Farmland soil temperature and humidity sensor layout method

Abstract

The invention discloses a farmland soil temperature and humidity sensor layout method, solves the problem of how to remarkably reduce the number of sensor nodes while meeting the soil monitoring coverage requirement, and belongs to the technical field of agricultural informatization. According to the method, an objective function and constraint conditions are constructed with the purpose of minimizing the total number of deployed sensor nodes, and the layout positions of farmland soil temperature and humidity sensors are taken as key points; and solving the objective function to obtain the number and the corresponding positions of the selected key points. According to the method, the steps of initialization matrix creation, node deployment, matrix optimization, a feature optimization algorithm and the like are utilized in the solving process, and the monitoring coverage range and the data representativeness can be improved to the maximum extent while it is ensured that the number of sensors is reduced.

Description

Technical Field

[0001] The invention relates to a method for arranging farmland soil temperature and humidity sensors, belonging to the technical field of agricultural informatization. Background Art

[0002] With the continuous advancement of agricultural modernization and the rapid development of smart agricultural technology, precision agriculture has become an important way to solve the problems of agricultural resource waste and environmental pollution. In precision agriculture, real-time monitoring of soil is of great significance to improving crop yields and optimizing resource utilization efficiency. Soil temperature and humidity sensors play an important role in agricultural production and environmental monitoring. They can monitor soil temperature and humidity in real time, provide a scientific basis for precision irrigation, fertilization optimization, and crop management, help improve resource utilization efficiency, improve soil health, and prevent salinization and other problems. In addition, soil temperature and humidity sensors are key IoT devices in smart agriculture. By linking with the automation system, they can achieve water saving and efficiency improvement, reduce costs, and also play an important role in climate change response and ecological environment monitoring, providing data support for sustainable agricultural development and scientific research.

[0003] At present, the diversified crop planting patterns have put forward higher requirements for soil monitoring technology. Traditional soil monitoring methods can no longer meet the actual needs of contemporary agriculture, and sensor-based intelligent soil monitoring has gradually become an important direction for the development of modern agriculture. Traditional soil monitoring mostly adopts random point distribution or regular grid deployment methods. Although this method is simple and easy to implement, it has problems such as low equipment utilization efficiency, uneven monitoring coverage and high operating costs when facing large-scale agricultural scenarios. In addition, the temperature and humidity of the soil have obvious spatiotemporal dynamic change characteristics, while traditional deployment methods are often fixed and lack dynamic adjustment capabilities, resulting in insufficient representativeness of monitoring results. In some agricultural areas with limited resources, it is often necessary to increase the number of nodes to ensure coverage, but this significantly increases the equipment procurement and communication costs.

[0004] In the process of transition from traditional agriculture to precision agriculture, in response to crop monitoring needs, existing technologies achieve monitoring coverage by adding a large number of sensor nodes or relying on experience-based deployment, resulting in a waste of resources and failing to meet the flexibility requirements in practical applications. Summary of the invention

[0005] In order to solve the problem of how to significantly reduce the number of sensor nodes while meeting the soil monitoring coverage requirements, the present invention provides a method for deploying farmland soil temperature and humidity sensors.

[0006] A method for deploying farmland soil temperature and humidity sensors of the present invention comprises:

[0007] S1. To minimize the total number of deployed sensor nodes, construct the objective function and constraint conditions, take the layout location of farmland soil temperature and humidity sensors as the key point, n represents the number of key points, and the objective function is:

[0008]

[0009] The constraints include: X i is 1, indicating that the position of key point i is selected, X i is 0, indicating that the position of key point i is not selected. The temperature characteristics are divided into several categories according to the temperature threshold range, and the humidity characteristics are divided into several categories according to the humidity threshold range. The temperature and humidity characteristics of each key point can be monitored. i,j represents the jth feature of the i-th key point, the value range of j is 1 to m, and m is the total category of temperature and humidity features; E(f i,j ) represents the average value of the jth feature of all key points, σ i,j represents the standard deviation of the jth feature, the transmission delay of each key point MD represents the maximum transmission delay value;

[0010] S2. Solve the objective function to obtain the number and corresponding positions of the selected key points.

[0011] Preferably, in S2, the method for solving the objective function to obtain the number and corresponding positions of the selected key points includes:

[0012] S21, create a matrix T with m rows and n columns, the matrix T includes the features of all key points, wherein the elements of each column correspond to a temperature and humidity eigenvalue of a key point, which is a valid eigenvalue or an invalid eigenvalue, and calculate the transmission delay of each key point;

[0013] S22, in the current matrix T, select the key point whose temperature feature and humidity feature are both valid features and the transmission delay is the smallest as the pre-selected point. When there is no key point whose temperature feature and humidity feature are both valid features, select the key point with valid features and the smallest transmission delay as the pre-selected point. If only one pre-selected point is selected in the current matrix T, then the pre-selected point is set as the current best point, and the other key points are used as ordinary key points, and the process goes to S23, otherwise it goes to S24;

[0014] S23, by deleting invalid key points and invalid features in the common key points, optimizing the matrix T, and going to S22 until there are no common key points;

[0015] S24, calculate the evaluation values ​​of the pre-selected points, set the one with the lowest evaluation value as the current best point, and use other key points as ordinary key points. If the evaluation values ​​of all pre-selected points are the same, use a random function to randomly select the best point from all pre-selected points, and go to S23.

[0016] Preferably, the temperature threshold range is divided into low temperature, normal temperature, and high temperature, and the humidity threshold range is divided into dry, moist, damp, and wet.

[0017] Preferably, the method for optimizing the matrix T by deleting invalid key points and invalid features in common key points includes:

[0018] Traverse the features of each common key point in the matrix T;

[0019] If the common key point i and the current best point have valid features of the same category, the feature value of the common key point i is set to an invalid feature;

[0020] If common key point i has no valid features, delete the common key point.

[0021] As a preference, the evaluation value of the pre-selected point is F i , k represents the number of valid features of the pre-selected points, and the evaluation value of the jth feature is

[0022] The beneficial effects of the present invention are as follows: the present invention realizes the scientific deployment of soil temperature and humidity sensors through the optimization algorithm, which significantly reduces the number of nodes, reduces equipment and operation costs, and ensures monitoring coverage and improves data representativeness compared with the prior art. Compared with the traditional method, the present method has stronger adaptability and higher resource utilization efficiency, and effectively solves the problems of low coverage efficiency, high cost and poor adaptability in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a flow chart of a method for solving an objective function according to the present invention;

[0024] Figure 2 Schematic diagram of the process of deploying the algorithm for nodes;

[0025] Figure 3 A schematic diagram for judging the situation. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0029] This implementation significantly reduces the number of nodes while ensuring monitoring coverage by constructing an objective function and an algorithm. The purpose of constructing the objective function in this implementation is to comprehensively monitor temperature and humidity with a minimum number of nodes while meeting monitoring coverage requirements and quality standards. The algorithm proposed in this implementation includes steps such as initialization matrix creation, node deployment, matrix optimization, and feature optimization algorithm. Ensure that while reducing the number of sensors, the coverage of monitoring and the representativeness of data can be maximized.

[0030] A method for deploying farmland soil temperature and humidity sensors in this embodiment includes:

[0031] Step 1: To minimize the total number of deployed sensor nodes, construct the objective function and constraints. The layout of farmland soil temperature and humidity sensors is taken as the key point, n represents the number of key points, and the objective function is:

[0032]

[0033] The constraints include:

[0034] (1) X i is 1, indicating that the position of key point i is selected, X i is 0, indicating that the position of key point i is not selected;

[0035] (2) The temperature and humidity characteristics of each key point can be monitored.

[0036] (3) According to the temperature threshold range, it is divided into several types of temperature features, and according to the humidity threshold range, it is divided into several types of humidity features, f i,j represents the jth feature of the i-th key point, the value range of j is 1 to m, and m is the total category of temperature and humidity features;

[0037] In this implementation mode, the temperature threshold range is divided into low temperature, normal temperature, and high temperature, and the humidity threshold range is divided into dry, moist, damp, and wet, so m=7.

[0038] The monitoring values ​​of the selected key points must be within two standard deviations of the feature mean:

[0039]

[0040] Among them, E(f i,j ) represents the average value of the jth feature of all key points, and σi,j represents the standard deviation of the jth feature;

[0041] (4) Transmission delay constraints for each key point, the transmission delay for each key point

[0042]

[0043] Wherein, MD represents the maximum transmission delay value;

[0044] Step 2: Solve the objective function to obtain the number and corresponding positions of the selected key points:

[0045] Step 21, initialize matrix T:

[0046] Create a matrix T with m rows and n columns, m = 7, representing low temperature, normal temperature, high temperature, humidity, dry, moist, damp, and wet respectively. The matrix T includes the features of all key points, where the elements of each column correspond to a key point temperature and humidity eigenvalue, which is a valid eigenvalue or an invalid eigenvalue, n = 12, as shown in Table 1:

[0047] Table 1

[0048]

[0049] In Table 1, the checked ones are valid features, and the blank ones are invalid features;

[0050] Then, the mean and standard deviation of each feature are calculated, the transmission delay of each key point is calculated, and the maximum transmission delay is determined.

[0051] Step 22, node deployment:

[0052] In the current matrix T, a key point whose temperature feature and humidity feature are both valid features and whose transmission delay is the smallest is selected as a pre-selected point. If only one pre-selected point is selected in the current matrix T, the pre-selected point is set as the current optimal point, and other key points are used as ordinary key points. All key points in Table 1 contain effective values ​​of temperature features and effective values ​​of humidity features. If the transmission delay of the first key point is the smallest, the first is selected as the optimal point, and the process goes to step 23, the matrix optimization algorithm. If the transmission delays of the first and third key points are the smallest and the same, these two are selected as pre-selected points, and the process goes to step 24 to execute the feature optimization algorithm.

[0053] There is another situation where, after subsequent processing, there may be only one valid feature in a common key point in the next iteration, that is, when there is no key point with both temperature and humidity features as valid features, the key point with valid features and the smallest transmission delay is selected as the pre-selected point;

[0054] Step 23: Matrix optimization algorithm:

[0055] By deleting invalid key points and invalid feature optimization matrix T in common key points, go to step 22 until there are no common key points;

[0056] Traverse each common key point in the matrix T and traverse the features of each common key point;

[0057] If the common key point i and the current best point have valid features of the same category, the feature value of the common key point i is set to an invalid feature; taking the first one as the best point as an example, after Table 1 is processed by step 23, Table 2 is obtained:

[0058] Table 2

[0059]

[0060] If the common key point i has no valid features, delete the common key point; if a feature has no valid nodes, delete the feature (that is, delete the row).

[0061] Step 24, feature optimization algorithm: select pre-selected points where the features are close to the average value;

[0062] Calculate the evaluation value of the pre-selected points, set the one with the lowest evaluation value as the current best point, and the other key points as ordinary key points;

[0063] The evaluation value of the jth feature of each pre-selected point is The evaluation value of the pre-selected point is the sum of the evaluation values ​​of all features of the corresponding key points, which is k represents the number of valid features of the pre-selected points, which may be 1 or 2;

[0064] If all pre-selected reviews have the same valuation, a random function is used to randomly select the best point from all pre-selected points, and the process goes to step 23 to enter the matrix optimization algorithm.

[0065] After several iterations of Table 2, Table 3 and Table 4 are obtained respectively:

[0066] Table 3

[0067]

[0068] Table 4

[0069]

[0070] This implementation method significantly reduces the number of sensor nodes while meeting the soil monitoring coverage requirements by optimizing the site selection strategy, thereby reducing deployment costs and later operating energy consumption. This implementation method can flexibly adjust the deployment plan of temperature and humidity sensors according to the needs of different crops, achieve efficient and economical real-time soil monitoring, and help modern agriculture upgrade to intelligence.

[0071] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A method for deploying farmland soil temperature and humidity sensors, characterized in that: include: S1. To minimize the total number of deployed sensor nodes, construct the objective function and constraint conditions, take the layout location of farmland soil temperature and humidity sensors as the key point, n represents the number of key points, and the objective function is: The constraints include: X i is 1, indicating that the position of key point i is selected, X i is 0, indicating that the position of key point i is not selected. The temperature characteristics are divided into several categories according to the temperature threshold range, and the humidity characteristics are divided into several categories according to the humidity threshold range. The temperature and humidity characteristics of each key point can be monitored. i,j represents the jth feature of the i-th key point, the value range of j is 1 to m, and m is the total category of temperature and humidity features; E(f i,j ) represents the average value of the jth feature of all key points, σ i,j represents the standard deviation of the jth feature, the transmission delay of each key point MD represents the maximum transmission delay value; S2. Solve the objective function to obtain the number and corresponding positions of the selected key points.

2. A method for deploying farmland soil temperature and humidity sensors according to claim 1, characterized in that: In S2, the objective function is solved to obtain the number of selected key points and the corresponding positions, including: S21, create a matrix T with m rows and n columns, the matrix T includes the features of all key points, wherein the elements of each column correspond to a temperature and humidity eigenvalue of a key point, which is a valid eigenvalue or an invalid eigenvalue, and calculate the transmission delay of each key point; S22, in the current matrix T, select the key point whose temperature feature and humidity feature are both valid features and the transmission delay is the smallest as the pre-selected point. When there is no key point whose temperature feature and humidity feature are both valid features, select the key point with valid features and the smallest transmission delay as the pre-selected point. If only one pre-selected point is selected in the current matrix T, then the pre-selected point is set as the current best point, and the other key points are used as ordinary key points, and the process goes to S23, otherwise it goes to S24; S23, by deleting invalid key points and invalid features in the common key points, optimizing the matrix T, and going to S22 until there are no common key points; S24, calculate the evaluation values ​​of the pre-selected points, set the one with the lowest evaluation value as the current best point, and use other key points as common key points. If all pre-selected points have the same evaluation value, use a random function to randomly select the best point from all pre-selected points, and go to S23.

3. A method for deploying farmland soil temperature and humidity sensors according to claim 2, characterized in that: According to the temperature threshold range, it is divided into low temperature, normal temperature, and high temperature; according to the humidity threshold range, it is divided into dry, moist, damp, and wet.

4. A method for deploying farmland soil temperature and humidity sensors according to claim 2, characterized in that: Methods for optimizing the matrix T by deleting invalid key points and invalid features from common key points include: Traverse the features of each common key point in the matrix T; If the common key point i and the current best point have valid features of the same category, the feature value of the common key point i is set to an invalid feature; If common key point i has no valid features, delete the common key point.

5. A method for deploying farmland soil temperature and humidity sensors according to claim 2, characterized in that: The evaluation value of the pre-selected point is F i , k represents the number of valid features of the pre-selected points, and the evaluation value of the jth feature is 6. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for deploying farmland soil temperature and humidity sensors as described in any one of claims 1 to 5 are implemented.

7. A device for deploying farmland soil temperature and humidity sensors, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of a method for deploying farmland soil temperature and humidity sensors as described in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of a method for deploying farmland soil temperature and humidity sensors as described in any one of claims 1 to 5 are implemented.

Images