Intelligent control system of small-particle-size precise film mulching and sowing all-in-one machine

Abstract

The invention belongs to the technical field of agricultural machinery and the technical field of intelligent control, and particularly relates to an intelligent control system of a small-particle-size precise film mulching and sowing all-in-one machine, which comprises a vehicle circuit module, a motor control module, a wireless transmission module, a GPS (Global Positioning System) module, a stock bin monitoring module and a single chip microcomputer, the motor control module realizes linkage control of a plurality of motors through a single-chip microcomputer, the wireless transmission module realizes wireless automatic control, the GPS module realizes accurate detection of a track range, and the stock bin monitoring module is characterized in that when seeds in a stock bin are lower than a warning line, an alarm flashes and gives out an alarm sound, and the single-chip microcomputer is integrated with a camera, so that the seeds in the stock bin can be accurately detected. A visual recognition system is formed, shot images can be processed, the deviation angle is calculated, and the vehicle track is automatically corrected. A plurality of modules are integrated, so that the functions of film mulching, soil covering, film pressing, hole making and seeding of the all-in-one machine are simultaneously performed, and the function integration is realized.

Description

Technical Field

[0001] The present invention belongs to the technical fields of agricultural machinery and intelligent control, and in particular to an intelligent control system for a small-diameter precision film-covering and seeding integrated machine. Background Art

[0002] Traditional agricultural production methods can no longer meet the growing demand for food and the requirements for sustainable agricultural development. Smart agriculture has emerged and become an important direction for the development of modern agriculture.

[0003] Intelligent agricultural machinery technology is an important part of smart agriculture. Through the intelligent transformation of agricultural machinery, agricultural machinery has functions such as autonomous decision-making, automatic control, and precise operation. It can adapt to different agricultural production environments and crop requirements, and improve the quality and efficiency of agricultural production.

[0004] Smart agriculture is a modern agricultural development model that leverages advanced technologies such as artificial intelligence, the Internet of Things, big data, and cloud computing to intelligently manage and optimize the entire agricultural production process, improving agricultural production efficiency and economic benefits. With the advancement of smart agriculture, intelligent agricultural machinery technology has experienced rapid development and innovation, playing a significant role in practical applications. It enables agricultural production to move toward precision, automation, and intelligence, thereby improving agricultural production efficiency, reducing agricultural production costs, and promoting sustainable agricultural development.

[0005] This article focuses on small-size film-mulching precision seeding machines. Most precision seeding machines available domestically and internationally are large and unsuitable for small-scale cultivation in facility agriculture. Existing machines can only perform one task in the planting process, with a single function. Most existing machines are mounted and manual, bulky and heavy, lack supporting monitoring equipment, and have a low level of intelligence, making them unsuitable for facility agriculture operations. Therefore, there is an urgent need to develop a new, multifunctional seeding machine equipped with an intelligent control system to improve planting conditions, increase intelligence, and thereby enhance production efficiency and create greater economic value. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems existing in the prior art and to propose an intelligent control system for a small-size precision film-covering and seeding integrated machine.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An intelligent control system for a small-size precision film-covering and seeding machine, comprising a vehicle circuit module, a motor control module, a wireless transmission module, a GPS module, a silo monitoring module, and a single-chip microcomputer;

[0009] The vehicle circuit module includes a 48v20ah battery pack, a junction box, a DC-DC step-down module, a relay, and a terminal block. The relay controls the operation of the hub motor of the integrated machine. The 48v20ah battery pack powers the hub motor of the integrated machine through the junction box. The terminal block and the DC-DC step-down module respectively power the stepper motor, brushless DC motor, electric sensor, PWM voltage conversion module, and single-chip microcomputer of the integrated machine.

[0010] The motor control module includes a stepper motor, a brushless DC motor, and a hub motor. The stepper motor uses PWM to achieve speed regulation and drives the acupuncture mechanism. The brushless DC motor works by giving a PWM signal and drives the seeding mechanism. The hub motor uses the PWM to voltage signal module to achieve speed regulation and drive the wheel.

[0011] The wireless transmission module includes a network debugging system and a software control system. The software control system controls the network debugging system to send instructions to the microcontroller to execute corresponding functions.

[0012] The GPS module outputs data to the microcontroller through the TXD serial port, and the microcontroller converts the data into longitude and latitude information;

[0013] The silo monitoring module includes an alarm and a photoelectric sensor. The photoelectric sensor uses the photoelectric effect to identify that the seed accumulation in the silo is lower than the preset height and outputs a signal to the alarm.

[0014] The single-chip microcomputer is integrated with a camera to form a visual recognition system, which can process the captured images, continuously calculate the deviation angle during the vehicle's driving process, transmit it to the single-chip microcomputer, and enable it to autonomously correct the vehicle's trajectory.

[0015] As a further preferred solution, in the vehicle circuit module, the positive and negative poles of the 48v20ah battery pack are respectively connected to the two ends of the two junction boxes, and the two junction boxes are respectively connected to the hub motor drivers of the integrated machine to form the power supply control circuit of the hub motor;

[0016] The relay is connected to the hub motor driver of the integrated machine to control the forward and reverse control line of the hub motor and the on and off of the brake line;

[0017] The DC-DC step-down module includes a 48V to 24V step-down module, a 48V to 12V step-down module, and a 48V to 5V step-down module; the junction box separates the positive and negative lines, and the lines separated through the wiring terminals are respectively connected to the 48V to 24V step-down module, the 48V to 12V step-down module, and the 48V to 5V step-down module. The 48V to 24V step-down module supplies power to the stepper motor and the DC brushless motor, the 48V to 12V step-down module supplies power to the photoelectric sensor and the PWM to voltage module, and the 48V to 5V step-down module supplies power to the relay and the microcontroller.

[0018] As a further preferred solution, the stepper motor includes a VCC interface, a GND interface, a PUL+ interface, a PUL- interface, a DIR+ interface, a DIR- interface, and an ENA- interface;

[0019] The VCC interface and GND interface are connected to the positive and negative poles of the 48V to 24V step-down module for power supply. The PUL- interface, DIR- interface and ENA- interface are short-circuited to form an internal circuit. The PUL+ interface, DIR+ interface and ENA- interface are connected to the microcontroller respectively. The PUL+ interface is connected to the PWM3 (IO47) port of the microcontroller, the DIR+ interface is connected to the +5v pin of the microcontroller, and the ENA- interface is connected to the GND pin.

[0020] As a further preferred solution, the brushless DC motor includes AI2 interface, COM interface, DC+ interface, DC- interface, REV / DI2 interface, and COM interface;

[0021] The DC+ and DC- interfaces are connected to the positive and negative poles of the power supply for power supply, and the REV / DI2 interface and COM interface are connected to form a short circuit; the AI2 interface is connected to the PWM2 (IO46) port of the microcontroller, and the COM interface is connected to the GND pin of the microcontroller.

[0022] As a further preferred solution, the hub motor is connected to the hub motor driver, the relay is connected to the brake system of the hub motor, and the PWM to voltage module controls the speed of the hub motor.

[0023] As a further preferred solution, the wireless transmission module is based on the TCP communication transmission protocol, uses network debugging software, utilizes the Wi-Fi module integrated in the microcontroller, writes communication code, and uses Socket communication.

[0024] As a further preferred solution, the visual recognition system includes an image acquisition module, an image preprocessing module, a ridge boundary detection module, and a navigation parameter calculation module;

[0025] The image acquisition module uses the PLC-integrated camera on the planting machinery to capture the farmland ridge scene in real time to obtain the ridge image;

[0026] The image preprocessing module imports the ridge image into the HSV-V grayscale enhancement model for grayscale conversion to form an original grayscale image. Then, the original grayscale image is converted into a black and white binary image through dynamic threshold binarization. Finally, a composite morphological filter chain operation is performed to obtain a preliminary processed image of the ridge.

[0027] The ridge boundary detection module, based on the preliminary ridge processing image, constructs a linear regression boundary fitting model through bidirectional boundary feature point extraction and left and right boundary scanning, and finally obtains the boundary contour line of the ridge;

[0028] The navigation parameter calculation module uses the left and right boundary equations to obtain the image theoretical center auxiliary line and image midline according to the boundary contour line of the field ridge. At the same time, it calculates the heading deviation of the planting machinery, obtains the agricultural machinery path deviation angle, and transmits it to the PLC. The PLC adjusts the heading of the planting machinery and issues obstacle warning information.

[0029] As a further preferred solution, the intelligent control system also includes a self-diagnosis module, which is used in conjunction with a visual recognition system. The visual recognition system includes a walking camera at the front of the all-in-one machine and a planting effect detection camera at the rear. The self-diagnosis module includes a walking detection part and a planting effect detection part.

[0030] Walking detection part:

[0031] In the first step, the walking camera analyzes the environment in front of the all-in-one machine. If an anomaly is found, the system self-checks the GPS data and determines that it is a change in the soil environment or a motor failure.

[0032] The second step is to identify changes in the soil environment, trigger a fault alarm, and adaptively plan and adjust the route. The wheel hub motors are linked to the wheel hub motors, adaptively changing the PWM signal to adjust the speed of the two wheels. After the speeds match, the steering is adjusted to return to the normal route.

[0033] The third step is to determine that it is a motor failure, self-diagnose the hub motor operation code, and make corrections;

[0034] Planting effect detection part:

[0035] In the fourth step, the planting effect detection camera analyzes the environment image in front of the all-in-one machine to determine whether there are any abnormalities in the hole making. If there are any problems, the stepper motor of the hole making module is tested and the motor control parameters are checked. If there are any faults, an alarm is issued and the parameters are adaptively adjusted.

[0036] The fifth step is to determine whether it is a sowing problem, check the parameters of the DC brushless motor of the seeding module, such as fault, alarm, and adaptive adjustment parameters.

[0037] The present invention has the following advantages:

[0038] 1. The motor control module design is based on the control principle of the CanMV K230 AI development board (single-chip microcomputer), which can realize the functions of film covering, soil covering, film pressing, hole making, and sowing at the same time, realizing functional integration and high integration.

[0039] 2. GPS vision module, based on single-chip WiFi module, GPS, and camera, can synchronize the vehicle's first-person perspective image on the APP in real time, and at the same time can realize remote positioning and motion trajectory drawing functions, realizing true remote control.

[0040] 3. Intelligent detection system, based on infrared sensor and single chip microcomputer control principle, can monitor and warn the material balance.

[0041] 4. Developed a visual recognition system that can autonomously identify and correct vehicle trajectories.

[0042] 5. A camera is used to run a visual recognition system to detect the sowing process. If any abnormality is found in sowing, the self-diagnosis system of each module of the seeder and the soil environment diagnosis system are started. If a functional failure occurs in the seeder, an alarm is issued and the work is stopped. If a major change is found in the soil environment, the travel and sowing parameters of the seeder are adaptively adjusted, thereby effectively improving the planting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is the overall system structure diagram of the present invention;

[0044] Figure 2 This is the logic diagram of the wireless transmission module;

[0045] Figure 3 Calculate the deviation angle for image processing in OpenCV in the visual recognition system;

[0046] Figure 4 This is a schematic diagram of the overall structure of the small-size precision film-covering and seeding integrated machine;

[0047] Figure 5 This is a schematic diagram of the acupuncture point making module structure;

[0048] Figure 6 This is a structural diagram of the seeding module;

[0049] Figure 7 Schematic diagram of the internal structure of the seed meter. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0051] The present invention simulates the integrated process of film mulching and sowing based on the planting method of small-particle seeds such as spinach in a single ridge and two rows, builds an EDEM simulation sowing model and a vehicle speed matching optimization model, determines the required motor speed and performance parameters through detailed calculations, and develops a multi-motor linkage control module with the help of the Canaan K230 development platform (single-chip microcomputer). With the help of Netassist network debugging software and 5G communication technology, the integrated functions of film mulching → soil covering → film pressing → punching → sowing are remotely realized; at the same time, a visual recognition module is introduced, and with the help of the GC2093 (60FPS) model camera that comes standard with the CSI2 interface, the contour of the work site is identified, the deviation angle is continuously calculated during the vehicle's driving process, and the single-chip microcomputer is enabled to autonomously identify and correct the vehicle trajectory; and with the help of a high-precision GPS positioning system, centimeter-level GPS positioning is achieved, and the longitude and latitude are fed back to the single-chip microcomputer in real time, thereby guiding the vehicle to follow the GPS-planned path.

[0052] The present invention belongs to agricultural machinery automation technology, precision agriculture technology, Internet of Things and data integration technology, and is a typical representative of the upgrade of modern agricultural equipment to intelligence and precision.

[0053] The present invention relates to a small-size precision mulching and seeding integrated machine, which includes a mulching module 1, a film pressing module 2, a soil covering module 3, a hole making module 4, and a seeding module 5 arranged in sequence from the front end to the rear end of the vehicle body to form an integrated operation.

[0054] The film covering module 1 is used to cover the film on the land, the film pressing module 2 is used to press the film into the soil as expected, the soil covering module 3 is used to gather the soil in the direction of the vehicle's forward movement, the hole making module 4 is used to drill holes in the soil, and the seeding module 5 is used to place seeds in the drilled holes.

[0055] The hole-making module 4 includes a stepper motor frame 41, a stepper motor 42, a crank 43, a connecting rod 44, a limiter 45, and a hole-making rod 46. The stepper motor frame 41 is fixed to the bottom of the vehicle body, the stepper motor 42 and the limiter 45 are installed on the stepper motor frame 41, and the hole-making rod 46 is located in the limiter 45 and moves up and down. The power output end of the stepper motor 42 is rotatably connected to one end of the crank 43, and one end of the connecting rod 44 is rotatably connected to the other end of the crank 43. The other end of the connecting rod 44 is hinged to the upper end of the hole-making rod 46, and the lower end of the hole-making rod 46 is a drill bit 47; the stepper motor 42, the crank 43, the connecting rod 44, and the hole-making rod 46 form a crank-connecting rod mechanism, and the drill bit 47 intermittently drives into the soil to make holes; a cylindrical baffle 48 is also provided at the bottom of the stepper motor frame 41, and the seed outlet of the seed-discharging module 5 corresponds to the cylindrical baffle 48.

[0056] The seeding module 5 includes a seeding positioning frame 51, a seeding device bracket 52, a seeding device 53, a brushless motor bracket 54, and a DC brushless motor 55. The seeding positioning frame 51 is fixed to the bottom of the vehicle body, the seeding device bracket 52 and the brushless motor bracket 54 are installed below the seeding positioning frame 51, the seeding device 53 is arranged in the seeding device bracket 52, and the DC brushless motor 55 is arranged in the brushless motor bracket 54. The DC brushless motor 55 controls the seeding device 53 to sow intermittently through the transmission shaft 56.

[0057] The seeding device 53 comprises, from top to bottom, a hopper 531, a seeding wheel 532, an arc-shaped block 539, and a seeding area 533. The seeding wheel 532 is partially embedded in the arc-shaped block 539, and the portion of the seeding wheel 532 outside the arc-shaped block 539 divides the hopper 531 and the seeding area 533. A discharge plate 534 is provided on the hopper 531, and a seeding guide rail 535 is provided at the bottom of the seeding area 533. The seeding guide rail 535 discharges the seeds to the position above the seed holes punched by the hole-making module 4. The seeding area 533 is also provided with an air jet pump 536, which blows air to the seeding guide rail 535 through an air pipe.

[0058] The seeding wheel 532 includes a blocking wheel 537 and a rotating drum 538. The blocking wheel 537 is fixed to one side of the seeding device 53. The rotating drum 538 is connected to the power output end of the DC brushless motor 55. There are socket holes on the rotating drum 538. The rotating drum 538 is attached to the arc-shaped stopper 539 for rotation. The blocking wheel 537 is an arc structure with the same width as the rotating drum 538. The outer wall of the arc structure has the same curvature as the inner wall of the rotating drum 538 and fits well. The arc opening faces the seeding area 533. The rotating drum 538 is located between the hopper 531 and the seeding area. The seeds in the hopper 531 enter the sockets of the rotating drum 538 as the drum rotates, and the sockets rotate with the rotating drum 538, passing through the arc-shaped stopper 539, and the seeds are brought into the seeding area 533 for discharge; a seed cleaning brush 5310 is provided near the arc-shaped stopper 539 in the hopper 531, and the seed cleaning brush 5310 removes excess seeds from the sockets, and a seeding brush 5311 is provided at the arc-shaped opening end of the blocking wheel 537, and the seeding brush 5311 promotes the seeds in the sockets to fall into the seeding area 533.

[0059] EDEM simulation seeding model and vehicle speed matching optimization model

[0060] The EDEM simulation seeding model simulates the seeding process, calculates the time it takes for seeds to fall, and establishes a speed optimization matching model to achieve an integrated process of seeding, hole making, and walking, realize precise seeding.

[0061] The rotation speed of the seed wheel has an important impact on the seeding quality. When the rotation speed is too high, the time for the seed holes to pass through the seed filling area is shortened, resulting in a decrease in seed filling capacity; when the rotation speed is too low, the fluidity of the seeds in the silo is poor, which is very likely to cause blockage. Therefore, the rotation speed setting is very important. Assuming the forward speed of the unit is v, the rotation speed of the seed wheel is n, and the plant spacing is x, the relationship between the forward speed of the unit and the rotation speed of the seed wheel is as follows

[0062]

[0063] Where: n1-seeding wheel speed (r / min), v-machine forward speed (m / s), x-plant spacing (cm), take 10cm, z-number of eyelets.

[0064] We calculated that the hole-making device speed was 240 r / min, the travel speed was 0.72 km / h, and the seed meter speed was 24 r / min. Based on these performance parameters and working results, we began to select motors and design each module of the control system.

[0065] The single-chip microcomputer of the present invention uses the Canaan CanMV K230 AI development board, which integrates a camera, a WIFI module, dual CPU super computing power, KPU (6TOPS equivalent computing power) and MicroSD large storage capacity.

[0066] The vehicle circuit module design of the present invention mainly designs the basic vehicle circuit, and applies the DC-DC step-down module, the selection of relay positions, the 48V20ah battery pack and other required equipment to provide a basis for the subsequent motor installation and power supply.

[0067] The positive and negative poles of the 48v20ah battery pack are connected to the two ends of the two junction boxes respectively, which supply power to the two hub motors respectively, and form the power supply control circuit of the hub motor with the hub motor driver. The relay controls the forward and reverse control line of the hub motor and the on and off of the brake line, thereby realizing the forward and reverse function switching and the braking function; at the same time, the positive and negative lines are separated from the junction box and connected to the terminal blocks respectively to form a one-input and four-output parallel circuit. So far, it is a 48v circuit. The next is the step-down circuit. At the same time, the separated lines are connected to the positive and negative poles of the DC-DC step-down module. There are two 48V to 24V step-down modules to power the stepper motor and the DC brushless motor, and two 48V to 12V step-down modules to power the photoelectric sensor and the PWM to voltage module respectively. Multiple 48V to 5V step-down modules are used to power the relay and the microcontroller.

[0068] The motor control module is designed to control the following multiple motors through a single-chip microcomputer, achieving their linkage, as well as starting, stopping and speed regulation.

[0069] The Z2BLD30-24GN-30 stepper motors are driven by a TB6600 driver at a set speed of 240 r / min. The two stepper motors used for acupuncture are regulated via PWM. During operation, the positive and negative terminals of the 48V to 24V step-down module are connected via VCC and GND. PUL-, DIR-, and ENA- are short-circuited to form the internal circuit.

[0070] Wiring with the MCU: PUL+, DIR+, and ENA- are connected to the CanMV K230 AI development board, PUL+ to the PWM3 (IO47) port, DIR+ to the MCU +5v pin, and ENA to the GND pin.

[0071] In terms of control, after the PWM speed regulation sets the frequency, the duty cycle is adjusted to control the motor speed, realize the acupuncture function, and realize adjustable acupuncture speed.

[0072] A brushless DC motor, model 57HSS22-8, drives the two seed metering units at 24 rpm via a connecting sleeve. This motor operates using a PWM signal. Forward and reverse rotation can be adjusted on the driver panel.

[0073] PWM speed regulation method: After setting the frequency to 2kHZ, adjust the duty cycle to control the motor speed to achieve the seeding function, and the seeding rate is adjustable.

[0074] Wiring method: When working, connect the positive and negative poles of the power supply through DC+ and DC-, and directly connect REV / DI2 and COM port to form a short circuit;

[0075] Wiring with the CanMV K230 AI development board: connect the AI2 port to the PWM2 (IO46) port, and the COM port to the GND pin. Adjust the duty cycle in the code through the CanMV K230 AI development board to control the direction and speed of the brushless DC motor and realize the seeding function.

[0076] The hub motor also involves the hub motor driver, brake device, PWM to voltage module, and relay. The wiring of the hub motor and the driver follows the existing battery vehicle motor technology. At the same time, we added a relay to the brake circuit, changed the mechanical braking method to remote relay control, set the brake handle to brake mode (normally squeeze the battery vehicle brake handle and fix this position), and use the relay to change the on and off of the brake circuit to determine the mode. At the same time, a relay (equivalent to a switch) is also added to the forward and reverse lines of the motor to control the on and off of the forward and reverse lines to realize the forward and reverse function conversion; the role of the PWM to voltage module is to replace the original handlebar. Like the above-mentioned motor control method, PWM speed regulation is used to change the duty cycle to achieve a change in the output voltage of the control circuit, thereby achieving speed regulation.

[0077] We have automated the entire vehicle circuit, using a PWM to voltage signal module instead of a potentiometer to achieve speed regulation, and controlling the on and off of relays to achieve reversing and braking functions. The travel speed is adjustable between 0.72km / h and 10km / h.

[0078] The wireless transmission module is used to remotely control the motor control module. The wireless transmission module also involves a network debugging system and a software control system, forming a control logic of software-network debugging assistant-microcontroller. The software interface is written using existing technology. Each function button is accompanied by an instruction, which is sent to the microcontroller with the help of the Netassist network debugging assistant to execute the corresponding function.

[0079] Based on the TCP communication transmission protocol, using Netassist network debugging software, utilizing the ESP32-C3wifi module that comes with the CanMV K230 AI development board, writing communication code, and using Socket communication, wireless automation control is achieved.

[0080] Sockets are the cornerstone of communication and the basic operational unit for network communications supporting the TCP / IP protocol. They are an abstract representation of an endpoint in the network communication process and contain five pieces of information essential for network communication: the connection protocol (usually TCP or UDP), the local host's IP address, the local process's protocol port, the remote host's IP address, and the remote process's protocol port. The socket abstraction layer lies between the transport layer and the application layer and is not necessarily related to TCP / IP. The socket programming interface was designed with the goal of adapting to other network protocols. Therefore, the emergence of sockets simply facilitates the use of the TCP / IP protocol stack, abstracting TCP / IP to form several basic function interfaces. Examples include create, listen, accept, connect, read, and write.

[0081] The GPS module utilizes the WTRTK-980 high-precision centimeter-level positioning system, enabling us to accurately detect trajectory ranges. This system is a high-performance BDS / GNSS full-constellation high-precision positioning and orientation system measuring 26x38mm, with excellent positioning performance.

[0082] While the vehicle is in operation, the GPS system collects information in GNGGA format at a baud rate of 115200 and outputs the data to the microcontroller via the TXD serial port. The microcontroller program converts the GNGGA information in a specific format into longitude and latitude information, which is then wirelessly transmitted to a tablet computer via the internet and displayed on the tablet's display interface.

[0083] The effective transmission distance of the software sending data through network hardware is within 40 meters. Connect the computer and the CanMV K230AI development board to the same WIFI, and send the letter instructions corresponding to the code to complete the remote operation of each motor.

[0084] The visual recognition system, implemented through a monolithic integrated camera, is a lightweight agricultural machinery visual navigation system based on the OpenCV open source framework. By optimizing the collaborative workflow of traditional image processing algorithms and deep learning models, it achieves low-cost, highly robust real-time detection and dynamic correction of field paths, solving the real-time and adaptability problems of traditional solutions in complex farmland environments.

[0085] The implementation method can use the camera of the CanMV K230 AI development board as the visual basis, use the KPU (KPU is a neural network processor inside the K230) to perform convolutional neural network calculations with low power consumption, obtain the size, coordinates and type of the detected target in real time, and detect and classify faces or objects.

[0086] During operation, the MCU's camera, located at the front of the vehicle, transmits captured images to the screen in real time. Image processing based on OpenCV continuously calculates the vehicle's deviation angle during driving and transmits it to the MCU, enabling it to autonomously correct the vehicle's trajectory.

[0087] Visual recognition is used to achieve vehicle obstacle avoidance and trajectory correction functions. The camera captures the path, performs algorithms, and sends instructions to the microcontroller. The GPS module is used as a path navigation module. It can not only specify the path to travel but also provide real-time feedback on longitude and latitude to determine the location, and plan the path based on the longitude and latitude.

[0088] Specifically, the visual recognition system includes an image acquisition module, an image preprocessing module, a ridge boundary detection module, a navigation parameter calculation module, an obstacle detection stage, and a multimodal output and decision feedback stage;

[0089] The image acquisition module uses the PLC-integrated camera on the planting machinery to capture the farmland ridge scene in real time to obtain the ridge image;

[0090] The image preprocessing module imports the ridge image into the HSV-V grayscale enhancement model for grayscale conversion to form an original grayscale image. Then, the original grayscale image is converted into a black and white binary image through dynamic threshold binarization. Finally, a composite morphological filter chain operation is performed to obtain a preliminary processed image of the ridge.

[0091] The ridge boundary detection module, based on the preliminary ridge processing image, constructs a linear regression boundary fitting model through bidirectional boundary feature point extraction and left and right boundary scanning, and finally obtains the boundary contour line of the ridge;

[0092] The step navigation parameter calculation module uses the left and right boundary equations to obtain the image theoretical center auxiliary line and image center line based on the boundary contour line of the field ridge. At the same time, it calculates the heading deviation of the planting machinery and obtains the deviation angle of the agricultural machinery path.

[0093] Obstacle detection stage: Obstacles are marked using the ridge image obtained in step 1;

[0094] Multimodal output and decision feedback stage: The agricultural machinery path deviation angle information in step 4 and the obstacle marking information in step 5 are transmitted to the PLC, which adjusts the heading of the planting machinery and issues obstacle warning information.

[0095] The intelligent control system also includes a self-diagnosis module, which is used in conjunction with the visual recognition system. The visual recognition system includes a walking camera at the front of the all-in-one machine and a planting effect detection camera at the rear. The self-diagnosis module includes a walking detection part and a planting effect detection part.

[0096] Walking detection part:

[0097] In the first step, the driving camera analyzes the image of the environment in front of the all-in-one machine. If an anomaly is found, the system self-checks the GPS data and determines that it is a change in the soil environment (travel path deviation or obstacle) or a motor failure. This step corresponds to the application method of the visual recognition system, which detects the environment in front and detects travel path deviation or obstacles.

[0098] The second step is to identify changes in the soil environment, trigger a fault alarm, and adaptively plan and adjust the route. The wheel hub motors are linked to the wheel hub motors, adaptively changing the PWM signal to adjust the speed of the two wheels. After the speeds match, the steering is adjusted to return to the normal route.

[0099] The third step is to determine that it is a motor failure, self-diagnose the hub motor operation code, and make corrections;

[0100] Planting effect detection part:

[0101] In the fourth step, the planting effect detection camera analyzes the environment image in front of the all-in-one machine to determine whether there are any abnormalities in the hole making. If there are any problems, the stepper motor of the hole making module is tested and the motor control parameters are checked. If there are any faults, an alarm is issued and the parameters are adaptively adjusted.

[0102] Step 5: Determine if it is a sowing problem, check the parameters of the brushless DC motor of the seeding module, if there is a fault, alarm, and adaptively adjust the parameters.

[0103] In the fourth and fifth steps, a camera is used to run a visual recognition system to detect the sowing effect. If any abnormality is found in sowing, the self-diagnosis system of each module of the seeder and the soil environment diagnosis system are started. If a functional failure occurs in the seeder, an alarm is issued and the work is stopped. If a major change is found in the soil environment, the seeder's travel and sowing parameters are adaptively adjusted, thereby effectively improving the planting effect.

[0104] Technical route: By collecting common fault characteristics during the implant process and then using the K230's built-in KPU training, fault identification and classification can be achieved, providing fault identification services for subsequent walking cameras and implant cameras. The specific process of fault self-diagnosis is as follows:

[0105] First, when the agricultural machinery is running, the camera responsible for monitoring the walking status finds walking abnormalities (such as speed changes too fast or too slow, deviation from the prescribed path, etc.), and the single-chip computer runs the program to check the GPS data (whether the agricultural machinery deviates from the predetermined path), and at the same time calculates the walking parameters of the agricultural machinery (walking speed, direction) through the coordinates. First, it is determined whether the soil environment has changed. After KPU training, if the walking direction is abnormal, the turning program will be used to adjust the walking direction and re-plan the route. If it is not a change in the soil environment, it is determined whether it is a motor failure. First, the hub motor is detected. Since the hub motor is connected to the PWM to voltage signal module instead of the potentiometer to achieve speed regulation, and the relay is connected to the K230 distribution The braking function is realized on the ordinary pins that have been set, so the hub motor fault can be detected by adjusting the PWM signal or checking the parameters of the opening and breaking of the control relay and sampling the motion stroke and speed with the walking camera for comparison. If a fault occurs, the fault alarm (photoelectric alarm, remote alarm reminder) will alarm or adaptive sowing adjustment parameters (set preset parameters and push rules for adaptive parameter change); secondly, if the camera responsible for monitoring the planting effect finds a planting quality problem, first cooperate with the GPS and walking camera to check whether it is a walking fault of the agricultural machinery, eliminate the walking problem, and then check the hole-making stepper motor. The stepper motor is connected to the K2305v pin by the DIR- port and the K230 by the ENA- port. GND pin, PUL- port is connected to the PWM port on K230. After the frequency is set for PWM speed regulation, the speed can be controlled by changing the duty cycle. Check the motor control parameters and use the implant camera to sample the acupuncture stroke and speed. If there is no problem, check the DC brushless motor. The DC brushless motor is also controlled by PWM signal. The AI2 port is connected to the PWM port on K230, and the COM port is connected to the GND pin. The direction and speed are also controlled by adjusting the duty cycle. If a fault occurs, an alarm will be issued or the motor parameters will be adaptively adjusted.

[0106] The silo monitoring module consists of a 12V power supply, an alarm, and a photoelectric sensor. The photoelectric sensor is placed inside the seed silo. During continuous seeding, the alarm sounds when it detects no seeds within a set distance. This unit uses an E3F-DS30C2 three-wire sensor and an alarm device, with an adjustable measurement distance of 10-50cm. Its operating principle is based on the photoelectric effect, converting input current into a light signal at the transmitter. The receiver then detects the target object based on the intensity or absence of the received light. Installed directly above the silo, the alarm flashes and sounds when the seed level falls below the warning line.

[0107] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An intelligent control system for a small-size precision film-covering and seeding machine, characterized by: Including vehicle circuit module, motor control module, wireless transmission module, GPS module, silo monitoring module, and single chip microcomputer; The vehicle circuit module includes a 48v20ah battery pack, a junction box, a DC-DC step-down module, a relay, and a terminal block. The relay controls the operation of the hub motor of the integrated machine. The 48v20ah battery pack powers the hub motor of the integrated machine through the junction box. The terminal block and the DC-DC step-down module respectively power the stepper motor, brushless DC motor, electric sensor, PWM voltage conversion module, and single-chip microcomputer of the integrated machine. The motor control module includes a stepper motor, a brushless DC motor, and a hub motor. The stepper motor uses PWM to achieve speed regulation and drives the acupuncture mechanism. The brushless DC motor works by giving a PWM signal and drives the seeding mechanism. The hub motor uses the PWM to voltage signal module to achieve speed regulation and drive the wheel. The wireless transmission module includes a network debugging system and a software control system. The software control system controls the network debugging system to send instructions to the microcontroller to execute corresponding functions. The GPS module outputs data to the microcontroller through the TXD serial port, and the microcontroller converts the data into longitude and latitude information; The silo monitoring module includes an alarm and a photoelectric sensor. The photoelectric sensor uses the photoelectric effect to identify that the seed accumulation in the silo is lower than the preset height and outputs a signal to the alarm. The single-chip microcomputer is integrated with a camera to form a visual recognition system, which can process the captured images, continuously calculate the deviation angle during the vehicle's driving process, transmit it to the single-chip microcomputer, and enable it to autonomously correct the vehicle's trajectory.

2. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 1 is characterized in that: In the vehicle circuit module, the positive and negative electrodes of the 48v20ah battery pack are connected to the two ends of the two junction boxes respectively, and the two junction boxes are respectively connected to the hub motor drivers of the integrated machine to form the power supply control circuit of the hub motor; The relay is connected to the hub motor driver of the integrated machine to control the forward and reverse control line of the hub motor and the on and off of the brake line; The DC-DC step-down module includes a 48V to 24V step-down module, a 48V to 12V step-down module, and a 48V to 5V step-down module; the junction box separates the positive and negative lines, and the lines separated through the wiring terminals are respectively connected to the 48V to 24V step-down module, the 48V to 12V step-down module, and the 48V to 5V step-down module. The 48V to 24V step-down module supplies power to the stepper motor and the DC brushless motor, the 48V to 12V step-down module supplies power to the photoelectric sensor and the PWM to voltage module, and the 48V to 5V step-down module supplies power to the relay and the microcontroller.

3. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 2 is characterized in that: The stepper motor includes a VCC interface, a GND interface, a PUL+ interface, a PUL- interface, a DIR+ interface, a DIR- interface, and an ENA- interface; The VCC interface and GND interface are connected to the positive and negative poles of the 48V to 24V step-down module for power supply. The PUL- interface, DIR- interface and ENA- interface are short-circuited to form an internal circuit. The PUL+ interface, DIR+ interface and ENA- interface are connected to the microcontroller respectively. The PUL+ interface is connected to the PWM3 (IO47) port of the microcontroller, the DIR+ interface is connected to the +5v pin of the microcontroller, and the ENA- interface is connected to the GND pin.

4. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 2, characterized in that: The brushless DC motor includes an AI2 interface, a COM interface, a DC+ interface, a DC- interface, a REV / DI2 interface, and a COM interface; The DC+ and DC- interfaces are connected to the positive and negative poles of the power supply for power supply, and the REV / DI2 interface and COM interface are connected to form a short circuit; the AI2 interface is connected to the PWM2 (IO46) port of the microcontroller, and the COM interface is connected to the GND pin of the microcontroller.

5. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 2, characterized in that: The hub motor is connected to the hub motor driver, the relay is connected to the brake system of the hub motor, and the PWM to voltage module controls the speed regulation of the hub motor.

6. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 1, characterized in that: The wireless transmission module is based on the TCP communication transmission protocol, uses network debugging software, utilizes the Wi-Fi module integrated in the single-chip microcomputer, writes communication code, and uses Socket communication.

7. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 1, characterized in that: The visual recognition system includes an image acquisition module, an image preprocessing module, a ridge boundary detection module, and a navigation parameter calculation module; The image acquisition module uses the PLC-integrated camera on the planting machinery to capture the farmland ridge scene in real time to obtain the ridge image; The image preprocessing module imports the ridge image into the HSV-V grayscale enhancement model for grayscale conversion to form an original grayscale image. Then, the original grayscale image is converted into a black and white binary image through dynamic threshold binarization. Finally, a composite morphological filter chain operation is performed to obtain a preliminary processed image of the ridge. The ridge boundary detection module, based on the preliminary ridge processing image, constructs a linear regression boundary fitting model through bidirectional boundary feature point extraction and left and right boundary scanning, and finally obtains the boundary contour line of the ridge; The navigation parameter calculation module uses the left and right boundary equations to obtain the image theoretical center auxiliary line and image midline according to the boundary contour line of the field ridge. At the same time, it calculates the heading deviation of the planting machinery, obtains the agricultural machinery path deviation angle, and transmits it to the PLC. The PLC adjusts the heading of the planting machinery and issues obstacle warning information.

8. The intelligent control system for a small-size precision film-covering and seeding machine according to claim 1, characterized in that: The intelligent control system also includes a self-diagnosis module, which is used in conjunction with a visual recognition system. The visual recognition system includes a walking camera at the front of the all-in-one machine and a planting effect detection camera at the rear. The self-diagnosis module includes a walking detection part and a planting effect detection part. Walking detection part: In the first step, the walking camera analyzes the environment in front of the all-in-one machine. If an anomaly is found, the system self-checks the GPS data and determines that it is a change in the soil environment or a motor failure. The second step is to identify changes in the soil environment, trigger a fault alarm, and adaptively plan and adjust the route. The wheel hub motors are linked to the wheel hub motors, adaptively changing the PWM signal to adjust the speed of the two wheels. After the speeds match, the steering is adjusted to return to the normal route. The third step is to determine that it is a motor failure, self-diagnose the hub motor operation code, and make corrections; Planting effect detection part: In the fourth step, the planting effect detection camera analyzes the environment image in front of the all-in-one machine to determine whether there are any abnormalities in the hole making. If there are any problems, the stepper motor of the hole making module is tested and the motor control parameters are checked. If there are any faults, an alarm is issued and the parameters are adaptively adjusted. The fifth step is to determine whether it is a sowing problem, check the parameters of the DC brushless motor of the seeding module, such as fault, alarm, and adaptive adjustment parameters.

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