Laying hen breeding environment monitoring system based on Internet of Things

Abstract

The invention discloses a laying hen breeding environment monitoring system based on the Internet of Things. The laying hen breeding environment monitoring system comprises a data acquisition module, a preprocessing module, a database module, a data processing module and a data output module. The preprocessing module is used for dividing the chicken house into a plurality of monitoring areas; the data processing module is used for matching and setting the breeding environment parameters of the chicken house, judging the abnormity of the environment parameters in the monitoring area and performing real-time control; according to the method, the temperature and humidity distribution state in the henhouse is analyzed according to historical data, the henhouse is divided into regions, and each region is monitored in real time; when the environmental parameters in the monitoring area in the henhouse are abnormal, the regulation and control requirements of the henhouse are deduced according to the position distribution of the abnormal area, and then targeted regulation and control are performed, so that the energy consumption in the henhouse operation process is reduced, the effective utilization degree of energy is improved, the laying hen breeding benefits are improved, and the sustainable development of the breeding industry is promoted.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management and control, and in particular to an egg-laying chicken breeding environment monitoring system based on the Internet of Things. Background Art

[0002] In laying hen farming, environmental monitoring is extremely necessary and important. Appropriate temperature, humidity, light and air quality are key factors for the healthy growth and efficient egg production of laying hens. Environmental monitoring can timely detect and adjust unfavorable conditions, prevent diseases, and improve the production performance of laying hens and egg quality. At the same time, environmental monitoring can also help save energy and reduce emissions, and optimize farming costs. Nowadays, large-scale laying hen farms detect the environmental parameters in the chicken house and conduct real-time control to ensure that the chicken house reaches a suitable growth environment. Since large-scale farms are generally large in area, the layout of chicken houses in different locations is different, resulting in a high density of laying hens in some areas. At the same time, accompanied by the loss of heat or cold air during the indoor temperature and humidity control process, there are differences in temperature and humidity between different breeding densities and breeding locations in the chicken house. During the production and operation of chicken farms, when there are abnormalities in the environment in the chicken house, the control equipment will be started to adjust and improve the environmental parameters of the entire chicken house. However, in fact, only some locations need to be adjusted due to the large temperature and humidity differences between themselves and the entire chicken house. Therefore, it will cause a large energy consumption, resulting in low effective energy utilization. To solve this problem, an laying hen breeding environment monitoring system based on the Internet of Things is proposed. Summary of the invention

[0003] The technical problem to be solved by the present invention is: how to solve the problem that the existing safety protection system has a single protection type, resulting in poor protection effect, which has a certain impact on the use of the safety protection system. A laying hen breeding environment monitoring system based on the Internet of Things is provided.

[0004] The present invention solves the above technical problems through the following technical solutions. The present invention includes a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module; The data acquisition module is used to collect real-time data information of the laying hen breeding process in the chicken house; The database module is used to store historical data information and standard data information of the chicken house; The preprocessing module is used to receive historical data information and standard data information, analyze the historical data information in combination with the standard data information, and divide the chicken house into multiple monitoring areas using preset rules; The data processing module is used to receive real-time data information, historical data information and standard data information. It first analyzes the historical data information, matches and sets the breeding environment parameters of the chicken house, and generates a basic parameter report; then it monitors the environmental parameters in each monitoring area in real time; it analyzes the real-time data information in combination with the standard data information to determine the abnormality of the environmental parameters in the monitoring area, performs real-time control, and generates a control report; The data output module is used to receive and output basic parameter reports and control reports.

[0005] Preferably, the historical data information includes historical temperature data, and the specific processing process of the preprocessing module is: Import standard data information and read the air inlet and outlet positions; Connecting the air inlet and the air outlet to obtain the first basic direction of air flow; Read the historical temperature data in the chicken house and divide the chicken house into areas using preset rules; The default rules are: Obtain historical temperature data of positions of different sizes from the air inlet in the first basic direction, which are Te1, Te2, Te3, ..., Ten respectively; Calculate the correlation r of temperature data at different size positions. The specific calculation process is: ; Where Tei is the temperature data of the i-th point, Lei is the distance between the i-th point and the air inlet, is the average value of the distance between each point and the air inlet, is the average temperature of each point; when >When the threshold Q1 is preset, the chicken house is divided into multiple monitoring areas by equally spaced divisions in the first basic direction using the preset size; when When the temperature is less than or equal to the preset threshold value Q1, the least square method is used to calculate the linear regression equation Te=β1+β2·Le when the temperature and distance size are in the first state and the second state of the chicken house respectively; The first state is that the chicken house is in hot air demand, and the second state is that the chicken house is in cold air demand; The specific calculation process is: ; ; Calculate the distance size difference Lr1 when the temperature difference of the chicken house is in the first state and is the preset threshold value Tr; Then calculate the distance size difference Lr2 when the temperature difference is the preset threshold value Tr when the chicken house is in the second state; use = (Lr1+Lr2) / 2 to calculate the segmentation spacing; by The chicken house is divided into a plurality of monitoring areas by equally dividing the chicken house in a first basic direction for spacing.

[0006] Preferably, the historical data information includes historical humidity data, and the monitoring area segmentation process further includes: Import historical data to obtain historical humidity data for each monitoring area in the chicken house under target conditions; Randomly obtain multiple groups of historical humidity data, each group of historical humidity data consists of two, and the two historical humidity data come from two adjacent monitoring areas respectively; Calculate the difference E1 of the two humidity data. The specific calculation process is: ; Among them, RH1 and RH2 are the historical humidity data of two adjacent monitoring areas; When E2>preset threshold Q2, the group of data is marked; Calculate the difference of each set of historical humidity data to obtain the marked data; Count the number of labeled data N1; Then count the number of groups N2 of randomly acquired historical humidity data; Calculate the mark ratio J. The specific calculation process is: J = N1 / N2; When the target state is the first state, obtaining the first mark proportion J1; When the target state is the second state, obtain the second mark proportion J2; When the first mark ratio J1> the preset threshold Q3 or the second mark ratio J2> the preset threshold Q3, the preset threshold Tr of the temperature difference is lowered and the segmentation interval is recalculated. .

[0007] Preferably, the historical data information includes historical egg production data, and the specific generation process of the basic parameter report is: Read historical egg production data under different environmental parameters; Establish the mapping relationship between different environmental parameters and egg production; Rank the different environmental parameters according to egg production from high to low; The environmental parameters with the highest egg production were selected as pre-selection parameters; Read the pre-selected parameters and import the egg production data within the preset time period under the pre-selected parameters; A rectangular coordinate system is established with egg production as the ordinate and time as the abscissa to obtain a first type of curve graph; Randomly select a base point and multiple verification points on the first type of curve graph; Calculate the slope K between the base point and each verification point respectively. The specific calculation process is: K = (An-Ai) / ∆ti; Among them, An is the egg production data at any verification point, Ai is the egg production data at the basic point, and ∆ti is the difference between the time at the verification point and the time at the basic point; Obtain the slope between the base point and each verification point, which are K1, K2, K3, ..., Kn respectively; Calculate the dispersion degree E3 between multiple slopes. The specific calculation process is: ; Among them, Ki is the slope between the ith verification point and the base point, is the average of multiple slopes; When the dispersion degree E3 is less than the preset threshold value Q4, the pre-selected parameters are marked to generate basic parameters, and a basic parameter report is generated; When the dispersion degree E3 ≥ the preset threshold value Q4, the data of the preselected parameters are cleared.

[0008] Preferably, the verification point selection process further includes: Read the horizontal coordinates of multiple verification points, which are B1, B2, B3, ..., Bn; Get the average value of the horizontal coordinates of multiple verification points ; Randomly select N3 points from multiple verification points to generate detection points; Calculate the horizontal coordinate and average value at the detection point The deviation degree E4, the specific calculation process is: ; in, is the horizontal coordinate of any detection point; When the deviation E4 is less than the preset threshold Q5, it is marked as abnormal data; Calculate the horizontal coordinates and average values ​​of the multiple detection points respectively The degree of deviation is used to count the number of abnormal data N4; Use the formula E5=N4 / N3 to calculate the first abnormal data ratio; When the first abnormal data proportion E5>the preset threshold Q6, the verification point is reselected.

[0009] Preferably, the real-time data information includes real-time environmental data, and the generation process of the control report is: Collect real-time environmental data in each monitoring area; The real-time environment data includes first type environment data Ce; Import standard data information and obtain the monitoring range (C1, C2) of the first type of environmental data Ce; When the first type of environmental data When the monitoring area is marked, the abnormal area is obtained; Collect the first type of environmental data Ce of each monitoring area to obtain the number N5 of abnormal areas; Count the number of monitoring areas N6; Use the formula E6=N5 / N6 to calculate the second anomaly ratio; When the second abnormality proportion E6> the preset threshold Q7, a control report on the overall control of the chicken house is generated; When the second abnormality proportion E6 ≤ the preset threshold Q7, a control report for local control of the monitoring area is generated.

[0010] Preferably, the real-time data information also includes real-time size data and real-time image data. When the second abnormality proportion E6>preset threshold value Q7, the generation process of the control report further includes: Collect real-time image data in the chicken house and obtain real-time picture data of the chicken house; Mark each abnormal area and obtain the geometric center points of multiple abnormal areas; Creating a circular area, wherein the circular area includes all geometric center points, and at least three geometric center points are located on the contour line of the circular area; The radius Re of the circular area to be collected; Read standard data information to obtain the area size Si of the chicken house; Calculate the abnormal area ratio E7. The specific calculation process is as follows: ; When the abnormal area ratio E7 is less than the preset threshold Q8, a control report for local control of the monitoring area is generated.

[0011] Preferably, the real-time data also includes real-time weight data and real-time egg production data. When the first type of environmental data Ce∈(C1, C2), the generation process of the control report also includes: Real-time egg production data collected continuously and in equal time periods; Randomly extract multiple groups of sample data; Calculate the increase in egg production G1. The specific calculation process is as follows: G1=(Ma-Mb) / (Ta-Tb); Wherein, Ma is the egg production of the chicken house at time point Ta, Mb is the egg production of the chicken house at time point Tb, and Ta>Tb; When G1<0, a control report for increasing the temperature of the chicken house is generated; When G1≥0, the total mass of the egg production in the chicken house at the corresponding time point is used to calculate the average egg weight increase G2. The specific calculation process is: ; Among them, Mu1 is the total mass of eggs produced in the chicken house at time point Ta, and Mu2 is the total mass of eggs produced in the chicken house at time point Tb; When the average egg weight increase G2 is less than 0, a control report for lowering the temperature of the chicken house is generated.

[0012] The system also includes a control module, which is used to receive a control report and take a control action.

[0013] Compared with the prior art, the present invention has the following advantages: the system first analyzes the temperature and humidity distribution state in the air duct direction in the chicken house according to historical data, divides the chicken house into areas while ensuring that the temperature and humidity differences in the same monitoring area are relatively stable, and then monitors each area in real time; when there are abnormal environmental parameters in the monitoring area in the chicken house, the control needs of the chicken house are inferred according to the position distribution of the abnormal area, and then the environmental parameters of the entire chicken house or the environmental parameters of the local area are targetedly regulated, thereby reducing energy consumption in the operation of the chicken house, improving the effective utilization of energy, improving the efficiency of laying hen farming and promoting the sustainable development of the farming industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the overall module diagram of the present invention. DETAILED DESCRIPTION

[0015] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0016] like Figure 1 As shown, this embodiment provides a technical solution: a laying hen breeding environment monitoring system based on the Internet of Things, including a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module; The data acquisition module is used to collect real-time data information of the laying hen breeding process in the chicken house; The database module is used to store historical data information and standard data information of the chicken house; It should be noted that the standard data information is a list of parameter information such as a pre-prepared standard parameter range and the position distribution data of the air inlet and outlet of the laying hen house and the area data of the chicken house.

[0017] The preprocessing module is used to receive historical data information and standard data information, analyze the historical data information in combination with the standard data information, and divide the chicken house into multiple monitoring areas using preset rules; The data processing module is used to receive real-time data information, historical data information and standard data information. It first analyzes the historical data information, matches and sets the breeding environment parameters of the chicken house, and generates a basic parameter report; then it monitors the environmental parameters in each monitoring area in real time; it analyzes the real-time data information in combination with the standard data information to determine the abnormality of the environmental parameters in the monitoring area, performs real-time control, and generates a control report; The data output module is used to receive and output basic parameter reports and control reports.

[0018] The system first analyzes the temperature and humidity distribution status in the air duct direction of the chicken house based on historical data, divides the chicken house into areas while ensuring that the temperature and humidity differences in the same monitoring area are relatively stable, and then monitors each area in real time; when there are abnormal environmental parameters in the monitoring area of ​​the chicken house, the control needs of the chicken house are inferred according to the location distribution of the abnormal area, and then the environmental parameters of the entire chicken house or the local area are targeted. Control, thereby reducing energy consumption in the operation of the chicken house, improving the effective utilization of energy, improving the efficiency of laying hen farming and promoting the sustainable development of the farming industry.

[0019] Among them, the historical data information includes historical temperature data, and the specific processing process of the preprocessing module is: Import standard data information and read the air inlet and outlet positions; Connecting the air inlet and the air outlet to obtain the first basic direction of air flow; Read the historical temperature data in the chicken house and divide the chicken house into areas using preset rules; The default rules are: Obtain historical temperature data of positions of different sizes from the air inlet in the first basic direction, which are Te1, Te2, Te3, ..., Ten respectively; Calculate the correlation r of temperature data at different size positions. The specific calculation process is: ; Where Tei is the temperature data of the i-th point, Lei is the distance between the i-th point and the air inlet, is the average value of the distance between each point and the air inlet, is the average temperature of each point; when >When the threshold Q1 is preset, the chicken house is divided into multiple monitoring areas by equally spaced divisions in the first basic direction using the preset size; when When the temperature is less than or equal to the preset threshold value Q1, the least square method is used to calculate the linear regression equation Te=β1+β2·Le when the temperature and distance size are in the first state and the second state of the chicken house respectively; The first state is that the chicken house is in hot air demand, and the second state is that the chicken house is in cold air demand; The specific calculation process is: ; ; Calculate the distance size difference Lr1 when the temperature difference of the chicken house is in the first state and is the preset threshold value Tr; Then calculate the distance size difference Lr2 when the temperature difference is the preset threshold value Tr when the chicken house is in the second state; use = (Lr1+Lr2) / 2 to calculate the segmentation spacing; by The chicken house is divided into a plurality of monitoring areas by equally dividing the chicken house in a first basic direction for spacing.

[0020] Due to the different stocking densities in the chicken house and the energy loss when the equipment delivers heating or cooling air to the chicken house, there are certain temperature and humidity differences in the air duct direction of the chicken house. This embodiment collects temperature data at different points in the air duct direction, analyzes the degree of difference and correlation of the temperature on the air duct, and then divides the chicken house into areas while ensuring that the temperature difference in the same monitoring area is small. Targeted temperature and humidity monitoring and control are carried out for different monitoring areas, which can reduce energy loss in the operation of the chicken farm and reduce the operating costs of the chicken farm.

[0021] Furthermore, the historical data information includes historical humidity data, and the monitoring area segmentation process also includes: Import historical data to obtain historical humidity data for each monitoring area in the chicken house under target conditions; Randomly obtain multiple groups of historical humidity data, each group of historical humidity data consists of two, and the two historical humidity data come from two adjacent monitoring areas respectively; Calculate the difference E1 of the two humidity data. The specific calculation process is: ; Among them, RH1 and RH2 are the historical humidity data of two adjacent monitoring areas; When E2>preset threshold Q2, the group of data is marked; Calculate the difference of each set of historical humidity data to obtain the marked data; Count the number of labeled data N1; Then count the number of groups N2 of randomly acquired historical humidity data; Calculate the mark ratio J. The specific calculation process is: J = N1 / N2; When the target state is the first state, obtaining the first mark proportion J1; When the target state is the second state, obtain the second mark proportion J2; When the first mark ratio J1> the preset threshold Q3 or the second mark ratio J2> the preset threshold Q3, the preset threshold Tr of the temperature difference is lowered and the segmentation interval is recalculated. .

[0022] Further analysis is conducted on the humidity in adjacent monitoring areas. When the humidity difference in adjacent monitoring areas is large and the large difference is common, the monitoring areas are re-divided by lowering the preset value of the temperature difference to prevent the system judgment error caused by excessive temperature and humidity differences in the same monitoring area, so as to achieve the purpose of stable monitoring of temperature and humidity in a unified monitoring area and improve the scientificity and rigor of the system use.

[0023] Among them, historical data information includes historical egg production data. The specific generation process of the basic parameter report is as follows: Read historical egg production data under different environmental parameters; Establish the mapping relationship between different environmental parameters and egg production; Rank the different environmental parameters according to egg production from high to low; The environmental parameters with the highest egg production were selected as pre-selection parameters; Read the pre-selected parameters and import the egg production data within the preset time period under the pre-selected parameters; A rectangular coordinate system is established with egg production as the ordinate and time as the abscissa to obtain a first type of curve graph; Randomly select a base point and multiple verification points on the first type of curve graph; Calculate the slope K between the base point and each verification point respectively. The specific calculation process is: K = (An-Ai) / ∆ti; Among them, An is the egg production data at any verification point, Ai is the egg production data at the basic point, and ∆ti is the difference between the time at the verification point and the time at the basic point; Get the slope between the base point and each verification point, which are K1, K2, K3, ..., Kn; Calculate the dispersion degree E3 between multiple slopes. The specific calculation process is: ; Among them, Ki is the slope between the ith verification point and the base point, is the average of multiple slopes; When the dispersion degree E3 is less than the preset threshold value Q4, the pre-selected parameters are marked to generate basic parameters, and a basic parameter report is generated; When the dispersion degree E3 ≥ the preset threshold value Q4, the data of the preselected parameters are cleared.

[0024] When this embodiment is implemented, a mapping relationship between different environmental parameters and egg production is first established, and then the environmental parameter information with the highest egg production is selected; the parameter is monitored, and a curve graph of egg production and time under the environmental parameter is established; the slopes of different points on the curve graph and the fixed points of the curve graph are analyzed, and the fluctuation of the curve is analyzed. When multiple slopes are relatively stable, it means that the egg production of laying hens is relatively stable under the environmental parameters and can be used; when multiple slopes fluctuate greatly, it means that the egg production of laying hens is unstable under the environmental parameters, and the environmental parameters are not suitable for the long-term life of laying hens, so they are abandoned; based on the analysis of historical data, the initial environmental monitoring parameters are matched to the system to ensure the normal operation of the chicken house.

[0025] The above scheme randomly selects verification points in real time, and there is a situation where the verification points are selected more concentratedly; when the curve on the graph fluctuates greatly, but some areas are relatively stable, the verification points are selected in the stable area of ​​the graph, and the difference with the basic point data is small, which will cause system misjudgment. To solve this problem, the following further technical solutions are proposed: Furthermore, the verification point selection process also includes: Read the horizontal coordinates of multiple verification points, which are B1, B2, B3, ..., Bn; Get the average value of the horizontal coordinates of multiple verification points ; Randomly select N3 points from multiple verification points to generate detection points; Calculate the horizontal coordinate and average value at the detection point The deviation degree E4, the specific calculation process is: ; in, is the horizontal coordinate of any detection point; When the deviation E4 is less than the preset threshold Q5, it is marked as abnormal data; Calculate the horizontal coordinates and average values ​​at multiple detection points respectively The degree of deviation is used to count the number of abnormal data N4; Use the formula E5=N4 / N3 to calculate the first abnormal data ratio; When the first abnormal data proportion E5>the preset threshold Q6, the verification point is reselected.

[0026] This scheme selects several verification points from multiple verification points as detection points for verification, and calculates the average value of the horizontal coordinates of multiple detection points; when the horizontal coordinate value of any detection point is slightly different from the calculated average value, it means that the verification points are selected more concentratedly, the horizontal coordinates of multiple verification points are slightly different, the data is not representative, and reselection is required; thereby reducing the system's misjudgment rate and improving the system's judgment accuracy and performance.

[0027] Among them, the real-time data information includes real-time environmental data, and the generation process of the control report is as follows: Collect real-time environmental data in each monitoring area; The real-time environment data includes first type environment data Ce; Import standard data information and obtain the monitoring range (C1, C2) of the first type of environmental data Ce; When the first type of environmental data When the monitoring area is marked, the abnormal area is obtained; Collect the first type of environmental data Ce of each monitoring area to obtain the number N5 of abnormal areas; Count the number of monitoring areas N6; Use the formula E6=N5 / N6 to calculate the second anomaly ratio; When the second abnormal proportion E6> the preset threshold Q7, a control report on the overall control of the chicken house is generated; When the second abnormality proportion E6 ≤ the preset threshold Q7, a control report for local control of the monitoring area is generated.

[0028] During the specific implementation of this embodiment, each environmental parameter in each monitoring area is monitored in real time, and each environmental parameter is compared with the standard monitoring range; when an environmental parameter is abnormal, it is marked as an abnormal area, and the number and proportion of abnormal areas are counted; when there are many abnormal areas, the chicken house environment control equipment is turned on to regulate the entire chicken house; when there are fewer abnormal areas, it means that the local area needs to be regulated, and targeted regulation is carried out to reduce energy consumption during chicken house environment regulation and improve the effective utilization rate of capacity.

[0029] When there are many abnormal areas and they are concentrated, regulating the entire chicken house to optimize the environmental data of the abnormal areas will still waste a lot of resources. In order to further optimize the regulation method and reduce energy consumption, the following further solutions are proposed: The real-time data information also includes real-time size data and real-time image data. When the second abnormality proportion E6> the preset threshold value Q7, the generation process of the control report also includes: Collect real-time image data in the chicken house and obtain real-time picture data of the chicken house; Mark each abnormal area and obtain the geometric center points of multiple abnormal areas; Create a circular area, which includes all geometric center points, and at least three geometric center points are located on the contour line of the circular area; The radius Re of the circular area to be collected; Read standard data information to obtain the area size Si of the chicken house; Calculate the abnormal area ratio E7. The specific calculation process is as follows: ; When the abnormal area ratio E7 is less than the preset threshold Q8, a control report for local control of the monitoring area is generated.

[0030] This scheme first obtains the geometric center of each monitoring area, and establishes a circular area containing all geometric centers to replace all abnormal areas; since there are some normal areas in the circular area, in order to further reduce the error, the circular area is established with reference to the geometric center of each monitoring area, and the area of ​​the monitoring area outside the circular area is discarded to balance the error caused by the normal area; by calculating the proportion of the circular area area to the chicken house area, the concentration of multiple abnormal areas is determined; when the proportion is large, it indicates that the distribution of multiple abnormal areas is relatively scattered and needs to be regulated as a whole; when the proportion is small, it means that the distribution of multiple abnormal areas is relatively concentrated, which can be improved and optimized through local regulation; it can reduce energy consumption and improve the effective utilization of energy.

[0031] Furthermore, the real-time data also includes real-time weight data and real-time egg production data. When the first type of environmental data Ce∈(C1, C2), the generation process of the control report also includes: Real-time egg production data collected continuously and in equal time periods; Randomly extract multiple groups of sample data; Calculate the increase in egg production G1. The specific calculation process is as follows: G1=(Ma-Mb) / (Ta-Tb); Wherein, Ma is the egg production of the chicken house at time point Ta, Mb is the egg production of the chicken house at time point Tb, and Ta>Tb; When G1<0, a control report for increasing the temperature of the chicken house is generated; When G1≥0, the total mass of the egg production in the chicken house at the corresponding time point is used to calculate the average egg weight increase G2. The specific calculation process is: ; Among them, Mu1 is the total mass of eggs produced in the chicken house at time point Ta, and Mu2 is the total mass of eggs produced in the chicken house at time point Tb; When the average egg weight increase G2 is less than 0, a control report for lowering the temperature of the chicken house is generated.

[0032] Under high temperature environment, the egg production and eggshell quality of laying hens will decrease significantly; laying hens are relatively cold-resistant, but too low temperature will lead to slow activity of laying hens, large feed loss and decreased production performance; this embodiment monitors the egg production when the chicken house is in normal environmental parameters; the temperature suitability is inferred by the egg production of laying hens in different periods of time; when the egg production of laying hens decreases, it means that the standard monitoring temperature is lower than the most suitable temperature for this type of laying hen, and the standard detection temperature is increased; then the individual quality of eggs laid by laying hens in different time periods is compared and analyzed; when the average weight of a single egg decreases, it means that the standard monitoring temperature is higher than the most suitable temperature for this type of laying hen, and the standard detection temperature is lowered to optimize the entire monitoring system; improve the use effect of the system, improve the quality of eggs, and increase the market competitiveness of eggs.

[0033] The system also includes a control module, which is used to receive a control report and take a control action.

[0034] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0035] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0036] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A laying hen breeding environment monitoring system based on the Internet of Things, characterized in that: It includes data acquisition module, preprocessing module, database module, data processing module and data output module; The data acquisition module is used to collect real-time data information of the laying hen breeding process in the chicken house; The database module is used to store historical data information and standard data information of the chicken house; The preprocessing module is used to receive historical data information and standard data information, analyze the historical data information in combination with the standard data information, and divide the chicken house into multiple monitoring areas using preset rules; The data processing module is used to receive real-time data information, historical data information and standard data information. It first analyzes the historical data information, matches and sets the breeding environment parameters of the chicken house, and generates a basic parameter report; then it monitors the environmental parameters in each monitoring area in real time; it analyzes the real-time data information in combination with the standard data information to determine the abnormality of the environmental parameters in the monitoring area, performs real-time control, and generates a control report; The data output module is used to receive and output basic parameter reports and control reports.

2. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 1 is characterized in that: The historical data information includes historical temperature data, and the specific processing process of the preprocessing module is: Import standard data information and read the air inlet and outlet positions; Connecting the air inlet and the air outlet to obtain the first basic direction of air flow; Read the historical temperature data in the chicken house and divide the chicken house into areas using preset rules; The default rules are: Obtain historical temperature data of positions of different sizes from the air inlet in the first basic direction, which are Te1, Te2, Te3, ..., Ten respectively; Calculate the correlation r of temperature data at different size positions. The specific calculation process is: ； Where Tei is the temperature data of the i-th point, Lei is the distance between the i-th point and the air inlet, is the average value of the distance between each point and the air inlet, is the average temperature of each point; when >When the threshold Q1 is preset, the chicken house is divided into multiple monitoring areas by equally spaced divisions in the first basic direction using the preset size; when When the temperature is less than or equal to the preset threshold value Q1, the least square method is used to calculate the linear regression equation Te=β1+β2·Le when the temperature and distance size are in the first state and the second state of the chicken house respectively; The first state is that the chicken house is in hot air demand, and the second state is that the chicken house is in cold air demand; The specific calculation process is: ； ； Calculate the distance size difference Lr1 when the temperature difference of the chicken house is in the first state and is the preset threshold value Tr; Then calculate the distance size difference Lr2 when the temperature difference is the preset threshold value Tr when the chicken house is in the second state; use = (Lr1+Lr2) / 2 to calculate the segmentation spacing; by The chicken house is divided into a plurality of monitoring areas by equally dividing the chicken house in a first basic direction for spacing.

3. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 2 is characterized in that: The historical data information includes historical humidity data, and the segmentation process of the monitoring area further includes: Import historical data to obtain historical humidity data for each monitoring area in the chicken house under target conditions; Randomly obtain multiple groups of historical humidity data, each group of historical humidity data consists of two, and the two historical humidity data come from two adjacent monitoring areas respectively; Calculate the difference E1 of the two humidity data. The specific calculation process is: ； Among them, RH1 and RH2 are the historical humidity data of two adjacent monitoring areas; When E2>preset threshold Q2, the group of data is marked; Calculate the difference of each set of historical humidity data to obtain the marked data; Count the number of labeled data N1; Then count the number of groups N2 of randomly acquired historical humidity data; Calculate the mark ratio J. The specific calculation process is: J = N1 / N2; When the target state is the first state, obtaining the first mark proportion J1; When the target state is the second state, obtain the second mark proportion J2; When the first mark ratio J1> the preset threshold Q3 or the second mark ratio J2> the preset threshold Q3, the preset threshold Tr of the temperature difference is lowered and the segmentation interval is recalculated. .

4. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 1 is characterized in that: The historical data information includes historical egg production data, and the specific generation process of the basic parameter report is as follows: Read historical egg production data under different environmental parameters; Establish the mapping relationship between different environmental parameters and egg production; Rank the different environmental parameters according to egg production from high to low; The environmental parameters with the highest egg production were selected as pre-selection parameters; Read the pre-selected parameters and import the egg production data within the preset time period under the pre-selected parameters; A rectangular coordinate system is established with egg production as the ordinate and time as the abscissa to obtain a first type of curve graph; Randomly select a base point and multiple verification points on the first type of curve graph; Calculate the slope K between the base point and each verification point respectively. The specific calculation process is: K = (An-Ai) / ∆ti; Among them, An is the egg production data at any verification point, Ai is the egg production data at the basic point, and ∆ti is the difference between the time at the verification point and the time at the basic point; Obtain the slope between the base point and each verification point, which are K1, K2, K3, ..., Kn respectively; Calculate the dispersion degree E3 between multiple slopes. The specific calculation process is: ； Among them, Ki is the slope between the ith verification point and the base point, is the average of multiple slopes; When the dispersion degree E3 is less than the preset threshold value Q4, the pre-selected parameters are marked to generate basic parameters, and a basic parameter report is generated; When the dispersion degree E3 ≥ the preset threshold value Q4, the data of the preselected parameters are cleared.

5. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 4 is characterized in that: The verification point selection process further includes: Read the horizontal coordinates of multiple verification points, which are B1, B2, B3, ..., Bn; Get the average value of the horizontal coordinates of multiple verification points ; Randomly select N3 points from multiple verification points to generate detection points; Calculate the horizontal coordinate and average value at the detection point The deviation degree E4, the specific calculation process is: ； in, is the horizontal coordinate of any detection point; When the deviation E4 is less than the preset threshold Q5, it is marked as abnormal data; Calculate the horizontal coordinates and average values ​​of the multiple detection points respectively The degree of deviation is used to count the number of abnormal data N4; Use the formula E5=N4 / N3 to calculate the first abnormal data ratio; When the first abnormal data proportion E5>the preset threshold Q6, the verification point is reselected.

6. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 1 is characterized in that: The real-time data information includes real-time environmental data, and the generation process of the control report is as follows: Collect real-time environmental data in each monitoring area; The real-time environment data includes first type environment data Ce; Import standard data information and obtain the monitoring range (C1, C2) of the first type of environmental data Ce; When the first type of environmental data When the monitoring area is marked, the abnormal area is obtained; Collect the first type of environmental data Ce of each monitoring area to obtain the number N5 of abnormal areas; Count the number of monitoring areas N6; Use the formula E6=N5 / N6 to calculate the second anomaly ratio; When the second abnormal proportion E6> the preset threshold Q7, a control report on the overall control of the chicken house is generated; When the second abnormality proportion E6 ≤ the preset threshold Q7, a control report for local control of the monitoring area is generated.

7. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 6 is characterized in that: The real-time data information also includes real-time size data and real-time image data. When the second abnormality proportion E6>preset threshold Q7, the generation process of the control report also includes: Collect real-time image data in the chicken house and obtain real-time picture data of the chicken house; Mark each abnormal area and obtain the geometric center points of multiple abnormal areas; Creating a circular area, wherein the circular area includes all geometric center points, and at least three geometric center points are located on the contour line of the circular area; The radius Re of the circular area to be collected; Read standard data information to obtain the area size Si of the chicken house; Calculate the abnormal area ratio E7. The specific calculation process is as follows: ； When the abnormal area ratio E7 is less than the preset threshold Q8, a control report for local control of the monitoring area is generated.

8. The layer chicken breeding environment monitoring system based on the Internet of Things according to claim 6 is characterized in that: The real-time data also includes real-time weight data and real-time egg production data. When the first type of environmental data Ce∈(C1, C2), the generation process of the control report also includes: Real-time egg production data collected continuously and in equal time periods; Randomly extract multiple groups of sample data; Calculate the increase in egg production G1. The specific calculation process is as follows: G1=(Ma-Mb) / (Ta-Tb); Wherein, Ma is the egg production of the chicken house at time point Ta, Mb is the egg production of the chicken house at time point Tb, and Ta>Tb; When G1<0, a control report for increasing the temperature of the chicken house is generated; When G1≥0, the total mass of the egg production in the chicken house at the corresponding time point is used to calculate the average egg weight increase G2. The specific calculation process is: ； Among them, Mu1 is the total mass of eggs produced in the chicken house at time point Ta, and Mu2 is the total mass of eggs produced in the chicken house at time point Tb; When the average egg weight increase G2 is less than 0, a control report for lowering the temperature of the chicken house is generated.

9. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 1, characterized in that: It also includes a control module, which is used to receive a control report and take a control action.

Images