Real-time detection method for non-GPS operation area of irregular and steep-gradient planting land in mountainous area

Abstract

The invention relates to the technical field of agricultural machinery, and discloses a real-time detection method for a non-GPS (Global Positioning System) operation area of irregular and steep-gradient planting land in a mountainous area. Comprising the following steps: multi-sensor data acquisition: acquiring the speed, attitude, operation state and boundary information of an operation machine tool through a rotating speed sensor, an attitude sensor, a vibration sensor, a load sensor and a boundary identification sensor; and data preprocessing: performing time synchronization, noise filtering and abnormity elimination on the acquired data. High-precision area detection is achieved through the multi-sensor fusion technology, the method adapts to irregular land parcels and large-gradient areas, the detection precision is remarkably improved, inclination angle compensation and dynamic width adjustment technologies are adopted, the method adapts to complex terrain conditions, the stable detection effect is ensured, the real-time area calculation function provides real-time operation feedback, adjustment is convenient for operators, and the operation efficiency is improved. Meanwhile, complete data are recorded, and the data utilization value is enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural machinery, and in particular to a real-time detection method for non-GPS operation areas of irregular and steep-slope planting lands in mountainous areas. Background Art

[0002] During agricultural planting in hilly and mountainous areas, the land plots are irregular in shape and the terrain is undulating, which poses a great challenge to the area detection of operating machinery. At present, the commonly used area detection method relies on GPS technology to calculate the operating area through the path record and width parameters of the machinery. However, the traditional GPS area detection method can still meet the needs in regular plots, but its detection accuracy is significantly reduced in low-speed and short-distance operations, frequent turns and complex terrain environments, and it is difficult to meet the requirements of mechanized agricultural operations in mountainous areas.

[0003] The existing technology uses a single sensor or a fixed-width detection method, and the path recording relies on GPS positioning, which lacks the ability to perceive the real-time status of the machine and complex terrain. This leads to obvious problems such as weak GPS signals, difficulty in eliminating turning paths, and uncompensated slope areas when operating in irregular plots and large slope areas. Especially in an environment with frequent turns, the area calculation error is large, and there is a lack of real-time feedback. Operators cannot adjust the operation method in time, further affecting the accuracy and efficiency of the detection results. These problems urgently need to be solved through multi-sensor fusion technology and terrain adaptability algorithms. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a real-time detection method for non-GPS operating areas in irregular and steep planting areas in mountainous areas, which solves the problems of low detection accuracy, poor real-time performance and lack of adaptability to turning paths and terrain changes in the prior art under complex terrain conditions.

[0005] To achieve the above objectives, the present invention is implemented by the following technical solutions: A real-time detection method for non-GPS operation area of ​​irregular and steep planting land in mountainous areas comprises the following steps:

[0006] Multi-sensor data acquisition: The speed, posture, working status and boundary information of the working tool are collected through speed sensors, posture sensors, vibration sensors, load sensors and boundary recognition sensors;

[0007] Data preprocessing: time synchronization, noise filtering and anomaly elimination of collected data;

[0008] Working state recognition: distinguishing effective working sections, turning sections and non-working sections based on multi-sensor data;

[0009] Real-time area calculation: Based on the effective working path and working width, the area is accumulated and calculated in combination with the slope angle information;

[0010] Data fusion and correction: Optimize the accuracy of path tracking and area calculation through multi-sensor fusion technology;

[0011] Job result output: store the area calculation results and path information and display them in real time.

[0012] Preferably, the speed sensor is installed on the driving wheel or track of the working tool to measure the real-time speed and accumulated travel distance of the tool.

[0013] Preferably, the posture sensor is an IMU sensor, which is used to collect the inclination angle, acceleration and angular velocity of the working tool, and monitor the movement state and slope changes of the tool in real time.

[0014] Preferably, the job status identification comprises the following steps:

[0015] Determine whether it is in an effective working state based on the signals of the vibration sensor and the load sensor;

[0016] Identify turning behavior based on the angular velocity signal of the attitude sensor;

[0017] Dynamically switch job status based on multi-sensor data.

[0018] Preferably, the area calculation comprises the following steps:

[0019] Eliminate turning segments and non-operating segments from the effective operating path;

[0020] Dynamically adjust the working width based on boundary recognition sensor data;

[0021] In plots with large slopes, the area calculation results are corrected by combining the inclination information.

[0022] Preferably, the multi-sensor fusion technology includes data weighted fusion and Kalman filtering to optimize the path tracking accuracy and the real-time performance of area calculation.

[0023] Preferably, the data preprocessing uses a low-pass filter and a Kalman filter to filter the noise of the collected data, and eliminates outliers based on statistical methods.

[0024] Preferably, the job result output includes the following contents:

[0025] Real-time display of current cumulative operation area, path trajectory and operation status;

[0026] Record complete job data for subsequent analysis.

[0027] Preferably, the method is applicable to irregular plots and steep terrains, and can adapt to area detection requirements under different terrain conditions by automatically adjusting operating parameters.

[0028] Preferably, the method is verified through multiple experiments, and the area detection error does not exceed 3%, and has good robustness and adaptability.

[0029] The present invention provides a real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas. It has the following beneficial effects:

[0030] 1. The present invention uses multi-sensor fusion technology to dynamically combine rotation speed, attitude and boundary data to achieve high-precision area detection of irregular plots and large slope areas. Compared with traditional GPS methods, the present invention can eliminate the interference of turns and non-operating paths, and compensate for slope changes in real time, significantly reducing detection errors. Especially under complex terrain conditions, the error rate can be controlled within a very small range.

[0031] 2. The present invention adopts inclination compensation and dynamic width adjustment technology, which can effectively adapt to various terrain environments. It dynamically adjusts the operating width through the boundary recognition sensor to avoid missing or miscalculating the boundary area, so that it can maintain a stable detection effect in regular plots, irregular plots and steep slopes.

[0032] 3. The present invention provides operators with instant operation feedback through the real-time area calculation function combined with the dynamic display of path trajectory and operation status. The display terminal can display the cumulative area, path trajectory and operation status in real time, which is convenient for operators to adjust operations in time. At the same time, the complete data is stored for subsequent analysis, which greatly improves the operation efficiency and data utilization value. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Please see attached Figure 1 The embodiment of the present invention provides a real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas, and realizes accurate real-time area detection and operation path tracking through dynamic operation state recognition, complex terrain adaptability algorithm and multi-source data fusion technology. The implementation methods of the present invention are described in detail below.

[0036] 1. System composition

[0037] The system of the present invention mainly includes the following modules:

[0038] Perception Module

[0039] Speed ​​sensor: Installed on the drive wheel or track of the working tool, used to detect the tool's travel speed and cumulative travel distance. Its output pulse signal is collected at a certain time interval and can accurately reflect the tool's path information.

[0040] Inertial Measurement Unit (IMU): used to detect the three-axis acceleration, angular velocity and inclination data of the work tool. The inclination data of the IMU is used for path compensation for slope operations, and the angular velocity is used to identify turning behavior.

[0041] Vibration sensor and load sensor: installed on the working parts of the machine, used to determine whether the machine is in an effective working state. The strength of the vibration signal and load signal is highly correlated with the working state of the machine.

[0042] Boundary recognition sensor: Uses lidar or ultrasonic sensors to monitor the operation boundary information in real time, which is used to dynamically adjust the operation width and avoid out-of-bounds calculations.

[0043] Control Module

[0044] The control module consists of an embedded controller and an algorithm processing unit, which is used to collect sensor data, perform data preprocessing, identify operating status and calculate area.

[0045] Display and storage module

[0046] The display module displays the accumulated area, operation status and path trajectory in real time through an LCD screen or a mobile terminal.

[0047] The storage module records the data of each operation, including area results, path information and operation status, for subsequent analysis and optimization.

[0048] 2. Technical principles and processes

[0049] The present invention performs real-time area detection based on the following technical principles:

[0050] Sensor data acquisition

[0051] Each sensor collects the movement status and working environment information of the machine according to a unified timestamp. The speed sensor is responsible for providing path data, the IMU is responsible for detecting the inclination and turning behavior, the vibration and load sensors determine the working status, and the boundary sensor obtains boundary information.

[0052] Data preprocessing

[0053] Data preprocessing consists of three parts:

[0054] Noise filtering: The sensor output data is smoothed by low-pass filter and Kalman filter to eliminate high-frequency noise and sudden interference.

[0055] Time synchronization: Time-align the data of each sensor to ensure the consistency of multi-source data.

[0056] Anomaly elimination: Detect and eliminate abnormal data points based on a set reasonable range to avoid calculation bias.

[0057] Job status identification

[0058] The operation status is divided into effective operation section, turning section and non-operation section:

[0059] Effective operation section: The vibration and load signals are both higher than the threshold, and the speed data is stable, indicating that the machine is in normal operation.

[0060] Turning segment: The angular velocity of the IMU exceeds the set threshold, indicating that the machine is turning or making a U-turn.

[0061] Non-operating section: The vibration and load signals are lower than the threshold, or the speed is close to zero, indicating that the machine is in a stopped or non-operating state.

[0062] Real-time area calculation

[0063] Path tracking: The machine's travel path is obtained based on the speed sensor data, eliminating turning sections and non-operating sections.

[0064] Dynamic Width Compensation: Combined with the real-time width data measured by the boundary recognition sensor, the working width is dynamically adjusted to ensure the accuracy of the calculation.

[0065] Area accumulation: The area of ​​the effective path segment is accumulated segment by segment, and the total area result is updated in real time. For slope operations, terrain compensation is performed through IMU inclination data to reduce the area deviation caused by terrain.

[0066] Data fusion and correction

[0067] Multi-sensor fusion technology is used to optimize data accuracy. Weighted fusion and Kalman filtering are used for real-time path tracking and smoothing of area calculation results respectively.

[0068] Result Output

[0069] The accumulated area and path trajectory are displayed in real time through the display terminal, and the results are stored for subsequent analysis after the job is completed.

[0070] 3. Specific implementation steps

[0071] The following are the specific implementation steps of the present invention:

[0072] After the machine is started, the sensor module is initialized and the controller sets the operating parameters (such as width calibration value, operating status threshold, etc.).

[0073] When the operation starts, each sensor collects real-time data at fixed time intervals, including path information, operation status and boundary information.

[0074] The controller synchronizes and preprocesses the collected data, filters out noise, removes abnormal values, and stores the data in a temporary buffer.

[0075] The current machine status (working section, turning section or non-working section) is dynamically determined through the working status recognition algorithm.

[0076] Calculate the area of ​​the effective operation section data:

[0077] Path tracking: Eliminate non-operating paths and turning paths and extract effective operating paths.

[0078] Dynamic compensation: Combine boundary sensor data to adjust the working width in real time, and combine IMU inclination data to correct the slope path area.

[0079] Multi-source data are fused and processed, and the accuracy of area calculation is improved through weighted fusion and Kalman filtering.

[0080] After the operation is completed, the total area and path information are stored in the controller and displayed in real time on the terminal device.

[0081] 4. Adaptability and effect verification

[0082] Adaptability

[0083] The present invention can operate stably under complex terrain conditions such as irregular plots and areas with large slopes, and can adapt to different terrains by dynamically adjusting the operating width and path compensation.

[0084] Effect verification

[0085] Experimental verification shows that in typical hilly areas and large slope conditions, the area detection error does not exceed 3%, which is much lower than the traditional GPS detection method.

[0086] Example 1: Detection of the working area on a regular plot of flat land

[0087] Specific steps:

[0088] Set the machine width to 1.5 meters.

[0089] The speed sensor is installed on the driving wheel to collect the travel speed and accumulated distance of the machine.

[0090] Use IMU to collect inclination and angular velocity data, and vibration and load sensors to determine the operating status.

[0091] The controller synchronizes, preprocesses and removes abnormal values ​​of the collected data.

[0092] Dynamically adjust the working width (within ±0.1m) and calculate the effective path area. Process parameters:

[0093] Plot type: Regular rectangular plot.

[0094] Plot area: 1 hectare.

[0095] Operating speed: 5km / h.

[0096] Data sampling frequency: 50Hz.

[0097] Comparative Example 1: Traditional GPS Area Detection Method

[0098] Specific steps:

[0099] Use a single GPS device to record the work path in real time.

[0100] Set the tool width to a fixed value of 1.5 meters without dynamic adjustment. Calculate the area as the product of the path length and the fixed width.

[0101] Process parameters: Same as Example 1.

[0102] Example 2: Detection of area of ​​large slope plot

[0103] Specific steps:

[0104] Set the machine width to 1.2 meters.

[0105] The IMU is used to collect the inclination angle, and the inclination angle data is used for slope compensation.

[0106] Dynamically adjust the width and path data to eliminate non-operating paths. Boundary sensors are used to prevent out-of-bounds calculations and display the operating area in real time. Process parameters:

[0107] Plot type: Terraced plot with a slope of 15°-20°.

[0108] Plot area: 0.5 hectares.

[0109] Operating speed: 4km / h.

[0110] Data sampling frequency: 50Hz.

[0111] Comparative Example 2: Single Sensor Path Recording Method

[0112] Specific steps:

[0113] Path data is recorded using a single speed sensor, without inclination compensation.

[0114] The working width is fixed at 1.2 meters and there is no dynamic adjustment.

[0115] Calculates area based on path length, ignoring errors caused by slope.

[0116] Process parameters: Same as Example 2.

[0117] Example 3: Irregular land area detection

[0118] Specific steps:

[0119] Set the machine width to 1.8 meters.

[0120] Use multi-sensor fusion technology to eliminate turns and non-operating paths.

[0121] Boundary sensors identify the boundaries of irregular plots and dynamically adjust the width. Real-time display of path trajectory and area results, and record operation data. Process parameters:

[0122] Plot type: irregular polygonal plot.

[0123] Plot area: 0.8 hectares.

[0124] Operating speed: 3km / h.

[0125] Data sampling frequency: 50Hz.

[0126] Comparative Example 3: Area Detection Method Using Only Speed ​​Sensor

[0127] Specific steps:

[0128] Only the speed sensor is used to record the path, without boundary detection.

[0129] The working width is fixed at 1.8 meters.

[0130] The area is calculated by multiplying the path length by the fixed width, without dynamic adjustment. Process parameters: the same as in Example 3.

[0131] Comparative Experimental Design

[0132] Experimental design:

[0133] Purpose: To verify the area detection accuracy of the present invention under different terrain conditions. Variables:

[0134] Plot type: regular rectangular plot, steep slope plot, irregular plot. Detection method: the method of the present invention (implementation) and the comparative method. Experimental environment: the same plot, the same machine, and the same operator. Measurement index:

[0135] Measured area (based on actual measurement).

[0136] Detection area.

[0137] Area detection error (%).

[0138] Experimental data table:

[0139]

[0140] Experiments show that the area detection accuracy of the present invention in regular plots, steep slope plots and irregular plots is significantly better than that of the comparative method, especially in complex terrain (such as steep slopes and irregular plots), where the traditional method significantly increases errors due to neglect of dynamic adjustment and compensation. The present invention has wide applicability and superior detection accuracy, and provides an efficient area detection solution for mechanized agriculture in mountainous areas.

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time detection method for non-GPS operation area in irregular and steep planting areas in mountainous areas, characterized in that: The following steps are involved: Multi-sensor data acquisition: The speed, posture, working status and boundary information of the working tool are collected through speed sensors, posture sensors, vibration sensors, load sensors and boundary recognition sensors; Data preprocessing: time synchronization, noise filtering and anomaly elimination of collected data; Working state identification: distinguishing effective working sections, turning sections and non-working sections based on multi-sensor data; Real-time area calculation: Based on the effective working path and working width, the area is accumulated and calculated in combination with the slope angle information; Data fusion and correction: Optimize the accuracy of path tracking and area calculation through multi-sensor fusion technology; Job result output: store the area calculation results and path information and display them in real time.

2. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The rotation speed sensor is installed on the driving wheel or crawler of the working machine and is used to measure the real-time speed and accumulated travel distance of the machine.

3. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The attitude sensor is an IMU sensor, which is used to collect the inclination angle, acceleration and angular velocity of the working tool and monitor the movement state and slope change of the tool in real time.

4. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The job status identification comprises the following steps: Determine whether it is in an effective working state based on the signals of the vibration sensor and the load sensor; Identify turning behavior based on the angular velocity signal of the attitude sensor; Dynamically switch job status based on multi-sensor data.

5. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The area calculation comprises the following steps: Eliminate turning segments and non-operating segments from the effective operating path; Dynamically adjust the working width based on boundary recognition sensor data; In plots with large slopes, the area calculation results are corrected by combining the inclination information.

6. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The multi-sensor fusion technology includes data weighted fusion and Kalman filtering to optimize the path tracking accuracy and the real-time performance of area calculation.

7. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The data preprocessing uses a low-pass filter and a Kalman filter to filter the noise of the collected data, and eliminates abnormal values ​​based on statistical methods.

8. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The job result output includes the following: Real-time display of current cumulative operation area, path trajectory and operation status; Record complete job data for subsequent analysis.

9. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1 is characterized in that: The method is applicable to irregular plots and large-slope terrains, and can adapt to area detection requirements under different terrain conditions by automatically adjusting operation parameters.

10. The real-time detection method for non-GPS operation area in irregular and steep planting land in mountainous areas according to claim 1, characterized in that: The method is verified through multiple experiments, and the area detection error does not exceed 3%, and has good robustness and adaptability.

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