Method and device for identifying and tracking high-voltage and low-voltage systems in weather situation chart, and medium

Abstract

The invention relates to a method and equipment for identifying and tracking a high-voltage system and a low-voltage system in a weather situation chart and a medium, and belongs to the technical field of weather situation identification. Collecting multi-source meteorological data, and constructing an isoline data set based on the multi-source meteorological data; closed isolines in the isoline data set are extracted, and the high-low voltage attribute of each closed isoline is judged through the recognition model of the high-low voltage system; calculating a minimum enclosing rectangle of the closed contour line; traversing all the closed isolines, judging whether the minimum enclosing rectangle is included by the minimum enclosing rectangles of other isolines or not, determining the closed isoline on the outermost layer, and forming a set U; a hierarchical high-low voltage system model is established based on an N-way tree structure, and a coverage area and an influence area of a high-low voltage system are calculated based on an N-way tree hierarchical structure so as to assist in meteorological prediction. The method does not need a large amount of prior training data, has good interpretability, and solves the problems that the existing recognition is limited and the recognition capability for specific high and low pressure weather situations is lacked.

Description

Technical Field

[0001] This application belongs to the technical field of weather situation recognition, and more particularly relates to a method, device, and medium for identifying and tracking high and low pressure systems in a weather situation map. Background Art

[0002] The identification of high and low pressure systems in a weather situation map mainly relies on the distribution of isobars. Initially, it mostly relied on experienced forecasters for subjective identification. With the development of computer technology, artificial intelligence, and image recognition algorithms, weather situation recognition based on intelligent technology began to emerge, but it was mainly applied to the automatic recognition of thick fog weather situations and low visibility weather, or the classification model was used to carry out weather situation recognition. Its main disadvantage is that a weather situation classification model needs to be pre-trained, which may lead to limitations in recognition accuracy and insufficient recognition ability for specific high and low pressure weather situations.

[0003] Therefore, how to solve the problems of limited automatic recognition and lack of recognition ability for specific high and low pressure weather situations in the prior art is a research topic worthy of study. Summary of the Invention

[0004] In view of the above analysis, embodiments of the present invention aim to provide a method, device, and medium for identifying and tracking high and low pressure systems in a weather situation map, aiming to solve the problems of limited automatic recognition and lack of recognition ability for specific high and low pressure weather situations in the prior art.

[0005] In the first aspect of this application, a method for identifying and tracking high and low pressure systems in a weather situation map is provided, including:

[0006] Collect multi-source meteorological data, including meteorological observation data, satellite remote sensing data, and radar data, and construct an isoline dataset based on the multi-source meteorological data;

[0007] Extract the closed isolines from the isoline dataset, and determine the high and low pressure attributes of each closed isoline through a high and low pressure system recognition model;

[0008] Calculate the minimum bounding rectangle of the closed isoline;

[0009] Traverse all closed isolines, determine whether the minimum bounding rectangle is included in the minimum bounding rectangles of other isolines, and determine the outermost closed isoline to form a set U;

[0010] Establish a hierarchical high and low pressure system model based on the N-ary tree structure. Take each closed isoline in the set U as the root node, traverse the isolines contained therein, and establish an N-ary tree hierarchical structure to represent the nested relationship of the high and low pressure systems;

[0011] Based on the N-ary tree hierarchical structure, calculate the coverage range and influence area of the high and low pressure systems to assist in meteorological prediction.

[0012] Optionally, it further includes:

[0013] Obtain the contour line data at different time steps, and respectively construct a first contour line set Set0 and a second contour line set Set1; wherein, the first contour line set Set0 is the contour line data corresponding to the previous moment T-1, and the second contour line set Set1 is the contour line data corresponding to the current moment T.

[0014] Establish a distance matrix between the first contour line set Set0 and the second contour line set Set1, and determine the matching relationship and moving direction between the contour lines at different time steps.

[0015] Use matrix operations to calculate the moving direction of the high and low pressure systems, and identify the situations of contour line merging, splitting, emergence, and disappearance to dynamically track the high and low pressure systems.

[0016] Optionally, the establishing a distance matrix between the first contour line set Set0 and the second contour line set Set1, and determining the matching relationship and moving direction between the contour lines at different time steps includes:

[0017] Establish an m×n two-dimensional matrix D; where Set0 has m contour lines and Set1 has n contour lines; D[i][j] represents the matching distance between the i-th contour line in Set0 and the j-th contour line in Set1.

[0018] Calculate the point-to-point distance between the contour lines in Set0 and Set1, and construct an a×b two-dimensional distance matrix; where a is the number of points of a certain contour line in Set0, b is the number of points of a certain contour line in Set1, calculate the distance between each point in a and each point in b, and fill the two-dimensional distance matrix.

[0019] Construct a boolean matching matrix; wherein, create matrix 0 and matrix 1; Matrix 0: For each row of the a×b distance matrix, find the position of the minimum value, set this position to 1, and set the remaining positions to 0; Matrix 1: For each column of the a×b distance matrix, find the position of the minimum value, set this position to 1, and set the remaining positions to 0; Perform a logical AND operation on matrix 0 and matrix 1 to obtain the boolean matching matrix; Through the boolean matching matrix, determine the matching relationship and moving direction between the contour lines at different time steps.

[0020] Optionally, the using matrix operations to calculate the moving direction of the high and low pressure systems includes:

[0021] Construct a high-low pressure tree matching matrix of c×d; where c is the number of high-low pressure trees at time T-1; d is the number of high-low pressure trees at time T; array0[i][j] represents how many isolines in the high-low pressure tree i at time T-1 are included in the high-low pressure tree j at time T; array1[i][j] represents how many isolines in the high-low pressure tree j at time T are included in the high-low pressure tree i at time T-1;

[0022] Calculate the final matching matrix array2. Set the positions of the row maximum values in matrix array0 to 1, and the rest to 0; set the positions of the column maximum values in matrix array1 to 1, and the rest to 0; perform a logical AND operation on array0 and array1 to obtain the final matching matrix array2;

[0023] Traverse array2 to find the matching high-low pressure systems, and calculate the movement direction vector of the matching isolines; calculate the average movement direction of all the matching isolines as the movement direction of the high-low pressure system.

[0024] Optionally, the identification of isoline merging, splitting, emergence, and disappearance situations includes:

[0025] When there is no matching high-low pressure system at T-1, it is identified that there is an isoline disappearance situation;

[0026] When there is no matching high-low pressure system at T, it is identified that there is an isoline emergence situation;

[0027] When multiple high-low pressure systems at T-1 match the same high-low pressure system at T, it is identified that there is an isoline merging situation;

[0028] When one high-low pressure system at T-1 matches multiple high-low pressure systems at T, it is identified that there is an isoline splitting situation.

[0029] Optionally, it further includes:

[0030] Collect high-low pressure system data at multiple time steps, and establish an evolution sequence of high-low pressure systems through the high-low pressure system matching results to form multiple high-low pressure system trajectories with time continuity;

[0031] For each trajectory, extract the corresponding time series features to form an evolution sample set of high-low pressure systems;

[0032] Input the evolution sample set into a high-low pressure system evolution model based on a long short-term memory network or a Transformer structure for training to obtain a deep learning model for predicting the position, intensity, and evolution trend of high-low pressure systems at future time steps

[0033] Optionally, the calculation of the coverage range and influence area of high-low pressure systems based on the N-ary tree hierarchical structure includes:

[0034] Traverse the N-ary tree, take the union of the minimum bounding rectangles of all isopleths, and obtain the coverage range of the high and low pressure systems;

[0035] Combine wind field data, temperature data, and humidity data to calculate the influence range of the high and low pressure systems.

[0036] Optionally, for the hierarchical high and low pressure system model established based on the N-ary tree structure, using each closed isopleth in the set U as the root node, traverse the isopleths contained therein, and the established N-ary tree hierarchical structure includes:

[0037] Traverse the isopleth set, and successively establish the N-ary tree hierarchical structure according to the nesting relationship between the isopleths to represent the nesting relationship of the high and low pressure systems;

[0038] Traverse the tree structure to check if there is a situation where the high and low pressure attributes of an isopleth are inconsistent with its root node; if inconsistent, prune the node to make it a new root node.

[0039] In a second aspect of the present application, there is provided an apparatus for identifying and tracking high and low pressure systems in a weather situation map, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for identifying and tracking high and low pressure systems in a weather situation map according to any one of the above.

[0040] In a third aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for identifying and tracking high and low pressure systems in a weather situation map according to any one of the above.

[0041] The method for identifying and tracking high and low pressure systems in a weather situation map provided by the present application represents the nested isopleth structure inside the high and low pressure systems with an N-ary tree model, making the center and hierarchical relationship of each system clear. This process is determined by the physical structure of the isobar distribution itself and does not rely on black-box model training. Using the direction and closed characteristics of the isopleths, directly judge the high or low pressure center, improving the physical interpretability and accuracy of the identification and avoiding the drawbacks of the traditional classification model's labeled identification. Compared with the traditional method that relies on manual identification or image classification models, the present application innovatively constructs an N-ary tree structure based on the isopleth nesting relationship to accurately represent the hierarchical relationship of the high and low pressure systems. This method does not require a large amount of prior training data, has good interpretability, structure, and scalability, and is a structured modeling method for automatic identification and prediction of high and low pressure systems. Further, the present application realizes the dynamic matching and system tracking of multi-time-step isopleths through geometric nesting and distance matrices, and further completes the structure identification and evolution trend analysis of the high and low pressure systems.

[0042] In addition, the present application also provides an identification and tracking device and medium for high and low pressure systems in a weather situation map having the above technical effects. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the embodiments of this specification. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of a specific implementation manner of the method for identifying and tracking high and low pressure systems in the weather situation map provided by the present application;

[0045] Figure 2 It is a schematic diagram of the implementation effect of the method for identifying and tracking high and low pressure systems in the weather situation map provided by the present application;

[0046] Figure 3 It is a flowchart of the tracking process of another specific implementation manner of the method for identifying and tracking high and low pressure systems provided by the present application;

[0047] Figure 4 It is a structural block diagram of the identification and tracking device for high and low pressure systems in the weather situation map provided by the present application. Specific Embodiments

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. It should be noted that, without conflict, the implementation manners and the features in the implementation manners in this disclosure can be combined, separated, interchanged, and / or rearranged. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0049] The terms used herein are for the purpose of describing particular embodiments and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are also intended to include the plural forms. In addition, when the terms "comprise" and / or "include" and their variants are used in this specification, it is stated that the stated features, integers, steps, operations, components, assemblies, and / or groups thereof exist, but do not preclude the existence or addition of one or more other features, integers, steps, operations, components, assemblies, and / or groups thereof. It should also be noted that, as used herein, the terms "substantially", "about", and other similar terms are used as approximate terms and not as terms of degree, and thus they are used to explain the inherent deviations of measured, calculated, and / or provided values that would be recognized by a person of ordinary skill in the art.

[0050] The flowchart of a specific implementation manner of the method for identifying and tracking high and low pressure systems in the weather situation map provided by this application is as Figure 1 shown, and the method specifically includes:

[0051] S101: Collect multi-source meteorological data, including meteorological observation data, satellite remote sensing data, and radar data, and construct an isoline dataset based on the multi-source meteorological data.

[0052] The multi-source meteorological data includes meteorological observation data, satellite remote sensing data, and radar data. Among them, the meteorological observation data can be obtained from surface meteorological stations and radiosonde stations to collect data such as air pressure, temperature, and humidity. The satellite remote sensing data is used to extract large-scale air pressure fields and temperature fields. The radar data is used to capture the edge structure changes of medium and small-scale weather systems.

[0053] Construct an isoline dataset based on the multi-source meteorological data. Specifically, spatial interpolation can be performed on variables such as air pressure, temperature, and humidity; generate continuous grid fields (such as isobaric fields, isothermal fields); extract isolines corresponding to specific isovalues; thus forming an isoline dataset that can be used for subsequent identification and modeling of high and low pressure systems.

[0054] S102: Extract the closed isolines in the isoline dataset, and determine the high and low pressure attributes of each closed isoline through the identification model of high and low pressure systems.

[0055] Judge the closed isolines according to whether the starting point and the ending point of the coordinate points are connected and the shape is closed.

[0056] The judgment of high and low pressure attributes can be: if the isobar is a closed structure with gradually increasing air pressure, it is judged as a high pressure attribute; if it is a closed structure with gradually decreasing air pressure, it is judged as a low pressure attribute.

[0057] As a specific implementation, a trained recognition model of the high and low pressure system can be used to judge the high and low pressure attributes.

[0058] S103: Calculate the minimum bounding rectangle of the closed contour line.

[0059] Calculate the minimum bounding rectangle for each closed contour line (a set of points).

[0060] S104: Traverse all closed contour lines, determine whether the minimum bounding rectangle is contained by the minimum bounding rectangles of other contour lines, identify the outermost closed contour line, and form a set U.

[0061] Traverse the minimum bounding rectangle of each closed contour line and determine whether it is contained by other contour lines; if not, it means it is on the outermost layer of the graph and can be used as the root node of the tree.

[0062] S105: Establish a hierarchical high and low pressure system model based on the N-ary tree structure. Using each closed contour line in set U as the root node, traverse the contour lines contained within it to establish an N-ary tree hierarchical structure to represent the nested relationship of the high and low pressure systems.

[0063] Traverse the set of contour lines, and successively establish an N-ary tree hierarchical structure according to the nested relationship between the contour lines. The tree structure is used to represent the nested relationship of the high and low pressure systems; traverse the tree structure to check whether there is a situation where the high and low pressure attributes of a contour line are inconsistent with its root node; if inconsistent, prune the node to make it a new root node.

[0064] S106: Based on the N-ary tree hierarchical structure, calculate the coverage range and influence area of the high and low pressure systems to assist in meteorological prediction.

[0065] Traverse the N-ary tree, take the union of the minimum bounding rectangles of all contour lines to obtain the coverage range of the high and low pressure systems.

[0066] Combine wind field data, temperature data, and humidity data to calculate the influence range of the high and low pressure systems.

[0067] The N-ary tree model is a data structure where each node can have no more than N child nodes. This structure provides greater flexibility and can more naturally represent certain complex relationships and data. It is widely used in multiple fields such as finance, management, and decision-making, and is an important tool for dealing with complex hierarchical relationships and data. However, there is currently no precedent for applying it to the recognition of high and low pressure systems in weather situation maps.

[0068] The high and low pressure systems are represented as multi-layer nested closed contour lines on the weather map, which increases the difficulty of organizing and analyzing on the weather map. By using deep learning to identify whether each closed contour line is a high pressure or a low pressure, and then through the method of N-ary tree growth, the topological structure of the high and low pressure systems is formed, and the characteristics such as the coverage area and depth of the high and low pressure systems are judged accordingly.

[0069] This application is based on the N-ary tree model, combines it with the traditional contour line drawing algorithm, and based on multi-source meteorological fusion data such as meteorological observation data, satellite remote sensing data, and radar data, uses deep learning methods to identify and draw the high pressure system and low pressure system in the weather situation map, which is used to support the analysis of their different impacts on the weather, and further supports the applications in the fields of meteorological and air quality forecasting and analysis and evaluation.

[0070] As a specific implementation, the specific construction process includes the following steps:

[0071] 1. Obtain the high and low pressure attributes of the closed contour lines, and store the minimum bounding rectangle (x_min, y_min, x_max, y_max) of each closed contour line in the computer memory. Each closed contour line has three attributes: number num, high pressure or low pressure H_L, and minimum bounding rectangle (x_min, y_min, x_max, y_max).

[0072] 2. Traverse the minimum bounding rectangle of each closed contour line (referred to as a rectangle later), judge whether the rectangle is contained by other rectangles, find the rectangles that are not contained by other rectangles, and form a set U with the corresponding closed contour lines.

[0073] 3. Traverse each closed contour line in the set U, find all the closed contour lines contained by this closed contour line, and form a set u. That is, there are as many different sets u as there are closed contour lines in the set U.

[0074] 4. Traverse each closed contour line in the set U. Each closed contour line is used as a root node, and each root node corresponds to a specific set u mentioned in step 3. Store each root node as a tree in the list of trees. In this way, each tree corresponds to the specific set u mentioned in step 3.

[0075] 5. Take the growth of a tree as an example to illustrate the tree growth method, and the corresponding specific set is u t 。

[0076] For u t Sort all the closed contour lines in it according to the contour line length, from long to short, to form a column list t 。

[0077] Traverse list in order tFor each closed contour line in [the object], traverse all the leaf nodes of the tree and determine whether the contour line is contained by a certain leaf node. If it is contained, add the closed contour line as a child node of the leaf node to the tree. If, after traversing all the leaf nodes, the closed contour line is not contained by any of them, then traverse the parent nodes of all the leaf nodes and determine whether it is contained by a certain parent node. If it is contained by a certain parent node, add the closed contour line as a child node of the parent node to the tree. Similarly, if the closed contour line is not contained by any of the parent nodes after traversing all of them, continue to search upward for the parent node of the parent node until a node in the tree that contains the closed contour line is found, and add the closed contour line as a child node of that node to the tree.

[0078] 6. Traverse each tree in the tree list level by level. If the high / low pressure attribute of a certain node is different from that of the root node of the tree, then cut out that node as the root node and store it in the tree list. Repeat step 6 until the length of the tree list no longer increases. The effect is as Figure 2 shown.

[0079] In addition, the present application can further use the point-to-point distance matrix in combination with the matching boolean matrix to complete the corresponding relationship between the contour lines; and then, based on the matching of the high / low pressure system tree, realize dynamic tracking at the high / low pressure system level, calculation of the moving direction, and recognition of merging / splitting.

[0080] As Figure 3 shown in the flowchart of the tracking process of another specific implementation manner of the method for identifying and tracking the high / low pressure system provided by the present application, this process specifically includes the following steps:

[0081] S301: Obtain the contour line data at different time steps, and respectively construct the first contour line set Set0 and the second contour line set Set1; wherein, the first contour line set Set0 is the contour line data corresponding to the previous moment T - 1, and the second contour line set Set1 is the contour line data corresponding to the current moment T.

[0082] S302: Establish the distance matrix of the first contour line set Set0 and the second contour line set Set1, and determine the matching relationship and moving direction between the contour lines at different time steps.

[0083] Establish an m×n two-dimensional matrix D; wherein, Set0 has m contour lines and Set1 has n contour lines; D[i][j] represents the matching distance between the i-th contour line in Set0 and the j-th contour line in Set1.

[0084] Calculate the point-to-point distance between the isopleths in Set0 and Set1, and construct a two-dimensional distance matrix of a×b; where a is the number of points on a certain isopleth in Set0, and b is the number of points on a certain isopleth in Set1. Calculate the distance between each point in a and each point in b, and fill the two-dimensional distance matrix.

[0085] Construct a Boolean matching matrix; where, create matrix 0 and matrix 1; Matrix 0: For each row of the a×b distance matrix, find the position of the minimum value, set this position to 1, and set the rest to 0; Matrix 1: For each column of the a×b distance matrix, find the position of the minimum value, set this position to 1, and set the rest to 0; Perform a logical AND operation on matrix 0 and matrix 1 to obtain the Boolean matching matrix; Through the Boolean matching matrix, determine the matching relationship and moving direction between the isopleths at different time steps.

[0086] S303: Use matrix operations to calculate the moving direction of the high and low pressure systems, identify the situations of isopleth merging, splitting, newborn and disappearance, so as to dynamically track the high and low pressure systems.

[0087] Construct a high and low pressure tree matching matrix of c×d; where c is the number of high and low pressure trees at time T-1; d is the number of high and low pressure trees at time T; array0[i][j] represents how many isopleths in the high and low pressure tree i at time T-1 are included in the high and low pressure tree j at time T; array1[i][j] represents how many isopleths in the high and low pressure tree j at time T are included in the high and low pressure tree i at time T-1.

[0088] Calculate the final matching matrix array2. Set the position of the maximum value in each row of matrix array0 to 1, and the rest to 0; Set the position of the maximum value in each column of matrix array1 to 1, and the rest to 0; Perform a logical AND operation on array0 and array1 to obtain the final matching matrix array2.

[0089] Traverse array2 to find the matching high and low pressure systems, and calculate the moving direction vector of the matching isopleths; Calculate the average moving direction of all matching isopleths as the moving direction of the high and low pressure systems.

[0090] The process of identifying the situations of isopleth merging, splitting, newborn and disappearance includes:

[0091] When the high and low pressure systems at T-1 cannot be matched, it is identified that there is a situation of isopleth disappearance.

[0092] When the high and low pressure systems at T cannot be matched, it is identified that there is a situation of isopleth newborn.

[0093] When multiple high and low pressure systems at T-1 are matched to the same high and low pressure system at T, it is identified that there is a situation of isopleth merging.

[0094] When multiple T high - low pressure systems are matched in a T - 1 high - low pressure system, it is recognized that the isoline has split.

[0095] As a specific implementation, the implementation process of the tracking module is as follows:

[0096] (1) Extract all the isolines at the previous moment T - 1, and set this ordered set as Set0. Extract all the isolines at the next moment T, and set this ordered set as Set1.

[0097] (2) Map the isolines in Set0 and Set1 to an image with a resolution of w*h, so that the isoline coordinates in Set0 and Set1 are all picture coordinates.

[0098] (3) Clean all the isolines in Set0 and Set1. Specifically: if the front and back points of the isoline are the same, only keep one.

[0099] (4) Group all the isolines in Set0 and Set1 according to the barometric pressure values of the isolines. The same barometric pressure value forms a group.

[0100] (5) Traverse all the barometric pressure values and process them according to the following steps:

[0101] ① Assume that the number of isolines in Set0 is m, and the number of isolines in Set1 is n. Establish a two - dimensional matrix of m*n.

[0102] ② Calculate the distance between the isolines in Set0 and Set1. Specifically: assume that the length of a certain isoline in Set0 is a, and the length of a certain isoline in Set1 is b. Establish a two - dimensional distance matrix of a*b, calculate the distance between each point in a and each point in b, and fill the above a*b two - dimensional distance matrix. Establish 2 empty a*b boolean matrices, coded as matrix 0 and matrix 1. Traverse each row in the a*b two - dimensional distance matrix, find the position of the minimum value in the row, and set the corresponding position in matrix 0 to 1, and the rest to 0. Traverse each column in the a*b two - dimensional distance matrix, find the position of the minimum value in the column, and set the corresponding position in matrix 1 to 1, and the rest to 0. Perform a logical AND operation on matrix 0 and matrix 1 to get matrix 2. Find the elements and corresponding positions in matrix 2 that are 1, find the corresponding distances according to the corresponding positions, calculate the average value of all the distances as the distance between the two isolines. Find the corresponding two points according to the corresponding positions, and calculate the average value of all the direction vectors according to the direction vectors of the two points as the moving direction of the isoline.

[0103] ③According to the method in step (5), obtain the distances between the isopleths in Set0 and Set1, fill the m*n two-dimensional matrix in step ((1)), and according to the method of processing the distance matrix in step ((2)), obtain a matrix 2 similar to that in step ②. Based on this matrix, obtain the matching relationship of the isopleths and the moving direction vector of the isopleths.

[0104] (6) According to the above five steps, the matching relationship of each isopleth is obtained. Of course, the isopleths in each high-low pressure tree, as a subset of all isopleths, also have their matching relationships obtained. Assume that the number of high-low pressure trees at the previous moment is c, and the number of high-low pressure trees at the next moment is d. Establish two c*d two-dimensional matrices, denoted as array0 and array1. Traverse each high-low pressure tree in c, and then traverse each high-low pressure tree in d. Determine the number of isopleths in each high-low pressure tree in c that are contained in each high-low pressure tree in d, and fill matrix array0. Determine the number of isopleths in each high-low pressure tree in d that are contained in each high-low pressure tree in c, and fill matrix array1. Set the positions of the row maxima in matrix array0 to 1, and the rest to 0. Set the positions of the column maxima in matrix array1 to 1, and the rest to 0. Perform a logical AND operation on matrix array0 and matrix array1 to obtain matrix array2. Find the positions in matrix array2 that are 1. The corresponding high-low pressure trees that match at the previous and next moments are obtained. Take the average of the moving direction vectors corresponding to the isopleths that can be matched in the two high-low pressure trees as the moving direction of the high-low pressure system at the previous and next moments. When a T-1 high-low pressure system cannot be matched, it is recognized that an isopleth disappearance occurs; when a T high-low pressure system cannot be matched, it is recognized that an isopleth emergence occurs; when multiple T-1 high-low pressure systems are matched to the same T high-low pressure system, it is recognized that an isopleth merger occurs; when a T-1 high-low pressure system is matched to multiple T high-low pressure systems, it is recognized that an isopleth split occurs.

[0105] In addition, based on any of the above embodiments, the present application may further include: collecting high-low pressure system data at multiple time steps, establishing an evolution sequence of the high-low pressure system through the high-low pressure system matching results, and forming multiple high-low pressure system trajectories with time continuity; for each trajectory, extracting corresponding time series features to form an evolution sample set of the high-low pressure system; inputting the evolution sample set into a high-low pressure system evolution model based on a long short-term memory network or a Transformer structure for training to obtain a deep learning model for predicting the position, intensity, and evolution trend of the high-low pressure system at future time steps.

[0106] In this embodiment, first, the above method is adopted to process meteorological data within multiple consecutive time steps (e.g., every hour), obtain the N-ary tree structure of high and low pressure systems at each time step, and establish the correspondence of high and low pressure systems between consecutive time steps through the distance matching matrix and the tree matching matrix. According to the matching results, the evolution trajectory of high and low pressure systems is formed, that is, the evolution path of each high and low pressure system at different time points.

[0107] Based on this trajectory, the time series features of high and low pressure systems are extracted to constitute an evolution sample sequence of a high and low pressure system over multiple time steps. By constructing a large number of such trajectory samples, an evolution sample set of high and low pressure systems can be formed.

[0108] Furthermore, the sample set is input into a deep neural network model based on the long short-term memory network (LSTM) or Transformer structure for training, and the training objective is to minimize the prediction error of features such as the position, intensity, and shape of high and low pressure systems within a certain number of future time steps. The model can be trained in a supervised learning manner, and the training labels come from the true identification results of high and low pressure systems in subsequent time steps in actual meteorological data.

[0109] After training, the evolution model can be used for the prediction task of future high and low pressure systems. Specifically, for the high and low pressure systems at a certain time point and their historical trajectories, they can be input into the trained model to output prediction results such as the moving path, intensity change trend, and structural evolution (such as splitting or merging) in the next few hours, assisting in the development of medium and short-term weather forecasts.

[0110] Through the high and low pressure system evolution modeling method in this embodiment, it is possible to realize the structured and sequential modeling of complex weather systems, improve the predictability of the behavior changes of high and low pressure systems, and provide more accurate data support for meteorological monitoring, disaster warning, etc.

[0111] In addition, the present application also provides an identification and tracking device for high and low pressure systems in a weather situation map, as Figure 4 shown. Specifically, the device includes a memory 41 and a processor 42. The memory 41 stores a computer program, and when the computer program is executed by the processor 42, it implements any one of the above-mentioned identification and tracking methods for high and low pressure systems in a weather situation map.

[0112] It can be understood that the identification and tracking device for high and low pressure systems in a weather situation map provided by the present application corresponds to each step in the above method, and its specific implementation can refer to the corresponding description content above, which will not be elaborated here.

[0113] In addition, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for identifying and tracking high and low pressure systems in the weather situation map according to any one of the above.

[0114] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0115] Those skilled in the art should further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0116] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROM, or any other form of storage medium known in the technical field.

[0117] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above are only specific embodiments of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for identifying and tracking high and low pressure systems in a weather situation map, characterized in that, Including: Collect multi-source meteorological data, including meteorological observation data, satellite remote sensing data, and radar data, and construct an isoline dataset based on the multi-source meteorological data; Extract the closed isolines in the isoline dataset, and determine the high and low pressure attributes of each closed isoline through an identification model of high and low pressure systems; Calculate the minimum bounding rectangle of the closed isoline; Traverse all closed isolines, determine whether the minimum bounding rectangle is contained by the minimum bounding rectangles of other isolines, and identify the outermost closed isoline to form a set U; Establish a hierarchical high and low pressure system model based on the N-ary tree structure. Take each closed isoline in the set U as the root node, traverse the isolines contained therein, and establish an N-ary tree hierarchical structure to represent the nesting relationship of high and low pressure systems; Based on the N-ary tree hierarchical structure, calculate the coverage range and influence area of the high and low pressure systems to assist in meteorological prediction.

2. The identification and tracking method for the high and low voltage system according to claim 1, characterized in that, Also including: Obtain isoline data at different time steps, and respectively construct a first isoline set Set0 and a second isoline set Set1; wherein, the first isoline set Set0 is the isoline data corresponding to the previous time step T-1, and the second isoline set Set1 is the isoline data corresponding to the current time step T; Establish a distance matrix between the first isoline set Set0 and the second isoline set Set1 to determine the matching relationship and moving direction between isolines at different time steps; Use matrix operations to calculate the moving direction of the high and low pressure systems, and identify the situations of isoline merging, splitting, emergence, and disappearance to dynamically track the high and low pressure systems.

3. The identification and tracking method of the high and low voltage system according to claim 2, characterized in that The establishing a distance matrix between the first isoline set Set0 and the second isoline set Set1 to determine the matching relationship and moving direction between isolines at different time steps includes: Establish a two-dimensional matrix D of m×n; where Set0 has m isolines and Set1 has n isolines; D[i][j] represents the matching distance between the i-th isoline in Set0 and the j-th isoline in Set1; Calculate the point-to-point distance between isolines in Set0 and Set1, and construct a two-dimensional distance matrix of a×b; where a is the number of points of a certain isoline in Set0, b is the number of points of a certain isoline in Set1, calculate the distance between each point in a and each point in b, and fill the two-dimensional distance matrix; Construct a Boolean matching matrix; where create matrix 0 and matrix 1; Matrix 0: For each row of the a×b distance matrix, find the position of the minimum value, set this position to 1, and the remaining positions to 0; Matrix 1: For each column of the a×b distance matrix, find the position of the minimum value, set this position to 1, and the remaining positions to 0; Perform a logical AND operation on matrix 0 and matrix 1 to obtain the Boolean matching matrix; Through the Boolean matching matrix, determine the matching relationship and moving direction between isolines at different time steps.

4. The recognition and tracking method for the high and low voltage system according to claim 3, characterized in that, The using matrix operations to calculate the moving direction of the high and low pressure systems includes: Construct a high-low pressure tree matching matrix of c×d; where c is the number of high-low pressure trees at time T-1; d is the number of high-low pressure trees at time T; array0[i][j] represents how many isolines in the high-low pressure tree i at time T-1 are included in the high-low pressure tree j at time T; array1[i][j] represents how many isolines in the high-low pressure tree j at time T are included in the high-low pressure tree i at time T-1. Calculate the final matching matrix array2. Set the positions of the row maxima in matrix array0 to 1 and the rest to 0; set the positions of the column maxima in matrix array1 to 1 and the rest to 0. Perform a logical AND operation on array0 and array1 to obtain the final matching matrix array2. Traverse array2 to find the matching high-low pressure systems, and calculate the movement direction vector of the matching isolines. Calculate the average movement direction of all matching isolines as the movement direction of the high-low pressure system.

5. The identification and tracking method of the high and low voltage system according to claim 3, characterized in that The identification of isoline merging, splitting, birth, and disappearance situations includes: When a high-low pressure system at T-1 cannot be matched, it is identified that an isoline disappearance situation exists. When a high-low pressure system at T cannot be matched, it is identified that an isoline birth situation exists. When multiple high-low pressure systems at T-1 are matched to the same high-low pressure system at T, it is identified that an isoline merging situation exists. When a high-low pressure system at T-1 is matched to multiple high-low pressure systems at T, it is identified that an isoline splitting situation exists.

6. The method for identifying and tracking a high and low voltage system according to any one of claims 1 to 5, characterized in that, It also includes: Collect high-low pressure system data at multiple time steps. Through the high-low pressure system matching results, establish an evolution sequence of high-low pressure systems to form multiple high-low pressure system trajectories with time continuity. For each trajectory, extract the corresponding time series features to form an evolution sample set of high-low pressure systems. Input the evolution sample set into a high-low pressure system evolution model based on a long short-term memory network or Transformer structure for training to obtain a deep learning model for predicting the position, intensity, and evolution trend of high-low pressure systems at future time steps.

7. The identification and tracking method for the high and low voltage system according to any one of claims 1 to 5, characterized in that, The calculation of the coverage range and influence area of high-low pressure systems based on the N-ary tree hierarchical structure includes: Traverse the N-ary tree, take the union of the minimum bounding rectangles of all isolines to obtain the coverage range of the high-low pressure system. Combine wind field data, temperature data, and humidity data to calculate the influence range of the high-low pressure system.

8. The identification and tracking method of the high and low voltage system according to any one of claims 1 to 5, characterized in that, The establishment of a hierarchical high-low pressure system model based on the N-ary tree structure, with each closed isoline in the set U as the root node, and traversing the isolines included therein to establish an N-ary tree hierarchical structure includes: Traverse the isoline set, and successively establish an N-ary tree hierarchical structure according to the nesting relationship between isolines to represent the nesting relationship of high-low pressure systems. Traverse the tree structure to check if there is a situation where the high-low pressure attribute of an isoline is inconsistent with its root node; if inconsistent, prune the node to make it a new root node.

9. An identification and tracking device for high and low pressure systems in a weather situation map, characterized in that, It includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the method for identifying and tracking high-low pressure systems in a weather situation map according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by a processor, it implements the method for identifying and tracking high and low pressure systems in the weather situation map according to any one of claims 1-8.

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