A big data-based mixed fertilizer production process parameter optimization management method and system

Abstract

The application discloses a kind of based on big data's mixed fertilizer production process parameter optimization management method and system, it is related to process optimization technical field.The application includes soil data acquisition, climate data acquisition, climate data prediction, obtains soil fertility flow storage capacity data, crop classification, obtains target area soil fertility demand data, market feedback data collection, carries out optimization to mixed fertilizer production process, process optimization method steps are as follows: analysis target area soil data and target area climate data, obtain formula adjustment direction;Using formula element adjustment formula, the proportion of various elements in formula is calculated, the proportion of element in adjusted formula is analyzed, and the raw material mixing parameter adjustment direction is obtained;Using raw material mixing parameter adjustment formula, the numerical value of each parameter during raw material mixing is calculated.The application can accurately optimize existing formula, formulate more accurate and effective compound fertilizer formula, so that compound fertilizer is more suitable for crops in target area.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of process optimization, in particular to a mixed fertilizer production process parameter optimization management method and system based on big data. BACKGROUND

[0002] Mixed fertilizers are obtained by mechanically mixing several single fertilizers, or a single fertilizer and a binary or ternary compound fertilizer, and sometimes some fillers are added to improve the physical and chemical properties of the fertilizers. For example, in order to prevent excessive acidification of mixed fertilizers made of ammonium nitrate and common calcium phosphate, some lime can be added to neutralize the excessive acidity of the soil, so as to create more favorable conditions for crop growth.

[0003] An organic fertilizer low-carbon adaptability production management system and method disclosed in an application publication No. CN118644108A, the system comprises: an organic fertilizer information input module configured to obtain organic fertilizer production related information in response to a user's input action; wherein the organic fertilizer production related information comprises organic fertilizer information, equipment information, transportation information and fossil fuel information; a carbon emission assessment module configured to perform carbon emission boundary accounting on the organic fertilizer production related information, generate accounting gas types and accounting energy consumption types as accounting results, and perform carbon emission assessment according to the accounting results to obtain a carbon emission assessment result; wherein the carbon emission assessment result comprises gas carbon emission, non-gas carbon emission and transportation carbon emission; a standard adaptability analysis module configured to establish a first constraint condition based on production demand according to production demand information of a target organic fertilizer production task, establish a second constraint condition based on carbon emission speed and a third constraint condition based on total carbon emission according to local carbon emission requirements, take the carbon emission assessment result of the target organic fertilizer production task as a state, take the production parameters in the target organic fertilizer production task as a strategy, and obtain the optimal production operation of the target organic fertilizer production task by iterative training using a reinforcement learning algorithm.

[0004] Crop growth and development need a variety of nutrients, usually we buy chemical fertilizer in production contains only one element or two elements, such as urea, ammonia, ammonium bicarbonate is a very single nitrogen fertilizer, potassium chloride is a very single potassium fertilizer, only a few compound fertilizers contain a variety of nutrients, at the same time, most of the existing land has been planted for years, not only the soil fertility is consumed, but also the soil pH value changes, these changes will lead to soil changes, blindly supplementing fertilizer can not help the soil restore as before, therefore, there is a special mixed fertilizer for the target area soil, the mixed fertilizer will be adjusted according to the target area soil test data, so as to adjust the production process, mainly according to the target area soil test data to adjust the production formula, although the mixed fertilizer improves the specificity and effectiveness of the fertilizer to a certain extent, but it still has certain limitations, specifically, the formula adjustment of mixed fertilizer mainly depends on the soil test data, and ignores the influence of other environmental factors, the big data technology is introduced into the formulation of fertilizer formula, through big data collection and analysis of multi-source data, including climate, soil test data and market feedback data, etc., the environmental conditions of crop growth can be more comprehensively understood, so as to develop more accurate and effective fertilizer formula, and the existing compound fertilizer process optimization often ignores the optimization of raw material mixing parameters in the process, resulting in uneven mixing or too long mixing time, increasing energy consumption and production cost. SUMMARY

[0005] The purpose of the present application is to provide a mixed fertilizer production process parameter optimization management method and system based on big data, to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a mixed fertilizer production process parameter optimization management method based on big data, the method comprises:

[0007] Soil data acquisition, using the recent soil data and historical soil data of the target area;

[0008] Climate data acquisition, collecting real-time climate data and historical climate data of the target area;

[0009] Climate data prediction, based on the real-time climate data and historical climate data of the target area, using climate prediction calculation method, obtaining the climate data of the target area at a predetermined time;

[0010] Obtain soil fertility retention capacity data, based on the climate data of the target area at a predetermined time and the soil data of the target area, using soil fertility retention capacity calculation method, obtaining the soil fertility retention capacity data of the target area at a predetermined time;

[0011] The crop classification is classified into multiple types according to the growth and fertility requirement calculation method, the growth and fertility requirements of multiple crops are obtained, and crops with similar growth and fertility requirements are classified into one type, so that the multiple crops are classified, and the corresponding mixed fertilizer production formula is designed;

[0012] The soil fertility requirement data of the target area is obtained, the soil fertility requirement data of the preset classified crops in the target area is obtained based on the fertility requirement data of the preset classified crops, and the soil fertility requirement calculation method is used;

[0013] Market feedback data collection collects customer feedback data and crop suitability data, and the corresponding process optimization weight is obtained based on the market feedback data using the weight calculation method;

[0014] The mixed fertilizer production process is optimized, and the process optimization method steps are as follows:

[0015] L1: Analyze the target area soil data and target area climate data to obtain the formula adjustment direction;

[0016] L2: Use the formula element adjustment formula to calculate the proportion of various elements in the formula, and the formula element adjustment formula is as follows:

[0017]

[0018] Wherein: Fj is the adjusted amount of fertilizer of formula element n, Fn is the original amount of fertilizer of formula element n calculated based on the soil fertility requirement data, Wcust is the weight of customer feedback data, Δcust is the adjustment coefficient of customer feedback data to formula element n, Wcrop is the weight of crop adaptability data, and Δcrop is the adjustment coefficient of crop adaptability data to formula element n. According to the calculation result, the amount of element in the formula is adjusted to improve the yield of crops;

[0019] L3: Analyze the proportion of elements in the adjusted formula to obtain the raw material mixing parameter adjustment direction;

[0020] L4: Use the raw material mixing parameter adjustment formula to calculate the values of various parameters during raw material mixing, and the raw material mixing parameter adjustment formula is as follows:

[0021]

[0022] Wherein: N a is the adjusted mixing equipment parameter, N b is the basic parameter, βF is the parameter adjustment coefficient, ΔF is the formula element change amount, F t is the total amount of formula elements, and the raw material mixing equipment parameter is adjusted according to the calculation result, so that the generating equipment is kept in the best operating state.

[0023] The climate prediction calculation method, the steps are as follows:

[0024] Step one: obtain the original climate data of time t;

[0025] Step two: obtain the original climate data of time t-1;

[0026] Step three: obtain the intercept term;

[0027] Step four: obtain the regression coefficient;

[0028] Step five: obtain the error value;

[0029] Step six: calculate by the climate prediction calculation formula, the climate prediction calculation formula is as follows:

[0030]

[0031] Wherein: Δyt is the climate data after time t is differentiated, yt is the original climate data of time t, yt-1 is the original climate data of time t-1;

[0032]

[0033] Wherein: ŷ is the predicted value of the overall climate condition of the next season, β0 is the intercept term, β1, β2,..., β n are regression coefficients, Δy t is the climate data after time t is differentiated, E is the error value, and the climate prediction result of the target area at a preset time is obtained.

[0034] The climate prediction calculation method, the steps are as follows:

[0035] Step one: obtain the original climate data of time t;

[0036] Step two: obtain the original climate data of time t-1;

[0037] Step three: obtain the intercept term;

[0038] Step four: obtain the regression coefficient;

[0039] Step five: calculate by the climate prediction calculation formula, the climate prediction calculation formula is as follows:

[0040]

[0041] Wherein: Δyt is the climate data after time t is differentiated, yt is the original climate data of time t, yt-1 is the original climate data of time t-1;

[0042]

[0043] wherein: y is the predicted value of the overall climate condition of the next season, β0 is the intercept term, β1, β2,..., β n are regression coefficients, Δy t is the difference of climate data at time t, to obtain the climate prediction result of the target area at a preset time.

[0044] The soil fertility retention ability calculation method comprises the following steps:

[0045] Step one: obtain the initial fertilizer element content of the soil before fertilization;

[0046] Step two: obtain the fertilizer element loss ratio caused by climate change;

[0047] Step three: obtain the intercept term;

[0048] Step four: obtain the regression coefficient;

[0049] Step five: calculate through the soil fertility retention ability data calculation formula, and the soil fertility retention ability data calculation formula is as follows:

[0050]

[0051] wherein: N r represents the fertilizer element content retained in the soil after fertilization, N i represents the initial fertilizer element content of the soil before fertilization, λR represents the fertilizer element loss ratio function caused by climate change, and ηF represents the fertilizer element utilization efficiency function, to obtain the soil fertility retention ability data.

[0052] The growth fertility demand calculation method comprises the following steps:

[0053] Step one: obtain the fertility demand of the crop i;

[0054] Step two: obtain the actual obtained fertility of the crop i;

[0055] Step three: obtain the maximum value of the fertility demands of all crops;

[0056] Step four: obtain the minimum value of the fertility demands of all crops;

[0057] Step five: calculate through the growth fertility demand calculation formula, and the growth fertility demand calculation formula is as follows:

[0058]

[0059] wherein: C i is the classification of the crop i, F i is the fertility demand of the crop i, Fr F is the actual obtained fertility of the crops i max F is the maximum value of the fertility requirement of all crops min F is the minimum value of the fertility requirement of all crops R is the representative value of the fertility requirement of the i type of crops

[0060] Crops with similar growth fertility requirements are classified into one type, and multiple crops are classified, facilitating the design of corresponding mixed fertilizer production formulas.

[0061] The soil fertility requirement calculation method has the following steps:

[0062] Step one: obtain the fertilizer utilization rate coefficient of nutrient n

[0063] Step two: obtain the demand amount of nutrient n of the crops

[0064] Step three: obtain the retention content of nutrient n in the soil

[0065] Step four: obtain the loss amount of nutrient n in the soil

[0066] Step five: based on the planting requirements of the preset classified crops, the soil fertility requirement data of the target area is calculated through the soil fertility requirement calculation formula, and the soil fertility requirement calculation formula is as follows:

[0067]

[0068] F is the amount of fertilizer that needs to be added, n represents the type of nutrient, F n is the fertilizer utilization rate coefficient of nutrient n, R is the demand amount of nutrient n of the crops, S is the retention content of nutrient n in the soil, S d is the loss amount of nutrient n in the soil l

[0069] The nutrient requirements of the target area for planting the preset classified crops are calculated, and the soil fertility requirement data of the target area for planting the preset classified crops are repeatedly calculated.

[0070] The weight calculation method has the following steps:

[0071] Step one: obtain the quality score of the customer feedback data

[0072] Step two: obtain the timeliness of the customer feedback data

[0073] Step three: obtain the total quality score of all considered factors

[0074] Step four: obtain the maximum timeliness value in all considered factors

[0075] ​Step five: calculate through the customer feedback data weight calculation formula, the customer feedback data weight calculation formula is as follows:

[0076]

[0077] Wherein: W cust The weight of customer feedback data, Q cust The quality score of customer feedback data, T cust The timeliness of customer feedback data, Q t The total quality score of all factors, T max The maximum timeliness value in all factors.

[0078] The weight calculation method, the steps are as follows:

[0079] Step one: obtain the nutrient adaptability score of crops;

[0080] Step two: obtain the economic value score of crops;

[0081] Step three: obtain the sum of the nutrient adaptability scores of all crops;

[0082] Step four: obtain the maximum value of economic value in all crops;

[0083] Step five: calculate through the crop adaptability data weight calculation formula, the crop adaptability data weight calculation formula is as follows:

[0084]

[0085] Wherein: W crop The weight of crop adaptability data, A crop The nutrient adaptability score of crops, E crop The economic value score of crops, A total The sum of the nutrient adaptability scores of all crops, E max The maximum value of economic value in all crops.

[0086] Compared with the prior art, the beneficial effects of the present application are:

[0087] The mixed fertilizer production process parameter optimization management method and system based on big data can obtain target region soil data and target region climate data through big data, can more comprehensively understand the environmental conditions of crop growth, can calculate the proportion of various elements in the formula through the formula element adjustment formula, can accurately optimize the existing formula, and can develop more accurate and effective compound fertilizer formula, so that the compound fertilizer is more suitable for crops in the target region.

[0088] By analyzing the proportion of various elements in the adjusted formula, the raw material mixing parameter adjustment direction is obtained, the values of various parameters during raw material mixing are calculated by using the raw material mixing parameter adjustment formula, and the raw material mixing equipment parameters are adjusted according to the calculation results, so that the mixing equipment can be kept in the best operating state, the problems of uneven raw material mixing or too long mixing time are avoided, and the production energy consumption and production cost are reduced. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 It is a schematic diagram of the data collection principle structure of the application;

[0090] Figure 2 It is a schematic diagram of the process optimization principle structure of the application;

[0091] Figure 3 It is a schematic diagram of the soil fertility loss principle structure of the application;

[0092] Figure 4 It is a schematic diagram of the climate prediction data principle structure of the application;

[0093] Figure 5 It is a schematic diagram of the soil fertility retention principle structure of the application;

[0094] Figure 6 It is a schematic diagram of the crop classification principle structure of the application;

[0095] Figure 7 It is a schematic diagram of the formula data adjustment principle structure of the application. DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0097] In the present application, the method steps used for convenience of understanding are not required to be performed according to the sequence of steps in the embodiments, and in other embodiments, these steps can be performed synchronously or in a changed sequence.

[0098] As shown in Figures 1-7 The application provides a technical solution: a mixed fertilizer production process parameter optimization management method based on big data, which comprises the following steps:

[0099] Soil data acquisition, using recent soil data and historical soil data of the target area;

[0100] Climate data collection, collecting real-time climate data and historical climate data of the target area;

[0101] Climate data prediction, based on real-time climate data and historical climate data of the target area, using climate prediction calculation method, obtaining climate data of the target area at a predetermined time;

[0102] Obtain soil fertility retention ability data, based on the climate data of the target area at a predetermined time and the soil data of the target area, using soil fertility retention ability calculation method, obtain soil fertility retention ability data of the target area at a predetermined time;

[0103] Crop classification, using growth fertility demand calculation method, obtaining growth fertility demand of multiple crops, classifying crops with similar growth fertility demand into one category, classifying multiple crops, facilitating the design of corresponding mixed fertilizer production formula;

[0104] Obtain target area soil fertility demand data, based on the fertility demand data of the classified crops, using soil fertility demand calculation method, obtain soil fertility demand data of the classified crops in the target area;

[0105] Market feedback data collection, collecting customer feedback data and crop suitability data, based on market feedback data, using weight calculation method, obtaining corresponding process optimization weight;

[0106] Optimize the mixed fertilizer production process, the process optimization method steps are as follows:

[0107] L1: analyze the target area soil data and the target area climate data, and obtain the formula adjustment direction;

[0108] L2: use the formula element adjustment formula to calculate the proportion of various elements in the formula, the formula element adjustment formula is as follows:

[0109]

[0110] Where: F j is the fertilizer amount of the adjusted formula element n, F n is the fertilizer amount of the original formula element n calculated based on the soil fertility demand data, W cust is the weight of customer feedback data, Δcust is the adjustment coefficient of customer feedback data to formula element n, W crop is the weight of crop adaptability data, Δcrop is the adjustment coefficient of crop adaptability data to formula element n, according to the calculation result, adjust the element quantity in the formula, improve the yield of crops;

[0111] It is important to note that the weight of customer feedback data is set according to customer feedback data, thus setting the weight and adjusting the formula, which is a common and effective method applied in many fields, including but not limited to food science, cosmetics industry, and fertilizer manufacturing in agriculture, etc. The weight of crop adaptability data is set by collecting crop data in the target area, and the weight of crop adaptability data is set to adjust the fertilizer formula, which is a common and scientific method, especially in precision agriculture and sustainable agriculture practices. This method can optimize the use of fertilizers according to the needs of specific crops, soil conditions and environmental factors, thereby improving crop yield, improving crop quality, and reducing negative impacts on the environment.

[0112] Data:

[0113] W crop =60;

[0114] W cust =76.5;

[0115] F n =48;

[0116] Δcust=0.1 (indicating that customer feedback data suggests increasing the original formula by 10%);

[0117] Δcrop=0.05 (indicating that crop adaptability data suggests increasing the original formula by 5%);

[0118] Result:

[0119] F j =48×(1+76.5×0.1+60×0.05);

[0120] =48×(1+7.65+3);

[0121] =48×11.65;

[0122] =559.2;

[0123] Therefore, the amount of fertilizer of element n in the adjusted formula is 559.2 units;

[0124] L3: Analyze the proportion of elements in the adjusted formula to obtain the direction of raw material mixing parameter adjustment;

[0125] L4: Use the raw material mixing parameter adjustment formula to calculate the values of each parameter during raw material mixing. The raw material mixing parameter adjustment formula is as follows:

[0126]

[0127] Where: N a is the adjusted mixing equipment parameter, Nb is the base parameter, βF is the parameter adjustment coefficient, ΔF is the formula element change amount, F t is the total amount of formula elements. Based on the calculation results, the parameters of the raw material mixing equipment are adjusted to keep the production equipment in the best operating state.

[0128] Substitute the data:

[0129] Nb = 100 (assuming the base parameter is 100);

[0130] βF = 0.5 (assuming the parameter adjustment coefficient is 0.5);

[0131] ΔF = 10 (assuming the formula element change amount is 10 units);

[0132] Ft = 100 (assuming the total amount of formula elements is 100 units, which means we are considering a whole or a specific proportion of formula change);

[0133] Substitute these data into the formula to get:

[0134] Na = 100 × (1 + 10010 × 0.5);

[0135] = 100 × (1 + 0.1 × 0.5);

[0136] = 100 × (1 + 0.05);

[0137] = 100 × 1.05;

[0138] = 105;

[0139] Therefore, the adjusted mixing equipment parameter Na is 105.

[0140] The climate prediction calculation method is as follows:

[0141] Step one: Obtain the original climate data at time t;

[0142] Step two: Obtain the original climate data at time t-1;

[0143] Step three: Obtain the intercept term;

[0144] Step four: Obtain the regression coefficient;

[0145] Step five: Obtain the error value;

[0146] Step six: Calculate through the climate prediction calculation formula, which is as follows:

[0147]

[0148] where: Δytis the difference of climate data at time t, ytis the original climate data at time t, yt-1is the original climate data at time t-1, and the regional soil data;

[0149]

[0150] where: ŷ is the predicted value of the overall climate condition of the next season, β0is the intercept term, β1, β2,..., β n are regression coefficients, Δy t is the difference of climate data at time t, and E is the error value, to obtain the climate prediction result of the target area at a preset time.

[0151] It should be noted that the climate system is often considered as a near-chaotic system due to its complex nonlinear dynamics, which means that there is significant uncertainty for long-term climate prediction (such as decades or longer), however, for seasonal prediction at shorter time scales (e.g. several months to a season), a certain degree of effective prediction can be achieved using existing scientific knowledge and technical means, for example, the sub-seasonal to seasonal to interannual integrated climate model prediction system developed by the China Meteorological Administration.

[0152] Substitute the data,

[0153] β0=10;

[0154] β1=0.5;

[0155] β2=−0.2; ...;

[0157] βn=0.1;

[0158] E=0.5;

[0159] Δy1=2;

[0160] Δy2=−1;

[0161] Δy3=0.5; ...;

[0163] Δyn=1 (this is a hypothetical value, only for demonstration);

[0164] Then:

[0165] Ŷ=10+0.5×2+(−0.2)×(−1)+0.1×0.5+...+0.1×1+0.5;

[0166] =10+1−0.2+0.05+...+0.1+0.5;

[0167] =11.85+...+0.5;

[0168] Thus, the predicted value of the overall climate condition of the next season is obtained;

[0169] It should be noted that the predicted value is only a prediction of the overall precipitation of the target area in the next season.

[0170] The climate prediction calculation method is as follows:

[0171] Step 1: Obtain the original climate data at time t;

[0172] Step 2: Obtain the original climate data at time t-1;

[0173] Step 3: Obtain the intercept term;

[0174] Step 4: Obtain the regression coefficients;

[0175] Step 5: Calculate by the climate prediction calculation formula, which is as follows:

[0176]

[0177] Where Δyt is the difference of climate data at time t, yt is the original climate data at time t, yt-1 is the original climate data at time t-1, and the soil data of the region;

[0178]

[0179] Where ŷ is the predicted value of the overall climate condition of the next season, β0 is the intercept term, β1, β2,..., βn are the regression coefficients, and Δy1, Δy2,..., Δyn are the differences of climate data at time t. n t

[0180] Substitute the data,

[0181] β0=10;

[0182] β1=0.5;

[0183] β2=−0.2; ...

[0185] βn=0.1;

[0186] Δy1=2;

[0187] Δy2=−1;

[0188] Δy3=0.5; ...

[0190] Δyn=1 (this is a hypothetical value, only for demonstration); ​​

[0191] Then:

[0192] Ŷ = 10 + 0.5 x 2 + (-0.2) x (-1) + 0.1 x 0.5 +... + 0.1 x 1;

[0193] = 10 + 1 - 0.2 + 0.05 +... + 0.1;

[0194] = 11.85 +...;

[0195] Thus, the prediction value of the overall precipitation climate condition of the next season is obtained.

[0196] The method for calculating the soil fertility retention capacity comprises the following steps:

[0197] Step 1: Obtain the initial fertilizer element content of the soil before fertilization;

[0198] Step 2: Obtain the loss rate of fertilizer elements caused by climate change;

[0199] Step 3: Calculate through the soil fertility retention capacity data calculation formula, and the soil fertility retention capacity data calculation formula is as follows:

[0200]

[0201] Wherein: N r represents the content of the fertilizer elements retained in the soil after fertilization, N i represents the initial fertilizer element content of the soil before fertilization, λR represents the loss rate function of the fertilizer elements caused by climate change, and ηF represents the fertilizer element utilization efficiency function, so as to obtain the soil fertility retention capacity data;

[0202] Substitute the data:

[0203] N i = 100 (the initial fertilizer element content of the soil before fertilization is 100 units);

[0204] λR = 0.2 (the loss rate of the fertilizer elements caused by climate change is 20%);

[0205] ηF = 0.8 (the utilization efficiency of the fertilizer elements is 80%);

[0206] Substitute these data into the formula, and we can obtain:

[0207] N r = 100 x (1 - 0.2) x 0.8;

[0208] = 100 x 0.8 x 0.8;

[0209] = 64;

[0210] Obtain the fertility retention capacity data of the soil.

[0211] The growth fertility demand calculation method has the following steps:

[0212] Step one: obtain the fertility demand of crop i;

[0213] Step two: obtain the actual fertility obtained by crop i;

[0214] Step three: obtain the maximum value of the fertility demand of all crops;

[0215] Step four: obtain the minimum value of the fertility demand of all crops;

[0216] Step five: calculate by the growth fertility demand calculation formula, which is as follows:

[0217]

[0218] Wherein: C i is the classification of crop i, F i is the fertility demand of crop i, F r is the actual fertility obtained by crop i, F max is the maximum value of the fertility demand of all crops, F min is the minimum value of the fertility demand of all crops, and R is the representative value of the fertility demand of crop i of class;

[0219] Substitute the data:

[0220] F i = 150 (the fertility demand of crop i is 150 units);

[0221] F r = 120 (the actual fertility obtained by crop i is 120 units);

[0222] F max = 200 (the maximum value of the fertility demand of all crops is 200 units);

[0223] F min = 50 (the minimum value of the fertility demand of all crops is 50 units);

[0224] R = {0.2, 0.5, 0.8} (assuming there are three categories, and the representative values of the fertility demands are 0.2, 0.5, and 0.8, respectively);

[0225] Note: The R value here is a representative value. For simplicity, we can assume that these representative values are already normalized values relative to F max − F min , if not, we need to normalize R first.

[0226] First, calculate:

[0227] (F i − F r ) / (F max − F min );

[0228] = (150−120) / (200-50);

[0229] = 30 / 150;

[0230] = 0.2;

[0231] Then, calculate the absolute value of the difference between each category and 0.2:

[0232] For R1=0.2: |0.2−0.2|=0;

[0233] For R2=0.5: |0.2−0.5|=0.3;

[0234] For R3=0.8: |0.2−0.8|=0.6;

[0235] Since 0 is the smallest difference value, the classification C i of crop i corresponds to R1=0.2;

[0236] Crops with similar growth fertility requirements are classified into one category, and multiple crops are classified to facilitate the design of corresponding mixed fertilizer production formulas.

[0237] The soil fertility requirement calculation method has the following steps:

[0238] Step one: Obtain the fertilizer utilization coefficient of nutrient n;

[0239] Step two: Obtain the demand of crop for nutrient n;

[0240] Step three: Obtain the retention content of nutrient n in the soil;

[0241] Step four: Obtain the loss amount of nutrient n in the soil;

[0242] Step five: Calculate the soil fertility requirement data of the target area based on the planting requirements of the pre-classified crops using the soil fertility requirement calculation formula, which is as follows:

[0243]

[0244] Where: F n is the amount of fertilizer needed to be added, n represents the type of nutrient, and F dis the fertilizer utilization coefficient of nutrient n, R is the demand of the crop for nutrient n, S is the retention content of nutrient n in the soil, S l is the loss of nutrient n in the soil;

[0245] Bring in the data:

[0246] For nutrient n (for example, nitrogen):

[0247] Fd=0.6 (fertilizer utilization coefficient is 60%);

[0248] R=200 (the demand of the crop for nutrient n is 200 units);

[0249] S=150 (the retention content of nutrient n in the soil is 150 units);

[0250] Sl=30 (the loss of nutrient n in the soil is 30 units);

[0251] Substitute these data into the formula, we can get:

[0252] F n =0.6×(200−(150−30));

[0253] =0.6×(200−120);

[0254] =0.6×80;

[0255] =48;

[0256] Therefore, for nutrient n, the amount of fertilizer to be added is 48 units.

[0257] Calculate the nutrient demand of the target area for planting the preset classification of crops, and repeat the calculation to obtain the soil fertility demand data of the target area for planting the preset classification of crops.

[0258] The weight calculation method is as follows:

[0259] Step one: obtain the quality score of customer feedback data;

[0260] Step two: obtain the timeliness of customer feedback data;

[0261] Step three: obtain the total quality score of all factors;

[0262] Step four: obtain the maximum timeliness value in all factors;

[0263] Step five: calculate through the customer feedback data weight calculation formula, the customer feedback data weight calculation formula is as follows:

[0264]

[0265] where: W cust is the weight of customer feedback data, Q cust is the quality score of customer feedback data, T cust is the timeliness of customer feedback data, Q t is the total quality score of all factors considered, T max is the maximum timeliness value among all factors considered.

[0266] Substitute the data:

[0267] Q cust = 85 (the quality score of customer feedback data is 85);

[0268] T cust = 0.9 (the timeliness of customer feedback data is 0.9, assuming this is a value normalized to between 0 and 1);

[0269] T max = 1.0 (the maximum timeliness value among all factors considered is 1.0, indicating the highest timeliness);

[0270] Substitute these data into the formula, we can get:

[0271] W cust = 1.085 x 0.9;

[0272] = 76.5;

[0273] So, the weight of customer feedback data is 76.5.

[0274] The weight calculation method, the steps are as follows:

[0275] Step one: obtain the nutrient adaptability score of crops;

[0276] Step two: obtain the economic value score of crops;

[0277] Step three: obtain the sum of all crop nutrient adaptability scores;

[0278] Step four: obtain the maximum value of economic value among all crops;

[0279] Step five: calculate through the crop adaptability data weight calculation formula, the crop adaptability data weight calculation formula is as follows:

[0280]

[0281] where: W crop is the weight of crop adaptability data, A crop is the nutrient adaptability score of crops, E crop is the economic value score of crops, Atotal The sum of the nutrient adaptability scores for all crops, E max The maximum value of economic value among all crops.

[0282] Substitute the data:

[0283] A crop = 80 (the nutrient adaptability score for the crop is 80 points);

[0284] E crop = 750 (the economic value score for the crop is 750 units, which can be a certain currency or value measurement unit);

[0285] E max = 1000 (the maximum value of economic value among all crops is 1000 units);

[0286] Substitute these data into the formula to obtain:

[0287] W crop = 80 x 750 / 1000;

[0288] = 60;

[0289] Therefore, the crop adaptability data weight is 60.

[0290] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A big data-based mixed fertilizer production process parameter optimization management method, characterized in that: The method comprises: Soil data collection, using recent soil data and historical soil data of the target area; Climate data collection, collecting real-time climate data and historical climate data of the target area; Climate data prediction, calculating the climate data of the target area at a preset time; Obtain soil fertility retention capacity data, use calculation method to obtain soil fertility retention capacity data of target area at a preset time; Crop classification, calculate the growth and fertility demand of various crops, and classify the crops; Obtain the soil fertility demand data of the target area, based on the fertility demand data of the classified crops, calculate the soil fertility demand data of the classified crops in the target area; Market feedback data collection, based on market feedback data, calculate the corresponding process optimization weight; Optimize the production process of mixed fertilizer, the process optimization method steps are as follows: L1: analyze the target area soil data and the target area climate data, and obtain the formula adjustment direction; L2: calculate the proportion of various elements in the formula, and the formula element adjustment formula is as follows: wherein: F j is the adjusted fertilizer amount for formula element n, F n is the original fertilizer amount for formula element n calculated based on soil fertility requirement data, W cust is the weight of customer feedback data, Δcust is the adjustment coefficient of customer feedback data for formula element n, W crop is the weight of crop adaptability data, Δcrop is the adjustment coefficient of crop adaptability data for formula element n; L3: analyze the proportion of elements in the adjusted formula, and obtain the raw material mixing parameter adjustment direction; L4: calculate the numerical value of each parameter in the raw material mixing, and the raw material mixing parameter adjustment formula is as follows: wherein: N a is the adjusted mixing device parameter, N b is the base parameter, βF is the parameter adjustment coefficient, ΔF is the formulation element change amount, F t is the total amount of formulation elements; The soil fertility retention capacity calculation method, steps are as follows: Step one: obtain the initial fertilizer element content of the soil before fertilization; Step two: obtain the fertilizer element loss ratio caused by climate change; Step three: obtain the intercept term; Step four: obtain the regression coefficient; Step five: calculate through the soil fertility retention capacity data calculation formula, the soil fertility retention capacity data calculation formula is as follows: where: N r represents the content of fertilizer elements remaining in the soil after fertilization, N i represents the initial content of fertilizer elements in the soil before fertilization, λR represents a function of the proportion of fertilizer element loss caused by climate change, and ηF represents a function of the utilization efficiency of fertilizer elements, to obtain the data of the soil's fertility retention capacity.

2. The big data-based mixed fertilizer production process parameter optimization management method according to claim 1, characterized in that: The climate prediction calculation method, steps are as follows: Step one: obtain the original climate data at time t; Step two: obtain the original climate data at time t-1; Step three: obtain the intercept term; Step four: obtain the regression coefficient; Step five: obtain the error value; Step six: calculate through the climate prediction calculation formula, the climate prediction calculation formula is as follows: where: Ay t is the difference in climate data at time t, y t is the original climate data at time t, y t-1 is the original climate data at time t-1, y wherein: is a predicted value of the overall climate condition of the next season, β0is an intercept term, β1, β2,..., β n are regression coefficients, Δy t is the climate data after the difference of time t, t = (1, 2,..., n), E is an error value, and the climate prediction result of the target area at a preset time is obtained.

3. The big data-based mixed fertilizer production process parameter optimization management method according to claim 1, characterized in that: The climate prediction calculation method, steps are as follows: Step one: obtain the original climate data at time t; Step two: obtain the original climate data at time t-1; Step three: calculate through the climate prediction calculation formula, the climate prediction calculation formula is as follows: where: Ay t is the difference in climate data at time t, y t is the original climate data at time t, y t-1 is the original climate data at time t-1, y wherein: is a predicted value of the overall climate condition of the next season, β0is an intercept term, β1, β2,..., β n are regression coefficients, Δy t is the climate data after the difference of time t, t = (1, 2,..., n), to obtain the climate prediction result of the target area at a preset time.

4. The big data-based mixed fertilizer production process parameter optimization management method according to claim 1, characterized in that: The growth and fertility demand calculation method, steps are as follows: Step one: obtain the fertility demand of crop i; Step two: obtain the actual obtained fertility of crop i; Step three: obtain the maximum value of the fertility demand of all crops; Step four: obtain the minimum value of the fertility demand of all crops; Step five: calculate through the growth and fertility demand calculation formula, the growth and fertility demand calculation formula is as follows: wherein: C i is the classification of the crop i, F i is the fertility requirement of the crop i, F r is the actual obtained fertility of the crop i, F max is the maximum value of all crop fertility requirements, F min is the minimum value of all crop fertility requirements, R is the representative value of the fertility requirement of the i-class crop; Classify the crops with similar growth and fertility demand into one category, classify the crops, and design the corresponding mixed fertilizer production formula.

5. The big data-based hybrid fertilizer production process parameter optimization management method according to claim 1, characterized in that: The weight calculation method, steps are as follows: Step one: obtain the quality score of customer feedback data; Step two: obtain the timeliness of customer feedback data; Step three: obtain the total quality score of all considered factors; Step four: obtain the maximum timeliness value in all considered factors; Step five: calculate through the customer feedback data weight calculation formula, the customer feedback data weight calculation formula is as follows: where: W cust is the weight of customer feedback data, Q cust is the quality score of customer feedback data, T cust is the timeliness of customer feedback data, Q t is the total quality score of all considerations, T max is the maximum timeliness value among all considerations.

6. The big data-based mixed fertilizer production process parameter optimization management method according to claim 1, characterized in that: The weight calculation method has the following steps: Step one: obtain the nutrient adaptability score of the crop; Step two: obtain the economic value score of the crop; Step three: obtain the sum of the nutrient adaptability scores of all crops; Step four: obtain the maximum value of the economic value among all crops; Step five: calculate through the crop adaptability data weight calculation formula, the crop adaptability data weight calculation formula is as follows: wherein: W crop is a weight for the crop suitability data, A crop is a nutrient suitability score for the crop, E crop is an economic value score for the crop, A total is the sum of the nutrient suitability scores for all crops, E max is the maximum value of economic value among all crops.

7. A big data based hybrid fertilizer production process parameter optimization management system characterized in that: The application uses the mixed fertilizer production process parameter optimization management method based on big data in any one of claims 1-6.

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