CN109740030B - Method for displaying soil detection result graph and automatically reading soil detection result graph - Google Patents
Method for displaying soil detection result graph and automatically reading soil detection result graph Download PDFInfo
- Publication number
- CN109740030B CN109740030B CN201811571171.4A CN201811571171A CN109740030B CN 109740030 B CN109740030 B CN 109740030B CN 201811571171 A CN201811571171 A CN 201811571171A CN 109740030 B CN109740030 B CN 109740030B
- Authority
- CN
- China
- Prior art keywords
- value
- soil
- data
- index
- qfz
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002689 soil Substances 0.000 title claims abstract description 311
- 238000001514 detection method Methods 0.000 title claims abstract description 128
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000007812 deficiency Effects 0.000 claims abstract description 30
- 235000015097 nutrients Nutrition 0.000 claims description 24
- 239000000843 powder Substances 0.000 claims description 16
- 235000021049 nutrient content Nutrition 0.000 claims description 15
- 239000004016 soil organic matter Substances 0.000 claims description 14
- 238000012937 correction Methods 0.000 claims description 13
- 238000005259 measurement Methods 0.000 claims description 10
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 claims description 8
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 claims description 8
- 235000019738 Limestone Nutrition 0.000 claims description 8
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims description 8
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 8
- 239000011575 calcium Substances 0.000 claims description 8
- 229910052791 calcium Inorganic materials 0.000 claims description 8
- 239000000460 chlorine Substances 0.000 claims description 8
- 229910052801 chlorine Inorganic materials 0.000 claims description 8
- 230000012010 growth Effects 0.000 claims description 8
- 239000006028 limestone Substances 0.000 claims description 8
- 239000011777 magnesium Substances 0.000 claims description 8
- 229910052749 magnesium Inorganic materials 0.000 claims description 8
- 239000011593 sulfur Substances 0.000 claims description 8
- 229910052717 sulfur Inorganic materials 0.000 claims description 8
- 238000011161 development Methods 0.000 claims description 6
- 239000010459 dolomite Substances 0.000 claims description 6
- 229910000514 dolomite Inorganic materials 0.000 claims description 6
- 239000004927 clay Substances 0.000 claims description 5
- 239000002253 acid Substances 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 claims description 4
- 150000004965 peroxy acids Chemical class 0.000 claims description 4
- 239000004576 sand Substances 0.000 claims description 4
- 238000012272 crop production Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000000383 hazardous chemical Substances 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- 230000002950 deficient Effects 0.000 claims 1
- 230000004720 fertilization Effects 0.000 abstract description 6
- 238000000638 solvent extraction Methods 0.000 abstract 1
- 238000012360 testing method Methods 0.000 description 9
- 238000012271 agricultural production Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000004579 marble Substances 0.000 description 2
- 235000020774 essential nutrients Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention is applicable to the field of soil detection results, and provides a method for displaying and automatically reading a soil detection result, which comprises the following steps: firstly, determining an optimal value (M), a deficiency value (QFZ) and an excess value (GLZ) of a soil index; calculating and determining a lower value (PDZ), a higher value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of the soil index; then, partitioning and drawing are carried out according to the optimal value (M), the deficiency value (QFZ), the excess value (GLZ), the lower value (PDZ), the higher value (PGZ), the minimum value (ZXZ) and the maximum value (ZDZ) of the soil index; and finally, an automatic interpretation model is constructed and soil detection results are automatically interpreted, so that the technical problems that the soil detection results are fed back to a user in a digital report form, a non-professional person cannot understand the soil detection results, and the soil detection results cannot be used for guiding fertilization are solved.
Description
Technical Field
The invention belongs to the field of soil detection results, and particularly relates to a method for displaying and automatically reading soil detection result graphs.
Background
Soil detection is an important work in modern agricultural production, the detected soil indexes mainly comprise soil pH, organic matters, soil available nutrient content and the like, and data related to soil properties can be known through soil detection and are very important for agricultural production. Nutrients in soil are essential nutrients for plant growth, the growth of crops can be influenced by too little or too much nutrients, the reasonable content of the nutrients in the soil is very important for the growth of the crops, and soil nutrient detection can help people to know the nutrient condition of the soil and guide fertilization work.
At present, soil detection results in China are fed back to users in a form of a digital report, the users need to find professionals to explain the soil detection results after taking the soil detection results, and the professionals often have strong subjective intentions in explanation, so that the professionals who take the same soil detection result to find different soil detection results can obtain different interpretations, and non-professionals often do not know the soil detection results and can not use the soil detection results to guide fertilization.
Disclosure of Invention
The invention aims to provide a method for displaying soil detection result graphs and automatically reading the soil detection result, and aims to solve the technical problems that the soil detection result is fed back to a user in a form of a digital report, and a non-professional person cannot know the soil detection result and cannot use the soil detection result to guide fertilization.
The invention is realized in such a way that a graph showing method of a soil detection result comprises the following steps:
step S1: firstly, determining an optimal value (M), a deficiency value (QFZ) and an excess value (GLZ) of soil indexes displayed by a soil detection result graph;
step S2: calculating and determining a lower value (PDZ), an upper value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of the soil index for the soil test result graph presentation according to the optimal value (M), the deficiency value (QFZ) and the excess value (GLZ) of the soil index;
and step S3: dividing a display screen of the display equipment into a detection item area, a detection result area, a metering unit area, a graph display area of soil detection results and an automatic interpretation area, taking an optimal value (M) as a main line, and subdividing the graph display area of the soil detection results into a lack area, a low area, a suitable area, a high area and an excess area according to the optimal value (M), a lack value (QFZ), an excess value (GLZ), a low value (PDZ), a high value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of soil indexes;
and step S4: further determining the abscissa (Min) of the left boundary of the lacking area, the abscissa (Down) of the boundary of the lacking area and the lower area, the abscissa (Under) of the boundary of the lower area and the suitable area, the abscissa (Mid) of the optimal value line, the abscissa (Above) of the boundary of the higher area and the suitable area, the abscissa (Up) of the boundary of the higher area and the excess area, and the abscissa (Max) of the right boundary of the excess area of the graph display area of the soil detection result;
step S5: converting an optimal value (M), a deficiency value (QFZ), an excess value (GLZ), a low-level value (PDZ), a high-level value (PGZ), a minimum value (ZXZ), and a maximum value (ZDZ) of the soil index into abscissa values (DT), and the minimum value (ZXZ) = Min; deficiency value (QFZ) = Down; lower value (PDZ) = Under; optimum value (M) = Mid; higher bias value (PGZ) = Above; excess value (GLZ) = Up; maximum value (ZDZ) = Max;
step S6: converting the measured value (Data) of the soil index into an abscissa value (DT);
step S7: drawing a line from Min to Data in a solid line manner, a dotted line manner, a character string manner or a dot manner on an abscissa value (Data) converted from a measured value (Data) of a soil index in a graph display area of a soil detection result; the length of the line represents the relative size of the soil detection index detection value, and the position of the rightmost end of the line represents the state of the soil index.
The further technical scheme of the invention is as follows: the optimal value (M) of the soil index in the step S1 is an optimal ideal value which is most beneficial to crop production and can avoid nutrient waste and environmental hazards; the deficiency value (QFZ) of the soil index means that when the value is less than the deficiency value, the soil index is in a deficiency state in the soil and can influence the growth and development of crops; the excess value (GLZ) of the soil index means that when the value is larger than the GLZ, the soil index is too high or excessive in the soil, which may affect the growth and development of crops or cause environmental damage.
The further technical scheme of the invention is as follows: the lower value (PDZ) of the soil index in the step S2 is a value between the lacking value (QFZ) of the soil index and the optimum value (M) of the soil index, and is PDZ = QFZ +2/3 (M-QFZ); the upper bound value (PGZ) of the soil index is a value between the excess value (GLZ) of the soil index and the optimum value (M) of the soil index, and is PGZ = M +1/3 (GLZ-M); the minimum value (ZXZ) of the soil index is the minimum value of the soil index in graphic display, the minimum value of the soil pH is set to be 3.5, the minimum value of the soil organic matter content is set to be 0.5g/kg, and the minimum value calculation formula of the soil nutrient content index is as follows: ZXZ = QFZ × QFZ/M; the maximum value (ZDZ) of the soil index is the maximum value of the soil index when a graph is displayed, the maximum value of the soil pH is set to be 9.0, the maximum value of the soil organic matter content is set to be 7.0g/kg, and the maximum value of the soil nutrient content index is calculated by the following formula: ZDZ = GLZ x (GLZ-M)/M.
The further technical scheme of the invention is as follows: the step S3 further includes the steps of:
step S31: outputting the project name of the soil index to a detection project area;
step S32: outputting the measured value (Data) of the soil index to a Data column corresponding to the detection result area; if the item is not detected, the item is not displayed or 'not detected' is output in the data column;
step S33: the measurement unit of the measured value (Data) of the soil index is outputted to the corresponding unit area.
The further technical scheme of the invention is as follows: in step S6, the measured value (Data) of the soil index is converted into an abscissa value (DT) by:
DT = Min +1/30 (Down-Min) when Data ≦ ZXZ;
DT = Min + (Data-ZXZ)/(QFZ-ZXZ) x (Down-Min) when ZXZ < Data < QFZ
DT = Down + (Data-QFZ)/(PDZ-QFZ) x (Under-Down) when QFZ < Data < PDZ)
DT = Under + (Data-PDZ)/(M-PDZ) x (Mid-Under) when PDZ < Data < M)
DT = Mid + (Data-M)/(PGZ-M) x (Above-Mid) when M < Data < PGZ
DT = Above + (Data-PGZ)/(GLZ-PGZ) x (Up-Above) when PGZ < Data < GLZ
DT = Up + (Data-GLZ)/(ZDZ-GLZ) x (Max-Up) when GLZ < Data < ZDZ
DT = Max when Data ≧ ZDZ.
It is another object of the present invention to provide an automatic interpretation of soil test result graphic representations
A method, the automatic interpretation method comprising the steps of:
step A1: determining buffer values of all soil indexes, and determining correction coefficients (J) of the buffer values of the soil indexes of the soils with different textures;
step A2: determining the soil weight W of each mu of plough layer through a calculation formula;
step A3: establishing a calculation model of the shortage or surplus quantity of the soil indexes on the basis of the optimal value (M) of the soil indexes;
step A4: establishing an automatic interpretation model of the soil detection value and automatically interpreting the detection value;
step A5: and outputting the interpretation result to a corresponding column in the automatic interpretation area.
The further technical scheme of the invention is as follows: the correction coefficient (J) of the buffer value in the step A1 is: sand and soil: j is more than or equal to 0.3 and less than or equal to 0.7; j is more than or equal to 0.5 and less than or equal to 0.9 in loam; clay J =1.
The further technical scheme of the invention is as follows: the calculation formula in the step A2 is as follows: w = plough layer thickness (m) × 667 × 1.1/1000 (million kg/mu).
The further technical scheme of the invention is as follows: the calculation model in the step A3 is: f = | (Data-M) × (1 + (C-1) × J) × W;
wherein F is the number of missing or surplus soil indexes (kg/mu); m-optimum value of soil index (mg/kg); data-the measurement of the current soil index (mg/kg); c, buffer value of soil index (kilogram/million kilogram); j-correction factor for soil texture; w-weight of soil in plough layer (million kilograms).
The further technical scheme of the invention is as follows: the automatic interpretation model of the soil detection value in the step A4 comprises an automatic interpretation model of a soil pH detection value, an automatic interpretation model of a soil organic matter detection value, automatic interpretation models of various soil nutrient content index measurement values and automatic interpretation models of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine of soil;
the automatic interpretation model of the soil pH detection value is as follows:
when Data < QFZ, it is automatically interpreted as "peracid, dolomite powder or limestone powder F kg/mu" is needed ";
when QFZ is not more than Data < PDZ, automatically reading as "meta-acid, requiring dolomite powder or limestone powder F kg/mu";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a more alkaline state";
when Data > GLZ, automatically read as "in an overbase state";
the automatic interpretation model of the soil organic matter detection value is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpret as "in rich state";
when Data > GLZ, auto-interpretation is "in very Rich State";
the automatic interpretation model of the measured values of various soil nutrient content indexes is as follows:
when Data < QFZ, it is automatically interpreted as "too low, lack of nutrients F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as 'lower and lack of nutrients F kg/mu';
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when the PGZ is more than Data and less than or equal to GLZ, automatically reading as high and surplus nutrients F kg/mu;
when Data is more than GLZ, automatically reading as 'overhigh and lack of surplus nutrients F kg/mu';
an automatic interpretation model of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine in soil is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a high state";
when Data > GLZ, it is automatically interpreted as "in an excessively high state".
The invention has the beneficial effects that: the existing soil detection result is fed back to a user in a digital report form mode, the user cannot understand the soil detection result after taking the soil detection result, and a professional person needs to be found to explain the soil detection result, so that the method is very inconvenient; by adopting the method, the soil detection result is provided for the user in a graphic display mode, the user can understand the soil detection result, and the user can clearly know the state of the soil index through automatic reading. The professional explains the soil detection result, which often has strong subjective will and sometimes has strong ambiguity; the automatic interpretation by the method can avoid subjective deviation, and a user can know which soil index is problematic, high or low, and how much is lacking or surplus can be made clear. This information helps users to develop a reasonable fertilization program and soil improvement program.
Drawings
FIG. 1 is a flowchart of a method for displaying a soil detection result graph according to an embodiment of the present invention;
FIG. 2 is a flowchart of an automatic interpretation method for soil test result graph representation according to an embodiment of the present invention;
FIG. 3 is a display screen partition of a display device of a method for graphically displaying soil detection results according to an embodiment of the present invention;
FIG. 4 is a soil test result graph showing a method according to an embodiment of the present invention;
fig. 5 is an interpretation result diagram of an automatic interpretation method graphically represented by soil detection results according to an embodiment of the present invention;
fig. 6 is a final display diagram of a method for displaying and automatically interpreting soil detection results according to an embodiment of the present invention.
Detailed Description
Fig. 1 to 6 illustrate a graph showing method of soil detection results provided by the present invention, the graph showing method comprising the following steps:
step S1: firstly, determining an optimal value (M), a deficiency value (QFZ) and an excess value (GLZ) of a soil index displayed by a soil detection result graph; the optimal value (M) of the soil index is an optimal ideal value which is most beneficial to crop production and can avoid nutrient waste and environmental hazards; the deficiency value (QFZ) of the soil index means that when the value is less than the deficiency value, the soil index is in a deficiency state in the soil and can influence the growth and development of crops; the excess value (GLZ) of the soil index means that when the value is larger than the GLZ, the soil index is too high or excessive in the soil, which may affect the growth and development of crops or cause environmental damage.
Step S2: calculating and determining a lower value (PDZ), an upper value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of the soil index for the soil test result graph presentation according to the optimal value (M), the deficiency value (QFZ) and the excess value (GLZ) of the soil index; the lower value of the soil index (PDZ) is a value between the absence value of the soil index (QFZ) and the optimum value of the soil index (M), and is PDZ = QFZ +2/3 (M-QFZ); the upper bound value (PGZ) of the soil index is a value between the excess value (GLZ) of the soil index and the optimum value (M) of the soil index, and is PGZ = M +1/3 (GLZ-M); the minimum value (ZXZ) of the soil index is the minimum value of the soil index in graphic display, the minimum value of the soil pH is set to be 3.5, the minimum value of the soil organic matter content is set to be 0.5g/kg, and the minimum value calculation formula of the soil nutrient content index is as follows: ZXZ = QFZ × QFZ/M; the maximum value (ZDZ) of the soil index is the maximum value of the soil index in the graphic display, the maximum value of the soil pH is set to be 9.0, the maximum value of the soil organic matter content is set to be 7.0g/kg, and the maximum value calculation formula of the soil nutrient content index is as follows: ZDZ = GLZ x (GLZ-M)/M.
And step S3: dividing a display screen of the display equipment into a detection item area, a detection result area, a metering unit area, a graph display area of soil detection results and an automatic interpretation area, taking an optimal value (M) as a main line, and subdividing the graph display area of the soil detection results into a lack area, a low area, a suitable area, a high area and an excess area according to the optimal value (M), a lack value (QFZ), an excess value (GLZ), a low value (PDZ), a high value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of soil indexes;
step S31: outputting the project name of the soil index to a detection project area;
step S32: outputting the measured value (Data) of the soil index to a Data column corresponding to the detection result area; if the item is not detected, the item is not displayed or 'not detected' is output in the data column;
step S33: the measurement unit of the measured value (Data) of the soil index is outputted to the corresponding unit area.
And step S4: further determining the abscissa (Min) of the left boundary of the lacking region, the abscissa (Down) of the boundary of the lacking region and the lower region, the abscissa (Under) of the boundary of the lower region and the suitable region, the abscissa (Mid) of the optimum value line, the abscissa (Above) of the boundary of the higher region and the suitable region, the abscissa (Up) of the boundary of the higher region and the excess region, and the abscissa (Max) of the right boundary of the excess region of the graph display region of the soil detection result;
step S5: converting an optimal value (M), a deficiency value (QFZ), an excess value (GLZ), a lower value (PDZ), a higher value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of the soil index into an abscissa value (DT), and the minimum value (ZXZ) = Min; deficiency value (QFZ) = Down; lower value (PDZ) = Under; optimum value (M) = Mid; higher bias value (PGZ) = Above; excess value (GLZ) = Up; maximum value (ZDZ) = Max;
step S6: converting the measured value (Data) of the soil index into an abscissa value (DT); the measured value (Data) of the soil index is converted into an abscissa value (DT) by:
DT = Min +1/30 (Down-Min) when Data ≦ ZXZ;
DT = Min + (Data-ZXZ)/(QFZ-ZXZ) x (Down-Min) when ZXZ < Data < QFZ
DT = Down + (Data-QFZ)/(PDZ-QFZ) x (Under-Down) when QFZ < Data < PDZ)
DT = Under + (Data-PDZ)/(M-PDZ) x (Mid-Under) when PDZ < Data < M)
DT = Mid + (Data-M)/(PGZ-M) x (Above-Mid) when M < Data < PGZ
DT = Above + (Data-PGZ)/(GLZ-PGZ) x (Up-Above) when PGZ < Data < GLZ
DT = Up + (Data-GLZ)/(ZDZ-GLZ) x (Max-Up) when GLZ < Data < ZDZ
DT = Max when Data ≧ ZDZ.
Step S7: drawing a line from Min to Data in a solid line manner, a dotted line manner, a character string manner or a dot manner on an abscissa value (Data) converted from a measured value (Data) of a soil index in a graph display area of a soil detection result; the length of the line represents the relative size of the soil detection index detection value, and the position of the rightmost end of the line represents the state of the soil index.
An automatic interpretation method for soil detection result graph display, comprising the following steps:
step A1: determining the buffer value of each soil index, and determining the correction coefficient (J) of the buffer values of the soil indexes of the soils with different textures; the correction coefficient (J) of the buffer value is: sand and soil: j is more than or equal to 0.3 and less than or equal to 0.7; j is more than or equal to 0.5 and less than or equal to 0.9 in loam; clay J =1.
Step A2: determining the soil weight W of each mu of plough layer through a calculation formula; the calculation formula is as follows: w = plough layer thickness (m) × 667 × 1.1/1000 (million kilograms/mu).
Step A3: establishing a calculation model of the shortage or surplus quantity of the soil indexes on the basis of the optimal value (M) of the soil indexes; the calculation model is as follows: f = | (Data-M) × (1 + (C-1) × J) × W; wherein F represents the quantity (kilogram/mu) of the lacking or surplus soil index; m-optimum value of soil index (mg/kg); data-the measurement of the current soil index (mg/kg); c, buffer value of soil index (kilogram/million kilogram); j-correction factor for soil texture; w-weight of soil in plough layer (million kilograms).
Step A4: establishing an automatic interpretation model of the soil detection value and automatically interpreting the detection value; the automatic interpretation model of the soil detection value comprises an automatic interpretation model of a soil pH detection value, an automatic interpretation model of a soil organic matter detection value, an automatic interpretation model of various soil nutrient content index measurement values and an automatic interpretation model of a ratio of effective calcium to effective magnesium and a ratio of effective sulfur to effective chlorine of soil;
the automatic interpretation model of the soil pH detection value is as follows:
when Data < QFZ, it is automatically interpreted as "peracid, requiring dolomite powder or limestone powder of F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as 'meta-acid, white marble powder or limestone powder F kg/mu';
when PDZ is less than or equal to Data and less than or equal to PGZ, automatically interpreting as being in a proper state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a more alkaline state";
when Data > GLZ, it is automatically interpreted as "in an overbase state";
the automatic interpretation model of the soil organic matter detection value is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in rich state";
when Data > GLZ, automatically interpret as "in very Rich State";
the automatic interpretation model of the measured values of various soil nutrient content indexes is as follows:
when Data < QFZ, it is automatically interpreted as "too low, lack of nutrients F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as 'lower and lack of nutrients F kg/mu';
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ is less than Data and less than or equal to GLZ, automatically reading as 'higher and surplus nutrient F kg/mu';
when Data is more than GLZ, automatically reading as 'overhigh and lack of surplus nutrients F kg/mu';
an automatic interpretation model of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine in soil is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a high state";
when Data > GLZ, it is automatically interpreted as "in an excessively high state".
Step A5: and outputting the interpretation result to a corresponding column in the automatic interpretation area.
The following describes a specific embodiment of a method for displaying a graph of a soil detection result and automatically reading the soil detection result provided by the method, taking soil a as an example. The results of the soil test are shown in Table 1, where the soil texture is clay and the plough layer thickness is 0.3 m.
Table 1, results of soil testing.
The optimum value (M), the deficiency value (QFZ), and the excess value (GLZ) of the soil test index are determined in the following 2 ways, in order of priority 1) to 2).
1) Determined according to the study results.
2) The optimum value (M), deficiency value (QFZ), and excess value (GLZ) determined in Table 2 were used.
Table 2, optimal (M), deficiency (QFZ) and excess (GLZ) for the common soil indicators.
Determining an optimal value (M), a deficiency value (QFZ) and an excess value (GLZ) of the soil index shown by the soil detection result graph; the lower value (PDZ), the upper value (PGZ), the minimum value (ZXZ) and the maximum value (ZDZ) of the soil index for the soil test result graph presentation were calculated based on the optimal value (M), the deficiency value (QFZ) and the excess value (GLZ) of the soil index, as shown in table 3.
Table 3, optimal value of soil index (M), deficiency value (QFZ), excess value (GLZ), lower value (PDZ), higher value (PGZ), minimum value (ZXZ), and maximum value (ZDZ).
The display screen of the display device is divided into a detection project area, a detection result area, a metering unit area, a soil detection result graph display area and an automatic interpretation area, the soil detection result graph display area takes an optimal value (M) as a main line, and the graph display area is further divided into a lack area, a low area, a suitable area, a high area and an excess area according to the optimal value (M), the lack value (QFZ), the excess value (GLZ), the low value (PDZ), the high value (PGZ), the minimum value (ZXZ) and the maximum value (ZDZ) of a soil index, wherein the detection project area, the detection result area, the metering unit area, the soil detection result graph display area and the automatic interpretation area are shown in figure 3.
Further determining the abscissa (Min) of the left boundary of the lacking area, the abscissa (Down) of the boundary of the lacking area and the lower area, the abscissa (Under) of the boundary of the lower area and the suitable area, the abscissa (Mid) of the optimal value line, the abscissa (Above) of the boundary of the higher area and the suitable area, the abscissa (Up) of the boundary of the higher area and the excess area, and the abscissa (Max) of the right boundary of the excess area of the graph display area of the soil detection result; converting an optimal value (M), a deficiency value (QFZ), an excess value (GLZ), a low-level value (PDZ), a high-level value (PGZ), a minimum value (ZXZ) and a maximum value (ZDZ) of the soil index into abscissa values (DT), and the minimum value (ZXZ) = Min; deficiency value (QFZ) = Down; lower value (PDZ) = Under; optimum value (M) = Mid; higher than normal (PGZ) = Above; excess value (GLZ) = Up; maximum value (ZDZ) = Max;
converting the measured value (Data) of the soil index into an abscissa value (DT); the measured value (Data) of the soil index is converted into an abscissa value (DT) by:
DT = Min +1/30 (Down-Min) when Data ≦ ZXZ;
DT = Min + (Data-ZXZ)/(QFZ-ZXZ) x (Down-Min) when ZXZ < Data < QFZ
DT = Down + (Data-QFZ)/(PDZ-QFZ) x (Under-Down) when QFZ < Data < PDZ)
DT = Under + (Data-PDZ)/(M-PDZ) x (Mid-Under) when PDZ < Data < M)
DT = Mid + (Data-M)/(PGZ-M) x (Above-Mid) when M < Data < PGZ
DT = Above + (Data-PGZ)/(GLZ-PGZ) x (Up-Above) when PGZ < Data < GLZ
DT = Up + (Data-GLZ)/(ZDZ-GLZ) × (Max-Up) when GLZ < Data < ZDZ
DT = Max when Data ≧ ZDZ.
Drawing a line from Min to Data in a solid line manner, a dotted line manner, a character string manner or a dot manner on an abscissa value (Data) converted from a measured value (Data) of a soil index in a graph display area of a soil detection result; the length of the line represents the relative size of the soil detection index detection value, the position of the rightmost end of the line represents the state of the soil index, and the graph of the soil detection result shows as shown in fig. 4.
An automatic interpretation method for soil detection result graph display, wherein buffer values (C) of all soil indexes are determined as shown in a table 3; the buffer value (C) of the soil index is determined in the following 2 ways, in the order of priority 1) to 2).
1) Determined according to the study results.
2) The buffer values for the soil indicators in table 4 were used.
Table 4 buffer values of soil indexes of soil.
Determining a correction coefficient (J) of the buffer value of the soil index of the soil with different textures; the correction coefficient (J) of the buffer value of the soil index of the soil with different textures is determined by the following 2 ways in the order of priority 1) to 2).
1) Determined according to the study results.
2) Soil texture correction factor using buffer values of commonly used soil indices: sand and soil: j =0.5; loam J =0.8; clay J =1.
Determining the soil weight W of each mu of plough layer through a calculation formula; the calculation formula is as follows: w = plough layer thickness (m) × 667 × 1.1/1000 (million kilograms/mu).
Establishing a calculation model of the shortage or surplus quantity of the soil indexes on the basis of the optimal value (M) of the soil indexes; the calculation model is as follows: f = | (Data-M) × (1 + (C-1) × J) × W; wherein F is the number of missing or surplus soil indexes (kg/mu); m-optimum value of soil index (mg/kg); data-the measurement of the current soil index (mg/kg); c, buffer value of soil index (kilogram/million kilogram); j-correction factor for soil texture; w-weight of soil in plough layer (million kilograms). Establishing an automatic interpretation model of the soil detection value and automatically interpreting the detection value; the automatic interpretation model of the soil detection value comprises an automatic interpretation model of a soil pH detection value, an automatic interpretation model of a soil organic matter detection value, automatic interpretation models of various soil nutrient content index measurement values and automatic interpretation models of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine in soil;
the automatic interpretation model of the soil pH detection value is as follows:
when Data < QFZ, it is automatically interpreted as "peracid, requiring dolomite powder or limestone powder of F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as 'meta-acid, white marble powder or limestone powder F kg/mu';
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a state of partial alkalinity";
when Data > GLZ, automatically read as "in an overbase state";
the automatic interpretation model of the soil organic matter detection value is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in rich state";
when Data > GLZ, auto-interpretation is "in very Rich State";
the automatic interpretation model of the measured values of various soil nutrient content indexes is as follows:
when Data < QFZ, it is automatically interpreted as "too low, lack of nutrients F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as 'lower and lack of nutrients F kg/mu';
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when the PGZ is more than Data and less than or equal to GLZ, automatically reading as high and surplus nutrients F kg/mu;
when Data is more than GLZ, automatically reading as 'overhigh and lack of surplus nutrients F kg/mu';
an automatic interpretation model of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine in soil is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a high state";
when Data > GLZ, the interpretation is "too high", and the automatic interpretation result is shown in fig. 5.
And outputting the interpretation result to a corresponding column in the automatic interpretation area.
And finally displaying the graphic display and the automatic interpretation of the soil detection result of the soil A on a display screen of the display device, as shown in FIG. 6.
The existing soil detection result is fed back to a user in a digital report form mode, the user cannot understand the soil detection result after taking the soil detection result, and a professional person needs to be found to explain the soil detection result, so that the method is very inconvenient; by adopting the method, the soil detection result is provided for the user in a graphic display mode, the user can understand the soil detection result, and the user can clearly know the state of the soil index through automatic reading. The professional explains the soil detection result, which often has strong subjective will and sometimes has strong ambiguity; the automatic interpretation by the method can avoid subjective deviation, and a user can know which soil index is problematic, high or low, and how much is lacking or surplus can be made clear. This information helps users to develop a reasonable fertilization program and soil improvement program.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A graph showing method for soil detection results is characterized by comprising the following steps:
step S1: firstly, determining an optimal value M, a deficiency value QFZ and an excess value GLZ of a soil index shown by a soil detection result graph;
step S2: calculating and determining a lower value PDZ, a higher value PGZ, a minimum value ZXZ and a maximum value ZDZ of the soil index for the soil detection result graph to display according to the optimal value M, the deficiency value QFZ and the excess value GLZ of the soil index;
and step S3: dividing a display screen of the display equipment into a detection item area, a detection result area, a metering unit area, a graph display area of a soil detection result and an automatic interpretation area, taking an optimal value M as a main line, and subdividing the graph display area of the soil detection result into a lack area, a low area, a suitable area, a high area and an excessive area according to the optimal value M, a lack value QFZ, an excessive value GLZ, a low value PDZ, a high value PGZ, a minimum value ZXZ and a maximum value ZDZ of soil indexes;
and step S4: further determining the abscissa Min of the left boundary of the lacking region, the abscissa Down of the boundary of the lacking region and the lower region, the abscissa Under of the boundary of the lower region and the suitable region, the abscissa Mid of the optimal value line, the abscissa Above of the boundary of the higher region and the suitable region, the abscissa Up of the boundary of the higher region and the excess region and the abscissa Max of the right boundary of the excess region of the soil detection result;
step S5: converting the optimal value M, the lack value QFZ, the excess value GLZ, the lower value PDZ, the upper value PGZ, the minimum value ZXZ and the maximum value ZDZ of the soil index into an abscissa value DT, and converting the minimum value ZXZ = Min; deficiency value QFZ = Down; lower value PDZ = Under; optimal value M = Mid; higher PGZ = Above; excess value GLZ = Up; maximum ZDZ = Max;
step S6: converting the measured value Data of the soil index into an abscissa value DT;
step S7: drawing a line from Min to Data in a solid line mode, a dotted line mode, a character string mode or a drawing point mode according to an abscissa value Data converted from the measured value Data of the soil index in a graph display area of the soil detection result; the length of the line represents the relative size of the soil detection index detection value, and the position of the rightmost end of the line represents the state of the soil index.
2. The graphic representation method according to claim 1, wherein the optimal value M of the soil index in step S1 is an optimal ideal value of the soil index that is most beneficial for crop production and can avoid nutrient waste and environmental damage; the deficiency value QFZ of the soil index means that when the value is less than the value, the soil index is in a deficiency state in the soil and can influence the growth and development of crops; the excess value GLZ of the soil index means that when the value is larger than the value, the soil index is in an excessively high or excessive state in the soil, and the growth and development of crops are influenced or environmental hazards are caused.
3. The graphic presentation method according to claim 2, wherein the lower value PDZ of the soil index in step S2 is a value between the deficiency value QFZ of the soil index and the optimum value M of the soil index, and is PDZ = QFZ +2/3 (M-QFZ); the higher value of the soil index, PGZ, is a value between the excess value of the soil index, GLZ, and the optimum value of the soil index, M, and is PGZ = M +1/3 (GLZ-M); the minimum value ZXZ of the soil index is the minimum value of the soil index in graphic display, the minimum value of the soil pH is set to be 3.5, the minimum value of the soil organic matter content is set to be 0.5g/kg, and the minimum value calculation formula of the soil nutrient content index is as follows: ZXZ = QFZ × QFZ/M; the maximum value ZDZ of the soil index is the maximum value of the soil index when the graph is displayed, the maximum value of the soil pH is set to be 9.0, the maximum value of the soil organic matter content is set to be 7.0g/kg, and the maximum value calculation formula of the soil nutrient content index is as follows: ZDZ = GLZ x (GLZ-M)/M.
4. The graphic presentation method according to claim 3, wherein the step S3 further comprises the steps of:
step S31: outputting the project name of the soil index to a detection project area;
step S32: outputting the measured value Data of the soil index to a Data column corresponding to the detection result area; if the item is not detected, the item is not displayed or 'not detected' is output in the data column;
step S33: and outputting the measurement unit of the measured value Data of the soil index to the corresponding unit area.
5. The figure representation method according to claim 4, wherein the measured value Data of the soil index is converted into an abscissa value DT in the step S6 by:
DT = Min +1/30 (Down-Min) when Data ≦ ZXZ;
DT = Min + (Data-ZXZ)/(QFZ-ZXZ) x (Down-Min) when ZXZ < Data < QFZ
DT = Down + (Data-QFZ)/(PDZ-QFZ) x (Under-Down) when QFZ < Data < PDZ)
DT = Under + (Data-PDZ)/(M-PDZ) x (Mid-Under) when PDZ < Data < M)
DT = Mid + (Data-M)/(PGZ-M) x (Above-Mid) when M < Data < PGZ
DT = Above + (Data-PGZ)/(GLZ-PGZ) x (Up-Above) when PGZ < Data < GLZ
DT = Up + (Data-GLZ)/(ZDZ-GLZ) x (Max-Up) when GLZ < Data < ZDZ
DT = Max when Data ≧ ZDZ.
6. The automatic interpretation method of the graphic presentation method according to any one of claims 1 to 5, wherein the automatic interpretation method comprises the steps of:
step A1: determining buffer values of all soil indexes, and determining correction coefficients J of the buffer values of the soil indexes of the soil with different textures;
step A2: determining the soil weight W of each mu of plough layer through a calculation formula;
step A3: establishing a calculation model of the shortage or surplus quantity of the soil indexes on the basis of the optimal value M of the soil indexes;
step A4: establishing an automatic interpretation model of the soil detection value and automatically interpreting the detection value;
step A5: and outputting the interpretation result to a corresponding column in the automatic interpretation area.
7. The automatic interpretation method according to claim 6, wherein the correction coefficient J of the buffer value in the step A1 is: sand and soil: j is more than or equal to 0.3 and less than or equal to 0.7; j is more than or equal to 0.5 and less than or equal to 0.9 in loam; clay J =1.
8. The automatic interpretation method according to claim 7, wherein the calculation formula in the step A2 is: w = plough layer thickness meter × 667 × 1.1/1000 million kg/mu.
9. The automatic interpretation method according to claim 8, wherein the calculation model in the step A3 is: f = | (Data-M) × (1 + (C-1) × J) × W; f represents the number of deficient or surplus soil indexes per mu; m is the optimal value of the soil index mg/kg; data-measured value of current soil index mg/kg; c, buffer value of soil index kilogram/million kilogram; j-correction factor for soil texture; w-million kilograms of soil on the plough layer.
10. The automatic interpretation method according to claim 9, wherein the automatic interpretation models of soil detection values in step A4 include an automatic interpretation model of a soil pH detection value, an automatic interpretation model of a soil organic matter detection value, an automatic interpretation model of various soil nutrient content index measurement values, and an automatic interpretation model of a ratio of available calcium to available magnesium and a ratio of available sulfur to available chlorine in soil;
the automatic interpretation model of the soil pH detection value is as follows:
when Data < QFZ, it is automatically interpreted as "peracid, requiring dolomite powder or limestone powder of F kg/mu";
when QFZ is not more than Data < PDZ, automatically reading as "meta-acid, requiring dolomite powder or limestone powder F kg/mu";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a state of partial alkalinity";
when Data > GLZ, automatically read as "in an overbase state";
the automatic interpretation model of the soil organic matter detection value is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in rich state";
when Data > GLZ, automatically interpret as "in very Rich State";
the automatic interpretation model of the measured values of various soil nutrient content indexes is as follows:
when Data < QFZ, it is automatically interpreted as "too low, lack of nutrients F kg/acre";
when QFZ is not more than Data < PDZ, automatically reading as 'lower and lack of nutrients F kg/mu';
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ is less than Data and less than or equal to GLZ, automatically reading as 'higher and surplus nutrient F kg/mu';
when Data is larger than GLZ, automatically reading as 'overhigh and lack of surplus nutrients F kg/mu';
an automatic interpretation model of the ratio of available calcium to available magnesium and the ratio of available sulfur to available chlorine in soil is as follows:
when Data < QFZ, it automatically interprets as "in a too low state";
when QFZ ≦ Data < PDZ, it is automatically interpreted as "in a low state";
when PDZ is not less than Data and not more than PGZ, automatically interpreting as being in an appropriate state;
when PGZ < Data ≦ GLZ, automatically interpreting as "in a high state";
when Data > GLZ, it is automatically interpreted as "in an excessively high state".
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811571171.4A CN109740030B (en) | 2018-12-21 | 2018-12-21 | Method for displaying soil detection result graph and automatically reading soil detection result graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811571171.4A CN109740030B (en) | 2018-12-21 | 2018-12-21 | Method for displaying soil detection result graph and automatically reading soil detection result graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109740030A CN109740030A (en) | 2019-05-10 |
| CN109740030B true CN109740030B (en) | 2022-12-06 |
Family
ID=66360934
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811571171.4A Active CN109740030B (en) | 2018-12-21 | 2018-12-21 | Method for displaying soil detection result graph and automatically reading soil detection result graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109740030B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419218A (en) * | 2008-12-02 | 2009-04-29 | 中国农业科学院农业资源与农业区划研究所 | Method for correcting graded index of soil nutrient |
| CN101949917A (en) * | 2010-08-16 | 2011-01-19 | 中国科学院南京土壤研究所 | Method for judging soil nutrient balance |
| CN106529133A (en) * | 2016-10-25 | 2017-03-22 | 安庆师范大学 | Method for determining suitable spatial ecological niche and environmental ecological niche of minimum population |
-
2018
- 2018-12-21 CN CN201811571171.4A patent/CN109740030B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101419218A (en) * | 2008-12-02 | 2009-04-29 | 中国农业科学院农业资源与农业区划研究所 | Method for correcting graded index of soil nutrient |
| CN101949917A (en) * | 2010-08-16 | 2011-01-19 | 中国科学院南京土壤研究所 | Method for judging soil nutrient balance |
| CN106529133A (en) * | 2016-10-25 | 2017-03-22 | 安庆师范大学 | Method for determining suitable spatial ecological niche and environmental ecological niche of minimum population |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109740030A (en) | 2019-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Arets et al. | Greenhouse gas reporting for the LULUCF sector in the Netherlands: Methodological background, update 2021 | |
| Seibert et al. | Virtual staff gauges for crowd-based stream level observations | |
| Sorgi et al. | Rating aggression in the clinical setting: A retrospective adaptation of the Overt Aggression Scale: Preliminary results. | |
| Lilburne et al. | Soil and informatics science combine to develop S-map: A new generation soil information system for New Zealand | |
| CN110991717B (en) | Intelligent ocean grid forecasting method and terminal | |
| CN110197035A (en) | A kind of navigation channel change of Underwater Topography analysis system and method | |
| CN109657922A (en) | Marine ecology suitability evaluation assembly apparatus | |
| Wondie et al. | Modelling the dynamics of landscape transformations and population growth in the highlands of Ethiopia using remote-sensing data | |
| CN102297850B (en) | Digital automatic coal rock component measuring method | |
| CN102323974A (en) | Method for evaluating degradation degree of alpine grassland based on visual vegetation indexes | |
| CN102200436B (en) | Real-time kinematic GPS (RTK-GRS) total station integrated topographic surveying method and system with encoded data | |
| CA2715312A1 (en) | Searchable electronic records of underground facility locate marking operations | |
| CN109740030B (en) | Method for displaying soil detection result graph and automatically reading soil detection result graph | |
| CN101655375A (en) | Method for automatically dividing display level of character marks in navigation electronic map and device thereof | |
| CN105467100A (en) | County territory soil erosion time-space dynamic monitoring method based on remote sensing and GIS | |
| Giorgini et al. | Woody species diversity as predictor of vascular plant species diversity in forest ecosystems | |
| CN116523476A (en) | Information system integrated project management system and management method | |
| CN104834757A (en) | Image semantic retrieval method and system | |
| CN112328948A (en) | Erosion calculation system for water and soil conservation | |
| CN107063210A (en) | The mapping method of project is renovated in a kind of reallocation of land | |
| CN105699243A (en) | Water and fertilizer integrated lysimeter measuring system adopting Internet of Things | |
| CN105403747B (en) | Multi-template synchronous detecting method and oscillograph | |
| CN217212396U (en) | Portable soil humidity detection device | |
| CN115906213A (en) | Editing method and system of visual chess evaluation statistical model | |
| CN102955736B (en) | Early-warning method and system for quality of software products |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221102 Address after: 530007, 174 East University Road, the Guangxi Zhuang Autonomous Region, Nanning Applicant after: GUANGXI ACADEMY OF AGRICULTURAL SCIENCES Address before: 530007, 174 East University Road, the Guangxi Zhuang Autonomous Region, Nanning Applicant before: AGRICULTURAL RESOURCE AND ENVIRONMENT Research Institute GUANGXI ACADEMY OF AGRICULTURAL SCIENCES |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |