Precise real-time monitoring system for abnormal body temperature of pigs in farm

Abstract

The invention discloses a pig body temperature abnormity accurate real-time monitoring system for a farm, and relates to the technical field of pig body temperature real-time monitoring. The system comprises a body temperature monitoring terminal, a data transmission module, an intelligent analysis platform and an early warning module, the intelligent analysis platform is in communication association with the plurality of body temperature monitoring terminals through the data transmission module, and is configured to execute the following operations in each monitoring period: acquiring pig body surface temperature data acquired by the body temperature monitoring terminals, and setting a standard body temperature interval based on pig variety and day age. According to the invention, non-contact accurate acquisition is realized through the infrared thermal imaging sensor, and interference of breeding environment fluctuation on temperature measurement data is effectively counteracted in cooperation with an environment temperature and humidity compensation algorithm; meanwhile, an exclusive standard body temperature interval is matched on the basis of pig varieties and day ages, data reliability coefficient verification is combined, the abnormal misjudgment rate is greatly reduced, and the problems that traditional equipment is insufficient in precision and poor in environmental adaptability are solved.

Description

Technical Field

[0001] This invention relates to the field of real-time pig body temperature monitoring technology, specifically a precise real-time monitoring system for abnormal pig body temperature in farms. Background Technology

[0002] In current large-scale pig farming, body temperature monitoring is a core means of disease prevention and control. Traditional monitoring methods mainly rely on manual, head-by-head contact temperature measurement, which has many technical limitations: First, it is inefficient, requiring a large amount of manpower per pig house, making it difficult to meet the real-time monitoring needs of farms with more than a thousand pigs; second, pigs have a strong stress response, and contact operation can easily cause stress-induced body temperature fluctuations, leading to data distortion and increasing the risk of disease transmission; third, the response is delayed, with long intervals between manual inspections, making it impossible to capture instantaneous abnormalities in body temperature in a timely manner, often missing the best opportunity for prevention and control.

[0003] In existing technologies, some non-contact monitoring devices, such as infrared thermometers, suffer from insufficient accuracy and susceptibility to environmental temperature interference. Meanwhile, single wireless sensing devices lack intelligent analysis capabilities, cannot distinguish individual identities, and struggle to achieve accurate traceability. Furthermore, existing systems do not consider the complexity of the farming environment (such as dust and humidity changes) and the impact of pig activity, leading to a high rate of false alarms. They also lack dynamic constraints on the operating status of monitoring equipment, making data interruptions likely in low-battery or signal interference scenarios, thus failing to balance accuracy, real-time performance, and stability.

[0004] Therefore, the technical problem to be solved by this invention is how to build a monitoring system that does not rely on human intervention, has strong resistance to environmental interference, and can achieve precise individual binding and real-time early warning of anomalies, so as to improve the efficiency of epidemic prevention and control while reducing labor costs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a precise real-time monitoring system for abnormal body temperature in pigs used in farms, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a precise real-time monitoring system for abnormal body temperature in pigs used in farms, comprising a body temperature monitoring terminal, a data transmission module, an intelligent analysis platform, and an early warning module; The intelligent analysis platform communicates and connects with multiple body temperature monitoring terminals through a data transmission module and is configured to perform the following operations in each monitoring cycle: acquire the surface temperature data of pigs collected by the body temperature monitoring terminals, calculate the temperature deviation value based on the standard body temperature range set according to the pig breed and age, and input the temperature deviation value into a nonlinear recognition function to determine whether the pig's body temperature is in an abnormal state. Simultaneously, the system acquires temperature and humidity data of the monitoring environment and calculates the rate of change of environmental parameters. It then compares the rate of change of environmental parameters with a preset environmental interference threshold. When the rate of change of environmental parameters exceeds the environmental interference threshold, it activates a temperature data compensation algorithm to correct the collected body surface temperature data. Furthermore, it acquires the remaining battery power and signal strength of the body temperature monitoring terminal, calculates the data reliability coefficient based on the remaining battery power and signal strength, couples the judgment result with the data reliability coefficient, obtains the final monitoring conclusion, and sends it to the early warning module.

[0007] According to the above technical solution, the body temperature monitoring terminal includes an infrared thermal imaging sensor and an RFID identification module. The temperature measurement range of the infrared thermal imaging sensor is 36℃-42℃, and the temperature measurement error is ≤±0.1℃. The intelligent analysis platform is configured to determine a normal state when the absolute value of the temperature deviation is lower than the first deviation threshold. When the absolute value of the temperature deviation is not lower than the first deviation threshold and not higher than the second deviation threshold, it is judged as a suspected abnormal state. When the absolute value of the temperature deviation exceeds the second deviation threshold, it is determined to be a serious abnormal state.

[0008] According to the above technical solution, the abnormal state determination follows the following relationship: ,in, To determine the result, This is the absolute value of the temperature deviation. For non-linear recognition functions, when At <0.3°C, normal; When 0.3°C≤ At ≤0.8°C, Suspected abnormality; when When >0.8°C, Seriously abnormal.

[0009] According to the above technical solution, the environmental interference threshold is an environmental temperature change rate ≥ 0.5℃ / min or an environmental humidity change rate ≥ 5% / min; the temperature data compensation algorithm is based on the difference between the environmental parameters and the standard environment (temperature 25℃, humidity 60%), using a linear correction formula: Calculate the corrected body temperature, where, To correct body temperature, For the collected body surface temperature, For ambient temperature, For ambient humidity, This is the temperature compensation coefficient (value 0.02). This is the humidity compensation factor (value 0.005).

[0010] According to the above technical solution, the data reliability coefficient is a function between 0 and 1, which is related to the remaining power (SOC) and signal strength (RSSI) of the monitoring terminal. When the remaining power is ≥30% and the signal strength is ≥-70dBm, the data reliability coefficient is 1. When the remaining power is <30% or the signal strength is <-70dBm, the data reliability coefficient decreases monotonically as the remaining power decreases or the signal strength weakens. The final monitoring conclusion is the product of the judgment result and the data reliability coefficient. When the product is ≥0.8, the corresponding warning level is triggered.

[0011] According to the above technical solution, the intelligent analysis platform is also configured to perform the following operations: synchronously acquire the activity status data of pigs (still / slowly active / vigorously active); calculate the time derivative of the temperature deviation value to obtain the rate of body temperature change; based on the synchronous criteria that the rate of body temperature change exceeds the rate threshold (≥0.2℃ / min) and the activity status is still, classify abnormal body temperature as pathological abnormality, otherwise classify it as stress abnormality; when it is determined to be a pathological abnormality, the early warning module synchronously sends the individual RFID identification and abnormal body temperature curve to the management terminal.

[0012] According to the above technical solution, before inputting the temperature deviation value into the nonlinear recognition function, the intelligent analysis platform is also configured to perform the following operations: acquire the breed parameters (finishing pigs / sows / piglets) and age data of the pigs; retrieve the corresponding standard body temperature range from the preset database based on the breed and age, and normalize the collected body surface temperature; wherein, the standard body temperature range for finishing pigs is 38.5℃-39.5℃, for sows it is 38.0℃-39.0℃, and for piglets it is 39.0℃-40.0℃.

[0013] According to the above technical solution, the intelligent analysis platform is also configured to perform the following operations when there is no abnormal body temperature data during the monitoring period: within a time window of 1 hour, calculate the variance of the data collected by the monitoring terminal as the data dispersion; based on the data dispersion, query and update the first deviation threshold and the second deviation threshold from a preset lookup table; when the data dispersion is ≥0.1℃, appropriately expand the range of the deviation threshold to avoid misjudgment.

[0014] According to the above technical solution, when the data reliability coefficient is less than 0.5, the intelligent analysis platform generates and sends a monitoring terminal maintenance reminder signal, which includes the terminal ID, remaining power, and installation location information.

[0015] This invention provides a precise real-time monitoring system for abnormal body temperature in pigs used in livestock farms. It has the following beneficial effects: (1) This invention uses an infrared thermal imaging sensor to achieve non-contact and accurate data acquisition. Combined with an environmental temperature and humidity compensation algorithm, it effectively offsets the interference of fluctuations in the breeding environment on the temperature measurement data. At the same time, based on the pig breed and age, it matches a dedicated standard body temperature range and combines data reliability coefficient verification (related to terminal power and signal strength) to significantly reduce the abnormal misjudgment rate and solve the problems of insufficient accuracy and poor environmental adaptability of traditional equipment.

[0016] (2) This invention completes body temperature collection, data transmission and analysis in a short cycle, with an early warning response time of ≤5 seconds, which is much faster than the 3-5 minutes of manual temperature measurement. It can capture instantaneous abnormalities in body temperature in a timely manner. The non-contact design avoids stress reactions in pigs, ensuring data authenticity and reducing the risk of disease transmission. At the same time, it replaces manual monitoring of each pig, significantly reducing the labor costs of large-scale farming. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system flow of the present invention.

[0018] In the diagram: 1. Body temperature monitoring terminal; 2. Data transmission module; 3. Intelligent analysis platform; 4. Early warning module. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Example 1: Verification of System Early Warning Effect A large-scale aquaculture simulation platform was built to simulate three scenarios: normal state, stress-induced abnormality, and pathological abnormality. The monitoring effects of the system of this invention were compared with those of traditional manual temperature measurement and ordinary infrared temperature measurement equipment. The experimental data are shown in the table below:

[0021] The test results show that the body temperature measurement accuracy and abnormality identification accuracy of the system of the present invention are better than those of traditional manual temperature measurement and ordinary infrared devices in different scenarios. Moreover, the early warning response time is controlled within 5 seconds, which is much faster than the 3-5 minutes of manual temperature measurement, and can realize real-time early warning of abnormal body temperature.

[0022] Example 2

[0023] To adapt the system to different farming scenarios, core parameters need to be calibrated: Deviation threshold calibration: Collect body temperature data of pigs from the target farm for 30 consecutive days, statistically analyze the distribution of body temperature deviation values ​​under 99% normal conditions, and set the first deviation threshold as the upper limit of this distribution (0.3℃ for fattening pigs, 0.25℃ for sows, and 0.35℃ for piglets); Environmental interference threshold calibration: Based on the operating parameters of the farm's ventilation and heating systems, record the maximum normal rate of change of ambient temperature and humidity, and set the environmental interference threshold to 1.5 times this value (temperature 0.5℃ / min, humidity 5% / min); Compensation coefficient calibration: Under standard environmental conditions (25℃, 60%) and different combinations of environmental parameters, simultaneously collect rectal temperature (true body temperature) and body surface temperature of pigs, and calculate the temperature compensation coefficient through linear regression. =0.02, humidity compensation coefficient =0.005.

[0024] Example 3

[0025] Simulate different abnormal scenarios to verify the system's accuracy in classifying pathological and stress-related abnormalities: Scenario 1: Pigs infected with classical swine fever have a body temperature that rises from 39.0℃ to 40.3℃ at a rate of 0.25℃ / min. Their activity level is currently at rest. The system classifies this as a pathological abnormality with 100% accuracy. Scenario 2: A pig is startled, its body temperature rises from 39.0℃ to 39.8℃ at a rate of 0.3℃ / min, and its activity level is vigorous. The system identifies this as an abnormal stress response, with a classification accuracy of 100%. Scenario 3: During the postoperative recovery period of pigs, the body temperature rises from 39.0℃ to 39.7℃, with a body temperature change rate of 0.15℃ / min. The activity state is slow. The system judges it as normal fluctuation, with no abnormal warning and a classification accuracy of 98%.

[0026] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A precise real-time monitoring system for abnormal body temperature in pigs used in farms, comprising a body temperature monitoring terminal (1), a data transmission module (2), an intelligent analysis platform (3), and an early warning module (4); The intelligent analysis platform (3) communicates with multiple body temperature monitoring terminals (1) through the data transmission module (2) and is configured to perform the following operations in each monitoring cycle: acquire the body surface temperature data of pigs collected by the body temperature monitoring terminal (1), calculate the temperature deviation value based on the standard body temperature range set by the pig breed and age; input the temperature deviation value into the nonlinear recognition function to determine whether the pig's body temperature is in an abnormal state. At the same time, the temperature and humidity data of the monitoring environment are acquired, and the rate of change of environmental parameters is calculated. The rate of change of environmental parameters is compared with the preset environmental interference threshold. When the rate of change of environmental parameters exceeds the environmental interference threshold, the temperature data compensation algorithm is started to correct the collected body surface temperature data. In addition, the remaining power and signal strength of the body temperature monitoring terminal (1) are acquired, and the data reliability coefficient is calculated based on the remaining power and signal strength. The judgment result is coupled with the data reliability coefficient to obtain the final monitoring conclusion and send it to the early warning module (4).

2. The accurate real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 1, characterized in that: The body temperature monitoring terminal (1) includes an infrared thermal imaging sensor and an RFID identification module. The temperature measurement range of the infrared thermal imaging sensor is 36℃-42℃, and the temperature measurement error is ≤±0.1℃. The intelligent analysis platform (3) is configured to determine a normal state when the absolute value of the temperature deviation is lower than the first deviation threshold. When the absolute value of the temperature deviation is not lower than the first deviation threshold and not higher than the second deviation threshold, it is judged as a suspected abnormal state. When the absolute value of the temperature deviation exceeds the second deviation threshold, it is determined to be a serious abnormal state.

3. The accurate real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 2, characterized in that: The abnormal state determination follows the following relationship: ,in, To determine the result, This is the absolute value of the temperature deviation. For non-linear recognition functions, when At <0.3°C, normal; When 0.3°C≤ At ≤0.8°C, Suspected abnormality; when When >0.8°C, Seriously abnormal.

4. The accurate real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 3, characterized in that: The environmental disturbance threshold is an environmental temperature change rate ≥ 0.5℃ / min or an environmental humidity change rate ≥ 5% / min; the temperature data compensation algorithm is based on the difference between the environmental parameters and the standard environment (temperature 25℃, humidity 60%), using a linear correction formula: Calculate the corrected body temperature, where, To correct body temperature, For the collected body surface temperature, For ambient temperature, For ambient humidity, This is the temperature compensation coefficient (value 0.02). This is the humidity compensation factor (value 0.005).

5. A precise real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 4, characterized in that: The data reliability coefficient is a function between 0 and 1, relating to the remaining battery power (SOC) and signal strength (RSSI) of the monitoring terminal. When the remaining battery power is ≥30% and the signal strength is ≥-70dBm, the data reliability coefficient is 1. When the remaining battery power is <30% or the signal strength is <-70dBm, the data reliability coefficient decreases monotonically as the remaining battery power decreases or the signal strength weakens. The final monitoring conclusion is the product of the judgment result and the data reliability coefficient. When the product is ≥0.8, the corresponding warning level is triggered.

6. The precise real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 5, characterized in that: The intelligent analysis platform (3) is also configured to perform the following operations: synchronously acquire the activity status data of pigs (still / slowly active / vigorously active); calculate the time derivative of the temperature deviation value to obtain the rate of body temperature change; classify abnormal body temperature as pathological abnormality based on the synchronous criterion that the rate of body temperature change exceeds the rate threshold (≥0.2℃ / min) and the activity status is still, otherwise classify it as stress abnormality; when it is determined to be a pathological abnormality, the early warning module (4) synchronously sends the individual RFID identification and abnormal body temperature curve to the management terminal.

7. A precise real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 6, characterized in that: Before inputting the temperature deviation value into the nonlinear recognition function, the intelligent analysis platform (3) is also configured to perform the following operations: obtain the breed parameters (fattening pigs / sows / piglets) and age data of the pigs; and call the corresponding standard body temperature range from the preset database based on the breed and age to normalize the collected body surface temperature; wherein, the standard body temperature range for fattening pigs is 38.5℃-39.5℃, for sows it is 38.0℃-39.0℃, and for piglets it is 39.0℃-40.0℃.

8. A precise real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 7, characterized in that: The intelligent analysis platform (3) is also configured to perform the following operations when there is no abnormal body temperature data during the monitoring period: within a time window of 1 hour, calculate the variance of the data collected by the monitoring terminal as the data dispersion; based on the data dispersion, query and update the first deviation threshold and the second deviation threshold from the preset lookup table; when the data dispersion is ≥0.1℃, appropriately expand the range of the deviation threshold to avoid misjudgment.

9. A precise real-time monitoring system for abnormal body temperature in pigs used in farms according to claim 8, characterized in that: When the data reliability coefficient is less than 0.5, the intelligent analysis platform (3) generates and sends a monitoring terminal maintenance reminder signal, which includes the terminal ID, remaining power and installation location information.

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