CN115143925A - Locomotive operation information processing method based on satellite positioning and running track analysis - Google Patents
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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 blocks according to the operating line; D. extracting land boundaries; E. and (5) repeatedly judging the 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 division result of the operating line is closer to the original track; 3. the land parcel is divided accurately, and the invalid area generated by road driving 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
Agricultural machinery operation intelligent monitoring system relies on installing sensor and positioner on agricultural machinery, reports agricultural machinery operation and positional data and state data to the backstage, satisfies the remote monitoring, the management demand of multiple types of operation such as the dark pine of agricultural machinery, dark turn over, rotary tillage, combine to reap, straw returning field, agricultural machinery plant protection, realizes the accurate operation and the precision height of agricultural production cultivation receipts and the real-time convenient agricultural machinery operation monitoring of reliability height.
In the agricultural machinery operation, the agricultural machinery operation area cannot be directly obtained. Moreover, due to the ubiquitous existence of the social service of the agricultural machinery, the problems of material stealing and material reducing, artificial reduction of the operation quality and the like caused by the non-uniform ownership of the land, the ownership of the agricultural machinery and the ownership of the operational service of the agricultural machinery exist commonly, and the occurrence of the national operation subsidy is caused when the national operation subsidy is selected. How to monitor the operation area and the operation quality of the agricultural machinery becomes the rigid requirement of the management of the current agricultural mechanism and the rigid requirement of the popularization of the operation of the agricultural machinery.
The existing platform processing flow is roughly divided into the following steps.
(1) Data acquisition: the agricultural machinery terminal collects the trajectory points in real time and uploads the trajectory points to the platform at a set frequency, and the platform is sorted according to a time ascending sequence. The data of each track point comprises fields such as longitude and latitude coordinates, acquisition time, operation state marks, course angles, speed and the like.
(2) Generating a working line by the track points: and generating a working line according to the relation of the working state, the angle and the like.
(3) The production line generates a land block: and dividing the land blocks according to the time distance relationship of the adjacent operating lines.
(4) Land boundary extraction: and acquiring a track point group wrapped outside each divided land block, sequentially connecting the track point groups to generate a land block boundary, solving the average value of all boundary points as the central point of the land block, carrying out GEOHASH coding on the central point of the land block, and storing the boundary point group, the central point and the GEOHASH coding as the identification information of the land block.
(5) Repeatedly judging the land parcel: the center point of the current land parcel is used as an index, other land parcel center points (generally center points for inquiring the same GEOHASH code) within a certain inquiry range from the land parcel are searched in a database, the corresponding land parcel is indexed, whether the current land parcel and the other searched land parcels have a common operation area is sequentially judged by using a geometric polygon judgment intersection method, if yes, a repeated operation area is subtracted from the current operation area, and then the real operation area of the agricultural machinery is obtained.
In the prior art, the prior agricultural platform at least has the following technical problems in the aspect of the treatment process: the quantity of uploaded data and process data stored by the platform is large, so that the storage and calculation performance of the platform is influenced; the operating line and the plot are divided inaccurately, which affects the accuracy of the operation area calculation and the repeated judgment of the plot; in addition, as the operation mode of the agricultural machinery is random, the types of the land blocks are various, the situations are complex, for example, the phenomena of hollow parts, inclusion relations among the land blocks, coexistence of the two conditions and the like occur in the land blocks, the geometric intersection method is easy to have leak, and the misjudgment is caused.
As shown in fig. 1, the land parcel 1 and the land parcel 2 do not intersect each other, and there is no overlapping area, but since the land parcel 2 has a hollow portion and the land parcel 1 is just in its hollow portion, when the land parcel 1 is judged by intersecting the geometric figure, both land parcels will be treated as solid plane figure, and the misjudgment will occur.
Disclosure of Invention
The invention aims to provide a locomotive operation information processing method based on satellite positioning and running track analysis, and the accuracy of judgment is improved aiming at special conditions.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a locomotive operation information processing method based on satellite positioning and traveling track analysis comprises the following steps: A. acquiring data uploaded by an agricultural machine terminal; B. generating an operation line according to the track points in the data; C. dividing land blocks according to the operating line; D. extracting land boundaries; E. repeatedly judging the land parcel;
the repeated judgment of the land parcel comprises the following steps:
e.1, judging whether agricultural machinery and farm tool information is lost or whether the operation of the land block reaches the standard, if the information is lost or the operation does not reach the standard, not calculating the repetition of the current land block and setting the repetition area as 0, otherwise, executing the step E.2;
e.2, traversing the suspected land parcels with the same GEOHASH codes as the current land parcels to obtain a list of the suspected repeated land parcels Bi with the same GEOHASH codes as the current land parcels, wherein i = 1.
E.3, traversing the list, generating a circumscribed rectangle Si of each suspected repeated land block Bi and a circumscribed rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each circumscribed rectangle Si is intersected with the circumscribed rectangle S, if so, keeping, and if not, rejecting;
step E.4, calculating a repeat region Pi of the current land and each reserved suspected repeat land Bi:
step E.4.1, generating contour areas OA and OBi of the current land and the reserved suspected repeated land Bi respectively by using a JTS function, and solving contour areas SOA and SOBi of the OA and OBi, working areas SA and SBi of the current land and the reserved suspected repeated land Bi and distances between the centers and the centers of gravity of the current land and the reserved suspected repeated land Bi respectively, namely DISA and DISBi;
step E.4.2, respectively solving and collecting a union set of buffer areas generated by each respective operation line of the current land parcel and the reserved suspected repeated land parcel Bi by taking the width as the width, and respectively marking the union set as AlineBufferUnion and BlineBufferUnion;
step E.4.3 uses JTS function to judge the inclusion relationship between OA and OBi:
step e.4.3.1, if the current parcel contains Bi and SA > = a × SOA, dis < = d, SBi > = a × SOBi and dis Bi < = d, pi = OBi;
step e.4.3.2, pi = AlineBufferUnion:nblinebufferunion if the current parcel contains Bi, and SA < a × SOA or dis > d, and SBi < a × SOBi or dis > d;
step e.4.3.3, if the current parcel contains Bi, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebuffermion union # OBi;
step e.4.3.4, if the current parcel contains Bi, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or disBi > d, pi = BlineBufferUnion;
step e.4.3.5, if Bi contains the current parcel and SA > = a × SOA and disA < = d and SBi > = a × SOBi and disBi < = d, pi = OA;
step e.4.3.6, pi = AlineBufferUnion:nblinebufferunion if Bi contains the current parcel, and SA < a × SOA or dis > d, and SBi < a × SOBi or dis > d;
step e.4.3.7, if Bi contains the current parcel, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebufferuion unit;
step e.4.3.8, if Bi contains the current parcel, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or dis Bi > d, pi = OA # blinebufferuion @;
step e.4.3.9, if the current parcel and Bi have no inclusion relationship, and SA > = a × SOA and disA < = d, and SBi > = a × SOBi and disBi < = d, pi = OA ≠ OBi;
step e.4.3.10, if the current parcel and Bi do not have an inclusion relationship, and SA < a × SOA or disA > d, and SBi < a × SOBi or disBi > d, pi = alinebufferuion union ≧ blinebufferuion union;
step e.4.3.11, if the current parcel and Bi do not have an inclusion relationship, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebufferuion unit # OBi;
step e.4.3.12, if the current parcel and Bi do not have an inclusion relationship, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or dis Bi > d, pi = OA # blinebufferuion;
step e.4.3.13, if OA = OBi, and SA > = a × SOA and dis a < = d, and SBi > = a × SOBi and dis bi < = 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 = alinebufferuunion: "blinebufferuion union;
step e.4.3.15, if OA = OBi, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebufferuion;
step e.4.3.16, if OA = OBi, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or disBi > d, pi = blinebufferuion;
in the above steps, a is a proportionality coefficient, and the numeric area is [0.6,1]; d is a distance threshold in meters, and the value range is [0,30]; n denotes the intersection region of two polygons by JTS library function interselect ().
E.5, combining all Pi with a unity () function 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 parcel;
and E.6, repeating the steps E.1-E.5, and repeatedly judging the next plot.
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 division result of the operating line is closer to the original track; 3. the land parcel is divided accurately, and the invalid area generated by road driving 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 view of a prior art plot 1 in a hollow portion of a plot 2.
FIG. 2 is a comparison graph of data uploaded by an agricultural machinery terminal before and after processing;
FIG. 3 is a comparison of trace points in data before and after processing to generate a line;
FIG. 4 is a comparison graph of land parcel division before and after grid clustering algorithm processing;
FIG. 5 is a comparison graph before and after point set data compression using a thinning algorithm;
FIG. 6 is a flow chart of a determination of a parcel repeat;
FIG. 7 is a schematic view of the absence of hollow regions within both plots A and Bi with Bi in A;
FIG. 8 is a schematic view of a land A and Bi both having a hollow region and Bi in A;
FIG. 9 is a schematic view of a plot A with hollow regions and Bi in A;
FIG. 10 is a schematic view of a land block Bi with hollow regions, A solid regions, and Bi in A;
FIG. 11 is a schematic view of a situation where no hollow region is present within both plots A and Bi and A is in Bi;
FIG. 12 is a schematic view of a land A and Bi both having hollow regions and A in Bi;
FIG. 13 is a schematic representation of a land A with hollow regions, bi solid regions, A in Bi;
FIG. 14 is a schematic diagram of a plot Bi with hollow regions and A in Bi;
FIG. 15 is a schematic view of a land A and Bi without hollow regions;
FIG. 16 is a schematic view showing the presence of hollow regions in both of plots A and Bi;
FIG. 17 is a schematic view of a plot A with void areas;
FIG. 18 is a schematic view showing a land Bi having a hollow region;
FIG. 19 is a schematic view of the contours of both blocks A and Bi completely coincident without the presence of a void region;
FIG. 20 is a schematic view of the structure of the land areas A and Bi with their outlines coinciding but both having hollow areas;
FIG. 21 is a schematic view of the structure of a land A and a land Bi having overlapping outlines but with A being a hollow portion;
FIG. 22 is a schematic view showing a structure in which the contours of the land A and Bi overlap each other, but a hollow portion of Bi exists.
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 data uploaded by the terminal all day, and when the terminal uploading frequency is high (once uploading in 2s at present), the platform stores 43200 pieces of data uploaded by the current terminal all 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 lot of data with low 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 the track distortion is caused, and the calculation of the working area is affected seriously.
The sensor installed on the agricultural machine can acquire the operation state mark of each track point, taking deep scarification as an example, the tilling depth of each track point can be detected, and if the set depth is greater than or equal to 25cm, the operating state is, and if the set depth is smaller than 25cm, the non-operating state is. Data in a non-operation state should be kept as little as possible because the data does not participate in area calculation, but the trajectory needs to be guaranteed not to be distorted. Therefore, each non-operation point collected in real time can be judged to be compared with the previous point, and whether the non-operation point is a track point with obvious characteristics such as turning or turning around or not can be judged, and if the non-operation point is not the track point, the non-operation point is uploaded, and if the non-operation point is not the track point, the non-operation point is not uploaded.
The method specifically comprises the following steps:
a.1, obtaining a positioning point Pi, if the Pi is an operation track point, directly uploading the Pi according to the original frequency, then judging whether a next positioning point Pi +1 is the operation track point, and if not, executing the step A.2.
And A.2, uploading the current positioning point Pi as an initial point, recording a heading angle head _ i of the initial point, and storing a temporary storage variable tmp = head _ i.
And A.3, judging whether the next positioning point Pi +1 of the Pi is a work 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 positioning point Pi +1 and the heading angle of the temporary storage variable tmp and the difference between the heading angle head _ i +1 and the heading angle head _ i of the previous positioning point Pi.
A.5, if the absolute value of the difference between the heading angle head _ i +1 and the temporary storage variable tmp is greater than ht or the absolute value of the difference between the heading angle head _ i +1 and the heading angle head _ i of the positioning point Pi is greater than hd, judging that Pi +1 is a characteristic point, uploading the characteristic point, updating the heading angle of the characteristic point to tmp, and = head _ i +1, i = i +1, and continuing to execute the step A.3, otherwise, not uploading, i = i +1, and continuing to execute the step A.3. Wherein ht unit is degree, and the numeric area is [10,90]; hd is in degrees and has a value in the range of [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 positioning point is in the uploaded point list, and if not, uploading the positioning point.
The method effectively solves the problems of 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 percent of the total number, only 450 non-operation track points are reserved after the steps, about 1.45 percent and about 1.04 percent 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 drawing 2, the non-operation track driven by a certain section of the agricultural machine is taken as a typical example, the left drawing in the attached drawing is the original track before processing, the right drawing is the original track before processing, the identified characteristic points in the section of track are marked, other unidentified parts are replaced by straight lines, and the effect is very obvious before and after comparison processing.
B. And generating the operation line according to the track points in the data.
After the operation of the agricultural machinery is finished, all generated track points need to be divided into operation lines in a reasonable mode and the operation finishing area is calculated according to each operation line, the division of the track lines is only divided 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 changes by 30 degrees, the track is divided into two lines, the logic is simple, the processing is convenient, and the defect is that the line breakage of an arc line is judged. If the agricultural machine runs straight, the agricultural machine turns a corner midway, so that the operating line is divided into two lines; the agricultural machine always runs in a slight angle arc line, so that only one operating line is judged. Due to different mountainous terrain in different areas, a larger error occurs when the mountain land terrain is used as an operation line for calculating the area.
Therefore, a reasonable line division method is as follows: by setting a position disturbance threshold, which can be 10m, each point is checked to the initial movement behavior (the line segment formed by the first two points of each new track) in turn according to the sequence of the track points
Distance of disturbance ofd gi ，i=1,…,nFinding out all characteristic points (points with disturbance distance larger than disturbance threshold), and dividing the track TR into stable sub-tracks SST (disturbance distances of all points in the track are smaller than the disturbance threshold)d 0 ). The main idea of the step is as follows: checking the value of each based on the current initial movement behaviord gi ，i=1,…,n. The method comprises the following specific steps.
B.1, sequencing all track points according to time, and taking the first two points to form an initial movement behavior
And adding the two points into the operation line trace point set STR.
B.2, traversing from the next point, and calculatingg i And
of (2) isd gi If, ifd gi ≤d 0 Then will beg i And adding the STR point set, and continuously traversing the next point, otherwise, executing the step B.3.
B.3, ifd gi >d 0 Then, thenSTR={g 1 ,…,g i-1 Dividing into an operation line, taking
Become the initial movement behavior of the next new line, will
Is marked as
And generates a next STR point set.
B.4, judgmentg i And if the point is the last point, continuing to execute B.2, if the point is not the last point, finishing the division of the operating line, and recording the characteristic points of all the divided line segments.
As shown in the attached figure 3, the operation track of a certain section of the agricultural machine is taken as a typical example, and after the step, the divided operation lines of each section are relatively close to the corresponding arc-shaped track lines.
C. And dividing the land blocks according to the operating lines.
If a land is divided into a plurality of lands or a plurality of lands are divided into one land, the result is inaccurate when the area is calculated by using the outline method. In addition, the track line of some agricultural machines on the road is judged to be in the working state for the road invalid area of the non-cultivated area, and the invalid area is generated, and the longer the track line is, the larger the invalid area is. Because the state can issue subsidies according to the operation area of the mobile phone, the unrecognized redundant invalid area on the road seriously influences the subsidy amount. Reasons for the presence of on-road area include inadequate sensor mounting, terrain factors in some areas, etc.
In the step, the operation line is divided into land blocks through a grid clustering algorithm, wherein in the grid clustering algorithm:
grid size:
density threshold value:
wherein, N is more than 0, lambda is more than 0, ploughgidth represents the width of the agricultural machinery, and the unit is as follows: and (4) rice. speed represents the track speed value, unit: meter/second, t represents the trace sampling time interval, unit: and second.
In this example, N =1, λ =0.5, speed in the grid size parameter is used to calculate the speed between two adjacent points, and the third quartile of the trajectory set speed value is taken. Speed for calculating the density threshold is max (speed, 1).
The track of a land where a normal agricultural machine works does not have only one direction, and the condition of turning around is certainly existed, namely, a reverse line exists, and the time difference with the reverse line is within a certain time range, which is different from the track characteristic on the road. Therefore, the step of filtering the land parcel according to the track points and the track lines in the divided land parcel is also included in the step, and the step comprises the following steps.
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, the filtered lines are considered to be on the way, otherwise, the step C.3 is executed.
C.3, traversing the line Li in the plot, reading the course angle Head _ Li, searching other lines of the plot line set, if a reverse line Lj exists, the time difference between the reverse line Lj and the line Li is less than td, the angle difference is greater than HL, and the distance is less than or equal to 2 × ploughWidth, determining the reverse line as a normal plot, otherwise, determining the reverse line as an on-road plot and filtering,
wherein point _ num is a point threshold value, the value range is that point _ num is more than or equal to 3, td unit is second, the value range is [600,900], HL unit is degree, and the value range is [160,180]. In this embodiment, point _ num =3, td takes 600 seconds, and HL takes 160 °.
As shown in fig. 4, in the original method, 9 working areas of the agricultural machinery are divided into 4 plots, track points are searched in the background, it is found that a large number of areas of the plot 2 are not worked, and because tracks of normal working states exist on the road, a plurality of plots are divided into 1, that is, problems occur in the division, which directly affects subsequent repeated area calculation (that is, if other agricultural machinery works in the areas covered by the plot 2, it is determined that the agricultural machinery is repeatedly worked). The method is adopted to accurately divide the land parcels into 9 independent land parcels which are not connected with each other, so that the land parcels which are divided by the step are more accurate.
D. And extracting the boundary of the land parcel. When the land parcel is large, the number of boundary points is large, which results in that the storage pressure of the platform is large on one hand, and the efficiency of generating the polygon on the boundary is low on the other hand.
When the boundary points forming the plot outline are more, the outline can be thinned by adopting a track compression mode, the number of data points can be greatly reduced after thinning, the shape of the outline can be kept undistorted, and the plot outline thinning step comprises the following steps:
d.1, traversing the plots, and extracting a plot boundary point set for any plot by adopting a JTS (mesh-text-to-series) outsourcing polygon method.
And D.2, compressing the data of the point sets 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 compressing; otherwise, entering the step (2).
(2) According to the arrangement sequence of the point sets, taking a head point P1 and a tail point P2, constructing a straight line Traj between the two points, traversing all other points on the Traj, finding a point Pi with the maximum distance Traj as a division point, and recording the distance asD max 。
(3) Setting an error threshold rho ifD max <ρ, a straight line Traj is approximated to the piece of data.
(4) IfD max And (6) dividing Traj into two sections by taking Pi as a dividing point, and respectively processing according to the steps (2) - (4).
(5) And sequentially connecting the broken lines subjected to the segmentation processing, and taking the obtained point set as a compressed result.
Wherein, the error threshold ρ of the algorithm can be adaptively set according to the number of the point sets, that is, 1 meter is set according to 100 points, and then ρ = Num/100.
As shown in fig. 5, taking a parcel 7 as an example, the parcel has 101 boundary points, and after compression by the thinning algorithm, the number of the boundary points is 16, which is about 15.8% of the number of the original data points, and the contour shape is substantially consistent with the original contour. The same method is used for treating other plots, wherein the total number of 1236 boundary points of 9 plots and the total number of 157 points after compression is about 12.7 percent. The storage overhead of the platform is further reduced, and the calculation efficiency is improved.
E. And repeatedly judging the land parcel.
In the prior art, as for a patent with an application number of 201910209752.1, an agricultural machinery repeated operation area determination method based on spatial analysis, the whole process of repeated judgment of a land parcel is designed, and the whole process comprises GEOHASH coding, GEOHASH neighborhood searching, suspected repeated land parcel coarse screening, final suspected repeated land parcel locking and the like. The method is based on the flow steps of the method, but the method does not distinguish the specific spatial characteristics of the operation land blocks so as to cause various misjudgments, so that the suspected repeated land blocks finally locked are accurately identified and effectively processed under various conditions.
Referring to fig. 6, taking the repeated judgment of the 9 plots operated by the agricultural machinery as an example, the method comprises the following steps:
e.1, judging whether the information of agricultural machinery and agricultural implements is lost or whether the operation of the land block reaches the standard, if the information is lost or the operation does not reach the standard, if so, not calculating the repetition of the current land block and setting the repetition area to be 0, otherwise, executing the step E.2.
If the information of the agricultural machinery and the agricultural implement is lost, the platform cannot track the information of the agricultural machinery and cannot repeatedly judge with other operation plots; if the information of agricultural machinery and agricultural implements is normal, but the operation does not reach the standard, namely the standard-reaching rate is less than 85%, the operation land is invalid according to the regulation, and repeated comparison with other operated land is not needed. Therefore, the premise of repeated judgment is that the information of agricultural machinery and agricultural implements is normal and the operation qualification rate meets the requirements.
And E.2, starting from the land parcel 1, marking the land parcel as a current land parcel A, traversing the suspected land parcel with the same GEOHASH code as the current land parcel to obtain a list of suspected repeated land parcels Bi with the same GEOHASH code as the current land parcel, wherein i = 1.
The GEOHASH coding is a quick and effective range search algorithm, and although the search range is relatively thick, the characteristic of quickly positioning the search range makes the search algorithm suitable for being applied to the scene. The GEOHASH code was used to find out 33 suspected duplicate plots.
And E.3, traversing the list, generating a circumscribed rectangle Si of the suspected repeated land block Bi and a circumscribed rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each circumscribed rectangle Si is intersected with the circumscribed rectangle S, if so, reserving, and otherwise, rejecting.
Because the coverage range of the GEOHASH codes is thick, a plurality of land parcels which are not intersected with the current land parcel completely are put into a list, coarse screening needs to be carried out again, the land parcels can be removed quickly and effectively by the external rectangle method, and 8 suspected repeated land parcels are remained through the steps.
And E.4, calculating a repeated area Pi of the current land and each reserved suspected repeated land Bi.
And E.4.1, generating contour areas OA and OBi of the current land and the reserved suspected repeated land Bi respectively by using a JTS function, and solving contour areas SOA and SOBi of the OA and OBi, working areas SA and SBi of the current land and the reserved suspected repeated land Bi and distances between the centers and the centers of gravity of the current land and the reserved suspected repeated land Bi respectively, namely DISA and DISBi.
In the prior art, for example, in a patent with application number 201910209752.1, an agricultural machinery repeated operation area determination method based on spatial analysis, the position relationship between the outline areas OA and OBi is simply determined by using an interject () function in a JTS library, so as to obtain the repeated areas, the method is simple and convenient, the calculation efficiency is high, and the method is also applicable to a land parcel in normal operation, wherein the normal operation refers to that the sum of buffer areas generated by each track line and the width in the land parcel a (or Bi) is very close to or the same as the sum of the buffer areas generated by the OA or the OBi, that is, the operation lines are dense, and no hollow area exists in the land parcel. However, in an actual operation scenario, this is not the case, as illustrated in fig. 1, and is not described herein again. Therefore, it is necessary to identify whether or not the parcel a (or Bi) is a hollow area by an index in advance at the time of judgment. In general, the presence of hollow areas in a plot is characterized by: the area of the completed job may be significantly smaller than the area of the profile or the center point of the plot may be significantly offset from its center point of gravity. For this purpose, a criterion for the presence of hollow areas in the land mass is given: the contour area of the land block is less than a, or the distance between the center of the land block and the center of gravity is more than d. Wherein a is a proportionality coefficient, the numeric area is [0.6,1], and the numeric area is 0.6; d is a distance threshold in meters, the value range is [0,30], here 20; namely SA <0.6 × SOA or DISA >20, A has an empty area, and Bi has the same reason.
And E.4.2, respectively solving a union set of buffer areas generated by each operation line of the current land parcel and each reserved suspected repeated land parcel Bi by taking the width as the width, and respectively marking as AlineBufferUnion and BlineBufferUnion.
Since the intersector () function in the JTS library represents a parcel of any shape as a solid region with the parcel outline as the side, a parcel with a hollow region will cause erroneous judgment when the judgment is repeated, and therefore, a method for accurately representing the actual coverage area of the parcel is required. The Buffer method Buffer in the JTS function can achieve this goal, the Buffer function generates an independent rectangular graph with each line and width, and can truly reflect the coverage area situation of the parcel, but it has the disadvantage of slow calculation, because it needs to traverse the intersection situation of each small rectangle of two parcels and other rectangles in turn, i.e. if the parcel a has p lines and the parcel Bi has q lines, it needs to compare p × q rectangular areas and then find the union, when the scales of p and q are large, although the result is accurate, the efficiency is very low, and it 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 and judgment strategies according to different conditions so as to find the optimal balance between the accuracy and the efficiency. The alinebufferuion union and blinebufferuion union generated in this step are used only when the use condition is triggered in the subsequent judgment step.
The following steps are descriptions of the possible 16 positional relationships between A and Bi and the corresponding processing method, including the spatial relationships such as mutual sub-regions of A and Bi, partial intersection of A and Bi, and complete overlapping of A and Bi, and the cases where A and Bi are solid regions or hollow regions in each spatial relationship.
Step e.4.3 uses JTS function to determine the OA-OBi containment relationship.
Step e.4.3.1 Pi = OBi if the current parcel contains Bi and SA > =0.6 soa, dis a < =20, SBi > =0.6 sobi and disti < = 20.
As shown in fig. 7, there is no hollow region in both the land areas a and Bi, and Bi is in a, then the repeated region is the outline region OBi of Bi, i.e. the area is the finished area SBi of Bi.
Step e.4.3.2 Pi = AlineBufferUnion ≠ BlineBufferUnion if the current parcel contains Bi, and SA <0.6 soa or disA >20, and SBi <0.6 sobi or disBi > 20.
As shown in fig. 8, both the land blocks a and Bi have hollow regions, and Bi is in a, then both a and Bi cannot be directly compared by using the contour region, but can only be compared by using the Buffer region, and the repeated region is the intersection region of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.3, if the current parcel contains Bi, and SA <0.6 × soa or dis >20, and SBi > =0.6 × sobi and disti < =20, pi = alinebuffermion union # Obi.
As shown in fig. 9, in this case, the land a has a hollow region, and Bi in a can be compared by the contour region, while a can be compared by the Buffer region only, and the repeated region is the intersection of alinebuffermunion and OBi.
Step e.4.3.4 if the current parcel contains Bi, and SA > =0.6 soa and disA < =20, and SBi <0.6 sobi or disBi >20, pi = BlineBufferUnion.
As shown in fig. 10, there are hollow areas in the land block Bi, and since a is a solid area and Bi is in a, the repeated area is the area where Bi actually works, i.e., blinebuffermion.
Step e.4.3.5 Pi = OA if Bi contains the current parcel 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 land areas a and Bi, and a is in Bi, then the repeating area is the outline area OA of a, i.e. the area is the finished area SA of a.
Step e.4.3.6 Pi = AlineBufferUnion: _ BlineBufferUnion if Bi contains the current parcel, and SA <0.6 soa or disA >20, and SBi <0.6 sobi or disBi > 20.
As shown in fig. 12, both the land blocks a and Bi have hollow regions, and a is in Bi, so that both a and Bi cannot be directly compared by using the contour region, and only the Buffer region can be used for comparison, and the repeated region is the intersection region of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.7 Pi = AlineBufferUnion if Bi contains the current parcel, and SA <0.6 soa or dis >20, and SBi > =0.6 sobi and disBi < = 20.
As shown in fig. 13, there are hollow areas in the land mass a, since Bi is a solid area, and a is in Bi, the repeated area is the area where a actually works, i.e., alinebuffermion.
Step e.4.3.8, pi = OA # blinebufferon if Bi contains the current parcel, and SA > =0.6 × soa and dis a < =20, and SBi <0.6 × sobi or disBi > 20.
As shown in FIG. 14, there are empty regions in the land block Bi, and A in Bi can be compared by the contour region, while Bi can be compared only by the Buffer region, and the repeated region is the intersection of BlineBufferUnion and OA.
Step e.4.3.9, if the current parcel and Bi have no inclusion relationship, and SA > =0.6 soa and disA < =20, and SBi > =0.6 sobi and disBi < =20, pi = OA ∞ OBi;
as shown in fig. 15, when both land areas a and Bi do not have hollow areas, the respective profiles can be directly compared, and the result is the intersection area of OA and OBi.
Step e.4.3.10 Pi = alinebuffermion union: _ blinebuffermion union if the current parcel and Bi have no inclusion relationship, and SA <0.6 soa or dis >20, and SBi <0.6 sobi or dis > 20.
As shown in fig. 16, both the land blocks a and Bi have hollow regions, and both a and Bi cannot be compared directly by using the contour region, but can be compared by using the Buffer region, and the repeated region is the intersection region of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.11, if the current parcel and Bi have no inclusion relationship, and SA <0.6 × soa or dis a >20, and SBi > =0.6 × sobi and distbi < =20, pi = alinebuffermion union # Obi.
As shown in fig. 17, in this case, a land area a has a hollow area, bi can be compared by using a contour area, a can only be compared by using a Buffer area, and a repeated area is the intersection of alinebuffermunion and OBi.
Step e.4.3.12, if the current parcel has no inclusion relation with Bi, and SA > =0.6 × soa and disA < =20, and SBi <0.6 × sobi or disBi >20, pi = OA # blinebufferon.
As shown in FIG. 18, in this case, the land block Bi has a hollow region, A can be compared by the contour region, while Bi can be compared only by the Buffer region, and the repeated region 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, when the contours of the blocks a and Bi completely overlap and there is no hollow area, the repeated areas are themselves, i.e., OA or OBi.
Step e.4.3.14 Pi = AlineBufferUnion # BlineBufferUnion if OA = OBi, and SA <0.6 soa or disA >20, and SBi <0.6 sobi or disBi > 20.
As shown in fig. 20, although the outlines of the land blocks a and Bi coincide, both of them have hollow regions (a operates the left part, bi operates the right part, and the middle part intersects with each other), repeated regions cannot be directly judged, and only Buffer regions are used for comparison, and the repeated regions are the intersecting regions of AlineBufferUnion and BlineBufferUnion.
Step e.4.3.15, if OA = OBi, and SA <0.6 soa or dis >20, and SBi > =0.6 sobi and dis bi < =20, pi = alinebufferuion;
as shown in fig. 21, although the contour of the land a and the contour of Bi are overlapped, if a has a hollow part, the Buffer region of a is a repeated region, i.e., alinebuffermion.
Step e.4.3.16, pi = BlineBufferUnion if OA = OBi, and SA > =0.6 soa and dis a < =20, and SBi <0.6 sobi or disBi > 20.
As shown in fig. 22, although the contour of the land a and the Bi are overlapped, the Bi has a hollow part, and the Buffer region of Bi is a repeated region, i.e., blinebufferuunion.
In the above step, n represents the intersection region of two polygons obtained by JTS library function inteselect ().
And E.5, combining all Pi by using a unity () function 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 parcel.
And E.6, repeating the steps E.1-E.5, and repeatedly judging the next plot.
In this example, after the above-described overlapping judgment operation was sequentially performed on 9 plots, the total historical overlapping area of the plots was determined to be 23.92 mu. Deducting the qualified area from 75.41 mu in the total operation completion area of the agricultural machine to obtain 51.49 mu in the current day of the agricultural machine.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (7)
1. A locomotive operation information processing method based on satellite positioning and traveling 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 blocks according to the operating line;
D. extracting land boundaries;
E. repeatedly judging the land parcel;
the repeated judgment of the land parcel comprises the following steps:
e.1, judging whether agricultural machinery and agricultural implement information is missing or whether the operation of the land parcel reaches the standard, if the information is lost or the operation does not reach the standard, not calculating the repetition of the current land parcel and setting the repetition area as 0, otherwise executing the step E.2;
e.2, traversing the suspected land parcels with the same GEOHASH codes as the current land parcels to obtain a list of the suspected repeated land parcels Bi with the same GEOHASH codes as the current land parcels, wherein i = 1.
E.3, traversing the list, generating a circumscribed rectangle Si of the suspected repeated land blocks Bi and a circumscribed rectangle S of the current land block for each suspected repeated land block Bi in the list, judging whether each circumscribed rectangle Si is intersected with the circumscribed rectangle S, if so, reserving, otherwise, rejecting;
step E.4, calculating a repeat region Pi of the current land and each reserved suspected repeat land Bi:
step E.4.1 of respectively generating contour areas OA and OBi of the current land and the reserved suspected repetitive land Bi by using a JTS function, and solving contour areas SOA and SOBi of the OA and OBi, working areas SA and SBi of the current land and the reserved suspected repetitive land Bi and distances between centers and barycenter of the current land and the reserved suspected repetitive land Bi DISA and DISBi;
step E.4.2, respectively solving a union set of buffer areas generated by each line of the current land block and each reserved suspected repeated land block Bi by taking the width as the width, and respectively marking the union set as AlineBufferUnion and BlineBufferUnion;
step E.4.3 uses JTS function to judge the inclusion relationship between OA and OBi:
step e.4.3.1, pi = OBi if the current parcel contains Bi and SA > = a × SOA, disA < = d, SBi > = a × SOBi, and disBi < = d;
step e.4.3.2, pi = AlineBufferUnion:nblinebufferunion if the current parcel contains Bi, and SA < a × SOA or dis > d, and SBi < a × SOBi or dis > d;
step e.4.3.3, if the current parcel contains Bi, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebuffermion union # OBi;
step e.4.3.4, if the current parcel contains Bi, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or disBi > d, pi = BlineBufferUnion;
step e.4.3.5, if Bi contains the current parcel and SA > = a × SOA and disA < = d and SBi > = a × SOBi and disBi < = d, pi = OA;
step e.4.3.6, pi = AlineBufferUnion:nblinebufferunion if Bi contains the current parcel, and SA < a × SOA or dis > d, and SBi < a × SOBi or dis > d;
step e.4.3.7, if Bi contains the current parcel, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebufferuion union;
step e.4.3.8, if Bi contains the current parcel, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or dis Bi > d, pi = OA # blinebufferuion @;
step e.4.3.9, if the current parcel and Bi do not have an inclusion relationship, and SA > = a × SOA and disA < = d, and SBi > = a × SOBi and disBi < = d, pi = OA ≧ andobi;
step e.4.3.10, if the current parcel and Bi do not have an inclusion relationship, and SA < a × SOA or disA > d, and SBi < a × SOBi or disBi > d, pi = alinebufferuion union ≧ blinebufferuion union;
step e.4.3.11, if the current parcel and Bi do not have an inclusion relationship, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebuffermion union # OBi;
step e.4.3.12, if the current parcel and Bi do not have an inclusion relationship, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or dis Bi > d, pi = OA # blinebufferuion;
step e.4.3.13, if OA = OBi, and SA > = a × SOA and dis a < = d, and SBi > = a × SOBi and dis bi < = 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 = alinebufferuunion: "blinebufferuion union;
step e.4.3.15, if OA = OBi, and SA < a × SOA or disA > d, and SBi > = a × SOBi and disBi < = d, pi = alinebufferuion union;
step e.4.3.16, if OA = OBi, and SA > = a × SOA and dis a < = d, and SBi < a × SOBi or disBi > d, pi = blinebufferuion;
in the above steps, a is a proportionality coefficient, and the numeric area is [0.6,1]; d is a distance threshold value, the unit is meter, the value range is [0,30], [ n ] represents that the intersected area is obtained by JTS library function interject (), the intersected area is obtained by the intersected area, and the intersected area is obtained by the intersected area;
e.5, combining all Pi by using a unit () function 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 plot.
2. The locomotive operation information processing method based on satellite positioning and traveling track analysis according to claim 1, wherein the uploading data by the locomotive terminal in the step A comprises the following steps:
a.1, obtaining a positioning point Pi, if the Pi is an operation track point, directly uploading the Pi according to the original frequency, then judging whether a next positioning point Pi +1 is the operation track point, and if not, executing the step A.2;
a.2, uploading a current positioning point Pi as an initial point, recording a course angle head _ i of the initial point, and storing a temporary storage variable tmp = head _ i;
a.3, judging whether the next positioning point Pi +1 of the Pi is an operation track point, if so, executing the step A.1, otherwise, executing the step A.4;
a.4, calculating the difference between the heading angle head _ i +1 of the positioning point Pi +1 and the heading angle of the temporary storage variable tmp and the difference between the heading angle head _ i +1 and the heading angle head _ i of the previous positioning point Pi;
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 positioning 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, and continuing to execute the step A.3, otherwise, not uploading, i = i +1, and continuing to execute the step A.3, wherein ht unit is degree, the value range is [10,90], 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 positioning point is in the uploaded point list, and if not, uploading the positioning point.
3. The locomotive operation information processing method based on satellite positioning and traveling track analysis according to claim 1, wherein a track point in the data is generated into an operation line by a track division algorithm in step B, comprising the following steps:
b.1, sequencing all track points according to time, and taking the first two points to form an initial movement behavior
Adding the two points into an operation line track point set STR;
b.2, from the next pointg i Starting to traverse and calculateg i And
of (2) isd gi If, ifd gi ≤d 0 Then will beg i Adding the STR point set into the STR point set, and continuously traversing the next point, otherwise, executing the step B.3;
b.3, ifd gi >d 0 Then, thenSTR={g 1 ,…,g i-1 Dividing the image into an operation line, and taking
Become the initial movement behavior of the next new line, will
Is marked as
And generating a next STR point set;
b.4, judgmentg i And if the point is the last point, continuing to execute B.2, and if the point is the last point, finishing the division of the operating line and recording the characteristic points of all the divided line segments.
4. The locomotive operation information processing method based on satellite positioning and traveling track analysis according to claim 1, wherein the operation line is divided into blocks by a grid clustering algorithm in step C, wherein:
grid size:
density threshold value:
wherein, N is more than 0, lambda is more than 0, ploughgidth represents the width of the agricultural machinery, and the unit is as follows: meter, speed denotes the track speed value in units: meter/second, t represents the trace sampling time interval, unit: and second.
5. The locomotive operation information processing method based on satellite positioning and traveling track analysis according to claim 4, wherein step C further comprises the step of filtering the land blocks according to track points and track lines in the divided land blocks, the step comprising:
c.1, reserving a line with the number of points of the center line of the land block being more than or equal to point _ num;
c.2, judging that the number of the filtered lines is less than or equal to 1, and if not, executing the step C.3;
c.3, traversing the line Li in the plot, reading the course angle Head _ Li, searching other lines of the plot line set, if a reverse line Lj exists, the time difference between the reverse line Lj and the line Li is less than td, the angle difference is greater than HL, and the distance is less than or equal to 2 × ploughWidth, determining the reverse line as a normal plot, otherwise, determining the reverse line as an on-road plot and filtering,
wherein point _ num is a point threshold value, the value range is that point _ num is more than or equal to 3, td unit is second, the value range is [600,900], HL unit is degree, and the value range is [160,180].
6. The method of claim 4, further comprising a block contour thinning step in step D, wherein the block contour thinning step comprises:
d.1, traversing the plots, and extracting a plot boundary point set for any plot by adopting a JTS (joint test system) outsourcing polygon method;
and D.2, compressing the data of the point sets by adopting a thinning algorithm for each block boundary point set.
7. The locomotive operation information processing method based on satellite positioning and traveling track analysis according to claim 6, wherein compressing the point set data by 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 compressing; otherwise, entering the step (2);
(2) According to the arrangement sequence of the point sets, taking a head point P1 and a tail point P2, constructing a straight line Traj between the two points, traversing all other points on the Traj, finding a point Pi with the maximum distance Traj as a division point, and recording the distance asD max ；
(3) Setting an error threshold rho ifD max <Rho, taking a straight line Traj as the approximation of the section of data;
(4) IfD max Not less than rho, taking Pi as a segmentation point to divide Traj into two sections, and respectively processing according to the steps (2) - (4);
(5) And sequentially connecting the broken lines subjected to the segmentation processing, and taking the obtained point set as a compressed result.
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