CN112070229A - Agricultural meteorological monitoring data processing method and system based on genetic algorithm - Google Patents
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Abstract
The invention relates to an agricultural meteorological monitoring data processing method and system based on a genetic algorithm. The method comprises the following steps: randomly generating parameters in a Kalman filtering algorithm; acquiring input data; preprocessing input data to obtain preprocessed input data; initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain the input data and errors of Kalman initial filtering, and taking the errors as fitness values; processing the fitness value by adopting a genetic algorithm to obtain a new fitness value; judging whether the new fitness value meets an ending condition or not; if not, returning to the step of carrying out selection operation, cross operation and mutation operation on the fitness value based on the genetic algorithm to obtain a new fitness value; if so, acquiring the optimal parameter value of Kalman filtering according to the new fitness value; and carrying out agricultural meteorological monitoring according to the optimal parameter values. The invention can improve the processing precision and efficiency of meteorological monitoring.
Description
Technical Field
The invention relates to the field of agricultural information analysis, in particular to an agricultural meteorological monitoring data processing method and system based on a genetic algorithm.
Background
With the continuous development of agricultural big data technology in China, the related range of the agricultural big data technology is wider and wider, and particularly, each link of the current agricultural activities is continuously increased, the quantity of generated data is more and more, and therefore, the related requirements on agricultural data processing are more and more. The agricultural big data has wide sources, various types, complex structures and potential values and is difficult to utilize, and although the agricultural big data technology in China is not mature, the development condition of the agricultural big data technology must be analyzed in time, a perfect agricultural big data processing technology system is established, and the development of the agricultural big data technology in China is further promoted.
The problem that noise points are difficult to remove exists in meteorological data processed by the prior art, and the problems of low processing precision and low efficiency exist in meteorological monitoring performed by means of the meteorological data processed by the prior art, so that the method cannot meet the requirement of agricultural information analysis.
Disclosure of Invention
The invention aims to provide an agricultural meteorological monitoring data processing method and system based on a genetic algorithm, which can improve the processing precision and efficiency of meteorological monitoring.
In order to achieve the purpose, the invention provides the following scheme:
an agricultural meteorological monitoring data processing method based on a genetic algorithm comprises the following steps:
randomly generating parameters in a Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power;
acquiring input data, wherein the input data is data in the parameters;
preprocessing the input data to obtain preprocessed input data;
initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain the input data and errors of Kalman initial filtering, and taking the input data and the errors of the Kalman initial filtering as fitness values;
processing the fitness value by adopting a genetic algorithm to obtain a new fitness value;
judging whether the new fitness value meets an end condition or not;
if not, returning to the step of carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value;
if so, acquiring an optimal parameter value of Kalman filtering according to the new fitness value;
and carrying out agricultural meteorological monitoring according to the optimal parameter value.
Optionally, the preprocessing the input data to obtain preprocessed input data specifically includes:
and carrying out standardization processing, normalization processing, filling processing and reliability processing on the input data to obtain preprocessed input data.
Optionally, the processing the fitness value by using a genetic algorithm to obtain a new fitness value specifically includes:
and carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
Optionally, the performing agricultural weather monitoring according to the optimal parameter value specifically includes:
substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result;
and carrying out agricultural meteorological monitoring according to the optimal filtering result.
An agricultural weather monitoring data processing system based on genetic algorithm, comprising:
the parameter generation module is used for randomly generating parameters in a Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power;
an input data acquisition module, configured to acquire input data, where the input data is data in the parameter;
the preprocessing module is used for preprocessing the input data to obtain preprocessed input data;
the fitness value determining module is used for initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain errors of the input data and Kalman initial filtering, and taking the errors of the input data and the Kalman initial filtering as fitness values;
the genetic algorithm processing module is used for processing the fitness value by adopting a genetic algorithm to obtain a new fitness value;
the judging module is used for judging whether the new fitness value meets an ending condition or not;
a returning module, configured to, when the new fitness value does not meet the end condition, "perform selection operation, crossover operation, and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value";
the optimal parameter value determining module is used for acquiring an optimal parameter value of Kalman filtering according to the new adaptability value when the new adaptability value meets an end condition;
and the agricultural weather monitoring module is used for carrying out agricultural weather monitoring according to the optimal parameter value.
Optionally, the preprocessing module specifically includes:
and the preprocessing unit is used for carrying out standardization processing, normalization processing, filling processing and reliability processing on the input data to obtain preprocessed input data.
Optionally, the genetic algorithm processing module specifically includes:
and the genetic algorithm processing unit is used for carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
Optionally, the agricultural weather monitoring module specifically includes:
the optimal filtering result determining unit is used for substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result;
and the agricultural weather monitoring unit is used for carrying out agricultural weather monitoring according to the optimal filtering result.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method combines the genetic algorithm and the Kalman filtering algorithm, and utilizes the genetic algorithm to encode the parameters in the Kalman filter so as to optimize the Kalman filtering algorithm, thereby improving the accuracy and efficiency of meteorological monitoring data processing.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for processing agricultural weather monitoring data based on a genetic algorithm according to the invention;
FIG. 2 is a diagram of the agricultural weather monitoring data processing system based on genetic algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an agricultural meteorological monitoring data processing method and system based on a genetic algorithm, which can improve the accuracy and efficiency of meteorological monitoring data processing.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in FIG. 1, the agricultural meteorological monitoring data processing method based on the genetic algorithm comprises the following steps:
step 101: and randomly generating parameters in a Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power.
Firstly, randomly generating parameters in a Kalman filtering algorithm, and initially operating the Kalman filtering algorithm so as to facilitate the subsequent genetic algorithm to encode an initial value, wherein the calculation process of the Kalman filtering algorithm is as follows:
firstly, a linear random differential equation is introduced, as shown in formula (1):
X(k)＝AX(k-1)+BU(K)+ω(k) (1)
where x (k) represents the system state at time k, u (k) represents the control quantity of the current system at time k, A, B is the system parameter value, and ω (k) represents the process noise. The system measurement is shown in equation (2):
Z(k)＝HX(k)+v(k) (2)
wherein Z (k) is the measurement value at time k, H is the measurement system parameter, and v (k) is the measurement noise. Here, let the covariances of ω (k) and v (k) be Q and R, respectively.
Assuming that the current system state is k, according to the system model, the current system state can be predicted according to the system state at the previous time, as shown in equation (3):
X(k|k-1)＝AX(k-1|k-1)+BU(k) (3)
where X (k | k-1) is the result predicted using the previous state, X (k-1| k-1) is the optimal result for the previous state, and U (k) is the control quantity for the current state, which may be made zero if there is no control quantity.
Step 102: and acquiring input data, wherein the input data is data in the parameters.
Step 103: and preprocessing the input data to obtain preprocessed input data, and performing standardization, normalization, filling and reliability processing on the input data to obtain preprocessed input data.
The error of the source data and Kalman initial filtering is taken as a fitness value: and taking the difference value of the original data and the Kalman filtered data as an algorithm fitness value. (the fitness value is a parameter that the algorithm needs to determine)
After the system result is updated, the covariance P corresponding to X (k | k-1) needs to be updated, as shown in equation (4):
P(k|k-1)＝AP(k-1|k-1)A'+Q (4)
where P (k | k-1) represents the covariance value of X (k | k-1), and correspondingly, P (k-1| k-1) represents the covariance value of X (k-1| k-1), and A' is the transpose of A.
Equations (3) and (4) are the predicted values of the current system, and the optimal estimated value of the current state of the system can be derived by combining the predicted values and the collected measured values of the current state:
X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1)) (4)
in formula (5), kg (k) represents kalman gain, as shown in formula (5):
Kg(k)＝P(k|k-1)H'[HP(k|k-1)H'+R]-1 (5)
finally, the covariance value of X (k | k) needs to be updated, so that the kalman filter continues to operate until the system process is finished, and the update formula is shown in equation (6):
P(k|k-1)＝[I-Kg(k)H]P(k|k-1) (6)
in formula (6), I is an identity matrix.
In addition, the prediction result of the current stage is used as the parameter value of the fitness function in the next stage.
Step 104: and initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain the input data and errors of Kalman initial filtering, and taking the input data and the errors of Kalman initial filtering as fitness values.
Step 105: processing the fitness value by adopting a genetic algorithm to obtain a new fitness value, which specifically comprises the following steps:
and carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
In the stage, each parameter of the Kalman filtering algorithm is optimized by using a genetic algorithm, each individual in the population comprises all parameters of the Kalman filtering algorithm, and the difference value of the source data and the initial Kalman filtering output value is used as a fitness value. The genetic algorithm process is as follows:
(a) and (6) selecting operation. Selecting the most excellent individuals from all the populations generated for heredity or generating new populations for heredity, mainly through the fitness F of each population iiTo evaluate. The selection is carried out by roulette, so that the probability of selection P for each individual populationiCan be expressed as:
(b) and (4) performing a crossover operation. And randomly selecting two population individuals for exchange combination. Let x and y be two populations, which intersect at position i, as follows:
wherein rd is a random number between [0,1 ].
(c) And (5) performing mutation operation. Randomly selecting a population individual from all the generated populations to perform variation. The variation of the population individual x at the ith position can be represented as:
wherein r and r1Are all [0,1]Random number between, xmaxAnd xminRespectively representing the upper and lower bounds of the individual of the population, and N and N respectively representing the current genetic algebra and the total genetic algebra.
And after the genetic algorithm performs mutation operation, calculating a new fitness value, setting a loop ending condition as a genetic algebra, entering a third stage if the genetic algebra reaches a set value, and otherwise, continuing to perform selection operation in a loop mode.
Step 106: judging whether the new fitness value meets an end condition or not;
step 107: if not, returning to the step of carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value;
step 108: if so, acquiring an optimal parameter value of Kalman filtering according to the new fitness value;
step 109: according to the optimal parameter value, carrying out agricultural meteorological monitoring, and specifically comprising the following steps:
and substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result, namely operating Kalman filtering again to obtain the optimal filtering result.
And carrying out agricultural meteorological monitoring according to the optimal filtering result.
The method is mainly applied to a data preprocessing stage in agricultural meteorological monitoring, and denoising and smoothing are carried out on data through a genetic algorithm and a Kalman filtering algorithm. The genetic algorithm and the Kalman filtering algorithm are combined, and the genetic algorithm is used for coding parameters in the Kalman filter so as to optimize the Kalman filtering algorithm. The algorithm is mainly divided into an initial stage (step 101-.
Corresponding to the agricultural weather-meteorological monitoring data processing method based on the genetic algorithm, the invention also provides an agricultural weather-meteorological monitoring data processing method based on the genetic algorithm, as shown in fig. 2, the agricultural weather-meteorological monitoring data processing system based on the genetic algorithm comprises:
and each parameter value generation module 201 is used for randomly generating parameters in the Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power.
An input data obtaining module 202, configured to obtain input data, where the input data is data in the parameter.
The preprocessing module 203 is configured to preprocess the input data to obtain preprocessed input data.
And an adaptability value determining module 204, configured to initially run a kalman filtering algorithm according to the preprocessed input data to perform initial value coding, obtain an error of the input data and kalman initial filtering, and use the error of the input data and kalman initial filtering as an adaptability value.
And the genetic algorithm processing module 205 is configured to process the fitness value by using a genetic algorithm to obtain a new fitness value.
A determining module 206, configured to determine whether the new fitness value satisfies an ending condition.
And a returning module 207, configured to, when the new fitness value does not meet the end condition, "perform selection operation, crossover operation, and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
And an optimal parameter value determining module 208, configured to, when the new fitness value meets an end condition, obtain an optimal parameter value of kalman filtering according to the new fitness value.
And the agricultural weather monitoring module 209 is used for performing agricultural weather monitoring according to the optimal parameter value.
The preprocessing module 203 specifically includes:
and the preprocessing unit is used for carrying out standardization processing, normalization processing, filling processing and reliability processing on the input data to obtain preprocessed input data.
The genetic algorithm processing module 205 specifically includes:
and the genetic algorithm processing unit is used for carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
The agricultural weather monitoring module 209 specifically includes:
and the optimal filtering result determining unit is used for substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result.
And the agricultural weather monitoring unit is used for carrying out agricultural weather monitoring according to the optimal filtering result.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An agricultural meteorological monitoring data processing method based on a genetic algorithm is characterized by comprising the following steps:
randomly generating parameters in a Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power;
acquiring input data, wherein the input data is data in the parameters;
preprocessing the input data to obtain preprocessed input data;
initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain the input data and errors of Kalman initial filtering, and taking the input data and the errors of the Kalman initial filtering as fitness values;
processing the fitness value by adopting a genetic algorithm to obtain a new fitness value;
judging whether the new fitness value meets an end condition or not;
if not, returning to the step of carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value;
if so, acquiring an optimal parameter value of Kalman filtering according to the new fitness value;
and carrying out agricultural meteorological monitoring according to the optimal parameter value.
2. The agricultural meteorological monitoring data processing method based on the genetic algorithm according to claim 1, wherein the preprocessing the input data to obtain preprocessed input data specifically comprises:
and carrying out standardization processing, normalization processing, filling processing and reliability processing on the input data to obtain preprocessed input data.
3. The agricultural meteorological monitoring data processing method based on the genetic algorithm according to claim 1, wherein the fitness value is processed by the genetic algorithm to obtain a new fitness value, and the method specifically comprises the following steps:
and carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
4. The agricultural meteorological monitoring data processing method based on the genetic algorithm, according to the optimal parameter value, the agricultural meteorological monitoring is carried out, and the method specifically comprises the following steps:
substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result;
and carrying out agricultural meteorological monitoring according to the optimal filtering result.
5. An agricultural weather monitoring data processing system based on genetic algorithm, which is characterized by comprising:
the parameter generation module is used for randomly generating parameters in a Kalman filtering algorithm, wherein the parameters comprise temperature, humidity, precipitation, illumination duration and wind power;
an input data acquisition module, configured to acquire input data, where the input data is data in the parameter;
the preprocessing module is used for preprocessing the input data to obtain preprocessed input data;
the fitness value determining module is used for initially operating a Kalman filtering algorithm according to the preprocessed input data to perform initial value coding to obtain errors of the input data and Kalman initial filtering, and taking the errors of the input data and the Kalman initial filtering as fitness values;
the genetic algorithm processing module is used for processing the fitness value by adopting a genetic algorithm to obtain a new fitness value;
the judging module is used for judging whether the new fitness value meets an ending condition or not;
a returning module, configured to, when the new fitness value does not meet the end condition, "perform selection operation, crossover operation, and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value";
the optimal parameter value determining module is used for acquiring an optimal parameter value of Kalman filtering according to the new adaptability value when the new adaptability value meets an end condition;
and the agricultural weather monitoring module is used for carrying out agricultural weather monitoring according to the optimal parameter value.
6. The agricultural weather-meteorological monitoring data processing system based on genetic algorithm according to claim 5, wherein the preprocessing module specifically comprises:
and the preprocessing unit is used for carrying out standardization processing, normalization processing, filling processing and reliability processing on the input data to obtain preprocessed input data.
7. The agricultural weather-meteorological monitoring data processing system based on genetic algorithm according to claim 5, wherein the genetic algorithm processing module specifically comprises:
and the genetic algorithm processing unit is used for carrying out selection operation, cross operation and mutation operation on the fitness value based on a genetic algorithm to obtain a new fitness value.
8. The agricultural weather-meteorological monitoring data processing system based on genetic algorithm of claim 5, wherein the agricultural weather-meteorological monitoring module comprises:
the optimal filtering result determining unit is used for substituting the optimal parameter value into a Kalman filtering formula to obtain an optimal filtering result;
and the agricultural weather monitoring unit is used for carrying out agricultural weather monitoring according to the optimal filtering result.
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Application publication date: 20201211 |