A saline-alkali land improvement method and system based on Internet of Things big data analysis

Abstract

The present invention provides a method and system for improving saline-alkali land based on Internet of Things big data analysis. The method includes: collecting soil samples from the target saline-alkali land, obtaining improvement objectives and preset improvement schemes through sample analysis and a knowledge base; randomly selecting multiple different types of areas and setting sensor nodes for hierarchical sampling; monitoring the changes in soil salt content and pH value as well as the growth of crops through a monitoring system; dynamically adjusting the sampling time interval; establishing a comprehensive evaluation model, and adjusting the improvement scheme according to the change values of soil salt content, pH value and the evaluation model. The system includes a sampling node setting module, a sampling module, and a model establishment and improvement scheme adjustment module. Through this method and system, sampling nodes are accurately set and accurate sampling is carried out, the data volume is minimized on the premise of accurately and comprehensively monitoring the situation of saline-alkali land, the monitoring efficiency is improved, and at the same time, a monitoring model is provided to comprehensively evaluate the effect of improvement.

Description

Technical Field

[0001] The present invention relates to the technical field of saline-alkali land improvement, and particularly to a method and system for improving saline-alkali land based on Internet of Things big data analysis. Background Art

[0002] Saline-alkali land is a type of land that affects crop growth due to excessive salt content in the soil. Globally, saline-alkali land is widely distributed, imposing great limitations on agricultural production. To improve the utilization efficiency of saline-alkali land, many researchers are committed to the study of saline-alkali land improvement methods. However, traditional saline-alkali land improvement methods mainly rely on experience and local experiments, lacking a comprehensive understanding and scientific planning of the entire region. With the development of Internet of Things and big data technologies, more and more researchers have started to explore the application of these technologies in saline-alkali land improvement. Through Internet of Things sensors, key parameters such as soil salt content and pH value can be monitored in real time. At the same time, through big data analysis, these data can be comprehensively processed to provide more scientific improvement suggestions.

[0003] However, there are still some problems with the existing saline-alkali land improvement methods based on Internet of Things big data analysis. Since the area of saline-alkali land is usually large and the Internet of Things data is also huge, how to set sampling nodes for accurate sampling while reducing sampling data is a problem; secondly, when monitoring soil salt content and pH value, the data of only a single sensor is often considered, affecting the accuracy and reliability of the data; in addition, the existing methods do not fully consider the influence of crop growth conditions, so they cannot provide a comprehensive reference for the improvement plan. Summary of the Invention

[0004] The present invention provides a method for improving saline-alkali land based on Internet of Things big data analysis, which is used to accurately set sampling nodes and conduct accurate sampling, minimize the data volume on the premise of accurately and comprehensively monitoring the situation of saline-alkali land, improve the monitoring efficiency, and at the same time provide a monitoring model to comprehensively evaluate the effect of improvement:

[0005] A method for improving saline-alkali land based on Internet of Things big data analysis proposed by the present invention, the method includes:

[0006] S1. Obtain the location of the saline-alkali land through a geographic information system and remote sensing data; collect soil samples of the target saline-alkali land, and obtain improvement objectives and preset improvement plans through sample analysis and a knowledge base; divide the target saline-alkali land into multiple regional types; randomly select multiple different types of regions, and set multiple sensor nodes for hierarchical sampling according to the improvement objectives;

[0007] S2. Obtain the soil conditions of the saline-alkali land through underground sensors; perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; monitor the changes in the soil salt content and pH value through a monitoring system; obtain the crop growth conditions of the saline-alkali land through an image sensor; set the sampling time interval of the sensor for sampling; dynamically adjust the sampling time interval according to the sampling results.

[0008] S3. Establish a comprehensive evaluation model based on the soil conditions and crop growth conditions of the saline-alkali land, and adjust the improvement plan according to the change value of the soil salt content, the change value of the pH value, and the change value of the evaluation model.

[0009] Further, a method for improving saline-alkali land based on Internet of Things big data analysis, wherein S1 includes:

[0010] Obtain the location of the saline-alkali land through a geographic information system and remote sensing data;

[0011] Collect soil samples of the saline-alkali land, and analyze the soil samples to obtain the soil type, salt content, and pH value of the corresponding saline-alkali land;

[0012] Obtain the improvement target and preliminary improvement plan of the corresponding saline-alkali land through sample analysis data and a knowledge base;

[0013] Divide the corresponding saline-alkali land into multiple first regions; among them, the first region includes a central region and a marginal region;

[0014] Obtain the altitude of different first regions of the target saline-alkali land through a geographic information system;

[0015] Divide the first region into second regions according to the altitude; obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second regions according to the levels;

[0016] Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement target; the number of selected second regions of each different type is N, and 2 ≤ N ≤ 5;

[0017] Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth of 30 - 60 cm as the second sampling depth;

[0018] Select sampling points at the first sampling depth and the second sampling depth respectively to set a sensor group for soil sampling; each sampling point includes multiple types of sensors; set an image sensor on the ground at the same sampling point.

[0019] Further, a method for improving saline-alkali land based on Internet of Things big data analysis, wherein S2 includes:

[0020] Obtain the soil conditions of saline-alkali land through underground sensors; the soil conditions of the saline-alkali land include salt content and pH value; monitor the changes in soil salt content and pH value through a monitoring system;

[0021] The soil salt content is:

[0022]

[0023] The soil pH value is:

[0024]

[0025] where Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the pH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2i is the pH value obtained by the sensor at the second depth; a1 and a2 are coefficients, and the range is (0, 1); m is the number of sampling points of the first or second depth sensor;

[0026] Obtain the crop growth conditions of saline-alkali land through an image sensor;

[0027] Set the sampling time interval of the underground sensor for sampling;

[0028] Dynamically adjust the sampling time interval according to the sampling results;

[0029] T = (1 - λ × avgD) × T0

[0030]

[0031] where T is the sampling time interval; T0 is the preset sampling time interval; C j-1 is the value of the previous sampling of a certain underground sensor, C j-2 is the value of the previous sampling of a certain underground sensor; ΔC y is the preset change value of such sensors; avgD is the average value of D obtained by multiple underground sensors; Min(E) is the minimum value of E obtained by multiple underground sensors; Max(E) is the maximum value of E obtained by multiple underground sensors.

[0032] Furthermore, a method for improving saline-alkali land based on Internet of Things big data analysis, where S3 includes:

[0033] Classify the growth conditions of crops; the classification includes very poor, poor, average, good, and excellent;

[0034] Establish a comprehensive evaluation model based on the soil conditions of saline-alkali land and the growth conditions of crops;

[0035] H = Z × [1 - λ × |avgD| × (w1 × A + w2 × B)]

[0036]

[0037] Wherein, H is the comprehensive evaluation score; Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the pH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2i is the pH value obtained by the sensor at the second depth; Y y is the target maximum salt content; P y is the target maximum pH value; w1, w2 are weights, and the range is (0, 1); m is the number of sampling points of the first or second depth sensor; a1, a2 are coefficients, and the range is (0, 1); Z is the crop growth score; the range is 2 - 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10;

[0038] Adjust the improvement plan according to the change value of soil salt content, the change value of pH value, and the change value of the evaluation model.

[0039] Furthermore, a saline - alkali land improvement method based on Internet of Things big data analysis, the classification of the growth situation of the crops includes:

[0040] Obtain the growth time series according to the crop growth time, and classify the growth situations of each growth time series of the same kind of crop to obtain the picture features of different classifications;

[0041] Extract the growth image features and time series of the crops corresponding to the target saline - alkali land;

[0042] Compare the image features of the crops corresponding to each sampling point with the features corresponding to different classifications of the same kind of crop in the database at the same time series, and obtain the classification corresponding to the feature with the highest similarity as the classification corresponding to the crop at this sampling point at this time series;

[0043] If there are multiple classification features corresponding to different sampling points of the target saline - alkali land at the same time series, then take the same classification corresponding to the most sampling points as the final classification of the crop at this time series.

[0044] The present invention provides a saline - alkali land improvement system based on Internet of Things big data analysis, and the system includes:

[0045] Sampling node setting module: Obtain the location of saline-alkali land through geographic information system and remote sensing data; Collect soil samples of the target saline-alkali land, obtain improvement objectives and preset improvement plans through sample analysis and knowledge base, and divide the target saline-alkali land into multiple regional types; Randomly select multiple different types of regions, and set multiple sensor nodes for hierarchical sampling according to the improvement objectives;

[0046] Sampling module: Obtain the soil conditions of saline-alkali land through underground sensors; Perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; Monitor the changes in the soil salt content and pH value through a monitoring system; Obtain the crop growth conditions of saline-alkali land through image sensors; Set the sampling time interval of the sensors for sampling; Dynamically adjust the sampling time interval according to the sampling results;

[0047] Model establishment and improvement plan adjustment module: Establish a comprehensive evaluation model according to the soil conditions and crop growth conditions of saline-alkali land, and adjust the improvement plan according to the change values of soil salt content, pH value, and the evaluation model.

[0048] Furthermore, a saline-alkali land improvement system based on Internet of Things big data analysis, the sampling node setting module includes:

[0049] Location acquisition module: Obtain the location of saline-alkali land through geographic information system and remote sensing data;

[0050] Sample analysis module: Collect soil samples of saline-alkali land, and analyze the soil samples to obtain the soil type, salt content, and pH value of the corresponding saline-alkali land;

[0051] Target setting module: Obtain the improvement objectives and preliminary improvement plans of the corresponding saline-alkali land through sample analysis data and knowledge base;

[0052] First regional division module: Divide the corresponding saline-alkali land into multiple first regions; Among them, the first region includes a central region and an edge region;

[0053] Second regional division module: Obtain the altitude of different first regions of the target saline-alkali land through geographic information system; Divide the first region into second regions according to the altitude; Obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second region according to the levels;

[0054] Regional selection module: Randomly select multiple different types of second regions, and set sensor nodes for sampling according to the improvement objectives; The number of each different type of second region selected is N, 2 ≤ N ≤ 5;

[0055] Sampling depth setting module: Set the depth from the ground surface of 0 to 30 cm as the first sampling depth, and the depth of 30 - 60 cm as the second sampling depth;

[0056] Stratified sampling module: Select sampling points at the first sampling depth and the second sampling depth respectively to set up a sensor group for soil sampling; Each sampling point includes multiple types of sensors; Set up an image sensor on the ground at the same sampling point.

[0057] Furthermore, a saline-alkali land improvement system based on Internet of Things big data analysis, the sampling module includes:

[0058] Soil condition acquisition module: Obtain the soil conditions of the saline-alkali land through underground sensors; The soil conditions of the saline-alkali land include salt content and pH value; Monitor the changes in soil salt content and pH value through a monitoring system;

[0059] The soil salt content is:

[0060]

[0061] The soil pH value is:

[0062]

[0063] Among them, Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the pH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2i is the pH value obtained by the sensor at the second depth; a1, a2 are coefficients, and the range is (0, 1); m is the number of sampling points of the sensor at the first or second depth;

[0064] Crop growth condition acquisition module: Obtain the crop growth conditions of the saline-alkali land through an image sensor;

[0065] Sampling time setting module: Set the sampling time interval of the underground sensors for sampling; Dynamically adjust the sampling time interval according to the sampling results;

[0066] T = (1 - λ × avgD) × T0

[0067]

[0068] Among them, T is the sampling time interval; T0 is the preset sampling time interval; C j-1 is the value of the previous sampling of a certain underground sensor, C j-2 is the value of the sampling before the previous sampling of a certain underground sensor; ΔC yPreset change values for such sensors; avgD is the average value of D obtained by multiple underground sensors; Min(E) is the minimum value of E obtained by multiple underground sensors; Max(E) is the maximum value of E obtained by multiple underground sensors.

[0069] Furthermore, a saline-alkali land improvement system based on Internet of Things big data analysis, the model establishment and improvement plan adjustment module includes:

[0070] Crop classification module: Classify the growth conditions of crops; the classification includes very poor, poor, average, good, and excellent;

[0071] Comprehensive evaluation module establishment module: Establish a comprehensive evaluation model according to the saline-alkali land soil conditions and crop growth conditions;

[0072] H = Z × [1 - λ × |avgD| × (w1 × A + w2 × B)]

[0073]

[0074]

[0075] Wherein, H is the comprehensive evaluation score; Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the PH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2i is the PH value obtained by the sensor at the second depth; Y y is the target maximum salt content; P y is the target maximum PH value; w1, w2 are weights, ranging from (0, 1); m is the number of sampling points of the first or second depth sensor; a1, a2 are coefficients, ranging from (0, 1); Z is the crop growth score; ranging from 2 to 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10;

[0076] Monitoring and optimization module: Adjust the improvement plan according to the change value of soil salt content, the change value of PH value, and the change value of the evaluation model.

[0077] Furthermore, a saline-alkali land improvement system based on Internet of Things big data analysis, the crop classification module includes:

[0078] Database crop feature classification module: Obtain the growth time series according to the crop growth time, and classify the growth conditions of each growth time series of the same crop to obtain the picture features of different classifications;

[0079] Target saline-alkali land crop feature extraction module: Extract the growth image features and time series of the crops corresponding to the target saline-alkali land.

[0080] Sampling point crop classification determination module: Compare the image features of the crops corresponding to each sampling point with the features corresponding to different classifications of the same crop in the database at the same time series, and obtain the classification corresponding to the feature with the highest similarity as the classification of the crop corresponding to this sampling point at this time series.

[0081] Final classification module: If there are multiple classification features corresponding to different sampling points of the target saline-alkali land at the same time series, then take the same classification corresponding to the most sampling points as the final classification of the crop at this time series.

[0082] Advantages of the present invention: Through the saline-alkali land improvement method and system based on Internet of Things big data analysis described in the present invention, the location of the saline-alkali land can be accurately obtained through the geographic information system and remote sensing data, realizing precise positioning; through sample analysis and knowledge base, the improvement objectives are obtained, the saline-alkali land is divided into different regions, and further divided according to the altitude; by setting sensor nodes for hierarchical sampling, while minimizing the number of sensor nodes, comprehensive data of the target saline-alkali land can be obtained. Through underground sensors to obtain soil conditions and image sensors to obtain crop growth conditions, multi-dimensional data of the saline-alkali land can be obtained, providing a comprehensive information basis; through the sampling results, adjust the sampling time interval to ensure the accuracy of sampling and reduce data; use the comprehensive evaluation model to dynamically adjust the improvement plan according to the changes in soil salt content and pH value and the changes in the evaluation model, making the improvement measures more flexible and effective; this method combines Internet of Things technology and big data analysis, realizes automated data collection and analysis, can quickly obtain the status information of the saline-alkali land, and adjusts the improvement plan according to the evaluation model, thereby improving the improvement efficiency. In summary, this saline-alkali land improvement method based on Internet of Things big data analysis can achieve benefits and effects such as precise positioning, targeted improvement, multi-dimensional data sampling, dynamic adjustment, and improvement of improvement efficiency, which is helpful for scientifically and effectively improving saline-alkali land, improving the land availability and crop yield. Description of the Drawings

[0083] Figure 1 It is a schematic diagram of a saline-alkali land improvement method based on Internet of Things big data analysis. Detailed Embodiments

[0084] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0085] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0087] An improved method for saline-alkali land based on Internet of Things big data analysis in this embodiment, the method includes:

[0088] S1. Obtain the location of the saline-alkali land through a geographic information system and remote sensing data; collect soil samples of the target saline-alkali land, obtain improvement targets and preset improvement plans through sample analysis and a knowledge base, and divide the target saline-alkali land into multiple regional types; randomly select multiple different types of regions, set sensor nodes for hierarchical sampling according to the improvement targets; connect the sensors to a monitoring platform through the Internet of Things;

[0089] S2. Obtain the soil conditions of the saline-alkali land through underground sensors; perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; monitor the changes in the soil salt content and pH value through a monitoring system; obtain the crop growth conditions of the saline-alkali land through an image sensor; set the sampling time interval of the sensor for sampling; dynamically adjust the sampling time interval according to the sampling results;

[0090] S3. Establish a comprehensive evaluation model according to the soil conditions and crop growth conditions of the saline-alkali land, and adjust the improvement plan according to the change values of the soil salt content, the change values of the pH value, and the change values of the evaluation model.

[0091] The working principle of the above technical solution is as follows: The location of saline-alkali land is obtained through the Geographic Information System (GIS) and remote sensing data. The GIS and remote sensing technology are used to locate the target saline-alkali land and obtain basic information such as topography. Soil samples are collected from the target saline-alkali land, and information such as the composition, texture, and structure of the soil samples is analyzed in the laboratory. Combining with the expert knowledge base, the improvement objectives and preset improvement plans are determined. According to the characteristics of the target saline-alkali land, it is divided into multiple regional types, and multiple different types of regions are randomly selected. According to the improvement objectives, sensor nodes are set for stratified sampling to obtain more accurate soil data. Through the Internet of Things technology, underground sensors are buried in the saline-alkali land to obtain parameters such as soil salt content and pH value. The data of multiple sensors are weighted and analyzed to obtain more accurate soil information. The changes in soil salt content and pH value are monitored in real time through the monitoring system to grasp the soil conditions and adjust the improvement plan in a timely manner. The growth situation of crops in the saline-alkali land is obtained through image sensors, including characteristics such as crop growth and color. Combining sensor data and image data, the growth status of crops is comprehensively evaluated. According to the sampling results, the sampling time interval of the sensors is dynamically adjusted to ensure the timeliness and accuracy of the data. According to the soil conditions of the saline-alkali land and the growth situation of crops, a comprehensive evaluation model is established to comprehensively evaluate the effect of the improvement plan. According to the change values of soil salt content, pH value, and the change values of the evaluation model, the improvement plan is adjusted and optimized to achieve the best improvement effect. By dynamically adjusting improvement measures such as applying appropriate improvement materials and adjusting irrigation water quality, the soil environment and crop growth conditions of the saline-alkali land are improved. Generally speaking, this method realizes the dynamic adjustment of the saline-alkali land improvement plan by obtaining the location information of the saline-alkali land, stratified sampling the soil and crop data of different regions, and analyzing based on the comprehensive evaluation model. Through this process, the saline-alkali land can be scientifically and efficiently improved, and the land availability and crop yield can be increased.

[0092] The effects of the above technical solutions are as follows: Through the geographical information system and remote sensing data, the location of saline-alkali land can be accurately obtained, achieving precise positioning; through sample analysis and the knowledge base, the improvement objectives are obtained, the saline-alkali land is divided into different regions, and further divided according to the altitude; by setting sensor nodes for stratified sampling, while minimizing the number of sensor nodes, comprehensive data of the target saline-alkali land can be obtained. Through underground sensors to obtain soil conditions and image sensors to obtain crop growth conditions, multi-dimensional data of the saline-alkali land can be obtained, providing a comprehensive information basis; based on the sampling results, the sampling time interval is adjusted to ensure the accuracy of sampling and reduce data; using the comprehensive evaluation model, according to the changes in soil salt content and pH value and the changes in the evaluation model, the improvement plan is dynamically adjusted, making the improvement measures more flexible and effective; this method combines Internet of Things technology and big data analysis to achieve automated data collection and analysis, can quickly obtain the status information of saline-alkali land, and adjust the improvement plan according to the evaluation model, thereby improving the improvement efficiency. In summary, this saline-alkali land improvement method based on Internet of Things big data analysis can achieve benefits and effects such as precise positioning, targeted improvement, multi-dimensional data sampling, dynamic adjustment, and improvement of improvement efficiency, which helps to scientifically and effectively improve saline-alkali land, improve the land availability and crop yields.

[0093] In an embodiment of a saline-alkali land improvement method based on Internet of Things big data analysis, the S1 includes:

[0094] Obtain the location of the saline-alkali land through the geographical information system and remote sensing data;

[0095] Collect soil samples of the saline-alkali land, and analyze the soil samples to obtain the soil type, salt content, and pH value of the corresponding saline-alkali land;

[0096] Obtain the improvement objectives and preliminary improvement plans for the corresponding saline-alkali land through sample analysis data and the knowledge base;

[0097] For example: According to the analysis results, it is found that the soil in this area is slightly to moderately saline-alkali soil, with a relatively high salt content and a relatively alkaline pH value.

[0098] Based on the reference of sample analysis data and the relevant knowledge base, the improvement objectives should be to reduce the soil salt content and increase the soil pH value to improve the soil quality and increase the suitability for plant growth.

[0099] Based on this information, the preliminary improvement plan may include the following measures:

[0100] Drainage improvement: By constructing a drainage system, improve the soil drainage condition and remove excess salt and water;

[0101] Salt flushing: Use desalinated water or low-salt water to flush the soil and wash away the salt in the soil;

[0102] Organic matter addition: Applying organic fertilizers or organic substances such as straw to improve soil structure and texture and enhance the soil's water retention capacity.

[0103] Gypsum addition: Appropriately adding soil conditioners such as gypsum to lower the soil pH value and improve soil acidity and alkalinity;

[0104] Selection of suitable crops: Selecting salt-tolerant crops for planting to adapt to the saline and alkaline soil environment;

[0105] Dividing the corresponding saline-alkali land into multiple first regions; among them, the first region includes a central region and a marginal region; making intersecting lines with the vertices of the inscribed rectangle in the region; taking the intersection point of the two intersecting lines as the center, selecting 4 points at 1 / 2 of the distance from the intersection point of the two intersecting lines to each vertex of the inscribed rectangle to form a rectangle, and the area inside this rectangle is the central region; the external region is the marginal region; the marginal region is divided into multiple regions according to the marginal position;

[0106] Obtaining the altitude of different first regions of the target saline-alkali land through a geographic information system;

[0107] Dividing the first region into a second region according to the altitude; obtaining the highest altitude and the lowest altitude of the target saline-alkali land, dividing the altitude difference between the highest altitude and the lowest altitude into multiple levels, and dividing the second region according to the levels in different regions; the number of levels is h = ceil((h max -h min ) / standard storey height), the function ceil() means rounding up a real number to the next larger integer, and the value range of the standard storey height is 30 - 40, with the unit of cm;

[0108] where h max is the highest altitude of the target saline-alkali land area; h min is the lowest altitude of the target saline-alkali land area;

[0109] For example, if the altitude difference between the highest altitude and the lowest altitude of the target saline-alkali land is 50 cm; then from the lowest altitude in the target saline-alkali land to the lowest altitude plus 30 cm is the first level, and from the lowest altitude plus 30 cm in the target saline-alkali land to the highest altitude is the second level; if the altitude difference between the highest altitude and the lowest altitude of the target saline-alkali land is 70 cm; then from the lowest altitude in the saline-alkali land to the lowest altitude plus 35 cm is the first level; from the lowest altitude plus 35 cm in the saline-alkali land to the highest altitude in the saline-alkali land is the second level;

[0110] If the altitude difference between the highest altitude and the lowest altitude of the target saline-alkali land is less than 30 cm, it is only divided into one level;

[0111] Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives; the number N of each different type of second region selected is 2 ≤ N ≤ 5;

[0112] Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth of 30 - 60 cm as the second sampling depth;

[0113] Select sampling points at the first sampling depth and the second sampling depth respectively, and set sensor groups for soil sampling; each sampling point includes multiple types of sensors;

[0114] Set an image sensor on the ground at the same sampling point.

[0115] The working principle of the above technical solution is as follows: First, obtain the location of the target saline-alkali land through the geographic information system and remote sensing data, and collect soil samples of the saline-alkali land for analysis to obtain key data such as the soil type, salt content, and pH value of the corresponding saline-alkali land. Then, obtain the improvement objectives and preliminary improvement plans for the corresponding saline-alkali land through sample analysis data and the knowledge base. Next, divide the corresponding saline-alkali land into multiple first regions, where the first region includes a central region and a marginal region. Divide the central region into rectangles by the way of intersecting lines, and the marginal region is further divided into multiple regions according to the marginal position. Obtain the altitude of different first regions of the target saline-alkali land through the geographic information system, and divide the first region into second regions according to the altitude. Divide the first region into second regions according to the altitude; obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second regions according to the levels in different regions. Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives. The number N of each different type of second region selected is 2 ≤ N ≤ 5. Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth of 30 - 60 cm as the second sampling depth, and include multiple types of sensors at each sampling point for soil sampling and monitoring.

[0116] At the same time, set an image sensor on the ground at the same sampling point for data collection and analysis. Through the analysis and processing of the sampling data, various key data such as the soil characteristics of the target saline-alkali land can be obtained, and then a more accurate saline-alkali land improvement plan can be formulated to achieve more targeted and better saline-alkali land treatment.

[0117] The effects of the above technical solutions are as follows: By using a geographic information system and remote sensing data to obtain the location of saline-alkali land, the distribution range of saline-alkali land can be accurately determined. Soil samples of saline-alkali land are collected and comprehensively analyzed, including indicators such as soil type, salt content, and pH value, so as to comprehensively understand the soil conditions of saline-alkali land. Based on the sample analysis data and knowledge base, combined with the goal of saline-alkali land improvement, a preliminary improvement plan is formulated. In this way, saline-alkali land can be improved targeted according to the actual situation. The saline-alkali land is divided into multiple first regions, including the central region and the edge region, which can better carry out regional management and improvement operations. According to the altitude difference of different regions of the target saline-alkali land, the first region is further divided into second regions. In this way, different levels of soil conditions and improvement strategies can be more accurately considered. Multiple different types of second regions are randomly selected, and sensor nodes are set up for sampling. In this way, the soil conditions of different regions can be comprehensively understood and reference data can be provided for subsequent improvement. By setting multiple sampling depths, including the depth from 0 to 30 cm on the ground and the depth from 30 to 60 cm, information on soils at different depths can be obtained. This helps to analyze the vertical distribution of saline-alkali land soil and guide more precise improvement strategies. An image sensor is set up on the ground at the sampling point to monitor the surface situation in real time. In this way, key information such as soil moisture distribution and vegetation growth can be discovered in a timely manner, providing an effective basis for adjustment and judgment during the improvement process. Through the above methods, saline-alkali land can be improved more scientifically, realizing the continuous improvement and restoration of the soil, improving soil fertility, increasing crop yields, reducing the salinization degree of cultivated land, and promoting the sustainable development of agriculture.

[0118] In this embodiment, a method for improving saline-alkali land based on Internet of Things big data analysis, the S2 includes:

[0119] Obtain the soil conditions of saline-alkali land through underground sensors; the soil conditions of saline-alkali land include salt content and PH value; monitor the changes in soil salt content and PH value through a monitoring system;

[0120] The soil salt content is:

[0121]

[0122] The soil PH value is:

[0123]

[0124] Wherein, Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the PH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2iThe pH value obtained by the sensor at the second depth; a1 and a2 are coefficients, with a range of (0, 1); m is the number of sampling points of the first or second depth sensor;

[0125] Obtain the crop growth situation of the saline-alkali land through an image sensor; the sampling frequencies of the image sensor and the underground sensor are kept consistent;

[0126] Set the sampling time interval of the underground sensor for sampling;

[0127] Dynamically adjust the sampling time interval according to the sampling results;

[0128] T = (1 - λ × avgD) × T0

[0129]

[0130] where T is the sampling time interval; T0 is the preset sampling time interval; C j-1 is the value of a certain underground sensor's previous sampling, C j-2 is the value of a certain underground sensor's sampling the time before last; ΔC y is the preset change value of such sensors; avgD is the average value of D obtained by multiple underground sensors; Min(E) is the minimum value of E obtained by multiple underground sensors; Max(E) is the maximum value of E obtained by multiple underground sensors.

[0131] The working principle of the above technical solution is as follows: Obtain the soil conditions of the saline-alkali land through underground sensors, including the salt content and pH value of the soil. The sensors are divided into two depths (the first depth and the second depth), and the salt content and pH value at the corresponding depths are obtained respectively. Monitor the changes in soil salt content and pH value in real time through the monitoring system. The monitoring system can record and display the dynamic changes of soil conditions to understand the soil conditions in real time. Perform weighted average calculation on the sensor sampling values at multiple depths obtained according to the weight coefficients to obtain the overall soil salt content and pH value. Among them, the weight coefficients a1 and a2 are determined according to the actual situation and are in the range of 0 and 1; Monitor the growth situation of crops on the saline-alkali land in real time through an image sensor. The image sensor can capture information such as the growth state of plants, leaf color, and canopy density to evaluate the improvement effect of the saline-alkali land. Dynamically adjust the sampling time interval of the sensor according to the sampling results and the preset change amount. Evaluate the stability of the soil conditions through the calculated D value (change difference degree). If the stability is high, the sampling time interval can be extended; on the contrary, if the stability is low, the sampling time interval needs to be shortened. According to the stability evaluation results, calculate the new sampling time interval T through the formula.

[0132] The effects of the above technical solution are as follows: Data on soil salt content and pH value are obtained through underground sensors, and their changes are monitored in real time through a monitoring system. This enables farmers and agricultural experts to understand the soil conditions of saline-alkali land in real time and take corresponding measures in a timely manner. By calculating the weighted average of the sampling values of multiple depth sensors, the overall soil salt content and pH value are obtained. This accurate assessment helps to better understand the soil characteristics of saline-alkali land and provides a reference for targeted improvement. An image sensor is used to monitor the growth of crops on saline-alkali land in real time. By monitoring information such as the growth status, leaf color, and canopy density of plants, problems can be discovered in a timely manner and appropriate measures can be taken, which helps to improve crop yield and quality. According to the sampling results, the stability evaluation index D is calculated, and the sampling time interval of the sensor is dynamically adjusted through a formula. By dynamically adjusting the sampling time interval, while ensuring data quality, redundant sampling can be reduced as much as possible, thereby saving energy and storage space. Since redundant data is reduced, the speed of the data processing stage will also increase accordingly. The adjustment of the sampling time interval takes into account the data change situations of multiple sensors. In this way, a more accurate data sampling time can be obtained, thereby improving data accuracy. In this way, changes in soil conditions can be captured more quickly, measures can be adjusted in a timely manner, and the improvement effect can be enhanced. By monitoring and accurately assessing soil conditions in real time, and dynamically adjusting the sampling time interval according to the sampling results, this method can more precisely guide the improvement work of saline-alkali land. For specific soil conditions and crop requirements, targeted measures can be taken, thereby improving the improvement effect, increasing crop yields, and reducing the adverse effects of soil salinity on plant growth. In summary, this saline-alkali land improvement method based on Internet of Things big data analysis can provide real-time and accurate soil information and crop growth monitoring, helping farmers and agricultural experts to effectively improve the soil conditions of saline-alkali land and increase crop yields and quality.

[0133] In this embodiment, a method for improving saline-alkali land based on Internet of Things big data analysis, where S3 includes:

[0134] Classify the growth conditions of crops; the classification includes very poor, poor, average, good, and excellent;

[0135] Establish a comprehensive evaluation model based on the soil conditions of saline-alkali land and crop growth;

[0136] H = Z × [1 - λ × |avgD| × (w1 × A + w2 × B)]

[0137]

[0138]

[0139] where H is the comprehensive evaluation score; Y 1i is the salt content value obtained by the sensor at the first depth; P 1iThe pH value obtained by the sensor at the first depth; Y 2i The salt content value obtained by the sensor at the second depth; P 2i The pH value obtained by the sensor at the second depth; Y y The target maximum salt content; P y The target maximum pH value; w1 and w2 are weights, ranging from (0, 1); m is the number of sampling points of the first or second depth sensor; a1 and a2 are coefficients, ranging from (0, 1); Z is the crop growth score; ranging from 2 to 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10;

[0140] C j-1 The value of the previous sampling of a certain underground sensor, C j-2 The value of the sampling before the previous sampling of a certain underground sensor; ΔC y The preset change value of such sensors; avgD is the mean value of D obtained by multiple underground sensors; Min(E) is the minimum value of E obtained by multiple underground sensors; Max(E) is the maximum value of E obtained by multiple underground sensors;

[0141] Adjust the improvement plan according to the change value of soil salt content, the change value of pH value and the change value of the evaluation model;

[0142] Compare the change values of soil salt content at different time points or different depths; if it is found that the salt content shows a gradually decreasing trend, it indicates that the improvement measures may already be working and the original improvement plan can be continued and strengthened; if the change value of salt content is not obvious or increases, it may be necessary to re-evaluate the effect of the improvement plan and consider taking further measures;

[0143] If the pH value gradually approaches the target maximum value during the improvement process, it indicates that the improvement plan is effective in adjusting the soil acidity; if the change value of pH value is not obvious or deviates from the target, it is possible to consider adjusting the improvement plan specifically, such as increasing or decreasing the application of acidic or alkaline substances to achieve the ideal pH value.

[0144] By comparing the change of the comprehensive evaluation score of the evaluation model at different time points, understand the effect of the improvement plan; if the comprehensive evaluation score gradually increases, it indicates that the improvement plan is effective and the original improvement measures can be continued; if the change value of the evaluation model shows that the comprehensive evaluation score decreases or fluctuates, it may be necessary to adjust the improvement plan, such as adjusting the fertilization amount, watering amount or adding other improvement means.

[0145] The working principle of the above technical solution is as follows: Classify the growth conditions of crops on saline-alkali land, including very poor, poor, average, good, and excellent levels; establish a comprehensive evaluation model based on the soil conditions of the saline-alkali land and the growth conditions of the crops to evaluate the improvement effect. This model calculates a comprehensive evaluation score H, which includes two parts A and B. Part A is obtained by calculating the deviations between the salt content value and pH value obtained by the first depth sensor and the target maximum salt content and pH value, and weighted summing according to the coefficients a1 and a2. Part B is also obtained by calculating the deviations between the salt content value and pH value obtained by the second depth sensor and the target maximum salt content and pH value, and weighted summing according to the coefficients a1 and a2.

[0146] The comprehensive evaluation score H uses weights w1 and w2 to perform weighted summation on A and B to obtain the final evaluation result. According to the classification, the crop growth condition is converted into a crop growth score Z, and its value range is 2 to 10, depending on the classification. For example, very poor corresponds to 2, poor corresponds to 4, average corresponds to 6, good corresponds to 8, and excellent corresponds to 10. By analyzing the change values of the soil salt content, pH value, and the evaluation model, the improvement plan for the saline-alkali land is adjusted. According to the change trends of the comprehensive evaluation score H and the crop growth score Z, the improvement measures can be optimized, such as adjusting the irrigation amount, applying appropriate salt treatment agents, changing the crop planting method, etc. In short, this method evaluates the improvement effect of the saline-alkali land through a comprehensive evaluation model and a crop growth score, and adjusts the improvement plan based on data analysis to improve the soil quality and crop yield of the saline-alkali land.

[0147] The effects of the above technical solution are as follows: By comprehensively evaluating the saline-alkali soil conditions and crop growth conditions, using parameters such as the salt content value and pH value obtained by sensors to calculate the comprehensive evaluation score H, the improvement effect of saline-alkali land can be objectively and accurately evaluated. The parameters avgD, λ, and the weights w1 and w2 in the formula are dynamically adjusted according to the sampling results of underground sensors, and the improvement of saline-alkali land can be accurately evaluated according to the actual situation, and the importance of different factors can be flexibly adjusted; Through the Internet of Things technology and big data analysis, it is possible to monitor and collect the changes of saline-alkali soil parameters in real time and evaluate the crop growth conditions in real time. In this way, problems can be discovered in time and corresponding measures can be taken to achieve the refined management of saline-alkali land. Through the change trends of the comprehensive evaluation score H and the crop growth score Z, it can provide scientific basis and reference for farmers, agricultural experts and decision-makers to guide the formulation of reasonable saline-alkali land improvement plans. According to the change value of the evaluation model, improvement measures can be adjusted, such as adjusting the irrigation volume, applying appropriate salt treatment agents, changing the crop planting method, etc., to improve the improvement effect. Through the improvement plan for saline-alkali land, parameters such as the salt content and pH value of the soil can be adjusted, the salt content in the soil can be reduced, the soil structure and water retention capacity can be improved, and the crop growth environment can be improved. This will help to increase the yield and quality of crops. Through accurate data analysis and evaluation, agricultural production factors such as water resources, fertilizers and pesticides can be rationally utilized, waste and pollution can be reduced, and the purpose of saving resources and protecting the environment can be achieved. In summary, the saline-alkali land improvement method based on Internet of Things big data analysis can provide accurate evaluation and guidance, achieve refined management, improve soil quality and crop yield, and save resources and protect the environment.

[0148] In this embodiment, a saline-alkali land improvement method based on Internet of Things big data analysis, the classification of the growth conditions of crops includes:

[0149] Obtain the growth time series according to the crop growth time, classify the growth conditions of each growth time series of the same crop to obtain the image features of different classifications, and the classifications include very poor, poor, average, good and excellent;

[0150] Extract the growth image features and time series of the crops corresponding to the target saline-alkali land;

[0151] Compare the image features of the crops corresponding to each sampling point with the features corresponding to different classifications of the same crop in the same time series in the database, and obtain the classification corresponding to the feature with the highest similarity as the classification corresponding to the crop at this sampling point and this time series;

[0152] If there are multiple classification features corresponding to different sampling points of the target saline-alkali land in the same time series, then use the same classification corresponding to the most sampling points as the final classification of the crop in this time series.

[0153] The working principle of the above technical solution is as follows: First, classify the growth conditions of the same crop in different growth time series. This can be determined by observing and recording the growth states of the same crop at different growth stages, such as leaf color, plant height, plant morphology, etc. The classification can include grades such as very poor, poor, average, good, and excellent. For each crop's growth time series, extract the growth image features of the corresponding crop. This can utilize computer vision technology to process and analyze the crop images and extract features related to the crop growth state, such as texture, shape, color, etc. Compare the extracted crop image features with the features corresponding to different classifications of the same crop in the database. By calculating the similarity between the features, find the classification corresponding to the features most similar to those of the crop to be classified. If multiple classification features correspond to different test points, select the same classification corresponding to the most detection points as the final classification result. This is to increase the accuracy and reliability of the classification. By voting or statistical analysis of the results of multiple detection points, select the classification with the highest frequency as the final result. Through the above steps, this method uses Internet of Things big data analysis and image feature extraction technology to classify the growth conditions of crops in saline-alkali land. By comparing the feature similarity and statistical analysis of multi-point detection results, accurate classification results can be obtained, providing a scientific basis for the improvement and management of saline-alkali land.

[0154] The effects of the above technical solution are as follows: By classifying the crop growth time series, the growth state of the crop can be accurately evaluated and described. Dividing the crop growth conditions into grades such as very poor, poor, average, good, and excellent can more specifically understand the adaptability and growth conditions of the crop in saline-alkali land. This method uses image feature extraction technology to convert the growth images of the crop into feature vectors. This classification method based on picture features can more objectively and quantitatively evaluate the growth of the crop, avoiding subjective and human interference factors. Compare the image features of the crop with the features in the database and calculate the similarity. By comparing the features corresponding to different classifications, find the features most similar to the crop to be classified, thereby determining the classification of the crop. In this way, the advantages of big data analysis and machine learning algorithms can be utilized to improve the accuracy and reliability of the classification. If multiple classification features correspond to different test points, this method uses the same classification corresponding to the most detection points as the final classification result. This method of integrating multi-point detection results can reduce misjudgments that may be caused by individual test points and improve the credibility and stability of the classification results. The saline-alkali land improvement method based on Internet of Things big data analysis can provide accurate crop growth condition evaluation and classification information for agricultural managers and decision-makers, helping them better understand the conditions of saline-alkali land and take corresponding improvement measures. At the same time, this method based on big data and image feature analysis overcomes the limitations of traditional subjective judgment and individual factors and has higher scientificity and objectivity.

[0155] In this embodiment, a saline-alkali land improvement system based on Internet of Things big data analysis, the system includes:

[0156] Sampling node setting module: Obtain the location of the saline-alkali land through the geographic information system and remote sensing data; collect soil samples of the target saline-alkali land, obtain improvement objectives and preset improvement plans through sample analysis and knowledge base, and divide the target saline-alkali land into multiple regional types; randomly select multiple different types of regions, and set sensor nodes for stratified sampling according to the improvement objectives.

[0157] Sampling module: Obtain the soil conditions of the saline-alkali land through underground sensors; perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; monitor the changes of the soil salt content and pH value through the monitoring system; obtain the crop growth conditions of the saline-alkali land through image sensors; set the sampling time interval of the sensors for sampling; dynamically adjust the sampling time interval according to the sampling results.

[0158] Model establishment and improvement plan adjustment module: Establish a comprehensive evaluation model according to the soil conditions and crop growth conditions of the saline-alkali land, and adjust the improvement plan according to the change values of the soil salt content, the change values of the pH value, and the change values of the evaluation model.

[0159] The working principle of the above technical solution is as follows: The location of saline-alkali land is obtained through the geographic information system and remote sensing data: By using the geographic information system (GIS) and remote sensing technology, the location and basic information such as topography of the target saline-alkali land are obtained. Soil samples are collected from the target saline-alkali land, and information such as the composition, texture, and structure of the soil samples is analyzed through a laboratory. Combining with the expert knowledge base, the improvement objectives and preset improvement plans are determined. According to the characteristics of the target saline-alkali land, it is divided into multiple regional types, and multiple different types of regions are randomly selected. According to the improvement objectives, sensor nodes are set for stratified sampling to obtain more accurate soil data. Through the Internet of Things technology, underground sensors are buried in the saline-alkali land to obtain parameters such as the salt content and pH value of the soil. The data of multiple sensors are weighted and analyzed to obtain more accurate soil information. The changes in the soil salt content and pH value are monitored in real time through a monitoring system to master the soil conditions and adjust the improvement plan in a timely manner. The growth situation of crops in the saline-alkali land, including characteristics such as the growth trend and color of the crops, is obtained through an image sensor. Combining the sensor data and the image data, the growth state of the crops is comprehensively evaluated. According to the sampling results, the sampling time interval of the sensors is dynamically adjusted to ensure the timeliness and accuracy of the data. According to the soil conditions of the saline-alkali land and the growth situation of the crops, a comprehensive evaluation model is established to comprehensively evaluate the effect of the improvement plan. According to the change values of the soil salt content, the change values of the pH value, and the change values of the evaluation model, the improvement plan is adjusted and optimized to achieve the best improvement effect. By dynamically adjusting improvement measures, such as applying appropriate improvement materials and adjusting the irrigation water quality, the soil environment and crop growth conditions of the saline-alkali land are improved; Generally speaking, this method realizes the dynamic adjustment of the improvement plan for saline-alkali land by obtaining the location information of the saline-alkali land, stratified sampling the soil and crop data of different regions, and analyzing based on the comprehensive evaluation model. Through this process, the saline-alkali land can be scientifically and efficiently improved, and the land availability and crop yield can be increased.

[0160] The effects of the above technical solutions are as follows: Through the geographic information system and remote sensing data, the location of saline-alkali land can be accurately obtained, achieving precise positioning; through sample analysis and knowledge base, the improvement objectives are obtained, the saline-alkali land is divided into different regions, and further divided according to the altitude; by setting sensor nodes for hierarchical sampling, while minimizing the number of sensor nodes, comprehensive data of the target saline-alkali land can be obtained. Through underground sensors to obtain soil conditions and image sensors to obtain crop growth conditions, multi-dimensional data of saline-alkali land can be obtained, providing a comprehensive information basis; based on the sampling results, the sampling time interval is adjusted to ensure the accuracy of sampling and reduce data; using the comprehensive evaluation model, according to the changes in soil salt content and pH value and the changes in the evaluation model, the improvement plan is dynamically adjusted, making the improvement measures more flexible and effective; this method combines Internet of Things technology and big data analysis, realizing automated data collection and analysis, can quickly obtain the status information of saline-alkali land, and adjust the improvement plan according to the evaluation model, thereby improving the improvement efficiency. In summary, this saline-alkali land improvement method based on Internet of Things big data analysis can achieve benefits and effects such as precise positioning, targeted improvement, multi-dimensional data sampling, dynamic adjustment, and improvement of improvement efficiency, which helps to scientifically and effectively improve saline-alkali land, improve the land availability and crop yields.

[0161] In this embodiment, a saline-alkali land improvement system based on Internet of Things big data analysis, the sampling node setting module includes:

[0162] Location acquisition module: Obtain the location of saline-alkali land through the geographic information system and remote sensing data;

[0163] Sample analysis module: Collect soil samples of saline-alkali land, and analyze the soil samples to obtain the soil type, salt content and pH value of the corresponding saline-alkali land;

[0164] Target setting module: Obtain the improvement objectives and preliminary improvement plans for the corresponding saline-alkali land through sample analysis data and knowledge base; For example: According to the analysis results, it is found that the soil in this area is light to medium saline-alkali soil, with a relatively high salt content and an alkaline pH value;

[0165] Based on the reference of sample analysis data and relevant knowledge base, the improvement objective should be to reduce the soil salt content and increase the soil pH value to improve the soil quality and increase the suitability for plant growth.

[0166] Based on this information, the preliminary improvement plan may include the following measures:

[0167] Drainage improvement: By building a drainage system, improve the soil drainage condition, and drain the excess salt and water;

[0168] Salt flushing: Use desalinated water or low-salt water to flush the soil to wash away the salt in the soil;

[0169] Organic matter addition: Applying organic fertilizers or organic substances such as straw to improve soil structure and texture and enhance the soil's water retention capacity.

[0170] Gypsum addition: Appropriately adding soil conditioners such as gypsum to lower the soil pH value and improve soil acidity and alkalinity;

[0171] Selection of suitable crops: Selecting salt-tolerant crops for planting to adapt to the saline and alkaline soil environment;

[0172] First area division module: Dividing the corresponding saline-alkali land into multiple first areas; among them, the first area includes a central area and a marginal area;

[0173] Taking the vertices of the inscribed rectangle of the area to make intersecting lines; taking the intersection point of the two intersecting lines as the center, selecting 4 points at 1 / 2 of the distance from the intersection point of the two intersecting lines to each vertex of the inscribed rectangle to make a rectangle, and the area inside this rectangle is the central area; the external area is the marginal area; the marginal area is divided into multiple areas according to the marginal position;

[0174] Second area division module: Obtaining the altitude of different first areas of the target saline-alkali land through a geographic information system; dividing the first area into second areas according to the altitude; obtaining the highest altitude and the lowest altitude of the target saline-alkali land, dividing the height difference between the highest altitude and the lowest altitude into multiple levels, and dividing the second area according to the levels; the number of levels is h = ceil((h max -h min ) / standard storey height), the function ceil() means rounding up a real number to the next larger integer, and the value range of the standard storey height is 30 - 40, with the unit of cm;

[0175] where h max is the highest altitude of the target saline-alkali land area; h min is the lowest altitude of the target saline-alkali land area;

[0176] For example, if the height difference between the highest altitude and the lowest altitude of the target saline-alkali land is 50 cm; then from the lowest altitude in the target saline-alkali land to the lowest altitude plus 30 cm is the first level, and from the lowest altitude plus 30 cm in the target saline-alkali land to the highest altitude is the second level; if the height difference between the highest altitude and the lowest altitude of the target saline-alkali land is 70 cm; then from the lowest altitude in the saline-alkali land to the lowest altitude plus 35 cm is the first level; from the lowest altitude plus 35 cm in the saline-alkali land to the highest altitude in the saline-alkali land is the second level;

[0177] If the height difference between the highest altitude and the lowest altitude of the target saline-alkali land is less than 30 cm, it is only divided into one level;

[0178] Region selection module: Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives; the number of selected second regions of each different type is N, where 2 ≤ N ≤ 5;

[0179] Sampling depth setting module: Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth of 30 - 60 cm as the second sampling depth;

[0180] Stratified sampling module: Select sampling points at the first sampling depth and the second sampling depth respectively to set up sensor groups for soil sampling; each sampling point includes multiple types of sensors; set up image sensors on the ground at the same sampling points.

[0181] The working principle of the above technical solution is as follows: First, obtain the location of the target saline-alkali land through the geographic information system and remote sensing data, and collect soil samples of the saline-alkali land for analysis to obtain key data such as the soil type, salt content, and pH value of the corresponding saline-alkali land. Then, obtain the improvement objectives and preliminary improvement plans for the corresponding saline-alkali land through sample analysis data and the knowledge base. Next, divide the corresponding saline-alkali land into multiple first regions, where the first regions include a central region and a marginal region. Divide the central region into rectangles by means of intersecting lines, and the marginal region is further divided into multiple regions according to the marginal positions. Obtain the altitude of different first regions of the target saline-alkali land through the geographic information system, and divide the first regions into second regions according to the altitude. Divide the first regions into second regions according to the altitude; obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second regions according to the levels in different regions. Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives. The number N of selected second regions of each different type is 2 ≤ N ≤ 5. Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth of 30 - 60 cm as the second sampling depth, and include multiple types of sensors at each sampling point for soil sampling and monitoring.

[0182] At the same time, set up image sensors on the ground at the same sampling points for data collection and analysis. Through the analysis and processing of the sampling data, various key data such as the soil characteristics of the target saline-alkali land can be obtained, and then a more accurate saline-alkali land improvement plan can be formulated to achieve more targeted and better saline-alkali land treatment.

[0183] The effects of the above technical solutions are as follows: By using a geographic information system and remote sensing data to obtain the locations of saline-alkali lands, the distribution range of saline-alkali lands can be accurately determined. Soil samples of saline-alkali lands are collected and comprehensively analyzed, including indicators such as soil type, salt content, and pH value, so as to comprehensively understand the soil conditions of saline-alkali lands. Based on the sample analysis data and the knowledge base, combined with the goals of saline-alkali land improvement, a preliminary improvement plan is formulated. In this way, saline-alkali lands can be improved targeted according to the actual situation. The saline-alkali land is divided into multiple first regions, including the central region and the edge region, which can better carry out regional management and improvement operations. According to the elevation differences in different regions of the target saline-alkali land, the first region is further divided into a hierarchy of second regions. In this way, the soil conditions and improvement strategies at different levels can be more accurately considered. Multiple different types of second regions are randomly selected, and sensor nodes are set up for sampling. In this way, the soil conditions in different regions can be comprehensively understood, and reference data can be provided for subsequent improvement. By setting multiple sampling depths, including the depth from 0 to 30 cm on the ground and the depth from 30 to 60 cm, information on soils at different depths can be obtained. This helps to analyze the vertical distribution of soils in saline-alkali lands and guide more precise improvement strategies. An image sensor is set up above the sampling point to monitor the surface conditions in real time. In this way, key information such as soil moisture distribution and vegetation growth can be discovered in a timely manner, providing an effective basis for adjustments and judgments during the improvement process. Through the above methods, saline-alkali lands can be improved more scientifically, achieving continuous improvement and restoration of the soil, increasing soil fertility, increasing crop yields, reducing the salinization degree of cultivated land, and promoting the sustainable development of agriculture.

[0184] In this embodiment, a saline-alkali land improvement system based on Internet of Things big data analysis, the sampling module includes:

[0185] Soil condition acquisition module: Obtain the soil conditions of the saline-alkali land through underground sensors; the soil conditions of the saline-alkali land include salt content and PH value; monitor the changes in soil salt content and PH value through a monitoring system;

[0186] The soil salt content is:

[0187]

[0188] The soil PH value is:

[0189]

[0190] Wherein, Y 1i is the salt content value obtained by the sensor at the first depth; P 1i is the PH value obtained by the sensor at the first depth; Y 2i is the salt content value obtained by the sensor at the second depth; P 2iThe pH value obtained by the sensor at the second depth; a1 and a2 are coefficients, with a range of (0, 1); m is the number of sampling points of the first or second depth sensor;

[0191] Crop growth condition acquisition module: acquires the crop growth conditions of the saline-alkali land through an image sensor; the sampling frequencies of the image sensor and the underground sensor are kept consistent;

[0192] Sampling time setting module: sets the sampling time interval of the underground sensor for sampling; dynamically adjusts the sampling time interval according to the sampling results;

[0193] T = (1 - λ × avgD) × T0

[0194]

[0195] where T is the sampling time interval; T0 is the preset sampling time interval; C j-1 is the value of a certain underground sensor's previous sampling, C j-2 is the value of a certain underground sensor's sampling the time before last; ΔC y is the preset change value of such sensors; avgD is the average value of D obtained by multiple underground sensors; Min(E) is the minimum value of E obtained by multiple underground sensors; Max(E) is the maximum value of E obtained by multiple underground sensors.

[0196] The working principle of the above technical solution is as follows: The soil conditions of the saline-alkali land, including the salt content and pH value of the soil, are obtained through underground sensors. The sensors are divided into two depths (the first depth and the second depth), and the salt content and pH value at the corresponding depths are obtained respectively. The changes in the soil salt content and pH value are monitored in real time through the monitoring system. The monitoring system can record and display the dynamic changes of the soil conditions to understand the soil conditions in real time. The sensor sampling values at multiple depths obtained are weighted and averaged according to the weight coefficients to obtain the overall soil salt content and pH value. Among them, the weight coefficients a1 and a2 are determined according to the actual situation and are in the range of 0 and 1; the growth conditions of the crops on the saline-alkali land are monitored in real time through the image sensor. The image sensor can capture information such as the growth state of the plants, the leaf color, and the canopy density to evaluate the improvement effect of the saline-alkali land. According to the sampling results and the preset change amount, the sampling time interval of the sensor is dynamically adjusted. The stability of the soil conditions is evaluated through the calculated D value (change difference degree). If the stability is high, the sampling time interval can be extended; conversely, if the stability is low, the sampling time interval needs to be shortened. According to the stability evaluation results, the new sampling time interval T is calculated through the formula.

[0197] The effects of the above technical solution are as follows: The data of soil salt content and pH value are obtained through underground sensors, and their changes are monitored in real time through a monitoring system. This enables farmers and agricultural experts to understand the soil conditions of saline-alkali land in real time and take corresponding measures in a timely manner. By calculating the weighted average of the sampling values of multiple depth sensors, the overall soil salt content and pH value are obtained. This accurate assessment helps to better understand the soil characteristics of saline-alkali land and provides a reference for targeted improvement. The growth conditions of crops on saline-alkali land are monitored in real time using image sensors. By monitoring information such as the growth status, leaf color, and canopy density of plants, problems can be discovered in a timely manner and appropriate measures can be taken, which helps to improve crop yield and quality. According to the sampling results, the stability evaluation index D is calculated, and the sampling time interval of the sensor is dynamically adjusted through a formula. By dynamically adjusting the sampling time interval, while ensuring data quality, redundant sampling can be reduced as much as possible, thereby saving energy and storage space. Since redundant data is reduced, the speed of the data processing stage will also increase accordingly. The adjustment of the sampling time interval takes into account the data change situations of multiple sensors. In this way, a more accurate data sampling time can be obtained, thereby improving data accuracy. This can capture changes in soil conditions faster, adjust measures in a timely manner, and improve the improvement effect. By monitoring and accurately assessing soil conditions in real time, and dynamically adjusting the sampling time interval according to the sampling results, this method can more precisely guide the improvement work of saline-alkali land. For specific soil conditions and crop requirements, targeted measures can be taken, thereby improving the improvement effect, increasing crop yields, and reducing the adverse effects of soil salinity on plant growth. In summary, this saline-alkali land improvement method based on Internet of Things big data analysis can provide real-time and accurate soil information and crop growth monitoring, helping farmers and agricultural experts to effectively improve the soil conditions of saline-alkali land and increase crop yields and quality.

[0198] In this embodiment, a saline-alkali land improvement system based on Internet of Things big data analysis, the model establishment and improvement plan adjustment module includes:

[0199] Crop classification module: Classify the growth conditions of crops; the classification includes very poor, poor, average, good, and excellent;

[0200] Comprehensive evaluation module establishment module: Establish a comprehensive evaluation model based on the soil conditions of saline-alkali land and the growth conditions of crops;

[0201] H = Z × [1 - λ × |avgD| × (w1 × A + w2 × B)

[0202]

[0203] where H is the comprehensive evaluation score; Y 1i is the salt content value obtained by the sensor at the first depth; P 1iThe pH value obtained by the sensor at the first depth; Y 2i The salt content value obtained by the sensor at the second depth; P 2i The pH value obtained by the sensor at the second depth; Y y The target maximum salt content; P y The target maximum pH value; w1 and w2 are weights, ranging from (0, 1); m is the number of sampling points of the first or second depth sensor; a1 and a2 are coefficients, ranging from (0, 1); Z is the crop growth score; ranging from 2 to 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10;

[0204] Monitoring and optimization module: Adjust the improvement plan according to the change value of soil salt content, the change value of pH value, and the change value of the evaluation model.

[0205] Compare the change values of soil salt content at different time points or different depths; if it is found that the salt content shows a gradually decreasing trend, it indicates that the improvement measures may already be taking effect, and the original improvement plan can be continued and strengthened; if the change value of salt content is not obvious or increases, it may be necessary to re-evaluate the effect of the improvement plan and consider taking further measures;

[0206] If the pH value gradually approaches the target maximum value during the improvement process, it indicates that the improvement plan is effective in adjusting the soil acidity and alkalinity; if the change value of the pH value is not obvious or deviates from the target, it is possible to consider adjusting the improvement plan specifically, such as increasing or decreasing the application of acidic or alkaline substances to achieve the ideal pH value.

[0207] By comparing the change of the comprehensive evaluation score of the evaluation model at different time points, understand the effect of the improvement plan; if the comprehensive evaluation score gradually increases, it indicates that the improvement plan is effective, and the original improvement measures can be continued; if the change value of the evaluation model shows that the comprehensive evaluation score decreases or fluctuates, it may be necessary to adjust the improvement plan, such as adjusting the fertilization amount, watering amount, or adding other improvement means.

[0208] The working principle of the above technical solution is: Classify the growth conditions of crops on saline-alkali land, including very poor, poor, average, good, and excellent levels; establish a comprehensive evaluation model according to the soil conditions of saline-alkali land and the growth conditions of crops to evaluate the improvement effect. This model obtains a comprehensive evaluation score H through calculation, which includes two parts A and B. Part A is obtained by calculating the deviation between the salt content value and pH value obtained by the first depth sensor and the target maximum salt content and pH value, and weighted summing according to the coefficients a1 and a2. Part B is also obtained by calculating the deviation between the salt content value and pH value obtained by the second depth sensor and the target maximum salt content and pH value, and weighted summing according to the coefficients a1 and a2.

[0209] The comprehensive evaluation score H uses weights w1 and w2 to perform weighted summation on A and B to obtain the final evaluation result. According to the classification, the crop growth situation is converted into a crop growth score Z, whose value range is 2 to 10, depending on the classification. For example, very poor corresponds to 2, relatively poor corresponds to 4, average corresponds to 6, good corresponds to 8, and excellent corresponds to 10. By analyzing the change values of soil salt content, pH value, and the evaluation model, the improvement plan for saline-alkali land is adjusted. According to the change trends of the comprehensive evaluation score H and the crop growth score Z, improvement measures can be optimized, such as adjusting the irrigation volume, applying appropriate salt treatment agents, changing the crop planting method, etc. In short, this method evaluates the improvement effect of saline-alkali land through a comprehensive evaluation model and crop growth score, and adjusts the improvement plan based on data analysis to improve the soil quality and crop yield of saline-alkali land.

[0210] The effects of the above technical solutions are as follows: By comprehensively evaluating the soil conditions and crop growth conditions of saline-alkali land, using parameters such as the salt content value and pH value obtained by sensors to calculate the comprehensive evaluation score H, the improvement effect of saline-alkali land can be objectively and accurately evaluated. The parameters avgD, λ, and weights w1 and w2 in the formula are dynamically adjusted according to the sampling results of underground sensors, and the improvement of saline-alkali land can be accurately evaluated according to the actual situation, and the importance of different factors can be flexibly adjusted; Through the Internet of Things technology and big data analysis, the changes of soil parameters of saline-alkali land can be monitored and collected in real time, and the crop growth situation can be evaluated in real time. In this way, problems can be discovered in time and corresponding measures can be taken to achieve the refined management of saline-alkali land. According to the change trends of the comprehensive evaluation score H and the crop growth score Z, it can provide scientific basis and reference for farmers, agricultural experts and decision-makers to guide the formulation of reasonable improvement plans for saline-alkali land. According to the change value of the evaluation model, improvement measures can be adjusted, such as adjusting the irrigation volume, applying appropriate salt treatment agents, changing the crop planting method, etc., to improve the improvement effect. Through the improvement plan for saline-alkali land, parameters such as soil salt content and pH value can be adjusted, the salt content in the soil can be reduced, the soil structure and water retention capacity can be improved, and the crop growth environment can be improved. This will help to increase the crop yield and quality. Through precise data analysis and evaluation, agricultural production factors such as water resources, fertilizers and pesticides can be reasonably utilized, waste and pollution can be reduced, and the purpose of saving resources and protecting the environment can be achieved. In summary, the saline-alkali land improvement method based on Internet of Things big data analysis can provide accurate evaluation and guidance, achieve refined management, improve soil quality and crop yield, and save resources and protect the environment. <000> <000>

[0211] In an embodiment of the present invention, a saline-alkali land improvement system based on Internet of Things big data analysis, the crop classification module includes:

[0212] Database Crop Feature Classification Module: Obtain the growth time series according to the crop growth time, and classify the growth conditions of each growth time series of the same crop to obtain picture features of different classifications;

[0213] Target Saline-Alkali Land Crop Feature Extraction Module: Extract the growth image features and time series of the crops corresponding to the target saline-alkali land;

[0214] Sampling Point Crop Classification Determination Module: Compare the image features of the crops corresponding to each sampling point with the features corresponding to different classifications of the same crop in the database at the same time series, and obtain the classification corresponding to the feature with the highest similarity as the classification corresponding to the crop at this sampling point and this time series;

[0215] Final Classification Module: If there are multiple classification features corresponding to different sampling points of the target saline-alkali land at the same time series, then select the same classification corresponding to the most sampling points as the final classification of the crop at this time series.

[0216] The working principle of the above technical solution is as follows: First, classify the growth conditions of the same crop under different growth time series. This can be determined by observing and recording the growth states of the same crop at different growth stages, such as leaf color, plant height, plant morphology, etc. The classification can include grades such as very poor, poor, average, good, and excellent. For each crop's growth time series, extract the growth image features of the corresponding crop. This can utilize computer vision technology to process and analyze the crop images and extract features related to the crop growth state, such as texture, shape, color, etc. Compare the extracted crop image features with the features corresponding to different classifications of the same crop in the database. By calculating the similarity between the features, find the classification corresponding to the feature most similar to the features of the crop to be classified. If there are multiple classification features corresponding to different test points, then select the same classification corresponding to the most detection points as the final classification result. This is to increase the accuracy and reliability of the classification. By voting or statistics on the results of multiple detection points, select the classification with the highest frequency of occurrence as the final result. Through the above steps, this method uses Internet of Things big data analysis and image feature extraction technology to classify the growth conditions of crops in saline-alkali land. By comparing the feature similarity and statistically analyzing the results of multiple points of detection, accurate classification results can be obtained, providing a scientific basis for the improvement and management of saline-alkali land.

[0217] The effects of the above technical solution are as follows: By classifying the crop growth time series, the growth status of crops can be accurately evaluated and described. Classifying the crop growth conditions into very poor, poor, average, good, and excellent grades can provide a more specific understanding of the adaptability and growth status of crops in saline-alkali land. This method uses image feature extraction technology to convert the growth images of crops into feature vectors. This classification method based on picture features can more objectively and quantitatively evaluate the growth of crops, avoiding subjective and human interference factors. The image features of the crops are compared with the features in the database, and the similarity is calculated. By comparing the features corresponding to different classifications, the features most similar to the crop to be classified are found, thereby determining the classification of the crop. In this way, the advantages of big data analysis and machine learning algorithms can be utilized to improve the accuracy and reliability of classification. If multiple classification features correspond to different test points, this method uses the same classification corresponding to the most detected points as the final classification result. This way of integrating the detection results of multiple points can reduce the misjudgment that may be caused by individual test points and improve the credibility and stability of the classification results. The saline-alkali land improvement method based on Internet of Things big data analysis can provide accurate crop growth situation evaluation and classification information for agricultural managers and decision-makers, helping them better understand the situation of saline-alkali land and take corresponding improvement measures. At the same time, this method, based on the analysis of big data and image features, overcomes the limitations of traditional subjective judgment and individual factors and has higher scientificity and objectivity.

[0218] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A saline-alkali land improvement method based on Internet of Things big data analysis, characterized in that, The method includes: S1. Obtain the location of saline-alkali land through a geographic information system and remote sensing data; collect soil samples of the target saline-alkali land, obtain improvement objectives and preset improvement plans through sample analysis and a knowledge base; divide the target saline-alkali land into multiple regional types; randomly select multiple regions of different types, and set multiple sensor nodes for stratified sampling according to the improvement objectives. S2. Obtain the soil conditions of the saline-alkali land through underground sensors; perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; monitor the changes in the soil salt content and pH value through a monitoring system; obtain the crop growth conditions of the saline-alkali land through an image sensor; set the sampling time interval of the sensor for sampling; dynamically adjust the sampling time interval according to the sampling results. S3. Establish a comprehensive evaluation model based on the soil conditions and crop growth conditions of the saline-alkali land, and adjust the improvement plan according to the change values of the soil salt content, the change value of the pH value, and the change value of the evaluation model. The S3 includes: Classify the growth conditions of the crops; the classification includes very poor, poor, average, good, and excellent. Establish a comprehensive evaluation model based on the soil conditions and crop growth conditions of the saline-alkali land. = Among them, H is the comprehensive evaluation score; is the salt content value obtained by the sensor at the first depth; is the pH value obtained by the sensor at the first depth; is the salt content value obtained by the sensor at the second depth; is the pH value obtained by the sensor at the second depth; is the target maximum salt content; is the target maximum pH value; w1, w2 are weights, and the range is (0, 1); m is the number of sampling points of the first or second depth sensor; 、 are coefficients, and the range is (0, 1); Z is the crop growth score; the range is 2 - 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10; is the value of the previous sampling of a certain underground sensor, is the value of the sampling before the previous sampling of a certain underground sensor; is the preset change value of such sensors; D is the average value of D obtained by multiple underground sensors; is the minimum E value obtained by multiple underground sensors; is the maximum E value obtained by multiple underground sensors; Adjust the improvement plan according to the change values of the soil salt content, the change value of the pH value, and the change value of the evaluation model.

2. The method for improving saline-alkali land based on Internet of Things big data analysis according to claim 1, wherein The S1 includes: Obtain the location of the saline-alkali land through a geographic information system and remote sensing data. Collect soil samples of the saline-alkali land, and analyze the soil samples to obtain the soil type, salt content, and pH value of the corresponding saline-alkali land. Obtain the improvement objectives and preliminary improvement plans of the corresponding saline-alkali land through sample analysis data and a knowledge base. Divide the corresponding saline-alkali land into multiple first regions; among them, the first region includes a central region and a marginal region. Obtain the altitude of different first regions of the target saline-alkali land through a geographic information system. Divide the first region into second regions according to the altitude; obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second regions according to the levels. Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives; the number of each different type of second region selected is N, and 2 ≤ N ≤ 5. Take the depth from 0 to 30 cm from the ground as the first sampling depth, and take the depth from 30 to 60 cm as the second sampling depth. Select sampling points at the first sampling depth and the second sampling depth respectively to set a sensor group for soil sampling; each sampling point includes multiple types of sensors; set an image sensor on the ground at the same sampling point.

3. The method for improving saline-alkali land based on Internet of Things big data analysis according to claim 1, wherein, The S2 includes: Obtain the soil conditions of the saline-alkali land through underground sensors; the soil conditions of the saline-alkali land include salt content and pH value; monitor the changes in the soil salt content and pH value through a monitoring system. The soil salt content is: The soil pH value is: Wherein, is the salt content value obtained by the sensor at the first depth; is the pH value obtained by the sensor at the first depth; is the salt content value obtained by the sensor at the second depth; is the pH value obtained by the sensor at the second depth; , are coefficients, with a range of (0, 1); m is the number of sampling points of the first or second depth sensor; Obtain the crop growth conditions of the saline-alkali land through an image sensor. Set the sampling time interval of the underground sensor for sampling. Dynamically adjust the sampling time interval according to the sampling results. = Wherein, T is the sampling time interval; is the preset sampling time interval; is the value of the previous sampling of a certain underground sensor, is the value of the sampling before the last sampling of a certain underground sensor; is the preset change value of such sensors; D is the mean value of D obtained by multiple underground sensors; is the minimum E value obtained by multiple underground sensors; is the maximum E value obtained by multiple underground sensors.

4. A method for improving saline-alkali land based on Internet of Things big data analysis according to claim 1, characterized in that, The classification of the growth conditions of the crops includes: Obtain the growth time series according to the crop growth time, and classify the growth conditions of each growth time series of the same crop to obtain the image features of different classifications; Extract the growth image features and time series of the corresponding crops in the target saline-alkali land; Compare the image features of the crops corresponding to each sampling point with the features corresponding to different classifications of the same crop in the same time series in the database, and obtain the classification corresponding to the feature with the highest similarity as the classification corresponding to the crop at this sampling point and this time series; If there are multiple classification features corresponding to different sampling points of the target saline-alkali land in the same time series, then take the same classification corresponding to the most sampling points as the final classification of the crop in this time series.

5. A saline-alkali land improvement system based on Internet of Things big data analysis, characterized in that The system includes: Sampling node setting module: Obtain the location of the saline-alkali land through the geographic information system and remote sensing data; Collect soil samples of the target saline-alkali land, obtain the improvement target and preset improvement plan through sample analysis and knowledge base, and divide the target saline-alkali land into multiple regional types; Randomly select multiple different types of regions, and set multiple sensor nodes for hierarchical sampling according to the improvement target; Sampling module: Obtain the soil conditions of the saline-alkali land through underground sensors; Perform weighted analysis on the data of multiple sensors to obtain the soil salt content and pH value; Monitor the changes of the soil salt content and pH value through the monitoring system; Obtain the crop growth conditions of the saline-alkali land through image sensors; Set the sampling time interval of the sensors for sampling; Dynamically adjust the sampling time interval according to the sampling results; Model establishment and improvement plan adjustment module: Establish a comprehensive evaluation model according to the soil conditions of the saline-alkali land and the crop growth conditions, and adjust the improvement plan according to the change value of the soil salt content, the change value of the pH value, and the change value of the evaluation model; The model establishment and improvement plan adjustment module includes: Crop classification module: Classify the growth conditions of the crops; The classifications include very poor, poor, average, good, and excellent; Comprehensive evaluation module establishment module: Establish a comprehensive evaluation model according to the soil conditions of the saline-alkali land and the crop growth conditions; = Among them, H is the comprehensive evaluation score; is the salt content value obtained by the sensor at the first depth; is the pH value obtained by the sensor at the first depth; is the salt content value obtained by the sensor at the second depth; is the pH value obtained by the sensor at the second depth; is the target maximum salt content; is the target maximum pH value; w1, w2 are weights, and the range is (0, 1); m is the number of sampling points of the first or second depth sensor; 、 are coefficients, and the range is (0, 1); Z is the crop growth score; the range is 2 - 10, depending on the classification; very poor is 2, poor is 4, average is 6, good is 8, excellent is 10; is the value of the previous sampling of a certain underground sensor, is the value of the sampling before the previous one of a certain underground sensor; is the preset change value of this type of sensor; is the average value of D obtained by multiple underground sensors; is the minimum E value obtained by multiple underground sensors; is the maximum E value obtained by multiple underground sensors; Monitoring and optimization module: Adjust the improvement plan according to the change value of the soil salt content, the change value of the pH value, and the change value of the evaluation model.

6. The saline-alkali land improvement system based on Internet of Things big data analysis according to claim 5, characterized in that, The sampling node setting module includes: Location acquisition module: Obtain the location of the saline-alkali land through the geographic information system and remote sensing data; Sample analysis module: Collect soil samples of the saline-alkali land, and analyze the soil samples to obtain the soil type, salt content, and pH value of the corresponding saline-alkali land; Target setting module: Obtain the improvement target and preliminary improvement plan of the corresponding saline-alkali land through sample analysis data and knowledge base; First regional division module: Divide the corresponding saline-alkali land into multiple first regions; Among them, the first region includes the central region and the edge region; Second regional division module: Obtain the altitude of different first regions of the target saline-alkali land through the geographic information system; Divide the first region into second regions according to the altitude; Obtain the highest altitude and the lowest altitude of the target saline-alkali land, divide the height difference between the highest altitude and the lowest altitude into multiple levels, and divide the second region according to the levels; Region selection module: Randomly select multiple second regions of different types, and set sensor nodes for sampling according to the improvement objectives; the number of selected second regions of each different type is N, where 2 ≤ N ≤ 5; Sampling depth setting module: Set the depth from 0 to 30 cm from the ground as the first sampling depth, and the depth from 30 to 60 cm as the second sampling depth; Stratified sampling module: Select sampling points at the first sampling depth and the second sampling depth respectively to set up sensor groups for soil sampling; each sampling point includes multiple types of sensors; set up image sensors on the ground at the same sampling points.

7. An improved saline-alkali land system based on Internet of Things big data analysis according to claim 5, characterized in that, The sampling module includes: Soil condition acquisition module: Obtain the soil conditions of saline-alkali land through underground sensors; the soil conditions of the saline-alkali land include salt content and pH value; monitor the changes in soil salt content and pH value through the monitoring system; The soil salt content is: The soil pH value is: Among them, is the salinity value obtained by the sensor at the first depth; is the pH value obtained by the sensor at the first depth; is the salinity value obtained by the sensor at the second depth; is the pH value obtained by the sensor at the second depth; , are coefficients, with a range of (0, 1); m is the number of sampling points of the first or second depth sensor; Crop growth condition acquisition module: Obtain the crop growth conditions of saline-alkali land through image sensors; Sampling time setting module: Set the sampling time interval of underground sensors for sampling; dynamically adjust the sampling time interval according to the sampling results; = Among them, T is the sampling time interval; is the preset sampling time interval; is the value of the previous sampling of a certain underground sensor, is the value of the sampling before the last sampling of a certain underground sensor; is the preset change value of such sensors; is the mean value of D obtained by multiple underground sensors; is the minimum value of E obtained by multiple underground sensors; is the maximum value of E obtained by multiple underground sensors.

8. An improved saline-alkali land system based on Internet of Things big data analysis according to claim 5, characterized in that The crop classification module includes: Database crop feature classification module: Obtain the growth time series according to the crop growth time, and classify the growth conditions of the same crop in each growth time series to obtain the picture features of different classifications; ​ ​ ​

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