, rejecting;
step E.4, calculating a repetition area Pi between the current block and each reserved suspected repetition block Bi:
Step E.4.1, generating outline areas OA and OBi of the current land block and the reserved suspected repeated land block Bi respectively by using a JTS function, and solving outline areas SOA and SOBi of the OA and OBi respectively, operation areas SA and SBi of the current land block and the reserved suspected repeated land block Bi respectively and distances between centers of the current land block and the reserved suspected repeated land block Bi and the centers of gravity disA and disBi respectively;
Step E.4.2, respectively obtaining union sets of buffer areas which are generated by using the width as the width for each operation line of the current land block and the reserved suspected repeated land block Bi, and respectively marking the union sets as AlineBufferUnion and BlineBufferUnion;
step E.4.3 using JTS function to determine the inclusion relationship between OA and OBi:
Step e.4.3.1 pi= OBi if the current block contains Bi and SA > =a×soa, disA < =d, SBi > =a× SOBi and disBi < =d;
step e.4.3.2 if the current block contains Bi, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.3 if the current block contains Bi and SA < a > SOA or disA > d and SBi > =a > SOBi and disBi < =d, pi= AlineBufferUnion n OBi;
Step e.4.3.4 pi= BlineBufferUnion if the current block contains Bi, and SA > =a×soa and disA < =d, and SBi < a× SOBi or disBi > d;
Step e.4.3.5 pi=oa if Bi comprises the current block, and SA > =a×soa and disA < =d, and SBi > =a× SOBi and disBi < =d;
Step e.4.3.6 if Bi comprises the current plot, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
step e.4.3.7 if Bi comprises the current block, and SA < a×soa or disA > d, and SBi > =a× SOBi and disBi < =d, pi= AlineBufferUnion;
step e.4.3.8 if Bi comprises the current block, and SA > = a SOA and disA < = d, and SBi < a SOBi or disBi > d, pi=oa n BlineBufferUnion;
Step e.4.3.9 if the current block and Bi have no inclusion relationship, and SA > = a×soa and disA < = d, and SBi > = a× SOBi and disBi < = d, pi=oa n OBi;
Step e.4.3.10 if the current block and Bi have no inclusion relationship, and SA < a×soa or disA > d, and SBi < a× SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.11 if the current block and Bi have no inclusion relationship, and SA < a×soa or disA > d, and SBi > =a× SOBi and disBi < =d, pi= AlineBufferUnion n OBi;
step e.4.3.12 if the current block and Bi have no inclusion relationship, and SA > = a×soa and disA < = d, and SBi < a× SOBi or disBi > d, pi=oa n BlineBufferUnion;
Step e.4.3.13 if oa= OBi and SA > =a×soa and disA < =d and SBi > =a× SOBi and disBi < =d, pi=oa or OBi;
Step e.4.3.14 if oa= OBi, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.15 if oa= OBi and SA < a > SOA or disA > d and SBi > =a > SOBi and disBi < =d, pi= AlineBufferUnion;
Step e.4.3.16 if oa= OBi and SA > =a SOA and disA < =d and SBi < a SOBi or disBi > d, pi= BlineBufferUnion;
In the step, a is a proportionality coefficient, and the value range is [0.6,1]; d is a distance threshold, the unit is meter, and the value range is [0,30]; and ∈d denotes the intersection region for two polygons using JTS library function intersect ().
E.5, merging all Pis by using union () functions in JTS library functions to obtain P, and calculating the contour area of the P to obtain the historical repeated area of the current land block;
and E.6, repeating the steps E.1-E.5, and repeatedly judging the next land parcel.
The beneficial technical effects of the invention are as follows: 1. the scale of the platform for storing the original data and some process data is greatly reduced; 2. the dividing result of the operation line is more similar to the original track; 3. the land block is accurately divided, and the invalid area generated by road running is removed; 4. the repeated judgment of the operation area ensures the execution efficiency on the premise of accuracy.
The present invention will be described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a land parcel 1 in the prior art when it is in a hollow portion of land parcel 2.
FIG. 2 is a comparison of the data uploaded by an agricultural machine terminal before and after processing;
FIG. 3 is a graph comparing trace points in data before and after processing;
FIG. 4 is a plot division comparison diagram before and after processing by a grid clustering algorithm;
FIG. 5 is a graph comparing the data of the compressed point set with the thinning algorithm;
FIG. 6 is a flow chart of a plot repeat determination;
FIG. 7 is a schematic diagram of plots A and Bi with no open areas within A and Bi in A;
FIG. 8 is a schematic diagram of plots A and Bi with hollow areas and Bi in A;
FIG. 9 is a schematic diagram of plot A with a hollow area and Bi in A;
FIG. 10 is a schematic diagram of plots Bi with open areas, A with solid areas, bi in A;
FIG. 11 is a schematic diagram of plots A and Bi with no open areas within plots A and Bi and A in Bi;
FIG. 12 is a schematic diagram of plots A and Bi with a hollow region and A in Bi;
FIG. 13 is a schematic diagram of land A with hollow areas, bi being solid areas, and A being Bi;
FIG. 14 is a schematic diagram of plots Bi with hollow areas and A in Bi;
FIG. 15 is a schematic view of plots A and Bi without the presence of a hollow region;
FIG. 16 is a schematic diagram of plots A and Bi with open areas;
FIG. 17 is a schematic diagram of land A with a hollow area;
FIG. 18 is a schematic diagram of a plot Bi with a hollow area;
FIG. 19 is a schematic view of plots A and Bi with the contours completely coincident and without the hollow region;
FIG. 20 is a schematic view of the structure of plots A and Bi with coincident profiles but with hollow areas;
FIG. 21 is a schematic diagram of the structure of plots A and Bi with overlapping contours but with a void in A;
FIG. 22 is a schematic diagram of the structure of plots A and Bi with overlapping contours but with Bi present in the hollow.
Detailed Description
The invention provides a locomotive operation information processing method based on satellite positioning and running track analysis, which comprises the following steps.
A. And acquiring data uploaded by the agricultural machinery terminal.
The platform receives the data uploaded by the terminal all the day, and when the uploading frequency of the terminal is higher (the current uploading is carried out for 2 seconds), the platform stores 43200 pieces of data uploaded by the current terminal all the day. When the operation is in a busy season, the operation agricultural machinery is more, the uploading data volume is large, the platform storage pressure is also large, and a large amount of data with little use for area calculation can be stored; however, if the uploading frequency is reduced, some important track points (such as turning points) are not uploaded, so that track distortion is caused, and more serious, calculation of the working area is affected.
The sensor installed on the agricultural machine can acquire the operation state mark of each track point, for example, deep scarification can detect the ploughing depth of each track point, and if the set depth is greater than or equal to 25cm and is in an operation state, the set depth is less than 25cm and is in a non-operation state. The data in the non-working state should be kept as little as possible because it does not participate in area calculation, but it is necessary to ensure that the trajectory is not distorted. Therefore, whether each real-time collected non-operation point is a track point with obvious characteristics such as turning or turning around or not can be judged to be compared with the previous point, if yes, uploading is performed, and if not, uploading is not performed.
The method specifically comprises the following steps:
and A.1, acquiring a positioning point Pi, if Pi is a working track point, directly uploading according to the original frequency, then judging whether the next positioning point Pi+1 is the working track point, and if not, executing the step A.2.
And A.2, uploading the current positioning point Pi as an initial point, recording the heading angle head_i of the point and storing a temporary variable tmp=head_i.
And A.3, judging whether the next positioning point Pi+1 of the Pi is a job track point, if so, executing the step A.1, otherwise, executing the step A.4.
And A.4, calculating the difference between the heading angle head_i+1 of the locating point Pi+1 and the heading angle of the temporary variable tmp, and the difference between the heading angle head_i+1 and the heading angle head_i of the previous locating point Pi.
And A.5, if the absolute value of the difference between the heading angle head_i+1 and the temporary storage variable tmp is larger than ht or the absolute value of the difference between the heading angle head_i+1 and the heading angle head_i of the locating point Pi is larger than hd, judging Pi+1 as a characteristic point, uploading the characteristic point, updating the heading angle of the point to tmp, wherein tmp=head_i+1, i=i+1, continuing to execute the step A.3, otherwise, not uploading, i=i+1, and continuing to execute the step A.3. Wherein, the ht unit is the degree, and the value range is [10,90]; hd is in degrees and the value range is [10,45]. In this embodiment, ht and hd are both 10 °.
And A.6, acquiring the last positioning point at which the operation is finished, judging whether the last positioning point is in the uploaded point column, and if the last positioning point is not in the uploaded point column, uploading the last positioning point.
The method effectively solves the problems encountered by the storage performance and the calculation performance of the platform, in the example, 31000 non-operation track points in 43200 track points uploaded by the agricultural machine on a certain day account for 71.7% of the total number, only 450 non-operation track points are reserved after the steps are carried out, about 1.45% and about 1.04% of the total number, the data scale is greatly reduced, the track shape is basically consistent with the original track, as shown in the attached figure 2, the non-operation track from a certain section of the agricultural machine is taken as a typical sample, the original track before processing is shown in the left graph in the attached figure, the characteristic points identified in the section of track are marked in the right graph, other unidentified parts are replaced by straight lines, and the effect is very obvious before and after the comparison processing.
B. and generating a working line according to the track points in the data.
After the agricultural machine is operated, all generated track points are required to be divided into operation lines in a reasonable mode and the operation completion area is calculated according to each operation line, the track line is divided only by the fact that the angle change of the track points is larger than a certain set threshold value, if the angle of two adjacent points is changed by 30 degrees, the track is divided into two lines, logic is simple, processing is convenient and fast, and the defect of the method is that the broken line judgment of an arc line is achieved. If the agricultural machinery runs straight, the agricultural machinery turns in the middle, so the working line is divided into two parts; the agricultural machinery always runs in a small angle arc, so that the working line is only judged to be one. The mountain land topography in different areas is different, so that a large error occurs when the area is calculated by the working line.
Therefore, a reasonable line dividing method is as follows: the initial movement behavior (line segment consisting of the first two points of each new track) can be checked sequentially from each point to the initial movement behavior in track point order by setting the position disturbance threshold, here 10m can be takenI=1, …, n, find all feature points (points with a disturbance distance greater than the disturbance threshold) and divide the track TR into stationary sub-tracks SST (all points in the track have a disturbance distance less than the disturbance threshold d 0). The main idea of the step is as follows: the value d gi, i=1, …, n of each is checked based on the current initial movement behavior. The specific steps are as follows.
B.1, ordering all track points according to time, and taking the first two points to form initial movement behaviorAnd adds these two points to the set of line trace points STR.
B.2, traversing from the next point, calculating g i andIf d gi≤d0, add g i to the STR point set and continue traversing the next point, otherwise go to step b.3.
B.3 if d gi>d0, dividing STR= { g 1,…,gi-1 } into one working line, takingInitial movement behavior for the next new line willIs marked asAnd generates the next set of STR points.
And B.4, judging whether g i is the last point, if not, continuing to execute the step B.2, if so, ending the line division, and recording the characteristic points of all the dividing line segments.
As shown in fig. 3, a typical example is taken as a working track of a certain section of the agricultural machine, and after the step, each section of the segmented working line is relatively close to a corresponding arc-shaped track line.
C. Dividing the land according to the working line.
If a plot is divided into a plurality or if a plurality of plots is divided into one, the result is inaccurate when the area is calculated using the outer contour method. In addition, the trace line of some agricultural machines on the road in the non-cultivated land area is also determined to be in the working state, and the longer the trace line, the larger the generated trace line. Since the country can issue the subsidy according to the operation area of the machine hand, the amount of the subsidy is seriously affected by the unrecognized redundant area on the road. The reasons for the area of the road are that the sensor is not installed in place, the topography factors of some areas, etc.
In the step, the operation line is divided into plots by a grid clustering algorithm, wherein:
Grid size:
density threshold:
Wherein, N >0, lambda >0, plowing width represents the breadth of the agricultural machinery, unit: and (5) rice. speed represents the track velocity value in units of: meter/second, t represents the trace sample time interval in units of: second.
In this example, n=1, λ=0.5, and speed in the grid size parameter is calculated as the speed between two adjacent points, and the third quartile of the speed value of the track set is taken. The speed of the density threshold is calculated to get max (speed, 1).
The track of a normal agricultural work plot should not have only one direction, and there must be a u-turn condition, i.e. a reverse line exists, and the time difference from the reverse line should be within a certain time range, which is different from the track characteristics on the road. Thus, the step of filtering the plot according to the trace points and trace lines in the divided plot is also included in the present step, and includes the steps of.
And C.1, reserving a line with the point number of the center line of the land block being equal to or more than point_num.
And C.2, the number of the filtered lines is less than or equal to 1, and the line is considered as a road, otherwise, the step C.3 is executed.
Traversing the line Li in the land, reading the course angle head_Li, searching other lines of the land line set, if a reverse line Lj with the time difference smaller than td, the angle difference larger than HL and the distance smaller than or equal to 2x plowing width exists, the land is considered as a normal land, otherwise, the land is considered as an on-road land, and filtering is carried out,
Wherein, point_num is the point threshold, the value range is that point_num is more than or equal to 3, td units are seconds, the value range is [600,900], HL units are degrees, and the value range is [160,180]. In this embodiment, point_num=3, td takes 600 seconds, and HL takes 160 °.
Referring to fig. 4, the original method divides the 9 working areas of the agricultural machine into 4 plots, searches track points in the background, finds that a large number of areas of the plot 2 are not worked, and divides the plurality of plots into 1 due to the track in the normal working state of the road, that is, the division has a problem, which directly affects the subsequent repeated area calculation (that is, if other agricultural machines work in the area covered by the plot 2, it is determined to be repeated). By adopting the method, the method is accurately divided into 9 independent plots which are not connected with each other, so that the plot division after the step is more accurate.
D. And (5) extracting the land parcel boundary. When the land is large, the boundary points are more, so that the storage pressure of the platform is high on one hand, and the efficiency of generating polygons by the boundary is low on the other hand.
When the boundary points forming the contour of the land block are more, the contour can be thinned in a track compression mode, the number of data points can be greatly reduced after thinning, the shape of the contour can be kept undistorted, and the contour thinning step of the land block comprises the following steps:
And D.1, traversing the land parcels, and extracting a land parcel boundary point set by adopting a JTS outsourcing polygon method for any land parcel.
And D.2, compressing point set data by adopting a thinning algorithm for each block boundary point set.
The method for compressing the point set data by adopting the thinning algorithm comprises the following steps:
(1) Traversing the boundary point set, counting the number Num of the point set, and if the number of the points is less than three, not needing to compress; otherwise, the step (2) is entered.
(2) Taking the first and last points P1 and P2 according to the arrangement sequence of the point set, constructing a straight line Traj between the two points, traversing all other points on Traj, finding out the point Pi with the largest distance Traj as a division point, and recording the distance as D max.
(3) If D max < ρ, the error threshold ρ is set, the straight line Traj is set as an approximation of the piece of data.
(4) If D max is more than or equal to ρ, dividing Pi as a dividing point into two sections, and processing according to steps (2) - (4) respectively.
(5) And sequentially connecting the fold lines subjected to the segmentation treatment, and taking the obtained point set as a compressed result.
The error threshold ρ of the algorithm can be adaptively set according to the number of point sets, i.e. 1 meter according to 100 points, and ρ=num/100.
As shown in fig. 5, taking a plot 7 as an example, the number of boundary points of the plot is 101, the number of boundary points is 16 after the plot is compressed by using a thinning algorithm, the number of the boundary points is about 15.8% of the number of the original data points, and the outline shape is basically consistent with the original outline. Other plots were treated in the same manner, with a total of 1236 boundary points for 9 plots, 157 points after compression, accounting for approximately 12.7% of the total. The storage overhead of the platform is further reduced, and the computing efficiency is improved.
E. And (5) repeatedly judging the land parcels.
In the prior art, as in the patent with the application number 201910209752.1 and the agricultural machinery repeated operation area judging method based on space analysis, the whole flow of repeated land parcel judgment is designed, including GEOHASH coding, GEOHASH neighborhood searching, rough screening of suspected repeated land parcel, final suspected repeated land parcel locking and the like. The method is based on the flow steps of the patent method, but considers that the method does not distinguish specific spatial features of the operation land block, thereby causing the problem of multiple misjudgment, and therefore accurately identifying and effectively processing multiple situations of the finally locked suspected repeated land block.
Referring to fig. 6, taking an example of sequentially performing repeated judgment on 9 plots of the agricultural machinery operation, the method comprises the following steps:
e.1, judging whether agricultural tool information of the agricultural machinery is missing or whether the land block meets the operation standard, if the information is missing or the operation does not meet the operation standard, the current land block does not calculate repetition and sets the repetition area to be 0, otherwise, executing the step E.2.
If the agricultural tool information of the agricultural machine is lost, the platform cannot track the information of the agricultural machine and cannot repeatedly judge with other operation plots; if the agricultural tool information of the agricultural machine is normal, but the operation is not up to standard, namely the standard reaching rate is less than 85 percent, and the operation land block is invalid according to the rule, repeated comparison with other operated land blocks is not needed. Therefore, the precondition of repeated judgment is that the agricultural tool information of the agricultural machine is normal and the operation qualification rate meets the requirement.
And E.2, starting from the land block 1, marking the land block as a current land block A, traversing suspected land blocks with the same coding as GEOHASH of the current land block to obtain a list of suspected repeated land blocks Bi with the same coding as GEOHASH of the current land block, wherein i=1.
GEOHASH is a quick and effective range search algorithm, and although the search range is thicker, the characteristic of the search range is quickly positioned so that the search range is suitable for being applied to the scene. And using GEOHASH codes to find out 33 suspected repeated plots.
E.3, traversing the list, generating an external rectangle Si of the suspected repeated land block Bi and an external rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each external rectangle Si is intersected with the external rectangle S, if so, reserving, otherwise, rejecting.
Because GEOHASH codes have thicker coverage, a plurality of plots which are completely disjoint with the current plot are put into the list, coarse screening is needed again, the plots can be rapidly and effectively removed by the circumscribed rectangle method, and through the step, 8 suspected repeated plots are remained.
And E.4, calculating a repetition area Pi of the current land parcel and each reserved suspected repetition land parcel Bi.
Step E.4.1, generating outline areas OA and OBi of the current land block and the reserved suspected repeated land block Bi respectively by using a JTS function, and solving outline areas SOA and SOBi of the OA and OBi respectively, working areas SA and SBi of the current land block and the reserved suspected repeated land block Bi respectively and distances between centers of the current land block and the reserved suspected repeated land block Bi and the centers of gravity disA and disBi respectively.
In the prior art, as in the patent with the application number 201910209752.1 and the method for judging the area of the repeated operation of the agricultural machinery based on space analysis, the intersect () function in the JTS library is used for simply judging the position relation of the outline areas OA and OBi, so as to obtain the repeated area, the method is simple and convenient, the calculation efficiency is higher, and the method is also applicable to the land block of normal operation, wherein the normal operation refers to that the sum of the buffer areas generated by each track line and the width in the land block A (or Bi) is very close to or the same as OA or OBi, namely, the operation line is denser, and no hollow area exists in the land block. However, in the actual working scenario, this is not the only case, as illustrated in fig. 1, and will not be described here again. It is therefore necessary to identify whether or not the land block a (or Bi) is a void region by an index in advance at the time of judgment. In general, the presence of a hollow area in a plot is characterized by: the area of the work is obviously smaller than the outline area, or the center point of the land block and the center point of the land block are obviously deviated. For this purpose, a criterion is given for the presence of a hollow region in the block: the method satisfies the condition that the finishing area is less than a, or the distance between the center of the land and the center of gravity is greater than d. Wherein a is a proportionality coefficient, and the value range is [0.6,1], and 0.6 is taken here; d is a distance threshold, in meters, and the range of values is [0,30], here 20; i.e. SA <0.6 x soa or disA >20 a is present in the hollow region and Bi is the same.
And E.4.2, respectively obtaining a union set of buffer areas generated by each line of the current land block and the reserved suspected repeated land block Bi with the width being the width, and respectively marking the union set as AlineBufferUnion and BlineBufferUnion.
Since the intersect () function in the JTS library represents any shape of a block as a solid area with the contour of the block as a side, a block with a hollow area will cause erroneous judgment when the judgment is repeated, and therefore, a method for accurately representing the actual coverage area of the block is required. The Buffer method Buffer in the JTS function can achieve the purpose, the Buffer function generates independent rectangular graphs with each line and breadth, and the coverage area situation of the land block can be truly reflected, but the Buffer function has the defect of slow calculation, because the intersecting situation of each small rectangle and other rectangles of two land blocks needs to be traversed in sequence, namely if land block A has p lines and land block Bi has q lines, p times q rectangular areas need to be compared to obtain a union set, and when p and q are large in scale, the result is accurate, but the efficiency is extremely low, and the Buffer function can only be used when necessary. Therefore, the method identifies the position relation and the spatial characteristics of the two plots in advance, and adopts different comparison judgment strategies aiming at different conditions so as to find the best balance between accuracy and efficiency. AlineBufferUnion and BlineBufferUnion generated in this step are used in the subsequent determination step only when the use condition is triggered.
The following steps are description of 16 possible positional relationships between A and Bi and corresponding processing methods, including spatial relationships that A and Bi are mutually opposite sub-regions, A and Bi are partially intersected, A and Bi are completely overlapped, and the like, and the cases that A and Bi are solid regions or hollow regions exist in each spatial relationship.
Step E.4.3 uses JTS function to determine the inclusion relationship of OA and OBi.
Step e.4.3.1 pi= OBi if the current block contains Bi and SA > =0.6×soa, disA < =20, SBi > =0.6×sobi, and disBi < =20.
As shown in fig. 7, there is no hollow area in both land areas a and Bi, and Bi is in a, then the repeated area is the outline area OBi of Bi, and the area is the finished area SBi of Bi.
Step e.4.3.2 if the current block contains Bi and SA <0.6 x soa or disA >20 and SBi <0.6 x sobi or disBi >20, pi= AlineBufferUnion n BlineBufferUnion.
As shown in fig. 8, the plots a and Bi both have hollow areas, and Bi is in a, so that a and Bi cannot be directly compared with each other by using the contour area, but can only be compared by using the Buffer area, and the repeated area is the intersection area of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.3 if the current block contains Bi and SA <0.6 x soa or disA >20 and SBi > =0.6 x sobi and disBi < =20, pi= AlineBufferUnion n Obi.
As shown in FIG. 9, at this point, there is a hollow area in plot A, bi is in A, bi can be compared with the outline area, A can only be compared with the Buffer area, and the repeated area is the intersection of AlineBufferUnion and OBi.
Step e.4.3.4 pi= BlineBufferUnion if the current block contains Bi, and SA > =0.6×soa and disA < =20, and SBi <0.6×sobi or disBi > 20.
As shown in FIG. 10, in this case, the land Bi has a hollow area, and since A is a solid area and Bi is in A, the repeated area is the area where Bi actually works, i.e., blineBufferUnion.
Step e.4.3.5 pi=oa if Bi contains the current block, and SA > =0.6×soa and disA < =20, and SBi > =0.6×sobi and disBi < =20.
As shown in fig. 11, there is no hollow area in both plots a and Bi, and a is in Bi, then the repeated area is the outline area OA of a, and the area is the finished area SA of a.
Step e.4.3.6 if Bi comprises the current plot and SA <0.6 x soa or disA >20 and SBi <0.6 x sobi or disBi >20, pi= AlineBufferUnion n BlineBufferUnion.
As shown in fig. 12, plots a and Bi both have hollow areas, and a is in Bi, so that a and Bi cannot be directly compared with each other by using a contour area, but can only be compared by using a Buffer area, and the repeated area is the intersection area of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.7 pi= AlineBufferUnion if Bi contains the current block and SA <0.6 x soa or disA >20 and SBi > =0.6 x sobi and disBi < =20.
As shown in fig. 13, at this time, the land a has a hollow area, and since Bi is a solid area, a is in Bi, and the repeated area is the area where a actually works, that is, alineBufferUnion.
Step e.4.3.8 pi=oa n BlineBufferUnion if Bi contains the current block, and SA > =0.6×soa and disA < =20, and SBi <0.6×sobi or disBi > 20.
As shown in FIG. 14, the land block Bi has a hollow area, A is in Bi, A can be compared by using a contour area, bi can only be compared by using a Buffer area, and the repeated area is the intersection of BlineBufferUnion and OA.
Step e.4.3.9 pi=oa OBi if the current block and Bi have no inclusion relationship, and SA > =0.6×soa and disA < =20, and SBi > =0.6×sobi and disBi < =20;
As shown in FIG. 15, where neither land areas A and Bi have a hollow area, the respective profiles can be directly compared, resulting in the intersection of OA and OBi.
Step e.4.3.10 pi= AlineBufferUnion n BlineBufferUnion if the current block and Bi have no inclusion relationship, and SA <0.6 x soa or disA >20, and SBi <0.6 x sobi or disBi > 20.
As shown in fig. 16, the plots a and Bi have hollow areas, and thus, a and Bi cannot be directly compared with each other by using the contour area, but can be compared only by using the Buffer area, and the repeated area is the intersection area of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.11 pi= AlineBufferUnion n Obi if the current block and Bi have no inclusion relationship, and SA <0.6 x soa or disA >20, and SBi > =0.6 x sobi and disBi < =20.
As shown in FIG. 17, at this point, block A has a hollow area, bi can be compared with a contour area, A can only be compared with a Buffer area, and the repeated area is the intersection of AlineBufferUnion and OBi.
Step e.4.3.12 pi=oa n BlineBufferUnion if the current block and Bi have no inclusion relationship, and SA > =0.6×soa and disA < =20, and SBi <0.6×sobi or disBi > 20.
As shown in FIG. 18, at this time, the land Bi has a hollow area, A can be compared by a contour area, bi can be compared only by a Buffer area, and the repeated area is the intersection of BlineBufferUnion and OA.
Step e.4.3.13 pi=oa or OBi if oa= OBi and SA > =0.6×soa and disA < =20 and SBi > =0.6×sobi and disBi < =20.
As shown in FIG. 19, where the contours of plots A and Bi are completely coincident and there are no open areas, then the repeated areas are themselves, namely OA or OBi.
Step e.4.3.14 if oa= OBi and SA <0.6 x soa or disA >20 and SBi <0.6 x sobi or disBi >20, pi= AlineBufferUnion n BlineBufferUnion.
As shown in fig. 20, although the outlines of land block a and Bi overlap, both have hollow areas (a for left side portion, bi for right side portion, and middle portion are intersected), and the repeated areas cannot be directly judged, and only the Buffer areas can be used for comparison, and the repeated areas are the intersection areas of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.15 if oa= OBi and SA <0.6 x soa or disA >20 and SBi > =0.6 x sobi and disBi < =20, pi= AlineBufferUnion;
as shown in fig. 21, although the outlines of land areas a and Bi overlap, a has a hollow portion, and the Buffer area of a is a repeat area, that is, alineBufferUnion.
Step e.4.3.16 pi= BlineBufferUnion if oa= OBi and SA > =0.6×soa and disA < =20 and SBi <0.6×sobi or disBi > 20.
As shown in fig. 22, although the outlines of land a and Bi overlap, bi has a hollow portion, and the Buffer region of Bi is a repeat region, that is BlineBufferUnion.
In the above steps, n represents the intersection area for two polygons obtained by JTS library function intersect ().
And E.5, merging all Pis by using union () functions in the JTS library function to obtain P, and calculating the contour area of the P to obtain the historical repeated area of the current land block.
And E.6, repeating the steps E.1-E.5, and repeatedly judging the next land parcel.
In this example, after the above-described repeated judgment operation was sequentially performed on each of the 9 plots, a total of 23.92 mu of the historical repeated area of the plots was obtained. Deducting the total operation completion area of the agricultural machine from 75.41 mu, and obtaining the qualified operation area 51.49 mu of the agricultural machine on the same day.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same; while the invention has been described in detail with reference to the preferred embodiments, those skilled in the art will appreciate that: modifications may be made to the specific embodiments of the present invention or equivalents may be substituted for part of the technical features thereof; without departing from the spirit of the invention, it is intended to cover the scope of the invention as claimed.
Claims (7)
1. A locomotive operation information processing method based on satellite positioning and running track analysis is characterized by comprising the following steps:
A. acquiring data uploaded by an agricultural machine terminal;
B. generating a working line according to the track points in the data;
C. dividing land according to the working line;
D. Extracting the land parcel boundary;
E. Repeatedly judging the land parcels;
The repeated judgment of the land block comprises the following steps:
E.1, judging whether agricultural tool information of the agricultural machinery is missing or whether the land block meets the operation standard, if the information is missing or the operation does not meet the operation standard, not calculating repetition of the current land block and setting the repetition area to be 0, otherwise, executing the step E.2;
E.2, traversing suspected plots with the same GEOHASH codes as the current plots to obtain a list of suspected repeated plots Bi with the same GEOHASH codes as the current plots, wherein i=1, n, n is the number of list elements;
E.3, traversing the list, generating an external rectangle Si of the suspected repeated land block Bi and an external rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each external rectangle Si is intersected with the external rectangle S, if so, reserving, otherwise, rejecting;
step E.4, calculating a repetition area Pi between the current block and each reserved suspected repetition block Bi:
Step E.4.1, generating outline areas OA and OBi of the current land block and the reserved suspected repeated land block Bi respectively by using a JTS function, and solving outline areas SOA and SOBi of the OA and OBi respectively, operation areas SA and SBi of the current land block and the reserved suspected repeated land block Bi respectively and distances between centers of the current land block and the reserved suspected repeated land block Bi and the centers of gravity disA and disBi respectively;
Step E.4.2, respectively obtaining union sets of buffer areas which are generated by using the width as the width for each operation line of the current land block and the reserved suspected repeated land block Bi, and respectively marking the union sets as AlineBufferUnion and BlineBufferUnion;
step E.4.3 using JTS function to determine the inclusion relationship between OA and OBi:
Step e.4.3.1 pi= OBi if the current block contains Bi and SA > =a×soa, disA < =d, SBi > =a× SOBi and disBi < =d;
step e.4.3.2 if the current block contains Bi, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.3 if the current block contains Bi and SA < a > SOA or disA > d and SBi > =a > SOBi and disBi < =d, pi= AlineBufferUnion n OBi;
Step e.4.3.4 pi= BlineBufferUnion if the current block contains Bi, and SA > =a×soa and disA < =d, and SBi < a× SOBi or disBi > d;
Step e.4.3.5 pi=oa if Bi comprises the current block, and SA > =a×soa and disA < =d, and SBi > =a× SOBi and disBi < =d;
Step e.4.3.6 if Bi comprises the current plot, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
step e.4.3.7 if Bi comprises the current block, and SA < a×soa or disA > d, and SBi > =a× SOBi and disBi < =d, pi= AlineBufferUnion;
step e.4.3.8 if Bi comprises the current block, and SA > = a SOA and disA < = d, and SBi < a SOBi or disBi > d, pi=oa n BlineBufferUnion;
Step e.4.3.9 if the current block and Bi have no inclusion relationship, and SA > = a×soa and disA < = d, and SBi > = a× SOBi and disBi < = d, pi=oa n OBi;
Step e.4.3.10 if the current block and Bi have no inclusion relationship, and SA < a×soa or disA > d, and SBi < a× SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.11 if the current block and Bi have no inclusion relationship, and SA < a×soa or disA > d, and SBi > =a× SOBi and disBi < =d, pi= AlineBufferUnion n OBi;
step e.4.3.12 if the current block and Bi have no inclusion relationship, and SA > = a×soa and disA < = d, and SBi < a× SOBi or disBi > d, pi=oa n BlineBufferUnion;
Step e.4.3.13 if oa= OBi and SA > =a×soa and disA < =d and SBi > =a× SOBi and disBi < =d, pi=oa or OBi;
Step e.4.3.14 if oa= OBi, and SA < a > SOA or disA > d, and SBi < a > SOBi or disBi > d, pi= AlineBufferUnion n BlineBufferUnion;
Step e.4.3.15 if oa= OBi and SA < a > SOA or disA > d and SBi > =a > SOBi and disBi < =d, pi= AlineBufferUnion;
Step e.4.3.16 if oa= OBi and SA > =a SOA and disA < =d and SBi < a SOBi or disBi > d, pi= BlineBufferUnion;
in the step, a is a proportionality coefficient, and the value range is [0.6,1]; d is a distance threshold, the unit is meter, the value range is [0,30], and the U represents that the JTS library function intersect () is used for solving the intersection area of two polygons;
e.5, merging all Pis by using union () functions in JTS library functions to obtain P, and calculating the contour area of the P to obtain the historical repeated area of the current land block;
and E.6, repeating the steps E.1-E.5, and repeatedly judging the next land parcel.
2. The locomotive operation information processing method based on satellite positioning and running track analysis according to claim 1, wherein the uploading data of the agricultural locomotive terminal in the step a comprises the following steps:
A1, acquiring a positioning point Pi, if Pi is an operation track point, directly uploading according to the original frequency, then judging whether the next positioning point Pi+1 is the operation track point, and if not, executing the step A.2;
Uploading the current positioning point Pi as an initial point, recording a heading angle head_i of the point and storing a temporary variable tmp=head_i;
A.3, judging whether the next positioning point Pi+1 of Pi is a working track point, if so, executing the step A.1, otherwise, executing the step A.4;
calculating the difference between the heading angle head_i+1 of the locating point Pi+1 and the heading angle of the temporary variable tmp, and the difference between the heading angle head_i+1 and the heading angle head_i of the previous locating point Pi;
A5, if the absolute value of the difference between the heading angle head_i+1 and the temporary variable tmp is larger than ht or the absolute value of the difference between the heading angle head_i+1 and the heading angle head_i of the locating point Pi is larger than hd, judging Pi+1 as a characteristic point, uploading the point, updating the heading angle of the point to tmp, tmp=head_i+1, i=i+1, continuing to execute the step A.3, otherwise, not uploading, i=i+1, continuing to execute the step A.3, wherein the ht unit is degree, the value range is [10,90], the hd unit is degree, and the value range is [10,45];
And A.6, acquiring the last positioning point at which the operation is finished, judging whether the last positioning point is in the uploaded point column, and if the last positioning point is not in the uploaded point column, uploading the last positioning point.
3. The locomotive operation information processing method based on satellite positioning and travel track analysis according to claim 1, wherein the step B of generating the operation line from the track points in the data by the track dividing algorithm includes the steps of:
B.1, ordering all track points according to time, and taking the first two points to form initial movement behavior Adding the two points into a working line track point set STR;
b.2, starting from the next point g i, calculating g i and If d gi≤d0, adding g i to the STR point set and continuing to traverse the next point, otherwise, performing step b.3;
B.3 if d gi>d0, dividing STR= { g 1,…,gi-1 } into one working line, taking Initial movement behavior for the next new line willIs marked asAnd generating a next STR point set;
And B.4, judging whether g i is the last point, if not, continuing to execute the step B.2, and if so, ending the line division and recording the characteristic points of all the division line segments.
4. The locomotive operation information processing method based on satellite positioning and running track analysis according to claim 1, wherein in step C, the operation line is divided into plots by a grid clustering algorithm, wherein:
Grid size:
density threshold:
Wherein, N >0, lambda >0, plowing width represents the breadth of the agricultural machinery, unit: rice, speed, represents the track velocity value in units of: meter/second, t represents the trace sample time interval in units of: second.
5. The method for processing locomotive operation information based on satellite positioning and travel track analysis according to claim 4, further comprising the step of filtering the land parcel according to track points and track lines in the divided land parcel in step C, said step comprising:
c.1, reserving a line with the point number of the center line of the land block being more than or equal to point_num;
c.2, the number of the filtered lines is less than or equal to 1, and the lines are considered as roads, otherwise, the step C.3 is executed;
Traversing the line Li in the land, reading the course angle head_Li, searching other lines of the land line set, if a reverse line Lj with the time difference smaller than td, the angle difference larger than HL and the distance smaller than or equal to 2x plowing width exists, the land is considered as a normal land, otherwise, the land is considered as an on-road land, and filtering is carried out,
Wherein, point_num is the point threshold, the value range is that point_num is more than or equal to 3, td units are seconds, the value range is [600,900], HL units are degrees, and the value range is [160,180].
6. The method for processing locomotive operation information based on satellite positioning and travel track analysis according to claim 4, further comprising a land contour thinning step in step D, wherein the land contour thinning step comprises:
d.1, traversing the land, and extracting a land boundary point set for any land by adopting a JTS outsourcing polygon method;
and D.2, compressing point set data by adopting a thinning algorithm for each block boundary point set.
7. The method for processing locomotive operation information based on satellite positioning and travel track analysis according to claim 6, wherein compressing the point set data using the thinning algorithm comprises the steps of:
(1) Traversing the boundary point set, counting the number Num of the point set, and if the number of the points is less than three, not needing to compress; otherwise, entering the step (2);
(2) Taking the first and the last points P1 and P2 according to the arrangement sequence of the point set, constructing a straight line Traj between the two points, traversing all other points on Traj, finding out the point Pi with the largest distance Traj as a division point, and recording the distance as D max;
(3) Setting an error threshold ρ, and if D max < ρ, taking the straight line Traj as an approximation of the piece of data;
(4) If D max is more than or equal to ρ, dividing Pi as a dividing point into two sections, and processing according to the steps (2) - (4) respectively;
(5) And sequentially connecting the fold lines subjected to the segmentation treatment, and taking the obtained point set as a compressed result.
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| CN110763181B (en) * | 2019-11-13 | 2021-02-23 | 中电科卫星导航运营服务有限公司 | Operation area filtering method based on position data temporal analysis |
| CN111722252B (en) * | 2020-05-14 | 2023-04-11 | 江苏北斗卫星应用产业研究院有限公司 | High-precision operation surface identification method based on track data |
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