Intelligent pasture under the PLC and internet of things collaborative feeding control system of mutton sheep breeding

Abstract

This invention discloses a PLC and IoT-based collaborative feeding control system for sheep farming in a smart ranch, belonging to the field of livestock farming automation control technology. The system includes a local controller with a built-in PLC controller; a sensing terminal located inside the feed hopper; a remote management terminal including a cloud server and terminal devices communicating with the cloud server; and a data interaction terminal communicating between the local controller and the remote management terminal. This invention generates a baseline control quantity based on a preset feeding plan through a feedforward control branch, and simultaneously generates a compensation quantity by performing PID calculations on the deviation between the actual and target feeding rates through a feedback control branch. The two are then superimposed to form the final control quantity. This composite control strategy ensures the stability of the feeding process while rapidly responding to dynamic changes in sheep feeding behavior, avoiding the problems of insufficient feeding or feed waste in traditional open-loop control, and effectively reducing feed loss.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for livestock farming, and in particular to a PLC and Internet of Things collaborative feeding control system for sheep farming in a smart ranch. Background Technology

[0002] In recent years, Programmable Logic Controller (PLC) technology has been gradually introduced into the livestock farming field due to its advantages such as high reliability, strong anti-interference ability, and suitability for industrial environments. PLC controllers can automatically control feeding equipment according to preset logic programs, achieving timed and quantitative feed delivery, thus replacing manual operation to a certain extent. At the same time, the rapid development of Internet of Things (IoT) technology has also provided new technical means for remote monitoring and data management in farms—by deploying various sensors in key locations such as feed towers and pens, combined with the data storage and analysis capabilities of cloud platforms, real-time collection, remote viewing, and statistical analysis of livestock data can be achieved.

[0003] However, a common problem in existing technologies is that PLC control systems and IoT systems are often two independent systems. PLC control systems focus on real-time logic control on-site, but lack remote data interaction and intelligent decision-making capabilities; IoT systems focus on remote data acquisition and cloud analysis, but the issuance of control commands suffers from network latency, making it difficult to meet the real-time and reliability requirements during feeding. Furthermore, existing intelligent feeding systems have the following shortcomings in large-scale sheep farming scenarios: First, feed tower weighing sensors mostly use simple weight detection methods, lacking self-diagnostic capabilities for sensor installation status. Unstable installation can lead to systematic deviations in weighing data, affecting subsequent feed quantity calculations; second, feeding control strategies are mostly simple timed and quantitative open-loop control, lacking a closed-loop adjustment mechanism based on real-time feed intake feedback, and unable to cope with dynamic changes in sheep feeding behavior. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a PLC and IoT collaborative feeding control system for sheep farming in a smart ranch, which achieves the organic integration of real-time on-site control and remote intelligent management, thereby improving the feeding accuracy and feed utilization rate of large-scale sheep farming.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a PLC and IoT collaborative feeding control system for sheep farming in a smart ranch, comprising:

[0008] The local controller has a built-in PLC controller, which is connected to the feed line conveyor actuator and is used to drive the actuator to deliver feed to the target pen according to a preset feeding plan.

[0009] The sensing end is set inside the feed tower and includes at least a weighing sensor and a feeding monitoring unit. The weighing sensor is used to detect the remaining amount in the feed tower in real time, and the feeding monitoring unit is used to detect the feeding behavior parameters of the sheep in real time.

[0010] The remote management terminal includes a cloud server and a terminal device that communicates with the cloud server.

[0011] The data interaction terminal is a communication connection between the local controller and the remote management terminal. It is used to upload the field data collected by the sensing terminal to the cloud server and forward the control parameters issued by the remote management terminal to the local controller.

[0012] Preferably, the local controller calculates the actual feeding rate based on the rate of change of the feed tower balance fed back by the weighing sensor, compares the actual feeding rate with the target feeding rate, and adjusts the feed dispensing speed of the actuator in real time according to the comparison result until the target feeding amount is reached.

[0013] The remote management terminal generates feeding optimization parameters based on the field data uploaded by the data interaction terminal, and sends them to the local controller through the data interaction terminal to update the preset feeding plan.

[0014] Preferably, redundant communication links are used to connect the local controller and the data interaction terminal, as well as between the data interaction terminal and the remote management terminal. The redundant communication links include at least wireless communication links and wired communication links.

[0015] The data interaction terminal has a built-in data cache unit. When the wireless communication link is interrupted, the data interaction terminal temporarily stores the field data in the data cache unit and timestamps it. After the wireless communication link is restored, the temporarily stored data is retransmitted to the remote management terminal in the order of the timestamps.

[0016] The cache capacity of the data cache unit ,in The data generation rate per unit time. This is the preset maximum tolerable network outage duration.

[0017] Preferably, the weighing sensor includes multiple weighing modules respectively disposed on each support leg of the tower, and each weighing module outputs its detected load value in real time;

[0018] The local controller or the remote management terminal determines the installation stability of the tower based on the discrete distribution of the load values ​​output by each weighing module. When the deviation between the load values ​​of each support leg exceeds a preset threshold, an installation abnormality warning signal is generated.

[0019] During the period when the installation abnormality warning signal is triggered, the local controller compensates and corrects the remaining value of the material tower fed back by the weighing sensor according to the weighted average of the load values ​​of each support leg. The support leg with a smaller load value has a larger weight coefficient, and the sum of all weight coefficients is equal to 1.

[0020] Preferably, the local controller has a built-in feedforward-feedback composite control model, including a feedforward control branch and a feedback control branch;

[0021] The feedforward control branch generates a feedforward control quantity based on the target feeding amount and preset conveying speed in the preset feeding plan.

[0022] The feedback control branch generates a feedback compensation value by sequentially performing proportional, integral, and differential operations on the deviation between the actual feeding rate and the target feeding rate.

[0023] The local controller generates a final control quantity by superimposing the feedforward control quantity and the feedback compensation quantity, and adjusts the operating frequency of the actuator according to the final control quantity.

[0024] Preferably, the local controller monitors the cumulative actual amount of feed in real time based on the remaining amount of the feed tower fed by the weighing sensor. When the cumulative actual amount of feed reaches the target feeding amount, the controller controls the actuator to stop running and records the end time of the feeding.

[0025] The local controller calculates the average feeding rate for this feeding based on the target feeding amount and the time difference between the start and end times of this feeding.

[0026] When the relative deviation between the average feeding rate and the target feeding rate exceeds a preset threshold, the local controller adjusts the target feeding rate for the next feeding cycle by increasing or decreasing it.

[0027] Preferably, the remote management terminal includes:

[0028] The data fusion module is used to receive the field data uploaded by the data interaction terminal, perform time axis alignment and format unification processing on various types of time series data, and generate a standardized fusion dataset.

[0029] The decision analysis module is used to call the fused dataset to perform feeding consumption analysis, feed conversion ratio calculation and equipment operating efficiency evaluation, and generate the feeding optimization parameters based on the analysis results.

[0030] Preferably, the decision analysis module constructs a feed conversion ratio prediction model, which uses the cumulative feed consumption sequence and sheep weight gain sequence in the historical fusion dataset as training samples to fit the mapping relationship between the feed conversion ratio and feeding parameters and environmental parameters.

[0031] During the current breeding cycle, the decision analysis module inputs the current fused data into the feed conversion ratio prediction model to predict the final feed conversion ratio at the end of the current breeding cycle.

[0032] When the predicted result of the final feed conversion ratio exceeds the preset standard range, the decision analysis module performs reverse iteration by adjusting the feeding frequency and / or the amount of feed per feeding, so that the adjusted predicted feed conversion ratio approaches the target feed conversion ratio, and uses the solution result as the feeding optimization parameter.

[0033] Preferably, the local controller calculates the actual feeding rate based on the effective feeding time of each feeding trough and the total amount of feed delivered within the corresponding time period;

[0034] The local controller is also connected to an environmental sensor for real-time detection of the ambient temperature inside the enclosure.

[0035] The local controller has a feeding anomaly discrimination model. The model multiplies the feeding rate deviation and the ambient temperature deviation by preset weighting coefficients and then adds them together to obtain a comprehensive discrimination index. When the absolute value of the comprehensive discrimination index exceeds a preset warning threshold, a feeding anomaly warning signal is generated and uploaded to the remote management terminal.

[0036] Preferably, the data interaction terminal has a built-in protocol conversion unit and is pre-configured with multiple industrial fieldbus protocol stacks;

[0037] When the data interaction terminal connects to the local controller, the protocol conversion unit attempts to send handshake requests to the local controller for each protocol stack in turn, and locks the current protocol stack after receiving a correct response.

[0038] The data interaction terminal encapsulates the downlink control parameters from the remote management terminal into the protocol format currently matched by the local controller and then forwards them to the local controller.

[0039] (III) Beneficial Effects

[0040] Compared with existing technologies, this invention provides a PLC and IoT collaborative feeding control system for sheep farming in a smart ranch, which has the following beneficial effects:

[0041] I. This invention establishes an independent data interaction terminal as an intermediary layer between the local controller and the remote management terminal. The local controller is responsible for millisecond-level closed-loop feeding adjustment, unaffected by network latency, ensuring real-time and reliable control. The remote management terminal is responsible for global data aggregation and analysis and parameter optimization, distributing optimization results over the network. Both functions work in tandem, overcoming the network latency issues inherent in pure cloud-based control while compensating for the lack of data support and remote management capabilities in purely local control.

[0042] Second, this invention generates a baseline control quantity based on a preset feeding plan through a feedforward control branch, and simultaneously generates a compensation quantity by performing PID calculations on the deviation between the actual feeding rate and the target feeding rate through a feedback control branch. The two are then superimposed to form the final control quantity. This composite control strategy ensures the stability of the feeding process while rapidly responding to dynamic changes in the sheep's feeding behavior, avoiding the problems of insufficient feeding or feed waste in traditional open-loop control, and effectively reducing feed loss. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall system architecture of the present invention;

[0044] Figure 2 This is a schematic diagram illustrating the stability detection and calibration of the weighing sensor installation according to the present invention.

[0045] Figure 3 This is a functional schematic diagram of the feedforward-feedback composite control model of the present invention. Detailed Implementation

[0046] To better understand the purpose, structure, and function of this invention, the following description, in conjunction with the accompanying drawings, will further illustrate a PLC and IoT collaborative feeding control system for sheep farming in a smart ranch.

[0047] Example 1

[0048] like Figure 1 As shown, the system in this embodiment is deployed according to a four-terminal architecture consisting of a local controller, a sensing terminal, a data interaction terminal, and a remote management terminal.

[0049] This embodiment uses a large-scale sheep farm as the application scenario. The farm has 80,000 sheep, which are divided into multiple breeding pens. It is equipped with 66 intelligent feed towers, an automated feed line with a total length of more than 10,000 meters, and 500 sets of automatic feeding troughs.

[0050] Specifically, the local controller uses a Siemens 1214C PLC controller as the core control unit, installed in the control cabinet in the ranch's central control room. The PLC controller connects to remote I / O modules distributed throughout each pen via a Profibus fieldbus, expanding the number of analog inputs, digital inputs, and digital outputs. The PLC controller's analog output modules connect to the frequency converters of each feed line drive unit via shielded cables, controlling the inverter's operating frequency by outputting a 0-10V voltage signal, thereby adjusting the drive motor speed. The PLC controller's digital output modules connect to the contactors of each actuator via intermediate relays, controlling the start and stop of the drive unit.

[0051] Furthermore, the sensing end includes weighing sensors and a feed monitoring unit. The weighing sensors are strain gauge modules, one installed on each of the four support legs of each feed tower. Each weighing module integrates a Wheatstone bridge composed of high-precision strain gauges. When the feed tower is loaded with feed, the support legs experience slight deformation under stress, and the bridge outputs a millivolt-level differential voltage signal proportional to this deformation. This signal is amplified, filtered, and temperature-compensated by the built-in signal conditioning circuit, then converted into a standard industrial signal and transmitted via RS485 bus using the Modbus RTU protocol to the analog input module of the PLC controller. The feed monitoring unit installs a pair of infrared beam sensors above each feed trough—the transmitter and receiver are installed on opposite sides of the trough—when sheep extend their heads to feed, the infrared beam is blocked, and the receiver outputs a level change signal, which is sent to the PLC controller via the digital input module. The PLC controller obtains effective feeding time data by recording the duration of the level change in each feed trough.

[0052] Furthermore, the data interaction terminal adopts an industrial-grade IoT gateway, supporting 4G / 5G wireless communication and gigabit wired Ethernet communication. The IoT gateway connects to the PLC controller via an Ethernet interface, using the Modbus TCP protocol for data reading and writing. The IoT gateway internally configures an industrial-grade SD card as a data buffer and also incorporates a multi-protocol conversion unit, supporting automatic identification and switching of multiple protocol stacks such as Modbus RTU, Modbus TCP, and PROFIBUS.

[0053] The remote management terminal is deployed on a cloud server using a Linux operating system. It is equipped with a relational database to store basic aquaculture information and business data, and a time-series database to store high-frequency sensor data. The remote management terminal also provides a PC-based web management platform and a mobile app, allowing administrators to log in to the system from any terminal device to view data and adjust parameters.

[0054] like Figure 2 As shown, after the load cell is installed, the system will automatically perform an installation stability test.

[0055] Taking a material tower as an example, the initial load values ​​output by the weighing modules installed on the four support legs of the tower under no-load conditions are 245kg, 252kg, 238kg, and 265kg, respectively. After reading these four values, the local controller calculates the difference between the maximum and minimum values ​​and the average value to determine the dispersion of the load distribution. The system's preset installation stability threshold is 5%. Since the current maximum deviation exceeds this threshold, the system determines that the tower installation is unstable, generates an "installation anomaly warning signal," and pushes it to the management personnel's mobile APP. After on-site inspection, the management personnel found that the shim thickness of one of the support legs was inappropriate, causing the load on that support leg to be too low. After readjusting the shim, the load values ​​of the four weighing modules became 252kg, 255kg, 248kg, and 253kg, with the deviation controlled within 5%. The system determined that the installation was qualified and automatically exited the warning state.

[0056] like Figure 3 As shown, the core control logic of the local controller is a feedforward-feedback composite control model. The following section will explain its working process in detail using a complete feeding cycle as an example.

[0057] Suppose a pen currently houses 500 fattening sheep, and the feeding plan stipulates that feeding will begin at 6:00 AM, with a target feed amount of 500 kg and a target feed intake rate of 10 kg / min.

[0058] At 6:00 AM sharp, the clock interrupt inside the PLC controller triggers the feeding start command. The PLC's digital output module sends a start signal to the corresponding feed line drive unit, the contactor engages, the drive motor starts running, and feed is transported from the central feed tower through the feed line to the feeding trough in the pen.

[0059] During feeding, the PLC controller continuously reads the real-time remaining amount of the feed tower at a sampling cycle of 5 seconds. In the first sampling cycle, the PLC reads a decrease of 0.8 kg in the feed tower's remaining amount, calculating the current instantaneous feeding rate to be 9.6 kg / min, a deviation of 0.4 kg / min from the target feeding rate of 10 kg / min. This deviation is fed into the PID calculation module. The proportional loop provides an immediate adjustment based on the current deviation, the integral loop eliminates static errors based on the accumulation of previous deviations, and the derivative loop predicts changes based on the trend of the deviation. These three factors are combined to generate a feedback compensation quantity. Simultaneously, the feedforward control branch generates a baseline control quantity based on the target feeding amount and the preset conveying speed. The feedback compensation quantity is superimposed on the feedforward control quantity to form the final control quantity. The PLC controller adjusts the inverter's operating frequency based on the final control quantity, thereby controlling the drive motor's speed.

[0060] In simple terms, if the sheep eat slower than expected, the system will appropriately increase the feeding speed; if the sheep eat faster than expected, the system will appropriately decrease the feeding speed. The entire adjustment process is repeated every 5 seconds, which means that the system is constantly running in a cycle of sensing, comparing, and adjusting, ensuring a dynamic match between the feeding speed and the eating speed throughout the feeding process.

[0061] Furthermore, when the cumulative actual feed intake reaches 500 kg, the PLC controller immediately issues a stop signal, the feed line drive stops operating, and the feeding session ends. Assuming this feeding session started at 6:00 and ended at 6:42:30, lasting 42.5 minutes and consuming 500 kg of feed, the average feed intake rate for this session was approximately 11.76 kg / min. This deviates from the target feed intake rate of 10 kg / min by approximately 17.6%, exceeding the system's preset tolerance threshold of 10%. Based on this, the system determines that the sheep's feeding capacity is significantly higher than the preset value and automatically adjusts the target feed intake rate for the next feeding cycle by approximately 30% towards the current actual value, achieving autonomous optimization of control parameters.

[0062] In this embodiment of the invention, the data interaction terminal is deployed in the ranch's central control room, communicating with the cloud server via a 4G / 5G wireless network, while using a wired Ethernet link as a backup. In actual operation, the ranch's location occasionally experiences unstable 4G signals.

[0063] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A PLC and IoT collaborative feeding control system for sheep farming in a smart ranch, characterized in that, include: The local controller has a built-in PLC controller, which is connected to the feed line conveyor actuator and is used to drive the actuator to deliver feed to the target pen according to a preset feeding plan. The sensing end is set inside the feed tower and includes at least a weighing sensor and a feeding monitoring unit. The weighing sensor is used to detect the remaining amount in the feed tower in real time, and the feeding monitoring unit is used to detect the feeding behavior parameters of the sheep in real time. The remote management terminal includes a cloud server and a terminal device that communicates with the cloud server. The data interaction terminal is a communication connection between the local controller and the remote management terminal. It is used to upload the field data collected by the sensing terminal to the cloud server and forward the control parameters issued by the remote management terminal to the local controller.

2. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 1, characterized in that, The local controller calculates the actual feeding rate based on the rate of change of the feed tower balance fed back by the weighing sensor, compares the actual feeding rate with the target feeding rate, and adjusts the feed dispensing speed of the actuator in real time according to the comparison result until the target feeding amount is reached. The remote management terminal generates feeding optimization parameters based on the field data uploaded by the data interaction terminal, and sends them to the local controller through the data interaction terminal to update the preset feeding plan.

3. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 2, characterized in that, The local controller and the data interaction terminal, as well as the data interaction terminal and the remote management terminal, are connected by redundant communication links. The redundant communication links include at least wireless communication links and wired communication links. The data interaction terminal has a built-in data cache unit. When the wireless communication link is interrupted, the data interaction terminal temporarily stores the field data in the data cache unit and timestamps it. After the wireless communication link is restored, the temporarily stored data is retransmitted to the remote management terminal in the order of the timestamps. The cache capacity of the data cache unit ,in The data generation rate per unit time. This is the preset maximum tolerable network outage duration.

4. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 1, characterized in that, The weighing sensor includes multiple weighing modules respectively installed on each support leg of the tower, and each weighing module outputs its detected load value in real time; The local controller determines the installation stability of the tower based on the discrete distribution of the load values ​​output by each weighing module. When the deviation between the load values ​​of each support leg exceeds a preset threshold, an installation abnormality warning signal is generated. During the period when the installation abnormality warning signal is triggered, the local controller compensates and corrects the remaining value of the material tower fed back by the weighing sensor according to the weighted average of the load values ​​of each support leg. The support leg with a smaller load value has a larger weight coefficient, and the sum of all weight coefficients is equal to 1.

5. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 1, characterized in that, The local controller has a built-in feedforward-feedback composite control model, including a feedforward control branch and a feedback control branch; The feedforward control branch generates a feedforward control quantity based on the target feeding amount and preset conveying speed in the preset feeding plan. The feedback control branch generates a feedback compensation value by sequentially performing proportional, integral, and differential operations on the deviation between the actual feeding rate and the target feeding rate. The local controller generates a final control quantity by superimposing the feedforward control quantity and the feedback compensation quantity, and adjusts the operating frequency of the actuator according to the final control quantity.

6. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 5, characterized in that, The local controller monitors the cumulative actual amount of feed in real time based on the remaining amount of the feed tower fed by the weighing sensor. When the cumulative actual amount of feed reaches the target feeding amount, the controller controls the actuator to stop running and records the end time of the feeding. The local controller calculates the average feeding rate for this feeding based on the target feeding amount and the time difference between the start and end times of this feeding. When the relative deviation between the average feeding rate and the target feeding rate exceeds a preset threshold, the local controller adjusts the target feeding rate for the next feeding cycle by increasing or decreasing it.

7. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 1, characterized in that, The remote management terminal includes: The data fusion module is used to receive the field data uploaded by the data interaction terminal, perform time axis alignment and format unification processing on various types of time series data, and generate a standardized fusion dataset. The decision analysis module is used to call the fused dataset to perform feeding consumption analysis, feed conversion ratio calculation and equipment operating efficiency evaluation, and generate the feeding optimization parameters based on the analysis results.

8. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 7, characterized in that, The decision analysis module constructs a feed conversion ratio prediction model, which uses the cumulative feed consumption sequence and sheep weight gain sequence in the historical fusion dataset as training samples to fit the mapping relationship between the feed conversion ratio and feeding parameters and environmental parameters. During the current breeding cycle, the decision analysis module inputs the current fused data into the feed conversion ratio prediction model to predict the final feed conversion ratio at the end of the current breeding cycle. When the predicted result of the final feed conversion ratio exceeds the preset standard range, the decision analysis module performs reverse iteration by adjusting the feeding frequency and the amount of feed per feeding, so that the adjusted predicted feed conversion ratio approaches the target feed conversion ratio, and the solution result is used as the feeding optimization parameter.

9. The PLC and IoT collaborative feeding control system for sheep farming in a smart ranch according to claim 1, characterized in that, The local controller calculates the actual feeding rate based on the effective feeding time of each feeding trough and the total amount of feed delivered within the corresponding time period. The local controller is also connected to an environmental sensor for real-time detection of the ambient temperature inside the enclosure. The local controller has a feeding anomaly discrimination model. The model multiplies the feeding rate deviation and the ambient temperature deviation by preset weighting coefficients and then adds them together to obtain a comprehensive discrimination index. When the absolute value of the comprehensive discrimination index exceeds a preset warning threshold, a feeding anomaly warning signal is generated and uploaded to the remote management terminal.

10. A PLC and IoT collaborative feeding control system for sheep farming in a smart ranch, as described in claim 1, is characterized in that... The data interaction terminal has a built-in protocol conversion unit and is pre-configured with multiple industrial fieldbus protocol stacks. When the data interaction terminal connects to the local controller, the protocol conversion unit attempts to send handshake requests to the local controller for each protocol stack in turn, and locks the current protocol stack after receiving a correct response. The data interaction terminal encapsulates the downlink control parameters from the remote management terminal into the protocol format currently matched by the local controller and then forwards them to the local controller.

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