Straw mulching effect evaluation method and system based on multi-source data analysis

Abstract

The application discloses a straw returning to field rot effect evaluation method and system based on multi-source data analysis, and relates to the technical field of straw returning to field. A neural network model is constructed by taking the influence factor parameters in the straw rotting process as input samples and taking the straw mass change as output samples, the neural network model is trained, the number of neurons in the hidden layer of the neural network model is adjusted to the expected value of the prediction result according to the training result, the correlation between the influence factor parameter samples and the straw mass change quantity samples is obtained through the constructed neural network model, the time sequence data of the influence factors in the straw rotting process in the monitoring area is obtained, the change quantity of the straw mass when the influence factor changes once in the rotting process is obtained according to the correlation, the cumulative value of the straw mass change quantity from the initial moment to moment t of the rotting process is calculated and obtained, and the rotting rate at moment t is calculated according to the obtained cumulative value of the straw mass change quantity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of straw returning to field, and particularly relates to a straw returning to field accelerated decomposition effect evaluation method and system based on multi-source data analysis. BACKGROUND

[0002] Straw returning to field is one of the important ways of straw resource utilization, and straw can supplement soil organic matter, and its rapid decomposition plays an important role in balancing soil C / N and improving soil fertility.

[0003] Measuring straw decomposition rate is one of the important methods for measuring rice straw decomposition effect, and the size of straw decomposition percentage can directly reflect the straw decomposition effect. Straw decomposition is a cumulative process under the influence of multiple factors, and the decomposition rate of straw returning to field is mainly affected by straw size, soil properties, environmental water and heat conditions and other factors. Lu's research shows that the smaller the straw, the larger the specific surface area, and the more conducive to the contact between soil microorganisms and enzymes and straw, and the faster the straw decomposition. Some scholars have studied the texture, pH and nutrients of soil, and found that the straw decomposition rate is different in different texture soils, among which the decomposition rate is the highest in sandy soil, the second is in silty soil, and the lowest is in clay soil; pH mainly affects the activity of microorganisms to affect the decomposition rate of straw.

[0004] At present, the change of straw weight in the decomposition process is mainly measured by sampling method to reflect the decomposition degree, and the decomposition of straw in the monitoring area cannot be obtained by analyzing the change trend of environmental water and heat conditions, and the evaluation mechanism of straw decomposition degree under the action of multiple influencing factors is lacking. Therefore, we propose a straw returning to field accelerated decomposition effect evaluation method and system based on multi-source data analysis. SUMMARY

[0005] The main purpose of the present application is to provide a straw returning to field accelerated decomposition effect evaluation method and system based on multi-source data analysis, which can effectively solve the problems in the background art.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is:

[0007] The straw returning to field accelerated decomposition effect evaluation method based on multi-source data analysis comprises:

[0008] A neural network model is constructed with the influencing factor parameters in the straw decomposition process as input samples and the straw mass change as output samples, the neural network model is trained, the number of neurons in the hidden layer of the neural network model is adjusted to the expected value according to the training result, the correlation relationship f from the influencing factor parameter sample to the straw mass change sample is obtained through the constructed neural network model, and the expression of the correlation relationship is: f={x i x i∈X}→{Y}, where x i Let {x} be the i-th influencing factor parameter in the straw decomposition process; i x i {X} represents the set of influencing factor parameters; {Y} represents the set of straw mass change quantities, wherein the influencing factors include temperature, humidity, soil pH, and decomposition time;

[0009] The neural network model is a three-layer BP neural network model consisting of an input layer, an intermediate layer, and an output layer. The number of neurons in the intermediate layer is determined by the following formula:

[0010]

[0011] In the formula, s represents the number of neurons in the intermediate layer; p represents the number of neurons in the input layer; and q represents the number of neurons in the output layer. Indicated as to The value is rounded up; a is a constant coefficient and is an integer in the interval [1,9]; where the number of neurons in the input layer is equal to the type of influencing factor, in this model, p=4, q=1;

[0012] The formula for calculating the expected value is as follows:

[0013]

[0014] Where E(Y) represents the expected value; N represents the number of input samples to the neural network; f(x) i ) represents the output function of the neural network; x i Let i be the i-th output sample of the neural network;

[0015] Obtain time-series data of factors influencing the straw decomposition process within the monitoring area, and based on the correlation f, obtain the change in straw mass ΔDr when the influencing factor undergoes a single change during the decomposition process. k ,ΔDr k This represents the change in straw mass when the influencing factor undergoes a change for the kth time, where the first change refers to any one of the influencing factors—temperature, humidity, or soil pH—exceeding a threshold value. The threshold values ​​for temperature, humidity, and soil pH are determined using the following empirical formulas:

[0016]

[0017] In the formula, ct j Let ΔDr represent the threshold value of the j-th influencing factor, where j = 1, 2, 3; jmax This represents the j-th influencing factor parameter value corresponding to the maximum change in straw mass when a single change occurs; ΔDr jminThis represents the j-th influencing factor parameter value corresponding to the minimum change in straw mass when a change occurs; λ is a constant coefficient.

[0018] The range of values ​​for the constant coefficient λ is:

[0019] For the temperature change threshold, λ∈[2,10];

[0020] For the threshold of humidity change, λ∈[2,10];

[0021] For the threshold of soil pH change, λ∈[1,5];

[0022] Calculate the cumulative value ΔDr of the change in straw mass from the initial moment of the decomposition process to time t. t The calculation formula is: In the formula, m represents the total number of times the influencing factors change once from the initial moment of the decomposition process to time t;

[0023] Based on the cumulative value ΔDr of the obtained straw mass change t Calculate the decay rate Dr at time t t The calculation formula is: In the formula, qc is the initial mass of straw in the monitoring area.

[0024] The straw return-to-field decomposition effect evaluation system based on multi-source data analysis includes a data acquisition module, a neural network construction module, a data processing module, and a decomposition rate calculation module.

[0025] The data acquisition module is used to acquire time-series data of factors affecting the straw decomposition process and statistical data of straw quality changes within the monitoring area. The factors affecting the straw decomposition process include temperature, humidity, soil pH, and decomposition time.

[0026] The neural network construction module is used to construct a neural network model that takes the influencing factor parameters during straw decomposition as input samples and the straw mass change as output samples. The neural network model is trained, and the number of neurons in the hidden layers is adjusted according to the training results until the prediction result reaches the expected value. The constructed neural network model obtains the correlation f between the influencing factor parameter samples and the straw mass change samples. The expression for this correlation is: f = {x} i x i ∈X}→{Y}, where x i Let {x} be the i-th influencing factor parameter in the straw decomposition process; i x i {∈X} represents the set of influencing factor parameters; {Y} represents the set of straw mass change quantities;

[0027] The data processing module is used to obtain, based on the correlation f, the change in straw mass ΔDr when any one of the influencing factors, temperature, humidity, and soil pH, exceeds a threshold for the kth time during the decomposition process. k And calculate the cumulative value ΔDr of the change in straw mass from the initial moment of the decomposition process to time t. t The calculation formula is: In the formula, m represents the total number of times the influencing factors change once from the initial moment of the decomposition process to time t. The threshold values ​​for temperature, humidity, and soil pH are determined according to the following empirical formulas:

[0028]

[0029] In the formula, ct j Let ΔDr represent the threshold value of the j-th influencing factor, where j = 1, 2, 3; jmax This represents the j-th influencing factor parameter value corresponding to the maximum change in straw mass when a single change occurs; ΔDr jmin Let represent the j-th influencing factor parameter value corresponding to the minimum change in straw mass when a change occurs; λ is a constant coefficient; the range of values ​​for the constant coefficient λ is:

[0030] For the temperature change threshold, λ∈[2,10];

[0031] For the threshold of humidity change, λ∈[2,10];

[0032] For the threshold of soil pH change, λ∈[1,5];

[0033] The decomposition rate calculation module is used to calculate the cumulative value ΔDr of the obtained straw mass change. t Calculate the decay rate Dr at time t t The calculation formula is: In the formula, qc is the initial mass of straw in the monitoring area;

[0034] The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0035] The present invention has the following beneficial effects:

[0036] Compared with existing technologies, this method constructs a neural network model that uses parameters of influencing factors during straw decomposition as input samples and changes in straw mass as output samples. The neural network model is trained, and the number of neurons in the hidden layers is adjusted based on the training results until the prediction results reach the desired value. The constructed neural network model obtains the correlation f between the influencing factor parameter samples and the straw mass change samples, acquiring time-series data of influencing factors in the straw decomposition process within the monitoring area. Based on the correlation f, the change in straw mass is obtained when the influencing factors change once during the decomposition process. The cumulative value of the straw mass change from the initial time of the decomposition process to time t is calculated, and the decomposition rate at time t is calculated based on the cumulative value of the straw mass change. This allows for the analysis of the changing trends of environmental hydrothermal conditions to obtain the straw decomposition status within the monitoring area, thereby obtaining an assessment result of the degree of straw decomposition under the influence of multiple influencing factors. Attached Figure Description

[0037] Figure 1 This is a flowchart of the method for evaluating the effect of straw returning to the field to promote decomposition based on multi-source data analysis according to the present invention.

[0038] Figure 2 This is a structural block diagram of the straw return-to-field decomposition effect evaluation system based on multi-source data analysis of the present invention;

[0039] Figure 3 This is a structural diagram of the neural network constructed in the present invention. Detailed Implementation

[0040] The present invention will be further described below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size.

[0041] The specific implementation process of the technical solution of this invention includes the following steps:

[0042] Step 1: Construct a neural network model with the parameters of influencing factors in the straw decomposition process as input samples and the change in straw mass as output samples. Train the neural network model and adjust the number of neurons in the hidden layer of the neural network model according to the training results until the prediction result reaches the expected value.

[0043] It should be noted that the parameters of influencing factors and the parameters of straw mass change used to construct the neural network can be sampled by designing a decomposition experiment. The specific experimental design is as follows:

[0044] There are 15 residential areas, each with an area of ​​40 square meters.2 Rice straw, pulverized by a spreader, was collected. Longer straw was cut to approximately 5cm. 50g of rice straw (dry basis) was mixed evenly with 1kg of paddy soil and placed into 40-mesh nylon mesh bags. Twelve such bags were placed and vertically buried 10-15cm into the topsoil. Three samples were randomly taken each time, for a total of four samplings at 30, 60, 90, and 120 days after transplanting. After each straw sampling, the nylon mesh bags were soaked, and the straw was washed. After washing, the straw was dried at 60℃, and the result was compared with the initial dry weight to calculate the change in straw mass.

[0045] Among the influencing factors are temperature, humidity, soil pH, and decomposition time;

[0046] The neural network model is a three-layer backpropagation (BP) neural network model with an input layer, an intermediate layer, and an output layer, such as... Figure 3 As shown, the number of neurons in the intermediate layer is determined by the following formula:

[0047]

[0048] In the formula, s represents the number of neurons in the intermediate layer; p represents the number of neurons in the input layer; and q represents the number of neurons in the output layer. Indicated as to The value is rounded up; a is a constant coefficient and is an integer in the interval [1,9]; where the number of neurons in the input layer is equal to the type of influencing factor, in this model, p=4, q=1;

[0049] The formula for calculating the expected value is:

[0050]

[0051] Where E(Y) represents the expected value; N represents the number of input samples to the neural network; f(x) i ) represents the output function of the neural network; x i Let i be the i-th output sample of the neural network;

[0052] Step 2: Obtain the association f between the influencing factor parameter samples and the straw mass change samples through the constructed neural network model. The expression for the association is: f = {x} i x i ∈X}→{Y}, where x i Let {x} be the i-th influencing factor parameter in the straw decomposition process; i x i {∈X} represents the set of influencing factor parameters; {Y} represents the set of straw mass change quantities;

[0053] Step 3: Obtain real-time time-series data of factors influencing the straw decomposition process within the monitoring area, and obtain the change in straw mass ΔDr when a change in an influencing factor occurs during the decomposition process based on the correlation f. k ,ΔDr k This represents the change in straw mass when the influencing factor undergoes a single change for the kth time, where a single change occurs when any one of the influencing factors—temperature, humidity, or soil pH—exceeds a threshold value. The threshold values ​​for temperature, humidity, and soil pH are determined using the following empirical formulas:

[0054]

[0055] In the formula, ct j Let ΔDr represent the threshold value of the j-th influencing factor, where j = 1, 2, 3; jmax This represents the j-th influencing factor parameter value corresponding to the maximum change in straw mass when a single change occurs; ΔDr jmin This represents the j-th influencing factor parameter value corresponding to the minimum change in straw mass when a change occurs; λ is a constant coefficient.

[0056] The range of values ​​for the constant coefficient λ is:

[0057] For the temperature change threshold, λ∈[2,10];

[0058] For the threshold of humidity change, λ∈[2,10];

[0059] For the threshold of soil pH change, λ∈[1,5];

[0060] It should be noted that when an influencing factor changes once, that is, when the sampled value of any of the influencing factors, such as temperature, humidity, or soil pH, exceeds the change threshold, the influencing factor changes in this change. Each change corresponds to a decomposition time period. The decomposition process of straw can be regarded as the cumulative effect of several decomposition time periods. Within the same decomposition time period, temperature, humidity, and soil pH remain at the same level. Within different decomposition time periods, at least one of the influencing factors, such as temperature, humidity, or soil pH, changes beyond the threshold. From the initial decomposition time to the current time t, it is composed of several consecutive decomposition time periods. By accumulating the changes in straw mass within several decomposition time periods, the total change in straw mass can be obtained, which is the cumulative value of the change in straw mass.

[0061] Step 4: Calculate the cumulative value ΔDr of the change in straw mass from the initial moment of the decomposition process to time t. t The calculation formula is: In the formula, m represents the total number of times the influencing factors change once from the initial moment of the decomposition process to time t;

[0062] Step 5: Based on the cumulative value ΔDr of the obtained straw mass change... t Calculate the decay rate Dr at time t t The calculation formula is: In the formula, qc is the initial mass of straw in the monitoring area.

[0063] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the effect of straw returning to the field on promoting decomposition based on multi-source data analysis, characterized in that, include: A neural network model is constructed, using the influencing factors during straw decomposition as input samples and the change in straw mass as output samples. The neural network model is trained, and the number of neurons in the hidden layers is adjusted based on the training results until the prediction results reach the desired value. The constructed neural network model is used to obtain the correlation f between the influencing factor parameter samples and the straw mass change samples. The expression for this correlation is: f = {x} i |x i ∈X}→{Y}, where x i Let {x} be the i-th influencing factor parameter in the straw decomposition process; i |x i {∈X} represents the set of influencing factor parameters; {Y} represents the set of straw mass change quantities; Obtain time-series data of factors influencing the straw decomposition process within the monitoring area, and based on the correlation f, obtain the change in straw mass ΔDr when the influencing factor undergoes a single change during the decomposition process. k ,ΔDr k This represents the change in straw mass when the influencing factor undergoes its first change for the kth time. Calculate the cumulative value ΔDr of the change in straw mass from the initial moment of the decomposition process to time t. t The calculation formula is: In the formula, m represents the total number of times the influencing factors change once from the initial moment of the decomposition process to time t; Based on the cumulative value ΔDr of the obtained straw mass change t Calculate the decay rate Dr at time t t The calculation formula is: In the formula, qc is the initial mass of straw in the monitoring area; The influencing factors include temperature, humidity, soil pH, and decomposition time. A single change is defined as any one of the influencing factors, such as temperature, humidity, or soil pH, exceeding a threshold value. The threshold values ​​for changes in temperature, humidity, and soil pH are determined using the following empirical formulas: In the formula, ct j Let ΔDr represent the threshold value of the j-th influencing factor, where j = 1, 2, 3; jmax This represents the j-th influencing factor parameter value corresponding to the maximum change in straw mass when a single change occurs; ΔDr jmin Let λ represent the value of the j-th influencing factor parameter corresponding to the minimum change in straw mass when a change occurs; λ is a constant coefficient.

2. The method for evaluating the straw return-to-field decomposition effect based on multi-source data analysis according to claim 1, characterized in that: The neural network model is a three-layer BP neural network model consisting of an input layer, an intermediate layer, and an output layer. The number of neurons in the intermediate layer is determined by the following formula: In the formula, s represents the number of neurons in the intermediate layer; p represents the number of neurons in the input layer; and q represents the number of neurons in the output layer. Indicated as to The value is rounded up; a is a constant coefficient and is an integer in the interval [1,9]; where the number of neurons in the input layer is equal to the type of influencing factor, in this model, p=4, q=1.

3. The method for evaluating the straw return-to-field decomposition effect based on multi-source data analysis according to claim 1, characterized in that: The formula for calculating the expected value is as follows: Where E(Y) represents the expected value; N represents the number of input samples to the neural network; f(x) i ) represents the output function of the neural network; x i Let i be the i-th output sample of the neural network.

4. The method for evaluating the straw return-to-field decomposition effect based on multi-source data analysis according to claim 1, characterized in that: The range of values ​​for the constant coefficient λ is: For the temperature change threshold, λ∈[2,10]; For the threshold of humidity change, λ∈[2,10]; For the threshold of soil pH change, λ∈[1,5].

5. A straw return-to-field decomposition-promoting effect evaluation system based on multi-source data analysis, characterized in that, It includes a data acquisition module, a neural network construction module, a data processing module, and a decomposition rate calculation module; The data acquisition module is used to acquire time-series data of factors affecting the straw decomposition process and statistical data of straw quality changes within the monitoring area. The factors affecting the straw decomposition process include temperature, humidity, soil pH, and decomposition time. The neural network construction module is used to construct a neural network model that takes the influencing factor parameters during straw decomposition as input samples and the straw mass change as output samples. The neural network model is trained, and the number of neurons in the hidden layers is adjusted according to the training results until the prediction result reaches the expected value. The constructed neural network model obtains the correlation f between the influencing factor parameter samples and the straw mass change samples. The expression for this correlation is: f = {x} i |x i ∈X}→{Y}, where x i Let {x} be the i-th influencing factor parameter in the straw decomposition process; i |x i {∈X} represents the set of influencing factor parameters; {Y} represents the set of straw mass change quantities. The neural network model is a three-layer BP neural network model with an input layer, an intermediate layer, and an output layer. The number of neurons in the intermediate layer is determined by the following formula: In the formula, s represents the number of neurons in the intermediate layer; p represents the number of neurons in the input layer; and q represents the number of neurons in the output layer. Indicated as to The value is rounded up; 'a' is a constant coefficient, and 'a' is an integer in the interval [1, 9]; The number of neurons in the input layer is equal to the type of influencing factor. In this model, p = 4, q = 1; The formula for calculating the expected value is... Where E(Y) represents the expected value; N represents the number of input samples to the neural network; f(x) i ) represents the output function of the neural network; x i Let i be the i-th output sample of the neural network; The data processing module is used to obtain, based on the correlation f, the change in straw mass ΔDr when any one of the influencing factors, temperature, humidity, and soil pH, exceeds a threshold for the kth time during the decomposition process. k And calculate the cumulative value ΔDr of the change in straw mass from the initial moment of the decomposition process to time t. t The calculation formula is: In the formula, m represents the total number of times the influencing factors change once from the initial moment of the decomposition process to time t. The threshold values ​​for temperature, humidity, and soil pH are determined according to the following empirical formulas: In the formula, ct j Let ΔDr represent the threshold value of the j-th influencing factor, where j = 1, 2, 3; jmax This represents the j-th influencing factor parameter value corresponding to the maximum change in straw mass when a single change occurs; ΔDr jmin Let represent the j-th influencing factor parameter value corresponding to the minimum change in straw mass when a change occurs; λ is a constant coefficient; the range of values ​​for the constant coefficient λ is: For the temperature change threshold, λ∈[2,10]; For the threshold of humidity change, λ∈[2,10]; For the threshold of soil pH change, λ∈[1,5]; The decomposition rate calculation module is used to calculate the cumulative value ΔDr of the obtained straw mass change. t Calculate the decay rate Dr at time t t The calculation formula is: In the formula, qc is the initial mass of straw in the monitoring area.

6. The straw return-to-field decomposition-promoting effect evaluation system based on multi-source data analysis according to claim 5, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method according to any one of claims 1-4.

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