Pig house environment regulation control system and method based on multi-sensor

Abstract

The present invention discloses a pig house environment adjustment control system and method based on multiple sensors, belonging to the field of management systems. The present invention collects physical characteristic data of pigs in a pig house through a physical condition collection terminal, estimates suitable temperature data of the pigs through temperature data in the pig house and exercise data of the pigs in the pig house, imports the physical characteristic data of the pigs in the pig house into a physical abnormality analysis model to perform physical abnormality analysis of the pigs, imports the acquired suitable temperature data and physical abnormality analysis of each pig into an optimal temperature analysis model to perform optimal temperature analysis, and the temperature adjustment terminal adjusts the temperature in the pig house to the optimal temperature. The present application obtains the most suitable temperature for each pig in the pig house according to the historical activity intensity and historical temperature adjustment data of the pigs, and obtains the optimal temperature of the pig house according to the physical abnormality and the adapted temperature of each pig in the pig house, which is beneficial to the healthy growth of the pigs in the pig house and improves the growth quality of pigs with abnormal physical conditions.

Description

Technical Field

[0001] The present invention belongs to the field of management systems, and in particular relates to a piggery environment regulating control system and method based on multiple sensors. Background Art

[0002] The pig house environment is crucial to the health and welfare of pigs. Good environmental conditions help improve the appetite, sleep and activity levels of pigs, while reducing the incidence of disease. Pigs are very sensitive to temperature. The suitable temperature range varies depending on the age, breed and season of the pig. Generally speaking, newborn pigs grow best in an environment of 28-32 degrees Celsius, while adult pigs survive best in an environment of 22-25 degrees Celsius. The pig house environment can be effectively regulated and controlled by using appropriate equipment (such as air conditioners, fans, dehumidifiers, etc.) and following appropriate hygiene and epidemic prevention regulations. In addition, it is also crucial to regularly evaluate and adjust the pig house environmental parameters;

[0003] When controlling the temperature of a pig house, the temperature is usually maintained within a certain range suitable for survival (e.g., adult pigs are suitable for an environment of 22-25 degrees Celsius). Usually, several pigs are raised in one pig house, but the suitable temperature range varies depending on the age, breed, and season of the pigs. Existing technologies are unable to determine the most suitable temperature for each pig in the pig house based on the pigs' historical activity intensity and historical temperature adjustment data. Furthermore, they are unable to determine the optimal temperature for the pig house based on the physical abnormalities and temperature adaptation of each pig in the pig house, which is detrimental to the healthy growth of the pigs in the pig house. Most existing technologies have the above-mentioned problems.

[0004] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a pig house environment adjustment control system and method based on multiple sensors. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a pig house environment adjustment control system and method based on multiple sensors.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a pig house environment adjustment and control method based on multiple sensors, which includes the following specific steps:

[0007] The motion sensor is used to obtain the movement data of the pigs in the pig house, the temperature data in the pig house is collected through the temperature collection terminal, and the physical characteristic data of the pigs in the pig house is collected through the physical collection terminal;

[0008] Estimate the suitable temperature data of pigs through the temperature data in the pig house and the movement data of pigs in the pig house;

[0009] Importing the physical characteristic data of the pigs in the piggery into the physical abnormality analysis model to perform physical abnormality analysis on the pigs;

[0010] Import the obtained suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to analyze the optimal temperature;

[0011] The temperature control terminal adjusts the temperature in the pig house to the optimal temperature.

[0012] As a preferred technical solution for the multi-sensor based pig house environment regulation and control method, the specific contents of obtaining the movement data of the pigs in the pig house by the motion sensor, collecting the temperature data in the pig house by the temperature acquisition terminal, and collecting the physical characteristic data of the pigs in the pig house by the physical acquisition terminal are as follows:

[0013] S11, obtaining movement distance data and movement speed data of each pig in the pig house during the monitoring period through a motion sensor, and storing the data in a first storage terminal;

[0014] S12, collecting temperature data in the pig house in real time through the temperature collection terminal and storing it in the second storage terminal;

[0015] S13. The physical characteristic data of each pig in the pig house during the monitoring period are collected through the physical characteristic collection terminal, wherein the physical characteristic data of the pig include the pig's body temperature data, heart rate data, respiratory intensity and other data that can reflect the physical condition of the pig, and the physical characteristic data of each pig obtained are stored in the third storage terminal.

[0016] As a preferred technical solution of the pig house environment adjustment and control method based on multiple sensors, the method of estimating the suitable temperature data of the pigs based on the temperature data in the pig house and the movement data of the pigs in the pig house includes the following specific steps:

[0017] S21. Obtain the pig movement data in the pig house during the monitoring period, and import the data into the pig movement analysis value calculation formula to calculate the pig movement analysis value, wherein the pig movement analysis value calculation formula is: , where a is the coefficient of movement distance, S is the movement distance within the monitoring period, T is the duration of the monitoring period, vt is the instantaneous speed at time t within the monitoring period, v is the standard speed, dt is the time integral, Se is the standard movement distance, and the pig movement analysis difference is obtained by subtracting the pig movement analysis threshold from the pig movement analysis value;

[0018] It should be specifically explained here that, due to the characteristics of mammals, they will be tired of exercising when the temperature is usually high, and will increase the intensity of exercise because they need to keep warm when the temperature is usually low. Therefore, the movement distance and movement speed of the pigs in the pig house are used here to reflect the characteristics of the exercise intensity to reflect the pigs' perception of temperature. It should also be explained that the standard speed and standard movement distance in this step are obtained by obtaining the speed and movement distance of 500 pigs in the optimal survival temperature environment judged by experts, and taking the average value to obtain the standard speed and standard movement distance, wherein the optimal survival temperature environment is the optimal survival temperature for the pig's physique, that is, the corresponding survival temperature when the physical abnormality analysis value is the smallest, that is, the temperature most suitable for pig growth. The standard speed and standard movement distance are introduced into the pig movement analysis value calculation formula to obtain the pig movement analysis threshold;

[0019] S22. Obtain the pig motion analysis difference and the average temperature value of the pig house during the historical monitoring period, the pig motion analysis difference and the pig house temperature value of the temperature-adjusted pigs, and construct a recurrent neural network model whose input is the pig motion analysis difference and the pig house temperature value during the historical monitoring period and whose output is the pig house temperature value when the absolute value of the pig motion analysis difference is the smallest;

[0020] S23. Divide the extracted pig motion analysis difference and pig house temperature value during the historical monitoring period, and the pig motion analysis difference and pig house temperature value after temperature adjustment into a 70% parameter training set and a 30% parameter test set; input the 70% parameter training set into the recurrent neural network model for training to obtain an initial recurrent neural network model; use the 30% parameter test set to test the initial recurrent neural network model, and output the initial recurrent neural network model with the highest judgment accuracy of the pig house temperature value when the absolute value of the preset pig motion analysis difference is the smallest as the recurrent neural network model, wherein the output strategy formula of the specific neuron in the recurrent neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in layer n and neuron s in layer n+1, represents the Sigmoid activation function, m is the number of neurons in the nth layer;

[0021] S24. Import the obtained pig movement analysis difference during the monitoring period and the average temperature value of the pig house into the constructed recurrent neural network model to obtain the pig house temperature value when the absolute value of the pig movement analysis difference is the smallest, and set it as the suitable temperature for the corresponding pigs during the monitoring period.

[0022] Through this step, the historical movement data and historical suitable temperature values ​​of the pigs are comprehensively analyzed to obtain the suitable temperature of the corresponding pigs in the monitoring period.

[0023] As a preferred technical solution of the multi-sensor based pig house environment regulation and control method, the method of importing the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis of the pigs includes the following specific steps:

[0024] S31. Obtaining physical characteristic data of each pig in the pig house during the monitoring period;

[0025] S32. Import the obtained pig physical characteristic data into the physical abnormality analysis value calculation formula to calculate the physical abnormality analysis value, wherein the physical abnormality analysis value calculation formula is: , where N is the type of physical characteristic data, cj is the proportion coefficient of the j-th physical characteristic, kjt is the j-th physical characteristic data at time t, kjm is the median of the safety range of the j-th physical characteristic data, kjmax is the maximum value of the safety range of the j-th physical characteristic data, and kjmin is the minimum value of the safety range of the j-th physical characteristic data.

[0026] As a preferred technical solution for the multi-sensor based pig house environment regulation and control method, the method of importing the acquired suitable temperature data of each pig and the abnormal physical condition analysis results into the optimal temperature analysis model for optimal temperature analysis includes the following specific contents:

[0027] Obtain the suitable temperature data and physical abnormality analysis value of each pig, and import them into the optimal temperature value calculation formula to calculate the optimal temperature value. The optimal temperature value calculation formula is: , where U is the number of pigs in the pig house, Ku is the abnormal physical analysis value of the u-th pig in the pig house, and Tu is the suitable temperature data of the u-th pig in the pig house.

[0028] As a preferred technical solution of the pig house environment control method based on multiple sensors, the temperature control terminal adjusts the temperature in the pig house to the optimal temperature, including the following specific contents:

[0029] The optimal temperature value of the pig house is obtained, and the temperature control terminal adjusts the temperature in the pig house to the optimal temperature.

[0030] A multi-sensor based piggery environment control system, which is implemented based on the multi-sensor based piggery environment control method, specifically includes a data acquisition module, a suitable temperature estimation module, a physical abnormality analysis module, an optimal temperature analysis module, a temperature adjustment module and a control module;

[0031] The data acquisition module is used to acquire the movement data of the pigs in the pig house through the motion sensor, the temperature data in the pig house through the temperature acquisition terminal, and the physical characteristic data of the pigs in the pig house through the physical acquisition terminal;

[0032] The suitable temperature estimation module is used to estimate the suitable temperature data of the pigs based on the temperature data in the pig house and the movement data of the pigs in the pig house;

[0033] The physical abnormality analysis module is used to import the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs;

[0034] The optimal temperature analysis module is used to import the acquired suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to perform optimal temperature analysis;

[0035] The temperature regulating module is used for the temperature regulating terminal to adjust the temperature in the pig house to the optimal temperature;

[0036] The control module is used to control the operation of the data acquisition module, the suitable temperature estimation module, the physical abnormality analysis module, the optimal temperature analysis module and the temperature adjustment module.

[0037] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0038] The processor executes the above-mentioned pig house environment adjustment and control method based on multiple sensors by calling the computer program stored in the memory.

[0039] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned pig house environment adjustment and control method based on multiple sensors.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] The present application obtains the movement data of pigs in the pig house through a motion sensor, collects the temperature data in the pig house through a temperature acquisition terminal, and collects the physical characteristic data of the pigs in the pig house through a physical condition acquisition terminal. The suitable temperature data of the pigs is estimated through the temperature data in the pig house and the movement data of the pigs in the pig house. The physical characteristic data of the pigs in the pig house is imported into a physical abnormality analysis model to perform physical abnormality analysis of the pigs. The obtained suitable temperature data and physical abnormality analysis results of each pig are imported into an optimal temperature analysis model to perform optimal temperature analysis. The temperature adjustment terminal adjusts the temperature in the pig house to the optimal temperature. The present application obtains the most suitable temperature for each pig in the pig house based on the historical activity intensity and historical temperature adjustment data of the pigs, and obtains the optimal temperature of the pig house based on the physical abnormality and adaptive temperature of each pig in the pig house. This is beneficial to the healthy growth of the pigs in the pig house while improving the growth quality of pigs with abnormal physical conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the overall flow of the pig house environment adjustment and control method based on multiple sensors of the present invention;

[0043] Figure 2 Schematic diagram of step S1 of the piggery environment adjustment and control method based on multiple sensors of the present invention;

[0044] Figure 3 This is a schematic diagram of the overall framework of the piggery environment adjustment and control system based on multiple sensors of the present invention;

[0045] Figure 4 Schematic diagram of the recurrent neural network model structure of the present invention;

[0046] Figure 5 Schematic diagram of data transmission according to the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application, its application, or use.

[0048] Example 1

[0049] Here we will describe the application scenarios of this embodiment in detail: Figure 5As shown, the motion sensor acquires the movement data of the pigs in the pig house, the temperature data in the pig house is acquired through the temperature acquisition terminal, and the physical characteristic data of the pigs in the pig house is acquired through the physical characteristic acquisition terminal. The server imports the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs, imports the acquired suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to perform optimal temperature analysis, and the temperature adjustment terminal adjusts the temperature in the pig house to the optimal temperature;

[0050] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, the pig house environment adjustment and control method based on multiple sensors includes the following specific steps:

[0051] S1. Obtaining movement data of pigs in a piggery through a motion sensor, collecting temperature data in the piggery through a temperature acquisition terminal, and collecting physical characteristic data of the pigs in the piggery through a physical characteristic acquisition terminal;

[0052] In one specific embodiment, the specific contents of obtaining the movement data of the pigs in the pig house by the motion sensor, the temperature data in the pig house by the temperature acquisition terminal, and the physical characteristic data of the pigs in the pig house by the physical acquisition terminal are as follows:

[0053] S11, obtaining movement distance data and movement speed data of each pig in the pig house during the monitoring period through a motion sensor, and storing the data in a first storage terminal;

[0054] S12, collecting temperature data in the pig house in real time through the temperature collection terminal and storing it in the second storage terminal;

[0055] S13. Collecting physical characteristic data of each pig in the piggery during a monitoring period through the physical characteristic collection terminal, wherein the physical characteristic data of the pigs includes data reflecting the physical condition of the pigs, such as the pigs' body temperature data, heart rate data, and respiratory intensity, and the obtained physical characteristic data of each pig is stored in a third storage terminal;

[0056] S2. estimating the suitable temperature data of the pigs based on the temperature data in the pig house and the movement data of the pigs in the pig house;

[0057] In one specific embodiment, estimating the suitable temperature data of the pigs based on the temperature data in the pig house and the movement data of the pigs in the pig house includes the following specific steps:

[0058] S21. Obtain the pig movement data in the pig house during the monitoring period, and import the data into the pig movement analysis value calculation formula to calculate the pig movement analysis value, wherein the pig movement analysis value calculation formula is: , where a is the coefficient of movement distance, S is the movement distance within the monitoring period, T is the duration of the monitoring period, vt is the instantaneous speed at time t within the monitoring period, v is the standard speed, dt is the time integral, Se is the standard movement distance, and the pig movement analysis difference is obtained by subtracting the pig movement analysis threshold from the pig movement analysis value;

[0059] It should be specifically explained here that, due to the characteristics of mammals, they will be tired of exercising when the temperature is usually high, and will increase the intensity of exercise because they need to keep warm when the temperature is usually low. Therefore, the movement distance and movement speed of the pigs in the pig house are used here to reflect the characteristics of the exercise intensity to reflect the pigs' perception of temperature. It should also be explained that the standard speed and standard movement distance in this step are obtained by obtaining the speed and movement distance of 500 pigs in the optimal survival temperature environment judged by experts, and taking the average value to obtain the standard speed and standard movement distance, wherein the optimal survival temperature environment is the optimal survival temperature for the pig's physique, that is, the corresponding survival temperature when the physical abnormality analysis value is the smallest, that is, the temperature most suitable for pig growth. The standard speed and standard movement distance are introduced into the pig movement analysis value calculation formula to obtain the pig movement analysis threshold;

[0060] S22. Obtain the pig motion analysis difference and the average temperature value of the pig house during the historical monitoring period, the pig motion analysis difference and the pig house temperature value of the temperature-adjusted pigs, and construct a recurrent neural network model whose input is the pig motion analysis difference and the pig house temperature value during the historical monitoring period and whose output is the pig house temperature value when the absolute value of the pig motion analysis difference is the smallest;

[0061] The structure of the recurrent neural network model is as follows Figure 4 As shown, a recurrent neural network (a neural network model specifically used to process sequence data) is mainly composed of an input layer, a hidden layer, and an output layer. At each time point, it receives an input and then calculates a hidden state based on the current input and the previous hidden state. Finally, it generates an output based on the current hidden state. The following is a sample code to implement the above steps:

[0062] import numpy as np

[0063] from keras.models import Sequential

[0064] from keras.layers import LSTM, Dense

[0065] from sklearn.model_selection import train_test_split

[0066] from sklearn.preprocessing import MinMaxScaler

[0067] # Assume that there is already a data set containing pig movement analysis difference values ​​and pig house temperature values

[0068] # The dataset should be a two-dimensional array, where each row is the data of a time point, including the difference in pig movement analysis and the temperature of the pig house

[0069] Data preprocessing

[0070] # Standardize the data to have zero mean and unit variance

[0071] scaler = MinMaxScaler()

[0072] scaled_data = scaler.fit_transform(data)

[0073] # Split the dataset into training and test sets

[0074] X, y = scaled_data[:, :-1], scaled_data[:, -1] # X is the feature, y is the target temperature

[0075] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

[0076] # In order to adapt to the input requirements of LSTM, the data needs to be reshaped

[0077] # LSTM expects input shape to be (samples, time_steps, features)

[0078] X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1],1))

[0079] X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

[0080] # Build LSTM model

[0081] model = Sequential()

[0082] model.add(LSTM(50, activation='relu', input_shape=(X_train.shape[1],1)))

[0083] model.add(Dense(1))

[0084] # Compile the model

[0085] model.compile(optimizer='adam', loss='mean_squared_error')

[0086] # Train the model

[0087] model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test), verbose=1)

[0088] # Use the model to make predictions

[0089] predicted_temperatures = model.predict(X_test)

[0090] # Since standardization is used, the predicted values ​​need to be denormalized back to the original range

[0091] predicted_temperatures = scaler.inverse_transform(predicted_temperatures)

[0092] # Find the predicted temperature that minimizes the absolute value of the difference in pig movement analysis

[0093] # Assume there is a function to calculate the absolute value of the difference in pig movement analysis at a given temperature

[0094] # This is just a demonstration of how to find the minimum value in the predicted value

[0095] min_motion_diff_temp = predicted_temperatures[np.argmin(np.abs(predicted_temperatures))]

[0096] print(f"Predicted temperature that minimizes the absolute value ofpig motion analysis difference: {min_motion_diff_temp}")

[0097] S23. Divide the extracted pig motion analysis difference and pig house temperature value during the historical monitoring period, and the pig motion analysis difference and pig house temperature value after temperature adjustment into a 70% parameter training set and a 30% parameter test set; input the 70% parameter training set into the recurrent neural network model for training to obtain an initial recurrent neural network model; use the 30% parameter test set to test the initial recurrent neural network model, and output the initial recurrent neural network model with the highest judgment accuracy of the pig house temperature value when the absolute value of the preset pig motion analysis difference is the smallest as the recurrent neural network model, wherein the output strategy formula of the specific neuron in the recurrent neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in layer n and neuron s in layer n+1, represents the Sigmoid activation function, m is the number of neurons in the nth layer;

[0098] S24, importing the obtained pig movement analysis difference value and the average temperature value of the pig house during the monitoring period into the constructed recurrent neural network model to obtain the pig house temperature value when the absolute value of the pig movement analysis difference is the smallest, and setting it as the suitable temperature for the corresponding pigs during the monitoring period; through this step, a comprehensive analysis is performed on the historical pig movement data and the historical suitable temperature values ​​to obtain the suitable temperature for the corresponding pigs during the monitoring period;

[0099] S3, importing the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs;

[0100] In one specific embodiment, the physical characteristic data of the pigs in the piggery is imported into the physical abnormality analysis model to perform physical abnormality analysis on the pigs, including the following specific steps:

[0101] S31. Obtaining physical characteristic data of each pig in the pig house during the monitoring period;

[0102] S32. Import the obtained pig physical characteristic data into the physical abnormality analysis value calculation formula to calculate the physical abnormality analysis value, wherein the physical abnormality analysis value calculation formula is: , where N is the type of physical characteristic data, cj is the proportion coefficient of the j-th physical characteristic, kjt is the j-th physical characteristic data at time t, kjm is the median of the safety range of the j-th physical characteristic data, kjmax is the maximum value of the safety range of the j-th physical characteristic data, and kjmin is the minimum value of the safety range of the j-th physical characteristic data;

[0103] In this step, the degree of physical abnormality of the pigs is evaluated by integrating the physical data of the pigs, and the abnormal status of each pig in the pig house is evaluated;

[0104] S4. Importing the obtained suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to perform optimal temperature analysis;

[0105] In one specific embodiment, the obtained suitable temperature data of each pig and the physical abnormality analysis results are imported into the optimal temperature analysis model to perform the optimal temperature analysis, which includes the following specific contents:

[0106] Obtain the suitable temperature data and physical abnormality analysis value of each pig, and import them into the optimal temperature value calculation formula to calculate the optimal temperature value. The optimal temperature value calculation formula is: , where U is the number of pigs in the pig house, Ku is the physical abnormality analysis value of the u-th pig in the pig house, and Tu is the suitable temperature data of the u-th pig in the pig house. In this step, a comprehensive analysis is performed on the suitable temperature data and physical abnormality analysis values ​​of various pigs in the pig house, and a weighted calculation is performed based on the physical abnormality analysis values ​​of the pigs, which is conducive to the normal growth of pigs with physical defects.

[0107] S5. The temperature regulating terminal adjusts the temperature in the pig house to the optimal temperature;

[0108] In one specific embodiment, the temperature regulating terminal adjusts the temperature in the pig house to the optimal temperature including the following specific contents:

[0109] The optimal temperature value of the pig house is obtained, and the temperature control terminal adjusts the temperature in the pig house to the optimal temperature.

[0110] It should be noted here that the method for obtaining the values ​​of the coefficient of proportion of exercise distance and the coefficient of proportion of the jth physical characteristic is: obtain the exercise data of pigs in 500 groups of pig houses, the temperature data in the pig houses and the physical characteristic data of pigs in the pig houses, substitute them into the optimal temperature value calculation formula to calculate the optimal temperature value, obtain the growth yield rate of pigs in the pig houses at each temperature, import the growth temperature value with the maximum growth yield rate and the calculated optimal temperature value into the fitting software, and output the values ​​of the coefficient of proportion of exercise distance and the coefficient of proportion of the jth physical characteristic that meet the maximum temperature judgment accuracy.

[0111] The advantages of this embodiment over the prior art are as follows: the movement data of the pigs in the pig house are obtained through the motion sensor, the temperature data in the pig house is obtained through the temperature acquisition terminal, and the physical characteristic data of the pigs in the pig house are collected through the physical condition acquisition terminal. The suitable temperature data of the pigs are estimated through the temperature data in the pig house and the movement data of the pigs in the pig house, and the physical characteristic data of the pigs in the pig house are imported into the physical abnormality analysis model to perform physical abnormality analysis of the pigs. The obtained suitable temperature data and physical abnormality analysis results of each pig are imported into the optimal temperature analysis model to perform optimal temperature analysis. The temperature adjustment terminal adjusts the temperature in the pig house to the optimal temperature. This application obtains the most suitable temperature for each pig in the pig house based on the historical activity intensity and historical temperature adjustment data of the pigs, and obtains the optimal temperature of the pig house based on the physical abnormality and adaptive temperature of each pig in the pig house. This is beneficial to the healthy growth of the pigs in the pig house while improving the growth quality of pigs with abnormal physical conditions.

[0112] Example 2

[0113] like Figure 3 As shown, the pig house environment regulation and control system based on multiple sensors is implemented based on the above-mentioned pig house environment regulation and control method based on multiple sensors, and specifically includes a data acquisition module, a suitable temperature estimation module, a physical abnormality analysis module, an optimal temperature analysis module, a temperature regulation module and a control module;

[0114] The data acquisition module is used to acquire the movement data of the pigs in the pig house through the motion sensor, the temperature data in the pig house through the temperature acquisition terminal, and the physical characteristic data of the pigs in the pig house through the physical acquisition terminal;

[0115] The suitable temperature estimation module is used to estimate the suitable temperature data of pigs based on the temperature data in the pig house and the movement data of the pigs in the pig house;

[0116] The physical abnormality analysis module is used to import the physical characteristic data of pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs;

[0117] The optimal temperature analysis module is used to import the acquired suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to perform optimal temperature analysis;

[0118] The temperature regulation module is used for the temperature regulation terminal to adjust the temperature in the pig house to the optimal temperature;

[0119] The control module is used to control the operation of the data acquisition module, the suitable temperature estimation module, the physical abnormality analysis module, the optimal temperature analysis module and the temperature adjustment module.

[0120] Example 3

[0121] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0122] The processor executes the above-mentioned pig house environment adjustment and control method based on multiple sensors by calling the computer program stored in the memory.

[0123] This electronic device can vary significantly depending on its configuration or performance. It can include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the multi-sensor-based piggery environmental control method provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as a wired or wireless network interface and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0124] Example 4

[0125] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0126] When the computer program runs on a computer device, the computer device is caused to execute the above-mentioned pig house environment adjustment and control method based on multiple sensors.

[0127] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0128] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A pig house environment adjustment and control method based on multiple sensors, characterized in that: It includes the following specific steps: The motion data of the pigs in the pig house is obtained through the motion sensor, the temperature data in the pig house is collected through the temperature collection terminal, and the physical characteristic data of the pigs in the pig house is collected through the physical collection terminal; Estimate the suitable temperature data of pigs through the temperature data in the pig house and the movement data of pigs in the pig house; Import the physical characteristic data of pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs; Import the acquired suitable temperature data of each pig and the abnormal physical condition analysis results into the optimal temperature analysis model to analyze the optimal temperature; The temperature control terminal adjusts the temperature in the pig house to the optimal temperature; The method of estimating suitable temperature data of pigs by using temperature data in the pig house and movement data of pigs in the pig house comprises the following specific steps: S21, obtaining the movement data of the pigs in the pig house during the monitoring period, and importing it into the pig movement analysis value calculation formula to calculate the pig movement analysis value, wherein the pig movement analysis value calculation formula is: , where a is the coefficient of movement distance, S is the movement distance in the monitoring time period, T is the duration of the monitoring time period, vt is the instantaneous speed at time t in the monitoring time period, v is the standard speed, dt is the time integral, Se is the standard movement distance, and the pig movement analysis difference is obtained by subtracting the pig movement analysis threshold from the pig movement analysis value; S22, obtaining the pig movement analysis difference and the average temperature value of the pig house during the historical monitoring period, the pig movement analysis difference and the pig house temperature value of the temperature-adjusted pig, and constructing a recurrent neural network model whose input is the pig movement analysis difference and the pig house temperature value during the historical monitoring period, and whose output is the pig house temperature value when the absolute value of the pig movement analysis difference is the smallest; S23, dividing the extracted pig motion analysis difference and pig house temperature value in the historical monitoring period, and the pig motion analysis difference and pig house temperature value after temperature adjustment into 70% of parameter training set and 30% of parameter test set; inputting 70% of the parameter training set into the recurrent neural network model for training to obtain an initial recurrent neural network model; using 30% of the parameter test set to test the initial recurrent neural network model, and outputting the initial recurrent neural network model with the highest judgment accuracy of the pig house temperature value when the absolute value of the preset pig motion analysis difference is the smallest as the recurrent neural network model, wherein the output strategy formula of the specific neuron in the recurrent neural network model is: ,in is the output of n+1 layer s-term neurons, is the connection weight between neuron i in the nth layer and neuron s in the n+1th layer, represents the output of neuron i in layer n, represents the bias of the linear relationship between neuron i in the nth layer and neuron s in the n+1th layer, represents the Sigmoid activation function, m is the number of neurons in the nth layer; S24, importing the obtained pig movement analysis difference value and the average temperature value of the pig house during the monitoring period into the constructed recurrent neural network model to obtain the pig house temperature value when the absolute value of the pig movement analysis difference value is the smallest, and setting it as the appropriate temperature of the corresponding pig during the monitoring period; The method of importing the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs comprises the following specific steps: S31, obtaining physical characteristic data of each pig in the pig house during the monitoring period; S32, importing the obtained pig physical characteristic data into the physical abnormality analysis value calculation formula to calculate the physical abnormality analysis value, wherein the physical abnormality analysis value calculation formula is: , where N is the type of physical characteristic data, cj is the proportion coefficient of the j-th physical characteristic, kjt is the j-th physical characteristic data at time t, kjm is the median value of the safety range of the j-th physical characteristic data, kjmax is the maximum value of the safety range of the j-th physical characteristic data, and kjmin is the minimum value of the safety range of the j-th physical characteristic data; The method of importing the obtained suitable temperature data of each pig and the abnormal physical condition analysis results into the optimal temperature analysis model to perform the optimal temperature analysis includes the following specific contents: Obtain the appropriate temperature data and physical abnormality analysis value of each pig, and import them into the optimal temperature value calculation formula to calculate the optimal temperature value, where the optimal temperature value calculation formula is: , where U is the number of pigs in the pig house, Ku is the abnormal physical analysis value of the u-th pig in the pig house, and Tu is the suitable temperature data of the u-th pig in the pig house.

2. The pig house environment adjustment and control method based on multiple sensors as claimed in claim 1 is characterized in that: The specific contents of obtaining the movement data of the pigs in the pig house by the motion sensor, collecting the temperature data in the pig house by the temperature collection terminal, and collecting the physical characteristic data of the pigs in the pig house by the physical collection terminal are as follows: S11, obtaining movement distance data and movement speed data of each pig in the pig house during the monitoring period through a motion sensor, and storing the data in a first storage terminal; S12, collecting temperature data in the pig house in real time through the temperature collection terminal, and storing it in the second storage terminal; S13. Collect the physical characteristic data of each pig in the pig house during the monitoring period through the physical characteristic collection terminal, and store the obtained physical characteristic data of each pig in the third storage terminal.

3. The pig house environment adjustment and control method based on multiple sensors as claimed in claim 2 is characterized in that: The temperature regulating terminal regulates the temperature in the pig house to the optimal temperature, including the following specific contents: The optimal temperature value of the pig house is obtained, and the temperature control terminal adjusts the temperature in the pig house to the optimal temperature.

4. A pig house environment regulation and control system based on multiple sensors, which is implemented based on the pig house environment regulation and control method based on multiple sensors as claimed in any one of claims 1 to 3, characterized in that: It specifically includes a data acquisition module, a suitable temperature estimation module, a physical abnormality analysis module, an optimal temperature analysis module, a temperature adjustment module and a control module; The data acquisition module is used to acquire the movement data of the pigs in the pig house through the motion sensor, acquire the temperature data in the pig house through the temperature acquisition terminal, and acquire the physical characteristic data of the pigs in the pig house through the physical acquisition terminal; The suitable temperature estimation module is used to estimate the suitable temperature data of the pigs according to the temperature data in the pig house and the movement data of the pigs in the pig house; The physical abnormality analysis module is used to import the physical characteristic data of the pigs in the pig house into the physical abnormality analysis model to perform physical abnormality analysis on the pigs; The optimal temperature analysis module is used to import the acquired suitable temperature data of each pig and the physical abnormality analysis results into the optimal temperature analysis model to perform optimal temperature analysis; The temperature regulating module is used for the temperature regulating terminal to adjust the temperature in the pig house to the optimal temperature; The control module is used for controlling the operation of the data acquisition module, the suitable temperature estimation module, the physical abnormality analysis module, the optimal temperature analysis module and the temperature adjustment module.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the pig house environment adjustment and control method based on multiple sensors as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the pig house environment adjustment and control method based on multiple sensors as described in any one of claims 1 to 3.

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