Breeding environment comfort early warning system for pig breeding house

Abstract

The invention relates to the technical field of environment early warning, in particular to a pig breeding house breeding environment comfort early warning system which comprises an environment monitoring module, a data analysis module, a health prediction module, a sound analysis module, a disease prevention module and an adjustment execution module. According to the invention, through real-time monitoring and dynamic analysis of environmental data, the adaptability and response speed of the early warning system, environmental quality assessment and acoustic data analysis are obviously improved, so that early warning is not only based on a static threshold, but also based on real-time data change and live pig behavior response, and the health risk and pressure level are comprehensively assessed. A breeding manager is allowed to adjust the environment according to real-time data, so that the disease incidence is reduced, the growth conditions of live pigs are improved, an early warning mechanism based on comprehensive data analysis ensures the improvement of the breeding efficiency and the production quality, and particularly, more accurate management decision support can be provided under the rapidly changing environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental early warning, and in particular to an early warning system for the comfort level of a pig breeding house breeding environment. Background Art

[0002] The field of environmental early warning technology involves the development and application of systems and tools for monitoring and assessing environmental conditions and issuing early warnings when preset thresholds are reached, aiming to promptly inform potential environmental risks so as to initiate preventive measures or mitigation measures to protect environmental safety and ecological balance. Environmental early warning systems can cover a variety of environmental factors, such as climate change, air quality, water quality and soil conditions, as well as the impact on the living environment of specific groups such as animals and plants, and have a wide range of applications in agriculture, urban planning, natural disaster management, and public health management.

[0003] Among them, the pig breeding house breeding environment comfort warning system is a technical solution designed to improve pig breeding efficiency and welfare. The system monitors key environmental parameters such as temperature, humidity, gas concentration, etc. in the pig breeding house, evaluates the comfort of the pig house, and automatically issues warnings when conditions are not ideal, thereby helping farm managers to adjust environmental settings in a timely manner, which not only helps to improve the growth conditions of pigs, but also reduces the incidence of diseases and improves overall breeding efficiency and production quality.

[0004] Existing technologies have obvious deficiencies in real-time data processing and multi-dimensional analysis. They usually rely on preset environmental thresholds and lack the ability to respond quickly to environmental changes. Limitations result in the early warning system being unable to adjust in a timely manner when faced with drastic changes in the environment, which will miss critical opportunities for intervention and affect the health of pigs and the overall efficiency of farming. For example, when environmental parameters change suddenly, traditional systems may delay issuing early warnings due to failure to update data in real time, resulting in failure to take effective disease prevention measures in a timely manner. In addition, existing technologies often ignore acoustic monitoring, lack a comprehensive assessment of pig behavior and welfare, and are unable to accurately identify stress changes caused by the environment, increasing the risk of non-disease health problems. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a pig breeding house breeding environment comfort early warning system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A pig breeding house breeding environment comfort early warning system comprises: The environmental monitoring module installs temperature sensors, humidity sensors and ammonia concentration sensors inside the pig house to continuously collect data, calculate the real-time average temperature and humidity of the pig house environment, summarize the daily average values ​​of temperature and humidity, and form basic data of the pig house environment; The data analysis module receives the basic data of the pig house environment, integrates the ammonia concentration sensor data, uses normalization processing, calculates the environmental quality index of each monitoring point according to the data of the monitoring point, and obtains the comprehensive environmental quality assessment result of the pig house; The health prediction module uses the pig house comprehensive environmental quality assessment results to compare the environmental indicators with the health thresholds of different growth stages of pigs in the database in real time, calculates the deviation and summarizes the deviation degree of each indicator to obtain the pig house health risk data; The sound analysis module continuously monitors the pig calls through a microphone array, collects call samples, and performs frequency analysis on the samples to identify the pressure frequency characteristics, and evaluates the pressure level in combination with the sound intensity to obtain the pig acoustic pressure index; The disease prevention module combines the pig acoustic pressure index and the pig house health risk data to analyze the correlation between the potential health risks of the pig house and the pig calls, judge the possibility of pig disease occurrence, formulate corresponding prevention strategies, and obtain early warning and adjustment plans; The adjustment execution module dynamically adjusts the working parameters of the ventilation equipment, humidifier and dehumidifier in the pig house according to the early warning and adjustment plan, monitors the matching degree between the adjustment effect and the environmental indicators in real time, and records and outputs the environmental adjustment feedback record.

[0007] As a further scheme of the present invention, the basic data of the pig house environment include temperature records, humidity records and ammonia concentration records, the comprehensive environmental quality assessment results of the pig house include environmental quality index, monitoring point data and normalization results, the health risk data of the pig house include health threshold comparison results, indicator deviation and environmental risk map, the acoustic pressure indicators of live pigs include frequency characteristics, sound intensity and pressure level, the early warning and adjustment plan includes disease possibility analysis results, prevention strategies and early warning signals, and the environmental adjustment feedback records include adjustment parameter records, monitoring results and adjustment effects.

[0008] As a further solution of the present invention, the environmental monitoring module includes a data acquisition submodule, a data analysis submodule, and an environmental assessment submodule; The data acquisition submodule uses temperature sensors, humidity sensors and ammonia concentration sensors, which are installed in key locations of the pig house to capture data in real time, continuously record pig house environmental parameters, and obtain the original environmental data set; The data analysis submodule uses the original environmental data set to clean the data, remove outliers, calculate the real-time average environmental temperature and humidity, calculate the daily average temperature and humidity of the pig house, and generate daily average environmental parameters; The environmental assessment submodule analyzes the long-term data trend based on the daily average environmental parameters, evaluates the impact of seasonal changes on the pig house environment, and obtains basic data on the pig house environment.

[0009] As a further solution of the present invention, the data analysis module includes a data integration submodule, a quality index calculation submodule, and an environmental assessment submodule; The data integration submodule uses the basic data of the pig house environment, integrates the ammonia concentration sensor data, standardizes the data, and performs time series synchronization to obtain normalized environmental data; The quality index calculation submodule performs weighted average of the temperature, humidity and ammonia value of the monitoring point based on the normalized environmental data, calculates the environmental quality index, and summarizes and generates the monitoring point quality index; The environmental assessment submodule uses the monitoring point quality index to measure the overall environmental health status of the pig house by analyzing the changing rules and patterns of the index, and obtains the comprehensive environmental quality assessment results of the pig house.

[0010] As a further embodiment of the present invention, the environmental quality index is calculated according to the formula:

[0011] Calculate, where Represents the quality index of the monitoring point, Representative The actual measured values ​​of each monitoring point, including temperature, humidity and ammonia value, Represents the average value of all monitoring points. Indicates The absolute deviation between the measured value of each monitoring point and the average value, Representative The distance between each monitoring point and the nearest monitoring center point, is the sum of the distances to all monitoring points.

[0012] As a further solution of the present invention, the health prediction module includes a data comparison submodule, a deviation calculation submodule, and a risk summary submodule; The data comparison submodule checks the temperature, humidity and ammonia concentration environmental indicators item by item through the comprehensive environmental quality assessment results of the pig house, makes a preliminary match between the environmental conditions and the health thresholds, and obtains environmental health matching data; The deviation calculation submodule performs quantitative analysis on the difference between each environmental indicator and the threshold value based on the environmental health matching data, calculates the deviation value of the indicator, and integrates and generates indicator deviation data; The risk summary submodule uses the indicator deviation data to conduct a comprehensive risk assessment, and by analyzing the actual impact of the indicators on the health of the pig house, determines the overall health risk level of the pig house and obtains the health risk data of the pig house.

[0013] As a further solution of the present invention, the deviation value of the indicator is calculated according to the formula:

[0014] Calculate, where Represents the deviation value of the indicator, Representative The actual measured value of the environmental indicator, Representative The environmental threshold of the indicator, Is an indicator The sensitivity coefficient of Representative The weighted deviation of an indicator.

[0015] As a further solution of the present invention, the sound analysis module includes a sound collection submodule, a frequency analysis submodule, and a pressure assessment submodule; The sound collection submodule continuously monitors the pig calls through the microphone array, collects pig call samples in real time, and performs signal enhancement and noise elimination to obtain clear pig call samples; The frequency analysis submodule performs spectrum analysis technology based on the clear pig call sample, extracts the characteristics of key frequency bands, identifies and classifies the sound samples, and generates frequency characteristic data; The pressure assessment submodule uses the frequency characteristic data, combined with sound intensity and frequency response analysis, to determine the stress level of the pig according to the change pattern of the sound data, and obtains the pig acoustic pressure index.

[0016] As a further solution of the present invention, the disease prevention module includes a correlation analysis submodule, a risk judgment submodule, and a prevention strategy submodule; The correlation analysis submodule uses the pig acoustic pressure index and the pig house health risk data to analyze the correlation between the data, reveal the connection between the pig call characteristics and the health index, and generate health risk correlation data; The risk judgment submodule evaluates and judges the probability of disease occurrence based on the health risk correlation data, and comprehensively assesses the potential health risks in combination with the disease occurrence patterns under different environmental pressures to obtain the disease occurrence probability assessment results; The prevention strategy submodule uses the disease occurrence probability assessment results, combined with real-time monitoring data, to design disease prevention measures, adjust feeding management and optimize pig house environmental conditions to obtain early warning and adjustment plans.

[0017] As a further solution of the present invention, the adjustment execution module includes a parameter adjustment submodule, an effect monitoring submodule, and a feedback recording submodule; The parameter adjustment submodule dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan, updates the equipment settings to match the different environmental requirements, and generates equipment parameter adjustment records; The effect monitoring submodule monitors the adjusted pig house environment level in real time based on the equipment parameter adjustment record, analyzes the immediate effect of the adjustment, confirms the effectiveness of the equipment adjustment, and obtains the adjustment effect monitoring result; The feedback recording submodule records the actual changes and the behavioral responses of the pigs after each environmental adjustment based on the adjustment effect monitoring results, integrates the environmental data and the pig call feedback data, summarizes the information, and outputs the environmental adjustment feedback record.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by real-time monitoring and dynamic analysis of environmental data, the adaptability and response speed of the early warning system are significantly improved. Environmental quality assessment and acoustic data analysis make the early warning not only based on static thresholds, but real-time data changes and pig behavioral responses. The method of comprehensively evaluating health risks and stress levels allows breeding managers to adjust the environment according to real-time data, thereby reducing the incidence of diseases and improving the growth conditions of pigs. The early warning mechanism based on comprehensive data analysis ensures the improvement of breeding efficiency and production quality, and can provide more accurate management decision support, especially under rapidly changing environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the environment monitoring module of the present invention; Figure 3 It is a flow chart of the data analysis module of the present invention; Figure 4 This is a flow chart of the health prediction module of the present invention; Figure 5 is a flow chart of the sound analysis module of the present invention; Figure 6 is a flow chart of the disease prevention module of the present invention; Figure 7 This is a flow chart of the adjustment execution module of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0022] See also Figure 1 , a pig breeding house breeding environment comfort early warning system includes: The environmental monitoring module installs temperature sensors, humidity sensors and ammonia concentration sensors inside the pig house to continuously collect data, calculate the real-time average temperature and humidity of the pig house environment, summarize the daily average values ​​of temperature and humidity, and form basic data of the pig house environment; The data analysis module receives the basic data of the pig house environment, integrates the ammonia concentration sensor data, uses normalization processing, calculates the environmental quality index of each monitoring point based on the data of the monitoring point, and obtains the comprehensive environmental quality assessment result of the pig house; The health prediction module uses the comprehensive environmental quality assessment results of the pig house to compare the environmental indicators with the health thresholds of different growth stages of pigs in the database in real time, calculates the deviation and summarizes the deviation degree of each indicator to obtain the health risk data of the pig house; The sound analysis module continuously monitors the pig calls through a microphone array, collects call samples, and performs frequency analysis on the samples to identify the pressure frequency characteristics, and evaluates the pressure level in combination with the sound intensity to obtain the pig acoustic pressure index; The disease prevention module combines pig acoustic pressure indicators and pig house health risk data to analyze the correlation between the potential health risks of the pig house and the pig calls, determine the possibility of pig disease, formulate corresponding prevention strategies, and obtain early warning and adjustment plans; The adjustment execution module dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan, monitors the matching degree between the adjustment effect and the environmental indicators in real time, and records and outputs the environmental adjustment feedback records.

[0023] The basic data of the pig house environment include temperature records, humidity records and ammonia concentration records. The comprehensive environmental quality assessment results of the pig house include the environmental quality index, monitoring point data and normalization results. The health risk data of the pig house include health threshold comparison results, indicator deviation and environmental risk map. The acoustic stress indicators of live pigs include frequency characteristics, sound intensity and pressure level. The early warning and adjustment plan includes disease possibility analysis results, prevention strategies and early warning signals. The environmental adjustment feedback records include adjustment parameter records, monitoring results and adjustment effects.

[0024] See also Figure 2 ,The environmental monitoring module includes a data acquisition submodule, a data analysis submodule, and an environmental assessment submodule; The data acquisition submodule uses temperature sensors, humidity sensors and ammonia concentration sensors, which are installed in key locations of the pig house to capture data in real time, continuously record pig house environmental parameters, and obtain the original environmental data set; In the data acquisition submodule, temperature sensors, humidity sensors and ammonia concentration sensors at key locations capture and continuously record data in real time. First, the output data of each sensor is converted into a digital signal through an analog-to-digital converter (ADC). The data acquisition system reads data from the sensor every 5 minutes. The collected data is stored in a buffer. The sensor's calibration data is used to adjust the readings to match the actual measured values. Next, the data is transmitted to the main server via a wireless network. The time synchronization service running on the server ensures that all data timestamps are accurate. The sensor's power management system regularly checks the power status to ensure the continuous operation of the sensor. The data collected in this process includes the temperature, humidity and ammonia concentration of the environment. Each data point is marked with the precise collection time and sensor location identifier.

[0025] The data analysis submodule uses the original environmental data set to clean the data, remove outliers, calculate the real-time average environmental temperature and humidity, and calculate the daily average temperature and humidity of the pig house to generate the daily average environmental parameters. The data analysis submodule processes the environmental raw data set to calculate the real-time environmental average temperature and humidity, and to calculate the daily average temperature and humidity. In this process, first, the raw data set undergoes a preprocessing stage to remove obvious erroneous readings caused by sensor failure or external interference. Then, a moving average filter is applied to the data to smooth short-term fluctuations and reduce the impact of random noise. The daily data is grouped and the average temperature and humidity data within each group is calculated. In addition, the environmental data is stored in time series for subsequent daily average calculations, and the daily average temperature and humidity are output for future trend analysis and environmental assessment.

[0026] The environmental assessment submodule analyzes long-term data trends based on daily average environmental parameters, evaluates the impact of seasonal changes on the pig house environment, and obtains basic data on the pig house environment; The environmental assessment submodule analyzes long-term data trends based on daily average environmental parameters. Seasonal change data is obtained by comparing environmental data from different months. In the analysis, the monthly average is first generated based on the daily average, which includes averaging all daily averages within each month. Then, regression analysis techniques are used to evaluate the trend of temperature and humidity data with seasonal changes. In addition, long-term trend data is obtained by analyzing historical data from the past six months. The data reflects changes in environmental conditions in different seasons or years. Finally, the evaluation results reveal the specific impact of seasonal changes on the pig house environment and provide data support for future environmental regulation.

[0027] See also Figure 3 ,The data analysis module includes data integration submodule, quality index calculation submodule, and environmental assessment submodule; The data integration submodule uses the basic data of the pig house environment, integrates the ammonia concentration sensor data, standardizes the data, and synchronizes the time series to obtain normalized environmental data; The data integration submodule uses the basic data of the pig house environment and the ammonia concentration sensor data for standardization and time synchronization. The data is first formatted to convert the data from different sensors into a unified data format. Data standardization includes mapping each sensor data to a fixed range, such as 0 to 1, using the maximum-minimum normalization method. This step ensures the consistency and comparability of subsequent analysis. Time synchronization involves aligning the timestamps of data points from different sensors to ensure the time consistency of the data. During the processing, each data point is recalibrated according to a unified time standard. Time calibration uses the network time protocol to ensure precise synchronization. Finally, the normalized data provides an accurate and consistent data foundation for subsequent modules.

[0028] The quality index calculation submodule calculates the weighted average of the temperature, humidity, and ammonia value of the monitoring point based on the normalized environmental data, calculates the environmental quality index, and summarizes and generates the monitoring point quality index; Environmental quality index, according to the formula,

[0029] Calculate, where Represents the quality index of the monitoring point, Representative The actual measured values ​​of each monitoring point, including temperature, humidity and ammonia value, Represents the average value of all monitoring points. Indicates The absolute deviation between the measured value of each monitoring point and the average value, Representative The distance between each monitoring point and the nearest monitoring center point, is the sum of the distances of all monitoring points, which is used to normalize the distance weights to ensure The calculation of remains stable under different monitoring network densities.

[0030] The data of the five monitoring points are as follows: Monitoring point 1: temperature 22°C, humidity 50%, ammonia value 30ppm.

[0031] Monitoring point 2: temperature 20°C, humidity 55%, ammonia value 25ppm.

[0032] Monitoring point 3: temperature 23°C, humidity 52%, ammonia value 28ppm.

[0033] Monitoring point 4: temperature 21°C, humidity 54%, ammonia value 30ppm.

[0034] Monitoring point 5: temperature 22°C, humidity 53%, ammonia value 27ppm.

[0035] Calculate the average ammonia value at each monitoring point:

[0036] Calculate the absolute deviation of the ammonia value at each monitoring point from the average value:

[0037]

[0038]

[0039]

[0040]

[0041] The distance from each monitoring point to the center point is obtained based on the actual monitoring data. The center point is set at the origin of the coordinate system. The coordinates of each point are obtained by field measurement: Monitoring point 1: m Monitoring point 2: m Monitoring point 3: m Monitoring point 4: m Monitoring point 5: m Weight normalization requires calculating the total distance:

[0042] Apply the formula to calculate the quality index :

[0043]

[0044]

[0045] The results show that the comprehensive environmental quality index of the monitoring point is ,The low value indicates that the environmental status of the overall monitoring point does not deviate much from the average value, indicating that the pig house environment is relatively stable.

[0046] The environmental assessment submodule uses the monitoring point quality index to measure the overall environmental health of the pig house by analyzing the changing rules and patterns of the index, and obtains the comprehensive environmental quality assessment results of the pig house; The environmental assessment submodule evaluates the overall environmental health of the pig house through the monitoring point quality index. The process begins with collecting and organizing the monitoring point quality index, arranging the index in time series, and analyzing the data to identify possible trends and patterns. Trend analysis uses statistical methods such as moving average or exponential smoothing to determine long-term and short-term changes. The analysis of the changing patterns is based on the periodic comparison of past data and the correlation analysis with environmental events. The final evaluation results summarize the overall health of the pig house environment and provide a reference for future management decisions.

[0047] See also Figure 4 ,The health prediction module includes a data comparison submodule, a deviation calculation submodule, and a risk summary submodule; The data comparison submodule uses the comprehensive environmental quality assessment results of the pig house to check the temperature, humidity and ammonia concentration environmental indicators item by item, conduct a preliminary match between environmental conditions and health thresholds, and obtain environmental health matching data; The data comparison submodule checks the temperature, humidity and ammonia concentration environmental indicators against the health thresholds one by one. Data matching begins with a preliminary comparison of the environmental indicators with the preset health thresholds, which involves comparing the real-time monitored environmental data with the threshold database. The system quickly retrieves the current value of each indicator and the corresponding threshold. The comparison operation is performed in the database, using SQL query statements and standardized query language to achieve accurate matching. The matching results are stored as Boolean values ​​to indicate whether the health standards are met. The Boolean values ​​are then aggregated to generate environmental health matching data, which shows the consistency between the current environmental conditions and the preset health standards.

[0048] The deviation calculation submodule quantitatively analyzes the difference between each environmental indicator and the threshold value based on the environmental health matching data, calculates the deviation value of the indicator, and integrates and generates indicator deviation data; The deviation value of the indicator, according to the formula,

[0049] Calculate, where Represents the deviation value of the indicator, Representative The actual measured value of the environmental indicator, Representative The environmental threshold of the indicator, Is an indicator The sensitivity coefficient of Representative The weighted deviation of an indicator.

[0050] Considering specific indicators such as ammonia value, temperature, and humidity, the data and thresholds are as follows: ppm, ppm, (For ammonia NH3) ℃, ℃, (for temperature) %, %, (For humidity) Calculate the weighted deviation for each indicator:

[0051]

[0052]

[0053] Calculates the average deviation of the indicator.

[0054]

[0055] The results show that the average deviation is , indicating that there is a slight difference between the overall environmental conditions of the pig house and the set health threshold, and the deviation is not high, which means that the existing environment of the pig house will not have a significant impact on the health of pigs.

[0056] The risk summary submodule uses the indicator deviation data to conduct a comprehensive risk assessment, and by analyzing the actual impact of the indicators on the health of the pig house, it determines the overall health risk level of the pig house and obtains the health risk data of the pig house; The risk summary submodule uses indicator deviation data to conduct an overall health risk assessment of the pig house. It analyzes indicator deviation data to identify which indicators are most significantly beyond the safety range. It uses threshold judgment logic to compare the deviation value of each indicator with the risk acceptance threshold. Each comparison is determined by logical operations. If the deviation value exceeds the threshold, it is marked as high risk. Logical operations are implemented in programming languages ​​such as Python or Java. After completing the risk rating of all indicators, the system aggregates these ratings to derive the overall health risk level of the pig house, which is recorded in the pig house health risk data to provide decision support for managers.

[0057] See also Figure 5 ,The sound analysis module includes a sound collection submodule, a frequency analysis submodule, and a pressure assessment submodule; The sound collection submodule continuously monitors the pig calls through the microphone array, collects pig call samples in real time, and performs signal enhancement and noise elimination to obtain clear pig call samples; The sound collection submodule continuously monitors the pig calls through the microphone array to perform signal enhancement and noise elimination. First, when the system is initialized, the sensitivity and gain of the microphone array are set to adapt to the noise level of the pig house environment. During the data collection process, the sound signal monitored in real time is filtered through high-pass and low-pass filters to remove noise of non-target frequencies, and the amplitude of the sound signal is adjusted using dynamic range compression technology to ensure that the sound clarity is not affected by environmental noise. In addition, the sound samples are continuously recorded and noise elimination algorithms such as spectral subtraction are implemented through real-time digital signal processing software to further clean up the background noise. The processed clear pig call samples are used for subsequent frequency analysis.

[0058] The frequency analysis submodule uses spectrum analysis technology based on clear pig call samples to extract the characteristics of key frequency bands, identify and classify sound samples, and generate frequency feature data; The frequency analysis submodule uses spectrum analysis technology based on clear pig call samples. First, the clear sound samples are loaded into the analysis software, and the fast Fourier transform (FFT) technology is used to convert the time series sound signal into a frequency domain representation, revealing the amplitude and phase information of each frequency component. Then, a bandpass filter is used to highlight key frequency bands, such as specific low-frequency and high-frequency intervals. Frequency bands are usually associated with specific behavioral states of pigs. Next, key features are extracted from the frequency domain data, including the peak frequency, energy distribution, and frequency change rate. The generated frequency feature data is used for the next step of stress assessment.

[0059] The pressure assessment submodule uses frequency characteristic data, combined with sound intensity and frequency response analysis, to determine the stress level of pigs according to the changing pattern of sound data and obtain the pig acoustic pressure index; The stress assessment submodule uses frequency feature data combined with sound intensity and frequency response analysis. First, the stress level of the pigs is determined based on the changing pattern of the sound data. The system loads the frequency feature data and compares the data with the known sound patterns of the pigs' stress behavior. By using acoustic models, such as sound intensity and frequency response models, the performance characteristics of the pigs' sounds under stressful conditions are evaluated. Then, logistic regression or support vector machines are used to analyze the relationship between the sound features and the classified stress levels. The resulting pig acoustic stress index provides data support for pig welfare and health management.

[0060] See also Figure 6 ,The disease prevention module includes a correlation analysis submodule, a risk judgment submodule, and a prevention strategy submodule; The correlation analysis submodule uses pig acoustic stress indicators and pig house health risk data to analyze the correlation between the data, reveal the connection between pig call characteristics and health indicators, and generate health risk correlation data; The correlation analysis submodule uses pig acoustic stress indicators and pig house health risk data to analyze the correlation between the data, loads the two types of data, and uses data integration techniques, such as database connection operations, to ensure data consistency and integrity. Subsequently, correlation analysis methods, such as Pearson or Spearman correlation coefficient calculations, are applied to determine the statistical correlation between different data sets. The calculation process is performed through automated scripts to ensure the accuracy and repeatability of data processing. The generated health risk correlation data reveals the specific connection between pig vocal characteristics and health indicators.

[0061] The risk judgment submodule evaluates and judges the probability of disease occurrence based on health risk correlation data, combines the disease occurrence patterns under different environmental pressures, comprehensively assesses potential health risks, and obtains the disease occurrence probability assessment results; The risk judgment submodule evaluates and judges the probability of disease occurrence based on health risk correlation data. First, the correlation data is compared with the disease occurrence pattern database to analyze the data patterns under different environmental pressures. The system trains predictions based on historical data, analyzes the probability of disease occurrence, and directly displays it through data visualization tools to provide decision makers with intuitive risk assessment. The final disease occurrence probability assessment results guide future health management strategies.

[0062] The prevention strategy submodule uses the disease probability assessment results, combined with real-time monitoring data, to design disease prevention measures, adjust feeding management and optimize pig house environmental conditions to obtain early warning and adjustment plans; The prevention strategy submodule uses the disease probability assessment results, combined with real-time monitoring data, to design disease prevention measures, analyze the relationship between the disease probability and the current pig house environmental conditions, identify possible risk factors, and then adjust the feeding management strategy based on the analysis results, such as changing the feeding time or feed composition. At the same time, optimize the pig house environment, such as adjusting the ventilation system or temperature and humidity settings. The adjustments are implemented through automated control to ensure real-time feedback and adjustment accuracy. The generated warning and adjustment plans are notified to relevant personnel through the management software, providing plans and operating guidelines for the implementation of specific disease prevention measures.

[0063] See also Figure 7 ,The adjustment execution module includes a parameter adjustment submodule, an effect monitoring submodule, and a feedback recording submodule; The parameter adjustment submodule dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan, updates the equipment settings to match the different environmental requirements, and generates equipment parameter adjustment records; The parameter adjustment submodule dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan. First, after receiving the early warning and adjustment plan, the system automatically queries the current environmental settings and compares them with the new adjustment plan to identify the parameters that need to be changed. The adjustment operation is performed by the interface controlled by the microprocessor, which is directly connected to the control system of the ventilation equipment, humidifier and dehumidifier. The adjustment includes modifying the knob position and setting new temperature and humidity thresholds. The system confirms that the settings have been implemented through sensor feedback and records the details of each equipment parameter adjustment. The adjustment records are saved in the central database to form equipment parameter adjustment records.

[0064] The effect monitoring submodule monitors the environmental level of the adjusted pig house in real time based on the equipment parameter adjustment records, analyzes the immediate effect of the adjustment, confirms the effectiveness of the equipment adjustment, and obtains the adjustment effect monitoring results; The effect monitoring submodule monitors the adjusted pig house environment level in real time based on the equipment parameter adjustment records. Once the parameter adjustment is implemented, the monitoring system automatically starts to collect the adjusted environmental data, such as temperature, humidity and ventilation quality. The system uses a sensor network to continuously collect data and transmit the data to the central database. The difference between the data before and after is then compared to confirm the degree of environmental improvement. The monitoring results are displayed on the control panel in the form of charts and numerical values ​​to verify the effectiveness of the equipment adjustment and automatically generate adjustment effect monitoring results.

[0065] The feedback recording submodule records the actual changes and pigs’ behavioral responses after each environmental adjustment based on the results of the adjustment effect monitoring, integrates environmental data and pigs’ call feedback data, summarizes the information, and outputs environmental adjustment feedback records; The feedback record submodule is based on the adjustment effect monitoring results, records the actual changes and behavioral responses of pigs after each environmental adjustment, integrates environmental data and pig behavioral data, analyzes the data changes before and after environmental adjustment, associates the changes with the behavioral responses of pigs, identifies changes in behavioral patterns, integrates environmental data and behavioral responses to form a comprehensive view, and finally outputs environmental adjustment feedback records containing all relevant information to provide a basis for future environmental management decisions.

[0066] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A pig breeding house breeding environment comfort early warning system, characterized by: The system comprises: The environmental monitoring module installs temperature sensors, humidity sensors and ammonia concentration sensors inside the pig house to continuously collect data, calculate the real-time average temperature and humidity of the pig house environment, summarize the daily average values ​​of temperature and humidity, and form basic data of the pig house environment; The data analysis module receives the basic data of the pig house environment, integrates the ammonia concentration sensor data, uses normalization processing, calculates the environmental quality index of each monitoring point according to the data of the monitoring point, and obtains the comprehensive environmental quality assessment result of the pig house; The health prediction module uses the pig house comprehensive environmental quality assessment results to compare the environmental indicators with the health thresholds of different growth stages of pigs in the database in real time, calculates the deviation and summarizes the deviation degree of each indicator to obtain the pig house health risk data; The sound analysis module continuously monitors the pig calls through a microphone array, collects call samples, and performs frequency analysis on the samples to identify the pressure frequency characteristics, and evaluates the pressure level in combination with the sound intensity to obtain the pig acoustic pressure index; The disease prevention module combines the pig acoustic pressure index and the pig house health risk data to analyze the correlation between the potential health risks of the pig house and the pig calls, judge the possibility of pig disease occurrence, formulate corresponding prevention strategies, and obtain early warning and adjustment plans; The adjustment execution module dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan, monitors the matching degree between the adjustment effect and the environmental indicators in real time, and records and outputs the environmental adjustment feedback record.

2. The pig breeding house breeding environment comfort early warning system according to claim 1 is characterized by: The basic data of the pig house environment include temperature records, humidity records and ammonia concentration records; the comprehensive environmental quality assessment results of the pig house include environmental quality index, monitoring point data and normalization results; the health risk data of the pig house include health threshold comparison results, indicator deviation and environmental risk map; the acoustic pressure indicators of live pigs include frequency characteristics, sound intensity and pressure level; the early warning and adjustment plan includes disease possibility analysis results, prevention strategies and early warning signals; the environmental adjustment feedback records include adjustment parameter records, monitoring results and adjustment effects.

3. The pig breeding house breeding environment comfort early warning system according to claim 1 is characterized by: The environmental monitoring module includes a data acquisition submodule, a data analysis submodule, and an environmental assessment submodule; The data acquisition submodule uses temperature sensors, humidity sensors and ammonia concentration sensors, which are installed in key locations of the pig house to capture data in real time, continuously record pig house environmental parameters, and obtain the original environmental data set; The data analysis submodule uses the original environmental data set to clean the data, remove outliers, calculate the real-time average environmental temperature and humidity, and calculate the daily average temperature and humidity of the pig house to generate daily average environmental parameters; The environmental assessment submodule analyzes the long-term data trend based on the daily average environmental parameters, evaluates the impact of seasonal changes on the pig house environment, and obtains basic data on the pig house environment.

4. The pig breeding house breeding environment comfort early warning system according to claim 1 is characterized by: The data analysis module includes a data integration submodule, a quality index calculation submodule, and an environmental assessment submodule; The data integration submodule uses the basic data of the pig house environment, integrates the ammonia concentration sensor data, standardizes the data, and performs time series synchronization to obtain normalized environmental data; The quality index calculation submodule performs weighted average of the temperature, humidity and ammonia value of the monitoring point based on the normalized environmental data, calculates the environmental quality index, and summarizes and generates the monitoring point quality index; The environmental assessment submodule uses the monitoring point quality index to measure the overall environmental health status of the pig house by analyzing the changing rules and patterns of the index, and obtains the comprehensive environmental quality assessment results of the pig house.

5. The pig breeding house breeding environment comfort early warning system according to claim 4 is characterized by: The environmental quality index is calculated according to the formula: Calculate, where Represents the quality index of the monitoring point, Representative The actual measured value of each monitoring point, Represents the average value of all monitoring points. Indicates The absolute deviation between the measured value of each monitoring point and the average value, Representative The distance between each monitoring point and the nearest monitoring center point, is the sum of the distances to all monitoring points.

6. The pig breeding house breeding environment comfort early warning system according to claim 1 is characterized by: The health prediction module includes a data comparison submodule, a deviation calculation submodule, and a risk summary submodule; The data comparison submodule checks the temperature, humidity and ammonia concentration environmental indicators item by item through the comprehensive environmental quality assessment results of the pig house, makes a preliminary match between the environmental conditions and the health thresholds, and obtains environmental health matching data; The deviation calculation submodule performs quantitative analysis on the difference between each environmental indicator and the threshold value based on the environmental health matching data, calculates the deviation value of the indicator, and integrates and generates indicator deviation data; The risk summary submodule uses the indicator deviation data to conduct a comprehensive risk assessment, and by analyzing the actual impact of the indicators on the health of the pig house, determines the overall health risk level of the pig house and obtains the health risk data of the pig house.

7. The pig breeding house breeding environment comfort early warning system according to claim 6 is characterized by: The deviation value of the indicator is calculated according to the formula: Calculate, where Represents the deviation value of the indicator, Representative The actual measured value of the environmental indicator, Representative The environmental threshold of the indicator, Is an indicator The sensitivity coefficient of Representative The weighted deviation of an indicator.

8. The pig breeding house breeding environment comfort early warning system according to claim 1 is characterized by: The sound analysis module includes a sound collection submodule, a frequency analysis submodule, and a pressure assessment submodule; The sound collection submodule continuously monitors the pig calls through the microphone array, collects pig call samples in real time, and performs signal enhancement and noise elimination to obtain clear pig call samples; The frequency analysis submodule performs spectrum analysis technology based on the clear pig call sample, extracts the characteristics of key frequency bands, identifies and classifies the sound samples, and generates frequency characteristic data; The pressure assessment submodule uses the frequency characteristic data, combined with sound intensity and frequency response analysis, to determine the stress level of the pig according to the change pattern of the sound data, and obtains the pig acoustic pressure index.

9. The pig breeding house breeding environment comfort early warning system according to claim 1, characterized in that: The disease prevention module includes a correlation analysis submodule, a risk judgment submodule, and a prevention strategy submodule; The correlation analysis submodule uses the pig acoustic pressure index and the pig house health risk data to analyze the correlation between the data, reveal the connection between the pig call characteristics and the health index, and generate health risk correlation data; The risk judgment submodule evaluates and judges the probability of disease occurrence based on the health risk correlation data, and comprehensively assesses the potential health risks in combination with the disease occurrence patterns under different environmental pressures to obtain the disease occurrence probability assessment results; The prevention strategy submodule uses the disease occurrence probability assessment results, combined with real-time monitoring data, to design disease prevention measures, adjust feeding management and optimize pig house environmental conditions to obtain early warning and adjustment plans.

10. The pig breeding house breeding environment comfort early warning system according to claim 1, characterized in that: The adjustment execution module includes a parameter adjustment submodule, an effect monitoring submodule, and a feedback recording submodule; The parameter adjustment submodule dynamically adjusts the working parameters of the ventilation equipment, humidifiers and dehumidifiers in the pig house according to the early warning and adjustment plan, updates the equipment settings to match the different environmental requirements, and generates equipment parameter adjustment records; The effect monitoring submodule monitors the adjusted pig house environment level in real time based on the equipment parameter adjustment record, analyzes the immediate effect of the adjustment, confirms the effectiveness of the equipment adjustment, and obtains the adjustment effect monitoring result; The feedback recording submodule records the actual changes and the behavioral responses of the pigs after each environmental adjustment based on the adjustment effect monitoring results, integrates the environmental data and the pig call feedback data, summarizes the information, and outputs the environmental adjustment feedback record.

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