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

Abstract

According to the intelligent management system and method for growing and fattening pig breeding and the related equipment, the star topology structure network is adopted in the system, long-distance and low-power-consumption transmission of low-throughput data can be achieved in real time, real-time transmission of high-throughput data can also be achieved, and efficient data transmission and stable communication guarantee are achieved. Once one communication protocol in the first transmission module or the second transmission module fails, the other protocol can be automatically taken over, and stable operation of the system can be effectively ensured. Besides, the environment monitoring module and the pig detection module of the intelligent management system provided by the embodiment of the invention can accurately monitor the farm environment and the health state of the fattening pigs in real time through various sensors and cameras, so that the health problem of the fattening pigs can be found in time; accurate monitoring, scientific management and intelligent control of the whole growing process of the fattening pigs are effectively improved, the breeding efficiency can be improved, the pork quality is guaranteed, and the economic benefits of a farm are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent breeding technology, and in particular to an intelligent management system, method and related equipment for fattening pig breeding. Background Art

[0002] In modern animal husbandry, fattening pig farming is an important part of pork production. With the growth of population and the increasing demand for pork, traditional fattening pig farming methods face many challenges.

[0003] In the existing pig farm management technology, traditional fattening pig farm management usually relies on manual observation and experience judgment. Farmers need to frequently inspect the pig house to monitor the health status, diet and behavior of the pigs. However, this method has many limitations. Manual observation not only consumes a lot of time and manpower, but also makes it difficult to achieve real-time and comprehensive monitoring, and it is easy to miss key information. In terms of feed management, traditional methods often rely on estimates to put feed, and cannot accurately supply according to the actual needs of each pig, resulting in feed waste or unbalanced nutrition for pigs. For the monitoring of the health status of pigs, it is usually discovered after the pigs have shown obvious symptoms. At this time, treatment is often difficult, which not only increases the cost of treatment, but may also affect the growth of the pigs, or even cause death, causing economic losses to the farm. Environmental control is also a problem in traditional farming. Conditions such as temperature, humidity and ventilation are difficult to maintain in the most suitable range, affecting the growth rate and health status of fattening pigs.

[0004] With the development of science and technology, intelligent technology has been gradually applied to various fields, but its application in the field of fattening pig farming is still relatively lagging behind. At present, although some farms have begun to introduce some automated equipment, most of these equipment are single-function, lack of systematicity and integration, and cannot achieve comprehensive, accurate and efficient farm management. Therefore, how to develop a comprehensive fattening pig intelligent farm management system to achieve accurate monitoring, scientific management and intelligent control of the entire growth process of fattening pigs has become an urgent need to improve breeding efficiency, ensure pork quality and improve the economic benefits of farms. Summary of the invention

[0005] The present application aims to solve at least one of the above-mentioned technical defects. In view of this, the present application provides an intelligent management system, method and related equipment for fattening pig farming, which are used to solve the technical defect in the prior art that intelligent management of farming objects cannot be achieved in farms.

[0006] An intelligent management system for fattening pig farming, comprising: a remote device, a cloud server and a gateway module, wherein the gateway module comprises: a coordination control module, a first transmission module, a second transmission module, a power module, an environment detection module, and a pig detection module;

[0007] Wherein, the coordination control module is respectively connected to the environment detection module, the pig detection module, the power supply module, the first transmission module and the second transmission module;

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

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

[0010] The coordination control module receives the data transmitted by the environment detection module and the pig detection module, analyzes the data, and transmits the data to the cloud server through the first transmission module and the second transmission module respectively according to the data type for user reference;

[0011] The remote device is used for allowing the user to view the data of the cloud server, and receiving the target instruction made by the user based on the data of the cloud server, and transmitting it to the coordination control module, so that the coordination control module can adjust the operation strategy of the environment detection module and the pig detection module in real time according to the target instruction;

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

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

[0014] Based on this, the process of the environmental detection module collecting the environmental parameters of the target fattening pig farm includes:

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

[0016] The gas concentration detection unit is responsible for detecting the harmful gas concentration data 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 data 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 the activity data and status data of each target fattening pig in real time, analyzes the data and transmits the analyzed data to the coordination control module, including:

[0020] The image acquisition unit collects the activity trajectory data and status data of each of the target fattening pigs in real time and transmits them to the edge computing unit, wherein the collected activity trajectory data and status data of the target fattening pigs 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 on the data to determine the movement trajectory, behavior, ID and location information of each target fattening pig and transmits the data to the coordination control module.

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

[0023] Wherein, the emergency switch button is disposed on the control panel, and the emergency switch button is used for the user to close or start the feeding device based on a preset emergency closing or starting 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 waterer 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 the feed for each of the target fattening pigs;

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

[0028] The water basin is installed below the water feeder and is used to hold 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 of the water feeder, and the weight sensor is used to monitor the liquid weight data of the water feeder.

[0029] Preferably, 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 on the activity trajectory data and status data of each target fattening pig to determine the movement trajectory, behavior, ID and location information of each target fattening pig, including:

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

[0031] Perform multi-target tracking on each of the target fattening pigs, assign a unique ID to each of the target fattening pigs, and record the movement path of each of the target fattening pigs;

[0032] Extracting image data of each of the target fattening pigs based on the bounding box of each of the target fattening pigs;

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

[0034] Identify the edge information of the ID of each target fattening pig to obtain an identification result of the ID of each target fattening pig;

[0035] Calibrate the identification result of the ID of each target fattening pig with the ID determined by tracking it, and determine the ID of each target fattening pig;

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

[0037] Preferably, the control panel comprises a control unit and a display unit, wherein 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 data set status data of each target fattening pig collected by the pig detection module;

[0038] The control unit is used for allowing the user 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 of the target fattening pigs.

[0039] Preferably, 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 data and status data of each of the target fattening pigs based on the data of the cloud server, and judging whether the environment of the target fattening pig farm meets the growth needs of each of the target fattening pigs and judging whether there are abnormalities in the growth trend or growth status of each of the target fattening pigs. If it is determined that the environment of the target fattening pig farm does not meet the growth needs of each of the target fattening pigs or there are target fattening pigs with abnormal growth trends or growth status, an early warning signal of 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] An intelligent management method for fattening pig breeding is applied to any of the intelligent management systems for fattening pig breeding described above, and the method comprises:

[0041] Collect environmental parameters of target fattening pig farms;

[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] Real-time storage of the collected environmental parameters of the target fattening pig farm and the activity data and status data of the target fattening pigs;

[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 of the target fattening pigs; if the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pigs, issue an abnormal environment 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 data 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 of the target fattening pigs;

[0047] Analyze the growth conditions of each of the target fattening pigs to determine whether there are target fattening pigs with abnormal growth. If there are target fattening pigs with abnormal growth, issue fattening pig abnormality warning information based on the ID of the target fattening pig with abnormal growth, determine fattening pig abnormality warning information corresponding to the target fattening pig with abnormal growth, determine 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] Determining a control strategy for the target fattening pig farm based on the environmental parameters of the target fattening pig farm and the growth conditions of each of the target fattening pigs;

[0049] According to 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] An intelligent management device for fattening pig farming, comprising: one or more processors, and a memory;

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

[0052] A readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors implement the steps of the intelligent management method for fattening pig farming as described above.

[0053] It can be seen from the technical solutions introduced above that the intelligent management system provided by the embodiment of the present application may include a remote device, a cloud server and a gateway module, wherein the gateway module may include a coordination control module, a first transmission module, a second transmission module, a power module, an environmental detection module and a pig detection module; wherein the coordination control module is respectively connected to the environmental detection module, the pig detection module, the power module, the first transmission module and the second transmission module; the environmental detection module is responsible for collecting the environmental parameters of the target fattening pig farm and transmitting them to the coordination control module; the pig detection module is responsible for collecting the activity data and status data of each target fattening pig in real time, and after analyzing the data, transmitting the analyzed data to the coordination control module; the coordination control module receives the data transmitted by the environmental detection module and the pig detection module, and analyzes the data and then transmits the data to the coordination control module according to the data type, respectively transmitted to the cloud server through the first transmission module and the second transmission module for user review, wherein the first transmission module can be used to transmit high-throughput data, for example, it can transmit large data such as video streams, and the second transmission module can be used for long-distance, low-power transmission of low-throughput data; wherein, once a communication protocol in the first transmission module or the second transmission module fails, the other protocol can automatically take over, which can effectively ensure the stable operation of the system; the remote device is used for users to view the data of the cloud server, and receive the target instructions made by the user based on the data of the cloud server, and transmit it to the coordination control module, so that the coordination 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; the power module is responsible for powering other modules in the gateway module except the power module.

[0054] It can be seen that the target fattening pig farm is managed by the fattening pig intelligent management system provided by the embodiment of the present application. The fattening pig breeding management system provided by the embodiment of the present application adopts a star topology network, which 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, with efficient data transmission and stable communication guarantee. The system also has a redundant design. Once a communication protocol in the first transmission module or the second transmission module fails, the other protocol can automatically take over, which can effectively ensure the stable operation of the system. In addition, the environmental monitoring module and pig detection module of the intelligent management system provided by the embodiment of the present application can realize real-time and accurate monitoring of the farm environment and the health status of fattening pigs through a variety of sensors and cameras, timely discover health problems of fattening pigs, effectively improve the accurate monitoring, scientific management and intelligent control of the whole process of fattening pig growth, and can improve breeding efficiency, ensure pork quality and improve the economic benefits of the farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 A schematic diagram of a system architecture for realizing intelligent management of fattening pigs provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of another system architecture for realizing intelligent management of fattening pigs provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the front view structure of a feeding device provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the upper and lower isometric view of a feeding device provided in an embodiment of the present application;

[0060] Figure 5 A schematic diagram of the structure of a water feeder provided in an embodiment of the present application;

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

[0062] Figure 7 A schematic diagram of the structure of a gateway module provided in an embodiment of the present application;

[0063] Figure 8 A flowchart of a method for realizing intelligent management of fattening pig farming provided in an embodiment of the present application;

[0064] Fig. 9 A schematic diagram of the structure of an intelligent management device for fattening pig farming as an example of an embodiment of the present application;

[0065] Fig.10 This is a hardware structure block diagram of the intelligent management equipment for fattening pig farming disclosed in the embodiment of this application. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0067] Among the existing pig farm management technologies, manual inspection is the most traditional management method. Workers regularly inspect the pig farm to check the temperature and humidity, feed supply, and the health status and behavior of the pigs. Although this method is simple, it is extremely inefficient in large-scale farms and difficult to achieve real-time monitoring and management. 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 diseases or insufficient feed supply, increasing management costs and risks.

[0068] Some modern farms have introduced automated sensor monitoring systems. Although pigs wearing or injected sensors can record their movement trajectories, the equipment needs to be maintained, which is costly. In addition, wearing or injecting sensors may cause stress reactions in pigs, affecting their health and growth. At the same time, sensors are easily damaged during actual use, resulting in unstable monitoring data, further increasing the difficulty and cost of maintenance.

[0069] Traditional IoT networking methods usually use a single communication protocol for data transmission, which cannot take into account both low power consumption and communication stability. In addition, most existing IoT systems lack fault redundancy design. Once a communication link fails, the system may be paralyzed, resulting in data transmission interruption, increasing the complexity and cost of maintenance.

[0070] In view of the fact that most of the current intelligent management solutions for fattening pig farming are difficult to adapt to the complex and changeable business needs, for this reason, the applicant has studied an intelligent management solution for fattening pig farming. The fattening pig intelligent management system provided by the embodiment of the present application manages the target fattening pig farm. The fattening pig farming management system provided by the embodiment of the present application adopts a star topology network, which can realize long-distance and low-power transmission of low-throughput data in real time, and can also realize real-time transmission of high-throughput data, with efficient data transmission and stable communication guarantee. The system also has a redundant design. Once a communication protocol in the first transmission module or the second transmission module fails, the other protocol can automatically take over, which can effectively ensure the stable operation of the system. In addition, the environmental monitoring module and pig detection module of the intelligent management system provided by the embodiment of the present application can realize real-time and accurate monitoring of the farm environment and the health status of fattening pigs through a variety of sensors and cameras, timely discover the health problems of fattening pigs, effectively improve the accurate monitoring, scientific management and intelligent control of the whole process of fattening pig growth, and can improve breeding efficiency, ensure pork quality and improve the economic benefits of farms.

[0071] The method provided in the embodiments of the present application can be used in many general or special computing device environments or configurations, such as personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, and distributed computing environments including any of the above devices or equipment.

[0072] The embodiment of the present application provides an intelligent management method for fattening pig farming. The method can be applied to various farming management systems, and can also be applied to various computer terminals or smart terminals. The execution subject can be a processor or server of the computer terminal or smart terminal.

[0073] Combine the following Figure 1 , introduces an optional system architecture for realizing intelligent management of fattening pig farms given in the embodiment of the present application, such as Figure 1 As shown, the system architecture may include a remote device, a cloud server, and a gateway module.

[0074] In actual application, multiple functional nodes can be deployed according to the actual needs of the farm, such as Figure 2 In the system architecture shown, the remote device and the cloud server can communicate via WiFi, the cloud server and the gateway module can communicate via Ethernet, and multiple functional nodes can be deployed in the gateway module.

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

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

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

[0078] In actual application, the environmental detection module can deploy a variety of sensors to collect various environmental parameters of the farm according to the actual application needs of the farm.

[0079] For example, the types and quantity of detection units carried in the environmental detection 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, the environment detection module may include a temperature and humidity detection unit, a gas concentration detection unit, a pressure detection unit, and a liquid level detection unit.

[0081] Among them, the temperature and humidity detection unit can be responsible for real-time monitoring of the temperature and humidity data of the target fattening pig farm to provide a suitable growth environment for fattening pigs;

[0082] The gas concentration detection unit can be responsible for detecting the concentration data 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 the harmful gases from affecting the health of pigs;

[0083] The pressure detection unit can be responsible for detecting the weight data of the target fattening pigs so as to monitor the growth status of the fattening pigs. In the actual application process, in order to reduce the management cost, a pressure detection sensor can be installed in the pig house 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 data 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 the embodiment of the present application can be an efficient feeding system. It can effectively solve the eating and drinking problems of fattening pigs. Its design is particularly suitable for small and medium-sized pig houses, especially in a pen environment, when multiple pigs eat or drink at the same time, it can effectively alleviate crowding and improve feeding efficiency and animal welfare.

[0086] Figure 3 and Figure 4 An example of a feeding device structure diagram is shown, wherein: Figure 3 A schematic diagram of the front view of the feeding device provided in an embodiment of the present application; Figure 4 This is a schematic diagram of the upper and lower isometric views of the feeding device provided in an embodiment of the present 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 basin 5, a water basin 6, a waterer 7, and an emergency switch button 8.

[0088] Among them, the emergency switch button 8 can be deployed on the control panel 1, and the emergency switch button 8 can be used for the user to close or start the feeding device based on a preset emergency closing or starting strategy. For example, the emergency switch button can be used to deal with emergencies and minimize losses.

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

[0090] For example, in actual application, the control unit and the display unit can transmit data through the HDMI interface. The display unit can intuitively present the operating status of each node and the collected environmental data, pig behavior data, feed status and other information, providing a user-friendly operation interface. The control unit allows the user to adjust system parameters, query data and other operations through the interface. The system supports viewing historical data, and users can choose to view a data period of one month or one quarter, which is convenient for analyzing and evaluating the long-term operation of the farm.

[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, the feeder 4, the feed basin 5, the water basin 6 and the waterer 7 respectively;

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

[0093] The feeding port 3 can be installed above the feeder 4 and can be used to feed the feed for each target fattening pig;

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

[0095] For example, when it is necessary to add water or solvent or solid nutrient to the feed, the stirring device in the feeder 4 can be started so that the water, solvent or solid nutrient in the feeder 4 is fully mixed with the feed to improve 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, 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 can be used to monitor the liquid remaining data of the water feeder 7, and the weight sensor 11 is used to monitor the liquid weight data of the water feeder.

[0098] For example, in order to comprehensively monitor the health of pigs, high-resolution cameras can be deployed in the camera device to monitor the pigs' drinking and eating behaviors in real time. The data captured by the high-resolution camera can be used to calculate the number of pigs eating in real time. Combined with the pressure sensor, the remaining amount of feed and water in the feeder can be calculated, and the amount of feed and water can be adjusted dynamically.

[0099] In order to optimize the nutritional intake of animals, stirring devices can be introduced in feeders, and similar innovative designs are applied to waterers. With this design, the stirring device ensures that the water is fully and evenly mixed with any added nutrients, drugs or other solutions. This measure effectively prevents the precipitation of additives and ensures that the solution remains uniform at all times. In addition, the stirring device also significantly speeds up the dissolution rate of additives in water, especially for some difficult-to-dissolve substances, improving the dissolution efficiency. The evenly mixed solution avoids the problem of local high or low concentrations, ensuring that all nutrients and drugs are fully utilized and reducing waste. This not only improves the intake of drugs and nutrients by animals, contributing to their health and growth, but also prevents the formation of lumps or blockages in the waterer, which is conducive to the normal maintenance and operation of the equipment.

[0100] Staff can put feed and water in a timely and quantitative manner based on experience, or set parameters based on the statistical data provided by the system and the recommended time points and demand calculated. In this way, managers can combine professional knowledge and rely on the system's intelligent recommendations to scientifically manage the entire pig farm's feeding work.

[0101] The modular design of the feeding device provided in the embodiment of the present application is convenient for maintenance and upgrading, so that it has good adaptability and scalability in long-term use. Overall, this system has brought significant technological progress to the pig farming industry, promoting the dual improvement of pig farming efficiency and animal welfare.

[0102] The pig detection module can be responsible for collecting the activity data and status data of each target fattening pig in real time, and after analyzing it, transmit the analyzed data to the coordination control module.

[0103] The pig detection module may include an image acquisition unit and an edge computing unit, wherein the image acquisition unit may collect the activity trajectory data and status data of each target fattening pig in real time and transmit the data to the edge computing unit, wherein 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 perform real-time analysis on it to determine the movement trajectory, behavior, ID and location information of each target fattening pig and transmit it to the coordination control module.

[0105] For example, in the actual application process, the edge computing unit has local computing capabilities and can process and analyze the collected data in real time. For example, by analyzing the collected data of fattening pigs, the behavior, ID and location information of the fattening pigs can be identified, and 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 control module.

[0106] 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 on the data to determine the movement trajectory, behavior, ID and location information of each target fattening pig, which may include the following process:

[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 it through a preset detection and analysis model to obtain the boundary box and behavior classification results of each target fattening pig.

[0108] Among them, the preset detection and analysis model can be trained using the activity trajectory data set status data of the training target fattening pigs as training samples, and the activity trajectory data and status data of the training target fattening pigs, including the bounding box and behavior classification results of the training fattening pigs, as sample labels.

[0109] Among them, the preset detection and analysis model can be deployed in the edge computing unit to realize 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 the embodiment of the present application. Figure 6 As shown, the detection and analysis model provided in the embodiment of the present application can be a YOLO-CS model. The detection and analysis model provided in the embodiment of the present application adds a dual attention mechanism, adds a CA attention mechanism to the 5th layer of Backbone, and adds a SE attention mechanism to 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 the embodiment of the present application can use the GSConv module to replace the ordinary Conv module for downsampling, so as to effectively improve the operation efficiency, reduce the amount of calculation and the number of parameters, so as to increase the calculation speed.

[0112] In actual application, the GSConv calculation of the YOLO-CS model provided in the embodiment of the present application includes two stages of calculation, namely, the Squeeze stage and the Ghost generation stage.

[0113] (1) The standard convolution operation in the squeeze stage can obtain the feature map Y s , Y s The calculation formula can be:

[0114] Y s =W s *X+b s

[0115] in,

[0116] X is the input feature map, W s is the convolution kernel matrix; b s is the bias term; dimension is α×c out ; α is the compression factor. In practical application, the compression factor used in this application is 0.5, C out is the output channel; * indicates the convolution operation.

[0117] (2) In the ghost generation stage, a cheap linear transformation (the linear transformation used in this application is 1×1 convolution) is used to generate the output Y of the squeeze stage. s Generate the remaining Ghost feature map Y g These Ghost feature maps complement the remaining (1-α)×c out channel characteristics.

[0118]

[0119] is the i-th feature map output by the Squeeze stage, and φ(·) represents the linear transformation operation.

[0120] Step S12, performing multi-target tracking on each target fattening pig, assigning a unique ID to each target fattening pig, and recording the movement path of each target fattening pig.

[0121] For example, in actual 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.

[0122] By performing multi-target tracking tasks based on the ByteTrack tracker, assigning a unique ID number to each pig, and recording the movement path of each pig, it is possible to effectively avoid implanting tracker sensors in each fattening pig, avoiding the risk of fattening pigs becoming sick due to implanted trackers, while also effectively reducing costs and increasing efficiency.

[0123] Step S13, extracting image data of each target fattening pig based on the bounding box of each target fattening pig.

[0124] Step S14, preprocessing the image data of each target fattening pig to obtain edge information of the ID of each target fattening pig.

[0125] Step S15, identifying the edge information of each target fattening pig's ID to obtain an identification result of each target fattening pig's ID.

[0126] For example, in actual application, Canny edge detection can be used to pre-process the image data corresponding to each pig, making the edge information of the ID more prominent. The specific steps are as follows:

[0127] (1) Use a Gaussian filter to smooth the image and reduce the impact of noise on edge detection.

[0128] (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.

[0129] (3) Suppress non-maxima in the gradient direction, remove non-edge pixels, and retain local maxima.

[0130] (4) Use high threshold and low threshold to determine strong edges and weak edges respectively.

[0131] (5) The strong edge determined by the high threshold is connected to the weak edge to finally obtain the complete edge.

[0132] In actual application, 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.

[0133] Step S16, calibrating the identification result of each target fattening pig's ID with the ID determined by tracking it, and determining the ID of each target fattening pig.

[0134] For example, in actual application, the ID of each pig can be calibrated periodically, and the ID number can be written on each pig using bright and obvious paint that is not easy to fall off to distinguish them.

[0135] 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.

[0136] The coordination control module can receive the data transmitted by the environment detection module and the pig detection module, and after analyzing the data, can transmit 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;

[0137] In actual application, the first transmission module can adopt the WiFi protocol, and the second transmission module can adopt the LoRa transmission protocol.

[0138] For example, Figure 2 The system architecture shown in the figure, the intelligent management system provided by the embodiment of the present application can adopt a star topology network. In actual application, the gateway can be responsible for receiving data from each node and transmitting all collected information to the cloud server through the Ethernet interface. The gateway is compatible with both LoRa and WiFi protocols and can select the appropriate communication protocol according to the data type.

[0139] In actual application, the WiFi protocol, also known as wireless fidelity technology, is a technical standard that allows electronic devices to communicate wirelessly on local area networks (WLANs) and the Internet. The WiFi protocol supports the QoS (Quality of Service) mechanism, which provides different service qualities for different types of data packets through priority queues and bandwidth allocation, ensuring the data transmission quality of real-time applications such as voice and video. In terms of security, it supports a variety of encryption algorithms and authentication mechanisms, such as WEP, WPA, WPA2, etc. It also supports security measures such as hiding SSID and MAC address filtering to enhance the security of wireless networks.

[0140] Therefore, the first transmission module in the system provided in the embodiment of the present application can apply the WiFi transmission protocol to transmit high-throughput data, for example, it can transmit the collected video stream data.

[0141] LoRa (Long Range) is a low-power wide area network (LPWAN) communication technology protocol, mainly used for long-distance, low-power Internet of Things (IoT) devices. It can achieve long-distance communication of several kilometers to tens of kilometers with extremely low power consumption. LoRa uses spread spectrum modulation technology, specifically chirp spread spectrum (CSS) technology. This technology expands the signal in the frequency domain, making the signal have better anti-interference ability and longer transmission distance. It is like dispersing information over a wider frequency range, so that even if some frequencies are interfered with, the information can be partially recovered from other frequencies.

[0142] In open environments such as rural or suburban areas, the communication distance can reach 10-20 kilometers or even further; in urban environments, due to interference from obstacles such as buildings, the communication distance can also reach about 1-5 kilometers. This allows it to cover a larger area and reduce the number of network infrastructure deployments.

[0143] LoRa devices consume very low power in sleep mode, only a few microamps or even lower. In working mode, power consumption can also be controlled at a low level based on factors such as data transmission frequency and power. For example, a battery-powered LoRa sensor node can have a battery life of several years under proper configuration, greatly reducing equipment maintenance costs.

[0144] A LoRa gateway can connect thousands of terminal devices at the same time, which is very important for large-scale IoT application scenarios (such as environmental monitoring systems in smart cities, vehicle tracking in intelligent transportation systems, etc.), and can effectively support the access of a large number of devices.

[0145] Therefore, the second transmission module provided in the embodiment of the present application can use the LoRa transmission protocol to transmit low-throughput data.

[0146] It can be seen that the intelligent management system provided by the embodiment of the present application can use both LoRa and WiFi protocols for data transmission. Among them, the LoRa protocol is responsible for the transmission of low-throughput data, which is mainly used to collect low-capacity data such as environmental monitoring data and feed feeding commands in fattening pig farms, while the WiFi protocol is responsible for the transmission of high-throughput data, such as high-capacity data such as video streams, to ensure the real-time and high-efficiency of video transmission.

[0147] In actual application, each node communicates with the gateway module through the LoRa transmission module or the WiFi transmission module, and dual-protocol support takes into account the advantages of low power consumption, long-distance transmission, and high stability. At the same time, the transmission network of the intelligent management system provided in the embodiment of the present application adopts a fault redundancy design. When a 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 to ensure the stable operation of the system.

[0148] For example, Figure 7 An example of a schematic diagram of the structure of each module of a gateway module is shown.

[0149] In actual application, Figure 7 In the structure shown, the coordination control module is responsible for driving the operation of other modules and collecting relevant data from each module. The coordination control module can communicate with the LoRa transmission module through the serial port to transmit the collected low-throughput data, such as environmental parameters, feed feeding status and other information, to the cloud server. At the same time, the coordination control module can communicate with the pig detection module at high speed through the SPI bus to ensure that the video stream data collected by the pig detection module and the behavior, ID and location information of the fattening pigs are quickly processed and transmitted.

[0150] The remote device can be used for users to view the data of the cloud server, receive the target instructions made by the user based on the data of the cloud server, and transmit them to the coordination control module so that the coordination 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.

[0151] For example, in actual application, the remote device in the intelligent management system provided in the embodiment of the present application can be connected to the farm through the cloud server. The user can monitor the operating status of the farm in real time through the remote device and adjust the system parameters as needed.

[0152] For example, in actual application, after collecting data from each node, the coordination control module can transmit it to the remote server through the Ethernet interface to achieve centralized management and remote monitoring of the entire farm data.

[0153] Among them, the remote device can access the intelligent management system provided by the embodiment of the present application through web page authentication, and the remote device can be a mobile phone, a computer, or a mobile terminal.

[0154] The power module is responsible for supplying power to other modules in the gateway module except the power module. The main work of the power module is to complete the 220V step-up and step-down, as well as the AC to DC conversion, and supply specific voltage according to the actual needs of each module.

[0155] Furthermore, in order to better monitor the health status of the breeding objects in the farm, the intelligent breeding system for fattening pigs provided by the real-time traffic of the present application may also include a health monitoring system.

[0156] Among them, the health detection system can analyze the environmental conditions of the target fattening pig farm and the activity data and status data of each target fattening pig based on the data of the cloud server.

[0157] For example, based on the analysis results of the environmental conditions of the target fattening pig farm, it can be judged whether the environment of the target fattening pig farm meets the growth needs of each target fattening pig. If it is determined that the environment of the target fattening pig farm does not meet the growth needs of each target fattening pig, an abnormal environment warning signal can be issued, and corrective measures for the target fattening pig farm can be determined based on the abnormal environment warning signal to remind staff to intervene.

[0158] For example, it is also possible to determine whether there are abnormalities in the growth trend or growth status of each target fattening pig based on the analysis results of the activity data and status data of each target fattening pig. If it is determined that there are target fattening pigs with abnormal growth trends or growth status, an abnormal fattening pig warning signal can be issued; and maintenance adjustment measures for fattening pigs with growth abnormalities can be determined based on the abnormal fattening pig warning information to remind staff to intervene.

[0159] For example, when the health detection system detects unreasonable environmental conditions in the farm, such as abnormal temperature and humidity, or deteriorating air quality; or when it detects that the fattening pigs are suspected of being ill, the health detection system can automatically issue an alarm and provide recommended 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 the growth environment.

[0160] Furthermore, in order to better facilitate the staff to view abnormal information of the breeding farm or fattening pigs, the adjustment measures for the target fattening pig breeding farm or target fattening pigs can also be displayed on the display unit.

[0161] Combine the following Figure 8 , introduces the process of the intelligent management method for fattening pig farming given in the embodiment of the present application, such as Figure 8 As shown, the process can include the following steps:

[0162] Step S101, collecting environmental parameters of the target fattening pig farm.

[0163] Step S102, 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.

[0164] Step S103, saving the collected environmental parameters of the target fattening pig farm and the activity data and status data of the target fattening pigs in real time.

[0165] Step S104, analyzing the environmental parameters of the target fattening pig farm to determine whether the environment of the target fattening pig farm meets the growth needs of each of the target fattening pigs; if the environment of the target fattening pig farm does not meet the growth needs of the target fattening pigs, an abnormal environment warning signal is issued based on the environmental parameters of the target fattening pig farm, and environmental rectification measures for the target fattening pig farm are determined, and the environmental parameters of the target fattening pig farm are adjusted in real time.

[0166] Step S105, analyzing 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.

[0167] Step S106, analyzing the movement trajectory, behavior, location information and ID of the target fattening pigs to determine the growth status of each of the target fattening pigs.

[0168] Step S107, analyzing the growth conditions of each of the target fattening pigs to determine whether there are target fattening pigs with abnormal growth. If there are target fattening pigs with abnormal growth, abnormal fattening pig warning information is issued based on the ID of the target fattening pig with abnormal growth, and the fattening pig abnormal warning information corresponding to the target fattening pig with abnormal growth is determined, maintenance rectification measures for the target fattening pigs with abnormal growth are determined, and maintenance measures for the target fattening pigs with abnormal growth are adjusted according to the determined maintenance rectification measures.

[0169] Step S108, determining a control strategy for the target fattening pig farm based on the environmental parameters of the target fattening pig farm and the growth conditions of each of the target fattening pigs.

[0170] Step S109, adjusting 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.

[0171] It can be seen from the technical solutions introduced above that the method provided in the embodiments of the present application 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 guarantees, and can realize real-time and accurate monitoring of the farm environment and the health status of fattening pigs, timely discover health problems of fattening pigs, and effectively improve the accurate monitoring, scientific management and intelligent control of the entire growth process of fattening pigs, which can improve breeding efficiency, ensure pork quality and enhance the economic benefits of farms.

[0172] The intelligent management device for fattening pig farming provided in an embodiment of the present application is described below. The intelligent management device for fattening pig farming described below and the intelligent management method for fattening pig farming described above can be referenced to each other.

[0173] See also Fig. 9 , Fig. 9 This is a schematic diagram of the structure of an intelligent management device for fattening pigs disclosed in the embodiment of this application. Fig. 9 As shown, the intelligent management device for fattening pig farming may include:

[0174] The first collection unit 102 is used to collect environmental parameters of the target fattening pig farm;

[0175] The second collection unit 102 is used to collect the activity data and status data of each target fattening pig in real time based on the tracker of each target fattening pig;

[0176] A storage unit 103 is used to store the collected environmental parameters of the target fattening pig farm and the activity data and status data of the target fattening pigs in real time;

[0177] The first analysis unit 104 is used to 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 of the target fattening pigs. If the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pigs, an environmental abnormality warning signal is issued according to the environmental parameters of the target fattening pig farm, and environmental rectification measures for the target fattening pig farm are determined, and the environmental parameters of the target fattening pig farm are adjusted in real time;

[0178] 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;

[0179] The third analysis unit 106 is used to analyze the movement trajectory, behavior, location information and ID of the target fattening pigs to determine the growth status of each of the target fattening pigs;

[0180] The fourth analysis unit 107 is used to analyze the growth conditions of each of the target fattening pigs, determine whether there are target fattening pigs with abnormal growth, and if there are target fattening pigs with abnormal growth, issue fattening pig abnormality warning information according to the ID of the target fattening pig with abnormal growth, determine fattening pig abnormality warning information corresponding to the target fattening pig with abnormal growth, determine maintenance rectification measures for the target fattening pig with abnormal growth, and adjust the maintenance measures for the target fattening pig with abnormal growth according to the determined maintenance rectification measures;

[0181] A determination unit 108, configured to determine a control strategy for the target fattening pig farm according to the environmental parameters of the target fattening pig farm and the growth conditions of each of the target fattening pigs;

[0182] 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.

[0183] It can be seen from the technical solutions introduced above that the device of the embodiment of the present application 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, with 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 discover health problems of fattening pigs, effectively improve the accurate monitoring, scientific management and intelligent control of the whole process of fattening pig growth, and can improve breeding efficiency, ensure pork quality and enhance the economic benefits of farms. .

[0184] Among them, the specific processing procedures of each unit included in the above-mentioned intelligent management device for fattening pig farming can refer to the relevant introduction of the intelligent management method for fattening pig farming in the previous text, and will not be repeated here.

[0185] The fattening pig breeding intelligent management device provided in the embodiment of the present application can be applied to fattening pig breeding intelligent management equipment, such as terminals: mobile phones, computers, etc. Optionally, Fig.10 The hardware structure diagram of the intelligent management equipment for fattening pig farming is shown. Fig.10 The hardware structure of the fattening pig breeding intelligent management device 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.

[0186] In the embodiment of the present application, the number of the processor 1 , the communication interface 2 , the memory 3 , and the communication bus 4 is at least one, and the processor 1 , the communication interface 2 , and the memory 3 communicate with each other through the communication bus 4 .

[0187] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application, etc.;

[0188] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0189] Among them, the memory stores a program, and the processor can call the program stored in the memory, and the program is used to: implement each processing flow in the aforementioned terminal fattening pig breeding intelligent management solution.

[0190] An embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by a processor, and the program is used to: implement various processing flows of the aforementioned terminal in the intelligent management solution for fattening pig farming.

[0191] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0192] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0193] The above description of the disclosed embodiments enables professionals and technicians in the field to implement or use the present application. Various modifications to these embodiments will be apparent to professionals and technicians in the field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent management system for fattening pigs, characterized in that: include: A remote device, a cloud server and a gateway module, wherein the gateway module includes: a coordination control module, a first transmission module, a second transmission module, a power module, an environment detection module, and a pig detection module; Wherein, the coordination control module is respectively connected to the environment detection module, the pig detection module, the power supply module, the first transmission module and the second transmission module; The environmental detection module is responsible for collecting environmental parameters of the target fattening pig farm and transmitting them to the coordination control module; The pig detection module is responsible for collecting the activity data and status data of each target fattening pig in real time, and after analyzing the data, transmits the analyzed data to the coordination control module; The coordination control module receives the data transmitted by the environment detection module and the pig detection module, analyzes the data, and transmits the data to the cloud server through the first transmission module and the second transmission module respectively according to the data type for user reference; The remote device is used for allowing the user to view the data of the cloud server, and receiving the target instruction made by the user based on the data of the cloud server, and transmitting it to the coordination control module, so that the coordination control module can adjust the operation strategy of the environment detection module and the pig detection module in real time according to the target instruction; The power supply module is responsible for supplying power to other modules in the gateway module except the power supply module.

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 of the environmental detection module collecting the environmental parameters of the target fattening pig farm includes: The temperature and humidity detection unit is responsible for real-time monitoring of the temperature and humidity data of the target fattening pig farm; The gas concentration detection unit is responsible for detecting the harmful gas concentration data 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 data of feed or liquid in the feeding device of the target fattening pig.

3. The system according to claim 1, characterized in that The pig detection module includes an image acquisition unit and an edge computing unit. Based on this, the pig detection module collects the activity data and status data of each target fattening pig in real time, analyzes the data and transmits the analyzed data to the coordination control module, including: The image acquisition unit collects the activity trajectory data and status data of each of the target fattening pigs in real time and transmits them to the edge computing unit, wherein the collected activity trajectory data and status data of the target fattening pigs include image data and video stream data; 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 on the data to determine the movement trajectory, behavior, ID and location information of each target fattening pig and transmits the data to the coordination control module.

4. The system according to claim 2, characterized in that The feeding device comprises: a control panel, a camera device, a feeding port, a feeder, a feed basin, a water basin, a waterer, and an emergency switch button; Wherein, the emergency switch button is disposed on the control panel, and the emergency switch button is used for the user to close or start the feeding device based on a preset emergency closing or starting 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 waterer 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 the feed for each of the target fattening pigs; The feed basin is installed below the feeder and is used to hold feed for feeding each of the target fattening pigs. The feeder includes a first liquid level sensor, which is used to monitor the feed remaining data of the feeder; The water basin is installed below the water feeder and is used to hold 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 of the water feeder, and the weight sensor is used to monitor the liquid weight data of the water feeder.

5. The system according to claim 3, characterized in that 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 on the activity trajectory data and status data of each target fattening pig to determine the movement trajectory, behavior, ID and location information of each target fattening pig, including: The edge computing unit receives the activity trajectory data and status data of each of the target fattening pigs, and performs real-time detection on the data through a preset detection and analysis model to obtain the bounding box and behavior classification results of each of the target fattening pigs, wherein the preset detection and analysis model uses the activity trajectory data set status data of the training target fattening pigs as training samples, and uses the bounding box and behavior classification results of the training fattening pigs included in the activity trajectory data and status data of the training target fattening pigs as sample labels, and is trained to obtain; Perform multi-target tracking on each of the target fattening pigs, assign a unique ID to each of the target fattening pigs, and record the movement path of each of the target fattening pigs; Extracting image data of each of the target fattening pigs based on the bounding box of each of the target fattening pigs; Preprocessing the image data of each target fattening pig to obtain edge information of the ID of each target fattening pig; Identify the edge information of the ID of each target fattening pig to obtain an identification result of the ID of each target fattening pig; Calibrate the identification result of the ID of each target fattening pig with the ID determined by tracking it, and determine the ID of each target fattening pig; Based on the movement trajectory and ID of each of the target fattening pigs, the movement trajectory, behavior and location information of each of the target fattening pigs are determined.

6. The system according to claim 4, characterized in that The control panel includes a control unit and a display unit, wherein 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 data set status data of each target fattening pig collected by the pig detection module; The control unit is used for allowing the user 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 of the target fattening pigs.

7. The system according to claim 6, 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 data and status data of each target fattening pig based on the data of the cloud server, and judging whether the environment of the target fattening pig farm meets the growth needs of each target fattening pig and judging whether there is any abnormality 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 needs of each target fattening pig or there are target fattening pigs with abnormal growth trends or growth status, an early warning signal of environmental abnormality or fattening pig abnormality is issued; and adjustment measures for the target fattening pig farm or target fattening pig 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 pig are displayed on the display unit.

8. An intelligent management method for fattening pig farming, characterized in that: Applied to the intelligent management system for fattening pig farming according to any one of claims 1 to 7, the method comprises: Collect environmental parameters of target fattening pig farms; 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; Real-time storage of the collected environmental parameters of the target fattening pig farm and the activity data and status data of the target fattening pigs; 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 of the target fattening pigs; if the environment of the target fattening pig farm does not meet the growth requirements of the target fattening pigs, issue an abnormal environment 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 data 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 of the target fattening pigs; Analyze the growth conditions of each of the target fattening pigs to determine whether there are target fattening pigs with abnormal growth. If there are target fattening pigs with abnormal growth, issue fattening pig abnormality warning information based on the ID of the target fattening pig with abnormal growth, determine fattening pig abnormality warning information corresponding to the target fattening pig with abnormal growth, determine 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; Determining a control strategy for the target fattening pig farm based on the environmental parameters of the target fattening pig farm and the growth conditions of each of the target fattening pigs; According to 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.

9. An intelligent management device for fattening pig breeding, 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 8.

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

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