Intelligent Temperature Regulation System Based on the Internet of Things

Abstract

The present application provides an intelligent temperature regulation system based on the Internet of Things, which relates to the field of intelligent regulation technology. It includes: an instruction acquisition module for acquiring a temperature detection instruction; a real-time temperature detection module for activating a platinum resistance temperature sensor based on the temperature detection instruction to dynamically detect the target real-time temperature of the target digestive system at the real-time; a temperature transmission module for transmitting the target real-time temperature to the Internet of Things data processing cloud platform; a temperature prediction module for performing prediction analysis through the Internet of Things data processing cloud platform to obtain the predicted target temperature at the target time; if the predicted target temperature is not within the predetermined temperature threshold, an intervention regulation module issues a health regulation instruction to perform health intervention regulation on the target animal body. The present application solves the technical problem in the prior art that due to the lack of prediction analysis means, the temperature regulation lags behind, and achieves the technical effect of improving the recognition accuracy and foresight of temperature anomalies, thereby improving the temperature regulation response speed.

Description

Technical Field

[0001] This application relates to the field of intelligent regulation technology, and particularly to an intelligent temperature regulation system based on the Internet of Things. Background Art

[0002] The digestive system plays a key role in the processes of energy acquisition and nutrient absorption in an animal body. The stability and suitability of its temperature directly affect the activity of digestive enzymes, the living environment of microorganisms, etc., and thus affect the function of the entire digestive system. At present, the temperature regulation of the animal body's digestive system mainly measures the temperature of the animal body's digestive system through contact temperature measurement and non-contact temperature measurement, and then conducts intervention and regulation. Contact temperature measurement such as rectal temperature measurement, although with high accuracy, is cumbersome to operate, easily causes stress reactions in animals, and is difficult to achieve continuous monitoring. Non-contact temperature measurement technologies, such as infrared thermal imaging, temperature sensing systems based on biosensors, etc., have the advantages of non-invasive and fast, but are easily interfered by environmental factors (such as temperature, humidity, wind speed), resulting in inaccurate measurement results and unable to directly reflect the actual temperature changes of the digestive system. At the same time, these methods can only monitor the real-time temperature, resulting in that only after the temperature anomaly occurs, the management personnel take remedial measures for intervention and regulation. The lag of temperature regulation is difficult to meet the high-precision and high-efficiency requirements of modern aquaculture for animal health management. Summary of the Invention

[0003] This application provides an intelligent temperature regulation system based on the Internet of Things, which solves the technical problem of temperature regulation lag in the prior art due to the lack of prediction and analysis means, and achieves the technical effect of improving the recognition accuracy and forward-looking of temperature anomalies, and further improving the response speed of temperature regulation.

[0004] In view of the above problems, this application provides an intelligent temperature regulation system based on the Internet of Things. The system includes: an instruction acquisition module for acquiring a temperature detection instruction, where the temperature detection instruction is an instruction for the microcontroller to automatically issue a temperature detection of the target digestive system of the target animal body based on a predetermined detection frequency; a real-time temperature detection module for activating a platinum resistance temperature sensor based on the temperature detection instruction, and dynamically detecting the target real-time temperature of the target digestive system at the real-time time through the platinum resistance temperature sensor; a temperature transmission module for transmitting the target real-time temperature to the Internet of Things data processing cloud platform through a LoRa communicator; a temperature prediction module for performing prediction and analysis through the Internet of Things data processing cloud platform to obtain a predicted target temperature at the target time; an intervention and regulation module for issuing a health regulation instruction if the predicted target temperature is not within a predetermined temperature threshold, and performing health intervention and regulation on the target animal body based on the health regulation instruction.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] This application realizes automatic temperature detection triggering through the instruction acquisition module to ensure the regularity and continuity of monitoring; the real-time temperature detection module uses a high-precision platinum resistance sensor to dynamically capture temperature changes, providing an accurate data basis for subsequent temperature prediction and intervention adjustment; the temperature transmission module uses LoRa technology to efficiently transmit data to the cloud to achieve remote management and centralized processing of data; the temperature prediction module uses big data analysis and the computing power of the cloud platform to predict temperature change trends in advance and provide forward-looking decision support; the intervention adjustment module issues adjustment instructions in time according to the prediction results to achieve rapid and accurate intervention adjustment of animal body temperature. In summary, this application solves the problem of temperature regulation lag in the prior art, improves the accuracy and foresight of identifying temperature anomalies, thereby improving the temperature regulation response speed and realizing the intelligent and refined animal health management.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of an intelligent temperature control system based on the Internet of Things provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of a process for obtaining a predicted target temperature at a target time in an IoT-based intelligent temperature control system provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: instruction acquisition module 10 , real-time temperature detection module 20 , temperature transmission module 30 , temperature prediction module 40 , intervention and adjustment module 50 . DETAILED DESCRIPTION

[0011] The embodiment of the present application solves the technical problem of delayed temperature regulation due to the lack of predictive analysis means in the prior art by providing an intelligent temperature control system based on the Internet of Things, thereby achieving the technical effect of improving the accuracy and foresight of identifying temperature anomalies and thereby improving the temperature control response speed.

[0012] like Figure 1 As shown, the embodiment of the present application provides an intelligent temperature adjustment system based on the Internet of Things, and the system includes:

[0013] Instruction acquisition module 10, which is used to acquire a temperature detection instruction, where the temperature detection instruction refers to an instruction automatically issued by a microcontroller for temperature detection of a target digestive system of a target animal body based on a predetermined detection frequency.

[0014] Specifically, the target animal body is a specific animal that needs temperature monitoring, such as ruminants like cows and sheep. The target digestive system refers to the part of the digestive system in the target animal body that is of key concern, such as the rumen of a cow and other parts. The predetermined detection frequency is a preset time interval for temperature detection of the target animal body. For example, if it is set to detect the animal's body temperature every 10 minutes, this 10 minutes is the predetermined detection frequency.

[0015] The microcontroller automatically issues a temperature detection instruction for the target digestive system of the target animal body according to the predetermined detection frequency. The instruction acquisition module 10 automatically receives the temperature detection instruction from the microcontroller and triggers the real-time temperature detection module 20 to perform temperature monitoring according to this temperature detection instruction, ensuring the regularity and timeliness of temperature detection of the specific digestive system of the target animal body and laying a foundation for subsequent temperature monitoring and management.

[0016] Real-time temperature detection module 20, which is used to activate a platinum resistance temperature sensor based on the temperature detection instruction and dynamically detect the target real-time temperature of the target digestive system at real time through the platinum resistance temperature sensor.

[0017] Specifically, the platinum resistance temperature sensor is a temperature sensor with high precision and good stability. It uses the characteristic that the resistance of platinum material changes with temperature to sense temperature. After the instruction acquisition module 10 receives the temperature detection instruction and triggers the real-time temperature detection module 20, the real-time temperature detection module 20 activates the platinum resistance temperature sensor to monitor the temperature of the target animal's digestive system in real time. The platinum resistance temperature sensor has high-precision measurement ability, can dynamically sense temperature changes, and transmit data in real time. The platinum resistance temperature sensor measures the temperature of the target digestive system at real time by contacting or approaching the target digestive system of the target animal body and using the characteristic that its resistance changes with temperature to obtain the target real-time temperature. For example, the platinum resistance temperature sensor is attached to a contactable position near the rumen of a cow to accurately measure the current temperature of the rumen.

[0018] By dynamically monitoring the temperature of the target animal body, the real-time temperature of the target digestive system can be obtained in real time, providing accurate current temperature data for subsequent transmission, prediction, and intervention.

[0019] Temperature transmission module 30, which is used to transmit the target real-time temperature to the Internet of Things data processing cloud platform through a LoRa communicator.

[0020] Specifically, the LoRa communicator is a low-power and long-range wireless communication device. The temperature transmission module 30 transmits the target real-time temperature data collected by the sensor to the Internet of Things data processing cloud platform at a distance. This Internet of Things data processing cloud platform is used to process Internet of Things data and can perform operations such as receiving, storing, and analyzing data from sensors. Through the LoRa communicator, long-distance and low-power data transmission from the temperature detection end to the data processing platform is achieved, ensuring the timely and stable transmission of data and providing a data source for subsequent temperature prediction.

[0021] The temperature prediction module 40 is used to perform prediction analysis through the Internet of Things data processing cloud platform to obtain the predicted target temperature at the target time.

[0022] Specifically, after the Internet of Things data processing cloud platform receives the target real-time temperature from the temperature transmission module 30, the temperature prediction module 40 uses the algorithms inside the Internet of Things data processing cloud platform (such as based on historical data and machine learning algorithms, etc.) to perform prediction analysis on the temperature of the target digestive system and obtain the predicted target temperature at the target time. Among them, the target time is a certain time point or time period in the future. For example, by analyzing the temperature data of the bovine rumen in the past week and combining the current real-time temperature, the temperature of the bovine rumen in the next two hours is predicted.

[0023] Through temperature prediction analysis, the future temperature situation of the target animal body can be known in advance, providing a decision-making basis for early health intervention.

[0024] The intervention and regulation module 50 is used to issue a health regulation instruction if the predicted target temperature is not within the predetermined temperature threshold, and perform health intervention and regulation on the target animal body based on the health regulation instruction.

[0025] Specifically, the predetermined temperature threshold is the upper and lower limits of the pre-set normal temperature range. By comparing the predicted target temperature with the predetermined temperature threshold, when the predicted target temperature obtained by the temperature prediction module 40 is not within the range of the predetermined temperature threshold, the intervention and regulation module 50 will issue a health regulation instruction. This health regulation instruction is used to adjust the environment where the animal is located to ensure that the target digestive system of the target animal body remains within the range of the predetermined temperature threshold, such as adjusting the environmental temperature (such as turning on or off the heater, air conditioner, etc.), or performing special feeding management operations on the animal (such as adjusting the feed formula, etc.). For example, the normal temperature range of the bovine rumen is 38°C to 40°C (predetermined temperature threshold). If it is predicted that the temperature of the bovine rumen will be lower than 38°C, the heater can be turned on to increase the environmental temperature or some high-calorie feed can be provided to the cattle to adjust the body temperature of the cattle.

[0026] Timely perform health intervention on the animal body according to the prediction result, prevent possible temperature abnormalities in advance, and thus improve the response speed and forward-looking of temperature regulation.

[0027] Furthermore, as Figure 2 shown, the temperature prediction module 40 of the embodiment of the present application is further configured to perform the following steps:

[0028] Step P41: Obtain the preprocessor in the Internet of Things data processing cloud platform, where a predetermined signal loss correction function is embedded in the preprocessor.

[0029] Step P42: Perform loss correction analysis on the target real-time temperature according to the predetermined signal loss correction function to obtain the target actual temperature.

[0030] Step P43: Establish a correspondence between the target actual temperature and the real-time time to establish a target temperature signal time sequence.

[0031] Step P44: Perform predictive analysis on the target temperature signal time sequence through the Internet of Things data processing cloud platform to obtain the predicted target temperature at the target time.

[0032] Specifically, the temperature prediction module 40 first obtains the preprocessor from the Internet of Things data processing cloud platform. A predetermined signal loss correction function is embedded in the preprocessor, and this function is used to correct the errors in the temperature signal. For example, in a farm environment, the signals collected by temperature sensors may be affected by environmental noise or sensor accuracy limitations. The role of the preprocessor is to perform preliminary processing on these signals to improve the data quality.

[0033] Use the predetermined signal loss correction function in the preprocessor to correct the target real-time temperature. The correction function takes into account various factors such as signal attenuation, transmission distance, and noise interference, so as to obtain a more accurate target actual temperature. This target actual temperature is the temperature value after correction and is closer to the real temperature in the animal body.

[0034] Establish a correspondence between the corrected target actual temperature and the real-time time, and arrange these target actual temperatures according to the time sequence to form a target temperature signal time sequence. This target temperature signal time sequence can clearly show the changing trend of the temperature of the target digestive system of the target animal body over time.

[0035] Utilize the computing power of the Internet of Things data processing cloud platform to perform predictive analysis on the target temperature signal time sequence. Through statistical analysis and machine learning algorithms (such as linear regression, time series analysis, or neural networks), model and predict the time series data to estimate the temperature value at a future time point.

[0036] Through the above execution steps, the temperature prediction module 40 realizes the prediction of the future temperature of the target animal body, can understand the changing trend of the temperature in advance, provides a decision-making basis for the intervention adjustment module 50, and improves the accuracy and forward-looking of temperature adjustment.

[0037] Further, step P41 includes:

[0038] Step P411: Perform temperature detection simulation on the target digestive system to obtain a simulation record.

[0039] Step P412: Compare and calculate the simulated signal power and simulated noise power in the simulation record to obtain the target signal-to-noise ratio.

[0040] Step P413: Obtain the predetermined signal loss correction function according to the target signal-to-noise ratio and store it in the preprocessor.

[0041] Specifically, the real-time temperature measured by the temperature sensor is affected by various factors such as signal attenuation and interference. Directly processing these data may lead to deviations in the temperature prediction results. Therefore, it is necessary to use a correction function to correct the real-time temperature. To determine the specific predetermined signal loss correction function, it is necessary to perform temperature detection simulation on the target digestive system. Exemplarily, a simulation model of the target digestive system can be established, which can simulate the temperature conditions of the target digestive system in different environments and states. For example, a temperature simulation model of the target digestive system can be established using computer software, which takes into account factors such as the physiological structure of the target digestive system, the ambient temperature, and the heat generation during the digestion process, and then perform simulation detection to obtain a simulation record. This simulation record contains various data related to temperature detection during the simulation process, such as temperature changes, signal intensities, and noise levels at different time points. Through simulation, various situations that may occur during the temperature detection of the target digestive system can be more comprehensively understood, thus preparing for accurate signal loss correction.

[0042] Extract two key data, the simulated signal power and the simulated noise power, from the simulation record, and then perform a comparison calculation to obtain the target signal-to-noise ratio. The signal-to-noise ratio is an important parameter for measuring signal quality, defined as the ratio of signal power to noise power. The higher the signal-to-noise ratio, the better the signal quality and the more accurate the temperature data. By calculating the target signal-to-noise ratio, the relationship between the signal and the noise during the simulated temperature detection of the target digestive system can be quantified, providing a key basis for determining the predetermined signal loss correction function.

[0043] Determine a predetermined signal loss correction function according to the target signal-to-noise ratio. The establishment of the predetermined signal loss correction function is based on some preset algorithms or empirical formulas, and these algorithms and formulas will determine how to correct the possible signal loss according to the value of the target signal-to-noise ratio. For example, if the target signal-to-noise ratio is low, it means that the influence of noise on the signal is large, then the predetermined signal loss correction function may make a large adjustment to the temperature data; if the target signal-to-noise ratio is high, the adjustment amplitude may be smaller. After determining the predetermined signal loss correction function, store it in the preprocessor for use in subsequent steps.

[0044] Through the above steps, a predetermined signal loss correction function adapted to the simulated situation of the target digestive system temperature detection is established and stored in the preprocessor, providing a technical means for accurately correcting signal loss in the actual temperature detection process and improving the accuracy of temperature monitoring data.

[0045] Furthermore, the expression of the predetermined signal loss correction function is as follows: ; where refers to the actual temperature of the target, refers to the real-time temperature of the target, refers to the comprehensive loss function, where the comprehensive loss function includes a signal attenuation loss function, a transmission distance loss function, and an interference loss function, and the signal attenuation loss function is represented by for characterization, refers to the attenuation coefficient, refers to the transmission distance of the signal in the target digestive system, and the transmission distance loss function is represented by for characterization, refers to the transmission distance of the signal in space, and the interference loss function is represented by characterized by, refers to the target signal-to-noise ratio.

[0046] Specifically, the predetermined signal loss correction function quantifies the comprehensive loss of signal transmission in the temperature monitoring process through the signal attenuation loss function , the transmission distance loss function and the interference loss function (that is, the target signal-to-noise ratio calculated by the temperature detection simulation mentioned above). Among them, the signal attenuation loss function calculates the signal attenuation loss according to the propagation distance and the attenuation coefficient , and describes the attenuation of the signal intensity caused by medium absorption and other reasons when the signal is transmitted inside the target digestive system. During the signal propagation process, due to the impedance of the air or the characteristics of the transmission medium, the signal intensity will weaken as the distance increases. The transmission distance loss function It reflects the energy dispersion of the signal during spatial transmission as the distance increases. As the signal travels in space increases, the intensity of the signal decays according to the inverse square law. The longer the signal propagates, the weaker the received signal intensity. The interference loss function reflects the impact of external interference on the signal based on the target signal-to-noise ratio. The lower the signal-to-noise ratio, the greater the interference and the greater the impact on temperature measurement. Based on the attenuation loss, transmission distance loss, and interference loss, the real-time temperature is corrected to obtain the target actual temperature , effectively improving the accuracy of the target temperature data, reducing the errors caused by external interference, and enabling the temperature regulation system to provide high-quality data support under various environmental conditions.

[0047] Further, step P44 includes:

[0048] Step a: Obtain the target scatter plot of the target temperature signal timing.

[0049] Step b: Draw the first temperature curve based on the first scatter sample and determine whether the first temperature curve reaches a predetermined curve constraint.

[0050] Step c: If the first temperature curve does not reach the predetermined curve constraint, repeat step b until the predetermined curve constraint is reached, and output the first temperature curve at that time.

[0051] Step d: Perform trend prediction analysis on the first temperature curve in combination with the target time to obtain the predicted target temperature.

[0052] Specifically, from the established target temperature signal timing data, extract the time and corresponding temperature data, and then use a data visualization tool or programming library (such as the matplotlib library in Python) to draw the target scatter plot. These tools can accurately draw scatter plots based on the given data points, intuitively showing the change of temperature over time, which helps to quickly understand the general trend of temperature change and the degree of data dispersion.

[0053] Select a part of the sample data from the target scatter plot and denote it as the first scatter sample. For example, a part of the data points can be selected from all the data points in the scatter plot at regular time intervals or randomly as the first scatter sample. Use a data fitting method (such as the least squares method) to draw the first temperature curve based on the first scatter sample. The least squares method finds a curve that minimizes the sum of the squares of the vertical distances from the sample data points to this curve, thereby obtaining a curve equation that can better fit these data points, denoted as the first temperature curve. Then, compare the drawn first temperature curve with the predetermined curve constraint to determine whether it meets the requirements of the predetermined curve constraint. This predetermined curve constraint is a pre-set limitation on aspects such as the shape, trend, and smoothness of the temperature curve. For example, the predetermined curve constraint may require that the temperature curve is monotonically increasing or decreasing within a certain time range, or that the curvature of the curve is within a certain range, etc. If the first temperature curve meets the predetermined curve constraint, it indicates that this curve model conforms to the expected temperature change law to a certain extent; if it does not meet the requirement, adjustments are needed.

[0054] When the first temperature curve does not meet the predetermined curve constraint, repeat step b. In each repetition process, adjust the selection method of the first scatter sample or the parameters of the fitting method, etc. For example, if the number of scatter samples selected for the first time is too small resulting in inaccurate curve fitting, the number of scatter samples can be increased; or if the initial parameter settings are unreasonable when using the least squares method, these parameters can be adjusted. By continuously repeating this process until the obtained first temperature curve meets the predetermined curve constraint. Finally, output the first temperature curve that meets the conditions, improving the accuracy and reliability of the temperature curve, making the subsequent trend prediction analysis based on this curve more scientific and reasonable.

[0055] After obtaining the first temperature curve that meets the predetermined curve constraint, conduct trend prediction analysis on this curve in combination with the target time. For example, if the first temperature curve is a quadratic function curve, according to the equation of the curve and the position of the target time, the corresponding temperature value at the target time can be calculated, and this temperature value is the predicted target temperature.

[0056] Through curve fitting, the prediction of the temperature at the target time is realized, providing an important decision-making basis for the intervention adjustment module 50, which helps to timely conduct health intervention adjustment on the target animal body.

[0057] Furthermore, after step P411, it further includes:

[0058] Step P411-1: Activate the Internet of Things device group and conduct multi-dimensional monitoring on the target animal body through the Internet of Things device group to obtain Internet of Things information.

[0059] Step P411-2: Input the IoT information into a predetermined signal calibration analysis function obtained based on the simulation record to obtain a calibration coefficient.

[0060] Step P411-3: Calibrate and adjust the predicted target temperature using the calibration coefficient as a weight.

[0061] Specifically, after performing a temperature detection simulation on the target digestive system to obtain a simulation record, activate the IoT device group, which is a collection of IoT devices including various sensors such as temperature sensors, humidity sensors, and biosensors, as well as data collection and transmission devices. Then, each device in the IoT device group performs multi-dimensional monitoring on the target animal body according to its respective functions. For example, the temperature sensor measures the temperature around the animal body, the camera captures videos of the animal's behavior, and the biosensor detects the physiological indicators of the animal. These devices integrate the collected data to obtain IoT information. This IoT information is a collection of various data, such as temperature data, humidity data, and animal behavior data, stored and transmitted in a certain format, reflecting the state of the target animal body and its living environment.

[0062] Based on the simulation record of the aforementioned temperature detection simulation, determine a predetermined signal calibration analysis function. This function is based on the analysis and processing of the simulation record, considering various factors in the simulation process, such as signal characteristics and interference factors, to determine how to calibrate the predicted temperature according to the IoT information. Input the obtained IoT information into the predetermined signal calibration analysis function for analysis and calculation to obtain a calibration coefficient. This calibration coefficient is a weight coefficient used to calibrate and adjust the predicted target temperature, reflecting the influence degree of the IoT information on the predicted temperature.

[0063] Calibrate and adjust the predicted target temperature using the calibration coefficient as a weight. Obtain the calibrated temperature value by multiplying the predicted target temperature by the calibration coefficient. Through the calibration adjustment, considering the influence of the multi-dimensional information monitored by the IoT device group on the temperature prediction, the predicted target temperature is made more in line with the actual situation, further improving the accuracy of the temperature prediction, thereby providing a more reliable temperature data basis for subsequent operations such as animal health intervention and regulation.

[0064] Furthermore, step P411-2 includes:

[0065] Step P411-21: Extract the simulated actual temperature and simulated IoT information from the simulation record.

[0066] Step P411-22: Perform a correlation analysis on the first simulated IoT data corresponding to the first IoT indicator in the simulated IoT information and the simulated actual temperature to obtain a first correlation coefficient.

[0067] Step P411-23: Establish the predetermined signal calibration analysis function according to the correspondence between the first correlation coefficient and the first Internet of Things index.

[0068] Specifically, from the obtained simulation records, extract two parts of data: simulated actual temperature and simulated Internet of Things information. Among them, the simulated actual temperature is temperature data close to the real situation obtained during the temperature detection simulation of the target digestive system. The simulated Internet of Things information is environmental and physiological data related to the target animal body generated during the simulation process, such as simulated environmental humidity, animal behavior characteristics, etc. This information corresponds to the actual monitoring data and is used to calibrate and verify the prediction model.

[0069] The first Internet of Things index refers to any one of the Internet of Things indexes in the Internet of Things information. Traverse the simulated Internet of Things information and extract the simulated Internet of Things data corresponding to the first Internet of Things index, that is, the first simulated Internet of Things data. Use statistical analysis methods to perform a correlation analysis on the first simulated Internet of Things data and the simulated actual temperature. For example, the Pearson correlation coefficient calculation method can be used, and its calculation formula , where is the i-th data in the first simulated Internet of Things data, is the mean value of the first simulated Internet of Things data, is the i-th data in the simulated actual temperature, is the mean value of the simulated actual temperature, and n is the number of data points. Calculate the first correlation coefficient through the above formula. This first correlation coefficient is used to measure the strength and direction of the linear relationship between the first simulated Internet of Things data and the simulated actual temperature.

[0070] Establish a predetermined signal calibration analysis function according to the correspondence between the first correlation coefficient and the first Internet of Things index. Exemplarily, this function can be a weighted average expression based on the correlation coefficient. By constructing a predetermined signal calibration analysis function through the correlation analysis between the simulated Internet of Things information and the actual temperature, it is possible to flexibly adjust the errors in temperature prediction for different Internet of Things indexes and environmental factors, improving the accuracy and dynamic adaptability of the prediction.

[0071] Furthermore, the first Internet of Things index refers to any one of the predetermined Internet of Things indexes. The predetermined Internet of Things indexes include predetermined external factor Internet of Things indexes and predetermined internal factor Internet of Things indexes. The predetermined external factor Internet of Things indexes at least include environmental temperature, feed type, and feed quantity. The predetermined internal factor Internet of Things indexes at least include activity level, rumen fermentation, physiological state, water intake, and individual heat dissipation rate.

[0072] Specifically, the first IoT indicator is any one selected from the predetermined IoT indicators, which cover multiple aspects of internal and external factors, including the predetermined external IoT indicators related to the external environment of the target animal body and the predetermined internal IoT indicators related to the internal physiology and behavior of the target animal body, comprehensively considering various factors that may affect the temperature of the target animal body.

[0073] The predetermined external IoT indicators at least include environmental temperature, feed type, and feed quantity. The environmental temperature is the temperature of the environment around the target animal body, which directly affects the heat dissipation and body temperature regulation of the animal body. Different types of feed consumed by animals have different nutritional components, digestion and absorption processes, etc., which will affect the physiological state and body temperature of the animal body. The quantity of feed ingested by animals affects their energy intake and metabolic level, and thus affects body temperature. Consuming too much or too little feed may both have an impact on the body temperature regulation of the animal body.

[0074] The predetermined internal IoT indicators at least include activity level, rumen fermentation, physiological state, water intake, and individual heat dissipation rate. The activity level reflects the energy consumption of the animal body. An animal body with a large amount of activity generates more heat and its body temperature will also be affected. For ruminants, rumen fermentation is an important physiological process. Microbial fermentation in the rumen generates heat, and this part of the heat will affect the body temperature of the animal. The physiological state includes physiological factors such as the health status and reproductive state of the animal. Under different physiological states, the body temperature regulation mechanism and ability of the animal body may be different. For example, a sick animal may have an abnormal body temperature. Water intake affects the heat dissipation and metabolic processes of the animal body. Insufficient water intake may lead to difficulty in heat dissipation and affect body temperature. Different animal individuals have different heat dissipation rates due to factors such as their body size and fur, which is an important internal factor affecting the body temperature of animals.

[0075] By monitoring the predetermined external IoT indicators, the influence of external environmental factors on animal body temperature can be understood; by monitoring the predetermined internal IoT indicators, the influence of the animal's own physiological and behavioral factors on body temperature can be grasped. Considering these internal and external factors comprehensively, a temperature prediction and calibration system that is more in line with the actual situation can be constructed, thereby improving the accuracy of calibration and adjustment of the predicted target temperature.

[0076] In summary, the intelligent temperature regulation system based on the Internet of Things provided by the embodiments of this application has the following technical effects:

[0077] The command acquisition module 10 is used to realize automatic temperature detection triggering to ensure the regularity and continuity of monitoring; the real-time temperature detection module 20 uses a high-precision platinum resistance sensor to dynamically capture temperature changes, providing an accurate data basis for subsequent temperature prediction and intervention adjustment; the temperature transmission module 30 uses LoRa technology to efficiently transmit data to the cloud to achieve remote management and centralized processing of data; the temperature prediction module 40 uses big data analysis and the computing power of the cloud platform to obtain the signal loss correction function in the preprocessor, correct the temperature deviation caused by signal attenuation and interference, and thus obtain the actual temperature of the target animal. Further, trend analysis is performed based on the target temperature signal time series to predict the temperature changes of the target animal in the future. To ensure the accuracy of the prediction, by analyzing the correlation between the simulated actual temperature in the simulation record and various IoT indicators (such as activity level, feed type, etc.) in the IoT data, a signal calibration analysis function is established, and the calibration coefficient is used to correct the predicted temperature data, thereby improving the prediction accuracy and providing forward-looking decision support for subsequent temperature regulation; the intervention adjustment module 50 issues adjustment instructions in a timely manner according to the prediction results to achieve rapid and accurate intervention and adjustment of the animal body temperature.

[0078] Overall, the embodiments of the present application improve the accuracy and foresight of temperature regulation and the response speed of temperature regulation by combining real-time monitoring, signal correction and predictive analysis, and utilizing IoT data to dynamically calibrate and optimize temperature prediction, thereby providing a more scientific and effective means for animal health management.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. 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 temperature regulation system based on the Internet of Things, characterized in that, The intelligent temperature regulation system based on the Internet of Things includes: An instruction acquisition module, configured to acquire a temperature detection instruction, where the temperature detection instruction refers to an instruction for automatically performing temperature detection on a target digestive system of a target animal body issued by a microcontroller based on a predetermined detection frequency; A real-time temperature detection module, configured to activate a platinum resistance temperature sensor based on the temperature detection instruction, and dynamically detect a target real-time temperature of the target digestive system at a real-time moment through the platinum resistance temperature sensor; A temperature transmission module, configured to transmit the target real-time temperature to an Internet of Things data processing cloud platform through a LoRa communicator; A temperature prediction module, configured to perform predictive analysis through the Internet of Things data processing cloud platform to obtain a predicted target temperature at a target time; An intervention regulation module, configured to issue a health regulation instruction if the predicted target temperature is not within a predetermined temperature threshold, and perform health intervention regulation on the target animal body based on the health regulation instruction; Wherein, the temperature prediction module is further configured to perform the following steps: Obtain a preprocessor in the Internet of Things data processing cloud platform, where a predetermined signal loss correction function is embedded in the preprocessor; Perform loss correction analysis on the target real-time temperature according to the predetermined signal loss correction function to obtain a target actual temperature; Establish a correspondence relationship between the target actual temperature and the real-time time to establish a target temperature signal time sequence; Perform predictive analysis on the target temperature signal time sequence through the Internet of Things data processing cloud platform to obtain the predicted target temperature at the target time; The temperature prediction module is further configured to perform the following steps: Perform temperature detection simulation on the target digestive system to obtain a simulation record; Perform a comparison calculation on the simulation signal power and the simulation noise power in the simulation record to obtain a target signal-to-noise ratio; Obtain the predetermined signal loss correction function according to the target signal-to-noise ratio, and store it in the preprocessor; The expression of the predetermined signal loss correction function is as follows: ； Wherein, refers to the actual temperature of the target, refers to the real-time temperature of the target, refers to the comprehensive loss function. Among them, the comprehensive loss function includes a signal attenuation loss function, a transmission distance loss function, and an interference loss function, and the signal attenuation loss function is represented by for characterization, refers to the attenuation coefficient, refers to the transmission distance of the signal in the target digestive system. The transmission distance loss function is represented by for characterization, refers to the transmission distance of the signal in space. The interference loss function is characterized by characterization, refers to the signal-to-noise ratio of the target.

2. The intelligent temperature regulation system based on the Internet of Things according to claim 1, characterized in that, The temperature prediction module is further configured to perform the following steps: Step a: Obtain a target scatter plot of the target temperature signal time sequence; Step b: Draw a first temperature curve based on a first scatter sample, and determine whether the first temperature curve reaches a predetermined curve constraint; Step c: If the first temperature curve does not reach the predetermined curve constraint, repeat step b until the predetermined curve constraint is reached, and output the first temperature curve at that time; Step d: Perform trend prediction analysis on the first temperature curve in combination with the target time to obtain the predicted target temperature.

3. The intelligent temperature regulation system based on the Internet of Things according to claim 1, characterized in that, The temperature prediction module is further configured to perform the following steps: Activate an Internet of Things device group, and perform multi-dimensional monitoring on the target animal body through the Internet of Things device group to obtain Internet of Things information; Input the Internet of Things information into a predetermined signal calibration analysis function obtained based on the simulation record to obtain a calibration coefficient; Calibrate and adjust the predicted target temperature with the calibration coefficient as a weight.

4. The intelligent temperature regulation system based on the Internet of Things according to claim 3, characterized in that, The temperature prediction module is further configured to perform the following steps: Extract the simulated actual temperature and the simulated Internet of Things information in the simulation record; Perform a correlation analysis on the first simulated IoT data corresponding to the first IoT metric in the simulated IoT information and the simulated actual temperature to obtain a first correlation coefficient; Establish the predetermined signal calibration analysis function according to the corresponding relationship between the first correlation coefficient and the first IoT metric.

5. The intelligent temperature regulation system based on the Internet of Things according to claim 4, characterized in that, The first IoT metric refers to any one of the predetermined IoT metrics. The predetermined IoT metrics include predetermined external cause IoT metrics and predetermined internal cause IoT metrics. The predetermined external cause IoT metrics at least include environmental temperature, feed type, and feed quantity. The predetermined internal cause IoT metrics at least include activity level, rumen fermentation, physiological state, water intake, and individual heat dissipation rate.

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