A multi-target sensor placement optimization method for poultry houses based on conditional constraints

Abstract

The present invention discloses a method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints, belonging to the technical field of poultry house environmental monitoring. By placing K types of dynamic environmental sensors within the poultry house based on N initial locations where environmental information is to be obtained, the optimal placement of the sensors is studied under the combined effects of multiple sensor types and locations, robot constraints, and cost constraints. The method integrates error, information gain, coverage, and penalty factors to construct a final objective function and constraint conditions, thereby selecting the optimal placement location for each sensor type. This method can determine the placement of monitoring points based on user-defined evaluation preferences and identify key environmental monitoring points in the poultry house, thereby reducing the number of environmental monitoring points and improving the efficiency of poultry house environmental monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of poultry house environment monitoring, and in particular to a poultry house multi-target sensor layout optimization method based on conditional constraints. Background Art

[0002] In intensive poultry farming, monitoring of key environmental indicators within the poultry house is essential. Precision environmental control systems are a crucial component in maintaining a healthy poultry farming environment. These systems primarily collect environmental information through sensors distributed throughout the poultry house. Environmental control strategies then control the corresponding environmental control equipment to maintain the environmental conditions within the sensor locations within the poultry house within a set threshold. Currently used sensor placement schemes primarily rely on the plum blossom and diagonal methods, primarily using average values ​​as control indicators. This approach ignores the dynamic changes in environmental factors within the poultry house as a function of external factors. With the increasing demand for precision farming, environmental control is increasingly focused on precise microclimate control. Therefore, sensor placement must be representative, reflecting the actual microclimate within the poultry house to the greatest extent possible at a reasonable cost. Furthermore, with the development of artificial intelligence and informatization, while inspection robots have played an important role in poultry farming, their drawback lies in their limited spatial and temporal monitoring capabilities, making them unable to simultaneously monitor the entire poultry house environment. Furthermore, farms have varying positioning requirements for inspection robots. Therefore, combining the advantages of environmental sensors (static) with inspection robots (dynamic) to improve the efficiency and effectiveness of whole-house monitoring is crucial. At this stage, many studies mainly focus on the layout of fixed sensor monitoring points, without considering the constraints and synergies of multiple sensors, costs and other collaborative equipment. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-objective sensor layout optimization method for poultry houses based on conditional constraints. The sensor layout method is studied under the joint effects of multiple sensor types and positions, robot constraints, and cost constraints. Through this method, the monitoring point layout positions can be determined according to customized evaluation preferences, and key environmental monitoring points in the poultry house can be found, so as to achieve the purpose of reducing the number of poultry house environmental monitoring points, improving the efficiency of poultry house environmental monitoring, and solving the shortcomings of the existing technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints comprises the following steps:

[0006] S1: Place K types of environmental sensors at N initial locations in the poultry house where environmental information is to be obtained, and collect prior information on the target structure, including: obtaining a data matrix D(t) of all sensors with a dimension of N*K at time point t, where each element d i，k (t) represents the data value of the k-th sensor at the i-th monitoring point at time t;

[0007] S2: Collect information about the external environment and the operating status of environmental control equipment as features for predicting environmental factors within the poultry house:

[0008]

[0009] Where T out (t), T out (t) and V out (t) is the temperature, humidity and wind speed data outside the house, W angle (t), F status (t) and M status (t) is the operating status of the poultry house fan, wet curtain and heater;

[0010] S3: For each sensor data point i and characteristic factor D in the sensor data matrix D(t) ext (t) are used as inputs to predict the data of other sensor sites of the same type through the machine learning model, and then the error is calculated to obtain the error matrix E(t) with a dimension of N*N*K;

[0011] S4: The environmental monitoring capability of the inspection robot in the poultry house is coordinated with the fixed sensors in the poultry house to collect the coverage area C(t) of the inspection robot at each time point t of the fixed sensors:

[0012]

[0013] S5: Determine the number of fixed sensors in the area actually covered by the robot at time point t by inspecting the coordinates of the robot Calculate the coverage rate R of the environmental dynamic sensors carried by the inspection robot in one day cover =n*f / 1440, where n is the duration of each inspection by the inspection robot, and f is the inspection frequency of the inspection robot's constraint condition;

[0014] S6: The coverage rate R of the inspection robot within one day cover and the number of environmental dynamic sensors covered in time t N cover (t), the number of replaceable fixed sensors is R cover *N cover (t);

[0015] S7: Calculate the information gain IG for each sensor position i，k =H(X rest )-H(X rest |S i，k ),in is a measure of the initial uncertainty of the system, X rest is the set of possible data values ​​of the remaining monitoring points, P(x rest ) is the probability of a certain data value of the remaining points, IG i，k is the initial entropy of the system H(X rest ) and the remaining entropy H(X rest |S i，k ) difference;

[0016] S8: Calculate the comprehensive evaluation index of each type of sensor Where k represents the type of sensor, ε k represents the average prediction error of each type of sensor, IG k is the average information gain of various sensors W k is the importance weight of each type of sensor, α is the adjustment parameter of prediction error and information gain;

[0017] S9: Calculate the global priority, that is, under the budget constraint, allocate the number of each type of environmental dynamic sensors and set the objective function of the global priority where n k is the number of each sensor, and the constraint is And n k ≤NR cover *N cover (t), where C k For the unit cost of each type of sensor, solve n by mixed integer linear programming k ;

[0018] S10: Perform local optimization, i.e., select the specific location of each type of sensor: set an initial error threshold ε0, which represents the error of the sensor when predicting other points;

[0019] S11: Based on the error matrix, find the sensor with the smallest prediction error and the highest coverage for other sites;

[0020] S12: Combine the standardized error matrix and information gain to construct a comprehensive score for each sensor type and calculate the comprehensive evaluation index for each location Where β is a weight used to adjust the importance of information gain and error;

[0021] S13: After selecting the first sensor, mark all points covered by the sensor as covered, remove the already covered points, recalculate the error matrix and information gain of the remaining points, repeat steps S11 to S12, and so on to sort all monitoring points of each sensor type k;

[0022] S14: For monitoring points with large prediction errors, add a penalty term P(ε i，k ), P(ε i，k ) is based on the maximum value of the prediction error of each sensor;

[0023] S15: Constructing the objective function Select the best placement location for each type of sensor.

[0024] Furthermore, each element e in the error matrix E(t) in S3 i，k (t) represents the prediction error of the kth sensor at the i-th monitoring point at time t, e i，k (t)=|d i，k (t)-P i，k (t)|, where d i，k (t) is the actual observed value, and Pi,k(t) is the predicted value.

[0025] Furthermore, the coverage R in S5 cover Close to 1, it means that the inspection time of the inspection robot is long enough, and the coverage R cover If it is close to 0, it means that the inspection robot is not working most of the time and its data contribution is limited.

[0026] Furthermore, the information gain IG at all time points in S7 i，k This can reflect the improved prediction accuracy achieved by increasing the number of fixed sensor locations.

[0027] Furthermore, the specific method in S11 is: calculate the error coverage of each point for temperature, humidity, wind speed and carbon dioxide sensors respectively s [i, k], that is, the error of predicting other points is less than ε0:

[0028]

[0029] Where 1(ε i，k ≤ε0) means that if the prediction error is less than the threshold, the result is 1, otherwise it is 0. The selection of a certain type of sensor means the selection of coverage s The i with the largest [i, k] has priority over the first sensor location of this type.

[0030] Furthermore, the objective function constructed in S15 combines error, information gain, coverage and penalty factors. The constraints of the objective function are: nk is the total number of sensors of this type.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention's multi-objective sensor layout optimization method for poultry houses based on conditional constraints studies the sensor layout method under the joint effects of multiple sensor types and positions, robot constraints, and cost constraints. Through this method, the monitoring point layout positions can be determined according to customized evaluation preferences, and key environmental monitoring points in the poultry house can be found, in order to achieve the purpose of reducing the number of environmental monitoring points in the poultry house and improving the efficiency of poultry house environmental monitoring. DETAILED DESCRIPTION

[0033] The following will describe the embodiments of the present invention in detail, however, the embodiments of the present invention are not limited thereto. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] An embodiment of the present invention provides a method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints, comprising the following steps:

[0035] S1: Place K types of environmental dynamic sensors at N initial locations in the poultry house where environmental information is to be obtained, collect prior information on the target structure, and obtain a matrix D(t) of all sensor data with a dimension of N*K at time point t, where each element d i，k (t) represents the data value of the kth sensor at the i-th monitoring point at time t. For example, the sensor data matrix at time t is:

[0036]

[0037] Among them D T (t), D H (t), D H (t) and D C (t) represents the data of the temperature, humidity, wind speed and carbon dioxide sensors in the house at time t.

[0038] S2: Since the external environment and the operating status of environmental control equipment also affect the distribution of the environment inside the house, this part of information is also collected as a feature to predict the environmental factors inside the house:

[0039]

[0040] Where T out (t), Tout (t) and V out (t) is the temperature, humidity and wind speed data outside the house, W angle (t), F status (t) and M status (t) is the operating status of the poultry house fan, wet curtain and heater;

[0041] S3: For each sensor data point i and D ext (t) are used as inputs, and the data of other sensor sites of the same type are predicted by the machine learning model, and then the error is calculated to obtain the error matrix E(t) with a dimension of N*N*K. i，k (t) represents the prediction error e of the k-th sensor at the i-th monitoring point at time t i，k (t)=|d i，k (t)-P i，k (t)|, where d i，k (t) is the actual observed value, P i，k (t) is the predicted value. For example, for D at time point t T (t) Temperature field matrix, assuming that the initial site is 60 points, each point i (i = 1, 2, ..., 60) and D ext (t) is used as input to predict the temperature values ​​of other points, and then the prediction error of each point is calculated to finally obtain a 60*60 error matrix E;

[0042] S4: The purpose of using inspection robots in different chicken houses is different, so it cannot be used as a quantitative measure. However, its environmental monitoring capabilities can be coordinated with static sensors (fixed). The constraints of the inspection robot are that the inspection frequency is f and the duration of each inspection is n minutes. The coverage rate of the inspection robot within the monitoring interval of the fixed sensor is collected. Assume that the coverage area C(t) of the fixed sensor collection robot at each time point t is:

[0043]

[0044] S5: Since the data monitored by the inspection robot at time point t is not necessarily the position where the static sensors completely overlap, there will be a certain deviation. Therefore, the number of fixed environmental dynamic sensors in the actual coverage area of ​​the robot at time point t is first determined by the coordinates of the robot. Calculate the coverage rate R of the robot's environmental dynamic sensor (dynamic) in one day cover =n*f / 1440. If the coverage rate is close to 1, it means that the inspection robot has a long enough detection time. If the coverage rate is close to 0, it means that the robot is not working most of the time and the data contribution is limited.

[0045] S6: The robot’s daily coverage rate R cover , and the number of environmental dynamic sensors covered in time t N cover (t), so the number of fixed sensors that can be replaced is R cover *N cover (t);

[0046] S7: Calculate the information gain IG for each sensor position i，k =H(X rest )-H(X rest |S i，k ),in is a measure of the initial uncertainty of the system, X rest is the set of possible data values ​​of the remaining monitoring points, P(x rest ) is the probability IG of a certain data value of the remaining points i，k , is the initial entropy of the system H(X rest ) and the remaining entropy H(X rest |S i，k ) The information gain IG of all time points is considered comprehensively. i，k This can reflect the improved prediction accuracy achieved by increasing the number of fixed sensor locations;

[0047] S8: Since there are many types of environmental dynamic sensors, they have different focuses according to their monitoring needs. Therefore, we first calculate the global priority, that is, the number of each sensor is allocated under the budget constraint. Calculate the comprehensive evaluation of each sensor Where k represents the type of sensor, ε k represents the average prediction error of each type of sensor, IG k is the average information gain of various sensors w k is the importance weight of each type of sensor, α is the adjustment parameter of prediction error and information gain;

[0048] S9: Setting the global priority objective function where n k is the number of each sensor. The constraints are And n k ≤NR cover *N cover (t), where C k For the unit cost of each type of sensor, solve n by mixed integer linear programming k ;

[0049] S10: Then, local optimization is performed, i.e., the selection of specific locations for each type of sensor. An initial error threshold ε0 is set, which represents the error of the selected sensor when predicting other points.

[0050] S11: Based on the error matrix, find the sensor with the smallest prediction error and the highest coverage for other locations; specifically, calculate the error coverage of each point for the temperature, humidity, wind speed, and carbon dioxide sensors, that is, the error of the predicted other points is less than ε0:

[0051]

[0052] Where 1(ε i，k ≤ε0) means that if the prediction error is less than the threshold, the result is 1, otherwise it is 0. The selection of a certain type of sensor means the selection of coverage s [i, k] The largest i has the priority of being the first sensor location of this type;

[0053] S12: Combine the standardized error matrix and information gain to construct a comprehensive score for each sensor type. Calculate the comprehensive evaluation for each location Where β is a weight used to adjust the importance of information gain and error;

[0054] S13: After selecting the first sensor, mark all points covered by the sensor as covered and remove the already covered points. Recalculate the error matrix and information gain of the remaining points, repeat steps S11 to S12, and so on to sort all monitoring points of each sensor type k;

[0055] S14: In order not to ignore those monitoring points with large prediction errors, a penalty term P(ε i，k The penalty term is based on the maximum value of each sensor’s prediction error, ensuring that points with large errors are focused on.

[0056] S15: Constructing the objective function The optimal layout position of each type of sensor is selected, which combines the error, information gain, coverage and penalty factors. The objective function constraint is n k is the total number of sensors of this type.

[0057] In summary: The embodiment of the present invention provides a method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints. The method studies the sensor placement method under the joint effects of multiple sensor types and positions, robot constraints, and cost constraints. This method can determine the placement of monitoring points based on customized evaluation preferences, and find key environmental monitoring points in the poultry house, in order to reduce the number of environmental monitoring points in the poultry house and improve the efficiency of environmental monitoring in the poultry house.

[0058] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing the placement of multi-target sensors in poultry houses based on conditional constraints, characterized in that: The following steps are involved: S1: Place K types of environmental sensors at N initial locations in the poultry house where environmental information is to be obtained, and collect prior information on the target structure, including: obtaining a data matrix D(t) of all sensors with a dimension of N*K at time point t, where each element d i，k (t) represents the data value of the k-th sensor at the i-th monitoring point at time t; S2: Collect information about the external environment and the operating status of environmental control equipment as features for predicting environmental factors within the poultry house: Where T out (t), T out (t) and V out (t) is the temperature, humidity and wind speed data outside the house, W angle (t), F status (t) and M status (t) is the operating status of the poultry house fan, wet curtain and heater; S3: For each sensor data point i and characteristic factor D in the sensor data matrix D(t) ext (t) are used as inputs to predict the data of other sensor sites of the same type through the machine learning model, and then the error is calculated to obtain the error matrix E(t) with a dimension of N*N*K; S4: The environmental monitoring capability of the inspection robot in the poultry house is coordinated with the fixed sensors in the poultry house to collect the coverage area C(t) of the inspection robot at each time point t of the fixed sensors: S5: Determine the number of fixed sensors in the area actually covered by the robot at time point t by inspecting the coordinates of the robot Calculate the coverage rate R of the environmental dynamic sensors carried by the inspection robot in one day cover =n*f / 1440, where n is the duration of each inspection by the inspection robot, and f is the inspection frequency of the inspection robot's constraint condition; S6: The coverage rate R of the inspection robot within one day cover and the number of environmental dynamic sensors covered in time t N cover (t), the number of replaceable fixed sensors is R cover *N cover (t); S7: Calculate the information gain IG for each sensor position i，k =H(X rest )-H(X rest |S i，k ),in is a measure of the initial uncertainty of the system, X rest is the set of possible data values ​​of the remaining monitoring points, P(x rest ) is the probability of a certain data value of the remaining points, IG i，k is the initial entropy of the system H(X rest ) and the remaining entropy H(X rest |S i，k ) difference; S8: Calculate the comprehensive evaluation index of each type of sensor Where k represents the type of sensor, ε k represents the average prediction error of each type of sensor, IG k is the average information gain of various sensors W k is the importance weight of each type of sensor, α is the adjustment parameter of prediction error and information gain; S9: Calculate the global priority, that is, under the budget constraint, allocate the number of each type of environmental dynamic sensors and set the objective function of the global priority where n k is the number of each sensor, and the constraint is And n k ≤NR cover *N cover (t), where C k For the unit cost of each type of sensor, solve n by mixed integer linear programming k ; S10: Perform local optimization, i.e., select the specific location of each type of sensor: set an initial error threshold ε0, which represents the error of the sensor when predicting other points; S11: Based on the error matrix, find the sensor with the smallest prediction error and the highest coverage for other sites; S12: Combine the standardized error matrix and information gain to construct a comprehensive score for each sensor type and calculate the comprehensive evaluation index for each location Where β is a weight used to adjust the importance of information gain and error; S13: After selecting the first sensor, mark all points covered by the sensor as covered, remove the already covered points, recalculate the error matrix and information gain of the remaining points, repeat steps S11 to S12, and so on to sort all monitoring points of each sensor type k; S14: For monitoring points with large prediction errors, add a penalty term P(ε i，k ), P(ε i，k ) is based on the maximum value of the prediction error of each sensor; S15: Constructing the objective function Select the best placement location for each type of sensor.

2. The method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints according to claim 1, wherein: Each element e in the error matrix E(t) in S3 i，k (t) represents the prediction error of the kth sensor at the i-th monitoring point at time t, e i，k (t)=|d i，k (t)-P i，k (t)|, where d i，k (t) is the actual observed value, P i，k (t) is the predicted value.

3. The method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints according to claim 1, wherein: S5 medium coverage R cover Close to 1, it means that the inspection time of the inspection robot is long enough, and the coverage R cover If it is close to 0, it means that the inspection robot is not working most of the time and its data contribution is limited.

4. The method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints according to claim 1, wherein: Information gain IG at all time points in S7 i，k This can reflect the improved prediction accuracy achieved by increasing the number of fixed sensor locations.

5. The method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints according to claim 1, wherein: The specific method in S11 is: calculate the error coverage of each point for temperature, humidity, wind speed and carbon dioxide sensors respectively s [i, k], that is, the error of predicting other points is less than ε0: Where 1(ε i，k ≤ε0) means that if the prediction error is less than the threshold, the result is 1, otherwise it is 0. The selection of a certain type of sensor means the selection of coverage s The i with the largest [i, k] has priority over the first sensor location of this type.

6. The method for optimizing the placement of multi-target sensors in a poultry house based on conditional constraints according to claim 1, wherein: The objective function constructed in S15 combines error, information gain, coverage and penalty factors. The constraints of the objective function are: n k The total number of sensors of this type.

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