A synchronous control system and method for electric drive precision seeding based on CORS network RTK speed measurement

Abstract

The application discloses a kind of electric drive precision seeding synchronous control system and method based on CORS network RTK velocity measurement, it is related to intelligent agricultural equipment and high-precision motion control field;Including high-precision speed and displacement acquisition;Theoretical seeding amount calculation;Actual seeding amount feedback;Closed-loop synchronous control.The application discloses the traditional error source of ground wheel velocity measurement, and instead provides absolute spatial position information, which is independent of ground conditions, as the only reference for displacement measurement by national CORS network, which fundamentally eliminates all systematic velocity measurement errors caused by ground wheel skidding, sinking, wear and tear, etc., providing an indisputable accurate time and space reference for seeding control, while also ensuring uninterrupted seeding operations, control accuracy does not drop sharply, and adapting to various complex conditions such as hillside, forest and field, greatly improving equipment utilization and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of intelligent agricultural equipment and high-precision motion control, and in particular to an electric precision seeding synchronous control system and method based on CORS network RTK speed measurement. Background Technology

[0002] Precision seeding is a crucial step in modern agriculture to achieve cost reduction and efficiency improvement, as well as ensure crop yield and quality. Traditional mechanical seeders generally use ground wheel-driven seed metering devices, whose working principle dictates that seeding accuracy heavily depends on the adhesion between the ground wheel and the ground. In actual operation, especially in wet, soft, or straw-covered fields, ground wheel slippage is unavoidable, leading to a significant deviation between the actual seed spacing and the theoretical value. This results in missed seeding and double seeding, severely affecting the uniformity of emergence and the final yield.

[0003] To overcome the fundamental defect of wheel slippage, electric precision seeding technology has emerged. This technology uses independent servo motors to drive each seeding unit, decoupling the seeding action from the mechanical transmission of the wheels. However, the precise control of the electric drive system depends on the real-time and accurate measurement of the agricultural machinery's speed and displacement.

[0004] Existing mainstream technical solutions all have obvious limitations: 1) The scheme that relies on encoders to measure the speed of the ground wheel cannot eliminate the reference error introduced by the slippage of the ground wheel, and the speed measurement accuracy is greatly affected by the ground conditions; 2) While using independent speed sensors such as radar avoids the slippage problem, it is expensive and cannot provide absolute position information; 3) Single satellite navigation (GNSS) velocity measurement schemes have low update frequency, slow dynamic response, and are prone to failure when the signal is blocked.

[0005] In addition, to achieve high-precision positioning, most solutions require users to build their own RTK base stations, which brings high equipment purchase and maintenance costs and hinders the popularization of this technology in small and medium-sized agricultural production.

[0006] Therefore, there is an urgent need to develop an electric precision seeding control system that is low in cost, has an absolutely reliable speed measurement reference, completely eliminates reliance on easily slipping components such as ground wheels, and can flexibly adapt to the seeding requirements of different crops.

[0007] Therefore, this invention proposes an electric precision seeding synchronization control system and method based on CORS network RTK speed measurement. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by proposing an electric precision seeding synchronization control system and method based on CORS network RTK speed measurement.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: An electric precision seeding synchronization control system based on CORS network RTK speed measurement completely eliminates the need for a physical ground wheel for speed measurement, including: The high-precision displacement sensing unit's core is a multi-frequency GNSS-RTK module supporting multiple satellite systems such as BeiDou and GPS. This module has a built-in or external mobile communication module (such as 4G / 5G) that connects to the national or regional CORS network service center via wireless network. It receives and processes differential correction information to obtain the seeder's real-time absolute position coordinates (latitude, longitude, and elevation) at the centimeter (or even millimeter) level. This unit not only provides position but also obtains the instantaneous velocity vector through differential calculation of high-precision position coordinates between adjacent moments. More importantly, the system uses the integral of the high-precision position coordinates over time as the sole and irrefutable measurement benchmark for the seeder's high-precision absolute linear displacement since the start of operation. This benchmark is completely independent of ground adhesion conditions.

[0010] Main control and interaction unit: includes a high-performance microcontroller (MCU), parameter input devices (such as touch screen, buttons) and status display. The microcontroller is connected to the displacement sensing unit and is used to receive and process the absolute position and displacement information, and calculate the theoretical number of seeds to be sown in real time according to the target plant spacing (L) set by the user through the input device (Ntheoretical = cumulative displacement S / target plant spacing L).

[0011] Distributed seeding execution unit: includes at least one seeding unit, each seeding unit consisting of an independently controllable encoder-integrated servo motor for driving the seed metering device (such as a pneumatic suction type or finger clamp type seed metering disc); the servo motor receives speed and position commands from the main control unit through a high real-time fieldbus network.

[0012] Real-time feedback unit: A high-resolution photoelectric encoder or magnetic encoder integrated in each servo motor is used to measure the motor's rotation angle and speed in real time and feed this information back to the control system. The control system can accurately calculate the actual number of seeds (N actual) sown by the sowing unit based on the motor rotation angle, the mechanical transmission ratio of the seed metering device and the current number of holes (H) in the seed tray.

[0013] Synchronous control unit: It exists in the main control unit in the form of an embedded algorithm. This unit is configured to dynamically generate control commands using an advanced closed-loop control algorithm (such as PID, fuzzy PID, etc.) with the theoretical number of seeds to be sown N as the set value and the actual number of seeds sown N as the process variable (feedback value). The commands adjust the speed of each servo motor in real time and synchronously through the fieldbus network, so that the actual sowing action of all sowing units strictly follows the theoretical sowing rhythm based on absolute displacement calculation in time and space, thereby achieving precise plant spacing synchronization control and ensuring that every seed falls in the theoretical position.

[0014] Preferably, the system further includes a miniature inertial measurement unit (IMU), which typically comprises a three-axis accelerometer and a three-axis gyroscope. The main control unit employs an adaptive Kalman filter or complementary filtering algorithm to fuse the absolute position / velocity information provided by the GNSS-RTK module with the high-frequency acceleration and angular velocity information provided by the IMU. The IMU data is used for accurate short-term dead reckoning within the GNSS update interval, and effectively maintains the continuity and smoothness of displacement and velocity estimation when the GNSS signal is briefly interrupted or its quality degrades (e.g., changing from a "fixed solution" to a "floating solution"), significantly improving the robustness and usability of the system in complex field environments such as bridge crossings, forest underpasses, and slopes.

[0015] Preferably, the fieldbus network is a Controller Area Network (CAN) bus. The CAN bus features high reliability, strong anti-interference capabilities, and multi-master communication, ensuring high real-time performance and determinism in synchronously issuing control commands to multiple servo motor drivers, avoiding "skipping" problems caused by communication delays and asynchrony. Furthermore, each seeding unit can be configured with an independent secondary controller (as a sub-controller). This secondary controller communicates with the corresponding servo motor via industrial protocols such as RS-485 bus and Modbus RTU, specifically responsible for high-frequency acquisition of encoder data, reading motor operating status (current, temperature, fault codes), and summarizing and reporting this data to the main control unit. This achieves separation of control and monitoring, reduces the burden on the main control unit, and improves system response speed.

[0016] Preferably, the human-machine interface unit is designed with an intuitive graphical interface. The interface allows users to directly input numbers or select the target plant spacing (L) and the number of holes (H) of the currently installed seed tray or the seeding frequency coefficient from a list. The system control core algorithm, based solely on these two core parameters and combined with the absolute displacement S, can automatically adapt the seeding control to different crops and different seeders. The calculation formula is clear (e.g., base rotation speed RPM_base ∝ v / (L * H)). This "physical seed tray replacement + parameterized software settings" mode makes switching between multiple crops such as corn, soybeans, rapeseed, and vegetables extremely fast and simple. Users do not need to preset complex crop databases or perform tedious calibrations, truly realizing the multi-functionality of a single device.

[0017] A synchronous control method for electric precision seeding based on the above system, which completely eliminates the need for wheel speed measurement, includes the following steps: S1: High-precision velocity and displacement sensing: The GNSS-RTK module continuously connects to the CORS network, outputting centimeter-level absolute position data (P) and raw velocity data (v_gnss) for the seeder. Simultaneously, the IMU acquires acceleration (a_imu) and angular velocity data at a higher frequency. A Kalman filter is used to fuse the two: state prediction is performed using IMU data, and observation updates are performed using GNSS data (when it is a high-quality "fixed solution"). The filter dynamically adjusts the observation noise matrix based on the confidence level of the GNSS data (such as carrier phase variance and number of satellites), achieving adaptive fusion, and ultimately outputting the optimal high-precision, high-frequency velocity estimate (v_fused) and compensated absolute linear cumulative displacement S(t).

[0018] S2: Theoretical seeding calculation: Based on the target plant spacing L set by the user, the theoretical seeding quantity N corresponding to the current absolute displacement S(t) is calculated in real time: theoretical = floor(S(t) / L). Whenever S(t) accumulates to more than one L, theoretical N increases by 1, generating a theoretical seeding trigger signal.

[0019] S3: Actual seeding feedback: The secondary controller of each seeding unit reads the absolute position value (θ) of the corresponding servo motor's built-in encoder at fixed intervals (e.g., 10-50ms) via RS-485 bus. Based on the mechanical transmission ratio of the seed metering device and the current number of holes H in the seed tray, the actual number of seeds N sown by that unit since the start of operation is calculated: actual_i = floor( (θ_i / transmission ratio) * H / 360° ).

[0020] S4: Closed-loop synchronous control: For the i-th sowing unit, calculate the instantaneous sowing deviation E_i(t) = Ntheoretical(t) - Nactuali(t). Using a digital PID control algorithm, based on the deviation E_i and its integral and derivative, calculate in real-time the speed correction ΔRPM_i used to eliminate this deviation. Simultaneously, based on the current fusion speed v_fused and the target plant spacing L, calculate the base speed RPM_base required to maintain the theoretical plant spacing. Finally, the target speed command sent to the i-th row of motors is: RPM_target_i = RPM_base + ΔRPM_i. This command is broadcast via the CAN bus, and each motor driver identifies and executes the corresponding command according to its preset ID, thus enabling Nactuali to dynamically and accurately follow Ntheoretical.

[0021] Furthermore, the method also includes system health monitoring and fault handling steps: the main controller periodically polls each sub-controller via the Modbus RTU protocol to obtain real-time status data of each servo motor, including bus voltage, phase current, load rate, motor temperature, etc. The main controller has built-in fault diagnosis logic. When any motor current exceeds the limit, temperature is too high, communication timeout occurs, or encoder malfunction is detected, a high-brightness alarm is immediately displayed on the interactive interface, and an audible and visual alarm can be triggered. Preset safety strategies are also implemented, such as gradually reducing speed and stopping the motor or only stopping the faulty motor, to ensure operational safety.

[0022] The beneficial effects of this invention are as follows: Improved accuracy and complete elimination of slippage error: This invention abandons the traditional source of error, ground wheel speed measurement, and instead uses absolute spatial position information provided by the national CORS network, which is independent of ground conditions, as the sole reference for displacement measurement. This fundamentally and completely eliminates all systematic speed measurement errors caused by ground wheel slippage, sinking, wear, etc., and provides an irrefutable and accurate spatiotemporal reference for seeding control.

[0023] Lower costs and lower barriers to entry: The system directly utilizes the CORS public service infrastructure that has been invested in and built by the state and covers the whole country. Users do not need to build and maintain expensive RTK base stations, nor do they need to purchase radar, laser and other Doppler velocity sensors. The main hardware adopts mature, mass-produced industrial-grade GNSS modules, general MCUs and servo motors. High performance is achieved through innovative algorithms and architecture, which reduces the hardware cost of the whole system compared with the high-end solution of "self-built base station + radar speed measurement", which greatly lowers the threshold for popularization of high-end precision seeding technology.

[0024] The system is extremely robust and reliable: it adopts a deep adaptive fusion scheme of GNSS-RTK and IMU, which leverages the advantages of both: GNSS provides long-term absolute accuracy and stability, while IMU provides short-term high dynamic response and continuity during signal loss. Even in the extreme case of brief loss of satellite signal, the system can still rely on IMU to maintain high-precision dead reckoning for tens of seconds, ensuring that sowing operations are not interrupted and control accuracy does not drop sharply, adapting to various complex working conditions such as slopes, forests, and field edges.

[0025] Easy crop adaptation, achieving intelligent "one machine for multiple uses": This invention abstracts the sowing control into a mathematical relationship between "displacement" and "seed metering parameters". When switching crops, users only need to modify two parameters, "target plant spacing" and "number of holes in the seed tray", on the user-friendly interface. All control algorithms of the system will automatically re-adapt. This eliminates the trouble of preset complex parameter curves or replacing dedicated controllers for each crop, making it possible for a single seeder to quickly adapt to multiple crops such as corn, soybeans, cotton, and sugar beets, greatly improving equipment utilization and economic benefits.

[0026] Excellent scalability and information infrastructure: The centimeter-level high-precision absolute positioning trajectory data generated by the system can be directly used to generate sowing operation maps and seamlessly integrated with the farm management system, providing a natural data foundation for advanced precision agriculture applications such as variable sowing, yield map analysis, and operation quality traceability. Attached Figure Description

[0027] Figure 1 This is a logic diagram of the architecture of an electric precision seeding synchronization control system based on CORS network RTK speed measurement proposed in this invention. Detailed Implementation

[0028] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0029] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection or setting, a detachable connection or setting, or an integral connection or setting. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Example

[0030] This embodiment uses a five-element electric-driven pneumatic seeder as an example for illustration.

[0031] System hardware configuration: High-precision displacement sensing unit: Utilizing the Hexin Xingtong UM980 multi-frequency, multi-system GNSS RTK module as its core, this module incorporates a tactical-grade IMU (including a three-axis MEMS accelerometer and gyroscope). It receives satellite signals from multiple systems such as BeiDou and GPS via an external full-band measurement antenna (commonly known as a "mushroom antenna"). Through an external Huawei ME909s-821 4G communication module, it accesses the "China Location" CORS service network provided by China Mobile, receiving and parsing differential correction data (such as RTCM3.2 format) in real time to achieve real-time centimeter-level positioning. The module outputs latitude, longitude, elevation, velocity, and positioning status (fixed solution / floating solution / single-point solution, number of satellites, PDOP value, etc.) information in NMEA-0183 format via a UART serial port.

[0032] Main Control and Interaction Unit: The main controller uses an STMicroelectronics STM32F407 microcontroller, which boasts high computing performance and rich peripheral interfaces. The human-machine interface consists of a 7-inch IPS capacitive touchscreen display and several physical function buttons, running an embedded GUI system. The interface displays real-time operating speed, cumulative displacement, target / actual plant spacing, motor status for each row, alarm information, etc., and provides a parameter setting menu for setting the target plant spacing (L), the number of holes in the seed tray (H), PID control parameters, etc.

[0033] Distributed seeding execution and real-time feedback unit: Each of the five seeding units is equipped with an AIMotor MD42 series intelligent servo motor. This motor integrates a driver, a 17-bit multi-turn absolute encoder, and an RS-485 communication interface. The CAN_H and CAN_L interfaces of the five motors are connected in parallel to a CAN bus composed of shielded twisted-pair cable, which is connected to the CAN interface of the main controller (driven by a TJA1050 chip). Simultaneously, the RS-485 interface (A and B lines) of each motor is connected to an independent STM32F103 secondary controller. This secondary controller is specifically responsible for periodically (every 20ms) reading the absolute encoder value, real-time current, motor temperature, and other status information of the motor using the Modbus RTU protocol (115200 baud rate, 8-bit data, no parity).

[0034] System workflow and key algorithm implementation: Displacement sensing and fusion: The main controller reads the position (P), velocity (v_gnss), and positioning quality factor Q from the UM980 module at a frequency of 10Hz.

[0035] Meanwhile, the raw acceleration (a_imu) and angular velocity data of the UM980's built-in IMU are read via the SPI interface at a frequency of 200Hz, and temperature compensation and zero bias correction are performed.

[0036] Kalman filter design: Establish a state vector X = [position, velocity, zero-bias acceleration]^T. State prediction is based on IMU data: X_k|k-1 = F * X_k-1 + B * (a_imu - zero-bias estimate). Observation updates are based on GNSS data: When Q indicates a "fixed solution" and the number of satellites > 10 and PDOP < 2, the observation accuracy is considered high, and a small observation noise covariance R is assigned; when Q indicates a "floating solution" or "single-point solution," the value of R is automatically increased, and the GNSS observation weight is reduced. The filter finally outputs the optimal fused velocity v_fused and the high-precision cumulative absolute displacement S(t) smoothed by IMU compensation.

[0037] Control command generation: Theoretical planting trigger: Based on the plant spacing L (unit: meters) set by the user, the system internally maintains a theoretical planting counter N_theory. Whenever S(t) ≥ (N_theory + 1) * L, N_theory is incremented by 1. This event serves as the "time reference" for planting.

[0038] Base speed calculation: Based on the current v_fused (m / s), calculate the base motor speed (RPM) required to maintain the theoretical plant spacing L: RPM_base = (v_fused / L) * (60 / (2π * r)) * i Where r is the effective radius of the seed metering disc (meters), and i is the reduction ratio from the motor shaft to the seed metering disc. The number of seeds discharged per revolution of the seed metering disc is equal to the number of holes H.

[0039] Closed-loop calibration: Each sub-controller reports its motor's actual multi-turn encoder value Cnt_i every 20ms. The master controller converts this into the actual number of rows: N_actual_i = floor( (Cnt_i / encoder resolution) * i / H ). For the i-th row, the deviation e_i = N_theory - N_actual_i is calculated. This is then processed by a discrete PID controller: ΔRPM_i = Kp *e_i + Ki * Σe_i + Kd * (e_i - e_i_prev), where Kp, Ki, and Kd are adjustable parameters.

[0040] Command issuance and synchronization: The target speed ultimately sent to the motor in row i is: RPM_target_i = RPM_base + ΔRPM_i. The main controller encapsulates this command and the corresponding motor ID into a standard CAN 2.0B data frame and broadcasts it via the CAN bus (500kbps baud rate). Each motor driver listens to the bus, responds only to frames matching its own ID, and performs speed control.

[0041] Crop adaptation operation process: When switching from planting corn to planting soybeans, operators only need to perform the following two steps: a) Physical operation: Stop the machine and replace the corn seed trays (e.g., H=20) of the five air suction seeders with soybean seed trays (e.g., H=40).

[0042] b) Parameter settings: On the main touchscreen interface, click "Parameter Settings," change the "Target Plant Spacing" from 0.25 meters for corn to 0.10 meters for soybeans; change the "Number of Seed Tray Holes" from 20 to 40. Click Save.

[0043] Once the setup is complete, start the system. All control algorithms will automatically run based on the new L and H parameters, immediately adapting to soybean planting operations without any further calibration or debugging.

[0044] Effect verification: Field trials showed that, operating at speeds between 0-12 km / h, this system achieved an average corn planting spacing qualification rate (within ±2 cm deviation) of 99.3%. In slippery, muddy fields, traditional ground-wheel seeders experienced severe slippage, with a plant spacing variation coefficient exceeding 15%. However, this system, relying on absolute displacement benchmarks, maintained a plant spacing qualification rate above 99.1%, fully demonstrating its significant advantage in completely eliminating the impact of slippage. The overall hardware cost of the system (excluding the seeder itself) is approximately 60% lower than the "self-built RTK base station + radar speed measurement" solution.

[0045] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for synchronous control of electric precision seeding based on CORS network RTK speed measurement, characterized in that, Includes the following steps: S1: High-precision speed and displacement acquisition: The RTK positioning module on the seeder connects to the National Continuously Operating Reference Station (CORS) network via a communication network to acquire centimeter-level real-time positioning data of the seeder; simultaneously, the inertial measurement unit (IMU) collects the inertial data of the seeder; the Kalman filter algorithm is used to fuse the RTK positioning data and the IMU inertial data to output the high-precision real-time speed of the seeder and the absolute linear displacement distance obtained based on the integral of the speed; S2: Calculation of theoretical seeding quantity: Based on the absolute linear displacement distance obtained in step S1 and the target plant spacing set or adjusted through the human-computer interaction interface, the theoretical seeding quantity is calculated in real time. S3: Actual sowing quantity feedback: The encoder integrated into the servo motor is used to obtain the rotation number and angle information of the servo motor of each sowing unit in real time, and calculate the actual sowing quantity of each sowing unit. S4: Closed-loop synchronous control: Using the theoretical sowing quantity calculated in step S2 as the set value and the actual sowing quantity obtained in step S3 as the process variable, a closed-loop control algorithm is used to calculate the control quantity and adjust the speed of the corresponding servo motor in real time, so that the actual sowing quantity tracks the theoretical sowing quantity, thereby achieving synchronous control of the sowing particle spacing.

2. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 1, characterized in that, In step S1, the Kalman filter algorithm dynamically adjusts the weight of the RTK positioning data and the IMU inertial data in the fusion process based on the signal quality of the RTK positioning data. When the RTK signal quality is higher than a set threshold, the RTK data takes the lead. When the RTK signal quality is lower than the set threshold, the weight of the IMU data is increased to maintain the continuity of velocity and displacement estimation.

3. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 1, characterized in that, The closed-loop control algorithm in step S4 is a proportional-integral-derivative PID control algorithm; the control quantity is sent to the servo motor driver corresponding to each sowing unit through the controller local area network CAN bus.

4. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 1, characterized in that, In step S3, the encoder data is read by the independent sub-controller of each sowing unit. The sub-controller is connected to the servo motor through an RS-485 to TTL level conversion chip and reports the actual sowing quantity and motor status data to the main controller using the Modbus RTU protocol.

5. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 4, characterized in that, The method also includes a status monitoring and alarm step: the main controller periodically queries each sub-controller through the Modbus RTU protocol to obtain the voltage, current, load and temperature status data of the servo motor; when any motor is determined to be in an abnormal state, an audible and visual alarm and / or an interface display alarm is triggered.

6. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 1, characterized in that, The method also includes a crop rapid adaptation step: by selecting the type of crop to be sown through the human-computer interaction interface, the system automatically calls the pre-stored target plant spacing parameters and control parameters corresponding to the crop to adapt to the sowing needs of different crops.

7. The method for synchronous control of electric precision seeding based on CORS network RTK speed measurement according to claim 1, characterized in that, The RTK positioning module is a multi-frequency receiver that supports at least two of the following satellite navigation systems: BeiDou, GPS, GLONASS, and Galileo.

8. An electric-drive precision seeding synchronization control system for implementing the electric-drive precision seeding synchronization control method based on CORS network RTK speed measurement as described in any one of claims 1-7, characterized in that, include: The positioning and velocity measurement unit includes a multi-constellation RTK module for accessing the national CORS network, a communication module, and an IMU module, used to achieve the high-precision velocity and displacement acquisition as described in claim 1; The main control and interaction unit includes a microcontroller and a display and input devices connected thereto, for parameter setting, information display and execution of the theoretical seeding quantity calculation and closed-loop synchronous control algorithm as described in claim 1; The seeding execution and feedback unit includes at least one seeding unit, each seeding unit including an encoder-integrated servo motor, a driver, and a secondary controller for acquiring encoder data; the secondary controller is connected to the servo motor via an RS-485 to TTL chip; The communication network includes a CAN bus connecting the main control and interaction unit to each seeding unit driver, and an RS-485 bus connecting the main control and interaction unit to each sub-controller.

9. The electric precision seeding synchronous control system according to claim 8, characterized in that, The main control and interaction unit is configured to: query each sub-controller via the RS-485 bus and Modbus RTU protocol to monitor the motor status and issue an alarm when abnormal; and / or, its software system has a pre-stored database of sowing parameters for various crops, which can be accessed with one click.

10. The electric-driven precision seeding synchronous control system according to claim 8, characterized in that, The system does not include a physical ground wheel or radar speed measuring device for measuring the forward speed of the seeder; its forward speed and displacement information are provided entirely by the positioning and speed measuring unit through the fusion of CORS network RTK data and IMU data.

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