Method and system for improving operation precision of rotary tillage seeder

Abstract

The invention discloses a method and a system for improving the operation precision of a rotary tillage seeder. The method comprises the following steps: acquiring working environment and machine state information in real time through a soil hardness sensor, a humidity sensor, a gyroscope and a GNSS (Global Navigation Satellite System) module; the rotary tillage depth and the sowing depth are detected, and operation deviation is calculated based on a multi-source data fusion algorithm; according to the deviation result, the rotating speed of a rotary tillage cutter shaft, the downward pressure of the seeder and the posture of the machine are dynamically adjusted through a self-adaptive control strategy, and the seeding depth and the rotary tillage depth are consistent; the operation track is optimized in combination with GNSS navigation, and the row spacing and plant spacing precision is ensured; and after the operation is finished, evaluating the uniformity and consistency of the operation. The system comprises a data acquisition module, a data fusion module, a control decision module, an execution mechanism and an operation evaluation module. According to the method and the system, the operation precision and the stability of the rotary tillage seeder can be improved in a complex field environment.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for agricultural machinery, specifically to a method and system for improving the operational accuracy of a rotary tiller and seeder. Background Technology

[0002] Rotary tillage seeders are agricultural machines that integrate rotary tillage and sowing operations. Widely used in grain crop cultivation, they are highly efficient and require less labor, playing a vital role in agricultural production. However, during field operations, variations in soil hardness, moisture, and undulation make it difficult to maintain consistent tillage and sowing depths, leading to uneven sowing and seedling emergence. Furthermore, vibrations and changes in posture during operation can cause deviations in the working trajectory, resulting in insufficient row and plant spacing accuracy and impacting overall crop growth. In addition, most existing rotary tillage seeders rely on manual experience for parameter adjustments, lacking real-time monitoring and intelligent control mechanisms based on multi-source information. This makes it difficult to adapt to environmental changes and operational conditions, resulting in unstable sowing quality and insufficient operational precision, thus limiting their application in modern precision agriculture. Summary of the Invention

[0003] The purpose of this invention is to address the problems of uneven sowing depth, insufficient row and plant spacing accuracy, and lack of real-time adjustment capability in existing rotary tillers during field operations, by providing a method and system for improving the operational accuracy of rotary tillers. This method and system, through multi-source sensor information fusion and adaptive control, achieves consistency between rotary tillage depth and sowing depth, stability of the operating trajectory, and evaluability of the operating effect, thereby effectively improving the accuracy and reliability of sowing operations.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for improving the operating accuracy of a rotary tiller seeder includes the following steps: Step 1: Acquisition of working environment data. Soil characteristic parameters, equipment attitude and position information are collected in real time through soil hardness sensor, humidity sensor, gyroscope and GNSS module; Step 2: Monitoring the operation status, real-time detection of rotary tillage depth and sowing depth, and comparison with preset agronomic indicators; Step 3: Data fusion and deviation calculation. Use Kalman filtering or Bayesian estimation algorithms to fuse multi-source sensor data to obtain high-precision operating parameters and calculate the deviation between the actual value and the target value. Step 4: Adaptive control strategy generation. Based on the deviation results, PID control or fuzzy control algorithms are used to generate adjustment commands to dynamically adjust the rotary tiller shaft speed, seeder downforce, and implement posture. Step 5: Trajectory optimization and closed-loop adjustment. Combine GNSS navigation and equipment attitude data to correct the operation trajectory in real time, ensuring the accuracy of row spacing and plant spacing, and continuously correct through a closed-loop feedback mechanism. Step 6: Evaluation of operation results. After the operation is completed, statistical analysis is performed on the consistency of sowing depth, row spacing, and plant spacing errors, and an operation accuracy evaluation report is generated.

[0005] A system for improving the operational accuracy of a rotary tiller seeder includes a data acquisition module, a data fusion module, a control decision module, an execution mechanism, and an operation evaluation module. The data acquisition module collects information on soil hardness, moisture, and the machine's posture and position. The data fusion module fuses multi-source data and calculates deviations. The control decision module generates an adaptive control strategy based on the deviation results. The execution mechanism includes a rotary tiller blade drive device, a seeder downpressure adjustment device, and a machine posture adjustment device, used to execute control commands. The operation evaluation module performs accuracy analysis and outputs a result report after the operation is completed.

[0006] Compared with existing technologies, the beneficial effects of this invention are as follows: by introducing multi-sensor information fusion, the accuracy of soil and operation status detection is effectively improved; through adaptive control and closed-loop adjustment mechanisms, the consistency between rotary tillage depth and sowing depth is ensured, improving seedling uniformity and operational reliability; through GNSS navigation and trajectory optimization, high-precision control of row spacing and plant spacing is achieved; and through an operation effect evaluation mechanism, data support is provided for subsequent operation parameter optimization and management. The overall method is superior to existing technologies in terms of accuracy, efficiency, and stability, and has broad engineering application value. Attached Figure Description

[0007] Figure 1 Flowchart of Methods to Improve the Operating Precision of Rotary Tiller Seeders Figure 2 Schematic diagram of the rotary tiller's precision improvement system Figure 3 Data Fusion and Control Decision Module Block Diagram Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] Example 1 like Figure 1As shown, this embodiment provides a method for improving the operational accuracy of a rotary tiller seeder. Its core lies in achieving consistency between tillage depth and sowing depth, as well as high-precision control of row spacing and plant spacing, through multi-source information acquisition, data fusion, and adaptive control. Specifically, firstly, soil hardness sensors, humidity sensors, gyroscopes, and GNSS positioning modules are deployed on the rotary tiller seeder to acquire real-time operational environment information, including soil compaction, moisture content, implement posture, and travel trajectory. Simultaneously, depth sensors are installed on the rotary tiller shaft and seeder to detect the tillage depth and sowing depth in real time, comparing them with preset agronomic indicators to obtain preliminary operational deviation information. Subsequently, the data fusion module comprehensively processes the above multi-source data. To eliminate noise and redundancy between different sensors, this embodiment introduces a Kalman filter method to filter dynamic parameters, and combines a Bayesian estimation algorithm to perform weighted updates on the data, obtaining more accurate estimates of tillage depth, sowing depth, and trajectory, and calculating the differences between these estimates and the target indicators. Based on the fused deviation information, the control decision module initiates an adaptive control mechanism. When the deviation is small, PID control is used for rapid correction; when the deviation is large or environmental conditions are complex, fuzzy control is used to enhance nonlinear adjustment capabilities, and corresponding control commands are generated. Upon receiving the control commands, the actuator can correct the operating state by adjusting the rotary tiller shaft speed, seeder downforce, and implement attitude. Simultaneously, it optimizes the operating trajectory by combining GNSS navigation and gyroscope attitude information to avoid row spacing deviations caused by plot undulations or vibrations, thus forming a closed-loop control process of "deviation detection—control generation—execution adjustment—feedback correction." After the operation is completed, the system also performs statistical analysis on sowing depth consistency, row spacing consistency, and plant spacing accuracy, and generates an operation evaluation report to provide a basis for subsequent operation optimization and long-term agricultural management. Through the above steps, this embodiment not only improves the operating accuracy and stability of the rotary tiller seeder but also significantly improves seedling uniformity and crop planting uniformity, demonstrating strong engineering application value.

[0010] Example 2: System-based implementation like Figure 2As shown in the figure, this embodiment provides a rotary tiller seeder operation accuracy improvement system, which includes a data acquisition module, a data fusion module, a control decision module, an execution mechanism, and an operation evaluation module. The modules interact with each other through a communication bus or wireless network to form a complete operation accuracy improvement closed loop. The data acquisition module acquires real-time data on soil hardness, moisture, implement posture, GNSS location, and operational parameters such as rotary tillage depth and sowing depth to reflect the field environment and operational conditions. The data fusion module processes the acquired multi-source information, using Kalman filtering and Bayesian estimation algorithms to eliminate and correct redundant data and noise, outputting more stable and reliable operational parameters, and calculating the deviation from preset agronomic indicators. The control decision module receives the deviation information and generates adaptive control commands based on PID or fuzzy control algorithms to adjust the rotary tiller shaft speed, seeder pressure, and implement posture, thereby ensuring that the rotary tillage depth and sowing depth remain consistent and that the implement operates stably. The actuators specifically include a rotary tiller drive control device, a seeder pressure adjustment device, and an implement posture adjustment device, which change the mechanical operation status in real-time according to the control commands to avoid accuracy degradation caused by differences in soil conditions or implement vibration. Finally, the operation evaluation module stores and analyzes the operation data, statistically analyzes sowing depth consistency, row spacing consistency, and plant spacing accuracy, generates an operation effect report, and can upload it to the agricultural management system for subsequent optimization and parameter adjustment. Through the above structural design, the system can monitor, control and provide feedback on the entire operation process of the rotary tiller seeder in complex field environments, greatly improving the sowing accuracy and operational stability, and ensuring high quality and high efficiency in agricultural production.

[0011] Example 3: Implementation based on the data fusion and control decision module like Figure 3As shown in the diagram, this embodiment provides a data fusion and control decision module block diagram in a rotary tiller seeder operation accuracy improvement system. This module is the core of the entire system and is mainly used to realize the comprehensive processing of sensor information and intelligent decision control. The data fusion module receives multi-source information from soil hardness sensors, humidity sensors, gyroscopes, GNSS positioning modules, and depth sensors. Due to the complexity of the field environment and the variable working conditions of sensors, single data sources often have fluctuations and errors. Therefore, this module uses the Kalman filter method to filter and reduce noise in dynamic data, and combines it with the Bayesian estimation algorithm to weight the data from different sensors to obtain more stable and reliable estimates of rotary tillage depth, seeding depth, attitude angle, and position trajectory, and calculates the deviation between these estimates and the target agronomic indicators. The control decision module generates adjustment commands based on the deviation information. Its algorithm framework includes two modes: PID control and fuzzy control. When the deviation is small, PID control can make quick and accurate small adjustments to ensure timely and stable response. When the deviation is large or the environmental conditions are complex, fuzzy control can achieve nonlinear adjustment through rule reasoning, thereby enhancing the system's adaptability. Once the control command is generated, it is immediately transmitted to the actuator. The rotary tiller drive control device, seeder pressure adjustment device, and implement posture adjustment device complete the specific action adjustments, forming a closed-loop control system of "data acquisition—data fusion—deviation calculation—control decision—execution feedback". In this process, the data fusion and control decision module not only improves the accuracy and stability of parameter estimation, but also ensures the real-time performance and robustness of the control strategy, making the rotary tillage depth consistent with the sowing depth, and ensuring that the row spacing and plant spacing meet the set accuracy requirements. This significantly improves the operation quality and stability of the rotary tiller seeder in complex field environments.

[0012] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0013] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for improving the operating accuracy of a rotary tiller seeder, characterized in that, Includes the following steps: Step 1: Real-time acquisition of soil environmental parameters, equipment attitude and position trajectory information through soil hardness sensor, humidity sensor, gyroscope and GNSS module; Step 2: Real-time monitoring of rotary tillage depth and sowing depth to obtain operational status parameters; Step 3: Based on the multi-source data fusion algorithm, the operation parameters are fused to obtain the operation deviation value; Step 4: Generate an adaptive control strategy based on the deviation value to dynamically adjust the rotary tiller shaft speed, seeder downforce, and implement posture; Step 5: Correct the work trajectory by combining GNSS navigation data to ensure the accuracy of row spacing and plant spacing; Step 6: After the operation is completed, evaluate the uniformity of the operation depth, the consistency of row spacing, and the error of plant spacing.

2. The method according to claim 1, characterized in that, In step 3, Kalman filtering or Bayesian estimation methods are used to fuse sensor data in order to reduce the impact of single sensor errors on the results.

3. The method according to claim 1, characterized in that, The adaptive control strategy in step 4 uses PID control or fuzzy control algorithms to generate control commands.

4. The method according to claim 1, characterized in that, In step 5, the GNSS navigation module and gyroscope are used together to correct the travel deviation of the rotary tiller in real time.

5. The method according to claim 1, characterized in that, The evaluation of the work effect in step 6 includes generating a work accuracy report, which is used to perform statistical analysis on depth consistency, row spacing consistency, and plant spacing error.

6. A system for improving the operational accuracy of a rotary tiller seeder, characterized in that, include: The data acquisition module is used to obtain information on soil hardness, moisture, equipment posture, and location; The data fusion module is used to fuse multi-source sensor data and calculate operational deviations; The control decision module is used to generate control strategies based on deviations. The actuator is used to adjust the rotary tiller speed, seeder downforce, and implement posture according to the control strategy. The job evaluation module is used to analyze the uniformity and consistency of the job and generate an accuracy report.

7. The system according to claim 6, characterized in that, The data acquisition module includes a soil hardness sensor, a humidity sensor, a gyroscope, and a GNSS positioning module.

8. The system according to claim 6, characterized in that, The data fusion module uses Kalman filtering or Bayesian estimation algorithms for data processing.

9. The system according to claim 6, characterized in that, The actuator includes a rotary tiller drive control device, a seeder pressure adjustment device, and a implement posture adjustment device.

10. The system according to claim 6, characterized in that, The job evaluation module is used to output a job accuracy report after the job is completed and store the evaluation results in the job database for subsequent optimization reference.

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