Coastal saline-alkali soil monitoring data acquisition system

Abstract

The invention relates to the technical field of data acquisition, in particular to a coastal saline-alkali soil monitoring data acquisition system which comprises a soil data acquisition module, an underground river shape analysis module and an irrigation fresh water prediction module. The saline-alkali soil is equally divided into a plurality of same areas through the soil data acquisition module, then the underground river shape analysis module analyzes the areas with similar fresh water amount according to historical data in the divided areas, the conductivity difference value of the areas corresponding to the similar irrigation fresh water amount is calculated, and if the conductivity difference value is larger than the conductivity difference threshold value, the underground river shape analysis module determines that the saline-alkali soil is in the same area. If yes, judging that an underground river exists below the two corresponding divided areas, constructing the shape of the underground river, defining the divided areas A and B according to the shape of the underground river, and calculating the correlation coefficient of the conductivity and the fresh water irrigation amount in the divided area A, so that the fresh water irrigation amount of different soil conductivities can be calculated according to the correlation coefficient when fresh water is irrigated. And blind large-scale irrigation is avoided, and water resource waste caused by excessive irrigation is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of data acquisition, in particular to a coastal saline-alkali land soil monitoring data acquisition system. Background Art

[0002] High salinity and alkaline soil in coastal saline-alkali land have many adverse effects on crop growth. Salt ions in the soil can make it difficult for plant roots to absorb water, resulting in physiological drought. When the salt concentration in the soil solution is too high, the osmotic pressure of the root cells is lower than the osmotic pressure of the soil solution, and water cannot enter the root system normally, resulting in slow plant growth, stunted development, and even death. According to statistics, in unimproved coastal saline-alkali land, the survival rate of crops is usually less than 30%, and the yield is extremely low, which seriously restricts the development of local agriculture. Therefore, before carrying out saline-alkali land agricultural planting, staff need to regularly test different areas of the saline-alkali land, implement freshwater irrigation on the saline-alkali land based on the test results, and store the test data as historical data; However, in coastal areas, the geological structure is relatively complex. After long-term geological changes and seawater erosion, the structure of underground rock and soil layers has undergone many changes, creating conditions for the formation of underground rivers. Therefore, there will be underground rivers under the saline-alkali land, causing the irrigation fresh water, which should have slowly infiltrated the soil pores, to flow along the underground rivers. If the amount of irrigation fresh water required for different areas of saline-alkali land cannot be analyzed based on the existence of underground rivers, the effect of saline-alkali land irrigation fresh water will be affected. In view of this, we propose a coastal saline-alkali land soil monitoring data collection system. Summary of the invention

[0003] The purpose of the present invention is to solve the problem of fresh water irrigation in saline-alkali land. Under normal circumstances, the irrigation fresh water should slowly penetrate into the soil pores, gradually dissolve and take away the salt in the soil, and play a role in improving the soil. However, in coastal saline-alkali land, due to the rise and fall of sea levels in geological history, there are often underground rivers under the saline-alkali land. Once encountering the underground river, the fresh water will be affected by its strong attraction and flow away quickly along the underground river, shortening the residence time of the fresh water in the soil, and the problem of the amount of fresh water for irrigation in the underground river area cannot be analyzed.

[0004] To achieve the above-mentioned purpose, the present invention provides a coastal saline-alkali land soil monitoring data acquisition system that can collect data from historical data for analyzing the shape of underground rivers and predict the amount of fresh water for the next irrigation based on the shape of underground rivers, including a soil data acquisition module, an underground river shape analysis module and an irrigation fresh water prediction module; The soil data acquisition module divides the saline-alkali land into equal parts and uses the distance calculation method to determine the historical data of the divided areas; the underground river shape analysis module normalizes the historical data, calculates the difference between the water content and the conductivity in the historical data, determines the divided areas with similar irrigation freshwater amounts, and uses the graph theory method to establish the underground river shape; the irrigation freshwater prediction module defines the divided areas A and B based on the underground river shape, uses the change trend analysis method to analyze the effective freshwater irrigation depth, calculates the effective irrigation depth conductivity and the freshwater irrigation amount, and calculates the correlation coefficient between the two.

[0005] As a further improvement of the technical solution, the soil data acquisition module establishes a plane rectangular coordinate system with a corner point of the saline-alkali land as the origin, and senses the saline-alkali land in The length in the axial direction is ,exist The length in the axial direction is , the spacing of the equal-distance divisions is set to ; Number of rows to divide for: ,in Indicates rounding down; the number of columns to be divided for: .

[0006] As a further improvement of the technical solution, the working principle of the distance calculation method in the soil data acquisition module is as follows: Sense the detection coordinates in each divided area. If the number of detection coordinates is greater than , calculate the distance between the detection coordinates and the center coordinates of the divided area. The coordinates of the lower left corner vertex of the perception division area are , the coordinates of the upper right vertex are , then the coordinates of the center of the divided area are: ; ; The distance between each detection coordinate and the center coordinate of the divided area is: ,in Detection coordinates, is the center coordinate, For the The distance between the detection coordinates and the center coordinates, call out the minimum , which is the historical data of the divided areas.

[0007] As a further improvement of the technical solution, the calculation formula for the normalization processing of the dark river shape analysis module is as follows: ; in, When is the water content after normalization, is the minimum value of water content in historical data, is the maximum value of water content in all historical data; When is the normalized conductivity, is the minimum value of conductivity in historical data, is the maximum value of conductivity in all historical data, and the above conductivity is the average value of conductivity at different depths; As a further improvement of the technical solution, the dark river shape analysis module senses the normalized conductivity and water content of the irrigation fresh water: ,in, After the irrigation of fresh water The conductivity and water content of each divided area, For the The average conductivity at different depths in the divided area , For the The number of conductivity measurements at different depths in each divided area, For the The moisture content of each divided area; Calculate the difference between water content and conductivity in turn: ,in , is the difference value, is the number of difference values; The difference values ​​are sorted from large to small. Sort by difference value set , ; Setting judgment threshold , select the difference value set from large to small Data: ,in, Indicates rounding down. To select the number of difference data in the difference value set, The corresponding divided areas have similar amounts of fresh water for irrigation; Calculate the average value of the selected data as the difference threshold: The perception selected data is: , then the difference threshold is in, The number of selected data.

[0008] As a further improvement of the technical solution, the dark river shape analysis module senses the unselected difference value , is the number of unselected difference values, and the unselected difference values ​​and the difference threshold are In contrast, if , then the difference value corresponds to the divided area where irrigation has similar amount of fresh water. , it is judged that there is no fresh water for irrigation in the area corresponding to the difference value.

[0009] As a further improvement of the present technical solution, the graph theory method in the underground river shape analysis module calls out the conductivity of similar irrigation freshwater divided areas and sets a conductivity difference threshold. If the conductivity difference at the same depth is greater than the conductivity difference threshold, it is determined that there is an underground river under the two corresponding divided areas. The underground rivers under multiple divided areas are compared in turn and connected to obtain the shape of the underground river.

[0010] As a further improvement of the present technical solution, the change trend analysis method in the irrigation freshwater prediction module analyzes the change trend of conductivity at different depths. When the conductivity changes from decreasing to increasing, the corresponding detection depth is the effective freshwater irrigation depth, and its expression is: For the partition area A, at the depth The conductivity is recorded as , freshwater irrigation volume is recorded as ;when , When , the conductivity is judged to be in the decreasing stage; When , the conductivity is judged to be in the increasing stage. It is the effective irrigation freshwater depth of divided area A.

[0011] As a further improvement of the present technical solution, the correlation coefficient in the irrigation freshwater prediction module is used to predict the irrigation water volume at different irrigation depths when the regional irrigation freshwater is divided: the integrated effective irrigation depth conductivity is: ; Freshwater for irrigation ; Calculate the mean conductivity at the effective irrigation depth: , Average irrigation freshwater volume: ; Correlation coefficient .

[0012] As a further improvement of the present technical solution, the irrigation fresh water prediction module also defines the divided area above the underground river as divided area B. If the conductivity change trend in divided area B is the same as that in divided area B, divided area B is mapped to divided area A. If the conductivity change trend continues to decrease, it is judged that there is too much irrigation fresh water in divided area B.

[0013] Compared with the prior art, the present invention has the following beneficial effects: In the coastal saline-alkali land soil monitoring data acquisition system, the saline-alkali land is equidistantly divided into multiple identical areas through the soil data acquisition module, and then the underground river shape analysis module analyzes the areas with similar freshwater volume through the historical data in the divided areas, and calculates the conductivity difference of the corresponding areas with similar irrigation freshwater volume. If the conductivity difference is greater than the conductivity difference threshold, it is judged that there is an underground river under the two corresponding divided areas, and then the coordinates of the multiple divided areas are connected to construct the shape of the underground river. At this time, the irrigation freshwater prediction module defines the divided areas A and B according to the shape of the underground river, and calculates the correlation coefficient between the conductivity and the freshwater irrigation volume in the divided area A, so that when freshwater is irrigated, it can be predicted. The amount of freshwater irrigation for different soil conductivities is calculated based on the correlation coefficient, which avoids blind and large-scale irrigation and reduces the waste of water resources caused by excessive irrigation, especially in coastal areas where water resources are relatively scarce, thereby improving the efficiency of water resource utilization. In addition, based on the trend of conductivity changes, it is analyzed whether the existence of underground rivers will interfere with the judgment of freshwater irrigation effects. If not, the divided area B is mapped to the divided area A, and then based on the trend of conductivity changes in the divided area B, it is analyzed whether there is excessive irrigation of freshwater in history, to ensure the scientific nature of the irrigation strategy, so that freshwater can act more effectively on saline-alkali land improvement, improve the effect of irrigation on reducing soil salinity, and achieve precision irrigation.

[0014] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall module principle diagram of the present invention; Figure 2 It is a schematic diagram of the conductivity change trend of the present invention.

[0016] The meaning of each number in the figure is: 100. Soil data collection module; 200. Underground river shape analysis module; 300. Irrigation fresh water prediction module. DETAILED DESCRIPTION

[0017] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] refer to Figure 1-Figure 2As shown, the coastal saline-alkali land soil monitoring data collection system includes a soil data collection module 100, a dark river shape analysis module 200 and an irrigation fresh water prediction module 300; In order to avoid affecting the accuracy and comparability of data due to the uncertainty of detection coordinates during the detection of saline-alkali land (the topography of coastal saline-alkali land is often complex, with various terrains such as tidal flats, swamps, and wetlands. When conducting detection in these areas, due to terrain restrictions, it may be difficult for detection personnel to accurately reach the pre-set coordinate position, and they can only temporarily select nearby relatively easy-to-reach locations for detection, resulting in changes in detection coordinates), the soil data acquisition module 100 divides the saline-alkali land into equal intervals, and uses a distance calculation method to select historical data close to the center coordinates of the divided area (the historical data includes the water content of the saline-alkali land, the conductivity of the saline-alkali land at different depths before and after irrigation with fresh water, the amount of fresh water irrigated in different areas, irrigation coordinates, etc.) as historical data representing the area, specifically including the following steps: Step 1: Establish a plane rectangular coordinate system with a corner point of the saline-alkali land as the origin to perceive the saline-alkali land. The length in the axial direction is ,exist The length in the axial direction is , the spacing of the equal-distance divisions is set to ; Number of rows to divide for: ,in Indicates rounding down; the number of columns to be divided for: .

[0019] Step 2: Sense the detection coordinates in each divided area. If the number of detection coordinates is greater than 1, calculate the distance between the detection coordinates and the center coordinates of the divided area. The coordinates of the lower left corner vertex of the perception division area are , the coordinates of the upper right vertex are , then the coordinates of the center of the divided area are: ; ; The distance between each detection coordinate and the center coordinate of the divided area is: ,in Detection coordinates, is the center coordinate, For the The distance between the detection coordinates and the center coordinates, call out the minimum , which is the historical data of the divided areas.

[0020] After the saline-alkali land is irrigated with fresh water, the fresh water will mix with the salt in the soil as the fresh water is added. At this time, the proportion of salt ions in the total volume of the solution increases, resulting in a decrease in conductivity. Therefore, the relationship between water content and conductivity is an inverse relationship. The dark river shape analysis module 200 calculates the difference between water content and conductivity, sets a judgment threshold, and determines the division area of ​​irrigation with similar fresh water volume; Since water content and conductivity are two indicators with different physical meanings and dimensions, if the difference is calculated directly through historical data, some data with larger values ​​will dominate the calculation process, while smaller values ​​will be ignored. Therefore, first of all, the water content and conductivity are normalized, and the calculation formula is as follows: ,in, When is the water content after normalization, is the minimum value of water content in historical data, is the maximum value of water content in all historical data; When is the normalized conductivity, is the minimum value of conductivity in historical data, is the maximum value of conductivity in all historical data; and conductivity is the average value at different depths; Then, the normalized conductivity and water content of the irrigation fresh water are sensed: ,in, After the irrigation of fresh water The conductivity and water content of each divided area, For the The average conductivity at different depths in the divided area , For the The number of conductivity measurements at different depths in each divided area, For the The moisture content of each divided area; Calculate the difference between water content and conductivity in turn: ,in , is the difference value, is the number of difference values; The difference values ​​are sorted from large to small. Sort by difference value set , ; Setting judgment threshold , select the difference value set from large to small Data: ,in, Indicates rounding down. To select the number of difference data in the difference value set, The corresponding divided areas have similar amounts of fresh water for irrigation; Calculate the average value of the selected data as the difference threshold: The perception selected data is: , then the difference threshold is in, The number of selected data.

[0021] In order to avoid abnormal data generated by historical data due to measurement errors, local soil characteristics differences and other factors, resulting in the difference value calculated by abnormal data greatly deviating from the normal range, the dark river shape analysis module 200 senses the unselected difference value , is the number of unselected difference values, and the unselected difference values ​​and the difference threshold are In contrast, if , then the difference value corresponds to the divided area where irrigation has similar amount of fresh water. , it is judged that there is no fresh water for irrigation in the area corresponding to the difference value.

[0022] In order to analyze the shape of the underground river under the saline-alkali land, the underground river shape analysis module 200 calls out the conductivity (conductivity at different depths) of similar irrigation freshwater division areas, sets the conductivity difference threshold, and if the conductivity difference at the same depth is greater than the conductivity difference threshold, it is determined that there is an underground river under the two corresponding division areas, and the underground rivers under multiple division areas are connected by graph theory in turn, which is the shape of the underground river. Its expression is as follows: Perceive any two partitioned areas and , at depth The conductivity difference at for ; Graph theory is used to establish the shape of the dark river: each divided area is regarded as a node. (meaning that the conductivity difference at the same depth is greater than the conductivity difference threshold), then at the node and Add an edge between them, which is the underground river under the two areas; Considering further that in the geological history, the sea level has experienced many ups and downs. When the sea level dropped, the river cutting effect in the coastal area increased, and the rivers deepened and widened. As the sea level rose again, some ancient river channels were submerged by sea water, but their river channel structure underground was still preserved, becoming underground rivers. When some areas are irrigated with fresh water, there are underground rivers in the corresponding areas of irrigation fresh water. The underground rivers are equivalent to a hidden drainage channel in the soil, so that the irrigation fresh water, which should have slowly infiltrated in the soil pores, will be diverted by the strong attraction of the underground river. If a large amount of irrigation fresh water is diverted by the underground river, it will cause a waste of fresh water. This irrigation fresh water prediction module 300 senses the divided area irrigated with the same amount of fresh water and not located above the underground river, which is defined as divided area A, and retrieves the conductivity (conductivity at different depths after irrigation) and the amount of fresh water for irrigation in the divided area A. In order to avoid unclear treatment effects achieved by different amounts of fresh water for irrigation when treating saline-alkali land, the trend analysis method analyzes the change trend of conductivity at different depths. When the conductivity changes from decreasing to increasing, the corresponding detection depth is the effective fresh water irrigation depth. According to the effective irrigation depth and the amount of fresh water irrigation, the correlation coefficient between the two is calculated to predict the amount of irrigation water at different irrigation depths when irrigating fresh water in the divided area. Perception Sharing For the partition area A, at the depth The conductivity is recorded as , freshwater irrigation volume is recorded as ;when , When , the conductivity is judged to be in the decreasing stage; When , the conductivity is judged to be in the increasing stage. It is the effective irrigation freshwater depth of divided area A.

[0023] Correlation coefficient: The integrated effective irrigation depth conductivity is: ; Freshwater for irrigation ; Calculate the mean conductivity at the effective irrigation depth: , Average irrigation freshwater volume: ; Correlation coefficient ; Correlation coefficient Based on statistical principles, the numerator reflects the degree of coordinated change between variables, and the denominator is used for standardization to obtain a correlation coefficient between -1 and 1 to measure the degree of linear correlation between the two; Therefore, by analyzing the changing trend of conductivity at different depths in the divided area A and determining the effective freshwater irrigation depth, we can intuitively understand the scope and extent of the impact of irrigation freshwater on saline-alkali land in the soil. Combined with the amount of irrigation freshwater, the governance effect achieved by different irrigation amounts can be clarified, helping researchers and agricultural workers to accurately evaluate the progress and effectiveness of saline-alkali land governance.

[0024] In order to avoid the situation where the underground river exists under the saline-alkali land but does not affect the irrigation fresh water when the saline-alkali land is irrigated with fresh water due to its depth being too high, if it is not possible to distinguish whether the underground river has an impact on the irrigation fresh water of the saline-alkali land, it will be impossible to effectively reduce soil salinity by reasonably adjusting the irrigation strategy. Therefore, the division area where the irrigation fresh water prediction module 300 is located above the underground river is defined as division area B. If the conductivity change trend in division area B is the same as that in division area B, division area B is mapped to division area A. If the conductivity change trend continues to decrease, it is judged that there is too much irrigation fresh water in division area B, thereby preventing the existence of the underground river from interfering with the judgment of the irrigation effect. If such a distinction is not made, the impact of the underground river on soil conductivity will be attributed to an irrigation strategy problem, resulting in an inability to accurately evaluate the true effect of irrigation on saline-alkali land management.

[0025] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Coastal saline-alkali land soil monitoring data acquisition system, characterized by: It includes a soil data collection module (100), a dark river shape analysis module (200) and an irrigation fresh water prediction module (300); The soil data acquisition module (100) divides the saline-alkali land into equal intervals and uses a distance calculation method to determine the historical data of the divided areas; the underground river shape analysis module (200) normalizes the historical data, calculates the difference between the water content and the conductivity in the historical data, determines the divided areas with similar irrigation freshwater volume, and uses a graph theory method to establish the underground river shape; the irrigation freshwater prediction module (300) divides the areas A and B based on the underground river shape definition, uses a change trend analysis method to analyze the effective freshwater irrigation depth, calculates the effective irrigation depth conductivity and the freshwater irrigation volume, and calculates the correlation coefficient between the two.

2. The coastal saline-alkali land soil monitoring data acquisition system according to claim 1 is characterized in that: The soil data acquisition module (100) establishes a plane rectangular coordinate system with a certain corner point of the saline-alkali land as the origin, and senses the saline-alkali land in The length in the axial direction is ,exist The length in the axial direction is , the spacing of the equal-distance divisions is set to ; Number of rows to divide for: ,in Indicates rounding down; the number of columns to be divided for: .

3. The coastal saline-alkali land soil monitoring data acquisition system according to claim 2 is characterized in that: The working principle of the distance calculation method in the soil data acquisition module (100) is as follows: Sense the detection coordinates in each divided area. If the number of detection coordinates is greater than 1, calculate the distance between the detection coordinates and the center coordinates of the divided area. The coordinates of the lower left corner vertex of the perception division area are , the coordinates of the upper right vertex are , then the coordinates of the center of the divided area are: ; ; The distance between each detection coordinate and the center coordinate of the divided area is: ,in Detection coordinates, is the center coordinate, For the The distance between the detection coordinates and the center coordinates, call out the minimum , which is the historical data of the divided areas.

4. The coastal saline-alkali land soil monitoring data acquisition system according to claim 1 is characterized in that: The calculation formula for the normalization processing of the dark river shape analysis module (200) is as follows: ； in, When is the water content after normalization, is the minimum value of water content in historical data, is the maximum value of water content in all historical data; When is the normalized conductivity, is the minimum value of conductivity in historical data, is the maximum value of conductivity in all historical data, and the above conductivity is the average value of conductivity at different depths.

5. The coastal saline-alkali land soil monitoring data acquisition system according to claim 4 is characterized in that: The dark river shape analysis module (200) senses the normalized conductivity and water content of the irrigation fresh water: ,in, After the irrigation of fresh water The conductivity and water content of each divided area, For the The average conductivity at different depths in the divided area , For the The number of conductivity measurements at different depths in each divided area, For the The moisture content of each divided area; Calculate the difference between water content and conductivity in turn: ,in , is the difference value, is the number of difference values; The difference values ​​are sorted from large to small. Sort by difference value set , ; Setting judgment threshold , select the difference value set from large to small Data: ,in, Indicates rounding down. To select the number of difference data in the difference value set, The corresponding divided areas have similar amounts of fresh water for irrigation; Calculate the average value of the selected data as the difference threshold: The perception selected data is: , then the difference threshold is in, The number of selected data.

6. The coastal saline-alkali land soil monitoring data acquisition system according to claim 5 is characterized in that: The dark river shape analysis module (200) senses the unselected difference value , is the number of unselected difference values, and the unselected difference values ​​and the difference threshold are In contrast, if , then the difference value corresponds to the divided area where irrigation has similar amount of fresh water. , it is judged that there is no fresh water for irrigation in the area corresponding to the difference value.

7. The coastal saline-alkali land soil monitoring data acquisition system according to claim 6 is characterized in that: The graph theory method in the underground river shape analysis module (200) retrieves the conductivity of similar irrigation freshwater division areas and sets a conductivity difference threshold. If the conductivity difference at the same depth is greater than the conductivity difference threshold, it is determined that there is an underground river under the two corresponding division areas. The underground rivers under multiple division areas are compared in turn and connected to obtain the shape of the underground river.

8. The coastal saline-alkali land soil monitoring data acquisition system according to claim 1 is characterized in that: The change trend analysis method in the irrigation fresh water prediction module (300) analyzes the change trend of the conductivity at different depths. When the conductivity changes from decreasing to increasing, the corresponding detection depth is the effective fresh water irrigation depth. The expression is: For the partition area A, at the depth The conductivity is recorded as , freshwater irrigation volume is recorded as ;when , When , the conductivity is judged to be in the decreasing stage; When , the conductivity is judged to be in the increasing stage. It is the effective irrigation freshwater depth of divided area A.

9. The coastal saline-alkali land soil monitoring data acquisition system according to claim 8, characterized in that: The correlation coefficient in the irrigation fresh water prediction module (300) is used to predict the irrigation water volume at different irrigation depths when the regional irrigation fresh water is divided: the integrated effective irrigation depth conductivity is: ; Freshwater for irrigation ; Calculate the mean conductivity at the effective irrigation depth: , Average irrigation freshwater volume: ; Correlation coefficient .

10. The coastal saline-alkali land soil monitoring data acquisition system according to claim 9, characterized in that: The irrigation fresh water prediction module (300) further defines the divided area above the underground river as divided area B. If the conductivity change trend in divided area B is the same as that in divided area B, divided area B is mapped to divided area A. If the conductivity change trend continues to decrease, it is determined that there is too much irrigation fresh water in divided area B.

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