An Internet of Things-based monitoring system for the laying hen breeding environment

Abstract

The present invention discloses a monitoring system for the laying hen breeding environment based on the Internet of Things, which includes a data acquisition module, a preprocessing module, a database module, a data processing module, and a data output module; the preprocessing module is used to divide the chicken coop into multiple monitoring areas; the data processing module is used to match and set the breeding environment parameters of the chicken coop, and judge the abnormality of the environmental parameters in the monitoring area for real-time control; the present invention first analyzes the temperature and humidity distribution state in the chicken coop according to historical data, divides the chicken coop into areas, and then monitors each area in real time; when there are abnormal environmental parameters in the monitoring area of the chicken coop, the regulation requirements of the chicken coop are inferred according to the location distribution of the abnormal area, and then targeted regulation is carried out, so as to reduce the energy consumption in the operation process of the chicken coop, improve the effective utilization degree of energy, improve the laying hen breeding benefit and promote the sustainable development of the breeding industry.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management and control, and particularly to a monitoring system for the laying hen breeding environment based on the Internet of Things. Background Art

[0002] In laying hen breeding, it is extremely necessary and significant to monitor the environment; appropriate temperature, humidity, light, and air quality are the key factors for the healthy growth and high-efficiency egg production of laying hens; through environmental monitoring, adverse conditions can be detected and adjusted in a timely manner, preventing the occurrence of diseases, improving the production performance of laying hens and the quality of eggs; at the same time, environmental monitoring also helps to save energy and reduce emissions and optimize breeding costs;

[0003] Now 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; due to the large area of general large-scale farms and the differences in the layout of chicken houses in different positions, the laying hen density is large in some areas, and 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 in different breeding densities and breeding positions in the chicken house; now during the production and operation of chicken farms, when there are abnormalities in the environment in the chicken house, the regulation equipment will be started to adjust and improve the environmental parameters of the entire chicken house, but in fact, only some positions 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 amount of energy consumption and make the effective utilization rate of energy low; to solve this problem, a monitoring system for the laying hen breeding environment based on the Internet of Things is proposed. Summary of the Invention

[0004] 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 and having a certain impact on the use of the safety protection system, and provides a monitoring system for the laying hen breeding environment based on the Internet of Things.

[0005] 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;

[0006] The data acquisition module is used to collect real-time data information during the laying hen breeding process in the chicken house;

[0007] The database module is used to store the historical data information and standard data information of the chicken house;

[0008] The preprocessing module is used to receive the historical data information and standard data information, analyze the historical data information in combination with the standard data information, and use preset rules to divide the chicken house into regions, dividing the chicken house into multiple monitoring regions;

[0009] The data processing module is used to receive real-time data information, historical data information, and standard data information. First, it analyzes the historical data information to match and set 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 combines the standard data information to analyze and judge the abnormality of the environmental parameters in the monitoring area for the real-time data information, conducts real-time control, and generates a control report.

[0010] The data output module is used to receive and output the basic parameter report and the control report.

[0011] Preferably, the historical data information includes historical temperature data, and the specific processing process of the preprocessing module is as follows:

[0012] Import the standard data information, and read the positions of the air inlet end and the air outlet end.

[0013] Connect the air inlet end and the air outlet end to obtain the first basic direction of air flow.

[0014] Read the historical temperature data in the chicken house, and divide the chicken house into areas using a preset rule.

[0015] The preset rule is:

[0016] Obtain the historical temperature data at different positions from the air inlet in the first basic direction, which are Te1, Te2, Te3,..., Ten respectively.

[0017] Calculate the correlation r of the temperature data at different positions. The specific calculation process is as follows:

[0018] ;

[0019] Where Tei is the temperature data at the i-th point, Lei is the size from the i-th point to the air inlet, is the average value of the sizes from each point to the air inlet, is the average value of the temperatures at each point;

[0020] When > the preset threshold Q1, use a preset size to equally divide the chicken house at equal intervals in the first basic direction to divide the chicken house into multiple monitoring areas;

[0021] When ≤ the preset threshold Q1, use the least squares method to respectively deduce the linear regression equations Tei = β1 + β2·Lei when the chicken house is in the first state and the second state for temperature and distance size.

[0022] 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.

[0023] The specific calculation process is as follows:

[0024] ;

[0025] ;

[0026] Calculate the distance dimension difference Lr1 when the temperature difference in the chicken coop is the preset threshold Tr in the first state;

[0027] Then calculate the distance dimension difference Lr2 when the temperature difference in the chicken coop is the preset threshold Tr in the second state;

[0028] Use =(Lr1 + Lr2) / 2 to calculate the segmentation spacing;

[0029] With as the spacing, equally divide the chicken coop in the first basic direction to divide the chicken coop into multiple monitoring areas.

[0030] Preferably, the historical data information includes historical humidity data, and the segmentation process of the monitoring area further includes:

[0031] Import historical data to obtain the historical humidity data in each monitoring area when the chicken coop is in the target state;

[0032] Randomly obtain multiple groups of historical humidity data, each group of historical humidity data has two, and the two historical humidity data come from adjacent two monitoring areas respectively;

[0033] Calculate the difference degree E1 of the two humidity data, and the specific calculation process is:

[0034] ;

[0035] Among them, RH1 and RH2 are the historical humidity data of adjacent two monitoring areas respectively;

[0036] When E1 > the preset threshold Q2, mark this group of data;

[0037] Calculate the difference degree of each group of historical humidity data respectively to obtain the marked data;

[0038] Count the number N1 of the marked data;

[0039] Then count the number of groups N2 of the randomly obtained historical humidity data;

[0040] Calculate the marking ratio J, and the specific calculation process is:

[0041] J = N1 / N2;

[0042] When the target state is the first state, obtain the first marking ratio J1;

[0043] When the target state is the second state, obtain the second marker ratio J2;

[0044] When the first marker ratio J1 > the preset threshold Q3 or the second marker ratio J2 > the preset threshold Q3, reduce the preset threshold Tr of the temperature difference and recalculate the segmentation distance .

[0045] Preferably, the historical data information includes historical egg production data, and the specific generation process of the basic parameter report is as follows:

[0046] Read the historical egg production data under different environmental parameters;

[0047] Establish the mapping relationship between different environmental parameters and egg production;

[0048] Sort different environmental parameters according to the egg production from high to low;

[0049] Select the environmental parameter with the highest egg production as the preselected parameter;

[0050] Read the preselected parameter and import the egg production data within a preset time period under the preselected parameter;

[0051] Establish a rectangular coordinate system with egg production as the vertical coordinate and time as the horizontal coordinate to obtain the first type of curve graph;

[0052] Randomly select a basic point and multiple verification points on the first type of curve graph;

[0053] Calculate the slope K between the basic point and each verification point respectively, and the specific calculation process is as follows:

[0054] K = (An - Ai) / ∆ti;

[0055] Where 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 in time between the verification point and the basic point;

[0056] Obtain the slopes between the basic point and each verification point, which are K1, K2, K3,..., Kn respectively;

[0057] Calculate the dispersion degree E3 between multiple slopes, and the specific calculation process is as follows:

[0058] ;

[0059] Where Ki is the slope between the i-th verification point and the basic point, is the average value of multiple slopes;

[0060] When the dispersion degree E3 < the preset threshold Q4, label the preselected parameters to generate basic parameters and generate a basic parameter report;

[0061] When the dispersion degree E3 ≥ the preset threshold Q4, clear the data of the preselected parameters.

[0062] Preferably, the selection process of the verification points further includes:

[0063] Read the abscissas of multiple verification points, which are B1, B2, B3,..., Bn respectively;

[0064] Obtain the average value of the abscissas of multiple verification points ;

[0065] Randomly select N3 from multiple verification points to generate detection points;

[0066] Calculate the deviation degree E4 between the abscissa at the detection point and the average value The specific calculation process is as follows:

[0067] ;

[0068] Among them, is the abscissa at any detection point;

[0069] When the deviation degree E4 < the preset threshold Q5, mark it as abnormal data;

[0070] Calculate the deviation degrees between the abscissas at the multiple detection points and the average value respectively, and count the number N4 of abnormal data;

[0071] Use the formula E5 = N4 / N3 to calculate the proportion of the first abnormal data;

[0072] When the proportion of the first abnormal data E5 > the preset threshold Q6, reselect the verification points.

[0073] Preferably, the real-time data information includes real-time environmental data, and the generation process of the control report is as follows:

[0074] Collect the real-time environmental data in each monitoring area;

[0075] The real-time environmental data includes the first type of environmental data Ce;

[0076] Import the standard data information and obtain the monitoring range (C1, C2) of the first type of environmental data Ce;

[0077] When the first type of environmental data , mark the monitoring area to obtain an abnormal area;

[0078] Collect the first type of environmental data Ce for each monitored area, and obtain the number N5 of abnormal areas;

[0079] Count the number N6 of monitored areas;

[0080] Calculate the second abnormal proportion using the formula E6 = N5 / N6;

[0081] When the second abnormal proportion E6 > the preset threshold Q7, generate a control report for the overall control of the chicken coop;

[0082] When the second abnormal proportion E6 ≤ the preset threshold Q7, generate a control report for the local control of the monitored area.

[0083] Preferably, the real-time data information further includes real-time dimension data and real-time image data. When the second abnormal proportion E6 > the preset threshold Q7, the generation process of the control report further includes:

[0084] Collect the real-time image data in the chicken coop to obtain the real-time picture data of the chicken coop;

[0085] Mark each abnormal area and obtain the geometric center points of multiple abnormal areas;

[0086] Make a circular area that includes all the geometric center points and at least three geometric center points are located on the contour line of the circular area;

[0087] Collect the size radius Re of the circular area;

[0088] Read the standard data information to obtain the area size Si of the chicken coop;

[0089] Calculate the abnormal area proportion E7. The specific calculation process is:

[0090] ;

[0091] When the abnormal area proportion E7 < the preset threshold Q8, generate a control report for the local control of the monitored area.

[0092] Preferably, the real-time data further 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 further includes:

[0093] Collect the real-time egg production data at continuous equal time intervals;

[0094] Randomly select multiple groups of sample data;

[0095] Calculate the egg production increase rate G1. The specific calculation process is

[0096] G1 = (Ma - Mb) / (Ta - Tb);

[0097] Among them, Ma is the egg production of the chicken coop at time Ta, Mb is the egg production of the chicken coop at time Tb, and Ta > Tb;

[0098] When G1 < 0, a control report for raising the temperature of the chicken coop is generated;

[0099] When G1 ≥ 0, calculate the average egg weight increase G2 for the total mass of the egg production of the chicken coop at the corresponding time point. The specific calculation process is as follows:

[0100] ;

[0101] Among them, Mu1 is the total mass of the egg production of the chicken coop at time Ta, and Mu2 is the total mass of the egg production of the chicken coop at time Tb;

[0102] When the average egg weight increase G2 < 0, a control report for lowering the temperature of the chicken coop is generated.

[0103] The system further includes a control module, and the control module is used to receive the control report and make a control action.

[0104] The present invention has the following advantages compared with the prior art: The system first analyzes the temperature and humidity distribution state in the air duct direction in the chicken coop according to historical data, divides the chicken coop into regions while ensuring that the temperature and humidity difference in the same monitoring area is relatively stable, and then conducts real-time monitoring on each region separately; when there are abnormal environmental parameters in the monitoring area in the chicken coop, infer the regulation requirements of the chicken coop according to the position distribution of the abnormal area, and then selectively regulate the environmental parameters of the entire chicken coop or the environmental parameters of a local area, so as to reduce the energy consumption in the operation process of the chicken coop, improve the effective utilization degree of energy, improve the breeding efficiency of laying hens and promote the sustainable development of the breeding industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 is the overall module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0106] The following describes the embodiments of the present invention in detail. The embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0107] As Figure 1 shown, this embodiment provides a technical solution: An egg-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;

[0108] The data acquisition module is used to collect real-time data information during the breeding process of laying hens in the chicken house;

[0109] The database module is used to store historical data information and standard data information of the chicken house;

[0110] It should be noted that the standard data information is a list of parameter information such as the prefabricated standard parameter range in advance, the position distribution data of the air inlet end and the air outlet end of the laying hen house, and the area data of the chicken house.

[0111] 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 use preset rules to divide the chicken house into regions, dividing the chicken house into multiple monitoring regions;

[0112] The data processing module is used to receive real-time data information, historical data information and standard data information. First, it 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 region in real time; combines the standard data information to analyze and judge the abnormality of the environmental parameters in the monitoring region for real-time control, and generates a control report;

[0113] The data output module is used to receive and output the basic parameter report and the control report.

[0114] 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 regions on the premise of ensuring that the temperature and humidity difference in the same monitoring region is relatively stable, and then monitors each region in real time; when there are abnormal environmental parameters in the monitoring region of the chicken house, it infers the regulation requirements of the chicken house according to the position distribution of the abnormal region, and then selectively regulates the environmental parameters of the whole chicken house or the environmental parameters of the local region, so as to reduce the energy consumption in the operation process of the chicken house, improve the effective utilization degree of energy, improve the breeding efficiency of laying hens and promote the sustainable development of the breeding industry.

[0115] Among them, the historical data information includes historical temperature data, and the specific processing process of the preprocessing module is as follows:

[0116] Import the standard data information and read the positions of the air inlet end and the air outlet end;

[0117] Connect the air inlet end and the air outlet end to obtain the first basic direction of air flow;

[0118] Read the historical temperature data in the chicken house and use preset rules to divide the chicken house into regions;

[0119] The preset rules are as follows:

[0120] Obtain historical temperature data at different size positions from the air inlet in the first basic direction, which are Te1, Te2, Te3, ..., Ten respectively;

[0121] Calculate the correlation r of the temperature data at different size positions. The specific calculation process is as follows:

[0122] ;

[0123] where Tei is the temperature data at the i-th point, Lei is the size of the i-th point from the air inlet, is the average value of the sizes of each point from the air inlet, is the average value of the temperatures of each point;

[0124] When > the preset threshold Q1, use the preset size to equally divide the chicken house in the first basic direction to divide the chicken house into multiple monitoring areas;

[0125] When ≤ the preset threshold Q1, use the least squares method to respectively deduce the linear regression equations Tei = β1 + β2·Lei for the temperature and distance size when the chicken house is in the first state and the second state;

[0126] 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;

[0127] The specific deduction process is as follows:

[0128] ;

[0129] ;

[0130] Calculate the distance size difference Lr1 when the temperature difference is the preset threshold Tr when the chicken house is in the first state;

[0131] Then calculate the distance size difference Lr2 when the temperature difference is the preset threshold Tr when the chicken house is in the second state;

[0132] Use =(Lr1 + Lr2) / 2 to calculate the division spacing;

[0133] With as the spacing, equally divide the chicken house in the first basic direction to divide the chicken house into multiple monitoring areas.

[0134] Due to the different breeding densities in the chicken house and the energy loss when the equipment conveys warm air or cold air into the chicken house, there are certain temperature and humidity differences in the direction of the chicken house air duct; in this embodiment, the temperature data at different points in the direction of the air duct are collected, the difference degree and correlation of the temperature on the air duct are analyzed, and then the chicken house is divided into regions on the premise of ensuring a small temperature difference in the same monitoring area, and targeted temperature and humidity monitoring and control are carried out for different monitoring regions, which can reduce the energy loss in the operation process of the chicken farm and reduce the operation cost of the chicken farm.

[0135] Further, the historical data information includes historical humidity data, and the process of dividing the monitoring area further includes:

[0136] Import historical data to obtain the historical humidity data in each monitoring area when the chicken house is in the target state;

[0137] Randomly obtain multiple groups of historical humidity data, with two historical humidity data in each group, and the two historical humidity data come from adjacent two monitoring areas respectively;

[0138] Calculate the difference degree E1 of the two humidity data, and the specific calculation process is:

[0139] ;

[0140] where RH1 and RH2 are the historical humidity data of adjacent two monitoring areas respectively;

[0141] When E1 > the preset threshold Q2, mark this group of data;

[0142] Calculate the difference degree of each group of historical humidity data respectively to obtain the marked data;

[0143] Count the number N1 of the marked data;

[0144] Then count the number of groups N2 of the randomly obtained historical humidity data;

[0145] Calculate the marking ratio J, and the specific calculation process is:

[0146] J = N1 / N2;

[0147] When the target state is the first state, obtain the first marking ratio J1;

[0148] When the target state is the second state, obtain the second marking ratio J2;

[0149] When the first marking ratio J1 > the preset threshold Q3 or the second marking ratio J2 > the preset threshold Q3, reduce the preset threshold Tr of the temperature difference and recalculate the segmentation distance .

[0150] 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.

[0151] Among them, historical data information includes historical egg production data. The specific generation process of the basic parameter report is as follows:

[0152] Read historical egg production data under different environmental parameters;

[0153] Establish the mapping relationship between different environmental parameters and egg production;

[0154] Rank the different environmental parameters according to egg production from high to low;

[0155] The environmental parameters with the highest egg production were selected as pre-selection parameters;

[0156] Read the pre-selected parameters and import the egg production data within the preset time period under the pre-selected parameters;

[0157] 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;

[0158] Randomly select a base point and multiple verification points on the first type of curve graph;

[0159] Calculate the slope K between the base point and each verification point respectively. The specific calculation process is:

[0160] K = (An-Ai) / ∆ti;

[0161] 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;

[0162] Get the slope between the base point and each verification point, which are K1, K2, K3, ..., Kn;

[0163] Calculate the dispersion degree E3 between multiple slopes. The specific calculation process is:

[0164] ;

[0165] Among them, Ki is the slope between the ith verification point and the base point, is the average of multiple slopes;

[0166] 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;

[0167] When the dispersion degree E3≥the preset threshold Q4, clear the data of the preselected parameters.

[0168] In the specific implementation of this embodiment, first establish the mapping relationship between different environmental parameters and egg production, and then select the environmental parameter information with the largest egg production; monitor this parameter and establish a curve graph of egg production and time under this environmental parameter; analyze the slopes of different points on the curve graph with respect to the fixed point of the curve graph, and analyze the fluctuation of the curve. When multiple slopes are relatively stable, it indicates that the egg production of laying hens is relatively stable under this environmental parameter and can be used; when multiple slopes fluctuate greatly, it indicates that the egg production of laying hens is unstable under this environmental parameter, and this environmental parameter is not suitable for the long-term life of laying hens, so it is abandoned; according to the analysis of historical data, match the initial environmental monitoring parameters for the system to ensure the normal operation of the chicken house.

[0169] In the above scheme during the real-time process, the verification points are randomly selected, and there is a situation where the selection of verification points is relatively concentrated; when the curve on the curve graph fluctuates greatly, but some areas are relatively stable, if the verification points are selected in the stable area of the curve graph and the difference from the basic point data is small, it will cause misjudgment of the system. To solve this problem, the following further technical solution is proposed:

[0170] Further, the process of selecting verification points further includes:

[0171] Read the abscissas of multiple verification points, which are B1, B2, B3,..., Bn respectively;

[0172] Obtain the average value of the abscissas of multiple verification points ;

[0173] Randomly select N3 from multiple verification points to generate detection points;

[0174] Calculate the deviation degree E4 between the abscissa at the detection point and the average value , and the specific calculation process is as follows:

[0175] ;

[0176] Among them, is the abscissa at any detection point;

[0177] When the deviation degree E4<the preset threshold Q5, mark it as abnormal data;

[0178] Calculate the deviation degrees between the abscissas at multiple detection points and the average value respectively, and count the number N4 of abnormal data;

[0179] Use the formula E5 = N4 / N3 to calculate the proportion of the first abnormal data;

[0180] When the proportion E5 of the first abnormal data > the preset threshold Q6, reselect the verification points.

[0181] In this solution, several verification points are selected from multiple verification points as detection points for verification, and the average value of the abscissas of multiple detection points is obtained; when the abscissa value of any one detection point has a small difference from the obtained average value, it means that the selection of verification points is relatively concentrated, the abscissa differences of multiple verification points are small, and the data is not representative, and reselection is required; thereby reducing the false judgment rate of the system and improving the judgment accuracy and performance of the system.

[0182] Among them, the real-time data information includes real-time environmental data, and the generation process of the control report is as follows:

[0183] Collect the real-time environmental data in each monitoring area;

[0184] The real-time environmental data includes the first type of environmental data Ce;

[0185] Import the standard data information to obtain the monitoring range (C1, C2) of the first type of environmental data Ce;

[0186] When the first type of environmental data , mark the monitoring area to obtain the abnormal area;

[0187] Collect the first type of environmental data Ce of each monitoring area to obtain the number N5 of abnormal areas;

[0188] Count the number N6 of monitoring areas;

[0189] Use the formula E6 = N5 / N6 to calculate the second abnormal proportion;

[0190] When the second abnormal proportion E6 > the preset threshold Q7, generate a control report for the overall control of the chicken coop;

[0191] When the second abnormal proportion E6 ≤ the preset threshold Q7, generate a control report for the local control of the monitoring area.

[0192] When this embodiment is specifically implemented, 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 abnormal environmental parameter appears, it is marked as an abnormal area, and the proportion of the number of abnormal areas is counted; when there are many abnormal areas, turn on the chicken coop environmental control equipment to regulate the entire chicken coop; when there are few abnormal areas, it means that local areas need to be regulated, and targeted regulation is carried out to reduce the energy consumption during the chicken coop environmental regulation and improve the effective utilization rate of energy.

[0193] When there are many abnormal areas and they are concentrated, regulating the entire chicken coop to optimize the environmental data of the abnormal areas will still waste a large amount of resources. To further optimize the regulation method and reduce energy consumption, the following further solutions are proposed:

[0194] The real-time data information also includes real-time dimension data and real-time image data. When the second abnormal proportion E6 > the preset threshold Q7, the control report generation process further includes:

[0195] Collect the real-time image data in the chicken coop to obtain the real-time picture data of the chicken coop;

[0196] Mark each abnormal area and obtain the geometric center points of multiple abnormal areas;

[0197] Create a circular area that includes all geometric center points, and at least three geometric center points are located on the contour line of the circular area;

[0198] Collect the dimension radius Re of the circular area;

[0199] Read the standard data information to obtain the area dimension Si of the chicken coop;

[0200] Calculate the abnormal area proportion E7. The specific calculation process is:

[0201] ;

[0202] When the abnormal area proportion E7 < the preset threshold Q8, generate a control report for local control of the monitoring area.

[0203] This solution first obtains the geometric centers of each monitoring area and establishes a circular area containing all geometric centers to replace all abnormal areas; since there are some normal areas within the circular area, to further reduce the error, a circular area is established with the geometric centers of each monitoring area as a reference, and the monitoring area area outside the circular area is discarded to balance the error brought by the normal areas; by calculating the proportion of the circular area to the chicken coop area, the concentration degree of multiple abnormal areas is determined; when the proportion is large, it indicates that the multiple abnormal areas are more dispersed and need to be regulated as a whole; when the proportion is small, it means that the multiple abnormal areas are more concentrated and can be improved and optimized through local regulation; it can reduce energy consumption and improve the effective utilization rate of energy.

[0204] 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 control report generation process further includes:

[0205] Collect the real-time egg production data at continuous equal time intervals;

[0206] Randomly select multiple groups of sample data;

[0207] Calculate the increase rate G1 of egg production. The specific calculation process is as follows

[0208] G1 = (Ma - Mb) / (Ta - Tb);

[0209] 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;

[0210] When G1 < 0, generate a control report for raising the temperature of the chicken house;

[0211] When G1 ≥ 0, calculate the average egg weight increase rate G2 for the total mass of the egg production of the chicken house at the corresponding time point. The specific calculation process is as follows:

[0212] ;

[0213] Wherein, Mu1 is the total mass of the egg production of the chicken house at time point Ta, and Mu2 is the total mass of the egg production of the chicken house at time point Tb;

[0214] When the average egg weight increase rate G2 < 0, generate a control report for lowering the temperature of the chicken house.

[0215] In a high-temperature environment, the egg production and eggshell quality of laying hens will significantly decrease; laying hens are relatively cold-resistant, but too low temperature will cause the laying hens to move slowly, have large feed losses, and a decline in production performance; in this embodiment, the egg production situation of the chicken house under normal environmental parameters is monitored; the suitability of the temperature is inferred from the egg production of laying hens at different time periods; when the egg production of laying hens shows a downward trend, it indicates that the standard monitoring temperature is lower than the most suitable temperature for this breed of laying hens, and the standard detection temperature is increased; then, a comparative analysis is carried out on the individual quality of the eggs laid by laying hens at different time periods; when the weight of an average single egg decreases, it indicates that the standard monitoring temperature is higher than the most suitable temperature for this breed of laying hens, and the standard detection temperature is decreased 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.

[0216] The system further includes a control module, and the control module is used to receive the control report and make control actions.

[0217] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0218] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection 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 can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0219] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to 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; 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 Q1, the least square method is used to calculate the linear regression equations Tei=β1+β2·Lei of the temperature and distance size in the first and second states 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.

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 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 E1>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. .

3. 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.

4. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 3 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.

5. 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 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.

6. The layer chicken breeding environment monitoring system based on the Internet of Things according to claim 5 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.

7. The laying hen breeding environment monitoring system based on the Internet of Things according to claim 5 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.

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

Images