Smart soil moisture analysis system based on IoT terminals

Abstract

The present invention discloses an intelligent soil moisture analysis system based on an Internet of Things terminal. The present invention relates to the field of soil analysis and includes a soil moisture management platform, which is provided with a data acquisition module, a data processing module, a data transmission module, a data analysis module and a soil moisture analysis module; the data acquisition module is used to set the Internet of Things terminal to collect soil moisture data; the data processing module is used to set an edge computing node according to the Internet of Things terminal, and the edge computing node analyzes and processes the obtained soil moisture data, obtains predicted soil moisture data, and generates a sampling point data packet; the data transmission module is used to encrypt and transmit the sampling point data packet; the data analysis module is used to judge whether the corresponding data information is valid according to the sampling point data packet; the soil moisture analysis module is used to analyze and process the valid sampling point data packet to obtain soil moisture data; the present invention improves the stability of soil moisture analysis.

Description

Technical Field

[0001] The present invention relates to the field of soil analysis, and in particular to an intelligent soil moisture analysis system based on an Internet of Things terminal. Background Art

[0002] As the Internet of Things continues to mature, its application scope continues to expand. In addition to smart homes, the Internet of Things is also widely used in agriculture, landscaping, forestry, and fruit farming. In the process of agricultural application, regular soil moisture analysis can better understand the soil moisture status, thereby improving the scientificity and efficiency of agricultural production. Therefore, how to use IoT terminals to intelligently analyze soil moisture and make reasonable plans is a problem we need to solve.

[0003] A soil moisture prediction method and system based on soil moisture correlation analysis with publication number CN117494902B discloses a soil moisture prediction method and system based on soil moisture correlation analysis, which relates to the technical field of soil moisture prediction. The method analyzes the correlation between soil layers at different depths and soil moisture to obtain the Pearson correlation coefficient between soil moisture at different depths; the predicted soil is layered at preset intervals to obtain the soil bulk density of the first soil layer, and the current meteorological characteristic values ​​are collected to form input features of a soil moisture prediction model; the input features are input into a trained soil moisture prediction model to obtain a soil moisture prediction value for the first soil layer; based on the soil moisture prediction value for the first soil layer, soil moisture prediction values ​​at other depths are predicted according to the Pearson correlation coefficient between soil moisture at different depths; the present invention incorporates analysis of the correlation between soil layers at different depths and various meteorological data and soil moisture, and reconstructs the soil moisture prediction model to obtain a more accurate soil moisture prediction;

[0004] However, in the current process of soil moisture analysis, it is necessary to uniformly analyze and process the data information collected in the target area. The stability of the soil moisture data analysis in the target area cannot be guaranteed during the unified processing process. Moreover, the data information is collected through the Internet of Things terminal, and it is impossible to adjust and transform it according to the soil type and terrain conditions of the sampling point, which reduces the accuracy of the soil moisture analysis process to a certain extent. Therefore, how to improve the stability and accuracy of the soil moisture analysis process is a problem we need to solve. To this end, we now provide an intelligent soil moisture analysis system based on the Internet of Things terminal. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent soil moisture analysis system based on an Internet of Things terminal.

[0006] The object of the present invention can be achieved through the following technical solutions: an intelligent soil moisture analysis system based on an Internet of Things terminal, comprising a soil moisture management platform, wherein the soil moisture management platform is provided with a data acquisition module, a data processing module, a data transmission module, a data analysis module and a soil moisture analysis module;

[0007] The data acquisition module is used to collect elevation data in the area, classify the location of the soil in the area according to the elevation data, set sampling units according to the classification results, set sampling points and Internet of Things terminals according to the sampling units, and collect soil moisture data at the corresponding sampling points;

[0008] The data processing module is used to set corresponding edge computing nodes according to the Internet of Things terminal. Each edge computing node analyzes and processes the soil moisture data obtained by the Internet of Things terminal at the corresponding sampling point, obtains predicted soil moisture data, and generates a sampling point data packet to send to the data transmission module;

[0009] The data transmission module is used to encode the sampling point data packet generated by the corresponding edge computing node, encrypt it according to the encoding processing result, and send it to the data analysis module;

[0010] The data analysis module is used to determine whether the soil moisture data obtained at the corresponding sampling point is valid based on the data information obtained in the sampling point data packet, re-plan the sampling units in the target area according to the judgment result of the sampling point, set the sampling point planning information to be sent to the data acquisition module, and send the valid sampling point data packet to the soil moisture analysis module;

[0011] The soil moisture analysis module is used to analyze and process the effective soil moisture data in the area and obtain the soil moisture data in the corresponding area.

[0012] Furthermore, the data acquisition module sets a sampling unit, and the process of setting corresponding sampling points and IoT terminals according to the sampling unit includes:

[0013] The data acquisition module is provided with an information entry window, which is used by relevant staff in the soil moisture management platform to enter soil moisture analysis area information, wherein the soil moisture analysis area information includes area name, area location information and soil classification information, and the soil classification information includes three types: sandy soil, loam and clay;

[0014] Set the target area based on the regional location information, use GPS equipment to measure the elevation within the target area to obtain elevation data and its corresponding coordinates, and generate a regional elevation image; classify the terrain features based on the regional elevation image, and set up a corresponding multi-level image network based on the classification results;

[0015] Integrate the multi-level image networks corresponding to different classification results, divide the regional elevation image according to the integration results, and set corresponding sampling units according to the division results;

[0016] The sampling points in the corresponding sampling units are set according to the sampling point planning information set by the data analysis module, and the corresponding Internet of Things terminals are set in the sampling points.

[0017] Furthermore, sampling points and corresponding IoT terminals within the target area are obtained;

[0018] The IoT terminal includes multiple sensors, including a humidity sensor, a temperature sensor, and a light sensor, which are used to collect soil moisture data, soil temperature data, and soil light data, respectively. The obtained data information is marked as soil moisture data according to the sampling point corresponding to the IoT terminal and sent to the data processing module.

[0019] Furthermore, the process of the data processing module generating a sampling point data packet and sending it to the data transmission module includes:

[0020] Obtain a corresponding Internet of Things terminal, set a corresponding edge computing node based on the Internet of Things terminal, preset a sampling monitoring period for the corresponding edge computing node, set the soil moisture data of the same sampling point within the sampling monitoring period as a soil dataset, and the edge computing node obtains a corresponding machine learning algorithm based on the classification results of the sampling unit corresponding to the sampling point;

[0021] The soil moisture data in the soil dataset is analyzed and processed based on the machine learning algorithm to obtain predicted soil moisture data; the soil moisture data in the soil dataset is compared and analyzed with the predicted soil moisture data, and whether the sampling point is normal is determined based on the comparison result. If normal, the sampling point is marked as normal; if abnormal, the sampling point is marked as abnormal;

[0022] The data processing module generates a sampling point data packet based on the information obtained in the corresponding edge computing node and sends it to the data transmission module.

[0023] Furthermore, the process of the data transmission module transmitting the sampling point data packet includes:

[0024] Obtaining a sampling point data packet, the sampling point data packet including soil moisture data, predicted soil moisture data, and an analysis result of the sampling point, encoding each data information in the sampling point data packet to generate a corresponding data code; setting a first data table, a second data table, and a third data table according to different data in the sampling point data packet, and randomly mapping the data codes corresponding to the data information to the corresponding data tables respectively;

[0025] Analyze and process the data codes in the first data table, the second data table, and the third data table, set a starting position, obtain a character string at a corresponding position in the first data table according to the starting position, obtain a character string at a corresponding position in the second data table according to the data information represented by the character string at the position, obtain a character string at a corresponding position in the third data table according to the data information represented by the character string obtained in the second data table, and obtain a character string at a corresponding position in the first data table according to the data information represented by the character string obtained in the third data table, until the number of obtained character strings reaches a preset requirement;

[0026] An encryption key is generated from the obtained character string, the sampling point data packet is encrypted according to the encryption key, and the encrypted sampling point data packet is transmitted to the data analysis module.

[0027] Furthermore, the process of the data analysis module determining whether the soil moisture data obtained at the corresponding sampling point is valid based on the data information obtained in the sampling point data packet includes:

[0028] Obtain the sampling point data packets obtained by each edge computing node, map the analysis results of the corresponding sampling points in the sampling point data packets to the regional elevation image, and generate a sampling visualization image;

[0029] Obtain the sampling points marked as normal in the sampled visualization image, preset the sampling correlation radius, take the sampling point marked as normal as the center, and the sampling correlation radius as the radius, obtain the marking results corresponding to the sampling points within the range of the sampled visualization image, perform statistical analysis on the marking results, and obtain the proportion of sampling points marked as normal within the range of the sampled visualization image;

[0030] A percentage threshold is preset, and the obtained percentage value is compared and analyzed with the percentage threshold. When the percentage value is greater than or equal to the percentage threshold, the predicted soil moisture data corresponding to the sampling point is valid; when the percentage value is less than the percentage threshold, the predicted soil moisture data corresponding to the sampling point is invalid.

[0031] Furthermore, the data analysis module replans the sampling unit and sets the sampling point planning information, including:

[0032] Obtain the sampling points marked as abnormal in the sampling visualization image and the corresponding sampling point data packets, obtain the machine learning algorithm corresponding to the sampling units with different classification results corresponding to the sampling points, and sequentially analyze and process the soil moisture data obtained in the sampling point data packets to obtain verified predicted soil moisture data;

[0033] Analyze and process the verified predicted soil moisture data obtained by each machine learning algorithm with the corresponding soil moisture data to obtain similarity data, and obtain the type of sampling unit corresponding to the machine learning algorithm with the highest similarity data;

[0034] The sampling unit types corresponding to the similarity data obtained from two adjacent sampling points marked as abnormal are correlated when the sampling unit types are consistent; when the sampling unit types are inconsistent, the sampling units between the sampling points are marked as intervals;

[0035] According to the correlation results and interval marks, the elevation data of the corresponding coordinates in the regional elevation image are collected, the sampling units in the regional elevation image are reallocated, and the corresponding sampling point planning information is set according to the distribution of the sampling units and sent to the data acquisition module.

[0036] Furthermore, the soil moisture analysis module analyzes and processes the effective soil moisture data in the region, and the process of obtaining the soil moisture data in the corresponding region includes:

[0037] Obtain valid sampling point data packages within the target area, map the corresponding predicted soil moisture data and soil moisture data in the sampling point data packages to the corresponding coordinates in the regional elevation image, and use spatial interpolation methods to estimate the corresponding predicted soil moisture data and soil moisture data for other areas based on the mapping results;

[0038] The soil moisture classification standard is preset, and the obtained soil moisture data and predicted soil moisture data are compared and analyzed with the soil moisture classification standard respectively. The soil moisture data and predicted soil moisture data in the target area are obtained based on the comparative analysis.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. By setting up edge computing nodes to analyze and process the soil moisture data obtained by IoT terminals in corresponding sampling units, predicted soil moisture data is obtained. Based on the comparison results of the predicted soil moisture data and the soil moisture data, it is determined whether the soil type or terrain feature classification corresponding to the corresponding sampling point has changed, thereby improving the efficiency of data processing in the soil moisture analysis process to a certain extent;

[0041] 2. Set up a data transmission module to encrypt the obtained sampling point data packets and transmit the data information obtained by the corresponding edge computing node according to the encryption processing results, which ensures the security of data information during transmission to a certain extent;

[0042] 3. The data analysis module analyzes and processes the sampling points in the target area to determine whether the corresponding predicted soil moisture data is valid, and sends the valid information to the soil moisture analysis module, which improves the accuracy of the data processing process of the soil moisture analysis module to a certain extent. In addition, the sampling point planning information is set through the data analysis module, which improves the accuracy of information collection by the Internet of Things terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a schematic diagram of the intelligent soil moisture analysis system based on the Internet of Things terminal in an embodiment of this application. DETAILED DESCRIPTION

[0044] like Figure 1 As shown, the intelligent soil moisture analysis system based on the Internet of Things terminal includes a soil moisture management platform, in which a data acquisition module, a data processing module, a data transmission module, a data analysis module and a soil moisture analysis module are provided;

[0045] The data acquisition module is used to collect elevation data in the area, set sampling points according to the elevation data, set corresponding Internet of Things terminals according to the sampling points, and use the Internet of Things terminals to collect corresponding soil moisture data. The specific implementation process includes:

[0046] The data acquisition module is provided with an elevation acquisition unit and a data acquisition unit;

[0047] The elevation acquisition unit is used to collect elevation data within a corresponding area and set corresponding sampling points according to the elevation data;

[0048] The elevation acquisition unit is provided with an information entry window, which is used for relevant staff in the soil moisture management platform to enter soil moisture analysis area information, and the soil moisture analysis area information includes area name, area location information, soil classification information, area management qualification information and area management manager information;

[0049] The soil classification information includes three categories: sandy soil, loam and clay;

[0050] Verify the obtained soil moisture analysis area information. If the verification process passes, the area is granted access to the soil moisture management platform; if the verification process fails, the area is not granted access to the soil moisture management platform.

[0051] Acquire regional location information stored in a regional distributed database, set the acquired regional location information as a target area, perform elevation measurement of the acquired target area using a GPS device within the target area, acquire elevation data and its corresponding coordinates, and send them to a data acquisition unit;

[0052] The data acquisition unit is provided with a point analysis subunit and a data acquisition subunit;

[0053] The point analysis subunit is used to obtain corresponding elevation data and its corresponding coordinates, and set sampling points in the corresponding area according to the obtained elevation data. The specific implementation process includes:

[0054] The point analysis subunit obtains the regional position information and elevation data corresponding to the corresponding area; generates a regional image according to the regional position information, maps the obtained elevation data to the corresponding regional image according to its corresponding coordinates, and generates a regional elevation image;

[0055] Based on the regional elevation image, terrain features are classified. The terrain feature classification standards include three types: slope classification standard, aspect classification standard, and geomorphic unit classification standard.

[0056] The slope classification standards include flat land, gentle slope land, steep slope land and super-steep slope land;

[0057] The slope analysis criteria include north slope, south slope, east slope and west slope;

[0058] The classification standards of geomorphic units include mountains, hills, plains and basins;

[0059] Obtain soil classification information corresponding to the corresponding coordinates in the regional elevation image;

[0060] Mark the soil classification information and terrain feature classification results corresponding to the corresponding coordinates in the regional elevation image, and set up a corresponding multi-level image network according to the marking results;

[0061] When different classification results in the multi-level image network are consistent, the consistent areas are marked as network units, and each obtained network unit is marked as a multi-level image network;

[0062] Analyze and process the multi-level image network corresponding to different classification results, obtain the grid corresponding to the corresponding coordinates in the regional elevation image, set the corresponding sampling units, integrate all the sampling units, and set the regional spatial sampling network;

[0063] and sending it to the data analysis module, which selects the corresponding sampling unit to set as the sampling point, and sends the sampling point to the data acquisition subunit;

[0064] The data acquisition subunit obtains corresponding sampling points and sets corresponding Internet of Things terminals according to the sampling points;

[0065] The IoT terminal is equipped with multiple sensors, including a humidity sensor, a temperature sensor, and a light sensor, which are used to collect soil moisture data, soil temperature data, and soil light data, respectively. The obtained data information is marked as soil moisture data according to the sampling point corresponding to the IoT terminal and sent to the data processing module.

[0066] The data processing module obtains soil moisture data from the IoT terminal, sets a soil data set corresponding to the sampling point and a corresponding edge computing node, and the edge computing node analyzes and processes the soil data set to obtain the soil type. The specific implementation process includes:

[0067] The data processing module is provided with a sampling processing unit and a data processing unit;

[0068] The sampling processing unit obtains soil moisture data obtained at each sampling point, and sets an edge computing node according to the sampling point where the Internet of Things terminal is located. The edge computing node is used to analyze and process the soil moisture data obtained by the corresponding Internet of Things terminal at the corresponding sampling point, wherein a historical data storage pool is provided;

[0069] The historical data storage pool is used to store soil moisture data obtained by the corresponding IoT terminal at the corresponding sampling point, classify the soil moisture data according to the marking results of the corresponding sampling points, classify the soil moisture data with the same sampling point into the same type, and set the soil moisture data of the same type as the soil dataset;

[0070] Obtain the corresponding soil dataset, analyze and process the soil moisture data in the soil dataset, obtain the sampling units of the spatial sampling network in the area where the soil dataset is located, and obtain the machine learning algorithm for the corresponding sampling unit based on the soil type and terrain characteristics classification results of the sampling unit;

[0071] The machine learning algorithm is used to obtain soil moisture data in a soil data set, analyze and process the obtained soil moisture data, and obtain predicted soil moisture data;

[0072] Compare and analyze the soil moisture data in the soil dataset with the predicted soil moisture data, obtain corresponding deviation data, preset a deviation threshold, and compare and analyze the obtained deviation data with the corresponding deviation threshold;

[0073] When the deviation data is less than or equal to the deviation threshold, the soil moisture data is marked as normal;

[0074] When the deviation data is greater than the deviation threshold, the soil moisture data is marked as abnormal;

[0075] Preset a sampling monitoring period, obtain the abnormality rate of corresponding soil water analysis data marked as abnormal within the sampling monitoring period, preset an abnormality rate threshold, and compare and analyze the obtained abnormality rate with the abnormality rate threshold;

[0076] When the abnormality rate is greater than or equal to the abnormality rate threshold, the sampling point is marked as abnormal, and sampling abnormality information is generated and sent to the data transmission module;

[0077] When the abnormality rate is less than the abnormality rate threshold, the sampling point is marked as normal and sent to the data processing subunit;

[0078] The data processing subunit is used to analyze and process the soil moisture data marked as normal on the corresponding edge computing node;

[0079] The edge computing node obtains corresponding soil moisture data, wherein the soil moisture data includes soil humidity data, soil temperature data, and soil light data; marks the obtained soil moisture data as initial soil moisture data, and sets a moisture analysis model based on other data types in the soil moisture data. The moisture analysis model is used to analyze and process the soil moisture data to obtain predicted soil moisture data;

[0080] It should be further explained that, during the specific implementation process, the data processing module generates a sampling point data packet for the soil moisture data obtained in the corresponding sampling point, the predicted soil moisture data and the data analysis results obtained in the sampling processing unit, and sends it to the data transmission module.

[0081] The data transmission module is used to transmit data according to the analysis results obtained by the edge computing node corresponding to the Internet of Things terminal, and set a corresponding transmission method to transmit the data information obtained by the corresponding edge computing node. The specific implementation process includes:

[0082] The data transmission module is provided with a data pre-processing unit and a data transmission unit;

[0083] The data preprocessing unit obtains corresponding sampling point data packets, preprocesses the obtained sampling point data packets, and sends them to the data transmission unit for transmission according to the preprocessing results;

[0084] Obtaining a sampling point data packet, generating a data table for the data information in the sampling point data packet according to the data type, wherein the soil moisture data, the predicted soil moisture data, and the sampling abnormality information or the sampling normal information obtained in the sampling processing unit correspond to the first data table, the second data table, and the third data table, respectively; encoding the obtained soil moisture data, the predicted soil moisture data, and the sampling abnormality information or the sampling normal information, and mapping them to the corresponding type of data table respectively;

[0085] Analyze and process the corresponding coded data in the first data table, the second data table, and the third data table, and map the character strings of the coded data to the corresponding data table according to the number of the character strings. If the number is insufficient, loop through the character strings of the coded data until a character string exists at each position in the data table;

[0086] Analyze and process the data codes in the first data table, the second data table, and the third data table, set a starting position, obtain a character string at a corresponding position in the first data table according to the starting position, obtain a character string at a corresponding position in the second data table according to the data information represented by the character string at the position, obtain a character string at a corresponding position in the third data table according to the data information represented by the character string obtained in the second data table, and obtain a character string at a corresponding position in the first data table according to the data information represented by the character string obtained in the third data table, until the number of obtained character strings reaches a preset requirement;

[0087] Generate an encryption key from the obtained character string, encrypt the sampling point data packet according to the encryption key, and transmit the encrypted sampling point data packet to the data analysis module;

[0088] It should be further explained that, in a specific implementation, the corresponding character strings in the first data table, the second data table and the third data table are obtained in sequence in horizontal and vertical order.

[0089] The data analysis module is used to determine whether the soil moisture data obtained at the corresponding sampling point is valid based on the data information obtained in the sampling point data packet, re-plan the sampling units in the target area based on the judgment result of the sampling point, set the sampling point planning information to be sent to the data acquisition module, and send the valid sampling point data packet to the soil moisture analysis module. The specific implementation process includes:

[0090] Obtain the sampling point data packets obtained by each edge computing node, map the analysis results of the corresponding sampling points in the sampling point data packets to the regional elevation image, and generate a sampling visualization image;

[0091] Obtain the sampling points marked as normal in the sampled visualization image, preset the sampling correlation radius, take the sampling point marked as normal as the center, and the sampling correlation radius as the radius, obtain the marking results corresponding to the sampling points within the range of the sampled visualization image, perform statistical analysis on the marking results, and obtain the proportion of sampling points marked as normal within the range of the sampled visualization image;

[0092] A percentage threshold is preset, and the obtained percentage value is compared with the percentage threshold for analysis. When the percentage value is greater than or equal to the percentage threshold, the predicted soil moisture data corresponding to the sampling point is valid; when the percentage value is less than the percentage threshold, the predicted soil moisture data corresponding to the sampling point is invalid.

[0093] Obtain the sampling points marked as abnormal in the sampling visualization image and the corresponding sampling point data packets, obtain the machine learning algorithm corresponding to the sampling units with different classification results corresponding to the sampling points, and sequentially analyze and process the soil moisture data obtained in the sampling point data packets to obtain verified predicted soil moisture data;

[0094] Analyze and process the verified predicted soil moisture data obtained by each machine learning algorithm with the corresponding soil moisture data to obtain similarity data, and obtain the type of sampling unit corresponding to the machine learning algorithm with the highest similarity data;

[0095] The sampling unit types corresponding to the similarity data obtained from two adjacent sampling points marked as abnormal are correlated when the sampling unit types are consistent; when the sampling unit types are inconsistent, the sampling units between the sampling points are marked as intervals;

[0096] According to the correlation results and interval marks, the elevation data of the corresponding coordinates in the regional elevation image are collected, the sampling units in the regional elevation image are reallocated, and the corresponding sampling point planning information is set according to the distribution of the sampling units;

[0097] Obtain the distribution of sampling units in the target area, preset an average equally divided area, divide the sampling unit by the obtained average equally divided area, obtain the equally divided area according to the division result, set corresponding sampling points in each equally divided area, the sampling points are the center positions of the equally divided areas, and the coordinate information of the sampling points is marked. The coordinate information of the sampling points corresponding to the different equally divided areas in each sampling unit is marked as sampling point planning information, and it is sent to the data acquisition module.

[0098] The soil moisture analysis module is used to analyze and process the effective soil moisture data in the region to obtain the soil moisture data in the corresponding region. The specific implementation process includes:

[0099] Obtain valid sampling point data packages within the target area, map the corresponding predicted soil moisture data and soil moisture data in the sampling point data packages to the corresponding coordinates in the regional elevation image, and use spatial interpolation methods to estimate the corresponding predicted soil moisture data and soil moisture data for other areas based on the mapping results;

[0100] The spatial interpolation method obtains the distance value between other areas and the sampling point, sets the weight coefficient of the corresponding distance according to the soil type and terrain feature type, calculates the weighted average according to the distance value and weight coefficient from the sampling point to the corresponding interpolation point, and marks the obtained weighted average as the soil moisture data and predicted soil moisture data of the corresponding interpolation point;

[0101] The soil moisture classification standard is preset, and the obtained soil moisture data and predicted soil moisture data are compared and analyzed with the soil moisture classification standard respectively. The soil moisture data and predicted soil moisture data in the target area are obtained based on the comparative analysis.

[0102] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The intelligent soil moisture analysis system based on the Internet of Things terminal includes a soil moisture management platform, which is characterized by: The soil moisture management platform is provided with a data acquisition module, a data processing module, a data transmission module, a data analysis module and a soil moisture analysis module; The data acquisition module is used to collect elevation data in the area, classify the location of the soil in the area according to the elevation data, set sampling units according to the classification results, set sampling points and Internet of Things terminals according to the sampling units, and collect soil moisture data at the corresponding sampling points; The data processing module is used to set up corresponding edge computing nodes according to the Internet of Things terminal. Each edge computing node analyzes and processes the soil moisture data obtained by the Internet of Things terminal at the corresponding sampling point, obtains predicted soil moisture data, and generates a sampling point data packet and sends it to the data transmission module. The process includes: obtaining the corresponding Internet of Things terminal, setting up the corresponding edge computing node according to the Internet of Things terminal, presetting a sampling monitoring cycle for the corresponding edge computing node, setting the soil moisture data of the same sampling point within the sampling monitoring cycle as a soil data set, and the edge computing node obtaining the corresponding machine learning algorithm based on the classification results of the sampling unit corresponding to the sampling point; The soil moisture data in the soil dataset is analyzed and processed based on the machine learning algorithm to obtain predicted soil moisture data; the soil moisture data in the soil dataset is compared and analyzed with the predicted soil moisture data, and whether the sampling point is normal is determined based on the comparison result. If normal, the sampling point is marked as normal; if abnormal, the sampling point is marked as abnormal; The data processing module generates a sampling point data packet based on the information obtained in the corresponding edge computing node and sends it to the data transmission module; The data transmission module is used to encode the sampling point data packet generated by the corresponding edge computing node, encrypt it according to the encoding processing result, and send it to the data analysis module; The data analysis module is used to determine whether the soil moisture data obtained at the corresponding sampling point is valid based on the data information obtained in the sampling point data packet. The process includes: obtaining the sampling point data packet obtained by each edge computing node, mapping the analysis results of the corresponding sampling point in the sampling point data packet to the regional elevation image, and generating a sampling visualization image; Obtain the sampling points marked as normal in the sampled visualization image, preset the sampling correlation radius, take the sampling point marked as normal as the center, and the sampling correlation radius as the radius, obtain the marking results corresponding to the sampling points within the range of the sampled visualization image, perform statistical analysis on the marking results, and obtain the proportion of sampling points marked as normal within the range of the sampled visualization image; A percentage threshold is preset, and the obtained percentage value is compared with the percentage threshold for analysis. When the percentage value is greater than or equal to the percentage threshold, the predicted soil moisture data corresponding to the sampling point is valid; when the percentage value is less than the percentage threshold, the predicted soil moisture data corresponding to the sampling point is invalid. According to the judgment results of the sampling points, the sampling units in the target area are replanned, the sampling point planning information is sent to the data acquisition module, and the valid sampling point data packets are sent to the soil moisture analysis module; The soil moisture analysis module is used to analyze and process the effective soil moisture data in the area and obtain the soil moisture data in the corresponding area.

2. The intelligent soil moisture analysis system based on the Internet of Things terminal according to claim 1 is characterized in that: The data acquisition module sets a sampling unit, and the process of setting corresponding sampling points and IoT terminals according to the sampling unit includes: The data acquisition module is provided with an information entry window, which is used by relevant staff in the soil moisture management platform to enter soil moisture analysis area information, wherein the soil moisture analysis area information includes area name, area location information and soil classification information, and the soil classification information includes three types: sandy soil, loam and clay; Set the target area based on the regional location information, use GPS equipment to measure the elevation within the target area to obtain elevation data and its corresponding coordinates, and generate a regional elevation image; classify the terrain features based on the regional elevation image, and set up a corresponding multi-level image network based on the classification results; Integrate the multi-level image networks corresponding to different classification results, divide the regional elevation image according to the integration results, and set corresponding sampling units according to the division results; The sampling points in the corresponding sampling units are set according to the sampling point planning information set by the data analysis module, and the corresponding Internet of Things terminals are set in the sampling points.

3. The intelligent soil moisture analysis system based on the Internet of Things terminal according to claim 2 is characterized in that: Obtain sampling points and corresponding IoT terminals within the target area; The IoT terminal includes multiple sensors, including a humidity sensor, a temperature sensor, and a light sensor, which are used to collect soil moisture data, soil temperature data, and soil light data, respectively. The obtained data information is marked as soil moisture data according to the sampling point corresponding to the IoT terminal and sent to the data processing module.

4. The intelligent soil moisture analysis system based on the Internet of Things terminal according to claim 3 is characterized in that: The process of transmitting the sampling point data packet by the data transmission module includes: Obtaining a sampling point data packet, the sampling point data packet including soil moisture data, predicted soil moisture data, and an analysis result of the sampling point, encoding each data information in the sampling point data packet to generate a corresponding data code; setting a first data table, a second data table, and a third data table according to different data in the sampling point data packet, and randomly mapping the data codes corresponding to the data information to the corresponding data tables respectively; Analyze and process the data codes in the first data table, the second data table, and the third data table, set a starting position, obtain a character string at a corresponding position in the first data table according to the starting position, obtain a character string at a corresponding position in the second data table according to the data information represented by the character string at the position, obtain a character string at a corresponding position in the third data table according to the data information represented by the character string obtained in the second data table, and obtain a character string at a corresponding position in the first data table according to the data information represented by the character string obtained in the third data table, until the number of obtained character strings reaches a preset requirement; An encryption key is generated from the obtained character string, the sampling point data packet is encrypted according to the encryption key, and the encrypted sampling point data packet is transmitted to the data analysis module.

5. The intelligent soil moisture analysis system based on the Internet of Things terminal according to claim 4 is characterized in that: The data analysis module replans the sampling unit and sets the sampling point planning information, including: Obtain the sampling points marked as abnormal in the sampling visualization image and the corresponding sampling point data packets, obtain the machine learning algorithm corresponding to the sampling units with different classification results corresponding to the sampling points, and sequentially analyze and process the soil moisture data obtained in the sampling point data packets to obtain verified predicted soil moisture data; Analyze and process the verified predicted soil moisture data obtained by each machine learning algorithm with the corresponding soil moisture data to obtain similarity data, and obtain the type of sampling unit corresponding to the machine learning algorithm with the highest similarity data; Compare the sampling unit types corresponding to the similarity data obtained from two adjacent sampling points marked as abnormal. If the sampling unit types are consistent, the sampling units between the sampling points are associated with each other; if the sampling unit types are inconsistent, the sampling units between the sampling points are marked as intervals. According to the correlation results and interval marks, the elevation data of the corresponding coordinates in the regional elevation image are collected, the sampling units in the regional elevation image are reallocated, and the corresponding sampling point planning information is set according to the distribution of the sampling units and sent to the data acquisition module.

6. The intelligent soil moisture analysis system based on the Internet of Things terminal according to claim 5 is characterized in that: The soil moisture analysis module analyzes and processes the effective soil moisture data in the region, and the process of obtaining the soil moisture data in the corresponding region includes: Obtain valid sampling point data packages within the target area, map the corresponding predicted soil moisture data and soil moisture data in the sampling point data packages to the corresponding coordinates in the regional elevation image, and use spatial interpolation methods to estimate the corresponding predicted soil moisture data and soil moisture data for other areas based on the mapping results; The soil moisture classification standard is preset, and the obtained soil moisture data and predicted soil moisture data are compared and analyzed with the soil moisture classification standard respectively. The soil moisture data and predicted soil moisture data in the target area are obtained based on the comparative analysis.

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