Patrol track adaptive correction processing and multi-parameter constraint simplification method and system

Abstract

The invention discloses a patrol track adaptive correction processing and multi-parameter constraint simplification method and system, and the method comprises the steps: firstly, obtaining patrol track data of a patrol worker in real time through a positioning device, and carrying out the preprocessing of the patrol track data; then, drift data in the preprocessed patrol track data are recognized, the patrol track data are removed from corresponding track points, and the situation that the track points corresponding to the drift data are mistakenly selected as track feature points in the follow-up track simplification process is avoided, so that a track point set is closer to an original unsimplified track. And finally, in combination with the set distance threshold value and the proportion threshold value, simplifying and constraining the patrol track data by adopting a Douglas-Peucker algorithm. In the simplification process, when the simplified track point set meets the distance condition and the proportion condition at the same time, selection of the track feature points is stopped, it is avoided that due to the fact that the distance threshold value is not reasonable enough, track simplification is too large, and the simplified track point set is closer to an original unsimplified track.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field for eliminating unnecessary data in given sequence data, and particularly relates to a patrol trajectory adaptive correction processing and multi-parameter constraint simplification method and system. BACKGROUND

[0002] A large amount of trajectory data will bring great storage and computing overhead, in order to reduce the trajectory storage space and improve the map display efficiency, the original trajectory needs to be simplified. The current trajectory simplification algorithm mostly uses distance threshold as the parameter of trajectory simplification process, only after the termination of the simplification process, the trajectory simplification ratio can be calculated. However, the setting of the distance threshold depends on the prior knowledge and the preliminary statistical result of the trajectory data, if the setting is not reasonable, the expected simplification ratio cannot be obtained, which will cause a large difference between the simplified trajectory mileage and the actual mileage, thereby affecting the examination statistics of the patrol personnel. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application provides a patrol trajectory adaptive correction processing and multi-parameter constraint simplification method and system, which can make the simplified trajectory point set closer to the original trajectory. The specific technical solutions are as follows: In a first aspect, a patrol trajectory adaptive correction processing and multi-parameter constraint simplification method is provided. In a first implementation manner of the first aspect, the method comprises: acquiring patrol trajectory data and pre-processing the patrol trajectory data; identifying drift data in the pre-processed patrol trajectory data and performing corresponding processing; simplifying the pre-processed patrol trajectory data based on a set distance threshold and a set ratio threshold using a Douglas-Pok algorithm.

[0004] In combination with the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the pre-processing of the patrol trajectory data comprises: deleting trajectory data with abnormal conditions in the patrol trajectory data.

[0005] In combination with the first or second implementation manner of the first aspect, in a third implementation manner of the first aspect, the identification of the drift data in the patrol trajectory data comprises: constructing a triangle based on three consecutive trajectory points in the patrol trajectory data and calculating a triangle area of the triangle; calculating a projection distance of a middle point to a connecting line of the other two points based on the triangle area, and comparing the projection distance with a set maximum allowable distance error; in response to the projection distance being greater than the maximum allowable distance error, determining that the middle point is drift data and removing the middle point.

[0006] In a fourth implementation form of the first aspect, according to the third implementation form of the first aspect, the distance maximum allowable error is set by: According to the equilateral triangle, the interval time and the moving speed of the two continuous track points are used to calculate the distance maximum allowable error.

[0007] In a fifth implementation form of the first aspect, according to the first or third implementation form of the first aspect, the drift data in the patrol track data is identified by: Three continuous track points in the patrol track data are extracted, and a path angle is calculated according to the path vectors among the three track points; The path angle is compared with a set angle maximum allowable error, and the middle point is removed in response to the path angle being less than the angle maximum allowable error.

[0008] In a sixth implementation form of the first aspect, according to the fifth implementation form of the first aspect, the angle maximum allowable error is set by: According to the projection distance of the middle point to the line segment connecting the other two points being equal to the distance maximum allowable error, and the perpendicular projection of the middle point to the line segment connecting the other two points coinciding with the line segment connecting the middle point and the latter point, the distance between the middle point and the other two points is used to calculate the angle maximum allowable error.

[0009] In a seventh implementation form of the first aspect, according to the first implementation form of the first aspect, the patrol track data is simplified by using a Douglas-Peucker algorithm, including: A head-tail line segment between the start point and the end point of the patrol track data is marked, and a perpendicular distance between each track point in the patrol track data and the head-tail line segment is calculated; The perpendicular distance corresponding to each track point is compared with the distance threshold, and the track point is marked as a feature point in response to the perpendicular distance exceeding the distance threshold; According to the number of feature points and the number of original track points in the patrol track data, a current simplification ratio of the patrol track data is calculated; The current simplification ratio of the patrol track data is compared with the ratio threshold; In response to the ratio threshold not exceeding the ratio threshold, the patrol track data is segmented into multiple sub-tracks by taking the feature points as segmentation points, and the feature points in each sub-track are continuously marked; The above process is repeated until the simplification ratio exceeds the ratio threshold.

[0010] In an eighth implementation form of the first aspect, according to the first implementation form of the first aspect, the method further includes: Generate a real-time digital map according to the inspection track data after processing the drift data.

[0011] With reference to the first implementation manner of the first aspect, in a ninth implementation manner of the first aspect, the method further includes: Upload the simplified inspection track data and the original inspection track data to a server.

[0012] The second aspect provides a system for adaptive correction processing of inspection track data and multi-parameter constraint simplification, including: An acquisition module configured to acquire inspection track data and pre-process the inspection track data; A correction module configured to identify drift data in the pre-processed inspection track data and perform corresponding processing; A simplification module configured to simplify the processed inspection track data based on a set distance threshold and a set proportion threshold using a Douglas-Peucker algorithm.

[0013] The method and system for adaptive correction processing of inspection track data and multi-parameter constraint simplification have the following beneficial effects. First, by identifying and processing drift data in the inspection track data, the drift data in the inspection track data can be removed, and the trajectory points corresponding to the drift data in the subsequent trajectory simplification process can be avoided from being mistakenly selected as trajectory feature points, so that the trajectory point set is closer to the original unsimplified trajectory. Second, by combining the vertical distance and the simplification proportion to constrain the trajectory, the balance of different trajectory simplification proportions is achieved. When the vertical distance reaches the distance threshold, and the simplification proportion reaches the proportion threshold, and both conditions are met, the selection of the trajectory feature points can be stopped, so that the trajectory simplification caused by an unreasonable distance threshold is avoided, and the trajectory point set after the multi-parameter constraint simplification is closer to the original unsimplified trajectory. BRIEF DESCRIPTION OF DRAWINGS

[0014] To more clearly illustrate the present application, the following will introduce the drawings needed in the specific embodiments. In all the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0015] Figure 1 A flowchart of the method for adaptive correction processing of inspection track data and multi-parameter constraint simplification provided by an embodiment of the present application; Figure 2 A schematic diagram of a triangle formed by three consecutive trajectory points; Figure 3 A schematic diagram for calculating the maximum allowable error of an included angle; Figure 4 A schematic diagram of trajectory simplification using the method for adaptive correction processing of inspection track data and multi-parameter constraint simplification of the present application. DETAILED DESCRIPTION

[0016] The embodiments of the technical solutions of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, but cannot limit the protection scope of the present application.

[0017] Embodiment one As Figure 1 shown in the flow chart of the adaptive correction processing of the patrol trajectory and the multi-parameter constraint simplification method, the simplification method includes: Step 1, obtaining patrol trajectory data and pre-processing the patrol trajectory data; Step 2, identifying the drift data in the pre-processed patrol trajectory data and performing corresponding processing; Step 3, based on the set distance threshold and the proportion threshold, using the Douglas-Pok algorithm to simplify the processed patrol trajectory data.

[0018] Specifically, first, the patrol trajectory data of the patrol personnel can be obtained in real time by the positioning device carried by the patrol personnel, and the obtained patrol trajectory data is pre-processed. Then, when the patrol personnel is in a dense forest area, a building or the like, the satellite signal will disappear, and at this time, the data will drift. Therefore, according to the characteristics of the drift data, the drift data in the pre-processed patrol trajectory data can be identified, and the corresponding trajectory points can be excluded from the patrol trajectory data, so as to avoid the trajectory points corresponding to the drift data being mistakenly selected as the trajectory feature points in the subsequent trajectory simplification process, so that the trajectory point set is closer to the original unsimplified trajectory. Finally, the Douglas-Pok algorithm can be used to simplify and constrain the patrol trajectory data in combination with the set distance threshold and the proportion threshold. In the simplification process, when the simplified trajectory point set simultaneously satisfies the distance condition and the proportion condition, the selection of the trajectory feature points can be stopped, so as to avoid the trajectory simplification being too large due to the unreasonable setting of the distance threshold, and make the simplified trajectory point set closer to the original unsimplified trajectory.

[0019] The distance condition can be set as: The proportion condition can be set as: . is the perpendicular distance from the feature point to the connecting line of the trajectory head and tail, is the distance threshold, is the simplification proportion of the patrol trajectory data, is the proportion threshold.

[0020] In this embodiment, optionally, obtaining the patrol trajectory data includes deleting the trajectory data with abnormal conditions in the patrol trajectory data.

[0021] Specifically, the patrol trajectory data is affected by factors such as rainy weather, device endurance, human error, and the like, and thus there are cases of partial data loss and partial data transmission errors. Therefore, when obtaining the patrol trajectory data, the initial collected data can be preprocessed to improve the accuracy of the patrol trajectory data, so that the simplified trajectory point set is closer to the original unsimplified trajectory.

[0022] The originally collected trajectory data includes user , patrol , longitude, latitude, collection time, and the like, and the contents of data preprocessing mainly include the following aspects: (1) deleting abnormal trajectories with patrol time or number of trajectory points less than the effective value; (2) deleting records with missing attributes; (3) for repeated records with the same user and collection time, only one of them is retained; (4) deleting abnormal records obviously beyond the patrol range.

[0023] In this embodiment, optionally, drift data in the preprocessed patrol trajectory data is identified and processed accordingly, including: constructing a triangle based on three consecutive trajectory points in the patrol trajectory data, and calculating the area of the triangle; calculating the projection distance of the middle point to the line connecting the other two points according to the area of the triangle, and comparing the projection distance with the maximum allowable error of distance; in response to the projection distance being greater than the maximum allowable error of distance, eliminating the middle point.

[0024] Specifically, first, three consecutive trajectory points , , in the patrol trajectory data can be extracted, which are regarded as a triangle, the middle point is the perpendicular projection of the line connecting , as shown in Figure 2 . According to the Heron formula, the area of the triangle can be obtained, and the specific calculation formula is as follows: ; wherein, is the semi-perimeter of the triangle, , , are the lengths of the three sides of the triangle, i.e., the distances between the three trajectory points.

[0025] Then, according to the area of the triangle, the projection distance of the middle point to The vertical distance h of the line is calculated as follows: .

[0026] The projection distance of the line is compared with the maximum allowable error of the distance of the drift data. If the projection distance is greater than the maximum allowable error of the distance, it indicates that the trajectory point is a drift trajectory point, and the trajectory point can be deleted from the patrol trajectory data.

[0027] In this embodiment, the maximum allowable error of the distance is optionally set, including: According to the equilateral triangle, the maximum allowable error of the distance is calculated according to the interval time and the moving speed of the two consecutive trajectory points.

[0028] Specifically, when setting the maximum allowable error of the distance, three consecutive trajectory points can be extracted from the patrol trajectory data to form an equilateral triangle, and the maximum allowable error of the distance is set according to the projection distance of the middle point to the line connecting the other two points. Alternatively, the maximum allowable error of the distance is calculated according to the interval time and the moving speed of the patrol personnel between the two consecutive trajectory points The specific calculation formula is as follows: .

[0029] In this embodiment, the Douglas-Pok algorithm is optionally used to simplify the patrol trajectory data, including: The first and last lines between the start point and the end point of the patrol trajectory data are marked, and the vertical distance between each trajectory point and the first and last lines in the patrol trajectory data is calculated; The vertical distance corresponding to each trajectory point is compared with the distance threshold value, and in response to the vertical distance exceeding the distance threshold value, the trajectory point is marked as a feature point; According to the number of feature points and the original number of trajectory points of the patrol trajectory data, the current simplification ratio of the patrol trajectory data is calculated; The current simplification ratio of the patrol trajectory data is compared with the ratio threshold value; In response to the ratio threshold value not exceeding the ratio threshold value, the patrol trajectory data is divided into multiple sub-trajectories with the feature points as the division points, and the feature points in each sub-trajectory are marked; This is repeated until the simplification ratio exceeds the ratio threshold value.

[0030] Specifically, after removing drift data, the Douglas-Puk algorithm can be used to simplify the patrol trajectory data. First, the start and end points of the patrol trajectory data are marked as feature points, and the start and end points are connected to form a line connecting them. The perpendicular distances from other trajectory points in the patrol trajectory data to this line are then calculated. Next, each trajectory point in the patrol trajectory data is traversed, and the perpendicular distance corresponding to each trajectory point is compared with a set distance threshold. If the perpendicular distance exceeds the threshold, the trajectory point is marked as a feature point and added to the feature point set. Otherwise, the trajectory point is not marked.

[0031] After traversing all trajectory points, the number of trajectory points in the feature point set can be counted, and the current simplification ratio of the patrol trajectory data can be calculated based on this. Then, the calculated simplification ratio can be compared with a set threshold. If the simplification ratio does not reach the set threshold, the patrol trajectory data is divided into multiple sub-trajectories using each feature point as a dividing point. The start and end points of each sub-trajectory are marked as feature points and connected to form a line connecting the beginning and end. The same method is used to continue marking feature points in the sub-trajectories. This process is repeated recursively until the simplification ratio reaches the set threshold. At this point, marking trajectory feature points can be stopped, thus avoiding excessive trajectory simplification due to an unreasonable distance threshold setting.

[0032] like Figure 4 As shown, the trajectory to be simplified is First, set the starting point of the trajectory. and the finish line Set as feature points and connect the first and last points. Then, calculations can be performed separately. , ... Connect the beginning and end The vertical distance. Where, due to the trajectory point... Connecting the beginning and end The vertical distance is the farthest and exceeds the distance threshold, therefore the trajectory point is... These points are marked as feature points and added to the feature point set. However, due to the small number of trajectory points in the feature point set, the simplification ratio did not reach the set threshold. Therefore, the simplification process continues with the trajectory points... The patrol trajectory data is segmented to obtain sub-trajectories. and The above process is then recursively repeated for each of these two sub-trajectories until the simplification ratio reaches the set threshold. The final feature points are then determined as follows. , , , , with , the simplified trajectory is .

[0033] In this embodiment, optionally, further comprising: According to the patrol trajectory data after processing the drift data, a real-time digital map is generated.

[0034] Specifically, timeliness is an important key factor in the mobile patrol scene. A corresponding real-time digital map can be generated according to the patrol trajectory data after removing the drift data, to intuitively show the patrol personnel and facilitate the patrol personnel to understand their real-time trajectory.

[0035] In this embodiment, optionally, further comprising: uploading the simplified patrol trajectory data and the original patrol trajectory data to a server.

[0036] Timeliness is an important key factor in the mobile patrol scene. Although the patrol personnel usually need to show the simplified and corrected trajectory in real time, these require a large amount of computing resources, which will cause delay in trajectory display. In order to solve the contradiction between timeliness and accuracy, an online and offline combined way is adopted to process and store the patrol trajectory data.

[0037] Specifically, the current real-time collected trajectory feature point set is stored in the local memory of the patrol personnel's mobile phone, and the current position and patrol trajectory data of the patrol personnel are displayed on the map in real time, to meet the needs of the on-site personnel for real-time trajectory viewing. Secondly, when a patrol task is completed and submitted, a trajectory simplification and adaptive correction processing program is started to process the real-time trajectory. Finally, the optimized trajectory and the original trajectory are submitted to the server for storage, as a basis for historical trajectory viewing and patrol examination statistics, to meet the needs of patrol task examination business for data accuracy.

[0038] Embodiment two Embodiment two is substantially the same as embodiment one, the main difference is that the drift data in the patrol trajectory data is processed, including: Extracting three consecutive trajectory points in the patrol trajectory data, and calculating the path angle between the three trajectory points according to the path vector; Comparing the path angle with the set maximum allowable error of the angle, and in response to the path angle being less than the maximum allowable error of the angle, removing the middle point.

[0039] Specifically, when removing the drift data, not only can the drift trajectory point be identified according to the maximum allowable error of the distance alone, but also can the drift trajectory point be identified according to the maximum allowable error of the angle alone. Or the drift trajectory point is identified in combination with the maximum allowable error of the distance and the maximum allowable error of the angle.

[0040] Specifically, first, three continuous trajectory points in the patrol trajectory data can be extracted 、 、 , and the path angle of the three trajectory points is calculated according to the dot product formula of the vector, and the specific calculation formula is as follows: .

[0041] , wherein 、 are vectors of the line segment and the line segment , respectively, 、 are the lengths of the vectors of the line segment and the line segment , respectively.

[0042] Then the calculated path angle is compared with the set maximum allowable error of the angle, and if the path angle is less than the maximum allowable error of the angle, it indicates that the trajectory point is a drift trajectory point, that is, the trajectory point is deleted from the patrol trajectory data, so that the two judgment indicators of the perpendicular projection distance and the path angle are combined, and the accuracy of the drift data processing is improved.

[0043] In the embodiment, optionally, the maximum allowable error of the angle is set, including: The maximum allowable error of the angle is calculated according to the condition that the projection distance of the middle point to the line segment connecting the other two points is equal to the maximum allowable error of the distance, and the perpendicular projection of the middle point to the line segment connecting the two points coincides with the line segment connecting the middle point and the subsequent point.

[0044] Specifically, as shown in Figure 3 , when setting the maximum allowable error of the angle, the triangles corresponding to the patrol trajectory data can be first screened out from all the triangles, in which the projection distance is equal to the maximum allowable error of the distance, and the perpendicular projection of the middle point to the line segment connecting the two points coincides with the line segment connecting the middle point and the subsequent point. Then the maximum allowable error of the angle is calculated according to the side length of the triangle, and the specific calculation formula is as follows: .

[0045] A patrol trajectory adaptive correction processing and multi-parameter constraint simplification system, comprising: An acquisition module configured to acquire patrol trajectory data and pre-process the patrol trajectory data; A correction module configured to identify drift data in the pre-processed patrol trajectory data and perform corresponding processing; The simplification module is configured to simplify the processed patrol track data by using a Douglas-Peucker algorithm based on a set distance threshold and a proportion threshold.

[0046] Specifically, the simplification system comprises an acquisition module, a correction module and a simplification module. The acquisition module can acquire the patrol track data of the patrol personnel in real time by using the positioning device carried by the patrol personnel, and pre-process the patrol track data. The correction module can identify the drift data in the patrol track data according to the characteristics of the drift data, and eliminate the identified drift data from the patrol track data. The simplification module can combine the set distance threshold and the proportion threshold, and use the Douglas-Peucker algorithm to simplify and constrain the patrol track data. In the simplification process, when the simplified track point set meets the distance condition and the proportion condition at the same time, the selection of the track feature point can be stopped, so as to avoid the over-simplification of the track caused by the unreasonable distance threshold setting, and make the simplified track point set closer to the original non-simplified track.

[0047] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the same. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some or all of the technical features can be replaced by equivalents. Such modifications or replacements do not change the essence of the corresponding technical solutions, and should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for trajectory adaptive correction processing and multi-parameter constraint simplification, characterized in that, The method comprises the following steps: Obtaining patrol trajectory data and preprocessing the patrol trajectory data; Identifying drift data in the preprocessed patrol trajectory data and performing corresponding processing; Simplifying the processed patrol trajectory data based on a set distance threshold and a proportion threshold by using a Douglas-Peucker algorithm.

2. The method of claim 1, wherein the method is characterized by, The preprocessing of the patrol trajectory data comprises: Deleting trajectory data with abnormal conditions in the patrol trajectory data.

3. The method of claim 1 or 2, wherein, The identification of the drift data in the patrol trajectory data comprises: Constructing a triangle based on three consecutive trajectory points in the patrol trajectory data, and calculating the area of the triangle; Calculating the projection distance of the middle point to the line connecting the other two points, and comparing the projection distance with a set maximum allowable distance error; In response to the projection distance being greater than the maximum allowable distance error, determining that the middle point is drift data and removing it.

4. The method of claim 3, wherein, The setting of the maximum allowable distance error comprises: When the triangle is an equilateral triangle, calculating the maximum allowable distance error based on the interval time and the moving speed of the two consecutive trajectory points.

5. The simplified method for patrol trajectory adaptive correction processing and multi-parameter constraint according to claim 1 or 3, characterized in that, The identification of the drift data in the patrol trajectory data comprises: Extracting three consecutive trajectory points in the patrol trajectory data, and calculating the path angle based on the path vectors between the three trajectory points; Comparing the path angle with a set maximum allowable angle error, and in response to the path angle being less than the maximum allowable angle error, determining that the middle point is drift data and removing it.

6. The method of claim 5, wherein, The setting of the maximum allowable angle error comprises: When the projection distance of the middle point to the line connecting the other two points is equal to the maximum allowable distance error, and the perpendicular projection of the middle point to the line connecting the two points coincides with the line connecting the middle point and the subsequent point, calculating the maximum allowable angle error based on the distance between the middle point and the other two points.

7. The method of claim 1, wherein, The simplification of the patrol trajectory data by using the Douglas-Peucker algorithm comprises: Marking the first and last connecting lines between the start point and the end point of the patrol trajectory data, and calculating the perpendicular distance between each trajectory point and the first and last connecting lines; Comparing the corresponding perpendicular distance of each trajectory point with the distance threshold, and in response to the perpendicular distance exceeding the distance threshold, marking the trajectory point as a feature point; Calculating the current simplification proportion of the patrol trajectory data based on the number of feature points and the original number of trajectory points in the patrol trajectory data; Comparing the current simplification proportion of the patrol trajectory data with the proportion threshold; In response to the proportion threshold not exceeding the proportion threshold, dividing the patrol trajectory data into multiple sub-trajectories by taking the feature points as the division points, and continuing to mark the feature points in each sub-trajectory; Repeating the above steps until the simplification proportion exceeds the proportion threshold.

8. The method of claim 1, wherein, The method further comprises: Generating a real-time digital map based on the patrol trajectory data after processing the drift data.

9. The method of claim 1, wherein, The method further comprises: Uploading the simplified patrol trajectory data and the original patrol trajectory data to a server.

10. A system for track inspection adaptive correction processing and multi-parameter constraint simplification, characterized in that, The method comprises the following steps: An obtaining module configured to obtain patrol trajectory data and preprocess the patrol trajectory data; A correction module configured to identify drift data in the preprocessed patrol trajectory data and perform corresponding processing; The simplification module is configured to simplify the processed patrol track data based on a set distance threshold and a scale threshold using a Douglas-Peucker algorithm.

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