CN117313555A - Distributed storage-based adaptive OATS-AJSA improved GRU humidity prediction method - Google Patents

Distributed storage-based adaptive OATS-AJSA improved GRU humidity prediction method Download PDF

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CN117313555A
CN117313555A CN202311599596.7A CN202311599596A CN117313555A CN 117313555 A CN117313555 A CN 117313555A CN 202311599596 A CN202311599596 A CN 202311599596A CN 117313555 A CN117313555 A CN 117313555A
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秦华旺
葛东明
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Nanjing University of Information Science and Technology
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Abstract

The invention discloses a distributed storage self-adaptive OATS-AJSA-based improved GRU humidity prediction method, which specifically comprises the following steps: acquiring historical multi-element meteorological data and storing the data in a MinIO database in a distributed manner; identifying abnormal values in the multi-element meteorological data set by using an isolated forest method, and replacing the abnormal values; performing dimension reduction on the replaced data set by adopting a t-SNE method to obtain a dimension reduced data set, and normalizing the dimension reduced data set to obtain a normalized data set; establishing an OATS-AJSA-GRU humidity prediction model, and optimizing the super parameters in the GRU by using an OATS-AJSA optimization algorithm to obtain optimized super parameters; and placing the optimized super-parameters and the normalized data set into the GRU for prediction to obtain a humidity prediction result. The method realizes higher accuracy, stability and efficiency in the aspect of humidity prediction, and simultaneously has automatic super-parameter optimization and wide practical application potential.

Description

Distributed storage-based adaptive OATS-AJSA improved GRU humidity prediction method
Technical Field
The invention relates to a distributed storage self-adaptive OATS-AJSA-based improved GRU humidity prediction method, and belongs to the technical field of meteorological data prediction.
Background
The humidity prediction plays a very key role in weather disaster early warning, is an important research content in the fields of weather science and environmental science, and has important significance in understanding and predicting the change of the moisture content in the atmosphere. Humidity has profound effects on aspects such as weather, agriculture, energy, ecosystem, health, and the like. Humidity is one of the important factors in meteorology, in predicting weather and climate change, affecting cloud formation, precipitation generation and energy transfer in the atmosphere. In the agricultural field, humidity prediction can help farmers to rationally arrange irrigation and crop management to maximize yield. The energy sector needs accurate humidity prediction to optimize power production and distribution, as humidity changes can affect the water vapor content in the air, and thus the efficiency of wind power generation and solar power generation. Accurate humidity prediction can early warn in advance, and loss caused by disasters is greatly reduced. The conventional method for predicting humidity is low in prediction accuracy because the problem of time series is not considered. Meanwhile, with the progress of technology, the collection speed and the collection quantity of multi-element meteorological data are continuously increased, and challenges are brought to the traditional data storage and processing method.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the humidity prediction method based on distributed storage self-adaptive OATS-AJSA is provided, and the accuracy of humidity prediction is further improved.
The invention adopts the following technical scheme for solving the technical problems:
a distributed storage self-adaptive OATS-AJSA-based improved GRU humidity prediction method comprises the following steps:
step 1, acquiring historical multi-element meteorological data and storing the data in a MinIO database in a distributed manner;
step 2, extracting multi-element meteorological data from a MinIO database as a prediction data set, identifying abnormal values in the prediction data set by using an isolated forest method, and replacing the abnormal values by using a linear interpolation method to obtain a replaced data set;
step 3, reducing the dimension of the replaced data set obtained in the step 2 by adopting a t distribution-random adjacent embedding method to obtain a dimension-reduced data set, and normalizing the dimension-reduced data set to obtain a normalized data set;
step 4, an OATS-AJSA-GRU humidity prediction model is established, and the ultra-parameters in the GRU are optimized by utilizing an OATS-AJSA optimization algorithm to obtain optimized ultra-parameters;
and 5, putting the optimized super parameters and the normalized data set obtained in the step 3 into GRU for prediction, and obtaining a humidity prediction result.
As a preferable scheme of the invention, the specific process of the step 1 is as follows:
1) Accessing the ECMWF by using the API and downloading the NC data file;
2) Analyzing the downloaded NC data file by using a xarray library of Python to obtain a CSV data file;
3) Configuring and connecting to a MinIO server, uploading the CSV data file obtained in the step 2 to a MinIO socket by using a MinIO SDK, and taking the CSV data file as a historical multi-element weather database, wherein the multi-element weather data comprises temperature, humidity, wind speed and pressure data;
4) Storing user information by using a MySQL database, creating an API endpoint by using a Python Web framework, and calling the historical multi-element weather data in the historical multi-element weather database by the user stored in the MySQL database.
As a preferred scheme of the present invention, in step 2, the identifying abnormal values in the predicted data set by using the isolated forest method specifically includes:
1) Setting the predicted data set to，/>Each data point +.>By->Individual meteorological features, i.e.，/>Representing data points +.>Is>Meteorological characteristics, from->Is selected at random->The data points form a subset->Subset +.>As the root node of an orphan tree;
2) Randomly assigned dimensionsAnd->In subset->A cutting point is randomly generated>The formula is as follows:
，
wherein,representing data points +.>Is>Individual weather features;
3) According to the cutting pointCreating a hyperplane, utilizing the hyperplane to pair sub-planes>Dividing, i.e. sub-set->Less than->Put the data point of (2) into the left child node, subset +.>Middle greater than or equal to->Placing the data point of the node in the right child node;
4) Repeating the steps 2) and 3) until all leaf nodes of the isolated tree contain only one data point or the height of the isolated tree reaches a preset upper height limit, namely generating an isolated tree;
5) Repeating the steps 1) -4) to obtain an isolated forest;
6) For the followingEach data point +.>Traversing each isolated tree in the isolated forest to obtain data points +.>The heights in each isolated tree are averaged to obtain the average height, and the average heights of all data points are normalized;
7) Using normalized flatAverage height ofCalculating outlier score ++>Taking data points corresponding to abnormal value fractions which are larger than or equal to a preset threshold value as abnormal values; the outlier score is calculated as follows:
，
wherein,，/>；/>representing +.>Calculating average heights in all the isolated trees, and taking an index after normalization; />Representing a parametric function->Represents the Euler-Mascheroni constant plus +.>Natural logarithm of (a).
As a preferred scheme of the present invention, in step 2, the replacing of the outlier by using the linear interpolation method specifically includes:
1) Finding adjacent data points of the abnormal value, namely two normal values before and after the abnormal value;
2) A straight line is constructed by using two normal values before and after the abnormal value, namely:
，
wherein,alternative values representing outliers, +.>Normal values before and after the abnormal value, respectively, ">Indicating that the outlier is +.>And->The position ratio between->；
3) And finding out the corresponding ordinate value along the constructed straight line according to the position of the outlier on the abscissa, namely, the replacement value of the outlier.
As a preferred scheme of the present invention, in step 3, the dimension of the replaced data set obtained in step 2 is reduced by adopting a t distribution-random adjacent embedding method, so as to obtain a dimension-reduced data set, which specifically includes the following steps:
1) Setting the replaced data set, namely the high-dimensional data set, asThe data set after dimension reduction is as follows，/>Representing high-dimensional data points in a high-dimensional dataset, +.>Representing low-dimensional data points in a reduced-dimension dataset using GaussianDistribution calculation high-dimensional similarity probability +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the Gaussian distribution formula is as follows:
，
wherein,representing high-dimensional data points +.>Generate->Conditional probability of->A variance parameter representing the distance calculation,representation->And->Euclidean distance of>Representing high-dimensional data points in a high-dimensional dataset;
2) Calculating low-dimensional similarity probabilities using t-distributionWherein the t distribution formula is as follows:
，
wherein,representing low dimensionalityPoint->Generate->Conditional probability of->Representing low-dimensional data points in the reduced-dimension dataset;
3) Using the difference between the KL-divergence Heng Lianggao-dimensional probability distribution and the low-dimensional probability distribution, the formula is as follows:
，
wherein,represents KL divergence;
4) Updating the low-dimensional representation using a gradient descent method, the updating rule being as follows:
，
wherein,indicates learning rate (I/O)>Representing global parameters->Is a parameter used to scale the gradient when updating the low-dimensional representation;
5) Repeating 2) -4) until a convergence condition is reached, i.e. a preset number of iterations is reached or the difference between the KL-divergence of the current iteration and the KL-divergence of the last iteration is smaller than a preset threshold.
As a preferred scheme of the present invention, the specific process of the step 4 is as follows:
step 41, initializing parameters of an artificial jellyfish searching algorithm by using an orthogonal table testing method, wherein the parameters of the artificial jellyfish searching algorithm comprise population quantity, searching radius and a population initializing strategy;
step 42, taking the super parameters of the GRU as jellyfish, calculating the fitness value of the jellyfish by using a cross entropy loss function in each iteration, and taking the jellyfish position with the highest fitness value as the optimal individual position of the jellyfish population in the current iteration; the fitness value is calculated as follows:
，
wherein,representing a cross entropy loss function, ">Is the number of iterations, +.>Is learning rate (I/O)>Is dropout proportion,/>Is the number of neurons in the hidden layer, < > and the number of neurons in the hidden layer>Is the number of samples, +.>Indicate->Actual tag of individual sample,/>Representation model pair->Predictive probability of individual samples +.>Indicating fitness;
step 43, a time control mechanism is introduced to determine that the current iteration jellyfish enters the ocean current stage or jellyfish group stage, namely:
，
wherein,control function representing a time control mechanism, +.>Representing the current iteration number, +.>Represents a random number between 0 and 1, < >>Representing the initially set maximum iteration number;
the maximum iteration number is dynamically adjusted by using an adaptive adjustment method, and the formula is as follows:
，
wherein,representing the number of remaining iterations, +.>Representing a minimum number of iterations;
step 44, judging according to the time control function, ifThe jellyfish enters the ocean current stage and updates the individual position according to the following formula:
，
wherein,indicate->Only the updated position of jellyfish +.>Indicate->Only the current iteration position of the jellyfish,representing the optimal individual position in the current iteration jellyfish population, < >>Is a distribution coefficient>，/>Representing the average position of all jellyfish in the population;
if it isThe jellyfish enters the jellyfish group stage, further according to +.>Judging whether jellyfish actively moves or passively moves in jellyfish group stage if the jellyfish is more than or equal to 0.5, if yes>Representing that jellyfish is passively moved in the jellyfish group stage, and updating the individual position according to the following formula:
，
wherein,is the motion coefficient, +.>，/>Respectively representing an upper bound and a lower bound of jellyfish population search space;
if it isRepresenting that jellyfish is actively moving in the jellyfish group stage, and updating the individual position according to the following formula:
，
wherein,indicates the direction of jellyfish movement, +.>Represents the distance the jellyfish moves in the direction of movement, < >>Represents the random selection of +.>Only jellyfish current iteration position,/->Respectively represent +.>Only the adaptability value of jellyfish at the current iteration position;
step 45, correcting the jellyfish position according to the clip function:
，
wherein,indicating corrected jellyfish position +.>Respectively representing minimum and maximum allowable values of the position;
step 46, judging the current iteration numberAnd threshold constant->If the current iteration number +.>Less than threshold constant->And continuing iteration, otherwise, outputting the optimal value of the super parameter.
As a preferred embodiment of the present invention, the specific process of step 41 is as follows:
411, determining parameter ranges of three parameters, namely population quantity, search radius and population initialization strategy, and selecting different combinations of the three parameters, namely population quantity, search radius and population initialization strategy, according to the parameter ranges;
step 412, performing an ASJA experiment on each combination of the L16 orthogonal table, and selecting the corresponding combination when the experimental result is optimal as the optimal combination of three parameters, namely, the optimal parameter of the artificial jellyfish searching algorithm, such as population number, searching radius and population initializing strategy.
A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, which when executed implements the steps of a distributed storage adaptive oat-AJSA based GRU humidity prediction method as described above.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of a distributed storage adaptive oat-AJSA based GRU humidity prediction method as described above.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. according to the invention, the GRU model is optimized by using the distributed storage and self-adaptive OATS-AJSA algorithm, so that the storage problem of massive meteorological data can be solved, and the humidity can be predicted more accurately, thereby improving the accuracy of meteorological prediction.
2. Compared with the method of adopting random initialization for the initialization of the optimization algorithm, the method of the invention uses the orthogonal table test method to initialize the parameters of the artificial jellyfish search algorithm, which can improve the efficiency, accuracy and stability of the algorithm, and is helpful to find the optimal parameter combination faster instead of relying on random attempt.
3. The invention adopts distributed storage and efficient data processing flow, so that the data acquisition and processing are more efficient. In addition, by means of self-adaptive adjustment of the maximum iteration times and the like, the parameter searching process is optimized, and the computing efficiency is improved.
4. According to the invention, an artificial jellyfish search algorithm is introduced to automatically find the optimal GRU model super-parameters, so that manual adjustment is not needed, and the time and labor cost are saved.
5. The method realizes higher accuracy, stability and efficiency in the aspect of humidity prediction, and simultaneously has automatic super-parameter optimization and wide practical application potential, thereby having remarkable technical advantages in the field of weather prediction.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the present invention for constructing an orphan forest;
FIG. 3 is a flow chart of the data dimension t-reduction distribution-random proximity embedding method of the present invention;
FIG. 4 is a flow chart of the OATS-AJSA-GRU predictive moisture model of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
A distributed storage self-adaptive OATS-AJSA-based improved GRU humidity prediction method comprises the following specific steps:
as shown in fig. 1, the multi-element weather data is downloaded and stored in a database in a distributed manner, and a user can call the multi-element weather data in the database, which comprises the following specific steps:
step 1: acquiring meteorological data from ECMWF, using API to access data service and downloading data file and storing in database;
step 2: analyzing the downloaded NC data file by using a xarray library of Python, and extracting the required meteorological variables and geographic coordinates;
step 3: configuring and connecting to a MinIO server, and uploading the obtained CSV file to a MinIO socket by using a MinIO SDK to serve as a historical multi-element meteorological database;
step 4: the MySQL database is used to store user information data for use in authenticating a user to log into the system. An API endpoint is created using a Python's Web framework (e.g., a flash framework) for a user to request weather data within a specified date range, thereby creating a callable interface to call historical multi-element weather data from a Minio database.
The invention uses historical multi-element meteorological data in a Minio database as a data source of a method for improving GRU prediction humidity by self-adapting OATS-AJSA.
The method of using the isolated forest processes the meteorological data stored in the database with outliers, according to the specific steps of fig. 2, as follows:
in an orphan tree, each node is of two types: external nodes and internal nodes. The external node has no child nodes, while the internal node has two child nodesAnd->) And a test condition consisting of the attribute and the division point. Sample points are assigned to either the left or right subtree depending on the test conditions. For sample point->Its path length in an isolated tree +.>Defined as the number of edges that pass from the root node to the leaf node. In other words, a->Representing sample points in an isolated tree +.>In the determination, the number of test conditions to be passed.
After defining the isolated tree and path length, an isolated forest needs to be built, specifically as follows:
step 1: the meteorological data set extracted from the database isWherein each data point->By->Composition of individual meteorological features, i.e.)>. From->Is selected at random->A plurality of sample points forming a subset->This subset is taken as the root node initial data of the orphan tree.
From the slaveOne dimension is randomly designated in the individual dimensions>（/>) In subset->A cutting point is randomly generated>The formula is as follows:
，
in the above-mentioned method, the step of,representing data points +.>Is>And a dimension.
Step 2: using cutting pointsCreating a hyperplane, sub-set +.>Divided into two subspaces. In this division, it will be smaller than +.>Is placed in the left child node, will be greater than or equal toIn->Is placed into the right child node. This partitioning facilitates the distribution of data samples into different child nodes in the tree structure according to the cut point setting.
Step 3: by repeatedly performing the above procedure, the cut point and hyperplane can be continuously applied into the data space until one of two conditions is reached: one condition is that all leaf nodes contain only one sample point, and the other condition is that the height of the orphan tree reaches a pre-specified threshold. Until a complete orphan tree is successfully generated.
Step 4: and repeatedly executing the steps 1-3 until the isolated forest is successfully generated. This process is an iterative process in which the algorithm performs data cutting, hyperplane generation and node construction again and again until an isolated forest is formed.
For each data pointEach isolated tree in the forest is traversed and the path length (or height) of the data point in the tree, i.e., the number of edges traversed from the root node to the leaf node of the tree, is calculated. All these path lengths (heights) will be collected and their average calculated to get an average height. The average height of all data points is normalized to further analyze or classify whether the data point is outliers.
The outlier score is calculated according to the following formula:
，
wherein the method comprises the steps ofThe formula is as follows:
，
representing Meteorological data points->In the course of->Degree of abnormality in isolated tree constructed of individual samples, +.>Representing +.>Calculating the average height in all the isolated trees, then taking the index, ++>Is a parameter function whose value depends on +.>，/>Is the Euler-Mascheroni constant plus +.>Natural logarithm of (a).
For representing data points +.>Degree of anomaly in the orphan tree. This degree is based on the average height of the data points in the plurality of isolated trees and is normalized and adjusted by a series of parameters. Its value ranges from 0 to 1]Between them. When the majority of training samples +.>Values are all close to 0.5, meaning meteorological numbersThere is no obvious abnormality in the construction of the orphan tree. In other words, if the degree of abnormality of most training samples approaches the intermediate value of 0.5, it can be concluded that the meteorological data as a whole is free from prominent abnormalities.
，
Wherein,is for each meteorological data point->Abnormal value score of>Is a threshold.
For the method that the isolated forest is used for identifying possible abnormal points in meteorological data and marking the abnormal points as the abnormal points, the abnormal points are replaced by adopting a linear interpolation method, and the method specifically comprises the following steps:
two normal values before and after the outlier, i.e., adjacent data points of the outlier, are found.
A straight line is constructed using these two normal values, connecting the two points.
And finding the ordinate value of the corresponding position along the straight line according to the position of the outlier on the abscissa, namely, the estimated outlier substitution value. The calculation formula of the specific linear interpolation is as follows:
，
wherein the outlier isIts previous normal value is +.>The latter normal value is +.>. Assume that the position of the outlier in the data is +.>It is->And->The position ratio between them is->（）。/>Representing an estimated replacement value for the outlier.
This formula is expressed byAnd->Linear interpolation between, use ∈>Is used to weight the calculated estimate. />The value of (2) represents the relative position of the abnormal value between the preceding and following data points, the closer to the preceding normal value +.>，/>The closer to 0, the closer to the latter normal value +.>，/>The closer to 1.
And carrying out one-hot coding on the analyzed meteorological data according to months, wherein the specific coding form is shown in table 1.
TABLE 1
In order to achieve the following objectives in processing meteorological data: the method comprises the steps of storing most information, reducing the data size and dimension, removing noise, and performing dimension reduction operation by adopting a t-distribution-random adjacent embedding method (t-distributed Stochastic Neighbor Embedding, t-SNE) method, so that a lower-dimension representation is obtained. This approach facilitates rapid processing of data while avoiding the effects of duplicate or redundant information. According to fig. 3, the specific steps are as follows:
first, consider the original high-dimensional dataset to be represented asWherein->Is a high-dimensional data point, the invention refers to multi-element meteorological data processed by an isolated forest, the target of t-SNE is to map the high-dimensional data to a low-dimensional space, the dimension of the low-dimensional space is assumed to be D, and the mapped low-dimensional data set is expressed as>Wherein->Is the corresponding low-dimensional data point.
Step 1: calculating high-dimensional similarity probabilityFor high dimension data points->And->The similarity between them is measured using a gaussian distribution. The gaussian distribution formula is as follows:
，
wherein,representing data points +.>Generate->Conditional probability of->A variance parameter representing the distance calculation. />Represented in a high-dimensional space, data points +.>Generate data point->Conditional probability of->Representing a high-dimensional probability of similarity for measuring data points +.>And->Similarity between them. />Is estimated by Gaussian distribution>Is a probability of (2). />The square of the euclidean distance (Euclidean distance) is represented and used to measure the distance between two vectors.
Step 2: calculating low-dimensional similarity probabilitiesFor low-dimensional data points->And->The t-distribution is used to measure the similarity between them. the t distribution formula is as follows:
，
wherein,to represent data point +.>Generate->Conditional probability of (2).
Step 3: the KL-divergence is calculated for the difference between the Heng Lianggao-dimensional probability distribution P and the low-dimensional probability distribution Q. The calculation formula is as follows:
，
wherein,representing the divergence->Representing a high-dimensional similarity probability, +.>Representing a low dimensional similarity probability.
Step 4: updating a low-dimensional representation using gradient descentTo reduce +.>Divergence. The updated rules are:
，
wherein the method comprises the steps ofIs the learning rate, controlling the updated step size in each iteration. />Is a global parameter for controlling the probability of similarity in high-dimensional space>Is a distribution of (a). It is used in the t-SNE algorithm to balance the parameters of local and global similarity. Higher +.>The values will result in more high-dimensional data points being considered similar, affecting the global structure of the t-SNE.Is for each low-dimensional data point +.>For controlling the probability of similarity in a low-dimensional space +.>Is a distribution of (a). In the t-SNE algorithm, +.>Is variable and is updated continuously with the iterative process to adjust for the similarity between the low-dimensional data points. Specifically, & gt>Is a parameter used to scale the gradient when updating the low-dimensional representation that helps maintain the relative distance between the data points.
Repeating the steps 2 to 4 until reaching the convergence condition, and reaching a certain iteration number orThe variation in the divergence is small. When the iteration is ended, the resulting low-dimensional representation is the final result.
The weather data after the t-SNE dimension reduction is normalized, and the specific formula is as follows:
，
wherein,representing the normalized value of the meteorological data, +.>Representing the minimum value of the meteorological data, +.>Representing the maximum value of the meteorological data.
The structure of the OATS-AJSA-GRU humidity prediction model was established according to fig. 4: the method comprises the following steps:
step 1: the over-parameters (iteration range, dropout ratio, number of neurons in hidden layer (hidden-size), learning rate size) in the GRU are optimized optimally using the OATS-ASJA optimization algorithm. Iteration range [1, 50], dropout ratio [0.01,0.20], hidden-size range [10, 200], learning rate range [0.001,0.01].
Step 2: parameters of an artificial jellyfish search algorithm (Artificial Jellyfish Search Algorithm, AJSA) are initialized by using an orthogonal table test method (Orthogonal Array Testing Strategy, OATS), and the parameters are selected from population quantity, search radius and population initialization strategies.
Step 3: determining the number of the population, searching the radius, initializing the parameter range of the strategy of the population, and filling in the orthogonal table. The L16 orthogonal table is selected to fill in the number of the population, the searching radius and the combination of three parameters of the population initialization strategy, and the specific orthogonal table is shown in the table 2.
TABLE 2
Step 4: ASJA experiments are carried out according to the combination of the orthogonal tables, the population quantity, the searching radius and the population initialization strategy are optimized. The results of each experiment were recorded and the optimal parameter configuration was obtained.
Step 5: calculating the fitness value of jellyfish, and calculating the fitness value of jellyfish by using a cross entropy loss function, wherein the specific calculation formulas of the cross entropy loss function and the fitness value of jellyfish are as follows:
，
wherein,is the number of iterations (iterations), +.>Is learning rate (learning_rate),>is a dropout proportion (dropout_rate),>is the hidden layer neuron number (hidden_neurons), +.>Is the number of samples, +.>Is->Actual tag of individual sample,/>Is model pair->Predictive probability of individual samples +.>Is the fitness.
Step 6: and (3) determining the optimal individual positions of the population, sorting all jellyfish individuals according to the fitness value calculated in the step (5), and arranging the jellyfish individuals from high to low according to the fitness value. The optimal individual is the jellyfish individual with the highest fitness value. Thus, after ranking, the first individual (highest fitness) is the optimal individual. The position is the optimal individual position of the population.
Step 7: because the ocean current contains a large amount of food, jellyfish gathers in the ocean current, the jellyfish can form jellyfish groups along with the movement of the ocean current along with the time, and the jellyfish becomes an ocean current stage, and the position of each jellyfish in the ocean current movement can be updated, and the specific formula is as follows:
，
wherein,is->Only jellyfish updated position->Is->Position of jellyfish only,/->Is the optimal position in the current jellyfish population, < >>Is a distribution coefficient>。/>Is the average position of all jellyfish in the population, < >>Is a random number of 0 to 1.
However, the temperature and wind direction of the ocean current will affect the distribution of jellyfish groups, so as to form new jellyfish groups, and at this time, jellyfish will move inside the jellyfish groups, which is called as jellyfish group stage. In the initial generation stage of jellyfish population, most jellyfish will follow the jellyfish population, namely jellyfish will move around its own position and become passive movement, so the position of jellyfish will be updated, and the specific formula is as follows:
，
wherein,is the upper and lower bounds of the search space of the jellyfish population,/->Is the motion coefficient, +.>。
Over time, jellyfish gradually develops subjective motility itself and gradually approaches to a companion who can find more foods, which becomes active movement, and the specific calculation formulas of the movement direction of jellyfish and the position after update are as follows:
，
wherein,is the direction of jellyfish movement,/->Is the distance the jellyfish moves in the direction of movement, < >>Is randomly selected from jellyfish population +.>Position of jellyfish only,/->Respectively represent +.>Only the adaptation value of jellyfish at the current position.
In order to regulate the movement of jellyfish between following ocean current movement and movement inside jellyfish groups, a time control mechanism is introduced to control the active movement or the passive movement during switching, and a specific formula of a time control function is as follows:
，
wherein,is the control function of the time control mechanism, in particular a control function with iteration number +.>In interval [0, 1]]Random value in->Is the current iteration number, +.>Is the maximum number of iterations initially set. The rule for the number of iterations is as follows:
dynamic adjustment of maximum iteration number during the operation of the AJSA algorithm using adaptive adjustmentIn the initial stage of the algorithm, the invention sets the initial iteration number to 50 times, calculates the proportion of the current iteration number and the total iteration number in each iteration, gradually reduces the iteration number, and dynamically adjusts the rest iteration number according to the proportion of the current iteration number by using a linear decreasing function. The linearly decreasing function ensures that the number of iterations is relatively large at the early stages of the algorithm in order to fully explore the global search space, then gradually decreasing the number of iterations, and finally allowing the algorithm to explore more intensively in the local search space. When the number of remaining iterations decreases to a set threshold, it may be selected to stop the algorithm's iterations. The linear decreasing iteration formula is as follows:
，
wherein,is the minimum number of iterations, +.>Is the current iteration number. The above formula dynamically calculates the remaining iteration number according to the current iteration number>Thereby gradually reducing the iteration times in the iteration process. Along with->Increase of->Will approach->So that the local search is more focused at a later stage of the algorithm.
Step 8: judging according to the time control function, ifThe jellyfish enters the ocean current stage and updates the individual position according to the formula. If->The jellyfish enters the jellyfish group stage and according to +.>Whether the jellyfish moves actively or passively in the jellyfish group stage is judged by being greater than or equal to 0.5, whether the jellyfish moves passively in the jellyfish group stage is judged by being greater than or equal to 0.5, and whether the jellyfish moves actively in the jellyfish group stage is judged by being less than or equal to 0.5, and the individual position is updated according to a formula.
Step 9: the jellyfish can judge whether the position of the jellyfish exceeds the defined searching range in the searching process, and correct the jellyfish, and the specific judging and correcting method is as follows:
definition of the definitionThe content of the function is:
，
wherein,is a value to be limited, +.>Is the minimum allowed if +.>Below this value, it is repairedJust +.>，/>Is the maximum allowed if->Above this value, it is corrected to +.>. Briefly, a->The function will ensure +.>Fall at->And->If it is outside this range, it is corrected to the nearest boundary value.
According toThe formula for correcting jellyfish position is:
，
wherein,is the new position calculated,/->Is the minimum and maximum allowable value of the position.
And updating the corrected jellyfish position, performing GRU model training, recalculating the fitness value, and updating the optimal individual position of the jellyfish population.
Step 10: judging the iteration timesAnd threshold constant->Size, if->Less than or equal to->And iterating again, otherwise, outputting the optimal solution and the optimal value.
Step 11: and putting the processed meteorological data and the GRU super parameters after optimizing into a GRU unit for humidity prediction.
Based on the same inventive concept, the embodiments of the present application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the foregoing adaptive oat-AJSA based distributed storage improvement GRU humidity prediction method when executing the computer program.
Based on the same inventive concept, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the foregoing distributed storage adaptive oat-AJSA based GRU humidity prediction method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow in the flowchart, and combinations of flows in the flowchart, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.

Claims (9)

1. The distributed storage self-adaptive OATS-AJSA-based improved GRU humidity prediction method is characterized by comprising the following steps of:
step 1, acquiring historical multi-element meteorological data and storing the data in a MinIO database in a distributed manner;
step 2, extracting multi-element meteorological data from a MinIO database as a prediction data set, identifying abnormal values in the prediction data set by using an isolated forest method, and replacing the abnormal values by using a linear interpolation method to obtain a replaced data set;
step 3, reducing the dimension of the replaced data set obtained in the step 2 by adopting a t distribution-random adjacent embedding method to obtain a dimension-reduced data set, and normalizing the dimension-reduced data set to obtain a normalized data set;
step 4, an OATS-AJSA-GRU humidity prediction model is established, and the ultra-parameters in the GRU are optimized by utilizing an OATS-AJSA optimization algorithm to obtain optimized ultra-parameters;
and 5, putting the optimized super parameters and the normalized data set obtained in the step 3 into GRU for prediction, and obtaining a humidity prediction result.
2. The distributed storage adaptive oat-AJSA-based improved GRU humidity prediction method according to claim 1, wherein the specific procedure of step 1 is as follows:
1) Accessing the ECMWF by using the API and downloading the NC data file;
2) Analyzing the downloaded NC data file by using a xarray library of Python to obtain a CSV data file;
3) Configuring and connecting to a MinIO server, uploading the CSV data file obtained in the step 2 to a MinIO socket by using a MinIO SDK, and taking the CSV data file as a historical multi-element weather database, wherein the multi-element weather data comprises temperature, humidity, wind speed and pressure data;
4) Storing user information by using a MySQL database, creating an API endpoint by using a Python Web framework, and calling the historical multi-element weather data in the historical multi-element weather database by the user stored in the MySQL database.
3. The distributed storage adaptive oat-AJSA-based improved GRU humidity prediction method according to claim 1, wherein the identifying abnormal values in the predicted dataset by using the isolated forest method in step 2 is specifically:
1) Setting the predicted data set to，/>Each data point +.>By->Composition of individual meteorological features, i.e.)>，Representing data points +.>Is>Meteorological characteristics, from->Is selected at random->The data points form a subset->Subset +.>As the root node of an orphan tree;
2) Randomly assigned dimensionsAnd->In subset->A cutting point is randomly generated>The formula is as follows:
，
wherein,representing data points +.>Is>Individual weather features;
3) According to the cutting pointCreating a hyperplane, utilizing the hyperplane to pair sub-planes>Dividing, i.e. sub-set->Less than->Put the data point of (2) into the left child node, subset +.>Middle greater than or equal to->Placing the data point of the node in the right child node;
4) Repeating the steps 2) and 3) until all leaf nodes of the isolated tree contain only one data point or the height of the isolated tree reaches a preset upper height limit, namely generating an isolated tree;
5) Repeating the steps 1) -4) to obtain an isolated forest;
6) For the followingEach data point +.>Traversing each isolated tree in the isolated forest to obtain data points +.>The heights in each isolated tree are averaged to obtain the average height, and the average heights of all data points are normalized;
7) Using normalized average heightCalculating outlier score ++>Taking data points corresponding to abnormal value fractions which are larger than or equal to a preset threshold value as abnormal values; the outlier score is calculated as follows:
，
wherein,，/>；/>representing +.>Calculating average heights in all the isolated trees, and taking an index after normalization; />Representing a parametric function->Represents the Euler-Mascheroni constant plus +.>Natural logarithm of (a).
4. The distributed storage adaptive oat-AJSA-based improved GRU humidity prediction method according to claim 1, wherein the replacing of the outlier with the linear interpolation method in step 2 is specifically:
1) Finding adjacent data points of the abnormal value, namely two normal values before and after the abnormal value;
2) A straight line is constructed by using two normal values before and after the abnormal value, namely:
，
wherein,alternative values representing outliers, +.>Respectively representing normal values before and after the abnormal value,indicating that the outlier is +.>And->The position ratio between->；
3) And finding out the corresponding ordinate value along the constructed straight line according to the position of the outlier on the abscissa, namely, the replacement value of the outlier.
5. The method for predicting humidity of a GRU based on distributed storage adaptive oat-AJSA according to claim 1, wherein in step 3, the dimension of the replaced dataset obtained in step 2 is reduced by using a t-distribution-random proximity embedding method, so as to obtain a dimension-reduced dataset, which is specifically as follows:
1) Setting the replaced data set, namely the high-dimensional data set, asThe data set after dimension reduction is +.>，Representing high-dimensional data points in a high-dimensional dataset, +.>Representing low-dimensional data points in the dimensionality reduced dataset, computing a high-dimensional similarity probability using a Gaussian distribution>The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the Gaussian distribution formula is as follows:
，
wherein,representing high-dimensional data points +.>Generate->Conditional probability of->Variance parameter representing distance calculation, ++>Representation->And->Euclidean distance of>Representing high-dimensional data points in a high-dimensional dataset;
2) Calculating low-dimensional similarity probabilities using t-distributionWherein the t distribution formula is as follows:
，
wherein,representing low-dimensional data points>Generate->Conditional probability of->Representing low-dimensional data points in the reduced-dimension dataset;
3) Using the difference between the KL-divergence Heng Lianggao-dimensional probability distribution and the low-dimensional probability distribution, the formula is as follows:
，
wherein,represents KL divergence;
4) Updating the low-dimensional representation using a gradient descent method, the updating rule being as follows:
，
wherein,indicates learning rate (I/O)>Representing global parameters->Is a parameter used to scale the gradient when updating the low-dimensional representation;
5) Repeating 2) -4) until a convergence condition is reached, i.e. a preset number of iterations is reached or the difference between the KL-divergence of the current iteration and the KL-divergence of the last iteration is smaller than a preset threshold.
6. The distributed storage adaptive oat-AJSA-based improved GRU humidity prediction method according to claim 1, wherein the specific procedure of step 4 is as follows:
step 41, initializing parameters of an artificial jellyfish searching algorithm by using an orthogonal table testing method, wherein the parameters of the artificial jellyfish searching algorithm comprise population quantity, searching radius and a population initializing strategy;
step 42, taking the super parameters of the GRU as jellyfish, calculating the fitness value of the jellyfish by using a cross entropy loss function in each iteration, and taking the jellyfish position with the highest fitness value as the optimal individual position of the jellyfish population in the current iteration; the fitness value is calculated as follows:
，
wherein,representing a cross entropy loss function, ">Is the number of iterations, +.>Is learning rate (I/O)>Is dropout proportion,/>Is the number of neurons in the hidden layer, < > and the number of neurons in the hidden layer>Is the number of samples, +.>Indicate->Actual tag of individual sample,/>Representation model pair->Predictive probability of individual samples +.>Indicating fitness;
step 43, a time control mechanism is introduced to determine that the current iteration jellyfish enters the ocean current stage or jellyfish group stage, namely:
，
wherein,control function representing a time control mechanism, +.>Representing the current iteration number, +.>Represents a random number between 0 and 1, < >>Representing the initially set maximum iteration number;
the maximum iteration number is dynamically adjusted by using an adaptive adjustment method, and the formula is as follows:
，
wherein,representing the number of remaining iterations, +.>Representing a minimum number of iterations;
step 44, judging according to the time control function, ifThe jellyfish enters the ocean current stage and updates the individual position according to the following formula:
，
wherein,indicate->Only the updated position of jellyfish +.>Indicate->Only jellyfish current iteration position,/->Representing the optimal individual position in the current iteration jellyfish population, < >>Is a distribution coefficient>，/>Representing the average position of all jellyfish in the population;
if it isThe jellyfish enters the jellyfish group stage, further according to +.>Whether the jellyfish is greater than or equal to 0.5 is judged to be actively moved or not in the jellyfish group stagePassive exercise, if->Representing that jellyfish is passively moved in the jellyfish group stage, and updating the individual position according to the following formula:
，
wherein,is the motion coefficient, +.>，/>Respectively representing an upper bound and a lower bound of jellyfish population search space;
if it isRepresenting that jellyfish is actively moving in the jellyfish group stage, and updating the individual position according to the following formula:
，
wherein,indicates the direction of jellyfish movement, +.>Represents the distance the jellyfish moves in the direction of movement, < >>Represents the random selection of +.>Only jellyfish current iteration position,/->Respectively represent +.>Only the adaptability value of jellyfish at the current iteration position;
step 45, correcting the jellyfish position according to the clip function:
，
wherein,indicating corrected jellyfish position +.>Respectively representing minimum and maximum allowable values of the position;
step 46, judging the current iteration numberAnd threshold constant->If the current iteration number +.>Less than threshold constant->And continuing iteration, otherwise, outputting the optimal value of the super parameter.
7. The distributed storage adaptive oat-AJSA based improved GRU humidity prediction method of claim 6 wherein the specific procedure of step 41 is as follows:
411, determining parameter ranges of three parameters, namely population quantity, search radius and population initialization strategy, and selecting different combinations of the three parameters, namely population quantity, search radius and population initialization strategy, according to the parameter ranges;
step 412, performing an ASJA experiment on each combination of the L16 orthogonal table, and selecting the corresponding combination when the experimental result is optimal as the optimal combination of three parameters, namely, the optimal parameter of the artificial jellyfish searching algorithm, such as population number, searching radius and population initializing strategy.
8. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor, when executing the computer program, implements the steps of the distributed storage adaptive oat-AJSA-based GRU humidity prediction method of any of claims 1 to 7.
9. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the distributed storage adaptive oat-AJSA based GRU humidity prediction method of any one of claims 1 to 7.
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