Digital twinborn intelligent monitoring platform and method for small-particle-size vegetable precision seeder

Abstract

The invention discloses a digital twinborn intelligent monitoring platform and method for a small-particle-size vegetable precision seeder. The platform comprises a mechanical system, a detection module, a control module, a communication module and a digital twin module, and the mechanical system comprises a chassis mechanism, a seed metering mechanism and a soil covering mechanism; the detection module comprises speed detection, seeding condition detection and operation time detection, the detection module is carried on a mechanical system, for example, a speed detection sensor is carried on a frame near a tire shaft and is provided with a monitoring point, and a seeding condition detection sensor is placed at a seed falling opening; the control module comprises an STM32 single-chip microcomputer, and the STM32 single-chip microcomputer is connected with the sensor in the detection module and controls the sensor to detect various data. The communication module sends data detected by the detection module to the digital twin module; and the digital twin module visually presents the data and analyzes the health condition of the mechanical equipment through the data.

Description

Technical Field

[0001] This invention relates to the field of mechanical digital twin technology, specifically to a digital twin intelligent monitoring platform and monitoring method for precision seeders of small-diameter vegetables. Background Technology

[0002] Traditional precision seeding in my country is a crucial link in ensuring safe agricultural production, and its quality directly determines crop emergence rate, yield, and overall economic benefits. However, the quality assessment of traditional seeders still mainly relies on outdated manual spot checks, severely lacking real-time feedback and control capabilities. Digital twin intelligent monitoring platforms, as the core means of intelligent seeding equipment, demonstrate significant application advantages by relying on multi-source information perception, virtual-real dynamic interaction, and intelligent decision-making technologies: They possess full-process controllability, enabling real-time monitoring and closed-loop control of key parameters such as seed metering speed, plant spacing, and sowing depth, increasing the seeding qualification rate to over 98%; they achieve adaptive regulation, dynamically optimizing parameters based on sowing mechanism models and real-time operational data, reducing seed breakage rate to below 0.5%; and they support visual traceability and decision-making, achieving traceability of operational quality and big data-assisted decision-making by constructing a digital mapping of the entire sowing process, providing key technical support for promoting the development of precision agriculture. Summary of the Invention

[0003] The purpose of this invention is to provide a digital twin intelligent monitoring platform and its monitoring method for precision seeders for small-diameter vegetables, so as to realize real-time monitoring of the seeding status and intelligent diagnosis of operational problems during the operation of the seeder.

[0004] The technical solution adopted to achieve the purpose of this invention is:

[0005] A digital twin intelligent monitoring platform and monitoring method based on a precision seeder for small-particle vegetables are disclosed. The platform mainly includes a mechanical system, a detection module, a control module, a communication module, and a digital twin module. The mechanical system includes a chassis mechanism, a seed metering mechanism, and a soil covering mechanism. The detection module includes speed detection, seeding status detection, and operation time detection. These modules are mounted on the mechanical system; for example, speed sensors are mounted on the frame near the tire axles with monitoring points, and seeding status detection sensors are placed at the seed inlet. The control module includes an STM32 microcontroller connected to the sensors in the detection module, controlling the sensors to detect various data. The communication module sends the data detected by the detection module to the digital twin module. The digital twin module visualizes the data and analyzes the health status of the machinery. This implementation can prevent equipment malfunctions during use and extend the service life of the machinery. This monitoring platform and method are innovative.

[0006] The mechanical system includes a chassis mechanism, a seed metering mechanism, and a soil covering mechanism. The chassis mechanism primarily supports the seed metering mechanism and its movement. The seed metering mechanism operates by using a cylinder push rod and a cam to perform reciprocating telescopic motion, creating positive and negative pressure. The chassis mechanism includes wheels and a frame, primarily supporting the seed metering and soil covering mechanisms. A speed detection sensor is mounted on the walking mechanism. The seed metering mechanism consists of seven seed metering units, each including a seed metering device and an infrared photoelectric sensor positioned within the seed delivery guide. The soil covering mechanism completes the soil covering after sowing.

[0007] The control module is composed of an STM32 microcontroller. The STM32 is connected to various sensors to control the data acquisition of the sensors, and is also connected to the communication module to control the data transmission of the communication module.

[0008] The detection module consists of various measurement sensors. The detection module measures data and sends the data to the control module.

[0009] The communication module consists of an ESP8266 WiFi module. The communication module transmits the machine status data collected by the STM32 to the digital twin platform, which then analyzes and displays the data.

[0010] The digital twin platform includes system performance parameters, system operating status, global status legend, equipment operation monitoring, layout of the sowing operation system in the facility environment, detailed diagram of the seed metering device movement, single-rotation flow rate of the seed metering device, and total seeding volume for the past 7 days. The digital twin platform displays the data collected by the detection module in the corresponding locations and shows the real-time operating status. The APP detection terminal sends signals to the control module to realize remote control and monitoring of the seeder.

[0011] Compared with the prior art, the present invention, by adopting the above technical solution, has the following advantages:

[0012] This invention designs a digital twin intelligent monitoring platform and monitoring method for precision seeders of small-particle vegetables to monitor the seeding data and status of the seeder in real time, enabling intelligent decision-making on the machine body and preventing unnecessary situations. Attached Figure Description

[0013] Figure 1 The diagram shows the control flow of the digital twin intelligent monitoring platform.

[0014] Figure 2 The image shown is an isometric view of a precision seeder for small-particle vegetables.

[0015] Figure 3 The image shown is a left view of a precision seeder for small-particle vegetables.

[0016] Figure 4The image shown is of a precision seeder for small-particle vegetables.

[0017] Figure 5 The system architecture of the digital twin intelligent monitoring platform and its monitoring method for a precision seeder for small-particle vegetables is shown.

[0018] Figure 6 The image shows the visualization interface of the digital twin system.

[0019] Figure labels: 1. Seeding mechanism; 2. Chassis mechanism; 3. Soil covering mechanism; 4. Precision seeder system parameters; 5. STM32 control module; 6. ESP8266 WIFI communication module; 7. Cloud server; 8. Digital twin platform; 81. System performance parameters; 82. System operating status; 83. Global status diagram; 84. Seeder movement details and seeding operation system layout in facility environment; 85. Total seeding volume in the past 7 days; 86. Seeder single-rotation flow rate; 87. Equipment operation monitoring; 9. APP control and detection terminal. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0021] like Figure 1 As shown, a digital twin intelligent monitoring platform for precision seeders of small-diameter vegetables includes a mechanical system, a detection module, a control module, a communication module, and a digital twin module. Figure 2 , 3 As shown, the mechanical system includes a seed metering mechanism 1, a chassis mechanism 2, and a soil covering mechanism 3. Various sensors in the detection module are mounted on the seed metering mechanism 1 to measure sowing data, and on the chassis mechanism 2 to measure operating speed, etc. Figure 5 The system architecture shown involves a detection module that measures the precision seeder system parameters 4, sends these parameters 4 to the STM32 control module 5 via a communication protocol, and then sends them to the ESP8266 WIFI communication module 6 via serial communication. The ESP8266 WIFI communication module 6 transmits the data to the cloud server 7, and the cloud server 7 sends the data to the digital twin platform 8 and the APP control and detection terminal 9. Figure 6 The transmitted data is displayed in the system performance parameters 81 and the equipment operation monitoring 87. The digital twin platform 8 analyzes the data and displays the detailed diagram of the seed metering device movement and the layout of the seeding operation system 84 in the facility environment, allowing for intuitive observation of the operating status of the precision seeder for small-diameter vegetables.

[0022] A digital twin intelligent monitoring platform and its monitoring method based on a small-particle-size vegetable precision seeder are characterized in that the digital twin intelligent monitoring platform includes: a mechanical system, a detection module, a control module, a communication module, and a digital twin module, wherein...

[0023] The mechanical system is equipped with sensors in the control module;

[0024] The control module is connected to the detection module and collects the data collected by the detection module;

[0025] The control module is connected to the communication module;

[0026] The communication module is connected to the digital twin platform and sends the detection data to the digital twin platform;

[0027] The mechanical module mainly includes a chassis mechanism, a seeding mechanism, and a soil covering mechanism;

[0028] The control module mainly includes an STM32 microcontroller;

[0029] The detection module mainly includes an infrared photoelectric sensor for detecting sowing, including the detection of missed sowing rate and re-sowing rate; a visual recognition module for detecting plant spacing and row spacing data; a speed detection sensor; and an operation time accumulation module.

[0030] The digital twin smart construction monitoring platform includes: system performance parameters, system operating status, global status legend, equipment operation monitoring, seed metering single-turn discharge rate, total seeding volume in the past 7 days, layout of the seeding operation system under the facility environment, detailed diagram of seed metering movement, and an APP control and detection terminal to cooperate with it.

[0031] 3. The digital twin intelligent monitoring platform and monitoring method for the precision seeder for small-particle vegetables according to claim 2, characterized in that the system parameters include sowing speed, sowing frequency, pass rate, missed sowing rate, and reseeding rate; the system operating status includes running and stopped; the global status legend includes normal and abnormal. Equipment operation monitoring includes operating time, remaining battery power, and effective seeding units.

[0032] The sowing speed is calculated from the walking speed detection and sowing detection data in the detection module;

[0033] The sowing frequency is obtained by statistically analyzing the measurement data of the infrared photoelectric sensor in the detection module every second.

[0034] The pass rate is the ratio of actual seed-falling data measured by the infrared photoelectric sensor to theoretical data, as shown in Equation 1;

[0035]

[0036] --The number of seeds falling per second is measured by an infrared photoelectric sensor;

[0037] --Theoretical number of seeds falling per second;

[0038] The sowing omission rate is the ratio of the difference between the actual sowing data measured by the infrared photoelectric sensor and the theoretical data to the theoretical sowing data, as shown in Equation 2;

[0039]

[0040] The replay rate is the ratio of the number of actual seeding intervals measured by the infrared photoelectric sensor per second to the number of theoretical seeding intervals, as shown in Equation 3.

[0041]

[0042] --The number of times the actual seed-falling time interval measured by the infrared photoelectric sensor per second is less than the theoretical seed-falling time interval.

[0043] --Number of theoretical time intervals for seeding;

[0044] When the system performance parameters deviate from the normal range, the global status legend will immediately issue a warning by changing the color and popping up an alarm, transforming reactive maintenance into proactive prevention and predictive maintenance. Problems are detected before equipment failures actually occur, causing downtime and production losses, thus minimizing unplanned downtime.

[0045] The system operating status is used to display the real-time operating status of the machine;

[0046] The global status graph is used for anomaly detection and fault early warning;

[0047] The operation time in the equipment operation monitoring is the core of production efficiency calculation, and it is a key input for calculating the overall efficiency of the equipment. By comparing the planned operation time with the actual operation time, the equipment utilization rate can be clearly seen, and wasted time in the production process can be identified. Continuous monitoring of single operation time and historical cumulative operation time enables predictive maintenance of key moving parts. Automatic accumulation of operating time and automatic generation of maintenance work orders when the maintenance cycle is approaching achieve a leap from "on-time maintenance" to "on-demand maintenance".

[0048] The device operation monitoring system not only displays the remaining battery power, but also calculates and predicts the remaining available operating time in real time based on historical energy consumption data. This allows the scheduling system to make intelligent decisions.

[0049] The effective seeding unit in the equipment operation monitoring refers to the number of seeders on the seeder that are functioning normally. The digital twin platform maps the real-time status of each seeding unit: normal, blocked, or malfunctioning. When a seeder is blocked, the corresponding virtual model area immediately highlights an alarm and accurately pinpoints the specific row number. Operators do not need to leave the machine to check row by row; they can simply view the digital twin screen to accurately identify the problem and proceed with the solution, saving diagnostic and repair time and ensuring operational efficiency. Each seeder corresponds to a specific seeding row, monitoring whether the seeding operation is "complete." The difference between the actual and theoretical seeding area is calculated in real time, and combined with the number of seeder actions, the actual seeding quantity is estimated, thus accurately calculating seed consumption.

[0050] The seed metering device's single-turn discharge rate is used for rapid diagnosis, horizontal comparison of each seed metering device's discharge rate, display of interval anomalies and instantaneous faults, and segmented statistics of work quality.

[0051] The total seeding volume over the past 7 days, and the overall trend of the line graph, directly reflect the production rhythm. By comparing the actual seeding volume with the reference line, the difference between the actual line and the reference line provides a clear assessment of whether production tasks are progressing as planned, accurately pinpoints the time of delays, and analyzes the causes. Using data-driven methods to control the production rhythm and ensure that planting targets are achieved on schedule is a core tool for achieving intelligent and refined management.

[0052] The layout of the seeding operation system in the facility environment presents the current virtual operating status and compares it with the current actual operating status. When there is a deviation between the virtual model and the physical entity data, the system triggers a warning, accurately locates abnormal equipment, and forms a precise control closed loop of "comparison-early warning-adjustment-execution", which greatly improves the automation level and reliability of facility agriculture.

[0053] The detailed diagram of the seed metering device's movement shows a virtual demonstration of the seed metering device's operation process.

[0054] The APP control and detection terminal receives data from the communication module, monitors the equipment status at any time via mobile phone, and controls the running speed of the seeder through the APP.

Claims

1. A digital twin intelligent monitoring platform based on a precision seeder for small-particle vegetables, characterized in that: include: Mechanical system, detection module, control module, communication module, digital twin module, among which, The mechanical system is equipped with sensors in the control module; The control module is connected to the detection module and collects the data collected by the detection module; The control module is connected to the communication module; The communication module is connected to the digital twin platform and sends the detection data to the digital twin platform; The mechanical module includes a chassis mechanism, a seed dispensing mechanism, and a soil covering mechanism; The control module includes an STM32 microcontroller; The detection module includes an infrared photoelectric sensor for detecting sowing, including the detection of missed sowing rate and reseeding rate; and a visual recognition module for detecting plant spacing and row spacing data. Includes a speed detection sensor; a work time accumulation module; The mechanical system includes a seed metering mechanism, a chassis mechanism, and a soil covering mechanism. Various sensors in the detection module are mounted on the seed metering mechanism to measure sowing data and on the chassis mechanism to measure operating speed. The detection module measures the precision seeder system parameters and sends these parameters to the STM32 control module via a communication protocol. The parameters are then sent to the ESP8266 WIFI communication module via serial communication. The ESP8266 WIFI communication module transmits the data to the cloud server, which in turn sends the data to the digital twin platform and the APP control and detection terminal. The transmitted data is displayed in the system performance parameters and equipment operation monitoring. The digital twin platform analyzes the data and displays detailed diagrams of the seed metering device's movement and the layout of the sowing operation system in the facility environment, allowing for intuitive observation of the operating status of the precision seeder for small-diameter vegetables.

2. The digital twin intelligent monitoring platform for the precision seeder for small-particle vegetables according to claim 1, characterized in that, The digital twin smart construction monitoring platform includes: system performance parameters, system operating status, global status legend, equipment operation monitoring, seed metering single-turn discharge rate, total seeding volume in the past 7 days, layout of the seeding operation system under the facility environment, detailed diagram of seed metering movement, and an APP control and detection terminal to cooperate with it.

3. A digital twin intelligent monitoring method for a small-particle-size vegetable precision seeder implemented using the platform described in claim 2, characterized in that, The system parameters include sowing speed, sowing frequency, pass rate, missed sowing rate, and re-sowing rate; the system operating status includes running and stopped; the global status legend includes normal and abnormal; the equipment operation monitoring includes operation time, remaining battery power, and effective seeding units; The sowing speed is calculated using walking speed detection and sowing detection data in the detection module; The sowing frequency is obtained by statistically analyzing the measurement data of the infrared photoelectric sensor in the detection module every second. The pass rate is the ratio of the actual seed-falling data measured by the infrared photoelectric sensor to the theoretical data, as shown in equation (1); , --The number of seeds falling per second is measured by an infrared photoelectric sensor; --Theoretical number of seeds falling per second; The sowing omission rate is the ratio of the difference between the actual sowing data measured by the infrared photoelectric sensor and the theoretical data to the theoretical sowing data, as shown in equation (2); The replay rate is the ratio of the number of actual seeding intervals measured by the infrared photoelectric sensor per second to the number of theoretical seeding intervals, as shown in equation (3). , --The number of times the actual seed-falling time interval measured by the infrared photoelectric sensor per second is less than the theoretical seed-falling time interval. --Number of theoretical time intervals for seeding; When the system performance parameters deviate from the normal range, the global status legend will immediately issue a warning by changing the color and popping up an alarm, turning post-event maintenance into pre-event prevention and predictive maintenance; it can detect problems before a failure actually occurs, causing downtime and production losses, and reduce unplanned downtime; The system operating status is used to display the real-time operating status; The global status graph is used for anomaly detection and fault early warning; The operation time in the equipment operation monitoring is the core of production efficiency calculation, and the operation time is the key input for calculating the overall efficiency of the equipment. By comparing the planned operation time with the actual operation time, we can intuitively see the level of equipment utilization and identify time waste in the production process; It continuously monitors the duration of each operation and the historical cumulative operation time, and performs predictive maintenance on key moving parts; it automatically accumulates the running time and automatically generates maintenance work orders when the maintenance cycle is approaching, achieving a leap from "on-time maintenance" to "on-demand maintenance"; The remaining battery power in the equipment operation monitoring not only displays the remaining power, but also calculates and predicts the remaining available working time in real time based on historical energy consumption data; Allowing the scheduling system to make intelligent decisions; The effective seeding unit in the equipment operation monitoring refers to the number of seeders that can work normally on the seeder; the digital twin platform maps the real-time status of each seeding unit, including normal, blocked, and malfunctioning; when a seeder is blocked, the corresponding virtual model part will immediately highlight the alarm and accurately locate the specific row number; each seeder corresponds to a seeding row, monitoring whether the seeding operation "does not miss a single seed"; the difference between the actual seeding area and the theoretical seeding area is calculated in real time, and combined with the number of actions of the seeder, the actual seeding amount is estimated and the seed consumption is accurately counted; The seed metering device's single-turn discharge rate is used for rapid diagnosis, horizontal comparison of each seed metering device's discharge rate, display of interval anomalies and instantaneous faults, and segmented statistics of work quality. The total seeding volume over the past 7 days, and the overall trend of the line, directly reflect the production rhythm. By comparing the actual line with the reference line for seeding volume, the difference between the actual line and the reference line can be used to assess whether the production task is progressing as planned. The layout of the seeding operation system in the facility environment presents the current virtual operating status and compares it with the current actual operating status. When there is a deviation between the virtual model and the physical entity data, the platform triggers a warning, accurately locates abnormal equipment, and forms a precise control closed loop of "comparison-early warning-adjustment-execution", which improves the automation level and reliability of facility agriculture. The detailed diagram of the seed metering device's movement shows a virtual demonstration of the seed metering device's operation process.

4. The digital twin intelligent monitoring method for a precision vegetable seeder with small particle size according to claim 3, characterized in that, The APP control and detection terminal receives data from the communication module, monitors the equipment status at any time via mobile phone, and controls the running speed of the seeder through the APP.

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