Multi-feature based agricultural machine operation track recognition and field analysis method

Abstract

The application relates to the field of intelligent agricultural technology, in particular to a farmland operation track recognition and plot analysis method based on multiple features, time position speed direction information output by a Beidou terminal is collected and time sequence correlation is carried out, a farmland operation track time sequence expression is constructed, operation track segments are divided and recognized according to motion continuity, steering characteristics and path bending, same-direction track areas are formed by being combined based on spatial proximity and direction consistency, operation plot track areas are divided by analyzing transverse interval changes, edge track points are extracted and closed to generate plot boundaries, and a track and plot spatial correlation relationship is established. According to the application, track state expression is constructed in time sequence to constrain path form motion continuity and direction change relationship, operation track screening is completed based on continuity and geometric features, track areas are formed according to direction consistency and position adjacency, plots are divided by using advancing direction vertical interval changes, and complete plot contours are obtained by closing edge tracks.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture technology, and in particular to a method for identifying agricultural machinery operation trajectories and analyzing land parcels based on multiple features. Background Technology

[0002] The field of smart agriculture technology encompasses a comprehensive technical system centered on the collection, processing, analysis, and spatial representation of agricultural machinery operation information during agricultural production. The core content of this technical field involves methods for acquiring positioning trajectory data generated by agricultural machinery in different operation and driving scenarios such as fields and roads, methods for continuously describing trajectory points in time and space dimensions, criteria for distinguishing operational behaviors from environmental characteristics, and analytical paths for transforming discrete trajectory data into geographically significant spatial objects. Its overall technical system serves the need for digital representation of operational area, operational location, plot structure, and agronomic processes in agricultural production management.

[0003] Among them, the multi-feature-based agricultural machinery operation trajectory recognition and plot analysis method refers to a technical solution that uses continuous trajectory data generated by agricultural machinery equipped with positioning systems during operation, combined with multi-source sensing information, to identify and analyze the spatial relationship between agricultural machinery operation behavior and plots. Addressing technical challenges such as the difficulty in distinguishing between field operations and road travel in agricultural machinery trajectories, the difficulty in directly forming continuous plot boundaries from discrete trajectory points, and the difficulty in representing adjacent and complex plots, the method constructs a multi-modal feature set including positioning coordinate sequences, changes in travel speed and direction, operation time periods, and operation images. It uses convolutional neural networks to extract operation scene features from images, recurrent neural networks to model the trajectory time series, and performs spatial aggregation analysis on the field operation trajectory. Based on the spatial distribution relationship of trajectory points, it extracts plot boundaries and describes the spatial topological relationship between plots, thus forming a complete trajectory recognition and plot analysis technical process.

[0004] Existing technologies focus on analyzing trajectory behavior by relying on feature combinations and overall discrimination methods. However, in scenarios where there are pauses, U-turns, or dense and intersecting paths during the operation process, the expression of the temporal sequence and spatial continuity of the trajectory is insufficient, which can easily lead to local trajectory confusion. In parcel analysis, the overall aggregation method is often used to process spatial distribution. There is a lack of effective distinguishing criteria for adjacent operation areas with the same direction and close distance, resulting in blurred parcel boundaries and affecting the stability of operation scope delineation and spatial structure description. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for agricultural machinery operation trajectory recognition and land parcel analysis based on multiple features. The technical solution is as follows: A method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features includes the following steps: S1: Collect information on the time, spatial location, speed and direction of agricultural machinery operation, associate various types of information in time order, convert the spatial location into a planar representation to depict the path, combine the speed and direction of travel to depict the motion and turning, and form the time sequence expression result of agricultural machinery operation trajectory; S2: Based on the travel path, motion characteristics and turning changes in the time-series expression results of the agricultural machinery operation trajectory, the travel segments are divided into trajectory segments, the motion continuity, turning characteristics and path bends are described, the operation trajectory segments are distinguished and screened, and the field operation trajectory recognition results are formed. S3: Based on the trajectory segments in the field operation trajectory recognition results, the regions are divided according to the proximity of the trajectory segments in planar position and the consistency of the direction of travel. The direction of travel is extracted and compared, and the time and space continuous trajectory regions in the same direction are merged to form the result of the trajectory region with the same operation direction. S4: Based on the same-direction trajectory area results in the operation direction consistent trajectory area, extract the common travel direction in the trajectory area as the operation direction, analyze the lateral interval change of adjacent trajectory segments in the vertical direction, separate the areas by the position of the interval change, and form the operation plot trajectory area results.

[0006] As a further aspect of the present invention, the temporal representation result of the agricultural machinery operation trajectory includes a unified time reference, continuous planar trajectory description, motion state change characteristics, and turning change characteristics; the field operation trajectory identification result includes a set of operation trajectory segments, trajectory segment operation validity identifier, and trajectory segment continuity attribute; the operation direction consistent trajectory region result includes a set of same-direction trajectory regions, trajectory region direction consistency attribute, and trajectory region spatial continuity characteristic; and the operation plot trajectory region result includes a set of plot trajectory regions, plot operation direction attribute, and lateral separation characteristic between plots.

[0007] As a further aspect of the present invention, the step of obtaining S1 is as follows: S101: Collect time information, spatial location information, travel speed information and travel direction information output by the Beidou positioning terminal during the operation of agricultural machinery. Based on the time information, sequentially associate the spatial location, speed and direction data at the same time point to generate a time series dataset. S102: Based on the spatial location information in the time series dataset, perform planar coordinate transformation to convert the spatial information from latitude and longitude coordinates to location data in a planar coordinate system. Combine the displacement information at adjacent times to determine the movement direction and path change of each path segment and generate path segment change features. S103: Based on the changes in the path segments, combined with the information on travel speed and direction, organize the data in the time series, determine the travel status of the agricultural machinery at each moment, complete the overall description of the motion characteristics and direction changes of each path segment, and generate the time series expression result of the agricultural machinery operation trajectory.

[0008] As a further aspect of the present invention, the step of obtaining S2 is as follows: S201: Obtain the travel path, motion characteristics and turning changes in the time sequence expression result of the agricultural machinery operation trajectory, and segment the continuous travel path based on the direction of operation time advancement. Divide adjacent paths into consecutive travel segments according to the time sequence to generate continuous travel segments. S202: Based on the data in the continuous travel segment, the changes in travel speed, travel direction, and directional deviation in the path within each trajectory segment are described in detail. The motion continuity, turning characteristics, and path bending characteristics of each segment are calculated to obtain the feature information describing the trajectory segment and generate a trajectory segment feature description. S203: Based on the motion continuity, turning characteristics and path bending characteristics in the trajectory segment feature description, the operation trajectory is distinguished and processed, and trajectory segments that meet the characteristics of field operation are selected to obtain the field operation trajectory recognition result.

[0009] As a further aspect of the present invention, the step of obtaining S3 is as follows: S301: Obtain the trajectory segments in the field operation trajectory recognition results, divide the trajectory segments into spatial regions based on the proximity distribution characteristics and consistency of the travel direction in the planar position expression of the trajectory segments, determine the planar position characteristics of adjacent trajectory segments according to the positional relationship of the trajectory segments in each region, and generate the trajectory segment region division results. S302: Based on the trajectory segment region division result, extract the travel direction information corresponding to the trajectory segment in each trajectory region, compare the direction information, compare the direction consistency between adjacent trajectory segments, determine the trajectory region with consistent travel direction, mark the boundary of the region with consistent direction, and generate a trajectory region pointing consistent result. S303: Based on the trajectory segments with the same direction in the trajectory region pointing consistent result, determine the temporal continuity and the positional adjacency relationship between the same-direction trajectory segments in the planar position expression, and continuously merge the same-direction trajectory regions according to the relationship to obtain a complete region with the same operation direction, and generate the trajectory region result with the same operation direction.

[0010] As a further aspect of the present invention, the step of obtaining S4 is as follows: S401: Obtain the travel direction information of the trajectory segments in the trajectory area result with consistent work direction, extract the common travel direction of the trajectory segments in the trajectory area, use the vertical direction of the work travel direction in space as a reference, determine the arrangement order of the trajectory segments, and generate the work travel direction reference. S402: Based on the work travel direction reference, analyze the lateral interval changes between adjacent trajectory segments, compare the distance differences between adjacent trajectory segments, determine the positions where the lateral interval changes continuously, use these continuously changing spatial positions as the basis for trajectory area separation, and generate the lateral interval change positions. S403: Based on the lateral interval change position, continuously merge the same-direction trajectory regions in the trajectory region results with the same operation direction, and recombine the trajectory regions that meet the merging conditions according to the time sequence to obtain the complete trajectory region division of the operation plot and generate the trajectory region result of the operation plot.

[0011] As a further aspect of the present invention, the method further includes: S5: Based on the set of trajectory points contained within each work plot area in the work plot trajectory area results, filter the trajectory points on the edge of the work plot area, close the boundary line according to the connection relationship, limit the spatial range of the plot, establish the association relationship between the boundary line and the work trajectory, and obtain the intelligent recognition of agricultural machinery operation trajectory and plot analysis results; The intelligent recognition and land parcel analysis results of agricultural machinery operation trajectories include land parcel boundary information, land parcel spatial range data, and the correspondence between operation trajectories and land parcels.

[0012] As a further aspect of the present invention, the step of obtaining S5 is as follows: S501: Obtain the set of trajectory points contained within each work plot area in the work plot trajectory area result, filter out the trajectory points located at the edge of the work plot area, and connect the trajectory points sequentially according to the spatial connection relationship of the edge trajectory points to form a preliminary boundary line and generate an edge trajectory point set. S502: Based on the edge trajectory points in the set of edge trajectory points, determine the connection order of each trajectory point through spatial position relationship, connect the edge trajectory points in sequence to form a closed land parcel boundary line, and record the spatial position of each trajectory point to generate the land parcel boundary line; S503: Based on the land boundary line, determine the spatial range of the work plot, establish the correlation between the spatial range of the work plot and the trajectory points that participate in forming the boundary line, obtain accurate identification between the agricultural machinery operation trajectory and the land plot, and generate intelligent identification of agricultural machinery operation trajectory and land plot analysis results.

[0013] As a further aspect of the present invention, the process of selecting trajectory points located at the edge of the work site area specifically comprises: The set of trajectory points contained within the work area is subjected to planar coordinate denoising and smoothing processing, and duplicate trajectory points are removed. The outer envelope of the trajectory point set in planar coordinates is used as the candidate range. Within the candidate range, trajectory points that are sparsely distributed in the local neighborhood and located on the outer envelope are marked as trajectory points located at the edge of the work area, thus forming the edge trajectory point set.

[0014] As a further aspect of the present invention, the process of sequentially connecting the trajectory points based on their spatial connection relationship is specifically as follows: Adjacent connection pairs are established in the order of time information of trajectory points in the set of edge trajectory points, and intersection detection is performed on the connection pairs. If there is an intersection, the connection pairs are adjusted according to the spatial proximity relationship to eliminate the intersection, thus obtaining the preliminary boundary line. The specific steps for determining the connection order of each trajectory point based on spatial location relationships are as follows: Starting from the endpoints of the initial boundary line, the boundary line is expanded point by point according to the principle of spatial nearest neighbor while maintaining the continuity of the connection direction until the endpoints of the initial boundary line satisfy the closure condition in the plane coordinates, thus forming the closed land boundary line. The spatial position of each trajectory point in the closed land boundary line is recorded.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, a trajectory state expression based on time sequence is constructed to form a stable constraint relationship between path shape, motion continuity and directional changes. In the trajectory recognition stage, the operation trajectory is screened based on continuity and geometric features. In the spatial organization stage, a clear trajectory region is formed based on directional consistency and positional adjacency. In the plot division stage, the interval change in the vertical dimension of the travel direction is used to characterize the operation row structure, so that the boundaries of adjacent plots present clear divisions. Finally, the complete plot outline is obtained through edge trajectory closure, realizing the interpretable expression and stable output of the relationship between trajectory and plot. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of S1 in this invention; Figure 3 This is a flowchart illustrating the acquisition process of S2 in this invention; Figure 4 This is a flowchart illustrating the acquisition process of S3 in this invention; Figure 5 This is a flowchart illustrating the acquisition process of S4 in this invention; Figure 6 This is a flowchart of the acquisition process for S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a technical solution: a method for identifying agricultural machinery operation trajectories and analyzing land parcels based on multiple features, comprising the following steps: S1: Collect time information, spatial location information, travel speed information, and travel direction information continuously output by the Beidou positioning terminal during the operation of agricultural machinery in the field. Using time information as the sequential basis, the various types of information are correlated to form a complete description of the agricultural machinery's travel status at each moment. The spatial location information is converted into a planar position expression. The travel path is depicted based on the planar position changes between adjacent moments. The continuous motion characteristics are depicted based on the travel speed information. The path turning changes are depicted in combination with the travel direction information. The travel path, motion characteristics, and turning changes are kept consistent in time sequence to form the time sequence expression result of the agricultural machinery operation trajectory. S2: Based on the travel path, motion characteristics, and turning changes in the time-series expression of agricultural machinery operation trajectory, the continuous travel path is continuously divided along the direction of operation time to form consecutive travel segments. Adjacent travel segments are selected as trajectory segments. Within each trajectory segment, the motion continuity presented by changes in travel speed, the turning characteristics presented by changes in travel direction, and the path bending characteristics formed by continuous directional deviations in the travel path are described. Based on the motion continuity, turning characteristics, and path bending characteristics presented by the trajectory segments, operation trajectory differentiation processing is performed to select trajectory segments that conform to the characteristics of field operations and form the field operation trajectory recognition results. S3: Based on the trajectory segments in the field operation trajectory recognition results, the trajectory segments are divided into regions according to the proximity distribution characteristics in the planar position expression and the consistent direction of the trajectory segments in space. The direction information corresponding to the trajectory segments is extracted in each trajectory region. The direction information in the trajectory region is compared to determine the trajectory regions with consistent direction. According to the continuous relationship of the same-direction trajectory segments in time sequence and the first and last adjacent relationship in the planar position expression, the same-direction trajectory regions are continuously merged to form the result of the trajectory region with consistent operation direction. S4: Based on the trajectory regions where the trajectory segments in the trajectory regions with consistent operation directions maintain the same direction, extract the common direction of movement of the trajectory segments within the trajectory regions as the operation direction. Using the vertical direction of the operation direction in space as the benchmark, arrange the trajectory segments, analyze the changes in the lateral interval between adjacent trajectory segments, determine the spatial location where the lateral interval between adjacent trajectory segments changes continuously, and use the continuously changing spatial location as the basis for trajectory region separation. Divide the trajectory regions with consistent operation directions to form the trajectory region results of the operation plots. S5: Based on the set of trajectory points contained within each work plot area in the work plot trajectory area results, select trajectory points located at the edge of each work plot area, and connect the edge trajectory points sequentially according to the spatial connection relationship of the trajectory points in the same work plot area to form a closed plot boundary line. Based on the plot boundary line, define the spatial range of the plot, establish the correlation between the spatial range of the plot and the work trajectories that participate in forming the plot boundary line, and obtain the intelligent recognition of agricultural machinery operation trajectory and plot analysis results.

[0023] The temporal representation results of agricultural machinery operation trajectories include a unified time reference, continuous planar trajectory description, motion state change characteristics, and turning change characteristics. The field operation trajectory identification results include a set of operation trajectory segments, trajectory segment operation validity identifiers, and trajectory segment continuity attributes. The results of trajectory regions with consistent operation directions include a set of trajectory regions in the same direction, trajectory region direction consistency attributes, and trajectory region spatial continuity characteristics. The results of operation plot trajectory regions include a set of plot trajectory regions, plot operation direction attributes, and lateral separation characteristics between plots. The intelligent identification and plot analysis results of agricultural machinery operation trajectories include plot boundary line information, plot spatial range data, and the correspondence between operation trajectories and plots.

[0024] Please see Figure 2 The steps to obtain S1 are as follows: S101: Collect time information, spatial location information, travel speed information and travel direction information output by the Beidou positioning terminal during the operation of agricultural machinery. Based on the time information, sequentially associate the spatial location, speed and direction data at the same time point to generate a time series dataset. The system collects time, spatial location, speed, and direction of travel information output by the Beidou positioning terminal (including GNSS receiver module and microprocessor) during agricultural machinery operation. A data receiving buffer is established, and the system reads the data stream uploaded by the positioning terminal via RS-232 or CAN bus serial communication protocol in real time. The system parses the NMEA-0183 standard protocol statements in the data stream, extracts the $GNRMC (recommended minimum positioning information) statement segment as the raw data source, and checks whether the field identifying the positioning status in the positioning statement segment contains the specific character "A" representing valid positioning. Simultaneously, it extracts the... The field indicating the number of satellites in the sentence segment has a minimum threshold of 4. This threshold is based on the minimum number of equations required for 3D positioning calculations (longitude, latitude, elevation, clock difference) (4 equations for 4 unknowns). If the extracted satellite count is less than 4 or the positioning status field is "V" (invalid), the data is marked as invalid and discarded to avoid noise interference. If the verification passes, the comma-separated UTC time, latitude, longitude, ground velocity, and ground heading fields in the sentence segment are parsed sequentially. The parsed latitude and longitude data are retained to six decimal places (approximately 0.1 meters). (Accuracy) The ground speed unit is converted from knots to meters per second (m / s) by multiplying by a factor of 0.5144. UTC time is converted to local operation time value by adding time zone offset. The time synchronization tolerance is set to 0.5 seconds. The system checks whether there are duplicate records in the local operation time value within the allowable tolerance range. If duplicates are found, the set of data with more satellites (higher accuracy) is retained. If no duplicates are found, a new data record row is created. For example, in a tractor operation, the terminal outputs longitude 116.354210, latitude 39.951230, and speed 3.6 km / h (i.e., 1.944 knots). The parameters, including the heading (90 degrees) and timestamp (T1), are parsed and converted to obtain the longitude (116.354210), latitude (39.951230), speed (1.0 m / s), heading (90.0), and time (T1). These verified and formatted parameters are then written into a predefined structured storage array in ascending order of time values. A unique index key is created for each time node, mapping the spatial location, speed, and direction data to this index key. This forms a two-dimensional data table with time as the unique primary key and various operational parameters as attribute columns, generating a time series dataset.

[0025] S102: Based on the spatial location information in the time series dataset, perform planar coordinate transformation to convert the spatial information from latitude and longitude coordinates to location data in the planar coordinate system. Combine the displacement information at adjacent time points to determine the movement direction and path change of each path segment and generate path segment change characteristics. Based on the spatial location information in the time series dataset, the first longitude value and the first latitude value in the dataset are retrieved as the reference origin longitude of the work area. Latitude relative to the reference origin Iterate through each time point in the dataset. Extract the longitude of the current time point. with latitude Introducing the Earth's average radius constant R, with a value of 6,371,393 meters, and performing a planarization operation using the Mercator projection approximation algorithm, the latitude and longitude difference is converted into radians: and Execute the formula Calculate the x-coordinate of the plane; Execute formula Calculate the ordinate of the plane. This process converts the spherical coordinates into a local Cartesian coordinate system. For example, for a point with longitude 116.354300 and latitude 39.951300, the coordinates are relative to the origin with longitude 116.354210 and latitude 39.951230. Substituting into the calculation, the plane coordinates are: Meters, the calculated plane coordinates Write back to the corresponding row of the time series dataset and extract adjacent moments in chronological order. and plane coordinates and ; The displacement vector is obtained by performing vector subtraction. Calculate the magnitude of the displacement vector. As the distance traveled on this path segment, calculate the angle between this displacement vector and the positive Y-axis of the planar coordinate system. This is then converted to a 0-360 degree range and used as the calculated heading angle for that path segment. The calculated heading angle and the distance traveled are stored as attributes for that time period. For example, the calculated heading angle is... to The displacement distance is 1.5 meters, and the calculated heading angle is 45 degrees. This is used to quantitatively describe the positional changes between adjacent time points, determine the movement direction and path changes of each path segment, and generate path segment change characteristics.

[0026] S103: Based on the characteristics of path segment changes, combined with the information on travel speed and direction, organize the data in the time series, determine the travel status of the agricultural machinery at each moment, complete the overall description of the motion characteristics and direction changes of each path segment, and generate the time series expression result of the agricultural machinery operation trajectory; Based on the path segment variation characteristics, combined with travel speed and direction information, each record in the time series dataset is traversed to extract the instantaneous velocity values ​​directly output by the BeiDou terminal. With instantaneous heading value Simultaneously, extract the path segment movement distance calculated in the previous step. With calculation of heading angle The static velocity threshold is set to 0.1 m / s. This threshold is based on the upper limit of zero-drift noise of the GPS receiver in a stationary state, and the instantaneous velocity values ​​are compared. Compared with the static determination speed threshold, if If the speed is less than 0.1 m / s, the current travel status is marked as "stationary". The heading deviation tolerance threshold is set to 15 degrees. This threshold is set based on the normal turbulence amplitude during agricultural machinery field operations and the GPS heading update delay. The absolute difference between the instantaneous heading value and the calculated heading angle is calculated. (The case of a boundary spanning 360 degrees needs to be handled; the minimum included angle should be taken.) If Greater than 0.1 m / s and If the angle is less than 15 degrees, the agricultural machinery is considered to be in a "stable straight-line driving" state. Greater than 0.1 m / s and If the angle is greater than or equal to 15 degrees, the agricultural machinery is determined to be in a "steering or slipping" state (because the direction of the vehicle's front is inconsistent with the actual direction of displacement). For example, if the instantaneous heading is 90 degrees and the calculated heading is 85 degrees, the difference of 5 degrees is less than the threshold, and it is marked as straight-line driving. The determined state label "stationary," "stable straight-line driving," or "steering or slipping" is added as a new field to the dataset, and the planar coordinates are also added. Instantaneous velocity Instantaneous heading The status labels are combined to form a feature vector. By concatenating all feature vectors in chronological order, a state matrix for the entire operation cycle is constructed. The data in the time series is organized to determine the movement status of the agricultural machinery at each moment, thus completing the overall description of the motion characteristics and directional changes of each path segment and generating the time-series representation of the agricultural machinery operation trajectory.

[0027] Please see Figure 3 The steps to obtain S2 are as follows: S201: Obtain the travel path, motion characteristics and turning changes from the time-series representation of agricultural machinery operation trajectory. Based on the direction of operation time advancement, segment the continuous travel path and divide adjacent paths into consecutive travel segments according to time sequence to generate continuous travel segments. To obtain the travel path, motion characteristics, and turning changes from the time-series representation of agricultural machinery operation trajectories, a time window cursor for dividing travel segments is established, and a basic time window length threshold is set. The threshold is set at 10 seconds, based on the average time required for the agricultural machinery to complete one routine turning maneuver. The starting index of the first travel segment is initialized as the first point of the trajectory sequence. The trajectory point sequence is traversed in the time increment direction, and the current cursor position is calculated in real time. and the start time of the current travel segment The difference ,Compare and The size, if If the time is less than 10 seconds, the current trajectory point will be added to the current travel segment set that is being constructed. If the time reaches or exceeds 10 seconds, the segmentation judgment logic is triggered, and the instantaneous heading values ​​of all trajectory points within the current time window are read. Calculate the standard deviation of the heading values ​​within this window. Set the heading stability threshold The threshold is 5 degrees, which is set based on the allowable course fluctuation range during straight-line operations. A value less than 5 degrees indicates that the vehicle is currently in a stable straight-line driving state, so the time window length will be adjusted accordingly. Temporarily extend the time by 5 seconds to continue absorbing subsequent points, until... If the current travel segment is interrupted immediately, up to 5 degrees or upon encountering the "Stationary" status label, and the status label is "Turning or Slipping" at the end of the window, or if the continuity of the direction of temperature change in the monitored area (referring to status parameters such as the rate of change of heading) is inconsistent (i.e., changing from left to right, or from a straight line to a curve), the index will be changed. The current segment's end point and the next segment's start point are marked. For example, in a certain operation, the standard deviation of the heading from second 0 to second 15 is 3 degrees, and it is continuously classified into the first segment. At second 16, a sudden change in heading is detected, and the standard deviation rises to 12 degrees. Then, second 0 to second 16 is divided into the first travel segment. After second 16, the second travel segment is constructed. The above sliding window detection and truncation operations are performed on the full time-series trajectory data in sequence to form a structured list consisting of the start timestamp, the end timestamp, and the indexes of all included trajectory points. Adjacent paths are divided into consecutive travel segments according to time order to generate continuous travel segments.

[0028] S202: Based on the data in the continuous travel segment, the changes in travel speed, travel direction and directional deviation in the path within each trajectory segment are described in detail. The motion continuity, turning characteristics and path bending characteristics of each segment are calculated to obtain the feature information describing the trajectory segment and generate the trajectory segment feature description. Based on the data in the continuous movement segment, traverse each predefined trajectory segment. Extract the set of velocity values ​​from all sampling points within the segment. With heading value set Calculate the set of velocity values coefficient of variation ,in For the speed standard deviation, The average speed (denominator is protected against zero: if...) ,but Set to 0), As a quantitative indicator of the continuity of motion in this segment, a continuity judgment benchmark value of 0.2 is set. A value less than 0.2 indicates small velocity fluctuations and high motion continuity. For example, if the average velocity of a segment is 1.5 m / s and the standard deviation is 0.15 m / s, then... The condition is determined to be high continuity; for steering characteristics, the sum of the heading differences between adjacent points in the heading value set is calculated. (Correction needed for the jump from 359 degrees to 1 degree), take the absolute value of the sum as the cumulative steering angle, and simultaneously calculate the straight heading of the line connecting the first and last points. Actual headings of each point within the segment Maximum deviation The cumulative steering angle and the maximum deviation value are combined to describe the steering characteristics. For example, the cumulative steering angle of a U-turn segment is 175 degrees, and the maximum deviation value is 85 degrees. For path bending features, the planar coordinates of all points within the segment are extracted. Construct a path curvature sequence and use the three-point circumcircle method to calculate the average curvature of all points in the path. (unit: Set the bending threshold. If it is 0.05, If the value is greater than 0.05, the segment is determined to have significant bending characteristics. The motion continuity, turning characteristics and path bending characteristics of each segment are calculated to obtain the feature information describing the trajectory segment and generate a trajectory segment feature description.

[0029] S203: Based on the motion continuity, turning characteristics and path bending characteristics in the trajectory segment feature description, the operation trajectory is distinguished and processed, and trajectory segments that meet the characteristics of field operation are selected to obtain the field operation trajectory recognition result. Based on the motion continuity, steering characteristics, and path curvature features in the trajectory segment feature description, initialize the valid operation trajectory storage list and set the composite condition group for operation mode determination: velocity variation coefficient (Characterizing relatively stable speed), cumulative steering angle (Representing an approximate straight line), mean curvature (Representing a straight path), scan the feature parameters of each trajectory segment one by one. If a segment simultaneously meets the above three conditions, for example, a certain segment... Cumulative steering angle mean curvature If the segment meets the above conditions, it is determined to be a "straight-line operation segment in the field," marked as "valid operation," and stored in the list. If the segment does not meet the above conditions, it is further determined whether it meets the "turning at the edge of the field" characteristic. The turning determination condition is set as: cumulative turning angle. and average speed If the speed is less than 80% of the average normal operating speed (due to the need to slow down when turning), for example, if a segment has a cumulative turning angle of 178 degrees and an average speed of 0.8 m / s (normal speed is 1.5 m / s), it is judged as a "turn at the edge of the field" and marked as "auxiliary operation"; for segments that do not meet the conditions for either straight-line operation or turning at the edge of the field, such as the cumulative turning angle... And the speed fluctuates greatly ( Irregular driving segments of 0.6) are identified as "non-operational transfer" or "abnormal interference", marked as "invalid trajectory" and removed. Operational trajectory differentiation processing is performed to filter out trajectory segments that meet the characteristics of field operations, and the field operation trajectory recognition results are obtained.

[0030] Please see Figure 4 The steps to obtain S3 are as follows: S301: Obtain trajectory segments from the field operation trajectory recognition results. Based on the proximity distribution characteristics and consistency of travel direction in the planar position expression of the trajectory segments, divide the trajectory segments into spatial regions. Based on the positional relationship of the trajectory segments in each region, determine the planar position characteristics of adjacent trajectory segments and generate trajectory segment region division results. Obtain trajectory segments from the field operation trajectory recognition results, construct a spatial grid-based index structure, and set the grid size to [value missing]. This size is set as a multiple of the agricultural machinery operating width (e.g., 2.5 meters), for example, 50 meters to cover multiple operating widths; The work area is divided into several The rectangular cells are used to iterate through each trajectory segment marked as a "valid task". Extract its centroid coordinates ; According to the formula and Calculate the grid row and column number of the segment, and group all trajectory segments with the same grid row and column number into the same initial spatial bucket. For each non-empty spatial bucket, calculate the average heading of all trajectory segments within the bucket. Set heading consistency tolerance Given a 20-degree angle, determine the heading for each segment within the bucket. ; Calculate the deviation If the deviation is less than 20 degrees, the segment is kept in the current bucket. If the deviation is greater than or equal to 20 degrees (i.e., the directions are inconsistent), it is removed and attempted to be assigned to a bucket with a closer heading in an adjacent grid, or a new independent bucket is created. For example, if a grid contains 5 trajectory segments, 4 of which have a heading of approximately 90 degrees and 1 has a heading of 270 degrees (reverse operation), the 270-degree segment is separated to form two sub-regions. The minimum bounding rectangle range of each region after separation is calculated, and the list of trajectory segment IDs and their spatial boundary coordinates contained in each region are recorded. Based on the positional relationship of the trajectory segments in each region, the planar positional characteristics of adjacent trajectory segments are determined, and the trajectory segment region division results are generated.

[0031] S302: Based on the trajectory segment region division results, extract the travel direction information corresponding to the trajectory segment in each trajectory region, compare the direction information, compare the direction consistency between adjacent trajectory segments, determine the trajectory regions with consistent travel directions, mark the boundaries of the regions with consistent directions, and generate trajectory regions pointing in the same direction. Based on the trajectory segment region segmentation results, each segmented initial spatial region is traversed. Extract the heading vectors of all trajectory segments within the region. Performing vector summation operation yields the main direction vector of the region. Calculate the angle of the principal direction vector. As the governing direction for this region, for example, if the calculated governing direction for a certain region is 88.5 degrees, a neighborhood search is performed between regions. For each region... Retrieve all spatially adjacent regions Calculate the difference in the overall operation direction between the two regions. Set the region merging angle threshold It is 15 degrees, if If the difference is less than 15 degrees, the two adjacent regions are determined to have "pointing consistency" and are marked as "candidate pairs of the same direction". For example, if the main direction of region A is 88 degrees and the main direction of the adjacent region B is 92 degrees, the difference is 4 degrees, and they are determined to be in the same direction. For regions determined to be in the same direction, their boundary contact length is further checked. If the common boundary length is greater than 50% of the grid size, it is confirmed that the two are closely connected in space. Regions that meet the requirements of consistent direction and spatial connection are initially clustered, assigned the same region cluster ID, and the boundary of the regions with consistent direction is marked to generate trajectory regions pointing consistent results.

[0032] S303: Based on the trajectory segments with the same direction in the trajectory region pointing consistent result, determine the temporal continuity and the positional adjacency relationship between the same-direction trajectory segments in the planar position expression, and continuously merge the same-direction trajectory regions according to the relationship to obtain a complete region with the same operation direction, and generate the trajectory region result with the same operation direction. Based on the trajectory segments with consistent directions in the trajectory region pointing results, for each set of regions with the same region cluster ID, extract the timestamp intervals of all trajectory segments within the set. and the first and last coordinates According to the start and end points of time Sort the fragments in ascending order, traverse the sorted fragment sequence, and check adjacent fragments. and time interval Set a time continuity threshold The threshold is 300 seconds, set based on the average time it takes for agricultural machinery to turn around or temporarily stop for fertilization at the edge of the field. Less than 300 seconds, and the starting point of the next segment. End point of the previous segment Euclidean distance between If the distance is less than a set threshold (e.g., 50 meters), then the two segments are determined to belong to the same continuous operation process, and a merging operation is performed to connect them into a long operation chain; if If the time is greater than 300 seconds, but the two segments are extensions of the same work line in space (i.e., the lateral offset is less than half of the work width, which meets the characteristics of breakpoint continuation), then it is determined to be "breakpoint continuation". The same logic merging is performed, the attributes of the merged area are updated, the overall outer polygon boundary of the merged area is recalculated, all trajectory segments with temporal continuity or spatial continuity are thoroughly integrated, isolated noise areas are removed, and a complete area with the same work direction is obtained, generating a trajectory area result with the same work direction.

[0033] Please see Figure 5 The steps to obtain S4 are as follows: S401: Obtain the travel direction information of trajectory segments in the trajectory area result with consistent work direction, extract the common travel direction of trajectory segments in the trajectory area, use the vertical direction of the work travel direction in space as the reference, determine the arrangement order of trajectory segments, and generate the work travel direction reference. Obtain the travel direction information of trajectory segments in the trajectory region with consistent operation direction, and extract the global average heading angle of that region. Calculate the orthogonal direction vector for that angle: This vector represents the lateral reference axis perpendicular to the direction of operation, and iterates through the coordinates of the center points of all trajectory segments within the region. Project the coordinates of each center point onto the horizontal reference axis; Calculate the projected scalar value using the dot product formula Based on the projection value The size of all trajectory segments is sorted in ascending order to create a list of trajectory segments ordered by their lateral position. For example, if the average working direction of a certain area is due north (0 degrees) and the reference axis is due west (270 degrees), and the x-coordinates of the center points of each trajectory segment are 10m, 12m, 35m, and 37m respectively, then the projection values ​​will correspond accordingly. The order of the list remains unchanged after sorting. For cases where the projection values ​​are similar (difference less than 0.5 meters) due to overlapping or reciprocating operations, a secondary sort is performed based on the timestamp to ensure that each working path has a definite relative position index in the list. The vertical direction of the working direction in space is used as the reference to determine the arrangement order of the trajectory segments and generate the working direction reference.

[0034] S402: Based on the work travel direction reference, analyze the lateral interval changes between adjacent trajectory segments, compare the distance differences between adjacent trajectory segments, determine the locations where the lateral interval changes continuously, use these continuously changing spatial locations as the basis for trajectory area separation, and generate the lateral interval change locations. Based on the work movement direction reference, traverse the sorted list of trajectory segments and calculate the distance between two adjacent trajectory segments. and lateral projection distance difference between Introducing agricultural machinery operating width parameters (For example, set to 2.5 meters), calculate the standardized lateral spacing ratio. Set the threshold range for the normal overlap or adjacency interval of operations as follows: (Corresponding to an interval of 2m to 3m), set the interval ratio threshold for determining the land parcel separation. The threshold is 5.0. This threshold indicates that when the lateral distance between two work trajectories exceeds 5 times the work width, it is considered a cross-plot operation. If detected... Greater than 5.0, for example, at a certain location Meters, width 2.5 meters, Then mark the index at that location. and There are "plot dividing points" between plots. The spatial coordinates and projected positions of these dividing points are recorded, and statistics are also compiled. exist If there are more than 3 consecutive medium-amplitude interval jumps between abnormal intervals (representing abnormal operation intervals, but not meeting the segmentation standard), it is marked as "abnormal interval warning" and will not be segmented until it is confirmed that the subsequent intervals have returned to normal or the segmentation threshold has been reached. The distance difference between adjacent trajectory segments is compared to determine the location where the horizontal interval changes continuously. The spatial location of these continuous changes is used as the basis for trajectory area separation to generate the horizontal interval change location.

[0035] S403: Based on the position of the lateral interval change, the same-direction trajectory areas in the same-direction trajectory area results are continuously merged. The trajectory areas that meet the merging conditions are recombined according to the time sequence to obtain the complete trajectory area division of the work plot and generate the trajectory area result of the work plot. Based on the horizontal interval changes, an empty set of plot areas is established. Using the identified "plot segmentation points" as boundaries, the trajectory areas with the same original operating direction are divided into several sub-regions. Each sub-region is traversed, and the number of trajectory segments it contains is checked. If the number is less than 3, it is considered a noise area and is removed. If the number is greater than or equal to 3, it is considered an independent "candidate plot unit". The time span attribute of each candidate plot unit is extracted. For candidate units that are spatially segmented but temporally closely connected (e.g., with an interval of less than 10 minutes), a secondary verification is performed. If two segmented units are far apart laterally, but there is a "non-operational transfer trajectory" connecting them (identified by step S2, representing the process of agricultural machinery moving from one plot to another), and the start and end points of the transfer trajectory fall on the boundaries of the two units respectively, then it is determined that the two units are actually long-distance operation areas of the same plot (such as the two ends of an irregular plot), the segmentation operation is canceled, and they are merged again. Conversely, if there is no transfer trajectory connection or the time interval is too long, the segmentation is confirmed to be valid. The trajectory areas that meet the merging conditions are recombined according to the time sequence to obtain the complete trajectory area division of the operation plot and generate the trajectory area result of the operation plot.

[0036] Please see Figure 6 The steps to obtain S5 are as follows: S501: Obtain the set of trajectory points contained within each work plot area in the work plot trajectory area result, filter out the trajectory points located at the edge of the work plot area, and connect the trajectory points sequentially according to the spatial connection relationship of the edge trajectory points to form a preliminary boundary line and generate an edge trajectory point set. The process of selecting trajectory points located at the edge of the work area is as follows: The set of trajectory points contained within the work area is subjected to planar coordinate denoising and smoothing, and duplicate trajectory points are removed. The outer envelope of the trajectory point set in planar coordinates is used as the candidate range. Within the candidate range, trajectory points that are sparsely distributed in the local neighborhood and located on the outer envelope are marked as trajectory points located at the edge of the work area, forming an edge trajectory point set. The set of trajectory points contained within each work site area in the result of obtaining the work site trajectory area. For each point in the set coordinates Perform duplicate point detection and set a duplicate distance threshold. If the calculated Euclidean distance between two points is less than 0.2 meters, the point with the earlier timestamp is retained and the latter is removed to complete the deduplication process. A local weighted average is then performed. For each point, neighborhood points within a 1.5-meter radius are selected, and the mean coordinates of these neighborhood points are calculated as the smoothed new coordinates. A two-dimensional Delaunay triangulation of the trajectory points in this region is constructed. All outer edges of this triangulation are extracted to form an initial convex hull or approximately concave hull profile as a candidate outer envelope. traversal Each vertex on ,by Set the local density scan radius with the center as the center. Given a radius of 5 meters, count the total number of trajectory points contained within this circular area. Calculate local density Set an edge sparsity threshold The threshold is 0.05 per square meter. This threshold is set based on the characteristic that agricultural machinery operation boundaries typically have only one trajectory while the interior is densely overlapping. and ,like A value less than 0.05 indicates that the point is located at the boundary of a sparsely distributed trajectory region, and it is marked as an "edge feature point". Points with a density greater than or equal to 0.05 are considered high-density points near the interior and are excluded. For example, in a rectangular plot, the local density of points inside the plot is 0.2, while the density of points on the outermost edge is only 0.04. This accurately filters out the outermost points. All points marked as "edge feature points" are then extracted and stored in a list. Based on the spatial connection relationship of the edge trajectory points, the trajectory points are connected sequentially to form a preliminary boundary line, generating a set of edge trajectory points.

[0037] S502: Based on the edge trajectory points in the edge trajectory point set, determine the connection order of each trajectory point through spatial position relationship, connect the edge trajectory points in sequence to form a closed plot boundary line, and record the spatial position of each trajectory point to generate the plot boundary line; The process of connecting the trajectory points sequentially based on their spatial relationships is as follows: Adjacent connection pairs are established in the order of time information of trajectory points in the edge trajectory point set, and intersection detection is performed on the connection pairs. If there is an intersection, the connection pairs are adjusted according to the spatial proximity relationship to eliminate the intersection, thus obtaining the preliminary boundary line. Determining the connection order of each trajectory point based on spatial location relationships is as follows: Starting from the endpoints of the initial boundary line, the boundary line is expanded point by point according to the principle of spatial nearest neighbor while maintaining the continuity of the connection direction until the endpoints of the initial boundary line meet the closure condition in the plane coordinates to form a closed land boundary line, and the spatial position of each trajectory point in the closed land boundary line is recorded. Based on the edge trajectory points in the edge trajectory point set, from The point with the smallest timestamp is selected as the starting point. Find the point in the remaining set that is spatially closest and not yet connected. As the next connection point, set a maximum connection span threshold. The threshold is 30 meters; this threshold is used to prevent incorrect connections across plot gaps. If the distance is less than 30 meters, then establish a connecting line segment. ,Will Set this as the new current point and repeat the nearest neighbor search process described above, establishing a new connecting segment each time. At that time, perform line segment intersection detection to determine... Is it related to the already generated set of line segments? If any non-endpoint intersection occurs, and an intersection is detected (e.g., line segment A and line segment B intersect in an "X" shape, which is a physically impossible boundary shape), then backtrack one step, abandon the current nearest neighbor, and select the second nearest neighbor to attempt a connection, or swap the connection order of the endpoints of the intersecting line segments (adjusting A1-A2, B1-B2 to A1-B1, A2-B2) to eliminate the intersection, until all edge points have been traversed and connected. At this point, check the last connection point. and the starting point The distance is used to set a closure detection distance threshold. The distance is 10 meters. If the distance is less than 10 meters, then connect directly. and To form a closed loop, if the distance is greater than 10 meters, linear interpolation is used to fill in the gaps along the boundary extension direction until the closure condition is met, ultimately forming a sequence of polygonal vertices. Record the spatial location of each trajectory point to generate the land parcel boundary line.

[0038] S503: Based on the land parcel boundary line, determine the spatial range of the work parcel, establish the correlation between the spatial range of the work parcel and the trajectory points that participate in forming the boundary line, obtain accurate identification between the agricultural machinery operation trajectory and the land parcel, and generate intelligent identification of agricultural machinery operation trajectory and land parcel analysis results; Based on the land parcel boundary lines, analyze the vertex sequence of the closed boundary line polygon. Based on the geometric properties, a polygon ray tracing algorithm is used to construct a geometric mask of the land parcel's spatial extent, traversing every trajectory point in the original full trajectory dataset. ,judge If a point is located inside the geometric mask, update its "Plot ID" field to the currently generated boundary line ID, establish a one-to-many attribution index relationship, and calculate the area of ​​the polygon enclosed by the closed boundary line (using the shoelace formula). (Ensure the vertex sequence is closed, i.e.) The calculated area value (in square meters) is converted to "mu" (divided by 666.67). For example, an area of ​​5000 square meters is equivalent to 7.5 mu. At the same time, the total length of the effective work trajectory falling within this area is calculated. The average operating speed and the plot area, operating area in acreage, operating time, average speed, and boundary vertex coordinate sequence are encapsulated into a standardized JSON data packet, for example: FieldID:"F001",Area:7.5,Boundary:[[x1,y1],...],TrajectoryRefs:[T1,T2...] serves as the digital archive of the plot. It establishes the correlation between the spatial range of the operation plot and the trajectory points that participate in forming the boundary line, thereby obtaining accurate identification between the agricultural machinery operation trajectory and the plot, and generating intelligent identification of agricultural machinery operation trajectory and plot analysis results.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features, characterized in that, Includes the following steps: S1: Collect information on the time, spatial location, speed and direction of agricultural machinery operation, associate various types of information in time order, convert the spatial location into a planar representation to depict the path, combine the speed and direction of travel to depict the motion and turning, and form the time sequence expression result of agricultural machinery operation trajectory; S2: Based on the travel path, motion characteristics and turning changes in the time-series expression results of the agricultural machinery operation trajectory, the travel segments are divided into trajectory segments, the motion continuity, turning characteristics and path bends are described, the operation trajectory segments are distinguished and screened, and the field operation trajectory recognition results are formed. S3: Based on the trajectory segments in the field operation trajectory recognition results, the regions are divided according to the proximity of the trajectory segments in planar position and the consistency of the direction of travel. The direction of travel is extracted and compared, and the time and space continuous trajectory regions in the same direction are merged to form the result of the trajectory region with the same operation direction. S4: Based on the same-direction trajectory area results in the operation direction consistent trajectory area, extract the common travel direction in the trajectory area as the operation direction, analyze the lateral interval change of adjacent trajectory segments in the vertical direction, separate the areas by the position of the interval change, and form the operation plot trajectory area results.

2. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The temporal representation of the agricultural machinery operation trajectory includes a unified time reference, continuous planar trajectory description, motion state change characteristics, and turning change characteristics. The field operation trajectory identification results include a set of operation trajectory segments, an operation validity identifier for trajectory segments, and a continuous attribute for trajectory segments. The trajectory region results with consistent operation direction include a set of trajectory regions in the same direction, a trajectory region direction consistency attribute, and a trajectory region spatial continuity characteristic. The operation plot trajectory region results include a set of plot trajectory regions, plot operation direction attributes, and lateral separation characteristics between plots.

3. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The steps for obtaining S1 are as follows: S101: Collect time information, spatial location information, travel speed information and travel direction information output by the Beidou positioning terminal during the operation of agricultural machinery. Based on the time information, sequentially associate the spatial location, speed and direction data at the same time point to generate a time series dataset. S102: Based on the spatial location information in the time series dataset, perform planar coordinate transformation to convert the spatial information from latitude and longitude coordinates to location data in a planar coordinate system. Combine the displacement information at adjacent times to determine the movement direction and path change of each path segment and generate path segment change features. S103: Based on the changes in the path segments, combined with the information on travel speed and direction, organize the data in the time series, determine the travel status of the agricultural machinery at each moment, complete the overall description of the motion characteristics and direction changes of each path segment, and generate the time series expression result of the agricultural machinery operation trajectory.

4. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The steps for obtaining S2 are as follows: S201: Obtain the travel path, motion characteristics and turning changes in the time sequence expression result of the agricultural machinery operation trajectory, and segment the continuous travel path based on the direction of operation time advancement. Divide adjacent paths into consecutive travel segments according to the time sequence to generate continuous travel segments. S202: Based on the data in the continuous travel segment, the changes in travel speed, travel direction, and directional deviation in the path within each trajectory segment are described in detail. The motion continuity, turning characteristics, and path bending characteristics of each segment are calculated to obtain the feature information describing the trajectory segment and generate a trajectory segment feature description. S203: Based on the motion continuity, turning characteristics and path bending characteristics in the trajectory segment feature description, the operation trajectory is distinguished and processed, and trajectory segments that meet the characteristics of field operation are selected to obtain the field operation trajectory recognition result.

5. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The steps for obtaining S3 are as follows: S301: Obtain the trajectory segments in the field operation trajectory recognition results, divide the trajectory segments into spatial regions based on the proximity distribution characteristics and consistency of the travel direction in the planar position expression of the trajectory segments, determine the planar position characteristics of adjacent trajectory segments according to the positional relationship of the trajectory segments in each region, and generate the trajectory segment region division results. S302: Based on the trajectory segment region division result, extract the travel direction information corresponding to the trajectory segment in each trajectory region, compare the direction information, compare the direction consistency between adjacent trajectory segments, determine the trajectory region with consistent travel direction, mark the boundary of the region with consistent direction, and generate a trajectory region pointing consistent result. S303: Based on the trajectory segments with the same direction in the trajectory region pointing consistent result, determine the temporal continuity and the positional adjacency relationship between the same-direction trajectory segments in the planar position expression, and continuously merge the same-direction trajectory regions according to the relationship to obtain a complete region with the same operation direction, and generate the trajectory region result with the same operation direction.

6. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The steps for obtaining S4 are as follows: S401: Obtain the travel direction information of the trajectory segments in the trajectory area result with consistent work direction, extract the common travel direction of the trajectory segments in the trajectory area, use the vertical direction of the work travel direction in space as a reference, determine the arrangement order of the trajectory segments, and generate the work travel direction reference. S402: Based on the work travel direction reference, analyze the lateral interval changes between adjacent trajectory segments, compare the distance differences between adjacent trajectory segments, determine the positions where the lateral interval changes continuously, use these continuously changing spatial positions as the basis for trajectory area separation, and generate the lateral interval change positions. S403: Based on the lateral interval change position, continuously merge the same-direction trajectory regions in the trajectory region results with the same operation direction, and recombine the trajectory regions that meet the merging conditions according to the time sequence to obtain the complete trajectory region division of the operation plot and generate the trajectory region result of the operation plot.

7. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 1, characterized in that: The method further includes: S5: Based on the set of trajectory points contained within each work plot area in the work plot trajectory area results, filter the trajectory points on the edge of the work plot area, close the boundary line according to the connection relationship, limit the spatial range of the plot, establish the association relationship between the boundary line and the work trajectory, and obtain the intelligent recognition of agricultural machinery operation trajectory and plot analysis results; The intelligent recognition and land parcel analysis results of agricultural machinery operation trajectories include land parcel boundary information, land parcel spatial range data, and the correspondence between operation trajectories and land parcels.

8. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 7, characterized in that: The steps for obtaining S5 are as follows: S501: Obtain the set of trajectory points contained within each work plot area in the work plot trajectory area result, filter out the trajectory points located at the edge of the work plot area, and connect the trajectory points sequentially according to the spatial connection relationship of the edge trajectory points to form a preliminary boundary line and generate an edge trajectory point set. S502: Based on the edge trajectory points in the set of edge trajectory points, determine the connection order of each trajectory point through spatial position relationship, connect the edge trajectory points in sequence to form a closed land parcel boundary line, and record the spatial position of each trajectory point to generate the land parcel boundary line; S503: Based on the land boundary line, determine the spatial range of the work plot, establish the correlation between the spatial range of the work plot and the trajectory points that participate in forming the boundary line, obtain accurate identification between the agricultural machinery operation trajectory and the land plot, and generate intelligent identification of agricultural machinery operation trajectory and land plot analysis results.

9. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 8, characterized in that: The process of selecting trajectory points located at the edge of the work area is as follows: The set of trajectory points contained within the work area is subjected to planar coordinate denoising and smoothing processing, and duplicate trajectory points are removed. The outer envelope of the trajectory point set in planar coordinates is used as the candidate range. Within the candidate range, trajectory points that are sparsely distributed in the local neighborhood and located on the outer envelope are marked as trajectory points located at the edge of the work area, thus forming the edge trajectory point set.

10. The method for agricultural machinery operation trajectory recognition and plot analysis based on multiple features according to claim 8, characterized in that: The process of connecting the trajectory points sequentially based on their spatial connection relationship is as follows: Adjacent connection pairs are established in the order of time information of trajectory points in the set of edge trajectory points, and intersection detection is performed on the connection pairs. If there is an intersection, the connection pairs are adjusted according to the spatial proximity relationship to eliminate the intersection, thus obtaining the preliminary boundary line. The specific steps for determining the connection order of each trajectory point based on spatial location relationships are as follows: Starting from the endpoints of the initial boundary line, the boundary line is expanded point by point according to the principle of spatial nearest neighbor while maintaining the continuity of the connection direction until the endpoints of the initial boundary line satisfy the closure condition in the plane coordinates, thus forming the closed land boundary line. The spatial position of each trajectory point in the closed land boundary line is recorded.

Images