A method for dynamic airflow distribution and pressure regulation in parallel pipelines of an air-suction seeder

Abstract

A method for dynamic airflow distribution and pressure regulation in parallel pipelines of a pneumatic seeder is disclosed. The system includes a signal bus, a power bus, a control module, a human-machine interface module, branch pipe flow sensors, a pneumatic pipeline system, a fan control module, a power module, and a main pipe pressure sensor. The pneumatic pipeline system includes a main pipe and multiple parallel branch pipes. One branch pipe flow sensor is used to collect the inlet flow data of the leftmost branch pipe in real time. The main pipe pressure sensor monitors the inlet negative pressure value of the main pipe in real time. The system predicts the state of each branch pipe in the parallel pipeline using a branch pipe flow prediction model embedded in the control system, and outputs control signals to the fan control module through a PID control algorithm and dynamic feedback optimization strategy to dynamically adjust the fan's output power and speed, thereby achieving optimal control of branch pipe airflow distribution and main pipe negative pressure. This invention is simple to operate, easy to expand, and highly intelligent, belonging to the field of seeding technology.

Description

Technical Field

[0001] This invention relates to the field of seeding technology in agricultural machinery, specifically to a method for dynamic distribution and pressure regulation of airflow in parallel pipelines of an air-suction seeder. Background Technology

[0002] Currently, pneumatic seeders are developing towards multi-row control, precision seeding, and high-speed operation. Their supporting pneumatic systems mostly adopt a combination of "single fan + multi-branch pipelines" for centralized air supply. However, due to the parallel installation mode of the multi-row seed metering units at equal intervals, the current air supply system pipelines suffer from problems such as poor uniformity of airflow distribution in each branch pipe due to varying airflow transmission path lengths, and large pressure fluctuations in the main pipe due to airflow convergence. This further leads to problems such as inconsistent negative pressure suction of each row seed metering unit, uneven seeding rate, and poor operational stability during the seeding stage, seriously restricting the accuracy and reliability of seeding operations.

[0003] Meanwhile, because existing fans lack real-time airflow status feedback or branch pipe flow prediction mechanisms, current air supply system fans mostly operate at fixed speeds or are manually adjusted before sowing. This simplistic control method results in the system's inability to dynamically allocate and adjust pipeline flow and pressure based on real-time airflow information under dynamic conditions such as changes in operating scenarios, pipeline structure, and seeder / seed metering device switching. Therefore, there is an urgent need for a technology capable of real-time acquisition of branch pipe airflow status and stabilization of main pipe pressure loss. This technology should utilize precise mathematical models to dynamically control fan rotation, thereby achieving adaptive closed-loop control of the entire system. Summary of the Invention

[0004] In view of the technical problems existing in the prior art, the purpose of this invention is to provide a method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder. The system has a simple structure and fast response, and is suitable for large-scale production of pneumatic seeders for rice, wheat, corn, soybeans, rapeseed and other crops.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder is disclosed. The system includes a signal bus, a power bus, a control module, a human-machine interface module, branch pipe flow sensors, a pneumatic pipeline system, a fan control module, a power module, and a main pipe pressure sensor. The pneumatic pipeline system includes a main pipe and multiple parallel branch pipes. One branch pipe flow sensor is used to collect the inlet flow data of the leftmost branch pipe in real time. The main pipe pressure sensor monitors the inlet negative pressure value of the main pipe in real time. The airflow status of the pipeline is obtained in real time through the branch pipe flow sensors installed on the branch pipes and the main pipe pressure sensor installed on the main pipe. The status of each branch pipe in the parallel pipeline is predicted by the branch pipe flow prediction model embedded in the control system. A control signal is output to the fan control module through a PID control algorithm and a dynamic feedback optimization strategy to dynamically adjust the output power and speed of the fan, thereby achieving optimal control of the branch pipe airflow distribution and the main pipe negative pressure.

[0007] As a preferred embodiment, a method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder includes the following steps:

[0008] Step 1: The user starts the system through the human-computer interaction module and inputs the structural parameters of the multi-branch pneumatic pipeline system according to the structural parameters of the positive and negative pressure channels of the seed metering device for the current crop. The parameters are then transmitted to the control module via serial communication.

[0009] Step 2: The control module receives structural parameter information from the human-machine interaction module and, in conjunction with the real-time inlet flow value of the branch pipe equipped with the branch pipe flow sensor, calls the preset branch pipe flow prediction model to prepare for subsequent calculations.

[0010] Step 3: The main pipe pressure sensor monitors the pressure changes at the main pipe inlet in real time and sends the detection data to the control module via the signal bus;

[0011] Step 4: The branch pipe flow sensor monitors the real-time inlet flow of the branch pipe equipped with the branch pipe flow sensor and transmits the data synchronously to the control module via the signal bus.

[0012] Step 5: The control module integrates the branch pipe flow data from Step 4 and the main pipe pressure data from Step 3, calculates the inlet flow value and inlet flow variation coefficient of each of the remaining branches based on the built-in branch pipe flow prediction model, and transmits the calculation results to the human-machine interaction module to realize the visualization display of the real-time flow, inlet flow variation coefficient and main pipe pressure information of each branch of the pneumatic pipeline system, so as to facilitate users to monitor the system operation status in real time.

[0013] Step 6: Based on the real-time flow data of the branch pipe in Step 4 and the model calculation results in Step 5, and with the dual control objectives of minimizing the coefficient of variation of inlet flow and minimizing the pressure fluctuation of the main pipe, the control module generates a fan speed adjustment command through an incremental PID control algorithm and sends it to the fan control module.

[0014] Step 7: The fan control module receives the control signal from the control module and dynamically adjusts the fan's operating status, thereby achieving reasonable distribution of airflow in each branch pipe of the pneumatic pipeline system and stable control of the main pipe's air pressure.

[0015] As a preferred embodiment, in step five, the real-time inlet flow rate of the branch pipe measured by the branch pipe flow sensor is Q1, and the inlet flow rate of the other branch pipes is Q... i The calculation formula is:

[0016]

[0017] In the formula, x is a variable factor, f(x) is the function model under that variable factor, and x can be any factor among Q1, d, l, δ, γ, Δ, and D. When x is determined to be a certain factor, the product of the function expressions of the remaining factors forms the constant term λ. At this time, ξ is the error compensation coefficient to improve the calculation accuracy of the formula; Q1: is the real-time inlet flow rate of the leftmost branch pipe, in m³ / s. 3 ·s -1 ; d is the inner diameter of the branch pipe, in mm; l is the length of the branch pipe, in mm; δ is the spacing between the branch pipes, in mm; γ is the inner diameter of the manifold, in mm; Δ is the length of the main pipe, in mm; D is the inner diameter of the main pipe, in mm.

[0018] The formula for calculating the coefficient of variation of inlet flow is:

[0019]

[0020] In the formula, n is the number of branches; j is the serial number of each branch, arranged from left to right, ranging from 1 to 10; Q j The inlet flow rate of each branch pipe is expressed in cubic meters per second (m³). 3 ·s -1 Q m This is the average flow rate at the inlet of each branch pipe, in m³. 3 ·s -1 .

[0021] As a preferred option, in step six, the fan speed regulation algorithm design adopts the standard incremental PID algorithm, and the control signal generation process includes:

[0022] (1) Error Calculation: The deviation of the branch pipe flow variation coefficient and the deviation of the main pipe pressure are weighted and fused to generate a composite error signal e. c (k):

[0023] e c (k)=α·e flow (k)+β·e pressure (k),

[0024] In the formula, e flow (k) represents the deviation of the branch flow variation coefficient at the current moment, which is the set value minus the measured value; e pressure (k) represents the current pressure deviation of the main pipe, which is the target value minus the measured value; the weighting coefficients α and β are dynamically adjusted according to the system priority.

[0025] (2) PID Output: The incremental PID control algorithm is used to calculate the fan speed adjustment Δu:

[0026]

[0027] In the formula, e c (k) represents the current composite error signal deviation, which is the difference between the set value and the measured value; e c (k-1) represents the composite error signal deviation at the previous moment, which is the difference between the set value and the measured value; e c (k-2) represents the value in e c The deviation of the composite error signal from the previous time step based on time (k-1) is the set value minus the measured value; T d T i T and I are the differential time constant, integral time constant, and sampling period, respectively. The values ​​of P, I, and D are dynamically adjusted according to the system priority.

[0028] As a preferred configuration, the signal bus is used for data communication and control command transmission; the power bus is used for stable DC power transmission; the control module is used for system information reception, data processing, and control command transmission; the human-machine interface module is used to realize information interaction between the user and the control module; the pneumatic pipeline system is used for airflow transmission and distribution, and is the core actuator for air pressure regulation; the fan control module is responsible for receiving control commands issued by the control module and regulating the fan's operating status in real time; the power module is used to provide stable power output and also has overvoltage and overcurrent protection functions; the main pipe air pressure sensor transmits data to the control module via wired connection, serving as reference data for the control module to determine whether the fan needs further regulation.

[0029] As a preferred embodiment, the pneumatic piping system includes branch tees, branch pipes, manifold connectors, main tees, main pipes, branch inlet flow measurement pipes, manifold end caps, manifold end connectors, negative pressure measurement pipes, corrugated pipes, clamps, fans, Pitot tubes, and rubber hoses. The pneumatic piping system comprises three main components: the main pipe, manifolds, and branch pipes. The manifold includes branch tees, manifold connectors, manifold end caps, and manifold end connectors. The manifold connectors connect adjacent branch tees, and the manifold end connectors are located on the outside of the branch tees at both ends and are connected via... The manifold is sealed at the end; branch pipes are connected to the branch tee channels perpendicular to the manifold direction through an equidistant parallel layout; the main pipe is located in the middle of the manifold and is connected to the manifold through the main tee, and connected to the fan inlet channel through the series connection of the negative pressure measuring pipe and the corrugated pipe; the corrugated pipe is fixed to the outlet of the negative pressure measuring pipe and the fan inlet with clamps; the branch inlet flow measuring pipe is set on the leftmost branch pipe, and the branch flow sensor is installed on the branch inlet flow measuring pipe; the negative pressure measuring pipe, Pitot tube, rubber hose, and main pipe pressure sensor are connected in sequence.

[0030] As a preferred option, the branch pipe flow sensor uses the thermal model induction principle to obtain the real-time inlet flow of the branch pipe. The branch pipe inlet flow measuring tube is vertically arranged at the airflow inlet of the branch pipe, and the data is transmitted to the control module via a wired connection. The main pipe pressure sensor uses the differential pressure principle to measure the real-time negative pressure at the main pipe outlet, and the data is transmitted to the control module via a wired connection.

[0031] As a preferred option, the signal bus uses the RS485 communication protocol to complete data communication; the power bus is configured according to the system power; the power module uses a battery pack, equipped with overvoltage, overcurrent, and short-circuit protection circuits and real-time power status display function, to provide continuous and reliable DC power to the control module, human-machine interaction module, branch flow sensor, fan control module and main pipe pressure sensor.

[0032] As a preferred option, the control module uses an STM32 microcontroller as the core processor, which is responsible for receiving and processing various signal inputs from the system, as well as the airflow information collected by the branch flow sensor and the main pipe pressure sensor. The control module also processes the structural parameters of the multi-branch pneumatic pipeline system input by the human-machine interface module.

[0033] As a preferred option, the human-computer interaction module uses a touch screen or tablet, transmits string data to the control module via a wired connection, and displays the system operating parameters to the user intuitively through a serial port screen.

[0034] The present invention has the following advantages:

[0035] 1. This invention deploys only two high-precision sensors at the main pipe and branch pipes. Through the mathematical model and PID control algorithm embedded in the control module, combined with real-time air pressure and flow rate data, it achieves dual optimized regulation of the stability of the main pipe air pressure and the uniformity of the branch pipe flow rate in a multi-branch parallel pipeline system. This method not only reduces the number of regulating devices and the complexity of the control logic, significantly reducing the system's manufacturing and maintenance costs, but also improves the system's reliability and applicability.

[0036] 2. Clear control objectives and optimized control logic: This invention takes "uniform distribution of branch pipe flow" as the core control objective. By using real-time flow monitoring data of a branch pipe, combined with mathematical prediction models and PID control, it achieves efficient coordination of branch pipe flow in parallel pipelines of multi-branch systems. The main pipe air pressure is used as a control feedback indicator to determine whether the fan has reached a relatively stable state after the branch pipe flow is uniformly distributed, rather than being the main control object. This avoids absolute dependence on the main pipe air pressure and improves the stability and response efficiency of the control.

[0037] 3. Advanced fan control method with fast response: This invention uses a DC fan as the core execution unit, which can quickly respond to control signals and has the advantages of high adjustment accuracy and fast feedback response. The entire operation process requires no manual intervention, realizing automation and high efficiency in the operation process. The DC fan control method of this invention is more suitable for various sowing operation scenarios and has good scalability and versatility.

[0038] 4. Flexible system structure design and strong adaptability: In terms of system structure, the present invention adopts a symmetrical parallel layout and detachable combination structure design. The structural parameters of key components such as manifolds, branch pipes and main pipes can be quickly replaced and adjusted, which can flexibly match the seeding mechanism of different types of seeders. It is suitable for a variety of application scenarios from small experimental equipment to large seeders, and meets the needs of large-scale and standardized production.

[0039] 5. Optimization of Air Pressure Fluctuation Prediction Mechanism and Control Strategy: Addressing the common problems of "long transmission path, slow fan response, and control lag" in traditional multi-branch air supply systems of air-suction seeders, this invention introduces a differential pressure negative pressure sensor at the end of the main pipe to capture fluctuation signals at the airflow outlet in real time. The control module then quickly identifies and predicts these fluctuations. Based on the prediction results, the control module issues control commands in advance, precisely adjusting the fan's operating state using a PID algorithm. This significantly reduces fan speed fluctuations and repetitive control phenomena, further improving the system's stability and energy efficiency.

[0040] 6. Functional Integration and Algorithm Optimization of the Control Module: The control module in this invention not only possesses basic signal acquisition and execution functions, but also integrates a branch pipe flow prediction model, a PID control algorithm, and a dynamic feedback optimization strategy—a triple control logic. Through real-time data fusion processing from the branch pipe flow sensor and the main pipe pressure sensor, the control module can dynamically predict and adjust the fan's operating status, reducing unnecessary fan starts and stops and speed fluctuations. It can also proactively respond and compensate for deviations before they occur, significantly improving the overall intelligence level and energy-saving performance of the machine.

[0041] 7. Intelligent visualization and adjustable functions of the human-machine interaction module: The human-machine interaction module is designed based on a serial port screen system and supports the dynamic display function of branch flow uniformity indicators (such as coefficient of variation CV). Users can not only monitor the airflow status of the pipeline in real time, but also customize parameters such as wind pressure adjustment threshold and control frequency to meet the sowing needs of different crops and different machine models, thereby improving the system adaptability and user experience.

[0042] 8. Power Supply Stability and System Safety Assurance Mechanism of the Power Module: The DC power supply system of this invention is equipped with safety measures such as voltage fluctuation protection and reverse connection protection, effectively avoiding system malfunctions caused by power interference. It also supports a replaceable power interface design, adapting to seeding equipment with different voltage levels of 12V / 24V / 48V, ensuring stable operation of the air supply system under different working conditions, and improving the overall versatility and reliability of the machine.

[0043] 9. Integrated Adaptation Design of Modules and Piping Structure: The control module, power module and pneumatic piping system adopt a modular integrated design scheme, which can be directly assembled into the standard mounting position of the seeder frame. The internal wiring is short and the signal transmission delay is low, avoiding problems such as inconsistent response and signal interference caused by distributed installation in traditional equipment.

[0044] 10. This invention constructs a higher-performance, more adaptable, and more scalable gas supply control system through integrated optimization of control logic, intelligent upgrade of interaction methods, power system protection design, and deep coupling with the airflow pipeline system. It has achieved an overall transformation from "traditional function superposition" to "innovative function integration".

[0045] 11. This invention adopts modular programming and hardware assembly methods, combined with RS485 communication protocol, which makes the system highly compatible, reliable, and expandable, and has strong anti-interference ability. It can effectively adapt to the farmland operation environment and help users make targeted adjustments and expansion development according to actual operation needs. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the hardware components of the present invention.

[0047] Figure 2 This is a schematic diagram of the pneumatic pipeline system structure of the present invention.

[0048] Figure 3 This is a schematic diagram of the human-computer interaction interface of the present invention.

[0049] Figure 4 This is a flowchart of the control system operation of the present invention.

[0050] The labels in the diagram are as follows: 1-Signal bus, 2-Power bus, 3-Control module, 4-Human-machine interface module, 5-Branch flow sensor, 6-Pneumatic pipeline system, 7-Fan control module, 8-Power module, 9-Main pipe pressure sensor, 601-Branch tee, 602-Branch, 603-Manifold connection pipe, 604-Main pipe tee, 605-Main pipe, 606-Branch inlet flow measurement pipe, 607-Manifold end cap, 608-Manifold end connection pipe, 609-Negative pressure measurement pipe, 610-Corrugated pipe, 611-Clamp, 612-Fan, 613-Pitto tube, 614-Rubber hose. Detailed Implementation

[0051] The present invention will now be described in further detail with reference to specific embodiments.

[0052] Depend on Figure 1 , 2 It is known that a method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder includes a signal bus 1, a power bus 2, a control module 3, a human-machine interface module 4, a branch pipe flow sensor 5, a pneumatic pipeline system 6, a fan control module 7, a power module 8, and a main pipe air pressure sensor 9. The pneumatic pipeline system 6 is the core actuator for airflow transmission and distribution in the entire system, including a branch pipe tee 601, a branch pipe 602, a manifold connecting pipe 603, a main pipe tee 604, a main pipe 605, a branch pipe inlet flow measuring pipe 606, a manifold end cap 607, a manifold end connecting pipe 608, a negative pressure measuring pipe 609, a corrugated pipe 610, a clamp 611, a fan 612, a Pitot tube 613, and a rubber hose 614. Figure 2 As shown, there are 10 branch pipes in total, numbered 1-10. The branch pipe with the inlet flow measurement tube is branch pipe 1, and they are numbered sequentially.

[0053] In this embodiment, the control module 3 receives and processes signal inputs from various modules and sensors within the system, including the structural parameters of the multi-branch pneumatic pipeline system input by the human-machine interface module 4, the real-time inlet flow data of the branch pipes collected by the branch pipe flow sensor 5, and the negative pressure information at the outlet of the main pipe 605 collected by the main pipe pressure sensor 9. The control module 3 comprehensively analyzes the above information, calls the corresponding accurate prediction model based on the specific pipeline structural parameter values, and uses matching operating logic to calculate the inlet flow of each branch pipe and the coefficient of variation of the branch pipe inlet flow. With the dual control objectives of minimizing the coefficient of variation of the branch pipe inlet flow and minimizing the pressure fluctuation of the main pipe, it generates a fan speed adjustment command through an incremental PID control algorithm to determine the appropriate fan speed and generates corresponding control signals to drive the fan control module 7 to dynamically control the operating status of the fan 612. Simultaneously, the control module 3 also controls the human-machine interface to display the inlet flow of each branch pipe, the coefficient of variation of the branch pipe inlet flow, and the negative pressure information of the main pipe in real time. The control module 3 and the human-machine interface module 4 communicate via a TTL serial port. Preferably, the control module 3 uses an STM32F103ZET6 microcontroller, which has strong data processing and control command output capabilities, and can meet the real-time and stability requirements of the system.

[0054] In this embodiment, the human-computer interaction module 4 is used to realize information interaction between the user and the control module 3, and to intuitively display the key operating parameters of the system to the user through the human-computer interaction interface; by Figure 3 As can be seen, the interface developed by the human-machine interaction module 4 includes a parameter setting section, a working status section, and four function buttons: start, stop, reset, and save. The parameter setting section is used to input the structural parameters of the gas supply pipeline system, specifically including the inner diameter of the main pipe, the length of the main pipe, the inner diameter of the manifold, the inner diameter of the branch pipes, the length of the branch pipes, and the spacing between the branch pipes. The working status section displays key operating data in real time, such as the inlet flow rate of each branch pipe (branch pipes 1-10), the coefficient of variation of the branch pipe flow rate, and the pressure of the main pipe. After clicking the "start" button, the user can enter the parameter setting section to input the geometric structural parameters of the multi-branch pipeline. After clicking the "stop" button, the parameter setting interface will be locked, and the user cannot input new parameters. After clicking the "reset" button, all previously entered parameters will be cleared. After clicking the "save" button, the entered structural parameters will be saved and used as the basis for the current stage of airflow calculation and control strategy generation. Through the above-mentioned structural parameter settings and real-time information display of the operating status, the human-machine interaction module 4 effectively realizes data transmission and functional collaboration with the control module 3. Preferably, the human-machine interaction module 4 uses a touch screen.

[0055] In this embodiment, the branch pipe flow sensor 5 is used to collect the inlet flow value of branch pipe 1 in real time and transmit the collected data to the control module 3 via RS485 bus. After receiving the real-time flow data, the control module 3, in conjunction with the pre-set pipeline structure parameters, calls the built-in mathematical prediction model to dynamically calculate and estimate the inlet flow of the remaining branch pipes. After completing the flow prediction, the control module 3 transmits the predicted inlet flow parameters of each branch pipe and related calculation results (including the branch pipe flow variation coefficient, etc.) to the human-machine interaction module 4 to realize the visualization display of the real-time flow information of each branch pipe. Preferably, the branch pipe flow sensor 5 adopts a dedicated pipeline measurement device, which is suitable for continuous operation in outdoor working environments, and the measurement error is less than 0.02%.

[0056] In this embodiment, the branch tees and main tees of the pneumatic pipeline system can be either equal-diameter or reducing tees. Both branch and main tees are available in various models, all using standard PVC pipes suitable for agricultural production. Pipes with different structural parameters can be flexibly replaced and assembled according to actual production needs. The initial inner diameter of the main pipe of the pneumatic pipeline system is 0.0426m, and the initial length is 0.25m; the initial inner diameter of the manifold connecting pipe is 0.057m, and the length of the manifold end connecting pipe is 0.1m; the initial inner diameter of the branch pipe is 0.034m, and the initial length is 0.15m. The spacing between adjacent branch pipes is 0.25m, and the initial flow target monitoring value for branch pipe 1 is 0.0027m. 3 ·s -1 .

[0057] In this embodiment, the fan control module 7 is used to receive control commands sent by the control module 3, and to perform real-time dynamic speed control of the fan 612 according to the received fan speed adjustment parameters, thereby ensuring stable and uniform airflow in the multi-branch pipeline of the pneumatic pipeline system, and meeting the requirements of continuous and responsive negative pressure air supply for sowing operations; preferably, the fan control module adopts a single-chip microcomputer of model STM32F103C8T6.

[0058] In this embodiment, the power module 8 includes an external power supply, a step-down / stabilizing device, and an overvoltage / overcurrent protector. It is mainly used to provide a stable and reliable DC power output for the entire system. The module provides the required power to key control units such as the control module 3, the human-machine interface module 4, the branch pipe flow sensor 5, the fan control module 7, and the main pipe air pressure sensor 9 through multiple power supply methods, ensuring the normal operation of each module and the overall stability of the system. Preferably, the external power supply adopts an independent battery pack to provide the system with a stable DC voltage of 48V, 24V, or 12V, which can be flexibly configured according to different operational needs and is suitable for continuous power supply needs in outdoor operating environments.

[0059] In this embodiment, the main pipe pressure sensor 9 is used to monitor the negative pressure value at the main pipe inlet in real time. The sensor is based on the L-shaped Pitot tube and differential pressure measurement principle to obtain dynamic negative pressure data of the airflow inside the main pipe, and transmits the measurement results to the control module 3 via RS485 bus, which serves as an important basis for determining whether further adjustment of the fan operation status is needed. Preferably, the main pipe pressure sensor adopts an industrial-grade sensing device specifically designed for pipeline pressure measurement, which has good environmental adaptability and a measurement accuracy of ±1.5%, which can meet the requirements of multi-branch gas supply systems for pressure detection stability and responsiveness.

[0060] In this embodiment, the control module 3 calculates the inlet flow rate of the remaining branches and the coefficient of variation of the inlet flow rate of the branches based on the geometric parameters of the multi-branch pipelines of the pneumatic pipeline system 6 and the real-time inlet flow rate value of the branch 1. The inlet flow rate Q of the remaining branches i The calculation formula is:

[0061]

[0062] In the formula, x is a variable factor, f(x) is the function model under that variable factor, and x can be any factor among Q1, d, l, δ, γ, Δ, and D. When x is determined to be a certain factor, the product of the function expressions of the remaining factors forms the constant term λ. At this time, ξ is the error compensation coefficient to improve the calculation accuracy of the formula; Q1: is the real-time inlet flow rate of the leftmost branch pipe, in m³ / s. 3 ·s -1 ; d is the inner diameter of the branch pipe, in mm; l is the length of the branch pipe, in mm; δ is the spacing between the branch pipes, in mm; γ is the inner diameter of the manifold, in mm; Δ is the length of the main pipe, in mm; D is the inner diameter of the main pipe, in mm.

[0063] In this embodiment, when x is determined to be one of the factors Q1, d, l, δ, γ, Δ, and D, such as x = Q1, then f(x) is f(Q1). The function expressions of the other non-variable factors d, l, δ, γ, Δ, and D together form the constant product term λ, that is, λ = f(d)f(l)f(δ)f(γ)f(Δ)f(D). At this time, ξ is the error compensation coefficient to improve the calculation accuracy of the formula. The specific value of ξ is shown in the table. When Q1 is a variable, f(x) = f(Q1) = -1.9729 × 10 -9 (ρQ1 / μL) 4 +1.37541×10 -5 (ρQ1 / μL) 3 -0.033749(ρQ1 / μL )2 +46.3539(ρQ1 / μL)-10567.1, f(Q1) when Q1 is 0.0009-0.0045m 3 ·s -1The range varies, at which point λ=f(d)f(l)f(δ)f(γ)f(Δ)f(D) is a constant, where the specific functional expressions of f(d), f(l), f(δ), f(γ), f(Δ), and f(D) are:

[0064] f(d) = -9.93827 × 10 6 (d / L) 4 +9.27221×10 6 (d / L) 3 -1.69553×10 6 (d / L) 2 -598379(d / L)+189809;

[0065] f(l) = -54214.8 (l / L) 4 +325289 (l / L) 3 -716313(l / L) 2 +684632(l / L)-216648;

[0066] f(δ) = 2710.83 (δ / L) 4 -21686.8 (δ / L) 3 +62858.8 (δ / L) 2 -77939.8(δ / L)+56929.3;

[0067] f(γ) = -7.83082 × 10 6 (γ / L) 4 +1.94696×10 7 (γ / L) 3 -1.78177×10 7 (γ / L) 2 +7.0961×10 6 (γ / L)-1.01285×10 6 ;

[0068] f(Δ) = 32528.6(Δ / L) 4 -249386(Δ / L) 3 +707498(Δ / L) 2 -880476(Δ / L)+427574;

[0069] f(D) = -3.51961 × 10 6 (D / L) 4 +7.60284×10 6 (D / L) 3 -5.97048×10 6 (D / L)2 +2.0204×10 6 (D / L)-227064.

[0070] The relevant values ​​of d, l, δ, γ, Δ, and D in the formula can all be determined based on the pipeline foundation size data. At this time, when predicting the specific flow distribution of branch pipe 2-10, the error compensation coefficient ξ for branch pipe 2-10 can be taken as 1.00002, 0.99971, 0.99206, 1.00053, 0.99985, 0.99973, 1.00008, 1.01268, and 0.99927, respectively.

[0071] When x = d, f(x) is f(d), where Q1 is a constant, and λ is the product of the remaining non-variable elements including Q1, i.e., λ = f(Q1)f(l)f(δ)f(γ)f(Δ)f(D). When d is a variable, f(x) = f(d) = -9.93827 × 10 6 (d / L) 4 +9.27221×10 6 (d / L) 3 -1.69553×10 6 (d / L) 2 -598379(d / L)+189809, f(d) varies within the range of d from 0.0194 to 0.036m. At this time, λ=f(Q1)f(l)f(δ)f(γ)f(Δ)f(D) is a constant. The specific functional expressions of f(Q1), f(l), f(δ), f(γ), f(Δ), and f(D) are the same as those in the previous text. The relevant values ​​of Q1, l, δ, γ, Δ, and D in the formula are determined based on the pipeline basic data. At this time, when used to predict the specific flow distribution of branch pipe 2-10, the error compensation coefficient ξ for branch pipe 2-10 can be respectively 21969. The values ​​can be selected arbitrarily within the range of 5840~21987.4521, 27437.1898~27444.4839, 36089.1258~36484.0778, 50677.4851~50753.0387, 50812.4510~50826.2709, 36699.3045~36739.6089, 27168.2259~27198.4597, 21841.4516~22147.2313, 18924.4567~18936.2805, etc., based on the actual situation.

[0072] Table 1. Values ​​of ξ

[0073]

[0074] The formula for calculating the coefficient of variation of the inlet flow of each branch pipe is as follows:

[0075]

[0076] In the formula, n is the number of branches; j is the serial number of each branch, arranged from left to right, ranging from 1 to 10; Q j The inlet flow rate of each branch pipe is expressed in cubic meters per second (m³). 3 ·s -1 Q m This is the average flow rate at the inlet of each branch pipe, in m³. 3 ·s -1 .

[0077] Preferably, the applicable parameter range for multi-branch pneumatic pipeline systems includes: the applicable range for the real-time flow rate Q at the inlet of branch pipe 1 is 0.0009-0.0045 m³ / h. 3 ·s -1 The applicable ranges for the following structural parameters are: branch pipe inner diameter d: 0.0194-0.036m; branch pipe length l: 0.1-0.2m; branch pipe spacing δ: 0.2-0.3m; manifold inner diameter γ: 0.0426-0.0814m; main pipe length Δ: 0.2-0.3m; and main pipe inner diameter D: 0.0426-0.057m. Within these ranges, the mathematical prediction model called by control module 3 can dynamically calculate the inlet flow of the remaining branch pipes. The error between the predicted value and the actual measured value is controlled within 10%, demonstrating good adaptability and accuracy.

[0078] Depend on Figure 4 As can be seen, in this embodiment, the method for dynamic distribution and pressure regulation of airflow in the parallel pipeline of the air-suction seeder includes the following steps:

[0079] Step 1: The user starts the system through the human-computer interaction module and inputs the structural parameters of the multi-branch pneumatic pipeline system according to the structural parameters of the positive and negative pressure channels of the seed metering device for the current crop. The parameters are then transmitted to the control module via serial communication. The seed metering device is an existing structure.

[0080] Step 2: The control module receives structural parameter information from the human-machine interaction module and, in conjunction with the real-time inlet flow value of the branch pipe equipped with the branch pipe flow sensor, calls the preset branch pipe flow prediction model to prepare for subsequent calculations.

[0081] Step 3: The main pipe pressure sensor monitors the pressure changes at the main pipe inlet in real time and sends the detection data to the control module via the signal bus;

[0082] Step 4: The branch flow sensor measures the real-time inlet flow (Q) of the branch pipe equipped with the branch flow sensor. jHere, j is set to 1) for monitoring, and the data is synchronously transmitted to the control module via the signal bus;

[0083] Step 5: The control module integrates the branch pipe flow data from Step 4 and the main pipe pressure data from Step 3, and calculates the inlet flow rate (Q) of the remaining branches based on the built-in branch pipe flow prediction model. j Here, j is taken as 2-10) and the inlet flow variation coefficient (CV) is calculated, and the results are transmitted to the human-computer interaction module to realize the visualization of the real-time flow, inlet flow variation coefficient and pressure information of each branch pipe of the pneumatic pipeline system, so as to facilitate users to monitor the system operation status in real time.

[0084] Step Six: Based on the real-time flow data of the branch pipe in Step Four and the model calculation results in Step Five, and with the dual control objectives of minimizing the inlet flow variation coefficient and minimizing the pressure fluctuation of the main pipe, the control module generates a fan speed adjustment command through an incremental PID control algorithm and sends it to the fan control module; the control signal generation process includes:

[0085] (1) Error Calculation: The deviation of the branch pipe flow variation coefficient and the deviation of the main pipe pressure are weighted and fused to generate a composite error signal e. c (k):

[0086] e c (k)=α·e flow (k)+β·e pressure (k),

[0087] In the formula, e flow (k) represents the deviation of the branch flow variation coefficient at the current moment, which is the set value minus the measured value; e pressure (k) represents the current pressure deviation of the main pipe, which is the target value minus the measured value; the weighting coefficients α and β are dynamically adjusted according to the system priority (e.g., α = 0.7 emphasizes flow balance, β = 0.3 suppresses pressure fluctuation).

[0088] (2) PID Output: The incremental PID control algorithm is used to calculate the fan speed adjustment Δu:

[0089]

[0090] In the formula, e c (k) represents the current composite error signal deviation, which is the difference between the set value and the measured value; e c (k-1) represents the composite error signal deviation at the previous moment, which is the difference between the set value and the measured value; e c (k-2) represents the value in e c The deviation of the composite error signal from the previous time step based on time (k-1) is the set value minus the measured value; T d T iT and I are the differential time constant, integral time constant, and sampling period, respectively. The values ​​of P, I, and D are dynamically adjusted according to the system priority.

[0091] Step 7: The fan control module receives the control signal from the control module and dynamically adjusts the fan's operating status, thereby achieving reasonable distribution of airflow in each branch pipe of the pneumatic pipeline system and stable control of the main pipe's air pressure.

[0092] This invention constructs a simple and efficient monitoring network by installing branch pipe flow sensors and main pipe negative pressure sensors at key locations. Combined with the precise mathematical model embedded in the control module, it achieves rapid prediction and fine-grained control of flow in multiple branch pipes, thus replacing the traditional method of deploying multiple sensors throughout the pipeline. This significantly reduces the overall production cost and maintenance complexity. At the same time, it achieves optimized control of the entire pneumatic seeding system without affecting monitoring accuracy and control response speed. The system is suitable for precision cultivation of various common crops such as rice and vegetables, and has good versatility and market prospects.

[0093] The air supply system of this invention features a symmetrical layout and a detachable assembly structure for its parallel pipelines. The structural parameters of core components such as branch pipes, main pipes, and the main pipe support rapid replacement and adaptation, allowing for flexible matching with the geometric parameters of different types of seeders. This meets the needs of large-scale, standardized production applications, ranging from small-scale experimental equipment to large and medium-sized agricultural machinery. Addressing the problems of long transmission paths and slow pressure response in traditional air supply systems, which lead to delays in fan control and frequent adjustments, this invention introduces a differential pressure negative pressure sensor installed at the end of the main pipe. This sensor monitors the pressure fluctuation characteristics at the air outlet in real time. The auxiliary control module makes advance judgments based on real-time pressure data and issues dynamic adjustment commands to the control module through predictive control and PID control strategies. This effectively reduces repeated fan adjustments or speed fluctuations, improving the overall operational stability of the system.

[0094] This invention boasts a high level of intelligence. Users can input the structural parameters of the multi-branch pneumatic pipelines through the human-machine interface module. After reading the parameters, the control module calls upon the built-in mathematical model and, combined with real-time inlet airflow data collected by the branch flow sensors, quickly completes the mathematical prediction of the flow rates of the remaining branches and the calculation of the branch flow rate variation coefficient. Combined with a PID control algorithm, the system can determine the uniformity of the current airflow distribution based on the branch flow rate variation coefficient value and adaptively adjust the fan speed through the fan control module, achieving precise dynamic control of the sowing air pressure. Furthermore, the entire process requires no frequent manual intervention, greatly simplifying the operation process and effectively ensuring the continuous stability of the air pressure environment during operation. This fundamentally improves the overall sowing efficiency, sowing uniformity, and energy utilization efficiency of the air supply system, reducing economic and time costs during operation. It provides solid technical support for the widespread application of pneumatic seeders in various scenarios such as field operations and small-scale seeding.

[0095] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for dynamic airflow distribution and pressure stabilization control in parallel pipelines of a pneumatic seeder, characterized in that: The system includes a signal bus, power bus, control module, human-machine interface module, branch flow sensor, pneumatic pipeline system, fan control module, power module, and main pipe pressure sensor. The pneumatic pipeline system includes a main pipe, multiple parallel branch pipes, and a manifold. The manifold includes branch tees, manifold connectors, manifold end caps, and manifold end connectors. The manifold connectors connect adjacent branch tees. The manifold end connectors are located outside the branch tees at both ends and are sealed by the manifold end caps. The branch pipes are connected to the branch tees perpendicular to the manifold direction through an equidistant symmetrical parallel layout. The main pipe is located in the middle of the manifold and connects to the manifold and the fan inlet channel through the main pipe tee. The system employs one branch flow sensor to collect real-time inlet flow data from the leftmost branch; one main pipe pressure sensor to monitor the main pipe inlet negative pressure value in real-time; the control module integrates the branch flow data from the branch flow sensor and the main pipe pressure data from the main pipe pressure sensor, calculates the inlet flow values ​​and inlet flow variation coefficients of the remaining branches based on the embedded branch flow prediction model, and uses the minimization of inlet flow variation coefficients and the minimization of main pipe pressure fluctuations as dual control objectives. Through a standard incremental PID algorithm and dynamic feedback optimization strategy, the module outputs control signals to the fan control module to dynamically adjust the fan's output power and speed, thereby achieving reasonable distribution of branch airflow and stable control of main pipe negative pressure. Among them, the real-time inlet flow rate of the branch pipe measured by the branch pipe flow sensor is Q1, and the inlet flow rate of the other branch pipes is Q. i The calculation formula is: In the formula, x is a variable factor, f(x) is the function model under that variable factor, and x can be any factor among Q1, d, l, δ, γ, Δ, and D. When x is determined to be a certain factor, the product of the function expressions of the remaining factors forms the constant term λ. At this time, ξ is the error compensation coefficient to improve the calculation accuracy of the formula; Q1: is the real-time inlet flow rate of the leftmost branch pipe, in m³ / s. 3 ·s -1 ; d is the inner diameter of the branch pipe, in mm; l is the length of the branch pipe, in mm; δ is the spacing between the branch pipes, in mm; γ is the inner diameter of the manifold, in mm; Δ is the length of the main pipe, in mm; D is the inner diameter of the main pipe, in mm; μ is the aerodynamic viscosity; ρ is the air density; L is the length of the end of the manifold. The formula for calculating the coefficient of variation of inlet flow is: In the formula, n is the number of branches; j is the serial number of each branch, arranged from left to right, ranging from 1 to 10; Q j The inlet flow rate of each branch pipe is expressed in cubic meters per second (m³). 3 ·s -1 Q m This is the average flow rate at the inlet of each branch pipe, in m³. 3 ·s -1； The fan speed regulation algorithm adopts the standard incremental PID algorithm, and the control signal generation process includes: (1) Error calculation: The deviation of the branch pipe flow variation coefficient and the deviation of the main pipe pressure are weighted and fused to generate a composite error signal e. c (k): ， In the formula, e flow (k) represents the deviation of the branch flow variation coefficient at the current moment, which is the set value minus the measured value; e pressure (k) represents the current pressure deviation of the main pipe, which is the target value minus the measured value; the weighting coefficients α and β are dynamically adjusted according to the system priority. (2) PID output: The standard incremental PID algorithm is used to calculate the fan speed adjustment Δu: ， In the formula, e c (k) represents the current composite error signal deviation, which is the difference between the set value and the measured value; e c (k-1) represents the composite error signal deviation at the previous moment, which is the difference between the set value and the measured value; e c (k-2) represents the value in e c The deviation of the composite error signal from the previous time step based on time (k-1) is the set value minus the measured value; T d T i T and P represent the differential time constant, integral time constant, and sampling period, respectively; the values ​​of P, I, and D are dynamically adjusted according to system priority. k The current fan speed control value, u k-1 K represents the fan speed control value at the previous moment. p This is the proportionality coefficient.

2. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that, Includes the following steps: Step 1: The user starts the system through the human-computer interaction module and inputs the structural parameters of the multi-branch pneumatic pipeline system according to the structural parameters of the positive and negative pressure channels of the seed metering device for the current crop. The input is then transmitted to the control module via serial communication. Step 2: The control module receives structural parameter information from the human-machine interaction module and, in conjunction with the real-time inlet flow value of the branch pipe equipped with the branch pipe flow sensor, calls the preset branch pipe flow prediction model to prepare for subsequent calculations. Step 3: The main pipe pressure sensor monitors the pressure changes at the main pipe inlet in real time and sends the detection data to the control module via the signal bus; Step 4: The branch pipe flow sensor monitors the real-time inlet flow of the branch pipe equipped with the branch pipe flow sensor and transmits the data synchronously to the control module via the signal bus. Step 5: The control module integrates the branch pipe flow data from Step 4 and the main pipe pressure data from Step 3, calculates the inlet flow value and inlet flow variation coefficient of each of the remaining branches based on the built-in branch pipe flow prediction model, and transmits the calculation results to the human-machine interaction module to realize the visualization display of the real-time flow, inlet flow variation coefficient and main pipe pressure information of each branch of the pneumatic pipeline system, so as to facilitate users to monitor the system operation status in real time. Step six: Based on the real-time flow data of the branch pipe in step four and the model calculation results in step five, and with the dual control objectives of minimizing the coefficient of variation of inlet flow and minimizing the pressure fluctuation of the main pipe, the control module generates a fan speed adjustment command through a standard incremental PID algorithm and sends it to the fan control module. Step 7: The fan control module receives the control signal from the control module and dynamically adjusts the fan's operating status, thereby achieving reasonable distribution of airflow in each branch pipe of the pneumatic pipeline system and stable control of the main pipe's air pressure.

3. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that: The signal bus is used for data communication and control command transmission; the power bus is used for stable DC power transmission; the control module is used for system information reception, data processing, and control command transmission; the human-machine interface module is used to realize information interaction between the user and the control module; the pneumatic pipeline system is used for airflow transmission and distribution, and is the core actuator for air pressure regulation; the fan control module is responsible for receiving control commands issued by the control module and regulating the fan's operating status in real time; the power module is used to provide stable power output and also has overvoltage and overcurrent protection functions; the main pipe air pressure sensor transmits data to the control module via wired connection, serving as reference data for the control module to determine whether the fan needs further regulation.

4. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that: The pneumatic piping system also includes a negative pressure measuring tube, corrugated pipe, clamps, Pitot tubes, and rubber hoses; The main pipe is connected to the fan inlet channel via a series connection of a negative pressure measuring pipe and a corrugated pipe; the corrugated pipe is fixed to the outlet of the negative pressure measuring pipe and the fan inlet with clamps. The branch inlet flow measurement tube is set on the leftmost branch pipe, and the branch flow sensor is installed on the branch inlet flow measurement tube; The negative pressure measuring tube, pitot tube, rubber hose, and main air pressure sensor are connected in sequence.

5. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 4, characterized in that: The branch pipe flow sensor uses the thermal model induction principle to obtain the real-time inlet flow of the branch pipe. The branch pipe inlet flow measurement tube is vertically arranged at the airflow inlet of the branch pipe and transmits the data to the control module via a wired method. The main pipe pressure sensor uses the differential pressure principle to measure the real-time negative pressure at the main pipe outlet and transmits the data to the control module via a wired connection.

6. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that: The signal bus uses the RS485 communication protocol to complete data communication; the power bus is configured according to the system power; the power module uses a battery pack, equipped with overvoltage, overcurrent, and short-circuit protection circuits and real-time power status display function, to provide continuous and reliable DC power to the control module, human-machine interaction module, branch flow sensor, fan control module and main pipe pressure sensor.

7. The method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that: The control module uses an STM32 microcontroller as its core processor, which is responsible for receiving and processing various signal inputs from the system, as well as the pipeline airflow information collected by the branch flow sensor and the main pipe pressure sensor. The control module also processes the structural parameters of the multi-branch pneumatic pipeline system input by the human-machine interface module.

8. A method for dynamic distribution and pressure regulation of airflow in parallel pipelines of a pneumatic seeder according to claim 1, characterized in that: The human-computer interaction module uses a touch screen or tablet to transmit string data to the control module via a wired connection, and displays the system operating parameters to the user intuitively through a serial port screen.

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