Soil data analysis method and system

Abstract

The present invention relates to the field of soil data analysis, and specifically to a soil data analysis method and system. The method comprises the following steps: randomly dividing multiple soil regions using a genetic algorithm; obtaining characteristic data of the soil internal and external environments of the multiple soil regions; optimizing the characteristic data; constructing a multidimensional analysis model; constructing a soil intelligence analysis model; and judging the health status of the soil through the soil intelligence analysis model. The present invention overcomes the subjectivity and uncertainty of traditional methods by dividing soil regions using a genetic algorithm; by analyzing the internal and external environmental data of the soil from multiple dimensions in terms of soil properties, crop growth, and groundwater dynamics, and judging data anomalies step by step, the accuracy and practicality of data analysis are improved, providing a scientific basis for soil health assessment; by reasonably setting weight coefficients, it is possible to complete the individual and comprehensive analysis of soil data characteristics in a single model.

Description

Technical Field

[0001] The present invention relates to the field of soil data analysis, and in particular to a soil data analysis method and system. Background Art

[0002] Soil quality is crucial to agricultural production, and accurate soil data analysis is key to improving production efficiency. While existing big data-based soil data analysis methods offer some support, they still suffer from limitations such as a single analytical dimension and high subjectivity. Most of these methods rely on traditional data processing techniques and fail to fully consider both internal and external soil environmental factors, resulting in inaccurate and incomplete analysis results.

[0003] In existing technologies, soil regionalization usually relies on expert field investigations and empirical judgments, which are easily influenced by personal subjective factors, resulting in inconsistent and inaccurate division results. In soil data analysis, it is mainly limited to internal soil environmental data, such as moisture content and salinity, but fails to fully consider the combined influence of external environmental factors such as crop growth and groundwater dynamics. This has weakened the practicality and comprehensiveness of soil data analysis results to a certain extent. In terms of soil health status assessment, most of them rely on a single or simple combination of indicators, lacking a comprehensive assessment model that includes weight coefficients with significant discrimination and can fully reflect multi-dimensional characteristics, as well as scientific data analysis and judgment methods, resulting in insufficient accuracy and lack of comprehensiveness in the assessment results.

[0004] At present, there is not much research on soil data analysis targeting multiple environmental scenarios, multi-dimensional characteristics, and multi-weight considerations, and there is no specific soil data fusion analysis method that can comprehensively consider internal and external environmental factors of the soil. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a soil data analysis method and system.

[0006] In a first aspect, the present invention provides a soil data analysis method, comprising the following steps: randomly dividing multiple soil areas using a genetic algorithm; obtaining characteristic data of the internal and external environments of the soil in the multiple soil areas; optimizing the characteristic data to obtain multidimensional spatiotemporal characteristic data of the soil environmental characteristics of the multiple soil areas; based on the multidimensional spatiotemporal characteristic data, constructing a multidimensional analysis model for directly or indirectly analyzing soil data from the perspectives of basic soil properties, crop growth, and groundwater dynamics; constructing a soil intelligence analysis model for comprehensive analysis of soil data based on the multidimensional analysis model; and judging the health status of the soil through the soil intelligence analysis model. The present invention uses a genetic algorithm to randomly divide multiple soil areas, ensuring the diversity and representativeness of the soil area division, and providing a solid foundation for the acquisition and analysis of characteristic data; by optimizing the processing of characteristic data, multidimensional spatiotemporal characteristic data is obtained, which realizes the comprehensive capture and in-depth analysis of internal and external environmental factors of the soil, overcomes the deficiency of traditional methods being limited to soil internal environmental data, makes soil data analysis more accurate and comprehensive, and enhances the practicality of soil data analysis; by constructing a multidimensional analysis model and a soil intelligent analysis model, a comprehensive analysis of soil data from multiple angles such as basic soil properties, crop growth and groundwater dynamics is realized, providing a strong scientific basis for soil management and protection.

[0007] Optionally, the acquisition of characteristic data of the soil internal environment and the soil external environment of the multiple soil regions includes: acquiring characteristic data of moisture content, salt concentration, temperature, pH, and mud-sand ratio at different depths within the soil of the multiple soil regions; acquiring characteristic data of crop growth cycle and yield of different crop types in the multiple soil regions; and acquiring characteristic data of groundwater level and flow at different monitoring points in the multiple soil regions. The present invention achieves a comprehensive analysis of the internal structure and properties of the soil by acquiring characteristic data of moisture content, salt concentration, temperature, pH, and mud-sand ratio at different depths within the soil; reveals the close connection between crop growth and the soil environment by acquiring characteristic data of growth cycle and yield of different crop types outside the soil; and reveals the impact of groundwater dynamics on the soil environment by acquiring characteristic data of groundwater level and flow at different monitoring points outside the soil, providing a scientific basis for the comprehensive analysis of soil data and helping to maintain the health and stability of the soil ecosystem.

[0008] Optionally, optimizing the feature data to obtain multidimensional spatiotemporal data of soil environmental characteristics of multiple soil regions includes: preprocessing the feature data; performing spatiotemporal matching processing on the feature data; performing spatiotemporal interpolation processing on the feature data; performing spatiotemporal fusion processing on the feature data; through the preprocessing, the spatiotemporal matching processing, the spatiotemporal interpolation processing and the spatiotemporal fusion processing, multidimensional spatiotemporal data of internal and external soil environmental characteristics of multiple soil regions are obtained, and the multidimensional spatiotemporal data include spatiotemporal correlation data of internal soil environmental characteristics, spatiotemporal correlation data of external soil environmental characteristics and spatiotemporal fusion data of internal and external soil environmental characteristics. By preprocessing characteristic data, the present invention effectively improves the accuracy and reliability of the data, providing a high-quality data foundation for subsequent analysis; through spatiotemporal matching, interpolation and fusion processing, it successfully constructs the spatiotemporal correlation and fusion data of the internal and external environmental characteristics of the soil, realizes the comprehensive capture and in-depth analysis of soil characteristics in time and space, and provides a scientific basis for revealing the dynamic laws and trends of soil environmental changes; by obtaining multidimensional spatiotemporal data, it enriches the dimension and depth of soil data analysis, helps to improve the intelligent and refined level of soil management and protection, and provides strong support for the sustainable development of agriculture.

[0009] Optionally, the multidimensional analysis model constructed based on the multidimensional spatiotemporal feature data for directly or indirectly analyzing soil data from the perspectives of basic soil properties, crop growth, and groundwater dynamics includes: constructing an internal environment analysis model for directly analyzing soil data based on the multidimensional spatiotemporal feature data; and constructing an indirect analysis model for indirectly analyzing soil data based on the multidimensional spatiotemporal feature data, wherein the indirect analysis model includes a crop growth analysis model and a groundwater dynamic analysis model. By constructing an internal environment analysis model, the present invention directly utilizes multidimensional spatiotemporal feature data to conduct an in-depth analysis of basic soil properties, thereby accurately capturing and quantitatively evaluating changes in the soil internal environment, providing a scientific basis for soil improvement and fertility management; by constructing an indirect analysis model, the soil condition is indirectly reflected from the perspectives of crop growth and groundwater dynamics, revealing the complex relationship between the soil environment and crop growth and groundwater dynamics, thereby facilitating indirect analysis of the status of soil data; and by constructing a multidimensional analysis model, the accuracy and comprehensiveness of soil data analysis are improved, promoting the in-depth development of soil science research.

[0010] Optionally, the internal environment analysis model for directly analyzing soil data satisfies the following expression:

[0011]

[0012] in, for Soil environment analysis value at each moment, for Moment Areas at soil depth The soil moisture content at for Moment Areas at soil depth The soil salt concentration at for Moment Areas at soil depth The soil temperature at for Moment Areas at soil depth The soil pH, for Moment Areas at soil depth The soil-sand ratio at 、 、 、 、 Respectively Areas at soil depth The weight coefficients of soil moisture content, soil salt concentration, soil temperature, soil pH and soil mud-sand ratio are: is the total number of soil areas, is the initial soil depth, For the The total soil depth of the region; the crop growth analysis model for indirect analysis of soil data satisfies the following expression:

[0013]

[0014] in, for Crop growth analysis values ​​of the extra-soil environment at all times, for Moment The region's The inverse of the crop growth cycle, for Moment The region's The yield of crops, 、 Respectively The region's The weight coefficient of the growth cycle and yield of the crop, is the total number of soil areas, For the The total number of crop species in the region; the groundwater dynamic analysis model for indirect analysis of soil data satisfies the following expression:

[0015]

[0016] in, for Dynamic analysis value of groundwater in the soil environment at all times, for Moment The region's The groundwater level at each monitoring point, for Moment The first The groundwater flow at each monitoring point, is the total number of soil areas, For the The total number of monitoring points in each region, 、 Respectively The first The weight coefficients of groundwater level and groundwater flow at each monitoring point. The internal environment analysis model constructed by the present invention comprehensively considers multiple factors such as soil moisture, salinity, temperature, pH and mud-sand ratio, and assigns weight coefficients to different depths and regions, thereby achieving accurate quantification of soil internal environmental characteristics; the constructed crop growth analysis model and groundwater dynamic analysis model serve as tools for indirect analysis of soil data, reflecting soil conditions from the two dimensions of crop growth and groundwater dynamics respectively. By introducing key indicators such as the inverse of the growth cycle, yield, groundwater level and flow, combined with weight coefficients, a comprehensive assessment of soil external environmental characteristics is achieved; the constructed multidimensional analysis model integrates the methods of direct and indirect analysis of soil data, thereby improving the accuracy and comprehensiveness of soil data analysis.

[0017] Optionally, the direct analysis includes setting weight coefficients for the soil moisture content, the soil salinity, the soil temperature, the soil pH, and the soil mud-sand ratio, where the weight coefficients satisfy the following conditions:

[0018]

[0019] According to the weight coefficient, a fluctuation amplitude threshold of the soil internal environment analysis value is set, and the fluctuation amplitude threshold is set as: 、 、 、 and ; The absolute value of the fluctuation range of the soil internal environment analysis value Comparative analysis is performed with the fluctuation amplitude threshold to obtain comparative analysis results, which are as follows: When , the soil moisture content data is abnormal; when When , the soil salt concentration data is abnormal; when When , the soil temperature data is abnormal; when When , the soil pH data is abnormal; when When , the soil mud-sand ratio data is abnormal; when When the soil internal environment data is normal. The present invention accurately reflects the differences in various factors of the soil internal environment by reasonably setting weight coefficients, providing a scientific basis for setting the fluctuation amplitude threshold; by comparing and analyzing the absolute value of the fluctuation amplitude of the soil internal environment analysis value with the fluctuation amplitude threshold, it can quickly identify abnormal conditions in data such as soil temperature, pH, moisture, salt concentration and mud-sand ratio, thereby improving the efficiency and accuracy of soil internal environment data analysis.

[0020] Optionally, the indirect analysis includes: setting a maximum threshold and a minimum threshold for the indirect analysis value, the indirect analysis value including the crop growth analysis value and the groundwater dynamic analysis value; comparing the indirect analysis value with the maximum threshold and the minimum threshold to obtain a comparison result, the comparison result including: when the indirect analysis value is greater than or equal to the maximum threshold, the soil data quality is relatively good; when the indirect analysis value is less than the maximum threshold and greater than the minimum threshold, the soil data quality is average; when the indirect analysis value is less than the minimum threshold, the soil data quality is poor. The present invention provides a clear standard for evaluating soil data quality by setting a maximum threshold and a minimum threshold for the indirect analysis value; comparing the indirect analysis value with the threshold can quickly obtain the analysis results of the soil data quality, including three situations of relatively good, average and poor soil data quality; by combining the two dimensions of the crop growth analysis value and the groundwater dynamic analysis value, the soil data quality is comprehensively evaluated from multiple angles, more comprehensively reflecting the actual condition of the soil, and improving the accuracy and reliability of the soil data quality evaluation.

[0021] Optionally, the soil intelligence analysis model satisfies the following expression:

[0022]

[0023] in, for Fluctuation analysis value of soil data at each moment, for Soil environment analysis value at each moment, for Crop growth analysis values ​​of the extra-soil environment at all times, for Dynamic analysis value of groundwater in the soil environment at all times, 、 、 is the weight coefficient. By integrating soil internal environment analysis values, crop growth analysis values ​​of the external soil environment, and groundwater dynamic analysis values, the present invention achieves a comprehensive and integrated assessment of soil data. Compared with existing single-indicator assessments, this is more accurate and comprehensive, and helps reveal the true fluctuations of soil data. By introducing weight coefficients, the weights of each analysis value can be flexibly adjusted according to actual conditions, thereby more accurately reflecting the fluctuation characteristics of soil data, improving the flexibility and applicability of the model, and making it applicable to soil data analysis under different soil types and environmental conditions. By calculating the fluctuation analysis values ​​of soil data, a scientific basis is provided for dynamic monitoring and early warning of soil data.

[0024] Optionally, judging the health status of the soil through the soil intelligent analysis model includes: setting a maximum threshold, a minimum threshold, and a maximum stable state value of a fluctuation analysis value of soil data through the soil intelligent analysis model; comparing the fluctuation analysis value with the maximum threshold, the minimum threshold, and the maximum stable state value to obtain a comparison result, wherein the comparison result includes: when the fluctuation analysis value is greater than the maximum threshold, there is obvious fluctuation in the soil internal environment data, and the soil is judged to be in an unhealthy state; when the fluctuation analysis value is less than the maximum threshold and greater than the minimum threshold, if there is a significantly increased fluctuation in the crop growth data in the external soil environment data, the soil is judged to be in a sub-healthy state; if there is a significantly decreased fluctuation, the soil is judged to be in a healthy state; when the fluctuation analysis value is less than the minimum threshold and greater than the maximum stable state value, if there is a fluctuation in the groundwater dynamic data in the external soil environment that deviates from the maximum stable state value, the soil is judged to be in a sub-healthy state; if there is a fluctuation close to the maximum stable state value, the soil is judged to be in a healthy state; when the fluctuation analysis value is less than the maximum stable state value, the data of the internal and external soil environments are both stable, and the soil is judged to be in an extremely healthy state. The present invention combines historical data and sets maximum and minimum thresholds for fluctuation analysis values, providing clear criteria for soil health assessment. This makes the assessment results more objective and accurate, and helps to promptly identify soil health issues. By comprehensively considering the fluctuations in both internal and external soil environmental data, the soil health status is comprehensively assessed from multiple perspectives, improving the comprehensiveness and accuracy of the assessment. Based on the comparison results of the fluctuation analysis values ​​with the thresholds, the soil health status is divided into four levels: unhealthy, subhealthy, healthy, and extremely healthy, providing more refined decision-making support for soil management and protection.

[0025] In a second aspect, the present invention provides a soil data analysis system comprising an input device, a processor, an output device, and a memory, wherein the input device, the processor, the output device, and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the soil data analysis method. The system has the following creative effects: first, it realizes the comprehensive collection, efficient processing, and intuitive display of soil data, greatly improving the automation and intelligence level of soil data analysis; second, it can accurately assess the health status of the soil, timely discover potential problems, provide a scientific basis for soil management and protection, and enhance the accuracy and practicality of soil data analysis; third, it has good scalability and compatibility, can flexibly adapt to the data analysis needs of different soil types and different environmental conditions, and supports seamless connection with other soil management systems, realizing the sharing and collaborative analysis of soil data. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of a soil data analysis method according to an embodiment of the present invention;

[0027] Figure 2 A flowchart of a genetic algorithm according to an embodiment of the present invention;

[0028] Figure 3 A dynamic diagram of groundwater according to an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the structure of the soil data analysis system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0031] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0032] See Figure 1 , an embodiment of the present invention provides a soil data analysis method, the method comprising the following steps:

[0033] S1. Use genetic algorithm to randomly divide multiple soil areas.

[0034] See Figure 2 In one embodiment, the population is initialized first, that is, the soil areas are initially divided.

[0035] Specifically, first, the number of soil areas or the number of individuals to be initially divided.

[0036] Furthermore, an initial population is randomly generated, where each individual represents a soil region division scheme, which can be represented using binary coding or other suitable coding methods. In this embodiment, a bit vector is used to represent the region to which each soil sample belongs. Each bit of the vector corresponds to a region, 1 indicates that the sample belongs to the region, and 0 indicates that the sample does not belong to the region.

[0037] Furthermore, the fitness value of each individual is calculated using a fitness function designed based on factors such as similarity of soil properties and spatial continuity to evaluate the pros and cons of the partitioning scheme.

[0038] Furthermore, the iterative process is entered. A genetic algebra counter is set, and a maximum genetic algebra stopping condition is set. The iterative process is as follows: according to the fitness value of the individual, a part of excellent individuals are selected as parents for subsequent crossover and mutation operations; the selected parent individuals are paired, and the crossover operator is applied to generate new offspring individuals; the mutation operator is applied to the offspring individuals to introduce new genetic information. It should be noted that the crossover operator can be a single-point crossover, a multi-point crossover, a uniform crossover, etc. During the crossover process, it is necessary to ensure that the offspring individuals still meet the requirements of the soil sample's belonging area, that is, each soil sample can only belong to one area. The mutation operation can be a random flipping of the value of a gene bit, or a more complex mutation strategy.

[0039] Furthermore, for each individual in the newly generated offspring population, its fitness value is recalculated.

[0040] Furthermore, it is checked whether the maximum genetic generation stopping condition is reached. If so, the iteration process is stopped; otherwise, the iteration is continued.

[0041] Furthermore, after the iteration process is completed, the optimal solution is output, that is, the soil area division scheme represented by the individual with the highest fitness value. This scheme should be the most reasonable soil area division result based on the designed fitness function and the optimization process of the genetic algorithm.

[0042] S2. Acquire characteristic data of the soil internal environment and external environment of the multiple soil areas.

[0043] In one embodiment, based on the soil region division result of step S1, the characteristic data of the soil inside and outside of the soil regions are obtained. Specifically, the following steps are included:

[0044] S21. Obtain characteristic data of moisture content, salt concentration, temperature, pH, and mud-sand ratio at different depths in the soil of the multiple regions.

[0045] In this embodiment, within each divided soil area, sampling points at different depths are determined according to research requirements and soil characteristics.

[0046] Furthermore, soil sampling is carried out using professional tools such as soil drills and samplers to ensure the accuracy and representativeness of the sampling.

[0047] Furthermore, each sampling point was marked and information such as sampling depth, time, and location was recorded. The soil samples obtained were properly stored to avoid water loss and contamination.

[0048] Furthermore, by testing the soil sample, characteristic data of moisture content, salt concentration, temperature, pH and mud-sand ratio are obtained. Specifically, the following steps are included:

[0049] S211. Obtain data on moisture content at different depths in multiple soil areas.

[0050] In this embodiment, the obtained soil sample is weighed and the initial weight is recorded.

[0051] Furthermore, the soil samples were placed in an oven and dried at high temperature to constant weight.

[0052] Furthermore, the soil was weighed again to calculate the soil moisture content.

[0053] S212. Obtain data on salt concentration at different depths in multiple soil areas.

[0054] In this embodiment, the electrodes of the salt meter are inserted into the soil sample and the data is read after stabilization.

[0055] Furthermore, the soil salinity is calculated based on the relationship between the readings and the soil salinity.

[0056] S213. Obtain temperature data at different depths in multiple soil areas.

[0057] In this embodiment, a soil thermometer is inserted into a soil sample, and data is read after a period of time to directly obtain the temperature value of the soil sample.

[0058] S214. Obtain data on pH at different depths in multiple soil areas.

[0059] In this embodiment, the soil pH detection instrument is inserted into the soil sample, and the data is read after stabilization to directly obtain the pH value of the soil sample.

[0060] S215. Obtain data on the mud-to-sand ratio at different depths in multiple soil areas.

[0061] In this embodiment, the soil sample is dispersed into a suspension, and the settling velocity of the soil particles in the suspension is measured using a hydrometer.

[0062] Furthermore, the particle size distribution of soil particles is estimated based on the settling velocity, thereby determining the soil mud-sand ratio.

[0063] S22. Obtain characteristic data of crop growth outside the soil and groundwater dynamics in the multiple regions.

[0064] Wherein, S22 further includes the following steps:

[0065] S221. Obtain characteristic data of crop growth outside the soil of the multiple regions.

[0066] In one embodiment, temperature sensors, humidity sensors, and light sensors are deployed within a selected soil area to monitor extra-soil environmental parameters in real time. The sensors are deployed in appropriate locations to accurately reflect the true state of the crop growth environment.

[0067] Furthermore, crops are observed regularly and their growth status, such as plant height, leaf area, flowering, fruit development, etc., is recorded. When the crop growth reaches a specific state, it is recorded as a growth cycle.

[0068] Furthermore, within a specific time period, yield data of these crops are obtained, and the growth cycle conditions thereof are analyzed based on the yield data.

[0069] Furthermore, the above growth cycle data and yield data are collated to form characteristic data of crop growth.

[0070] S222. Obtain data on the dynamics of groundwater outside the soil in the multiple areas.

[0071] See Figure 3 In one embodiment, a monitoring drainage ditch is dug in a selected soil area to ensure that the ditch depth reaches the target aquifer. The monitoring ditch needs to be stabilized to ensure the ditch wall stability and prevent groundwater contamination.

[0072] Furthermore, different monitoring points are set up in the monitoring ditch, water level sensors are installed at these monitoring points, and data collectors are connected to ensure that water level changes can be recorded in real time or periodically.

[0073] Furthermore, water flow monitoring equipment is installed to monitor water flow indicators.

[0074] S3. Optimize the characteristic data to obtain multi-dimensional spatiotemporal characteristic data of soil environmental characteristics of multiple soil regions.

[0075] Among them, S3 includes the following steps:

[0076] S31. Preprocess the feature data.

[0077] In one embodiment, the characteristic data inside and outside the soil obtained in step S2 are preprocessed.

[0078] Specifically, the steps include:

[0079] S311. Data cleaning.

[0080] In this embodiment, first, duplicate, invalid or abnormal data records are deleted.

[0081] Furthermore, correct obvious erroneous data, such as unreasonable temperature, salt concentration, etc.

[0082] Furthermore, missing values ​​can be processed by interpolation, mean substitution or deletion of missing values.

[0083] S312. Data conversion and standardization.

[0084] In this embodiment, data in different units are converted into a unified unit, such as converting temperature into degrees Celsius and converting salt concentration into grams / kilogram.

[0085] Furthermore, the data are standardized to eliminate the influence of different dimensions on data analysis.

[0086] S313. Data integration.

[0087] In this embodiment, the characteristic data inside and outside the soil are integrated to form a complete data set.

[0088] S32. Perform spatiotemporal matching processing on the feature data.

[0089] Wherein, S32 further comprises the following steps:

[0090] S321. Perform time series analysis on the characteristic data.

[0091] In this example, time series analysis is performed on both soil and soil-derived characteristic data to reveal temporal trends. For example, seasonal or yearly variations in soil moisture content, salt concentration, temperature, pH, mud-sand ratio, crop growth, and groundwater dynamics are analyzed.

[0092] S322. Perform spatial distribution analysis on the characteristic data.

[0093] In this embodiment, a geographic information system (GIS) or other spatial analysis technology is used to draw a spatial distribution map of the characteristic data inside and outside the soil. The spatial distribution map is used to reveal the spatial distribution characteristics and differences of the characteristic data in different regions and at different soil depths.

[0094] S323. Based on the time series analysis and the spatial distribution analysis, perform spatiotemporal matching processing on the feature data.

[0095] In this example, the results of time series analysis and spatial distribution analysis are matched to reveal the spatiotemporal correlations and trends between soil characteristic data and those outside the soil. For example, the relationship between soil moisture content, salinity, temperature, pH, and mud-sand ratio can be analyzed, or the relationship between crop growth cycles and groundwater dynamics can be analyzed.

[0096] S33. Perform spatiotemporal interpolation processing on the feature data.

[0097] Wherein, S33 further includes the following steps:

[0098] S331. Perform spatial interpolation processing on the feature data.

[0099] In this embodiment, based on the characteristics of the data and research requirements, the inverse distance weighted interpolation method is selected to perform spatial interpolation processing on the feature data.

[0100] S332. Perform time interpolation processing on the feature data.

[0101] In this embodiment, according to the characteristics of the data and research requirements, a method combining linear interpolation and nonlinear interpolation is used to perform time interpolation processing on the characteristic data.

[0102] S333. Based on the spatial interpolation processing and the temporal interpolation processing, perform spatiotemporal interpolation processing.

[0103] In this embodiment, the spatial interpolation results and the temporal interpolation results are integrated to form a complete spatiotemporal dataset.

[0104] Furthermore, a spatiotemporal interpolation model is constructed based on the characteristics of spatiotemporal data and research needs.

[0105] Furthermore, the constructed spatiotemporal interpolation model is used to interpolate the soil characteristic data of unknown spatiotemporal points.

[0106] S34. Perform spatiotemporal fusion processing on the feature data.

[0107] In this example, a spatiotemporal fusion model was used to process the characteristic data. This model fuses the characteristic data within the soil with the characteristic data outside the soil. This fusion process yields fused data that maps characteristic data on soil moisture content, salinity, temperature, pH, and mud-sand ratio to characteristic data on crop growth cycles and groundwater dynamics, in a one-to-one correspondence based on temporal and spatial relationships.

[0108] S35. Through the preprocessing, the spatiotemporal matching processing, the spatiotemporal interpolation processing and the spatiotemporal fusion processing, multidimensional spatiotemporal data of the internal and external environmental characteristics of soil in multiple soil areas are obtained.

[0109] In this embodiment, the processing results of steps S31, S32, S33 and S34 are integrated to obtain multi-dimensional spatiotemporal data of internal and external soil characteristics of multiple soil regions.

[0110] It should be noted that the multidimensional spatiotemporal data includes spatiotemporal correlation data of soil internal environmental characteristics, spatiotemporal correlation data of soil external environmental characteristics, and spatiotemporal fusion data of soil internal and external characteristics.

[0111] S4. Based on the multidimensional spatiotemporal characteristic data, a multidimensional analysis model is constructed for directly or indirectly analyzing soil data from the perspectives of basic soil properties, crop growth, and groundwater dynamics.

[0112] Among them, S4 includes the following steps:

[0113] S41. Based on the multi-dimensional spatiotemporal characteristic data, a soil internal environment analysis model is constructed for directly analyzing soil data from the perspective of basic soil properties.

[0114] In one embodiment, based on step S3, a soil internal environment analysis model is constructed for directly analyzing soil data from the perspective of basic soil properties. The soil internal environment analysis model satisfies the following expression:

[0115]

[0116] in, for Soil environment analysis value at each moment, for Moment Areas at soil depth The soil moisture content at for Moment Areas at soil depth The soil salt concentration at for Moment Areas at soil depth The soil temperature at for Moment Areas at soil depth The soil pH, for Moment Areas at soil depth The soil-sand ratio at 、 、 、 、 Respectively Areas at soil depth The weight coefficients of soil moisture content, soil salt concentration, soil temperature, soil pH and soil mud-sand ratio are: is the total number of soil areas, is the initial soil depth, For the The total soil depth of the area.

[0117] It should be noted that one of the innovations of the present invention is that by judging The fluctuation value of soil temperature, soil pH, soil moisture, soil salt concentration and soil mud-sand ratio can be accurately found, thereby realizing direct analysis of soil data. The direct analysis is only a preliminary analysis of soil data.

[0118] Specifically, first, by reasonably setting the weight coefficient, the following conditions are met:

[0119]

[0120] Furthermore, five groups of soil internal environment analysis values ​​were set with fluctuation amplitude thresholds, namely 、 、 、 and ;

[0121] Furthermore, the absolute value of the fluctuation range of the soil internal environment analysis value is Compare with the fluctuation amplitude threshold to obtain a comparison result, the absolute value of the fluctuation amplitude Satisfies the following expression:

[0122]

[0123] in, Indicates the real-time change data of soil internal environmental analysis values. Represents the initial fixed data of the soil internal environmental analysis value.

[0124] Furthermore, the comparison results are as follows:

[0125] when When the soil moisture content data is abnormal, it deviates from the healthy state;

[0126] when When the soil salt concentration data is abnormal, it deviates from the healthy state;

[0127] when When soil temperature data is abnormal, it deviates from the healthy state;

[0128] when When the soil pH data is abnormal, it deviates from the healthy state;

[0129] when When the soil sediment ratio data is abnormal, it deviates from the healthy state.

[0130] when When the soil is warm, the overall internal environment data is stable and relatively healthy.

[0131] It should be noted that the situation where the specific data of the soil environment deviates from the healthy state should be checked one by one from large to small. For example, in this embodiment, when the absolute value of the fluctuation amplitude of the soil environment analysis value is Greater than or equal to When the temperature data is abnormal, the soil temperature is adjusted by using the temperature regulating device to change value until Less than Furthermore, this method is used to judge abnormalities and adjust and improve soil pH data, moisture data, salt concentration data, and mud-sand ratio data.

[0132] S42. Based on the multi-dimensional spatiotemporal feature data, a crop growth analysis model is constructed for indirectly analyzing soil data from the perspective of the crop growth cycle.

[0133] In one embodiment, based on step S3, a crop growth analysis model is constructed for indirectly analyzing soil data from the perspective of the crop growth cycle. The crop growth analysis model satisfies the following expression:

[0134]

[0135] in, for Analytical values ​​of crop growth cycle outside soil at all times, for Moment The first The inverse of the crop growth cycle, for Moment The first The yield of crops, 、 Respectively The first The weight coefficient of the growth cycle and yield of the crop, is the total number of soil areas, For the The total number of crop types in the region.

[0136] It should be noted that 、 and The value of will hardly change significantly in the short term. Soil data can be indirectly analyzed through the analysis model of crop growth. The details are as follows:

[0137] First, set Maximum threshold and minimum threshold :

[0138] Furthermore, comparison and as well as size;

[0139] when When , it means that the crop growth cycle is short and the yield is large. The soil data at this time is optimal and very suitable for crop growth.

[0140] when When , it means that the crop growth cycle is longer and the yield is larger. The soil data at this time is relatively good and suitable for crop growth.

[0141] when When , it means that the crop growth cycle is short and the yield is small, or the growth cycle is long and the yield is small. At this time, the soil data is poor, which is not suitable for crop growth or very unsuitable for crop growth.

[0142] It's important to note that the length of a crop's growth cycle can reflect certain soil characteristics. For example, sufficient soil fertility, proper water management, and good soil structure generally lead to rapid crop growth, shortening the growth cycle. Conversely, poor soil, salinization, acidification, or improper soil management can lead to slow crop growth and a prolonged growth cycle. Crop yield is a direct reflection of soil productivity. High yields generally indicate sufficient soil nutrients and suitable soil conditions for crop growth. Low yields, on the other hand, may reflect insufficient soil nutrients, poor soil structure, or other obstacles.

[0143] S43. Based on the multi-dimensional spatiotemporal characteristic data, a groundwater dynamic analysis model is constructed for indirectly analyzing soil data from a groundwater dynamic perspective.

[0144] In one embodiment, based on step S3, a groundwater dynamic analysis model is constructed for indirectly analyzing soil data from the perspective of groundwater dynamics. The groundwater dynamic analysis model satisfies the following expression:

[0145]

[0146] in, for Multi-dimensional analysis value of groundwater dynamics outside the soil at all times, for Moment The region's The groundwater level at each monitoring point, for Moment The region's The groundwater flow at each monitoring point, is the total number of soil areas, For the The total number of monitoring points in each region, 、 Respectively The region's The weight coefficients of groundwater level and groundwater flow at each monitoring point.

[0147] Soil data can be indirectly analyzed through the groundwater dynamic analysis model. The details are as follows:

[0148] First, set Maximum threshold and minimum threshold :

[0149] Furthermore, comparison and as well as size;

[0150] when When the soil is moist, it dilutes salt, accelerates material migration and chemical reactions, and affects nutrient distribution and pH. Therefore, soil data obtained under this condition is better.

[0151] when When the soil is saturated, aeration is poor, and salt and nutrients tend to accumulate in the surface layer, slowing material circulation and biological activity. The soil may experience alternating wet and dry conditions, with abundant groundwater recharge at deep or distant locations. The specific impact on the soil requires consideration of the interaction between water movement and soil properties. Therefore, soil data obtained under these conditions is relatively poor.

[0152] when When the soil is dry and prone to drought and salinization, it is not conducive to microbial activity and plant growth. The organic matter content decreases and the biological activity decreases. Therefore, the soil data obtained under this condition is poor.

[0153] S5. Based on the multi-dimensional analysis model, a soil intelligent analysis model is constructed for comprehensive analysis of soil data.

[0154] In one embodiment, a soil intelligent analysis model for comprehensive analysis of soil data is constructed based on a soil internal environment analysis model, a crop growth analysis model, and a groundwater dynamic analysis model. The soil intelligent analysis model satisfies the following expression:

[0155]

[0156] in, for Fluctuation analysis value of soil data at each moment, for Fluctuation analysis value of soil environment at all times, for Fluctuation analysis value of crop growth cycle outside soil at every moment, for Fluctuation analysis value of groundwater dynamics outside soil at all times, 、 、 is the weight coefficient.

[0157] S6. Obtain comprehensive analysis results of soil data based on the soil intelligent analysis model.

[0158] In one embodiment, a method for setting weight coefficients with great creativity is proposed.

[0159] Specifically, based on step S5, first, a large number of time series corresponding relationships are obtained. 、 and historical data.

[0160] Furthermore, using statistical analysis methods, we obtained The minimum value of The maximum and minimum values ​​of The maximum value of .

[0161] Furthermore, by setting a Value Change The size of The size is always greater than By setting a Value Change The size of The size is always and By setting a Value Change The size of The size is always smaller than .

[0162] Further, according to The size of the maximum threshold and minimum threshold .

[0163] Furthermore, the actual Value and maximum threshold and minimum threshold A comparative analysis was conducted, and the results are as follows:

[0164] when There are obvious fluctuations in soil environmental data.

[0165] when When , the soil internal environment data did not fluctuate significantly, while the crop growth data in the soil external environment data did fluctuate significantly. When , it means that the crop growth data shows reduced fluctuations; when , indicating that crop growth data exhibits increasing fluctuations.

[0166] when When the soil internal environment data and the crop growth data in the soil external environment data did not fluctuate significantly, the groundwater dynamic data in the soil external environment data fluctuated significantly. Indicates the maximum value when the soil internal and external environmental data are in a stable state. value.

[0167] when When the soil internal and external environmental data are in a stable state.

[0168] S7. Determine the health status of the soil based on the fluctuation analysis results.

[0169] In one embodiment, the health status of the soil is determined based on the fluctuation analysis result of step S6.

[0170] Specifically, the judgment results are as follows:

[0171] When soil environmental data fluctuates significantly, it means that characteristic data such as soil moisture content, salt concentration, temperature, pH, and mud-sand ratio are fluctuating significantly and deviate significantly from normal data. Therefore, the soil is in an unhealthy state.

[0172] When the internal soil environmental data does not fluctuate significantly, and the crop growth cycle data in the external soil environmental data fluctuates more, the soil is in a sub-healthy state; when the fluctuation decreases, the soil is in a healthy state.

[0173] When there is no significant fluctuation in the crop growth cycle data in both the internal soil environment data and the external soil environment data, and when the groundwater dynamic data in the external soil environment data fluctuates away from the stable state, the soil is in a sub-healthy state; when there is a fluctuation close to the stable state, the soil is in a healthy state.

[0174] When both the internal and external environmental data of the soil are in a stable state, the soil is in an extremely healthy state.

[0175] See Figure 4 , Figure 4 The following is a schematic diagram of the structure of a soil data analysis system according to an embodiment of the present invention. The system includes an input device, a processor, an output device, and a memory, wherein the input device, the processor, the output device, and the memory are interconnected. The memory is used to store a computer program including program instructions. The processor is configured to invoke the program instructions. The system utilizes a soil data analysis method described above.

[0176] In this embodiment, the input device is the front end of the soil data analysis system, responsible for inputting the original data of the soil sample into the system, including a high-precision soil sensor, a scanner, and a user interaction interface.

[0177] The processor is the core of the soil data analysis system and is responsible for executing computer programs stored in the memory, especially those program instructions related to soil data analysis, including a high-performance computing unit.

[0178] The output device is the backend of the soil data analysis system, responsible for presenting the processed data and analysis results in a user-friendly manner, including a display screen and a printer.

[0179] The memory adopts a high-speed solid-state hard disk, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It is mainly used to store data input by the input device and the result data processed by the processor, and can meet the needs of large data storage.

[0180] In summary, the present invention introduces a genetic algorithm to perform random division of soil areas, breaking through the subjectivity and uncertainty of traditional methods and realizing intelligent and automated soil area division; by comprehensively considering the data of the internal and external environment of the soil, a multi-dimensional comprehensive analysis of soil data is realized, which comprehensively reflects the true condition of the soil and improves the accuracy and practicality of the analysis; by directly or indirectly analyzing soil data from three perspectives: basic soil properties, crop growth, and groundwater dynamics, it is possible to accurately judge abnormal conditions of soil data step by step, providing a scientific basis for the assessment of soil health status; by reasonably setting the weight coefficients of soil characteristics in different regions, it is possible to perform separate and comprehensive analyses of multiple soil characteristics in a single soil data comprehensive analysis model.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A soil data analysis method, characterized in that: The method comprises the following steps: Genetic algorithm is used to randomly divide multiple soil regions; Acquiring characteristic data of the soil internal environment and external environment of the plurality of soil regions; Optimizing the characteristic data to obtain multidimensional spatiotemporal characteristic data of soil environmental characteristics of multiple soil regions; Based on the multidimensional spatiotemporal characteristic data, a multidimensional analysis model is constructed for directly or indirectly analyzing soil data from the perspectives of basic soil properties, crop growth, and groundwater dynamics, including: Based on the multidimensional spatiotemporal characteristic data, an internal environment analysis model for directly analyzing soil data is constructed; The internal environment analysis model for directly analyzing soil data satisfies the following expression: in, for Soil environment analysis value at each moment, for Moment Areas at soil depth The soil moisture content at for Moment Areas at soil depth The soil salt concentration at for Moment Areas at soil depth The soil temperature at for Moment Areas at soil depth The soil pH, for Moment Areas at soil depth The soil-sand ratio at 、 、 、 、 Respectively Areas at soil depth The weight coefficients of soil moisture content, soil salt concentration, soil temperature, soil pH and soil mud-sand ratio are: is the total number of soil areas, is the initial soil depth, For the Total soil depth in the area; Based on the multidimensional spatiotemporal characteristic data, constructing an indirect analysis model for indirectly analyzing soil data, the indirect analysis model including a crop growth analysis model and a groundwater dynamic analysis model; The crop growth analysis model for indirectly analyzing soil data satisfies the following expression: in, for Crop growth analysis values ​​of the extra-soil environment at all times, for Moment The first The inverse of the crop growth cycle, for Moment The first The yield of crops, 、 Respectively The first The weight coefficient of the growth cycle and yield of the crop, is the total number of soil areas, For the The total number of crop types in the region; The groundwater dynamic analysis model for indirect analysis of soil data satisfies the following expression: in, for Dynamic analysis value of groundwater in the soil environment at all times, for Moment The first The groundwater level at each monitoring point, for Moment The first The groundwater flow at each monitoring point, is the total number of soil areas, For the The total number of monitoring points in each region, 、 Respectively The first Weight coefficients of groundwater level and groundwater flow at each monitoring point; Based on the multi-dimensional analysis model, a soil intelligent analysis model for comprehensive analysis of soil data is constructed; The soil health status is judged by the soil intelligent analysis model.

2. A soil data analysis method according to claim 1, characterized in that: The acquiring of characteristic data of the soil internal environment and the external environment of the plurality of soil regions comprises: Acquiring characteristic data of water content, salt concentration, temperature, pH, and mud-sand ratio at different depths within the soil of the plurality of soil regions; obtaining characteristic data of crop growth cycles and yields of different crop species in the plurality of soil regions; Characteristic data of groundwater level and flow at different monitoring points in the plurality of soil regions are obtained.

3. A soil data analysis method according to claim 1, characterized in that: Optimizing the characteristic data to obtain multi-dimensional spatiotemporal characteristic data of soil environmental characteristics of multiple soil regions includes: Preprocessing the feature data; Performing spatiotemporal matching processing on the feature data; Performing spatiotemporal interpolation processing on the feature data; Performing spatiotemporal fusion processing on the feature data; Through the preprocessing, the spatiotemporal matching processing, the spatiotemporal interpolation processing and the spatiotemporal fusion processing, multidimensional spatiotemporal data of the internal and external environmental characteristics of the soil in multiple soil areas are obtained, and the multidimensional spatiotemporal data include the spatiotemporal correlation data of the internal environmental characteristics of the soil, the spatiotemporal correlation data of the external environmental characteristics of the soil, and the spatiotemporal fusion data of the internal and external environmental characteristics of the soil.

4. A soil data analysis method according to claim 1, characterized in that: The direct analysis includes: The weight coefficients of the soil moisture content, the soil salinity, the soil temperature, the soil pH, and the soil mud-sand ratio are set so that the following conditions are met: According to the weight coefficient, a fluctuation amplitude threshold of the soil internal environment analysis value is set, and the fluctuation amplitude threshold is set as: 、 、 、 and ; The absolute value of the fluctuation amplitude of the soil internal environment analysis value A comparative analysis is performed with the fluctuation amplitude threshold to obtain a comparative analysis result, which is as follows: when When , the soil moisture content data is abnormal; when When , the soil salt concentration data is abnormal; when When , the soil temperature data is abnormal; when When the soil pH data is abnormal; when When , the soil mud-sand ratio data are abnormal; when The soil environment data are normal.

5. A soil data analysis method according to claim 1, characterized in that: The indirect analysis includes: Setting a maximum threshold and a minimum threshold of indirect analysis values, wherein the indirect analysis values ​​include the crop growth analysis value and the groundwater dynamic analysis value; The indirect analysis value is compared with the maximum threshold and the minimum threshold to obtain a comparison result, which includes: when the indirect analysis value is greater than or equal to the maximum threshold, the soil data quality is relatively good; when the indirect analysis value is less than the maximum threshold and greater than the minimum threshold, the soil data quality is average; when the indirect analysis value is less than the minimum threshold, the soil data quality is relatively poor.

6. A soil data analysis method according to claim 1, characterized in that: The soil intelligence analysis model satisfies the following expression: in, for Fluctuation analysis value of soil data at each moment, for Soil environment analysis value at each moment, for Crop growth analysis values ​​of the extra-soil environment at all times, for Dynamic analysis value of groundwater in the soil environment at all times, 、 、 is the weight coefficient.

7. A soil data analysis method according to claim 6, characterized in that: The soil health status is judged by the soil intelligence analysis model including: By using the soil intelligent analysis model, the maximum threshold, minimum threshold and maximum stable state value of the fluctuation analysis value of the soil data are set; Compare the fluctuation analysis value with the maximum threshold, the minimum threshold, and the maximum stable state value to obtain a comparison result, the comparison result including: When the fluctuation analysis value is greater than the maximum threshold, the soil internal environmental data has obvious fluctuations, and the soil is judged to be in an unhealthy state; When the fluctuation analysis value is less than the maximum threshold and greater than the minimum threshold, if the crop growth data in the soil external environment data has a significantly increased fluctuation, the soil is judged to be in a sub-healthy state; if the crop growth data has a significantly decreased fluctuation, the soil is judged to be in a healthy state; When the fluctuation analysis value is less than the minimum threshold and greater than the maximum stable state value, if the groundwater dynamic data in the environment outside the soil fluctuates away from the maximum stable state value, the soil is judged to be in a subhealthy state; if the groundwater dynamic data fluctuates close to the maximum stable state value, the soil is judged to be in a healthy state; When the fluctuation analysis value is less than the maximum stable state value, the data of the internal and external environments of the soil are stable, and the soil is judged to be in an extremely healthy state.

8. A soil data analysis system, the system using a soil data analysis method according to any one of claims 1 to 7, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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