Precise seed separation control system based on small-particle-size vegetable seed sowing machine

Abstract

The invention relates to the technical field of intelligent control of agricultural machinery, and discloses a precise seed distribution control system based on a small-particle-size vegetable seed seeder, which comprises an indent wheel configuration module, a fixed point module, a seeding parameter adaptation module, an effect feedback module and a dynamic adjustment module, the indent wheel configuration module matches and adapts to an indent wheel, the fixed point module generates a cultivated land map, seed fixed point coordinates and an operation path, the seeding parameter adaptation module configures parameters based on the seed target depth and the fixed point coordinates, and the effect feedback module monitors miss-seeding and reseeding and triggers reseeding. And the dynamic adjustment module monitors data in real time and dynamically optimizes seeding parameters. According to the system, precise adaptation, path optimization and dynamic regulation and control of small-particle-size vegetable seed sowing are achieved, the sowing precision and the working efficiency are improved, and the system is adaptive to complex agricultural scenes.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for agricultural machinery, specifically to a precision seeding control system based on a small-diameter vegetable seed planter. Background Technology

[0002] As an integrated agricultural equipment, the vegetable seed planter's core functions revolve around the entire process of ditching, sowing, seeding, covering with soil, and fertilizing. It is also equipped with a human-machine interface screen that supports manual setting of parameters such as plant spacing, row spacing, depth, and speed, and displays the operation progress in real time, greatly improving the efficiency of vegetable seed sowing.

[0003] However, small-diameter vegetable seeds (such as spinach, leeks, rapeseed, and carrots) have unique physical characteristics and growth requirements, placing stringent demands on the precision of sowing operations. Traditional extensive sowing methods are no longer suitable. These seeds generally have a diameter between 1-4 mm, and their shapes include spherical, flattened oval, and irregular shapes. They are lightweight and have low inertia. During sowing, they are highly susceptible to environmental factors such as airflow disturbances, mechanical vibrations, and soil moisture, leading to problems such as deviation in their falling trajectory, stacking, or scattering, directly resulting in uneven distribution in the field. The greater friction between small seeds makes them prone to clumping and bridging during storage and transportation, preventing them from smoothly filling the orifices or channels of the sowing mechanism, causing seed supply interruptions, and consequently, missed sowing, severely impacting the germination rate per unit area. The germination rate and growth status of small-diameter seeds are far more sensitive to sowing depth and plant spacing than those of large-diameter seeds. Sowing too shallowly can cause seeds to lose water and freeze, while sowing too deep can hinder seedling emergence due to lack of oxygen. Uneven plant spacing leads to competition for nutrients and light, resulting in significant differences in growth and reducing yield per unit area and product quality. Furthermore, some high-quality small-diameter vegetable varieties are expensive, and extensive sowing practices leading to reseeding result in severe seed waste and increased planting costs. Therefore, the characteristics of small-diameter vegetable seeds place higher demands on precision sowing.

[0004] Currently, the mainstream vegetable planters on the market are mainly mechanical seed roller planters, which have significant shortcomings in structural design and control technology: Traditional planters have fixed seed roller aperture sizes, which cannot flexibly match small seeds of different sizes and shapes. Overly large apertures can lead to multiple seeds filling the seed hopper and causing double-seeding, while overly small apertures make it difficult for seeds to enter, resulting in missed seeding. Furthermore, the seed filling effect of the seed roller depends on the seed pressure inside the seed box. When the remaining seed level is low, pressure transmission is insufficient, the seed filling rate decreases, and the missed seeding rate increases significantly, failing to guarantee stable seeding throughout the process. Existing planters mostly use a single brush for seed cleaning, which is difficult to precisely control. Excessive force can scratch the seed coat, affecting the germination rate; insufficient force cannot remove excess seeds from the aperture, leading to double-seeding. In addition, the seed protection mechanism is simply designed. During the process of seeds falling from the seed roller into the seed furrow, they are prone to collisions and bouncing with components, disrupting the orderly movement and causing deviations in seeding position, further reducing seeding uniformity. Traditional seeding control requires manual parameter setting, which is highly subjective and results in large fluctuations in seeding accuracy when planting different varieties or different plots of land using the same equipment.

[0005] In summary, the sowing of small-diameter vegetable seeds has created a rigid demand for precision seeding, while existing seeders have many shortcomings. Therefore, in order to promote the development of precision and efficiency in the planting of small-diameter vegetable seeds, the existing seeding control system urgently needs to be upgraded. Summary of the Invention

[0006] The present invention aims to provide a precision seeding control system based on a small-diameter vegetable seed planter, which addresses the problems of inaccurate seed picking, uneven seeding, and unstable seeding caused by seed characteristics, working environment, and other factors in the sowing of small-diameter vegetable seeds, and achieves precise seeding to meet the requirements of refined agronomy.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A precision seeding control system based on a small-diameter vegetable seed planter includes a seeding wheel configuration module, a seeding positioning module, a seeding parameter adaptation module, an effect feedback module, and a seeding parameter dynamic adjustment module. The seeding wheel configuration module matches seeding wheel parameters from a pre-stored association model based on the physical characteristics of the small-diameter vegetable seeds to be sown, and changes the seeding wheel on the seeder according to the matching results; The fixed-point module acquires a digital farmland map of the land to be sown, automatically recommends row spacing based on seed variety from a pre-stored crop agronomic parameter library, automatically divides the sowing area based on the farmland map and row spacing parameters, generates seed fixed-point coordinates on the farmland, and plans the operation path. The sowing parameter adaptation module configures sowing parameters based on the target depth and fixed-point coordinates of the small-diameter vegetable seeds to be sown. The effect feedback module monitors the sowing process in real time and identifies missed sowing and reseeding. Based on the identification results, it triggers a micro reseeding device to reseed. The dynamic adjustment module for sowing parameters is used to monitor sowing process data in real time and dynamically adjust sowing parameters based on the monitoring results.

[0008] The principle and advantages of this solution are as follows: In practical applications, with the goals of accurate seed picking, uniform seeding, and stable seed placement, a closed-loop control system is constructed through multi-module collaboration: First, the seed picking component is accurately matched according to the physical characteristics of the seed by the seed picking module, solving the core pain points of easy missed or excessive seed picking for small-diameter seeds; then, the fixed-point module plans the operation path and fixed-point coordinates to ensure uniform distribution of seeding space; in conjunction with the sowing parameter adaptation module, key parameters such as depth and speed are locked to ensure seed placement stability; finally, the effect feedback and dynamic adjustment module corrects deviations in real time. This not only fundamentally solves the problems of inaccurate seed picking, uneven seeding, and unstable seed placement for small-diameter vegetable seeds, but also achieves optimization effects such as improved sowing accuracy, automated operation process, and enhanced scene adaptability, meeting the requirements of precision agronomy.

[0009] Preferably, as an improvement, the dynamic adjustment module for sowing parameters further includes a dynamic adaptation submodule for the entire seed lifecycle, employing a multi-feature fusion seed adaptive matching algorithm to achieve precise sowing parameter adaptation for individual seeds, including: The particle size, shape factor, and density of a single seed are collected in real time using a visual sensor. The pressure sensor collects the pressure at the bottom of the seed box, and the laser displacement sensor monitors the flatness of the seed surface to obtain the seed flow status inside the seed box. The SVM algorithm is used to classify individual seeds, and dynamic weights based on flow state are introduced. The seed classification parameters are corrected by a preset correction formula, and the grasping pressure of the seed-holding wheel, the rotation speed of the seed-cleaning brush, and the angle of the seed-feeding channel are adjusted in real time.

[0010] Technical effects: Real-time perception and precise parameter matching of the physical characteristics and flow state of individual seeds are achieved, the single-seed grabbing rate of the seed-grabbing wheel is improved, the seed jamming rate of the seed-feeding channel is reduced, and the seed-grabbing deviation problem caused by individual differences of small-diameter seeds is effectively solved.

[0011] Preferably, as an improvement, the seeding parameter dynamic adjustment module further includes a multi-field coupling interference suppression submodule, which establishes a coupling interference factor model, estimates the total interference through extended Kalman filtering, and then generates a compensation amount through an adaptive inverse control algorithm to dynamically offset the effects of composite interference.

[0012] Technical benefits: It accurately estimates and offsets the combined interference of soil resistance fluctuations, machine vibrations, and seed box pressure changes, controls the coefficient of variation of sowing depth, and ensures the consistency of sowing under complex operating conditions.

[0013] Preferably, as an improvement, the dynamic adjustment module for seeding parameters further includes an intelligent self-evolutionary submodule, which employs a self-evolutionary optimization algorithm combining reinforcement learning and transfer learning to achieve autonomous adaptation and continuous optimization of the system, including: A reinforcement learning framework is constructed, with the seeding parameter dynamic adjustment module as the agent, the operation scenario as the environment, the state including real-time monitoring data, the action as the parameter adjustment combination, and the reward including accuracy reward, efficiency reward, and energy consumption penalty. The DQN network is used to iteratively update the parameters, the samples are stored through the experience replay pool, the historical scenario experience is reused through transfer learning, and the domain adaptive algorithm is introduced to initialize the parameters of the new scenario. The wear degree of the component is included in the state, and the parameters are automatically adjusted to offset the wear effect.

[0014] Technical effects: The system can autonomously adapt to different seed varieties and different farmland scenarios, shortening the adaptation time to new scenarios and reducing the accuracy decay rate in long-term operation.

[0015] Preferably, as an improvement, the dynamic adjustment module for seeding parameters further includes a resource and accuracy balancing submodule, which employs a multi-objective optimization and scene weight dynamic allocation algorithm to achieve a dynamic balance between accuracy, efficiency, and energy consumption, including: A target optimization model is established, and the operation scenarios are classified by K-means clustering. The NSGA-Ⅲ algorithm is used to solve the Pareto optimal solution, the weights are dynamically adjusted, and the scenario-specific optimal parameter combination is output.

[0016] Technical benefits: While ensuring seeding accuracy, energy consumption is reduced and work efficiency is dynamically improved according to the scenario, effectively balancing the contradiction between high precision and low cost and high efficiency, and adapting to the work needs of different users.

[0017] Preferably, as an improvement, the seed lifecycle dynamic adaptation submodule, multi-field coupling interference suppression submodule, system intelligent self-evolution submodule, and resource and precision balancing submodule are linked through a data interaction interface and perform optimization operations in order of priority.

[0018] Technical effects: Avoids parameter conflicts in various optimization directions, shortens system response time, reduces seeding accuracy fluctuations in complex scenarios, and improves the coordination and efficiency of the overall system optimization.

[0019] Preferably, as an improvement, it also includes a parameter adjustment triggering module, used to dynamically adjust the seeding parameters according to a hierarchical triggering strategy; The tiered triggering strategy includes Level 1 basic threshold triggering, Level 2 trend prediction triggering, and Level 3 multi-parameter collaborative triggering. Level 1 basic threshold trigger: When the seeding parameters deviate from the threshold, the corresponding parameter adjustment rules are triggered for adjustment; Secondary trend prediction trigger: Based on the time series forecasting algorithm, the changing trend of sowing parameters is predicted. If the predicted value reaches the threshold, optimization is triggered. Level 3 multi-parameter collaborative triggering: When the seeding parameters are close to the breakthrough threshold at the same time, and the triggering condition of the first-level basic threshold has not been reached, but the collaborative effect can lead to a decrease in seeding accuracy, the combination optimization is triggered by fuzzy logic reasoning.

[0020] Technical effects: It achieves an upgrade from passive correction to proactive prediction and collaborative early warning, comprehensively improving the timeliness and pertinence of parameter adjustments.

[0021] Preferably, as an improvement, the sowing parameters are determined according to the sowing target, which includes fixed point coordinates and depth. The sowing parameters include, but are not limited to, the walking direction, walking speed, seeding frequency, furrowing depth, and soil covering depth of the seeder.

[0022] Technical effect: It strongly binds the sowing parameters to the fixed point coordinates and depth target, ensuring that the goal of uniform seeding and stable seeding is achieved.

[0023] Preferably, as an improvement, it also includes a sowing status visualization module, which is used to visualize and display the data of the entire sowing process, including: real-time annotation of sowing spatial data based on GIS map, the sowing spatial data including sowing progress, fixed point coordinate deviation, and missed sowing and re-sowing locations; displaying real-time statistical information in the form of dashboards and line graphs, the real-time statistical information including sowing parameters, accuracy indicators, and energy consumption data; and automatically generating a sowing quality report.

[0024] Technical benefits: It enables data transparency throughout the entire sowing process, allowing users to monitor sowing progress, accuracy deviations, and energy consumption in real time. This makes sowing results traceable and analyzable, reducing operational monitoring costs and improving troubleshooting efficiency.

[0025] Preferably, as an improvement, it also includes an intelligent human-computer interaction module for enabling efficient collaborative operation between the user and the system, including: It provides a dual interactive interface of touch screen and voice control, and supports users to customize seeding targets, modify scene weights, and adjust start and pause parameters; It features a collaborative function of parameter recommendation and manual intervention, recommending the optimal parameter combination based on historical data and the current scenario, allowing manual fine-tuning and real-time preview of the adjustment effect simulation; It provides step-by-step operation guidance, and automatically locates the problem and outputs a visual troubleshooting solution when the system malfunctions.

[0026] Technical benefits: Simplifies operation procedures and enhances the ease of use and practicality of the system. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention. Detailed Implementation

[0028] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown: A precision seeding control system based on a small-diameter vegetable seed planter includes a seeding wheel configuration module, a positioning module, a seeding parameter adaptation module, an effect feedback module, and a seeding parameter dynamic adjustment module.

[0029] The seeding wheel configuration module matches seeding wheel parameters from a pre-stored association model based on the physical characteristics of the small-diameter vegetable seeds to be sown, and changes the seeding wheel on the seeder according to the matching results.

[0030] The system has a built-in parameter library for common small-diameter vegetable seeds of all categories, including key data such as seed diameter (length / width / height), shape (spherical / flat / elliptical), density, and flowability coefficient. It also supports manual input of special seed parameters (such as custom seed size and morphological description). Through a correlation model between key parameters of small-diameter vegetable seeds and pore wheel parameters (pore size, shape, number, and arrangement), the system automatically recommends suitable solutions; for example, for seeds with a diameter of 1-2mm (such as chives): the recommended pore diameter is 1.5mm, hemispherical, and a double-row 48-pore pore wheel; for seeds with a diameter of 2-3mm (such as spinach): the recommended pore diameter is 2mm, hemispherical, and a double-row 48-pore pore wheel; for seeds with a diameter of 3-4mm (such as carrots): the recommended pore diameter is 2.5mm, conical, and a double-row 40-pore pore wheel.

[0031] The positioning module acquires a digital map of the arable land to be sown. From a pre-stored crop agronomic parameter library, it automatically recommends row spacing based on seed variety. Based on the arable land map and row spacing parameters, it automatically divides the sowing area, generates seed positioning coordinates on the arable land, and plans the planting path. Specifically, it combines GPS positioning and LiDAR scanning to quickly acquire information such as arable land boundaries, area, and terrain slope to generate a digital arable land map. It also allows manual drawing of irregular arable land boundaries (such as terraces or small plots) via a mobile app to adapt to complex plot shapes. The pre-stored crop agronomic parameter library automatically recommends row spacing based on seed variety (e.g., 15-25cm for leeks, 25-30cm for rapeseed), and allows for custom adjustments within a range of 10-50cm via the mobile app. Based on the arable land map and row spacing parameters, it automatically divides the sowing area, generating evenly distributed seed positioning coordinates on the arable land to ensure consistent plant spacing while avoiding obstacles within the plot, such as stones and tree roots. The A* algorithm is used in conjunction with the characteristics of farmland operations to optimize the shortest operating path, reducing the number of turns and empty driving distance of the seeder. The path includes the in-row sowing path and the inter-row transfer path, clearly marking the sowing order and direction. For terrain paths with a slope of ≥25°, compensation is provided to adjust the travel angle and avoid sideslip.

[0032] The sowing parameter adaptation module configures sowing parameters based on the target depth and fixed-point coordinates of the small-diameter vegetable seeds to be sown.

[0033] Specifically, the target depth for sowing small-diameter vegetable seeds is obtained through a pre-stored crop agronomic parameter library or user input; the sowing parameters include the walking direction of the seeder, walking speed, seed dispensing frequency, furrow depth, and soil covering depth.

[0034] The effect feedback module monitors the sowing process in real time and identifies missed sowing and reseeding. Based on the identification results, it triggers a micro-reseeding device for reseeding. Specifically, it uses a particle counting method to monitor seed flow rate, and combines the actual plant spacing with the theoretical plant spacing to analyze and obtain information on missed sowing rate, reseeding rate, and sowing quantity. The monitoring device (photoelectric counter) is installed close to the furrow opener and the seed delivery tube. Under normal operating conditions, the infrared beam passes through a convex lens and illuminates the receiver in a parallel manner. When a seed passes through the monitoring device, if it completely blocks one or more infrared receiving diodes, the receiver will detect a change in voltage level. Once this change is detected, the receiver transmits the corresponding voltage level signal to the microcontroller for processing. After receiving the voltage level signal, the microcontroller counts the time interval between seed falls based on the signal changes. In this way, it directly observes how many seeds have fallen. It also obtains the time interval between the falls of two adjacent seeds. , The product of the theoretical and actual particle spacing is the monitored value of the seeder's travel speed v. By comparing the theoretical and actual particle spacing, the following formula is used to determine whether there has been double-seeding or missed seeding: (Replay) (Missed broadcast) In the formula, Theoretical plant spacing for sowing .

[0035] The dynamic adjustment module for sowing parameters is used to monitor sowing process data in real time and dynamically adjust sowing parameters based on the monitoring results.

[0036] The sowing process data includes environmental parameters, soil data, and sowing effect data. Environmental parameters include wind speed, wind direction, and temperature and humidity. Wind speed and direction affect the seed trajectory. The walking speed and the initial velocity of the seed feed wheel are adjusted in real time based on wind speed and direction to improve seed placement accuracy. Soil data includes soil moisture and soil hardness. Combined with data from soil moisture and hardness sensors, the sowing parameters are dynamically adjusted. In heavy, sticky soil, the seed feed wheel speed is reduced to prevent seed sticking; in loose soil, the sowing height is increased to ensure consistent seed depth. Sowing effect data includes placement accuracy.

[0037] The dynamic adjustment module for sowing parameters also includes a dynamic adaptation submodule for the entire seed lifecycle. Small-diameter seeds exhibit individual heterogeneity (e.g., differences in particle size, density, and shape). Initial configuration based on average parameters leads to insufficient sowing precision for some seeds. Simultaneously, the seed flow state within the seed box changes with the remaining quantity, easily causing uneven seed filling. The dynamic adaptation submodule for the entire seed lifecycle employs a multi-feature fusion seed adaptive matching algorithm to achieve precise sowing parameter adaptation for individual seeds, including: The system uses a visual sensor to collect data in real time on the particle size, shape factor (roundness, ellipticity), and density of individual seeds (estimated indirectly by image grayscale values); a pressure sensor collects the pressure at the bottom of the seed box (reflecting the remaining amount of seeds), and a laser displacement sensor monitors the flatness of the seed surface (reflecting fluidity) to obtain the seed flow status within the seed box.

[0038] The adaptive matching algorithm employs an improved support vector machine (SVM) with dynamic weight allocation: It constructs a seed feature-seed classification parameter association model, classifying seeds into four categories: high-quality standard, small-particle-size, large-particle-size, and irregular, each corresponding to a preset seed classification parameter range; the improved support vector machine algorithm is used to classify individual seeds, using the following formula:

[0039] in, For single seeds, For a single seed feature vector, The kernel function is Gaussian; optimizing the kernel function parameters improves classification accuracy.

[0040] A dynamic weight w is introduced for the flow state, where w = 0.3 * pressure coefficient + 0.7 * smoothness coefficient. The parameters are then corrected using a formula:

[0041] These are the basic parameters corresponding to the seed category. The gripping pressure of the seed-holding wheel, the rotation speed of the seed-cleaning brush, and the angle of the seed-dispensing channel are adjusted in real time to ensure accurate dispensing of seeds with different characteristics.

[0042] The dynamic adjustment module for sowing parameters also includes a multi-field coupling interference suppression submodule. During sowing, there are coupled interferences from environmental fields (wind, temperature), mechanical fields (vibration, transmission errors), and soil fields (humidity, hardness, spatial heterogeneity). Adjusting a single parameter cannot offset these combined interferences, leading to fluctuations in sowing accuracy. The multi-field coupling interference suppression submodule employs an adaptive multi-field coupling interference suppression algorithm to counteract the combined interferences from the environment, machinery, and soil, including: Establish a coupling interference factor model:

[0043] Environmental interference (wind speed × wind direction coefficient + temperature deviation × temperature sensitivity coefficient). Mechanical interference (seeder vibration acceleration × vibration attenuation coefficient + transmission error × transmission compensation coefficient) Due to soil disturbance, , , The values ​​are 0.2, 0.3, and 0.5 respectively.

[0044] The interference factor is estimated using an extended Kalman filter, and the state equation is:

[0045] The observation equation is:

[0046] in, Let A be the state vector for sowing accuracy (plant spacing deviation, seeding deviation), B be the state transition matrix, and C be the disturbance input matrix. , This refers to process noise and observation noise.

[0047] An adaptive inverse control algorithm is used to generate compensation quantities, and an inverse model of the disturbance is constructed:

[0048] The inverse model parameters are iteratively optimized using the LMS (Least Mean Square) algorithm.

[0049]

[0050] in, To estimate interference, This is the step size factor.

[0051] The compensation amount is added to the sowing parameters (such as walking speed, sowing wheel speed, and seeding angle). The addition formula is:

[0052] in, This is the compensation coefficient.

[0053] The dynamic adjustment module for sowing parameters also includes an intelligent self-evolution submodule. Initial parameters rely on a preset database, requiring manual adjustment for new crops and soil types. Long-term operation can lead to wear and tear on mechanical components (such as wear on the seeding wheel and aging of the brushes), causing parameter mismatch and affecting sowing accuracy. The intelligent self-evolution submodule treats system optimization as a reinforcement learning agent-environment interaction process. It reuses historical scenario experience through transfer learning and dynamically updates the optimal parameter set based on real-time operational feedback, enabling the system to learn autonomously and continuously optimize. This includes: A reinforcement learning framework is constructed, with the dynamic adjustment module for sowing parameters as the agent, the operational scenario (seed variety, soil type, environmental conditions) as the environment, the state S containing real-time monitoring data (sowing accuracy, interference factors, component wear level), and the action A being a combination of parameter adjustments (walking speed, sowing wheel speed, furrowing depth, etc.); the reward... , In order to reward planting accuracy, , Plant spacing deviation, Rewards for efficiency , For walking speed, As a penalty for energy consumption, , This refers to the motor power.

[0054] Construct a DQN network, taking state S as input and outputting the Q-value (action value) of each action A, storing the values ​​(S, A, R, ...) in an experience replay pool. Samples are used to iteratively update network parameters:

[0055]

[0056] γ is the discount factor.

[0057] By reusing historical scenario experience through transfer learning, when encountering a new scenario (such as an unknown seed), the parameter set of historically similar scenarios is transferred to the new scenario through the domain adaptation algorithm (DA) to initialize the DQN network parameters and shorten the learning cycle; component wear adaptation is achieved by estimating the wear degree W through changes in motor current and seeding accuracy, and incorporating W into the state S. The DQN network automatically adjusts parameters to offset the wear effect (such as increasing the seeding wheel speed to compensate for grasping efficiency after the seeding wheel wears down).

[0058] The dynamic adjustment module for sowing parameters also includes a resource and precision balancing submodule. Initially, the system prioritizes sowing precision, which can easily lead to low operational efficiency and excessive energy consumption. Different operational scenarios have different requirements for precision, efficiency, and energy consumption (e.g., high precision is needed for greenhouse precision planting, while large-scale open-field planting requires high efficiency and low energy consumption), lacking a dynamic balancing mechanism. The resource and precision balancing submodule employs a multi-objective optimization and scene weight dynamic allocation algorithm to achieve a dynamic balance between precision, efficiency, and energy consumption, including: Establish a target optimization model: Minimize seeding error: ; Maximize work efficiency:

[0059] v is the walking speed, and b is the working width; Minimize energy consumption:

[0060] t represents the total motor power, and t represents the operating time.

[0061] Constraints:

[0062] K-means clustering was used to classify the planting scenarios into three categories: precision planting, high-efficiency planting, and energy-saving planting. The corresponding weight vectors are [0.6, 0.2, 0.2] (precision priority), [0.3, 0.5, 0.2] (efficiency priority), and [0.3, 0.2, 0.5] (energy consumption priority).

[0063] The NSGA-Ⅲ algorithm (Non-dominated sorting genetic algorithm Ⅲ) is used to solve the Pareto optimal solution. Population diversity is maintained through a niche selection mechanism, and the optimal parameter combination (walking speed, seeding wheel speed, motor power allocation) is generated iteratively. The weights are dynamically adjusted based on real-time operation feedback (if the accuracy does not meet the standard, the accuracy weight is increased; if the energy consumption exceeds the standard, the energy consumption weight is increased), and the scenario-specific optimal parameter combination is output.

[0064] It also includes a parameter adjustment trigger module, used to dynamically adjust sowing parameters according to a tiered triggering strategy. The tiered triggering strategy includes a first-level basic threshold trigger, a second-level trend prediction trigger, and a third-level multi-parameter collaborative trigger. The first-level basic threshold trigger: when the sowing parameters deviate from the threshold, the corresponding parameter adjustment rule is triggered for adjustment. The second-level trend prediction trigger: based on the time series prediction algorithm, the changing trend of the sowing parameters is predicted. If the predicted value reaches the threshold, optimization is triggered. The third-level multi-parameter collaborative trigger: when the sowing parameters are close to the threshold at the same time, and the first-level basic threshold triggering condition has not been met, but the collaborative effect can lead to a decrease in sowing accuracy, combined optimization is triggered through fuzzy logic reasoning.

[0065] It also includes a sowing status visualization module, which is used to visualize and display the data of the entire sowing process, including: real-time annotation of sowing spatial data based on GIS map, including sowing progress, fixed point coordinate deviation, and missed sowing and re-sowing locations; display of real-time statistical information in the form of dashboards and line graphs, including sowing parameters, accuracy indicators, and energy consumption data; and automatic generation of sowing quality reports.

[0066] It also includes an intelligent human-computer interaction module to enable efficient collaborative operation between users and the system, including: providing a dual interactive interface of touch screen and voice control, supporting users to customize seeding targets, modify scene weights, and start / pause parameter adjustments; having parameter recommendation and manual intervention collaborative functions, recommending the optimal parameter combination based on historical data and the current scene, allowing manual fine-tuning and real-time preview of adjustment effect simulation; providing step-by-step operation guidance, and automatically locating problems and outputting visual troubleshooting solutions when the system malfunctions.

[0067] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A precision seeding control system based on a small-diameter vegetable seed planter, characterized in that: Includes a seeding configuration module, a seeding point module, a seeding parameter adaptation module, an effect feedback module, and a seeding parameter dynamic adjustment module: The seeding wheel configuration module matches seeding wheel parameters from a pre-stored association model based on the physical characteristics of the small-diameter vegetable seeds to be sown, and changes the seeding wheel on the seeder according to the matching results; The fixed-point module acquires a digital farmland map of the land to be sown, automatically recommends row spacing based on seed variety from a pre-stored crop agronomic parameter library, automatically divides the sowing area based on the farmland map and row spacing parameters, generates seed fixed-point coordinates on the farmland, and plans the operation path. The sowing parameter adaptation module configures sowing parameters based on the target depth and fixed-point coordinates of the small-diameter vegetable seeds to be sown. The effect feedback module monitors the sowing process in real time and identifies missed sowing and reseeding. Based on the identification results, it triggers a micro reseeding device to reseed. The dynamic adjustment module for sowing parameters is used to monitor sowing process data in real time and dynamically adjust sowing parameters based on the monitoring results.

2. The precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: The dynamic adjustment module for sowing parameters also includes a dynamic adaptation submodule for the entire seed lifecycle, which employs a multi-feature fusion seed adaptive matching algorithm to achieve precise sowing parameter adaptation for individual seeds, including: The particle size, shape factor, and density of a single seed are collected in real time using a visual sensor. The pressure sensor collects the pressure at the bottom of the seed box, and the laser displacement sensor monitors the flatness of the seed surface to obtain the seed flow status inside the seed box. The SVM algorithm is used to classify individual seeds, and dynamic weights based on flow state are introduced. The seed classification parameters are corrected by a preset correction formula, and the grasping pressure of the seed-holding wheel, the rotation speed of the seed-cleaning brush, and the angle of the seed-feeding channel are adjusted in real time.

3. The precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: The dynamic adjustment module for seeding parameters also includes a multi-field coupling interference suppression submodule, which establishes a coupling interference factor model, estimates the total interference through extended Kalman filtering, and then generates compensation through an adaptive inverse control algorithm to dynamically offset the effects of composite interference.

4. The precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: The dynamic adjustment module for sowing parameters also includes an intelligent self-evolutionary submodule, which employs a self-evolutionary optimization algorithm combining reinforcement learning and transfer learning to achieve autonomous adaptation and continuous optimization of the system, including: A reinforcement learning framework is constructed, with the seeding parameter dynamic adjustment module as the agent, the operation scenario as the environment, the state including real-time monitoring data, the action as the parameter adjustment combination, and the reward including accuracy reward, efficiency reward, and energy consumption penalty. The DQN network is used to iteratively update the parameters, the samples are stored through the experience replay pool, the historical scenario experience is reused through transfer learning, and the domain adaptive algorithm is introduced to initialize the parameters of the new scenario. The wear degree of the component is included in the state, and the parameters are automatically adjusted to offset the wear effect.

5. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: The dynamic adjustment module for seeding parameters also includes a resource and accuracy balancing submodule, which employs a multi-objective optimization and scene weight dynamic allocation algorithm to achieve a dynamic balance between accuracy, efficiency, and energy consumption, including: A target optimization model is established, and the operation scenarios are classified by K-means clustering. The NSGA-Ⅲ algorithm is used to solve the Pareto optimal solution, the weights are dynamically adjusted, and the scenario-specific optimal parameter combination is output.

6. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 2, 3, 4, or 5, characterized in that: The seed lifecycle dynamic adaptation submodule, multi-field coupling interference suppression submodule, system intelligent self-evolution submodule, and resource and precision balancing submodule are linked through a data interaction interface and perform optimization operations in order of priority.

7. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: It also includes a parameter adjustment triggering module, which is used to dynamically adjust the seeding parameters according to the hierarchical triggering strategy; The tiered triggering strategy includes Level 1 basic threshold triggering, Level 2 trend prediction triggering, and Level 3 multi-parameter collaborative triggering. Level 1 basic threshold trigger: When the seeding parameters deviate from the threshold, the corresponding parameter adjustment rules are triggered for adjustment; Secondary trend prediction trigger: Based on the time series forecasting algorithm, the changing trend of sowing parameters is predicted. If the predicted value reaches the threshold, optimization is triggered. Level 3 multi-parameter collaborative triggering: When the seeding parameters are close to the breakthrough threshold at the same time, and the triggering condition of the first-level basic threshold has not been reached, but the collaborative effect can lead to a decrease in seeding accuracy, the combination optimization is triggered by fuzzy logic reasoning.

8. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that: The sowing parameters are determined based on the sowing objectives, which include fixed-point coordinates and depth. The sowing parameters include, but are not limited to, the walking direction, walking speed, seeding frequency, furrow depth, and soil covering depth of the seeder.

9. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that... It also includes a sowing status visualization module, which is used to visualize and display the data of the entire sowing process, including: real-time annotation of sowing spatial data based on GIS map, including sowing progress, fixed point coordinate deviation, and missed sowing and re-sowing locations; display of real-time statistical information in the form of dashboards and line graphs, including sowing parameters, accuracy indicators, and energy consumption data; and automatic generation of sowing quality reports.

10. A precision seeding control system based on a small-diameter vegetable seed planter according to claim 1, characterized in that... It also includes an intelligent human-computer interaction module, used to enable efficient collaborative operation between users and the system, including: It provides a dual interactive interface of touch screen and voice control, and supports users to customize seeding targets, modify scene weights, and adjust start and pause parameters; It features a collaborative function of parameter recommendation and manual intervention, recommending the optimal parameter combination based on historical data and the current scenario, allowing manual fine-tuning and real-time preview of the adjustment effect simulation; It provides step-by-step operation guidance, and automatically locates the problem and outputs a visual troubleshooting solution when the system malfunctions.

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