Intelligent management system and method for finishing pig breeding and related equipment

Abstract

The application provides a fattening pig breeding intelligent management system, method and related equipment, the system provided by the application adopts a star topology network, can realize long-distance and low-power consumption transmission of low-throughput data in real time, and can also realize real-time transmission of high-throughput data, has efficient data transmission and stable communication guarantee.Once a communication protocol in the first transmission module or the second transmission module is invalid, another protocol can automatically take over, which can effectively ensure stable operation of the system.In addition, the environment monitoring module and the pig detection module of the intelligent management system provided by the embodiment of the application can realize real-time and accurate monitoring of the breeding farm environment and the health status of the fattening pigs through various sensors and cameras, discover health problems of the fattening pigs in time, effectively improve accurate monitoring, scientific management and intelligent control of the whole growth process of the fattening pigs, and can improve breeding efficiency, guarantee pork quality and improve economic benefits of the breeding farm.

Description

Technical Field

[0001] This application relates to the field of intelligent farming technology, and in particular to an intelligent management system, method and related equipment for fattening pig farming. Background Technology

[0002] In modern animal husbandry, fattening pig farming is a crucial link in pork production. With population growth and increasing demand for pork, traditional fattening pig farming methods face numerous challenges.

[0003] In existing pig farm management techniques, traditional fattening pig farm management typically relies on manual observation and experience-based judgment. Farm staff need to frequently inspect the pigpens to monitor the pigs' health, feeding habits, and behavior. However, this approach has many limitations. Manual observation is not only time-consuming and labor-intensive, but also difficult to achieve real-time and comprehensive monitoring, easily overlooking crucial information. Regarding feed management, traditional methods often rely on estimations to provide feed, failing to accurately supply feed according to the actual needs of each pig, leading to feed waste or nutritional imbalances. Health monitoring is often only discovered after pigs have shown obvious symptoms, at which point treatment is often difficult, increasing treatment costs and potentially impacting growth or even causing death, resulting in economic losses for the farm. Environmental control is also a challenge in traditional farming. Maintaining optimal temperature, humidity, and ventilation conditions is difficult, affecting the growth rate and health of fattening pigs.

[0004] With the development of technology, intelligent technologies are gradually being applied to various fields, but their application in the fattening pig farming sector remains relatively lagging. Currently, although some farms have begun to introduce some automated equipment, most of this equipment is single-function, lacks systematization and integration, and cannot achieve comprehensive, precise, and efficient farm management. Therefore, developing a comprehensive intelligent farm management system for fattening pigs to achieve precise monitoring, scientific management, and intelligent control throughout the entire growth process has become an urgent need to improve farming efficiency, ensure pork quality, and enhance the economic benefits of farms. Summary of the Invention

[0005] This application aims to at least solve one of the aforementioned technical defects. In view of this, this application provides an intelligent management system, method and related equipment for fattening pig farming, which is used to solve the technical defect that the farm cannot achieve intelligent management of the farmed animals in the prior art.

[0006] A smart management system for fattening pig farming includes: remote devices, a cloud server, and a gateway module, wherein the gateway module includes: a coordination and control module, a first transmission module, a second transmission module, a power supply module, an environmental monitoring module, and a pig monitoring module;

[0007] The coordination control module is connected to the environmental detection module, the pig detection module, the power supply module, the first transmission module, and the second transmission module, respectively.

[0008] The environmental monitoring module is responsible for collecting environmental parameters of the target fattening pig farm and transmitting them to the coordination and control module;

[0009] The pig detection module is responsible for collecting activity and status data of each target fattening pig in real time, analyzing the data, and transmitting the analyzed data to the coordination and control module.

[0010] The coordination and control module receives data transmitted from the environmental detection module and the pig detection module, analyzes it, and then transmits it to the cloud server through the first transmission module and the second transmission module respectively, according to the data type, for users to view.

[0011] The remote device is used to allow users to view the data of the cloud server, and to receive the target instructions made by the user based on viewing the data of the cloud server, and transmit them to the coordination and control module so that the coordination and control module can adjust the operation strategies of the environmental detection module and the pig detection module in real time according to the target instructions.

[0012] The power module is responsible for supplying power to all modules in the gateway module except for the power module itself.

[0013] Preferably, the environmental monitoring module includes a temperature and humidity monitoring unit, a gas concentration monitoring unit, a pressure monitoring unit, and a liquid level monitoring unit;

[0014] Based on this, the process by which the environmental monitoring module collects environmental parameters of the target fattening pig farm includes:

[0015] The temperature and humidity detection unit is responsible for real-time monitoring of temperature and humidity data in the target fattening pig farm.

[0016] The gas concentration detection unit is responsible for detecting the concentration data of harmful gases in the air of the target fattening pig farm;

[0017] The pressure detection unit is responsible for detecting the weight data of the target fattening pig;

[0018] The liquid level detection unit is responsible for detecting the remaining amount of feed or liquid in the feeding device of the target fattening pig.

[0019] Preferably, the pig detection module includes an image acquisition unit and an edge computing unit. Based on this, the pig detection module collects activity data and status data of each target fattening pig in real time, analyzes the data, and transmits the analyzed data to the coordination and control module. The process includes:

[0020] The image acquisition unit collects the activity trajectory data and status data of each target fattening pig in real time and transmits them to the edge computing unit. The collected activity trajectory data and status data of the target fattening pig include image data and video stream data.

[0021] The edge computing unit receives the activity trajectory data and status data of each target fattening pig transmitted by the image acquisition unit, and performs real-time analysis to determine the movement trajectory, behavior, ID, and location information of each target fattening pig, and transmits it to the coordination and control module.

[0022] Preferably, the feeding device includes: a control panel, a camera device, a feeding port, a feeder, a feed trough, a water trough, a waterer, and an emergency switch button;

[0023] The emergency switch button is deployed on the control panel and is used by the user to turn off or start the feeding device based on a preset emergency shutdown or start strategy.

[0024] The camera device is connected to the control panel and is used to monitor the status of the feeding port, the feeder, the feed basin, the water basin, and the water feeder, respectively.

[0025] The control panel is used to control the operation of various parts of the feeding device;

[0026] The feeding port is installed above the feeder and is used to feed each of the target fattening pigs.

[0027] The feed trough is installed below the feeder and is used to hold the feed for feeding each of the target fattening pigs. The feeder includes a first liquid level sensor, which is used to monitor the remaining feed data of the feeder.

[0028] The water basin is installed below the water feeder and is used to hold the liquid for feeding the target fattening pigs; wherein, the water feeder includes a stirring device, a second liquid level sensor and a weight sensor, the stirring device is used to stir the liquid or solid in the water feeder, the second liquid level sensor is used to monitor the liquid remaining data in the water feeder, and the weight sensor is used to monitor the liquid weight data in the water feeder.

[0029] Preferably, the process by which the edge computing unit receives the activity trajectory data and status data of each of the target fattening pigs transmitted by the image acquisition unit, and performs real-time analysis to determine the movement trajectory, behavior, ID, and location information of each target fattening pig includes:

[0030] The edge computing unit receives the activity trajectory data and status data of each target fattening pig, and performs real-time detection on them through a preset detection and analysis model to obtain the bounding box and behavior classification results of each target fattening pig. The preset detection and analysis model is trained using the activity trajectory dataset and status data of the target fattening pig as training samples, and using the bounding box and behavior classification results of the training fattening pig included in the activity trajectory data and status data of the training target fattening pig as sample labels.

[0031] Multi-target tracking is performed on each target fattening pig, and a unique ID is assigned to each target fattening pig. At the same time, the movement path of each target fattening pig is recorded.

[0032] Based on the bounding boxes of each target fattening pig, extract image data for each target fattening pig;

[0033] The image data of each target fattening pig is preprocessed to obtain the edge information of the ID of each target fattening pig;

[0034] Edge information of the ID of each target fattening pig is identified to obtain the identification result of the ID of each target fattening pig;

[0035] The identification result of the ID of each target fattening pig is calibrated with the ID determined for tracking it to determine the ID of each target fattening pig;

[0036] Based on the movement trajectory and ID of each target fattening pig, the movement trajectory, behavior, and location information of each target fattening pig are determined.

[0037] Preferably, the control panel includes a control unit and a display unit, the display unit being used to display the operating status of each part of the feeding device, the environmental data collected by the environmental detection module, and the activity dataset status data of each target fattening pig collected by the pig detection module;

[0038] The control unit is used to allow users to intelligently adjust the parameters of the target fattening pig farm and query the historical data of the target fattening pig farm and the historical data of each target fattening pig.

[0039] Preferably, the system further includes: a health monitoring system, which is responsible for analyzing the environmental conditions of the target fattening pig farm and the activity and status data of each target fattening pig based on data from the cloud server, and determining whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig and whether there are any abnormalities in the growth trend or growth status of each target fattening pig. If it is determined that the environment of the target fattening pig farm does not meet the growth requirements of each target fattening pig or that there are target fattening pigs with abnormal growth trends or growth status, an early warning signal for environmental abnormality or fattening pig abnormality is issued; and adjustment measures for the target fattening pig farm or target fattening pigs are determined based on the environmental abnormality early warning signal or fattening pig abnormality early warning information; and the adjustment measures for the target fattening pig farm or target fattening pigs are displayed on the display unit.

[0040] A method for intelligent management of fattening pig farming, applied to any of the aforementioned intelligent management systems for fattening pig farming, the method comprising:

[0041] Collect environmental parameters from the target fattening pig farm;

[0042] Based on the tracker of each target fattening pig, the activity data and status data of each target fattening pig are collected in real time;

[0043] The environmental parameters of the target fattening pig farm and the activity and status data of the target fattening pigs are saved in real time.

[0044] Analyze the environmental parameters of the target fattening pig farm to determine whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig. If the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pig, issue an environmental anomaly warning signal based on the environmental parameters of the target fattening pig farm, determine environmental rectification measures for the target fattening pig farm, and adjust the environmental parameters of the target fattening pig farm in real time.

[0045] Analyze the activity and status data of the target fattening pigs to determine the movement trajectory, behavior, location information, and ID of each target fattening pig;

[0046] Analyze the movement trajectory, behavior, location information, and ID of the target fattening pigs to determine the growth status of each target fattening pig;

[0047] Analyze the growth of each target fattening pig to determine if there are any target fattening pigs with abnormal growth. If there are target fattening pigs with abnormal growth, issue a fattening pig abnormality warning message based on the ID of the target fattening pig with abnormal growth, determine the fattening pig abnormality warning message corresponding to the target fattening pig with abnormal growth, determine the maintenance and rectification measures for the target fattening pig with abnormal growth, and adjust the maintenance measures for the target fattening pig with abnormal growth based on the determined maintenance and rectification measures.

[0048] Based on the environmental parameters of the target fattening pig farm and the growth status of each target fattening pig, a control strategy for the target fattening pig farm is determined.

[0049] Based on the control strategy of the target fattening pig farm, the feed and liquid delivered to the feeding device of the target fattening pig farm are adjusted in real time.

[0050] A smart management device for fattening pig farming includes: one or more processors, and a memory;

[0051] The memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of the intelligent management method for fattening pig farming as described above.

[0052] A readable storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the intelligent management method for fattening pig farming as described above.

[0053] As can be seen from the technical solutions described above, the intelligent management system provided in this application embodiment may include a remote device, a cloud server, and a gateway module. The gateway module may include a coordination control module, a first transmission module, a second transmission module, a power supply module, an environmental monitoring module, and a pig monitoring module. The coordination control module is connected to the environmental monitoring module, the pig monitoring module, the power supply module, the first transmission module, and the second transmission module. The environmental monitoring module is responsible for collecting environmental parameters of the target fattening pig farm and transmitting them to the coordination control module. The pig monitoring module is responsible for collecting real-time activity and status data of each target fattening pig, analyzing it, and transmitting the analyzed data to the coordination control module. The coordination control module receives the data transmitted by the environmental monitoring module and the pig monitoring module, analyzes it, and then... The data is transmitted to the cloud server via a first transmission module and a second transmission module for user access. The first transmission module can transmit high-throughput data, such as large datasets like video streams, while the second transmission module can transmit low-throughput data over long distances with low power consumption. If either the first or second transmission module fails, the other can automatically take over, ensuring stable system operation. The remote device allows users to view data from the cloud server and receives user commands based on this data, transmitting them to the coordination and control module. The coordination and control module then adjusts the operating strategies of the environmental detection module and the pig detection module in real time according to these commands. The power module supplies power to all modules in the gateway module except the power module itself.

[0054] Therefore, the intelligent management system for fattening pigs provided in this application embodiment manages the target fattening pig farm. The system employs a star topology network, enabling real-time, long-distance, low-power transmission of low-throughput data, as well as real-time transmission of high-throughput data, providing efficient data transmission and stable communication. The system also features redundancy; if one communication protocol in the first or second transmission module fails, another protocol automatically takes over, effectively ensuring stable system operation. Furthermore, the environmental monitoring module and pig detection module of the intelligent management system provided in this application embodiment, through various sensors and cameras, can achieve real-time and accurate monitoring of the farm environment and the health status of fattening pigs, promptly detecting health problems and effectively improving the accurate monitoring, scientific management, and intelligent control of the entire fattening pig growth process. This can improve breeding efficiency, ensure pork quality, and enhance the economic benefits of the farm. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A schematic diagram of a system architecture for intelligent management of fattening pigs is provided as an embodiment of this application;

[0057] Figure 2 This application provides a schematic diagram of another system architecture for implementing intelligent management of fattening pigs.

[0058] Figure 3 This is a front view of a feeding device provided in an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the upper and lower isometric projections of a feeding device provided in an embodiment of this application;

[0060] Figure 5 This is a schematic diagram of a water feeder structure provided in an embodiment of this application;

[0061] Figure 6 A schematic diagram of the network structure of a detection and analysis model provided in an embodiment of this application;

[0062] Figure 7 This is a schematic diagram of the structure of a gateway module provided in an embodiment of this application;

[0063] Figure 8 A flowchart illustrating a method for intelligent management of fattening pig farming, provided in an embodiment of this application;

[0064] Figure 9 This is a schematic diagram illustrating the structure of an intelligent management device for fattening pig farming, as exemplified in an embodiment of this application.

[0065] Figure 10 This is a hardware structure block diagram of the intelligent management device for fattening pig farming disclosed in an embodiment of this application. Detailed Implementation

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] In existing pig farm management techniques, manual inspection is the most traditional method. Workers periodically inspect the pig farm to check temperature and humidity, feed supply, and the health and behavior of the pigs. While simple, this method is extremely inefficient in large-scale farms, making real-time monitoring and management difficult. Especially when there are sudden changes in the pig farm environment or the health of the pigs, manual inspections cannot detect problems in time, which can easily lead to the spread of disease or insufficient feed supply, increasing management costs and risks.

[0068] Some modern farms have introduced automated sensor monitoring systems. While the solution of wearing or injecting sensors on pigs can record the movement trajectory of pigs, it requires equipment maintenance, which is costly. In addition, wearing or injecting sensors may cause stress reactions in pigs, affecting their health and growth. At the same time, the sensors are easily damaged during actual use, resulting in unstable monitoring data, which further increases the difficulty and cost of maintenance.

[0069] Traditional IoT networking methods typically use a single communication protocol for data transmission, which cannot simultaneously achieve low power consumption and communication stability. Furthermore, most existing IoT systems lack fault redundancy design; if a communication link fails, the system may be paralyzed, leading to data transmission interruption and increasing maintenance complexity and cost.

[0070] Given that most current intelligent management solutions for fattening pig farming are ill-suited to the complex and ever-changing business needs, this applicant has researched and developed an intelligent management solution for fattening pig farming. The intelligent management system for fattening pigs provided in this application manages target fattening pig farms. This system employs a star topology network, enabling real-time, long-distance, low-power transmission of low-throughput data, as well as real-time transmission of high-throughput data, providing efficient data transmission and stable communication. The system also features redundancy; if one communication protocol in the first or second transmission module fails, another protocol automatically takes over, effectively ensuring stable system operation. Furthermore, the environmental monitoring module and pig detection module of the intelligent management system provided in this application utilize various sensors and cameras to achieve real-time and accurate monitoring of the farm environment and the health status of fattening pigs. This allows for timely detection of health problems in fattening pigs, effectively improving the accuracy, scientific management, and intelligent control of the entire fattening pig growth process, thereby increasing farming efficiency, ensuring pork quality, and enhancing the economic benefits of the farm.

[0071] The methods provided in this application can be used in a variety of general-purpose or special-purpose computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc.

[0072] This application provides an intelligent management method for fattening pig farming. This method can be applied to various farming management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0073] The following is combined Figure 1 This application introduces an optional system architecture for intelligent management of fattening pig farms, as provided in its embodiments. Figure 1 As shown, the system architecture may include remote devices, cloud servers, and gateway modules.

[0074] In practical applications, multiple functional nodes can be deployed according to the actual needs of the farm, such as... Figure 2 The system architecture shown allows for communication between remote devices and the cloud server via WiFi, and between the cloud server and the gateway module via Ethernet. Multiple functional nodes can also be deployed in the gateway module.

[0075] For example, multiple functional nodes can be deployed for the gateway module according to the needs of fattening pig farms.

[0076] For example, the gateway module may include a coordination and control module, a first transmission module, a second transmission module, a power supply module, an environmental monitoring module, and a pig monitoring module. The coordination and control module can be connected to the environmental monitoring module, the pig monitoring module, the power supply module, the first transmission module, and the second transmission module, respectively.

[0077] The environmental monitoring module is responsible for collecting environmental parameters of the target fattening pig farm and transmitting them to the coordination and control module.

[0078] In practical applications, the environmental monitoring module can deploy various sensors to collect various environmental parameters of the farm, according to the actual application needs of the farm.

[0079] For example, the types and number of detection units in the environmental monitoring module can be customized according to the specific needs of the farm to meet the needs of farms of different sizes and types.

[0080] For example, an environmental monitoring module may include a temperature and humidity monitoring unit, a gas concentration monitoring unit, a pressure monitoring unit, and a liquid level monitoring unit.

[0081] Among them, the temperature and humidity detection unit can monitor the temperature and humidity data of the target fattening pig farm in real time, so as to provide a suitable growth environment for the fattening pigs;

[0082] The gas concentration detection unit can be responsible for detecting the concentration of harmful gases in the air of the target fattening pig farm, such as ammonia and carbon dioxide, to ensure the air quality of the farm and prevent harmful gases from affecting the health of the pigs.

[0083] The pressure detection unit can detect the weight data of the target fattening pigs so as to monitor the growth status of the fattening pigs. In practical applications, in order to reduce management costs, pressure detection sensors can be installed in the pigsty where the target fattening pigs are active to monitor the weight data of each target fattening pig.

[0084] The liquid level detection unit can be responsible for detecting the remaining amount of feed or liquid in the feeding device of the target fattening pig. For example, the liquid level detection unit can detect the remaining amount of feed in the feed bucket to ensure the timeliness of feed supply.

[0085] The feeding device provided in this application embodiment can be a highly efficient feeding system. It can effectively solve the feeding and drinking problems of fattening pigs. Its design is particularly suitable for small and medium-sized pig farms, especially in penned environments where multiple pigs are eating or drinking at the same time. It can effectively alleviate overcrowding, improve feeding efficiency, and enhance animal welfare.

[0086] Figure 3 and Figure 4 An example is a schematic diagram of a feeding device structure, wherein, Figure 3 This is a front view of the feeding device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the upper and lower isometric projections of the feeding device provided in the embodiments of this application.

[0087] like Figure 3 As shown, the feeding device may include a control panel 1, a camera device 2, a feeding port 3, a feeder 4, a feed trough 5, a water trough 6, a waterer 7, and an emergency switch button 8.

[0088] The emergency switch button 8 can be deployed on the control panel 1. The emergency switch button 8 can be used by the user to turn the feeding device off or on based on a preset emergency shutdown or startup strategy. For example, the emergency switch button can be used to respond to emergencies and minimize losses.

[0089] The control panel 1 may include a control unit and a display unit. The display unit can be used to display the operating status of various parts of the feeding device, environmental data collected by the environmental detection module, and activity and status data of each target fattening pig collected by the pig detection module. The control unit can be used to allow users to intelligently adjust the parameters of the target fattening pig farm and query the historical data of the target fattening pig farm and the historical data of each target fattening pig.

[0090] For example, in practical applications, the control unit and display unit can transmit data via an HDMI interface. The display unit can intuitively present the operating status of each node, as well as collected environmental data, pig behavior data, feed status, and other information, providing a user-friendly interface. The control unit allows users to adjust system parameters and query data through this interface. The system supports viewing historical data; users can choose to view data for a month or a quarter, facilitating the analysis and evaluation of the farm's long-term operation.

[0091] The camera device 2 can be connected to the control panel 1 and can be used to monitor the status of the feeding port 3, feeder 4, feed basin 5, water basin 6 and waterer 7 respectively.

[0092] Control panel 1 is used to control the operation of various parts of the feeding device;

[0093] Feed inlet 3 can be installed above feeder 4 and can be used to feed various target fattening pigs;

[0094] The feed trough 5 can be installed below the feeder 4 and can be used to hold feed for feeding various target fattening pigs. The feeder 4 can include a first liquid level sensor, which can be used to monitor the remaining feed data of the feeder 4. In actual application, a stirring device can also be added to the feeder 4 according to actual breeding needs so that the feed in the feeder 4 can be stirred.

[0095] For example, when water, solvents, or solid nutrients need to be added to the feed, the stirring device in feeder 4 can be activated to ensure that the water, solvents, or solid nutrients in feeder 4 are fully mixed with the feed, thereby improving the feeding effect.

[0096] The water basin 6 can be installed below the water feeder 7 and can be used to hold liquid for feeding the target fattening pigs.

[0097] Among them, such as Figure 5 As shown in the schematic diagram of the water feeder structure, the water feeder 7 may include a stirring device 9, a second liquid level sensor 10, and a weight sensor 11. The stirring device 9 is used to stir the liquid or solid in the water feeder 7. The second liquid level sensor 10 is used to monitor the liquid balance data of the water feeder 7. The weight sensor 11 is used to monitor the liquid weight data of the water feeder.

[0098] For example, to comprehensively monitor the health of pigs, high-resolution cameras can be deployed in the monitoring system to monitor the pigs' drinking and eating behaviors in real time. The data captured by the high-resolution cameras can be used to calculate the number of pigs feeding in real time, and combined with pressure sensors, the remaining amount of feed and water in the feeder can be calculated, allowing for dynamic adjustments to the amount of feed and water provided.

[0099] To optimize animal nutrient intake, a stirring device can be introduced into the feeder, and a similar innovative design can be implemented in the waterer. This design ensures that water is thoroughly and evenly mixed with any added nutrients, medications, or other solutions. This effectively prevents additive precipitation and ensures the solution remains homogeneous. Furthermore, the stirring device significantly accelerates the dissolution rate of additives in water, especially for substances that are difficult to dissolve, improving dissolution efficiency. The uniformly mixed solution avoids localized high or low concentrations, ensuring that all nutrients and medications are fully utilized and reducing waste. This not only improves the effectiveness of medication and nutrient intake in animals, contributing to their health and growth, but also prevents clumping or blockage in the waterer, facilitating proper equipment maintenance and operation.

[0100] Staff can use their experience to feed and water at set times and in set quantities, or set parameters based on statistical data and calculated recommended times and quantities provided by the system. This allows managers to combine their professional knowledge with the system's intelligent recommendations to scientifically manage the entire pig farm's feeding operations.

[0101] The modular design of the feeding device provided in this application facilitates maintenance and upgrades, giving it good adaptability and scalability for long-term use. Overall, this system brings significant technological advancements to the pig farming industry, promoting both improved pig farming efficiency and animal welfare.

[0102] The pig detection module is responsible for collecting activity and status data of each target fattening pig in real time, analyzing the data, and then transmitting the analyzed data to the coordination and control module.

[0103] The pig detection module may include an image acquisition unit and an edge computing unit. The image acquisition unit can collect the activity trajectory data and status data of each target fattening pig in real time and transmit them to the edge computing unit. The collected activity trajectory data and status data of the target fattening pig may include image data and video stream data.

[0104] The edge computing unit can receive the activity trajectory data and status data of each target fattening pig transmitted by the image acquisition unit, and analyze them in real time to determine the movement trajectory, behavior, ID and location information of each target fattening pig and transmit them to the coordination and control module.

[0105] For example, in practical applications, edge computing units have local computing capabilities and can process and analyze the collected data in real time. For instance, by analyzing the collected data of fattening pigs, the behavior, ID, and location information of the fattening pigs can be identified. The identified behavior, ID, and location information of the fattening pigs can be further processed to finally determine the behavior, ID, and location information of the fattening pigs and transmit it to the coordination and control module.

[0106] The process by which the edge computing unit receives the activity trajectory data and status data of each target fattening pig transmitted by the image acquisition unit, and performs real-time analysis to determine the movement trajectory, behavior, ID, and location information of each target fattening pig, may include the following:

[0107] In step S11, the edge computing unit receives the activity trajectory data and status data of each target fattening pig, and performs real-time detection on them through a preset detection and analysis model to obtain the bounding box and behavior classification results of each target fattening pig.

[0108] The preset detection and analysis model can be trained by using the activity trajectory dataset and state data of the target fattening pig as training samples, and the bounding boxes and behavior classification results of the training fattening pig included in the activity trajectory and state data as sample labels.

[0109] The preset detection and analysis model can be deployed in the edge computing unit to achieve real-time detection, location tracking and behavior analysis of fattening pigs.

[0110] Figure 6 This is a schematic diagram of the network structure of the detection and analysis model provided in an embodiment of this application. Figure 6 As shown, the detection and analysis model provided in this application embodiment can be the YOLO-CS model. The detection and analysis model provided in this application embodiment adds a dual attention mechanism, adding a CA attention mechanism in the 5th layer of the backbone and an SE attention mechanism in the 9th layer, so as to improve the detection and analysis model's ability to process different information and effectively suppress invalid information.

[0111] The YOLO-CS model provided in this application embodiment can use the GSConv module to replace the ordinary Conv module for downsampling, so as to effectively improve the computational efficiency, reduce the amount of computation and the number of parameters, and thus improve the computational speed.

[0112] In practical applications, the GSConv calculation of the YOLO-CS model provided in this application includes two stages: the Squeeze stage and the Ghost generation stage.

[0113] (1) The standard convolution operation in the Squeeze stage can produce feature maps. , The calculation formula can be:

[0114] ;

[0115] in, For the input feature map, The kernel matrix; It is a bias term; the dimension is ; The compression factor is 0.5, which is used in practical applications. For output channels; This indicates a convolution operation.

[0116] (2) In the Ghost generation stage, a cheap linear transformation is used (the linear transformation used in this application is...). (convolution), from the output of the Squeeze stage Generate the remaining Ghost feature maps These Ghost feature maps supplement the remaining... Each channel feature.

[0117] ;

[0118] This is the first output of the Squeeze phase. Each feature map This represents a linear transformation operation.

[0119] Step S12: Perform multi-target tracking on each target fattening pig, assign a unique ID to each target fattening pig, and record the movement path of each target fattening pig.

[0120] For example, in practical applications, multi-target tracking tasks can be performed based on the ByteTrack tracker, and a unique ID number can be assigned to each pig, while the movement path of each pig can be recorded.

[0121] By using the ByteTrack tracker for multi-target tracking and assigning a unique ID to each pig while recording its movement path, the risk of disease caused by implanted tracker sensors can be effectively avoided by avoiding the need to implant tracker sensors in each fattening pig. This also effectively reduces costs and increases efficiency.

[0122] Step S13: Extract image data for each target fattening pig based on the bounding box of each target fattening pig.

[0123] Step S14: Preprocess the image data of each target fattening pig to obtain the edge information of the ID of each target fattening pig.

[0124] Step S15: Identify the edge information of the ID of each target fattening pig to obtain the identification result of the ID of each target fattening pig.

[0125] For example, in practical applications, Canny edge detection can be used to preprocess the image data corresponding to each pig, making the edge information of the ID more prominent. The specific steps are as follows:

[0126] (1) Use a Gaussian filter to smooth the image and reduce the impact of noise on edge detection.

[0127] (2) Use the Sobel operator to calculate the gradient of the image in the horizontal and vertical directions to obtain the intensity gradient and direction of the image.

[0128] (3) Suppress non-maximum values ​​in the gradient direction, remove non-edge pixels, and retain local maximum values.

[0129] (4) Use high threshold and low threshold to determine strong edges and weak edges respectively.

[0130] (5) By connecting the weak edges through the strong edges determined by the high threshold, the complete edge is finally obtained.

[0131] In practical applications, OCR (Optical Character Recognition) can be used to identify the ID number of each pig, obtain the ID number corresponding to each fattening pig, and calibrate the corresponding ID number in the tracker to ensure the stability of each pig's ID in a dynamic environment.

[0132] Step S16: The identification result of the ID of each target fattening pig is calibrated with the ID determined for tracking it to determine the ID of each target fattening pig.

[0133] For example, in practical applications, each pig can be periodically calibrated with an ID number written on it using bright, easily visible, and durable paint to distinguish it from others.

[0134] Step S17: Based on the movement trajectory and ID of each target fattening pig, determine the movement trajectory, behavior and location information of each target fattening pig.

[0135] The coordination and control module can receive data transmitted from the environmental monitoring module and the pig monitoring module, analyze it, and then transmit it to the cloud server through the first transmission module and the second transmission module according to the data type for users to view.

[0136] In practical applications, the first transmission module can use the WiFi protocol, and the second transmission module can use the LoRa transmission protocol.

[0137] For example, such as Figure 2 The system architecture shown in this application embodiment allows the intelligent management system to adopt a star topology network. In practical applications, the gateway is responsible for receiving data from each node and transmitting all collected information to the cloud server via an Ethernet interface. The gateway is compatible with both LoRa and WiFi protocols and can select the appropriate communication protocol based on the data type.

[0138] In practical applications, the WiFi protocol, also known as Wireless Fidelity, is a technical standard that allows electronic devices to communicate wirelessly over local area networks (WLANs) and the Internet. The WiFi protocol supports QoS (Quality of Service) mechanisms, providing different quality of service for different types of data packets through priority queuing and bandwidth allocation, ensuring the data transmission quality of real-time applications such as voice and video. In terms of security, it supports multiple encryption algorithms and authentication mechanisms, such as WEP, WPA, and WPA2, and also supports security measures such as SSID hiding and MAC address filtering to enhance the security of wireless networks.

[0139] Therefore, the first transmission module in the system provided in this application embodiment can use the WiFi transmission protocol to transmit high-throughput data, for example, it can transmit the collected video stream data.

[0140] LoRa (Long Range) is a low-power wide-area network (LPWAN) communication technology protocol primarily used for long-distance, low-power communication between Internet of Things (IoT) devices. It can achieve long-distance communication of several kilometers to tens of kilometers with extremely low power consumption. LoRa employs spread spectrum modulation technology, specifically chirped spread spectrum (CSS). This technology spreads the signal in the frequency domain, giving it better anti-interference capabilities and a longer transmission distance. It's like distributing information across a wider frequency range; even if some frequencies are interfered with, the information can be recovered from other frequencies.

[0141] In open environments such as rural or suburban areas, the communication distance can reach 10-20 kilometers or even longer; in urban environments, due to interference from buildings and other obstacles, the communication distance can still reach about 1-5 kilometers. This allows it to cover a larger area and reduce the number of network infrastructure deployments required.

[0142] LoRa devices consume extremely low power in sleep mode, only a few microamps or even less. In operating mode, power consumption can also be kept low depending on factors such as data transmission frequency and power. For example, a battery-powered LoRa sensor node, with proper configuration, can have a battery life of several years, significantly reducing device maintenance costs.

[0143] A single LoRa gateway can connect tens of thousands of terminal devices simultaneously, which is crucial for large-scale IoT applications (such as environmental monitoring systems in smart cities and vehicle tracking in intelligent transportation systems), effectively supporting the access of a large number of devices.

[0144] Therefore, the second transmission module provided in this application embodiment can use the LoRa transmission protocol to transmit low-throughput data.

[0145] Therefore, the intelligent management system provided in this application embodiment can use both LoRa and WiFi protocols for data transmission. The LoRa protocol is responsible for transmitting low-throughput data, mainly used for collecting low-volume data such as environmental monitoring data and feed feeding commands in fattening pig farms. The WiFi protocol, on the other hand, is responsible for transmitting high-throughput data, such as video streams, to ensure the real-time performance and efficiency of video transmission.

[0146] In practical applications, each node communicates with the gateway module via either a LoRa or WiFi transmission module, supporting both protocols and offering advantages such as low power consumption, long-distance transmission, and high stability. Furthermore, the transmission network of the intelligent management system provided in this embodiment employs a fault redundancy design. When one communication protocol (LoRa or WiFi) in the intelligent management system fails, the other communication protocol can temporarily take over the data transmission task and send a warning message to the gateway module, ensuring stable system operation.

[0147] For example, Figure 7 This example illustrates the structural diagram of the various modules of a gateway module.

[0148] In practical applications, such as Figure 7 As shown in the diagram, the coordination and control module is responsible for driving the operation of other modules and collecting relevant data from each module. The coordination and control module can communicate with the LoRa transmission module via a serial port to transmit low-throughput data, such as environmental parameters and feed feeding status, to the cloud server. Simultaneously, the coordination and control module can communicate at high speed with the pig detection module via the SPI bus, ensuring rapid processing and transmission of video stream data collected by the pig detection module, as well as information on the behavior, ID, and location of fattening pigs.

[0149] The remote device can be used to allow users to view data on the cloud server and receive target instructions made by users based on the data viewed on the cloud server, and transmit them to the coordination and control module so that the coordination and control module can adjust the operating strategies of the environmental detection module and the pig detection module in real time according to the target instructions.

[0150] For example, in practical applications, the remote devices in the intelligent management system provided in this application embodiment can be connected to the farm via a cloud server. Users can monitor the farm's operating status in real time through the remote devices and adjust the system parameters as needed.

[0151] For example, in practical applications, after collecting data from each node, the coordination and control module can transmit the data to a remote server via an Ethernet interface to achieve centralized management and remote monitoring of the entire farm's data.

[0152] In this application, remote devices can access the intelligent management system provided by the embodiment of the application through web page authentication. The remote devices can be mobile phones, computers, or mobile terminals.

[0153] The power module is responsible for supplying power to all other modules in the gateway module except for the power module itself. The main function of the power module is to perform 220V step-up and step-down conversion, AC and DC conversion, and to supply specific voltages according to the actual needs of each module.

[0154] Furthermore, in order to better monitor the health status of the animals raised in the farm, the intelligent fattening pig farming system provided by the real-time flow of this application may also include a health monitoring system.

[0155] Among them, the health monitoring system can analyze the environmental conditions of the target fattening pig farm and the activity and status data of each target fattening pig based on data from the cloud server.

[0156] For example, the analysis results of the environmental conditions of the target fattening pig farm can be used to determine whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig. If it is determined that the environment of the target fattening pig farm does not meet the growth requirements of each target fattening pig, an environmental anomaly warning signal can be issued, and the rectification measures for the target fattening pig farm can be determined based on the environmental anomaly warning signal to remind staff to intervene.

[0157] For example, the analysis results of the activity data and status data of each target fattening pig can be used to determine whether there are any abnormalities in the growth trend or growth status of each target fattening pig. If it is determined that there are target fattening pigs with abnormal growth trends or growth status, an early warning signal for the fattening pig abnormality can be issued. Furthermore, based on the early warning information for the fattening pig abnormality, maintenance and adjustment measures for the fattening pigs with abnormal growth can be determined to remind staff to intervene.

[0158] For example, when the health monitoring system detects that the farm's environmental conditions are unreasonable, such as abnormal temperature and humidity or declining air quality, or when it detects that fattening pigs are suspected of being sick, the health monitoring system can automatically issue an alarm and provide suggested measures in the display unit to help users deal with potential problems in a timely manner and ensure the health of the pigs and the stability of their growth environment.

[0159] Furthermore, to better facilitate staff in viewing abnormal information about farms or fattening pigs, adjustment measures for the target fattening pig farms or target fattening pigs can also be displayed on the display unit.

[0160] The following is combined Figure 8 This application describes the process of the intelligent management method for fattening pig farming as given in the embodiments, such as... Figure 8 As shown, the process may include the following steps:

[0161] Step S101: Collect environmental parameters of the target fattening pig farm.

[0162] Step S102: Based on the tracker of each target fattening pig, collect the activity data and status data of each target fattening pig in real time.

[0163] Step S103: Save in real time the collected environmental parameters of the target fattening pig farm and the activity and status data of the target fattening pigs.

[0164] Step S104: Analyze the environmental parameters of the target fattening pig farm to determine whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig. If the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pig, issue an environmental anomaly warning signal based on the environmental parameters of the target fattening pig farm, determine environmental rectification measures for the target fattening pig farm, and adjust the environmental parameters of the target fattening pig farm in real time.

[0165] Step S105: Analyze the activity data and status data of the target fattening pigs to determine the movement trajectory, behavior, location information and ID of each target fattening pig.

[0166] Step S106: Analyze the movement trajectory, behavior, location information and ID of the target fattening pigs to determine the growth status of each target fattening pig.

[0167] Step S107: Analyze the growth status of each target fattening pig, determine whether there are any target fattening pigs with abnormal growth, and if there are target fattening pigs with abnormal growth, issue a fattening pig abnormality warning information based on the ID of the target fattening pig with abnormal growth, determine the fattening pig abnormality warning information corresponding to the target fattening pig with abnormal growth, determine the maintenance and rectification measures for the target fattening pig with abnormal growth, and adjust the maintenance measures for the target fattening pig with abnormal growth based on the determined maintenance and rectification measures.

[0168] Step S108: Based on the environmental parameters of the target fattening pig farm and the growth status of each target fattening pig, determine the control strategy for the target fattening pig farm.

[0169] Step S109: Based on the control strategy of the target fattening pig farm, adjust the feed and liquid delivered to the feeding device of the target fattening pig farm in real time.

[0170] As can be seen from the technical solutions described above, the method provided in this application embodiment can realize long-distance, low-power transmission of low-throughput data in real time, and can also realize real-time transmission of high-throughput data. It has efficient data transmission and stable communication guarantee, and can realize real-time and accurate monitoring of the farm environment and the health status of fattening pigs, timely detection of health problems of fattening pigs, and effectively improve the accurate monitoring, scientific management and intelligent control of the entire growth process of fattening pigs. It can improve breeding efficiency, ensure pork quality and improve the economic benefits of farms.

[0171] The intelligent management device for fattening pig farming provided in the embodiments of this application is described below. The intelligent management device for fattening pig farming described below can be referred to in correspondence with the intelligent management method for fattening pig farming described above.

[0172] See Figure 9 , Figure 9 This is a schematic diagram of the structure of an intelligent management device for fattening pig farming disclosed in an embodiment of this application. Figure 9 As shown, the intelligent management device for fattening pig farming may include:

[0173] The first data acquisition unit 102 is used to collect environmental parameters of the target fattening pig farm.

[0174] The second acquisition unit 102 is used to collect activity data and status data of each target fattening pig in real time based on the tracker of each target fattening pig;

[0175] The storage unit 103 is used to save the collected environmental parameters of the target fattening pig farm and the activity data and status data of the target fattening pig in real time.

[0176] The first analysis unit 104 is used to analyze the environmental parameters of the target fattening pig farm, determine whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig, and if the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pig, then issue an environmental abnormality warning signal based on the environmental parameters of the target fattening pig farm, determine environmental rectification measures for the target fattening pig farm, and adjust the environmental parameters of the target fattening pig farm in real time.

[0177] The second analysis unit 105 is used to analyze the activity data and status data of the target fattening pigs, and determine the movement trajectory, behavior, location information and ID of each target fattening pig;

[0178] The third analysis unit 106 is used to analyze the movement trajectory, behavior, location information and ID of the target fattening pigs, and to determine the growth status of each target fattening pig.

[0179] The fourth analysis unit 107 is used to analyze the growth of each of the target fattening pigs, determine whether there are any target fattening pigs with abnormal growth, and if there are target fattening pigs with abnormal growth, issue a fattening pig abnormality warning information based on the ID of the target fattening pig with abnormal growth, determine the fattening pig abnormality warning information corresponding to the target fattening pig with abnormal growth, determine the maintenance and rectification measures for the target fattening pig with abnormal growth, and adjust the maintenance measures for the target fattening pig with abnormal growth based on the determined maintenance and rectification measures.

[0180] The determining unit 108 is used to determine the control strategy for the target fattening pig farm based on the environmental parameters of the target fattening pig farm and the growth status of each target fattening pig.

[0181] The adjustment unit 109 is used to adjust the feed and liquid delivered to the feeding device of the target fattening pig farm in real time according to the control strategy of the target fattening pig farm.

[0182] As can be seen from the technical solutions described above, the device in this application embodiment can realize long-distance, low-power transmission of low-throughput data in real time, and can also realize real-time transmission of high-throughput data. It has efficient data transmission and stable communication guarantee, and can realize real-time and accurate monitoring of the farm environment and the health status of fattening pigs, timely detection of health problems of fattening pigs, and effectively improve the accurate monitoring, scientific management and intelligent control of the entire growth process of fattening pigs. It can improve breeding efficiency, ensure pork quality and improve the economic benefits of farms.

[0183] The specific processing flow of each unit included in the aforementioned intelligent management device for fattening pig farming can be found in the previous section on intelligent management methods for fattening pig farming, and will not be repeated here.

[0184] The intelligent management device for fattening pig farming provided in this application embodiment can be applied to intelligent management equipment for fattening pig farming, such as terminals: mobile phones, computers, etc. Optionally, Figure 10 The hardware structure block diagram of the intelligent management equipment for fattening pig farming is shown. (Refer to...) Figure 10 The hardware structure of the intelligent management equipment for fattening pig farming may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4.

[0185] In this embodiment, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4.

[0186] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0187] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0188] The memory stores a program, which the processor can call. The program is used to implement the various processing steps in the aforementioned intelligent management solution for fattening pigs.

[0189] This application embodiment also provides a readable storage medium that can store a program suitable for processor execution, the program being used to: implement various processing flows of the aforementioned terminal in the intelligent management scheme for fattening pig farming.

[0190] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0191] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0192] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Various embodiments can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart management system for fattening pig farming, characterized in that, include: The remote device, cloud server, and gateway module, wherein the gateway module includes: a coordination and control module, a first transmission module, a second transmission module, a power module, an environmental detection module, and a pig detection module; The coordination control module is connected to the environmental detection module, the pig detection module, the power supply module, the first transmission module, and the second transmission module, respectively. The environmental monitoring module is responsible for collecting environmental parameters of the target fattening pig farm and transmitting them to the coordination and control module; The pig detection module includes an image acquisition unit and an edge computing unit. The image acquisition unit is responsible for acquiring the activity trajectory data and status data of each target fattening pig in real time and transmitting them to the edge computing unit. The acquired activity trajectory data and status data of the target fattening pig include image data and video stream data. The edge computing unit is responsible for receiving the activity trajectory data and status data of each target fattening pig, and performing real-time detection using a preset detection and analysis model to obtain the bounding box and behavior classification results of each target fattening pig. It also performs multi-target tracking on each target fattening pig, assigns a unique ID to each pig, and records the movement path of each pig. Based on the bounding box of each target fattening pig, it extracts image data of each pig and preprocesses the image data to obtain the edge information of each pig's ID. The edge information is identified to obtain the identification result of the ID of each target fattening pig; the identification result of the ID of each target fattening pig is calibrated with the ID determined for tracking to determine the ID of each target fattening pig; and based on the movement trajectory and ID of each target fattening pig, the movement trajectory, behavior and location information of each target fattening pig are determined and transmitted to the coordination and control module; wherein, the preset detection and analysis model is trained using the state data of the activity trajectory dataset of the training target fattening pigs as training samples, and using the bounding boxes and behavior classification results of the training fattening pigs included in the activity trajectory data and state data of the training target fattening pigs as sample labels; The coordination and control module receives data transmitted from the environmental detection module and the edge computing unit, analyzes it, and then transmits it to the cloud server through the first transmission module and the second transmission module respectively, according to the data type, for users to view. The remote device is used to allow users to view the data of the cloud server, and to receive the target instructions made by the user based on viewing the data of the cloud server, and transmit them to the coordination and control module so that the coordination and control module can adjust the operation strategies of the environmental detection module and the pig detection module in real time according to the target instructions. The power module is responsible for supplying power to all other modules in the gateway module except for the power module itself.

2. The system according to claim 1, characterized in that, The environmental detection module includes a temperature and humidity detection unit, a gas concentration detection unit, a pressure detection unit, and a liquid level detection unit; Based on this, the process by which the environmental monitoring module collects environmental parameters of the target fattening pig farm includes: The temperature and humidity detection unit is responsible for real-time monitoring of temperature and humidity data in the target fattening pig farm. The gas concentration detection unit is responsible for detecting the concentration data of harmful gases in the air of the target fattening pig farm; The pressure detection unit is responsible for detecting the weight data of the target fattening pig; The liquid level detection unit is responsible for detecting the remaining amount of feed or liquid in the feeding device of the target fattening pig.

3. The system according to claim 2, characterized in that, The feeding device includes: a control panel, a camera device, a feeding port, a feeder, a feed trough, a water trough, a waterer, and an emergency switch button; The emergency switch button is deployed on the control panel and is used by the user to turn off or start the feeding device based on a preset emergency shutdown or start strategy. The camera device is connected to the control panel and is used to monitor the status of the feeding port, the feeder, the feed basin, the water basin, and the water feeder, respectively. The control panel is used to control the operation of various parts of the feeding device; The feeding port is installed above the feeder and is used to feed each of the target fattening pigs. The feed trough is installed below the feeder and is used to hold the feed for feeding each of the target fattening pigs. The feeder includes a first liquid level sensor, which is used to monitor the remaining feed data of the feeder. The water basin is installed below the water feeder and is used to hold the liquid for feeding the target fattening pigs; wherein, the water feeder includes a stirring device, a second liquid level sensor and a weight sensor, the stirring device is used to stir the liquid or solid in the water feeder, the second liquid level sensor is used to monitor the liquid remaining data in the water feeder, and the weight sensor is used to monitor the liquid weight data in the water feeder.

4. The system according to claim 3, characterized in that, The control panel includes a control unit and a display unit. The display unit is used to display the operating status of each part of the feeding device, the environmental data collected by the environmental detection module, and the activity dataset status data of each target fattening pig collected by the pig detection module. The control unit is used to allow users to intelligently adjust the parameters of the target fattening pig farm and query the historical data of the target fattening pig farm and the historical data of each target fattening pig.

5. The system according to claim 4, characterized in that, The system also includes a health monitoring system, which is responsible for analyzing the environmental conditions of the target fattening pig farm and the activity and status data of each target fattening pig based on data from the cloud server. It determines whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig and whether there are any abnormalities in the growth trend or growth status of each target fattening pig. If it is determined that the environment of the target fattening pig farm does not meet the growth requirements of each target fattening pig or that there are target fattening pigs with abnormal growth trends or growth statuses, it issues an early warning signal for environmental abnormality or fattening pig abnormality. Based on the environmental abnormality early warning signal or the fattening pig abnormality early warning information, it determines adjustment measures for the target fattening pig farm or the target fattening pigs. The adjustment measures for the target fattening pig farm or the target fattening pigs are displayed on the display unit.

6. A method for intelligent management of fattening pig farming, characterized in that, The method, applied to the intelligent management system for fattening pig farming according to any one of claims 1-5, comprises: Collect environmental parameters from the target fattening pig farm; Based on the tracker of each target fattening pig, the activity data and status data of each target fattening pig are collected in real time; The environmental parameters of the target fattening pig farm and the activity and status data of the target fattening pigs are saved in real time. Analyze the environmental parameters of the target fattening pig farm to determine whether the environment of the target fattening pig farm meets the growth requirements of each target fattening pig. If the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pig, issue an environmental anomaly warning signal based on the environmental parameters of the target fattening pig farm, determine environmental rectification measures for the target fattening pig farm, and adjust the environmental parameters of the target fattening pig farm in real time. Analyze the activity and status data of the target fattening pigs to determine the movement trajectory, behavior, location information, and ID of each target fattening pig; Analyze the movement trajectory, behavior, location information, and ID of the target fattening pigs to determine the growth status of each target fattening pig; Analyze the growth of each target fattening pig to determine if there are any target fattening pigs with abnormal growth. If there are target fattening pigs with abnormal growth, issue a fattening pig abnormality warning message based on the ID of the target fattening pig with abnormal growth, determine the fattening pig abnormality warning message corresponding to the target fattening pig with abnormal growth, determine the maintenance and rectification measures for the target fattening pig with abnormal growth, and adjust the maintenance measures for the target fattening pig with abnormal growth based on the determined maintenance and rectification measures. Based on the environmental parameters of the target fattening pig farm and the growth status of each target fattening pig, a control strategy for the target fattening pig farm is determined. Based on the control strategy of the target fattening pig farm, the feed and liquid delivered to the feeding device of the target fattening pig farm are adjusted in real time.

7. An intelligent management device for fattening pig farming, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of the intelligent management method for fattening pig farming as described in claim 6.

8. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to implement the steps of the intelligent management method for fattening pig farming as described in claim 6.

Images