Intelligent henhouse environment concentration monitoring system

Abstract

The invention discloses an intelligent henhouse environment concentration monitoring system, and relates to the technical field of henhouse environment concentration monitoring. The influence of changes of environmental parameters in a henhouse in different seasons on chicken raising is solved. The system specifically comprises a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module, the sensor module acquires various environmental parameters in a henhouse and transmits the acquired data to the data acquisition module, and the data acquisition module receives the data transmitted by the sensor module, pre-processes the data and transmits the pre-processed data to the remote monitoring module. The preprocessed data is uploaded to the data processing module through the data transmission module, the data processing module analyzes and processes the data, and the collected original data is converted into useful information understood by a breeder. Parameters of the model can be obtained by fitting the model into historical data, and the parameters are used for predicting the influence of changes of environmental parameters in the henhouse on chicken raising in different seasons in the future.

Description

Technical Field

[0001] The present invention relates to the technical field of chicken house environment concentration monitoring, and in particular to an intelligent chicken house environment concentration monitoring system. Background Art

[0002] The chicken house environmental concentration monitoring system is a device used to monitor the air quality in the chicken house. Through sensors and data acquisition equipment, the chicken house environmental concentration monitoring system can monitor the concentration of pollutants in the chicken house in real time, and transmit the data to the central control system for analysis and recording. When the concentration of pollutants exceeds the set safety range, the system will automatically sound an alarm to remind the breeding staff to take timely measures to ensure the air quality in the chicken house. The emergence of this monitoring system effectively helps farmers monitor the air quality in the chicken house, reduces the harm of pollutants in the chicken house to chickens, and improves the growth efficiency and health level of chickens. At the same time, it also provides scientific data support for farmers, helping them to better manage the growth environment of chickens and improve breeding efficiency.

[0003] After searching, the Chinese patent application number CN2017109607193 discloses a chicken house environment monitoring system, including: a microcontroller, a power module, a camera, a temperature sensor, a humidity sensor, a light intensity sensor, a voice alarm module and a liquid crystal display module; the power module is used to power each power module, the camera, the temperature sensor, the humidity sensor and the light intensity sensor are all connected to the input end of the microcontroller, the voice alarm module and the liquid crystal display module are all connected to the output end of the microcontroller; it also includes a roller shutter and a motor drive module arranged on the chicken house shed, and the input end of the motor drive module is connected to the output end of the microcontroller. A chicken house environment monitoring system in the above patent has the following shortcomings:

[0004] Although the overall system can transmit the temperature, humidity and light intensity values ​​received in the chicken house to the LCD display module through the microcontroller for display so that the staff can conduct on-site observation and analysis, the environmental conditions in the chicken house are different in different seasons, and the monitored environmental parameter values ​​also change. The above system does not analyze and explain the impact of changing environmental parameter values ​​on chicken farming. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent chicken house environment concentration monitoring system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An intelligent chicken house environmental concentration monitoring system includes a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module. The sensor module collects various environmental parameters in the chicken house and transmits the collected data to the data acquisition module. The data acquisition module receives the data from the sensor module and pre-processes the data. The pre-processed data is uploaded to the data processing module through the data transmission module. The data processing module analyzes and processes the data and converts the collected raw data into useful information that a breeder can understand. The analyzed and processed data is used to display the environmental parameters to the breeder in real time through the remote monitoring module.

[0008] Preferably, the sensor module includes a temperature sensor, a humidity sensor, an ammonia sensor and a light sensor, and the specific working steps are as follows:

[0009] A1: Install the temperature sensor, humidity sensor, ammonia sensor and light sensor at the designated location of the chicken house;

[0010] A2: Debug various sensors, set thresholds for various sensors, and specify limit values ​​for various environmental parameters;

[0011] A3: Various sensors are wirelessly connected to the data acquisition module. Various sensors transmit the temperature, humidity, ammonia concentration and light intensity parameters in the chicken house to the data module.

[0012] Furthermore: the data transmission module has the following specific working steps:

[0013] B1: Various sensors send the collected data of environmental concentration parameters to the LoRa module;

[0014] B2: The LoRa module encapsulates the collected data and selects the appropriate channel and frequency for data transmission, automatically adjusting the data transmission rate and power according to the communication distance and channel conditions;

[0015] B3: The data acquisition module at the receiving end decodes and decrypts the encapsulated data and restores the data to the original data parameters;

[0016] B4: The data acquisition module verifies, removes duplications and stores the received data.

[0017] On the basis of the above scheme: in the step B2, the data transmission rate is achieved through an adaptive spreading factor. When the adaptive spreading factor is small, it has a faster data transmission rate. When the adaptive spreading factor is large, it has a longer data transmission distance. The data transmission power is achieved through adaptive power regulation. The transmission power is automatically adjusted according to the current communication distance and channel conditions, so that data transmission can achieve the best transmission performance under different distances and environmental conditions.

[0018] A better solution among the above solutions is: the data acquisition module has the following specific working steps:

[0019] C1: Adaptively adjust the thresholds of various sensors according to seasonal changes, and collect chicken house environmental parameter data in different seasons;

[0020] C2: Establish a database of environmental concentration parameters in different seasons, analyze the environmental parameters in different seasons, and detect abnormal situations in time;

[0021] C3: Based on the environmental concentration parameters collected in different seasons, an analysis model is established in the corresponding database to analyze the impact of changes in environmental parameters in the chicken house on chicken farming in different seasons.

[0022] As a further solution of the present invention: in the step C3, the formula of the analysis model is as follows:

[0023] Y(t)=c+φ1Y(t-1)+φ2Y(t-2)+...+φpY(tp)+θ1e(t-1)+θ2e(t-2)+...+θqe(tq)+ε(t)+Φ1Y(tm)+Φ2Y(t-2m)+...+ΦP*Y(t-pm);

[0024] Where Y(t) represents the observation at time t, c is a constant term, φ1, φ2, ..., φp are the autoregressive term coefficients, θ1, θ2, ..., θq are the moving average term coefficients, ε(t) is the white noise error term, m represents the seasonal period, and Φ1, Φ2, ..., ΦP are the seasonal autoregressive term coefficients.

[0025] At the same time, the data processing module has the following specific working steps:

[0026] D1: Analyze the environmental parameter data collected by the sensor module, compare the actual measured data with the preset data of each sensor, and define the data exceeding the threshold as an abnormal situation;

[0027] D2: When environmental parameters detect abnormal conditions, the data processing module triggers the early warning mechanism;

[0028] D3: Provide early warning displays to breeders through visualization tools and early warning notification tools;

[0029] D4: The data processing module records the time when the abnormal situation occurs and the specific changes in environmental parameters, and integrates the recorded historical data to form the original data for predicting the occurrence of abnormal situations;

[0030] D5: Bring the original data of the abnormal situation into the machine learning model for training, extract the problem characteristics of the abnormal situation and the attributes of the data, including the statistical characteristics and time series characteristics of the environmental parameters;

[0031] D6: Based on the data characteristics obtained from the analysis, possible anomalies in the chicken house environmental parameter monitoring process are predicted.

[0032] As a preferred embodiment of the present invention: in the step D6, the prediction formula is as follows:

[0033] y=w0+w1x1+w2x2+...+w n x n ;

[0034] Among them, y represents the predicted result, (x1, x2, x3,..., x n ) represents the input characteristic variables, including temperature, humidity, ammonia concentration and light intensity parameters, occurrence time and location, (w0, w1, w2, w3, ..., w n ) represents the coefficients of the model, including various environmental parameters, occurrence time and location.

[0035] At the same time, the specific working steps of the remote monitoring module are as follows:

[0036] E1: Establish a cloud server and upload various environmental parameters measured by sensors to the cloud server through wireless communication;

[0037] E2: Users can remotely monitor the chicken house through the web page or APP of the cloud platform and view the environmental parameters of the chicken house in real time;

[0038] E3: Set abnormal alarm rules in the monitoring system to automatically send alarm information to the breeder;

[0039] E4: Use data visualization tools to intuitively display the monitored environmental parameters in the form of charts and curves;

[0040] E5: Control the equipment in the chicken house through the cloud platform, including turning on ventilation equipment and adjusting heating equipment.

[0041] The beneficial effects of the present invention are:

[0042] 1. This is an intelligent chicken house environmental concentration monitoring system. The sensor module can realize the simultaneous collection of multiple environmental parameters, detect environmental anomalies in time, and take corresponding measures to ensure the quality of the environment in the chicken house; the data collected by the sensor module usually has high accuracy and precision, and can provide objective and reliable environmental parameter information to help breeders better understand the environmental conditions in the chicken house.

[0043] 2. This intelligent chicken house environmental concentration monitoring system dynamically adjusts the data transmission rate and power according to the actual communication environment and needs to achieve optimal data transmission. The adaptive performance enables LoRa technology to achieve long-distance, low-power wireless data transmission under different communication distances and environmental conditions. It is suitable for various Internet of Things and long-distance communication application scenarios, and automatically selects the appropriate adaptive spreading factor according to the current communication distance and channel conditions to achieve the best data transmission rate and transmission distance. At the same time, adaptive power control can minimize energy consumption and ensure the stability and reliability of data transmission.

[0044] 3. This intelligent chicken house environmental concentration monitoring system establishes an analysis model in the corresponding database based on the environmental concentration parameters collected in different seasons. By fitting the model to historical data, the model parameters can be obtained to predict the impact of changes in environmental parameters in the chicken house on chicken farming in different seasons in the future. This can help chicken farmers better manage the chicken house environment and improve the production efficiency and health level of chicken farming.

[0045] 4. This intelligent chicken house environmental concentration monitoring system can better capture the characteristics and patterns of abnormal situations by training machine learning models using actual abnormal data, thereby improving the accuracy of abnormal predictions; and by extracting the characteristics and attributes of abnormal data, it can customize personalized abnormal prediction models according to the specific chicken house environment conditions, which are more in line with actual conditions; based on the trained machine learning model, it can monitor environmental parameter data in real time, and trigger early warnings in time when abnormal situations are found, helping chicken farmers to respond quickly; reducing the workload of manual analysis and improving processing efficiency; through analysis and feature extraction of abnormal data, it can comprehensively consider the impact of multiple factors on abnormal situations, thereby improving the comprehensiveness and comprehensiveness of predictions.

[0046] 5. This intelligent chicken house environmental concentration monitoring system helps chicken farmers to understand the situation in the chicken house in a timely manner by remotely monitoring the changes in the environmental parameters of the chicken house. The monitoring system can be accessed remotely through the Internet to view the environmental parameters in the chicken house anytime and anywhere, which is convenient for management and monitoring. The monitoring system can set abnormal alarm rules. Once the environmental parameters exceed the preset range, the system can automatically send an alarm message to the relevant personnel to help deal with abnormal situations in a timely manner. At the same time, the sensor data can be stored, analyzed and displayed through the cloud platform, which can help users better understand the changes in the environmental parameters in the chicken house and provide data support for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a main flow chart of an intelligent chicken house environmental concentration monitoring system proposed by the present invention;

[0048] Figure 2 A flow chart of a sensor module in an intelligent chicken house environment concentration monitoring system proposed by the present invention;

[0049] Figure 3 This is a flow chart of a data transmission module in an intelligent chicken house environmental concentration monitoring system proposed by the present invention;

[0050] Figure 4 This is a flow chart of a data acquisition module in an intelligent chicken house environmental concentration monitoring system proposed by the present invention;

[0051] Figure 5 This is a flow chart of a data processing module in an intelligent chicken house environmental concentration monitoring system proposed by the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0053] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0054] An intelligent chicken house environmental concentration monitoring system, such as Figure 1As shown, it includes a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module. The sensor module collects various environmental parameters in the chicken house and transmits the collected data to the data acquisition module. The data acquisition module receives the data from the sensor module and pre-processes the data. The pre-processed data is uploaded to the data processing module through the data transmission module. The data processing module analyzes and processes the data, and converts the collected raw data into useful information that the breeder can understand. The analyzed and processed data is used to display the environmental parameters to the breeder in real time through the remote monitoring module.

[0055] In order to collect various environmental parameters in the chicken house; Figure 2 As shown, the sensor module includes a temperature sensor, a humidity sensor, an ammonia sensor and a light sensor, and its specific working steps are as follows:

[0056] A1: Install the temperature sensor, humidity sensor, ammonia sensor and light sensor at the designated location of the chicken house;

[0057] A2: Debug various sensors, set thresholds for various sensors, and specify limit values ​​for various environmental parameters;

[0058] A3: Various sensors are wirelessly connected to the data acquisition module. Various sensors transmit the temperature, humidity, ammonia concentration and light intensity parameters in the chicken house to the data module.

[0059] The sensor module can collect multiple environmental parameters at the same time, detect environmental anomalies in time, and take corresponding measures to ensure the quality of the environment in the chicken house. The data collected by the sensor module usually has high accuracy and precision, and can provide objective and reliable environmental parameter information to help breeders better understand the environmental conditions in the chicken house.

[0060] In order to ensure the accuracy of the transmission of chicken house environmental concentration parameters; Figure 3 As shown, the data transmission module has the following specific working steps:

[0061] B1: Various sensors send the collected data of environmental concentration parameters to the LoRa module;

[0062] B2: The LoRa module encapsulates the collected data and selects the appropriate channel and frequency for data transmission, automatically adjusting the data transmission rate and power according to the communication distance and channel conditions;

[0063] B3: The data acquisition module at the receiving end decodes and decrypts the encapsulated data and restores the data to the original data parameters;

[0064] B4: The data acquisition module verifies, removes duplications and stores the received data.

[0065] According to the actual communication environment and needs, the data transmission rate and power are dynamically adjusted to achieve optimal data transmission. The adaptive performance enables LoRa technology to achieve long-distance, low-power wireless data transmission under different communication distances and environmental conditions, and is suitable for various IoT and long-distance communication application scenarios.

[0066] Among them, in the B2 step, the data transmission rate is achieved through an adaptive spreading factor. When the adaptive spreading factor is small, it has a faster data transmission rate. When the adaptive spreading factor is large, it has a longer data transmission distance. The data transmission power is achieved through adaptive power regulation. The transmission power is automatically adjusted according to the current communication distance and channel conditions so that the data transmission can achieve the best transmission performance under different distances and environmental conditions; thus, the appropriate adaptive spreading factor is automatically selected according to the current communication distance and channel conditions to achieve the best data transmission rate and transmission distance. At the same time, adaptive power control can minimize energy consumption and ensure the stability and reliability of data transmission.

[0067] In order to realize the effects of environmental parameters collected in different seasons on chicken farming; e.g. Figure 4 As shown, the data acquisition module has the following specific working steps:

[0068] C1: Adaptively adjust the thresholds of various sensors according to seasonal changes, and collect chicken house environmental parameter data in different seasons;

[0069] C2: Establish a database of environmental concentration parameters in different seasons, analyze the environmental parameters in different seasons, and detect abnormal situations in time;

[0070] C3: Based on the environmental concentration parameters collected in different seasons, an analysis model is established in the corresponding database to analyze the impact of changes in environmental parameters in the chicken house on chicken farming in different seasons.

[0071] In the C3 step, the analysis model formula is as follows:

[0072] Y(t)=c+φ1Y(t-1)+φ2Y(t-2)+...+φpY(tp)+θ1e(t-1)+θ2e(t-2)+...+θqe(tq)+ε(t)+Φ1Y(tm)+Φ2Y(t-2m)+...+ΦP*Y(t-pm);

[0073] Where Y(t) represents the observation at time t, c is a constant term, φ1, φ2, ..., φp are the coefficients of the autoregressive term, θ1, θ2, ..., θq are the coefficients of the moving average term, ε(t) is the white noise error term, m represents the seasonal period, Φ1, Φ2, ..., ΦP are the coefficients of the seasonal autoregressive term;

[0074] By fitting the above model to historical data, the model parameters can be obtained to predict the impact of changes in environmental parameters in the chicken house on chicken farming in different seasons in the future. This can help chicken farmers better manage the chicken house environment and improve the production efficiency and health level of chickens.

[0075] In order to realize the early warning function when abnormal conditions occur in environmental parameter changes; Figure 5 As shown, the data processing module has the following specific working steps:

[0076] D1: Analyze the environmental parameter data collected by the sensor module, compare the actual measured data with the preset data of each sensor, and define the data exceeding the threshold as an abnormal situation;

[0077] D2: When environmental parameters detect abnormal conditions, the data processing module triggers the early warning mechanism;

[0078] D3: Provide early warning displays to breeders through visualization tools and early warning notification tools;

[0079] D4: The data processing module records the time when the abnormal situation occurs and the specific changes in environmental parameters, and integrates the recorded historical data to form the original data for predicting the occurrence of abnormal situations;

[0080] D5: Bring the original data of the abnormal situation into the machine learning model for training, extract the problem characteristics of the abnormal situation and the attributes of the data, including the statistical characteristics and time series characteristics of the environmental parameters;

[0081] D6: Based on the data characteristics obtained from the analysis, possible anomalies in the chicken house environmental parameter monitoring process are predicted.

[0082] In step D6, the prediction formula is as follows:

[0083] y=w0+w1x1+w2x2+...+w n x n ;

[0084] Among them, y represents the predicted result, (x1, x2, x3,..., x n ) represents the input characteristic variables, including temperature, humidity, ammonia concentration and light intensity parameters, occurrence time and location, (w0, w1, w2, w3, ..., wn ) represents the coefficients of the model, including various environmental parameters, occurrence time and location;

[0085] By training the machine learning model using actual abnormal data, the characteristics and patterns of abnormal situations can be better captured, thereby improving the accuracy of abnormal predictions; and by extracting the characteristics and attributes of abnormal data, a personalized abnormal prediction model can be customized according to the specific chicken house environment conditions, which is more in line with the actual situation; based on the trained machine learning model, environmental parameter data can be monitored in real time, and early warnings can be triggered in time when abnormal situations are found, helping chicken farmers to respond quickly; reducing the workload of manual analysis and improving processing efficiency; through the analysis and feature extraction of abnormal data, the impact of multiple factors on abnormal situations can be comprehensively considered, improving the comprehensiveness and comprehensiveness of the prediction.

[0086] In order to promote real-time remote monitoring of chicken house environmental parameters, ensure the security of various environmental parameter data in the chicken house, and protect the health of the chickens in the chicken house; the specific working steps of the remote monitoring module are as follows:

[0087] E1: Establish a cloud server and upload various environmental parameters measured by sensors to the cloud server through wireless communication;

[0088] E2: Users can remotely monitor the chicken house through the web page or APP of the cloud platform and view the environmental parameters of the chicken house in real time;

[0089] E3: Set abnormal alarm rules in the monitoring system to automatically send alarm information to the breeder;

[0090] E4: Use data visualization tools to intuitively display the monitored environmental parameters in the form of charts and curves;

[0091] E5: Control the equipment in the chicken house through the cloud platform, including turning on ventilation equipment and adjusting heating equipment.

[0092] By remotely monitoring the changes in environmental parameters of the chicken house, chicken farmers can be informed of the situation in the chicken house in a timely manner. The monitoring system can be accessed remotely through the Internet to view the environmental parameters in the chicken house anytime and anywhere, which is convenient for management and monitoring. The monitoring system can set abnormal alarm rules. Once the environmental parameters exceed the preset range, the system can automatically send an alarm message to the relevant personnel to help deal with abnormal situations in a timely manner. At the same time, the sensor data can be stored, analyzed and displayed through the cloud platform, which can help users better understand the changes in environmental parameters in the chicken house and provide data support for decision-making.

[0093] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent chicken house environmental concentration monitoring system, comprising a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module, characterized in that: The sensor module collects various environmental parameters in the chicken house and transmits the collected data to the data acquisition module. The data acquisition module receives the data from the sensor module, pre-processes the data, and uploads the pre-processed data to the data processing module through the data transmission module. The data processing module analyzes and processes the data and converts the collected raw data into useful information that the breeder can understand. The analyzed and processed data is used to display the environmental parameters to the breeder in real time through the remote monitoring module.

2. The intelligent chicken house environment concentration monitoring system according to claim 1 is characterized in that: The sensor module includes a temperature sensor, a humidity sensor, an ammonia sensor and a light sensor, and its specific working steps are as follows: A1: Install the temperature sensor, humidity sensor, ammonia sensor and light sensor at the designated location of the chicken house; A2: Debug various sensors, set thresholds for various sensors, and specify limit values ​​for various environmental parameters; A3: Various sensors are wirelessly connected to the data acquisition module. Various sensors transmit the temperature, humidity, ammonia concentration and light intensity parameters in the chicken house to the data module.

3. The intelligent chicken house environmental concentration monitoring system according to claim 1 is characterized in that: The data transmission module has the following specific working steps: B1: Various sensors send the collected data of environmental concentration parameters to the LoRa module; B2: The LoRa module encapsulates the collected data and selects the appropriate channel and frequency for data transmission, automatically adjusting the data transmission rate and power according to the communication distance and channel conditions; B3: The data acquisition module at the receiving end decodes and decrypts the encapsulated data and restores the data to the original data parameters; B4: The data acquisition module verifies, removes duplications and stores the received data.

4. The intelligent chicken house environment concentration monitoring system according to claim 3 is characterized in that: In the step B2, the data transmission rate is achieved through an adaptive spreading factor. When the adaptive spreading factor is small, the data transmission rate is faster. When the adaptive spreading factor is large, the data transmission distance is farther. The data transmission power is achieved through adaptive power regulation. The transmission power is automatically adjusted according to the current communication distance and channel conditions, so that data transmission can achieve the best transmission performance under different distances and environmental conditions.

5. The intelligent chicken house environment concentration monitoring system according to claim 1 is characterized in that: The data acquisition module has the following specific working steps: C1: Adaptively adjust the thresholds of various sensors according to seasonal changes, and collect chicken house environmental parameter data in different seasons; C2: Establish a database of environmental concentration parameters in different seasons, analyze the environmental parameters in different seasons, and detect abnormal situations in time; C3: Based on the environmental concentration parameters collected in different seasons, an analysis model is established in the corresponding database to analyze the impact of changes in environmental parameters in the chicken house on chicken farming in different seasons.

6. The intelligent chicken house environment concentration monitoring system according to claim 5 is characterized in that: In the C3 step, the formula of the analysis model is as follows: Y(t)=c+φ1Y(t-1)+φ2Y(t-2)+...+φpY(tp)+θ1e(t-1)+θ2e(t-2)+...+θqe(tq)+ε(t)+Φ1Y(tm)+Φ2Y(t-2m)+...+ΦP*Y(t-pm); Where Y(t) represents the observation at time t, c is a constant term, φ1, φ2, ..., φp are the autoregressive term coefficients, θ1, θ2, ..., θq are the moving average term coefficients, ε(t) is the white noise error term, m represents the seasonal period, and Φ1, Φ2, ..., ΦP are the seasonal autoregressive term coefficients.

7. The intelligent chicken house environment concentration monitoring system according to claim 1 is characterized in that: The data processing module has the following specific working steps: D1: Analyze the environmental parameter data collected by the sensor module, compare the actual measured data with the preset data of each sensor, and define the data exceeding the threshold as an abnormal situation; D2: When environmental parameters detect abnormal conditions, the data processing module triggers the early warning mechanism; D3: Provide early warning displays to breeders through visualization tools and early warning notification tools; D4: The data processing module records the time when the abnormal situation occurs and the specific changes in environmental parameters, and integrates the recorded historical data to form the original data for predicting the occurrence of abnormal situations; D5: Bring the original data of the abnormal situation into the machine learning model for training, extract the problem characteristics of the abnormal situation and the attributes of the data, including the statistical characteristics and time series characteristics of the environmental parameters; D6: Based on the data characteristics obtained from the analysis, possible anomalies in the chicken house environmental parameter monitoring process are predicted.

8. The intelligent chicken house environment concentration monitoring system according to claim 7 is characterized in that: In step D6, the prediction formula is as follows: y=w0+w1x1+w2x2+...+w n x n ; Among them, y represents the predicted result, (x1, x2, x3,..., x n ) represents the input characteristic variables, including temperature, humidity, ammonia concentration and light intensity parameters, occurrence time and location, (w0, w1, w2, w3, ..., w n ) represents the coefficients of the model, including various environmental parameters, occurrence time and location.

9. The intelligent chicken house environment concentration monitoring system according to claim 1 is characterized in that: The specific working steps of the remote monitoring module are as follows: E1: Establish a cloud server and upload various environmental parameters measured by sensors to the cloud server through wireless communication; E2: Users can remotely monitor the chicken house through the web page or APP of the cloud platform and view the environmental parameters of the chicken house in real time; E3: Set abnormal alarm rules in the monitoring system to automatically send alarm information to the breeder; E4: Use data visualization tools to intuitively display the monitored environmental parameters in the form of charts and curves; E5: Control the equipment in the chicken house through the cloud platform, including turning on ventilation equipment and adjusting heating equipment.

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