Pig epidemic disease evaluation system based on body temperature and abnormal behavior recognition

Abstract

The invention relates to the technical field of data processing, particularly discloses a live pig epidemic disease evaluation system based on body temperature and abnormal behavior recognition, and is used for solving the problems that in the prior art, abnormal behavior symptoms of live pigs are difficult to capture in time only depending on body temperature data, and health conditions of all live pigs cannot be mastered in real time. The method comprises the following steps: acquiring temperature distribution parameters of live pigs, evaluating whether heat exchange between the live pigs and a floor has influence on body temperature loss of the live pigs, acquiring real-time video images and motion data of the live pigs, identifying normal and abnormal behaviors, and generating behavior index scores; performing multi-source data time synchronization and fusion according to the body surface temperature parameters and behavior index scores, dynamically evaluating the epidemic disease risk, and performing automatic early warning on an abnormal state according to an evaluation result; according to the invention, through dual monitoring of body temperature and abnormal behaviors, data fusion and dynamic evaluation, precise monitoring of the health state of the live pigs and early warning of epidemic disease risks are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a pig disease assessment system based on body temperature and abnormal behavior recognition. Background Art

[0002] The health management and disease prevention and control of pigs have always been the core topics in modern pig farming. Traditional technologies usually focus on the body temperature monitoring of individual stages (such as neonatal piglets or growing and fattening pigs) or the local environmental temperature regulation, lacking the comprehensive monitoring of the body temperature of all pigs, and rarely combining the key indicator of pig behavior changes. The common practice in the past was to use infrared thermal imaging or temperature sensors to monitor the environmental temperature and the body surface temperature of some pigs, and judge whether there are abnormalities in pigs through manual observation or simple image monitoring. Infrared thermal imaging technology is a non-contact and real-time means of monitoring body temperature. By capturing the infrared radiation of pigs and converting it into a temperature distribution map, it can quickly identify the abnormal body temperature of pigs. Infrared thermal imaging technology can provide the surface temperature distribution of pigs and environmental temperature monitoring data without disturbing the activities of pigs, facilitating the timely discovery of pigs with low or high body temperature and taking intervention measures. In the actual breeding process, the temperature change of pigs is usually closely related to the pig house environmental temperature, floor temperature, and group temperature of pigs. When the floor temperature is uneven, the body temperature of pigs may drop, thereby inducing diseases, and the existing technology lacks a warning means for real-time monitoring of the temperature difference between pigs and their activity areas.

[0003] The existing technology usually only focuses on the key monitoring of neonatal piglets or specific delivery room areas, lacking systematic and continuous data collection for the entire pig population (including pigs of different ages or different partitions), resulting in blind spots in the monitoring data and being unable to grasp the health status of all pigs in real time. Once a disease occurs, the best intervention time may be missed; moreover, pigs often show subtle but important behavioral changes in the early stage of illness, such as reduced movement, loss of appetite, abnormal group distribution, etc. It is difficult to capture these abnormal behavior signs in time only relying on body temperature data; on the other hand, simple manual observation is easily affected by subjective factors and is difficult to promote on a large scale. To solve the above problems, a technical solution is provided now. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a pig disease assessment system based on body temperature and abnormal behavior recognition, which solves the problems that the prior art is difficult to capture the abnormal behavior signs of pigs in time only relying on body temperature data and is unable to grasp the health status of all pigs in real time through the dual monitoring of body temperature and abnormal behavior, data fusion and dynamic assessment, so as to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A pig disease assessment system based on body temperature and abnormal behavior recognition, including a whole-pig body temperature monitoring center, a pig abnormal behavior recognition platform, a dynamic disease risk assessment module, and an intelligent early warning and regulation module. The whole-pig body temperature monitoring center is used to obtain the temperature distribution parameters of pigs and evaluate whether the heat exchange between the pigs and the floor has an impact on the body temperature loss of the pigs; the pig abnormal behavior recognition platform is used to collect the real-time video images and motion data of pigs, identify normal and abnormal behaviors, and generate behavior index scores; the dynamic disease risk assessment module is used to perform multi-source data time synchronization and fusion based on the body surface temperature parameters and behavior index scores, and dynamically assess the disease risk; the dynamic disease risk assessment module includes a disease risk comprehensive assessment unit; the disease risk comprehensive assessment unit is used to construct a disease risk comprehensive assessment model based on the pig temperature difference value, behavior score, and the average change rate of the third temperature distribution parameter. The disease risk comprehensive assessment model obtains the influence intensity of the disease risk by taking the absolute value of the pig temperature difference value and then multiplying it by the weight coefficient; calculate the absolute value of the difference between the current behavior score and its historical average behavior score, and then multiply it by the weight coefficient to obtain the contribution factor of behavior abnormality; take the average change rate of the first temperature distribution factor over time and multiply it by the weight coefficient to obtain the influence factor of the body temperature change trend on the disease risk. The disease risk assessment value is equal to the sum of the influence intensity of the disease risk, the contribution factor of behavior abnormality, and the influence factor of the disease risk; the formula of the disease risk comprehensive assessment model is: ; In the formula: is the disease risk assessment value, is the index weight obtained by fitting historical data, is the pig temperature difference value, is the behavior score, is the average behavior score, is the average change rate of the third temperature distribution parameter.

[0006] As a further solution of the present invention, the whole-pig body temperature monitoring center includes a temperature parameter collection module, a temperature difference detection and analysis module, a body temperature loss analysis module, and a pig disease risk assessment module; the temperature parameter collection module is used to monitor the temperature distribution parameters of the pigsty in real time through infrared thermal imaging technology and obtain the normal temperature distribution parameters of the pigs as the first temperature distribution parameters; the temperature distribution parameters include the second temperature distribution parameters of the floor and the third temperature distribution parameters of the pigs; The temperature difference detection and analysis module is used to import the first temperature distribution parameter and the third temperature distribution parameter of the pigs into the temperature difference analysis model to calculate the pig temperature difference value; The body temperature loss analysis module is used to import the pig temperature difference value and the second temperature distribution parameter into the pig body temperature loss coefficient calculation formula to calculate the pig body temperature loss coefficient; The live pig disease risk assessment module is used to construct a live pig heat exchange model based on the temperature distribution parameters and the live pig body temperature loss coefficient, and to evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig.

[0007] As a further solution of the present invention, the temperature difference detection and analysis module is used to import the first temperature distribution parameter and the third temperature distribution parameter of the live pig into the live pig temperature difference analysis model to calculate the live pig temperature difference value: arrange the first temperature distribution parameter and the third temperature distribution parameter in ascending order respectively to obtain the first parameter sequence and the second parameter sequence, calculate the mean value of the first temperature distribution parameter according to the first parameter sequence as the first temperature distribution factor, calculate the mean value of the third temperature distribution parameter according to the second parameter sequence as the second temperature distribution factor, construct a live pig temperature difference analysis model based on the first temperature distribution parameter, the third temperature distribution parameter, the first temperature distribution factor and the second temperature distribution factor to calculate the live pig temperature difference value, calculate the normalized deviation of each element in the first parameter sequence relative to the distribution factor, calculate the normalized deviation of each element in the second parameter sequence relative to the distribution factor, sum up all the above normalized deviation values, and divide by the sum of the lengths of the two sequences to obtain the overall average deviation, take the absolute value of 1 minus the overall average deviation to obtain the live pig temperature difference value.

[0008] The formula of the live pig temperature difference analysis model is: ; In the formula: is the live pig temperature difference value, is the first temperature distribution parameter ranked at the i-th in the first parameter sequence, is the first temperature distribution factor, is the third temperature distribution parameter ranked at the j-th in the second parameter sequence, is the second temperature distribution factor, is the total number of the first temperature distribution parameters, is the total number of the third temperature distribution parameters.

[0009] As a further solution of the present invention, the live pig body temperature loss analysis module is used to import the live pig temperature difference value and the second temperature distribution parameter into the live pig body temperature loss coefficient calculation formula to calculate the live pig body temperature loss coefficient. Arrange the second temperature distribution parameter in ascending order to obtain the third parameter sequence, calculate the mean value of the second temperature distribution parameter according to the third parameter sequence as the third temperature distribution factor, construct a live pig body temperature loss coefficient calculation formula based on the third temperature distribution factor, the live pig temperature difference value and the second temperature distribution parameter, and calculate the arithmetic mean value of the third parameter sequence as the third temperature distribution factor, take the maximum value and the minimum value in the third parameter sequence, and combine the obtained live pig temperature difference value , calculate the normalized deviation of each parameter in the third parameter sequence relative to the factor, and then multiply by the coefficient , accumulate the results of all parameters to obtain the pig body temperature loss coefficient.

[0010] The calculation formula for the pig body temperature loss coefficient is: ; In the formula: is the pig body temperature loss coefficient, is the temperature difference value of the pig, is the number of the second temperature distribution parameters in the third parameter sequence, is the f-th second temperature distribution parameter in the third parameter sequence, is the third temperature distribution factor, is the maximum second temperature distribution parameter in the third parameter sequence, is the minimum second temperature distribution parameter in the third parameter sequence.

[0011] As a further solution of the present invention, the pig disease risk assessment module includes a parameter extraction unit, a heat loss balance analysis unit, and a body temperature change analysis unit; the parameter extraction unit is used to extract the second temperature distribution parameter, the first temperature distribution parameter, and the pig body temperature loss coefficient from the temperature distribution parameters; The heat loss balance analysis unit is used to analyze the first heat loss of the pig in contact with the floor, the second heat loss of the pig in contact with the air, and the third heat loss caused by the heat dissipation of the pig to the environment, and obtain the total heat loss of the pig based on the first heat loss, the second heat loss, and the third heat loss; The body temperature change analysis unit is used to analyze the body temperature change amount of the pig according to the law of conservation of heat, construct a pig body temperature change equation based on the total heat loss of the pig to predict the body temperature drop rate of the pig, and evaluate whether the heat exchange between the pig and the floor has an impact on the body temperature loss of the pig.

[0012] As a further solution of the present invention, the body temperature change analysis unit is used to analyze the body temperature change amount of the pig according to the law of conservation of heat, construct a pig body temperature change equation based on the total heat loss of the pig to predict the body temperature drop rate of the pig, and evaluate whether the heat exchange between the pig and the floor has an impact on the body temperature loss of the pig, specifically: Analyze the body temperature change amount of the pig according to the law of conservation of heat. Regarding the mass and specific heat capacity of the pig as constants, the change rate of the third temperature distribution parameter of the pig at any moment is proportional to the heat change amount, and the body temperature change amount of the pig is equal to the product of its mass, specific heat capacity, and the change rate of the third temperature distribution parameter: The calculation formula for the body temperature change amount of the pig is: ; In the formula: is the change in body temperature of the live pig, is the mass of the live pig, is the specific heat capacity of the live pig, is the third temperature distribution parameter ranked jth in the second parameter sequence, and t is time, is the average value of the change rate of the third temperature distribution parameter; Based on the total heat loss of the live pig, a live pig body temperature change equation is constructed. By adding the first heat loss value, the second heat loss value, and the third heat loss value, the obtained result is equal to the product of the mass of the live pig, the specific heat capacity, and the change rate of the first temperature distribution parameter, thereby establishing a prediction equation for the rate of decrease in the body temperature of the live pig: ; ; In the formula: is the total heat loss of the live pig, is the first heat loss value, is the second heat loss value, is the third heat loss value, is the change in body temperature of the live pig, is the mass of the live pig, is the specific heat capacity of the live pig, is the third temperature distribution parameter ranked jth in the second parameter sequence, and t is time, is the average value of the change rate of the third temperature distribution parameter; According to the average value of the change rate of the third temperature distribution parameter evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig. Real-time extract the average value of the change rate of the third temperature distribution parameter. If the average value of the change rate of the third temperature distribution parameter exceeds the preset range of the change rate of the third temperature distribution parameter, then the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig, and at this time, the intelligent early warning and control module will be triggered; if the average value of the change rate of the third temperature distribution parameter does not exceed the preset range of the change rate of the third temperature distribution parameter, then the heat exchange between the live pig and the floor has no impact on the body temperature loss of the live pig.

[0013] As a further solution of the present invention, the live pig abnormal behavior recognition platform includes an image data acquisition module, a target recognition and positioning module, and a behavior feature extraction module; the image data acquisition module is used to obtain real-time video images of the live pig through a high-definition camera and extract live pig motion data based on the real-time video images; the live pig motion data includes speed, acceleration, motion direction, and motion mode; The target recognition and positioning module is used to preprocess the video image and detect the position and boundary of each live pig by using a target detection algorithm; The behavior feature extraction module is used to establish a behavior recognition model based on a deep neural network. The input is a continuous sequence of video frames and the movement data of live pigs. It constructs a behavior feature library for the behavior states of live pigs (including walking, running, eating, and standing still), uses supervised learning to train the model, and for the abnormal behavior states of sick live pigs (including staying still for a long time, abnormal gait, frequent rolling, and rapid breathing), it obtains abnormal behavior feature values based on the abnormal behavior states and constructs a behavior differentiation scoring function to calculate the behavior differentiation score.

[0014] The technical effects and advantages of the live pig disease assessment system based on body temperature and abnormal behavior recognition of the present invention: By obtaining the temperature distribution parameters of live pigs, and evaluating whether the heat exchange between the live pigs and the floor has an impact on the body temperature loss of the live pigs, collecting the real-time video images and movement data of the live pigs to identify normal and abnormal behaviors and generate behavior index scores, synchronizing and fusing multi-source data according to the body surface temperature parameters and behavior index scores, dynamically evaluating the disease risk, and automatically warning of abnormal states based on the evaluation results; through the dual monitoring of body temperature and abnormal behavior, data fusion and dynamic evaluation, the present invention realizes the precise monitoring of the health status of live pigs and the early warning of disease risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic structural diagram of the live pig disease assessment system based on body temperature and abnormal behavior recognition provided by the present invention; Figure 2 is a real-time temperature distribution diagram in the pigsty provided by the present invention; Figure 3 is a trend diagram of the body temperature loss of live pigs provided by the present invention; Figure 4 is a heat exchange model diagram of live pigs and the outside world under different heat exchange methods provided by the present invention; Figure 5 is a schematic diagram of monitoring the second temperature distribution parameter by infrared thermal imaging technology provided by the present invention; Figure 6 is a schematic diagram of monitoring the third temperature distribution parameter by infrared thermal imaging technology provided by the present invention; Figure 7 is a real-time monitoring screen in the pigsty provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the drawings in the present invention. Obviously, the described technical solutions are only a part of the present invention, rather than all of them. All other technical solutions obtained by those of ordinary skill in the art based on the technical solutions in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] AsFigure 1 Schematic diagram of the structure of a pig disease assessment system based on body temperature and abnormal behavior recognition. The pig disease assessment system based on body temperature and abnormal behavior recognition includes a whole-pig body temperature monitoring center, a pig abnormal behavior recognition platform, a dynamic disease risk assessment module, and an intelligent early warning and control module. The whole-pig body temperature monitoring center and the pig abnormal behavior recognition platform are respectively connected to the dynamic disease risk assessment module, and the whole-pig body temperature monitoring center and the dynamic disease risk assessment module are respectively connected to the intelligent early warning and control module.

[0018] The whole-pig body temperature monitoring center is used to obtain the temperature distribution parameters of pigs and evaluate whether the heat exchange between the pigs and the floor has an impact on the body temperature loss of the pigs.

[0019] The pig abnormal behavior recognition platform is used to collect real-time video images and motion data of pigs, identify normal and abnormal behaviors, and generate behavior index scores.

[0020] The dynamic disease risk assessment module is used to perform multi-source data time synchronization and fusion according to the body surface temperature parameters and behavior index scores, and dynamically assess the disease risk.

[0021] The intelligent early warning and control module is used to automatically warn of abnormal states according to the evaluation results.

[0022] The whole-pig body temperature monitoring center includes a temperature parameter collection module, a temperature difference detection and analysis module, a body temperature loss analysis module, and a pig disease risk assessment module. The temperature parameter module is respectively connected to the temperature difference detection and analysis module, the body temperature loss analysis module, and the pig disease risk assessment module. The temperature difference detection and analysis module is connected to the body temperature loss analysis module, and the body temperature loss analysis module is connected to the pig disease risk assessment module.

[0023] The temperature parameter collection module is used to monitor the temperature distribution parameters of the pigsty in real time through infrared thermal imaging technology and obtain the normal temperature distribution parameters of the pigs as the first temperature distribution parameters. The temperature distribution parameters include the second temperature distribution parameter of the floor and the third temperature distribution parameter of the pigs, as Figure 5 shown in the schematic diagram of monitoring the second temperature distribution parameter by infrared thermal imaging technology and Figure 6 shown in the schematic diagram of monitoring the third temperature distribution parameter by the provided infrared thermal imaging technology.

[0024] The temperature difference detection and analysis module is used to import the first temperature distribution parameter and the third temperature distribution parameter of the pigs into the temperature difference analysis model to calculate the temperature difference value of the pigs.

[0025] The body temperature loss analysis module is used to import the pig temperature difference value and the second temperature distribution parameter into the pig body temperature loss coefficient calculation formula to calculate the pig body temperature loss coefficient.

[0026] The live pig disease risk assessment module is used to construct a live pig heat exchange model based on the temperature distribution parameters and the live pig body temperature loss coefficient, and evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig.

[0027] The temperature difference detection and analysis module is used to calculate the live pig temperature difference value by extracting the first temperature distribution parameter and the third temperature distribution parameter of the live pig and importing them into the live pig temperature difference analysis model: arrange the first temperature distribution parameter and the third temperature distribution parameter in ascending order respectively to obtain the first parameter sequence and the second parameter sequence, calculate the mean value of the first temperature distribution parameter according to the first parameter sequence as the first temperature distribution factor, calculate the mean value of the third temperature distribution parameter according to the second parameter sequence as the second temperature distribution factor, and construct a live pig temperature difference analysis model based on the first temperature distribution parameter, the third temperature distribution parameter, the first temperature distribution factor, and the second temperature distribution factor to calculate the live pig temperature difference value. The formula of the live pig temperature difference analysis model is: ; In the formula: is the live pig temperature difference value, is the first temperature distribution parameter ranked at the i-th position in the first parameter sequence, is the first temperature distribution factor, is the third temperature distribution parameter ranked at the j-th position in the second parameter sequence, is the second temperature distribution factor, is the total number of the first temperature distribution parameters, is the total number of the third temperature distribution parameters.

[0028] As Figure 2 shown in the real-time temperature distribution map of the pigsty floor, it shows the temperature values of different coordinate positions on the floor in the pigsty in the form of a heatmap. The abscissa (x-axis) represents the length direction of the pigsty floor (unit: m), and the ordinate (y-axis) represents the width direction of the pigsty floor (unit: m). Each grid corresponds to the temperature value of the area, and the color gradually changes from light to dark or from cold color to warm color. The area with higher values is more reddish or orange, and the area with lower values is more bluish or light yellow. The latest update time (such as 2024-01-18 15:30) is shown above the heatmap, indicating that this is the data obtained based on the real-time monitoring system, which can help quickly identify whether there are areas with too high or too low temperature on the pigsty floor.

[0029] As Figure 3The shown trend chart of the body temperature loss of live pigs shows two temperature curves changing over time, representing the surface temperature (blue curve) and the core temperature (green curve) of the live pigs respectively. Among them, the surface temperature is the third temperature distribution parameter, and the core temperature is the rectal temperature of the live pigs monitored in real time, or other similar differentiations. The abscissa (x-axis) is usually the time within a day (such as 0:00–24:00 or 0:00–22:00), and the ordinate (y-axis) is the temperature value (unit: °C). Real-time numerical labels are attached to the chart (such as at 23:00, the surface temperature is about 38.48 °C and the core temperature is about 39.30 °C) to accurately display the temperature situation at a specific moment. When the gap between the two curves is large, it often means that there may be problems such as poor heat dissipation, too low (or too high) environmental temperature, and stress response in the live pigs, and it is necessary to check in time and take corresponding measures.

[0030] By sorting the surface temperature distribution data of live pigs and using the mean calculation as a benchmark, the temperature differences in different regions and different individuals can be normalized, so as to obtain an objective temperature difference coefficient, providing an accurate indicator for subsequent health assessment; by summarizing and sorting all the temperature distribution data, and using the mean value obtained from the large sample data as a reference factor, the interference of a single abnormal temperature data on the overall assessment is effectively reduced, and the robustness and stability of the detection are improved; by using the first temperature distribution parameter and the third temperature distribution parameter to construct a temperature difference analysis model respectively, the changes in the body temperature of live pigs in different positions or states can be comprehensively considered, making the calculation of body temperature differences more comprehensive and more in line with the actual situation, which is conducive to early detection of health hazards; as a real-time indicator, the temperature difference coefficient can continuously monitor the changes in the body temperature of live pigs. If the temperature difference exceeds the preset threshold, the system can automatically trigger warning measures to timely remind the breeding management personnel to intervene, preventing the occurrence of diseases caused by abnormal body temperature.

[0031] The live pig body temperature loss analysis module is used to import the live pig temperature difference value and the second temperature distribution parameter into the live pig body temperature loss coefficient calculation formula to calculate the live pig body temperature loss coefficient. The second temperature distribution parameter is arranged in ascending order to obtain the third parameter sequence, and the mean value of the second temperature distribution parameter is obtained from the third parameter sequence as the third temperature distribution factor. Based on the third temperature distribution factor, the live pig temperature difference value and the second temperature distribution parameter, a live pig body temperature loss coefficient calculation formula is constructed. The live pig body temperature loss coefficient calculation formula is: ; In the formula: is the live pig body temperature loss coefficient, is the live pig temperature difference value, is the number of the second temperature distribution parameters in the third parameter sequence, is the second temperature distribution parameter ranked fth in the third parameter sequence, is the third temperature distribution factor, is the maximum second temperature distribution parameter in the third parameter sequence, is the minimum second temperature distribution parameter in the third parameter sequence.

[0032] By introducing the temperature difference value of live pigs and the second temperature distribution parameter, this module can quantitatively calculate the body temperature loss coefficient of live pigs, objectively reflect the degree of body temperature drop caused by heat exchange in live pigs, and provide accurate data support for subsequent health status assessment; by sorting the second temperature distribution parameters and using the normalization processing of the maximum value and the minimum value, it ensures that the calculation results are not affected by extreme temperature values and enhances the fault tolerance ability of the system to data fluctuations; by introducing the temperature difference coefficient in the body temperature loss coefficient calculation formula, it can capture the changing trend of the body temperature of live pigs in real time, help monitor the dynamic evolution of abnormal body temperature, and early warning of potential epidemic risks; by sorting and calculating the mean value of temperature data, a unified third temperature distribution factor is formed, making the body temperature loss assessment more general and standardized, facilitating popularization and use in different farms or different environmental conditions, reflecting the complexity of body temperature loss, and helping to achieve more accurate and comprehensive health risk assessment.

[0033] Specifically, the live pig epidemic risk assessment module includes a parameter extraction unit, a heat loss balance analysis unit, and a body temperature change analysis unit; the parameter extraction unit is connected to the heat loss balance analysis unit, and the heat loss balance analysis unit is connected to the body temperature change analysis unit.

[0034] The parameter extraction unit is used to extract the second temperature distribution parameter, the first temperature distribution parameter, and the body temperature loss coefficient of the live pig from the temperature distribution parameters.

[0035] The heat loss balance analysis unit is used to analyze the first heat loss of the live pig in contact with the floor, the second heat loss of the live pig in contact with the air, and the third heat loss caused by the heat dissipation of the live pig to the environment, and obtain the total heat loss of the live pig based on the first heat loss, the second heat loss, and the third heat loss.

[0036] The body temperature change analysis unit is used to analyze the change in the body temperature of the live pig according to the law of conservation of heat, construct a body temperature change equation of the live pig based on the total heat loss of the live pig to predict the body temperature drop rate of the live pig, and evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig.

[0037] Specifically, the heat loss balance analysis unit is used to analyze the first heat loss of the live pig in contact with the floor, the second heat loss of the live pig in contact with the air, and the third heat loss caused by the heat dissipation of the live pig to the environment, and obtain the total heat loss of the live pig based on the first heat loss, the second heat loss, and the third heat loss. Specifically: The calculation formula for the first heat loss of the live pig in contact with the floor is: ; Wherein: is the first heat loss value, is the first temperature distribution factor, is the second temperature distribution factor, is the contact area between the live pig and the floor, is the contact thermal resistance between the live pig and the floor.

[0038] The calculation formula for the second heat loss of the live pig in contact with the air is: ; Wherein: is the second heat loss value, is the convective heat transfer coefficient, is the surface area of the live pig, is the first temperature distribution factor, is the ambient temperature.

[0039] The calculation formula for the third heat loss caused by the heat dissipation of the live pig to the environment is: ; Wherein: is the third heat loss value, is the emissivity, is the Stefan-Boltzmann constant, is the surface area of the live pig, is the first temperature distribution factor, is the ambient temperature.

[0040] Based on the first heat loss, the second heat loss, and the third heat loss, the total heat loss of the live pig is obtained: ; Wherein: is the total heat loss of the live pig, is the first heat loss value, is the second heat loss value, is the third heat loss value.

[0041] Specifically, the body temperature change analysis unit is used to analyze the body temperature change amount of the live pig according to the heat conservation, construct a body temperature change equation of the live pig based on the total heat loss of the live pig to predict the body temperature drop rate of the live pig, and evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig. Specifically: Analyze the body temperature change amount of the live pig according to the heat conservation. The calculation formula for the body temperature change amount of the live pig is: ; Wherein: is the body temperature change amount of the live pig, For the quality of live pigs, For the specific heat capacity of live pigs, Is the third temperature distribution parameter ranked the jth in the second parameter sequence, t is time, Is the average rate of change of the third temperature distribution parameter.

[0042] Construct a live pig body temperature change equation based on the total heat loss of live pigs: ; ; In the formula: Is the total heat loss of live pigs, Is the first heat loss value, Is the second heat loss value, Is the third heat loss value, Is the change in the body temperature of live pigs, Is the quality of live pigs, Is the specific heat capacity of live pigs, Is the third temperature distribution parameter ranked the jth in the second parameter sequence, t is time, Is the average rate of change of the third temperature distribution parameter.

[0043] According to the average rate of change of the third temperature distribution parameter Evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig. Real-time extract the average rate of change of the third temperature distribution parameter. If the average rate of change of the third temperature distribution parameter exceeds the preset range of the rate of change of the third temperature distribution parameter, then the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig. At this time, the intelligent warning and control module will be triggered; if the average rate of change of the third temperature distribution parameter does not exceed the preset range of the rate of change of the third temperature distribution parameter, then the heat exchange between the live pig and the floor has no impact on the body temperature loss of the live pig.

[0044] Integrates three major functional units: parameter extraction, heat loss balance analysis, and body temperature change analysis. It not only extracts temperature distribution parameters and body temperature loss coefficients, but also quantifies the heat exchange between live pigs and the floor, air, and environment item by item, providing comprehensive and multi-dimensional data support for the health status of live pigs. It uses clear heat loss calculation formulas to quantitatively calculate the heat conduction loss when the live pig contacts the floor, the convective heat loss when it contacts the air, and the radiative heat loss to the environment respectively, and then superimposes the heat losses of each item to obtain the total heat loss, ensuring an accurate grasp of the heat energy exchange process and being able to more accurately reflect the true situation of the body temperature loss of live pigs. By using the mass, specific heat capacity, and heat loss of live pigs, a body temperature change equation is constructed to predict the body temperature decline rate from the perspective of heat conservation, which can quantitatively calculate the change in the body temperature of live pigs and can also evaluate in real time whether the heat exchange between live pigs and the floor has a significant impact on body temperature loss, thus providing a scientific basis for health risk early warning. It extracts the average value of the change rate of the third temperature distribution parameter in real time. Once the change rate exceeds the preset range, it is determined that the heat exchange between the live pig and the floor is abnormal, triggering the intelligent early warning and regulation module. This automated, real-time monitoring and early warning mechanism can intervene in time before the obvious downward trend of the live pig's body temperature, helping to detect potential disease risks early and take corresponding prevention and control measures.

[0045] Such as Figure 4 The heat exchange model diagram of the live pig and the outside world under different heat exchange methods shown in the figure shows the curves of the power / heat flux density (W / m²) of the live pig and the outside world changing with time under different heat exchange methods. The abscissa (x-axis) is also time, and the ordinate (y-axis) is the heat flux density or heat power (unit: W / m²). Each curve has different values at different time periods. For example, at 23:00, the convective heat dissipation is 47.2 W / m², the radiative heat dissipation is 42.8 W / m², the conductive heat dissipation is 64.1 W / m², and the heat generation is 174.9 W / m². Among them, the reason why the heat generation value is not equal to the sum of the conductive heat dissipation, convective heat dissipation, and radiative heat dissipation is that there is also evaporative heat dissipation, which mainly comes from the sweat secretion and respiratory evaporation of the live pig. The first heat loss value corresponds to the conductive heat dissipation, the second heat loss value corresponds to the convective heat dissipation, and the third heat loss value corresponds to the radiative heat dissipation. It helps the breeding personnel understand the energy exchange process between the live pig and the environment. When the heat dissipation and heat production are out of balance, the body temperature of the live pig will change accordingly, thus affecting the health status.

[0046] Specifically, the abnormal behavior recognition platform for live pigs includes an image data acquisition module, a target recognition and positioning module, and a behavior feature extraction module; the image data acquisition module is connected to the target recognition and positioning module, and the target recognition and positioning module is connected to the behavior feature extraction module; The image data acquisition module is used to obtain real-time video images of live pigs through a high-definition camera and extract pig motion data based on the real-time video images; the pig motion data includes speed, acceleration, motion direction, and motion mode; The target recognition and positioning module is used to preprocess the video images and detect the position and boundary of each live pig using a target detection algorithm; The behavior feature extraction module is used to establish a behavior recognition model based on a deep neural network. The input is a continuous sequence of video frames and pig motion data. A behavior feature library is constructed for the behavior states of live pigs (including walking, running, eating, and standing still), and the model is trained using supervised learning. For the abnormal behavior states of sick pigs (including staying still for a long time, abnormal gait, frequent rolling, and rapid breathing), an abnormal behavior feature value is obtained based on the abnormal behavior state, and an abnormal behavior differential scoring function is constructed. The formula for the abnormal behavior differential scoring function is: ; In the formula: is the behavior differential score, is the number of abnormal behavior parameters, is the weight parameter of the k-th abnormal behavior feature value, is the k-th abnormal behavior feature value.

[0047] It should be noted that the abnormal behavior feature values include the average motion speed, eating times, standing duration, rolling times, and breathing frequency of live pigs. Before constructing the abnormal behavior differential scoring function based on the abnormal behavior feature values, each feature value is normalized to make different features comparable.

[0048] Specifically, the specific steps for extracting pig movement data based on real-time video images are as follows: Preprocess the collected video, including noise reduction, removing light interference, and image stabilization processing, to eliminate the influence of environmental light changes on subsequent analysis; Use the background modeling algorithm to model the video sequence, extract the dynamic foreground, separate the background from the foreground, and obtain a binary image containing pig targets, thereby eliminating static background interference; In the image after background segmentation, use the deep learning object detection algorithm to detect the position of the pigs, calibrate the bounding boxes of the pig targets in each frame, and output the detection results, including target position, size, and confidence information; Use target tracking algorithms (such as KCF, SORT, DeepSORT, etc.) to track the pig targets detected in consecutive frames, establish the continuous trajectory of the targets in the time domain, and solve problems such as target occlusion, overlap, and fast movement during the tracking process to ensure that the trajectory of each pig can be continuously recorded; According to the continuous target position data obtained by tracking, calculate the movement trajectory, including the displacement path of the target in the two-dimensional plane, and extract the movement data of the pigs; The movement data of the pigs includes speed, acceleration, movement direction, and movement mode; The speed is obtained by calculating the instantaneous speed using the displacement between adjacent frames and the time interval; The acceleration is calculated according to the rate of change of speed; The movement direction is used to obtain the movement angle of the target by the direction of position change; The movement mode is used to identify the behavior state of the pigs (including walking, running, eating, and standing still) through trajectory analysis.

[0049] By collecting real-time video through high-definition cameras, the platform can continuously track the movement trajectories of each pig, ensuring that behavioral abnormalities can be captured in the first time and providing basic data for early warning; Using advanced deep learning object detection and tracking algorithms, the platform can accurately locate the boundaries and movement states of pigs, and can maintain stable tracking even in cases of occlusion, overlap, or fast movement, greatly improving the monitoring accuracy; By preprocessing the video images, background modeling, and target segmentation, the platform can not only extract basic movement data such as the speed, acceleration, and movement direction of pigs, but also analyze the movement mode, identify different states such as walking, running, eating, and standing still, so as to comprehensively reflect the behavior of pigs; The differential scoring of abnormal behaviors can quantify the behavioral differences of pigs, capture those abnormal signals that are not easily detected in the early stage, and help to timely warn of the risk of diseases.

[0050] Specifically, the dynamic disease risk assessment module includes a time series matching unit and a comprehensive disease risk assessment unit; The time series matching unit is connected to the comprehensive disease risk assessment unit; The time series matching unit is used to match the pig temperature difference value and the behavioral differential score according to the time stamp, and uses the interpolation algorithm and time window averaging (such as 5-minute, 10-minute windows) to smooth the data and eliminate sampling differences; The disease risk comprehensive assessment unit is used to evaluate the pig temperature difference, behavior score and the mean rate of change of the third temperature distribution parameter. Construct a comprehensive disease risk assessment model. The formula of the comprehensive disease risk assessment model is: ; Where: is the disease risk assessment value, is the indicator weight obtained by fitting historical data, is the temperature difference of pigs, Score the behavior. is the mean behavior score, is the mean value of the rate of change of the third temperature distribution parameter; The disease risk assessment value is compared with the preset disease risk assessment threshold. If the disease risk assessment value is greater than or equal to the preset disease risk assessment threshold, there is an abnormality, which will trigger the intelligent early warning and control module; if the disease risk assessment value is less than the preset disease risk assessment threshold, there is no abnormality.

[0051] Figure 5 Schematic diagram of the infrared thermal imaging technology provided by the present invention for monitoring the second temperature distribution parameter; a total of 27 cross temperature measurement points M1-M27 are equidistantly arranged on the surface of the pig, covering the head, neck, back, abdomen and roots of the limbs; the temperature color scale on the right shows that the temperature of this frame varies between 20.6°C (dark purple) and 39.1°C (bright yellow), providing basic data for the subsequent extraction of time series of each temperature measurement point and calculation of abnormal body temperature deviation.

[0052] Figure 6 Schematic diagram of the infrared thermal imaging technology provided by the present invention for monitoring the third temperature distribution parameter; after improvement, additional measuring points M28-M30 are added on the flank and buttocks, totaling 30, to refine the temperature distribution on the side of the body; the temperature range of this frame is 21.7°C-40.2°C, which can capture the hot spot of the pig's back with a maximum temperature of nearly 40°C; the skin temperature on the side of the pig fluctuates between 32-36°C, and some measuring points (such as M5 and M8) are higher due to their proximity to the heat source lamp, which helps to locate early signals of fever.

[0053] Figure 7 This is the real-time monitoring screen inside the pig house provided by the present invention; each live pig is marked with a blue rectangular frame and assigned a unique ID; the green curve shows the movement trajectory of the individual; the red rectangular frame represents the individual judged to be "abnormally still / abnormal posture", and the small red dot marks the current "crowding" hot spot location, providing intuitive support for behavioral feature extraction and subsequent disease risk assessment.

[0054] Integrate multiple indicators such as the temperature difference of live pigs, behavior scores, and the rate of body temperature change into the same model, comprehensively consider health information in different dimensions, so as to more comprehensively reflect the actual health status and disease risks of live pigs; through the time series matching unit, time alignment and data smoothing are performed on the temperature difference value and behavior scores. The module can effectively eliminate sampling differences, realize real-time monitoring and dynamic update of various indicators of live pigs, and ensure that the evaluation results timely reflect the latest status; convert the temperature difference, abnormal behavior fluctuations, and the rate of body temperature change into a quantifiable risk assessment value, which can accurately judge the current disease risk level and provide a basis for decision-making; compare the risk assessment value with a preset threshold. Once the risk value exceeds the threshold, the system will automatically trigger the intelligent early warning and control module to send a warning to the breeding management personnel in a timely manner, so as to achieve early detection and early intervention; the indicator weights are obtained by fitting historical data, which ensures that the model can adapt to the actual situation in different breeding environments and can be continuously optimized according to long-term operation data to improve the prediction accuracy.

[0055] In the embodiment of the present invention, by obtaining the temperature distribution parameters of live pigs and evaluating whether the heat exchange between the live pigs and the floor has an impact on the body temperature loss of the live pigs, collecting real-time video images and motion data of the live pigs to identify normal and abnormal behaviors to generate behavior index scores, performing multi-source data time synchronization and fusion according to the body surface temperature parameters and behavior index scores, dynamically evaluating the disease risks, and automatically warning of abnormal states based on the evaluation results; through the dual monitoring of body temperature and abnormal behaviors, data fusion and dynamic evaluation, the present invention realizes the precise monitoring of the health status of live pigs and the early warning of disease risks.

[0056] As described above, only the specific implementation manners of this application are provided, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0057] Finally: The above description is only a preferred solution of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A pig disease assessment system based on body temperature and abnormal behavior recognition, comprising a whole-pig body temperature monitoring center, a pig abnormal behavior recognition platform, a dynamic disease risk assessment module, and an intelligent early warning and regulation module, characterized in that, The whole-pig body temperature monitoring center is used to obtain the temperature distribution parameters of live pigs and evaluate whether the heat exchange between the live pigs and the floor has an impact on the body temperature loss of the live pigs; the abnormal pig behavior recognition platform is used to collect the real-time video images and motion data of the live pigs, identify normal and abnormal behaviors, and generate behavior index scores; the dynamic disease risk assessment module is used to synchronize and fuse multi-source data in time according to the body surface temperature parameters and behavior index scores, and dynamically assess the disease risk; the dynamic disease risk assessment module includes a comprehensive disease risk assessment unit; the comprehensive disease risk assessment unit is used to construct a comprehensive disease risk assessment model according to the pig temperature difference value, the behavior score, and the average change rate of the third temperature distribution parameter. The comprehensive disease risk assessment model obtains the influence intensity of the disease risk by taking the absolute value of the pig temperature difference value and then multiplying it by the weight coefficient; calculate the absolute value of the difference between the current behavior score and its historical average behavior score, and then multiply it by the weight coefficient to obtain the contribution factor of behavior abnormality; take the average change rate of the first temperature distribution factor over time and multiply it by the weight coefficient to obtain the influence factor of the body temperature change trend on the disease risk. The disease risk assessment value is equal to the sum of the influence intensity of the disease risk, the contribution factor of behavior abnormality, and the influence factor of the disease risk.

2. The pig disease assessment system based on body temperature and abnormal behavior recognition according to claim 1, characterized in that, The whole-pig body temperature monitoring center includes a temperature parameter collection module, a temperature difference detection and analysis module, a body temperature loss analysis module, and a live pig disease risk assessment module; the temperature parameter collection module is used to monitor the temperature distribution parameters of the pigsty in real time through infrared thermal imaging technology and obtain the normal temperature distribution parameters of the live pigs as the first temperature distribution parameters; the temperature distribution parameters include the second temperature distribution parameter of the floor and the third temperature distribution parameter of the live pigs. The temperature difference detection and analysis module is used to import the first temperature distribution parameter and the third temperature distribution parameter of the live pigs into the temperature difference analysis model to calculate the pig temperature difference value. The body temperature loss analysis module is used to import the pig temperature difference value and the second temperature distribution parameter into the live pig body temperature loss coefficient calculation formula to calculate the live pig body temperature loss coefficient. The live pig disease risk assessment module is used to construct a live pig heat exchange model according to the temperature distribution parameters and the live pig body temperature loss coefficient, and evaluate whether the heat exchange between the live pigs and the floor has an impact on the body temperature loss of the live pigs.

3. The hog epidemic disease assessment system based on body temperature and abnormal behavior recognition according to claim 2, wherein The temperature difference detection and analysis module is used to import the first temperature distribution parameter and the third temperature distribution parameter of the live pig into the live pig temperature difference analysis model to calculate the temperature difference value of the live pig: arrange the first temperature distribution parameter and the third temperature distribution parameter in ascending order respectively to obtain the first parameter sequence and the second parameter sequence, calculate the mean value of the first temperature distribution parameter according to the first parameter sequence as the first temperature distribution factor, calculate the mean value of the third temperature distribution parameter according to the second parameter sequence as the second temperature distribution factor, construct a live pig temperature difference analysis model based on the first temperature distribution parameter, the third temperature distribution parameter, the first temperature distribution factor and the second temperature distribution factor to calculate the temperature difference value of the live pig. Calculate the normalized deviation of each element in the first parameter sequence relative to the distribution factor, calculate the normalized deviation of each element in the second parameter sequence relative to the distribution factor, sum up all the above normalized deviation values, and divide by the sum of the lengths of the two sequences to obtain the overall average deviation. Take the absolute value of 1 minus the overall average deviation to obtain the live pig temperature difference value.

4. The pig disease assessment system based on body temperature and abnormal behavior recognition according to claim 2, wherein The live pig body temperature loss analysis module is used to import the live pig temperature difference value and the second temperature distribution parameter into the live pig body temperature loss coefficient calculation formula to calculate the live pig body temperature loss coefficient. The second temperature distribution parameter is arranged in ascending order to obtain the third parameter sequence. The mean value of the second temperature distribution parameter is obtained according to the third parameter sequence as the third temperature distribution factor. Based on the third temperature distribution factor, the live pig temperature difference value, and the second temperature distribution parameter, a live pig body temperature loss coefficient calculation formula is constructed. The arithmetic mean of the third parameter sequence is calculated as the third temperature distribution factor, and the maximum and minimum values in the third parameter sequence are taken, combined with the already obtained live pig temperature difference value , calculate the normalized deviation of each parameter in the third parameter sequence relative to the factor, and then multiply by the coefficient , and sum up the results of all parameters to obtain the live pig body temperature loss coefficient.

5. The pig disease assessment system based on body temperature and abnormal behavior recognition according to claim 2, characterized in that, The live pig disease risk assessment module includes a parameter extraction unit, a heat loss balance analysis unit and a body temperature change analysis unit; the parameter extraction unit is used to extract the second temperature distribution parameter, the first temperature distribution parameter and the live pig body temperature loss coefficient in the temperature distribution parameters; The heat loss balance analysis unit is used to analyze the first heat loss of the live pig in contact with the floor, the second heat loss of the live pig in contact with the air and the third heat loss caused by the heat dissipation of the live pig to the environment, and obtain the total heat loss of the live pig based on the first heat loss, the second heat loss and the third heat loss; The body temperature change analysis unit is used to analyze the body temperature change of the live pig according to the law of conservation of heat, construct a live pig body temperature change equation based on the total heat loss of the live pig to predict the body temperature drop rate of the live pig, and evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig.

6. The pig disease assessment system based on body temperature and abnormal behavior recognition according to claim 5, wherein, The body temperature change analysis unit is used to analyze the body temperature change of the live pig according to the law of conservation of heat, construct a live pig body temperature change equation based on the total heat loss of the live pig to predict the body temperature drop rate of the live pig, and evaluate whether the heat exchange between the live pig and the floor has an impact on the body temperature loss of the live pig. Specifically: Analyze the body temperature change of the live pig according to the law of conservation of heat. Regarding the mass and specific heat capacity of the live pig as constants, the change rate of the third temperature distribution parameter of the live pig at any moment is proportional to the heat change amount. The body temperature change amount of the live pig is equal to the product of its mass, specific heat capacity and the change rate of the third temperature distribution parameter: Construct a live pig body temperature change equation based on the total heat loss of the live pig: by adding the first heat loss value, the second heat loss value and the third heat loss value, the obtained result is equal to the product of the mass, specific heat capacity and the change rate of the third temperature distribution parameter of the live pig, thereby establishing a prediction equation for the body temperature drop rate of the live pig; Evaluate whether the heat exchange between the live pig and the floor affects the body temperature loss of the live pig according to the mean value of the change rate of the third temperature distribution parameter. Extract the mean value of the change rate of the third temperature distribution parameter in real time. If the mean value of the change rate of the third temperature distribution parameter exceeds the preset range of the change rate of the third temperature distribution parameter, the heat exchange between the live pig and the floor affects the body temperature loss of the live pig, and at this time, the intelligent early warning and regulation module will be triggered; if the mean value of the change rate of the third temperature distribution parameter does not exceed the preset range of the change rate of the third temperature distribution parameter, the heat exchange between the live pig and the floor has no effect on the body temperature loss of the live pig.

7. The live pig epidemic disease assessment system based on body temperature and abnormal behavior recognition according to claim 1, characterized in that The abnormal behavior recognition platform for live pigs includes an image data acquisition module, a target recognition and positioning module, and a behavior feature extraction module; the image data acquisition module is used to obtain the real-time video image of the live pig through a high-definition camera and extract the live pig movement data based on the real-time video image; the live pig movement data includes speed, acceleration, movement direction, and movement mode; The target recognition and positioning module is used to preprocess the video image and detect the position and boundary of each live pig by using a target detection algorithm; The behavior feature extraction module is used to establish a behavior recognition model based on a deep neural network. The input is a continuous video frame sequence and live pig movement data. A behavior feature library is constructed for the behavior state of the live pig. Supervised learning is used for model training. For the abnormal behavior states of sick live pigs, abnormal behavior feature values are obtained based on the abnormal behavior states, and a behavior differentiation scoring function is constructed to calculate the behavior differentiation score.

Images