Sand-dust weather identification method, computer device and readable storage medium

Abstract

This specification provides a method, computer device, and readable storage medium for identifying dust storms. The method includes: generating an inter-city path attribute table, including the starting city, ending city, distance, and azimuth; identifying cities whose air quality data meets a preset air quality threshold as experiencing dust storm events; using the city where the dust storm event occurred earliest as the starting city, obtaining the distances to adjacent cities based on the path attribute table, estimating the dust movement speed, and calculating the dust arrival time window for adjacent cities based on the distances and dust movement speed; if the time of a dust storm event in an adjacent city falls within the time window, it is determined to be the same dust storm event, and this process is recursively extended to its adjacent cities, and so on, until no more cities experiencing dust storm events are identified; and outputting a dust storm event table. This method solves the problem in existing technologies that cannot identify the changing trends of dust storms.

Description

Technical Field

[0001] This specification relates to the field of weather recognition technology, and in particular to a method for identifying sandstorm weather, a computer device, and a readable storage medium. Background Technology

[0002] There are four major dust storm belts in the world, located in North America, Central Asia, Central Africa, and Australia. my country is situated in the eastern part of the Central Asian dust storm belt. Every winter and spring, northern my country and even larger areas face the threat of dust storms. Statistics show that since the beginning of this century, my country has experienced over 300 large-scale dust storms, averaging more than ten per year. These dust storms pose risks to human respiratory and cardiovascular systems, cause traffic disruptions and hazards, lead to widespread ecological problems, and result in substantial economic losses. In response, the government has promulgated a series of laws, regulations, and policies to guide desertification control and prevention efforts.

[0003] The implementation of sandstorm prevention and control work requires the identification of the impact range and trajectory of dust storms in order to analyze their origin and dust-causing climatic conditions in greater depth. Currently, the identification of the impact range and trajectory of dust storms mainly relies on satellite remote sensing and related numerical models. Satellite remote sensing-based research suffers from insufficient long-term data and a massive data volume, limiting the development of long-term historical dust storm studies. Numerical model-based research, on the other hand, has certain uncertainties, and the quality of its results depends heavily on the accuracy of the meteorological driving data used. The acquisition of large-scale historical meteorological driving data and the demand for substantial computational resources also limit the application of this approach.

[0004] Therefore, existing research on large-scale dust storms mostly focuses on one or a few dust storm events. Such research is difficult to identify in depth the changing trends, which affects the overall planning and coordination of my country's overall sand prevention and control efforts. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this specification provides a method for identifying dusty weather, a computer device, and a computer-readable storage medium, which can solve the problem that the existing technology cannot identify the changing trends of dusty weather.

[0006] According to a first aspect of the embodiments of this specification, a method for identifying sandstorm weather is provided. The method includes: acquiring the geographical coordinates of multiple cities, constructing a city coordinate network based on the geographical coordinates of the cities, and generating an inter-city path attribute table, wherein the inter-city path attribute table includes a starting city, an ending city, a distance, and an azimuth. Acquire air quality data from multiple cities, and determine cities whose air quality data meets the preset air quality threshold as having experienced a sandstorm weather event; The city where the sandstorm event occurred in the earliest time period is taken as the starting city. The distance between the starting city and its neighboring cities is obtained according to the path attribute table between cities. The movement speed of the sandstorm is estimated. The arrival time window of the sandstorm in the neighboring cities is calculated based on the distance between the neighboring cities and the movement speed of the sandstorm. If the time of a sandstorm event in a neighboring city falls within the time window, it is determined to be the same sandstorm event, and this is recursively extended to its neighboring cities, and so on, until no city where a sandstorm event has occurred is identified. Output a dust storm weather event table, which includes a list of cities where dust storm weather events occurred and their start and end times.

[0007] Optionally, the step of constructing a city coordinate network based on the city's geographical coordinates further includes: A triangular network is constructed based on the geographical coordinates of the city.

[0008] Optionally, the step of constructing a triangular network based on the geographical coordinates of the city further includes: Generate a super triangle containing the geographic coordinates of all cities; Insert the city's geographical coordinates point by point and reconstruct the triangulation network according to the criterion of the empty circumcircle; The triangular network is generated by deleting triangles associated with the super triangle.

[0009] Optionally, the step of generating the inter-city path attribute table further includes: The distance is obtained based on the Earth's radius and the central angle, wherein the central angle is related to the longitude and latitude between two adjacent cities; The north and east components are obtained based on the latitudes of two adjacent cities, and the azimuth is obtained based on the north and east components.

[0010] Optionally, the step of estimating the movement speed of the sandstorm further includes: Obtain the cosine of the angle between the azimuth and wind direction of the line connecting the city where the sandstorm event occurred and its neighboring cities; The velocity component of the sand and dust movement is obtained based on the preset sand and dust movement speed and the cosine value.

[0011] Optionally, the identification method further includes: If no dust storm event occurs in the adjacent city within the time window, then the dust storm event in the starting city is an isolated dust storm event. If the duplication rate of at least two dust storm event tables is higher than the preset duplication rate threshold, then the dust storm event tables will be merged and the duplicate records will be deleted.

[0012] Optionally, the air quality data includes PM10 concentration; The step of determining a city whose air quality data meets a preset air quality threshold as having experienced a sandstorm weather event further includes: If the ratio of the current PM10 concentration to the PM10 concentration at a preset time is greater than a preset ratio threshold, and the current PM10 concentration is greater than a preset concentration threshold, then a sandstorm weather event is determined to have occurred.

[0013] Optionally, the step of using the city where the sandstorm event occurred earliest as the starting city further includes: If two or more non-adjacent cities are identified as having experienced sandstorm weather events within a preset time threshold, then the city where the sandstorm weather event occurred within the preset time threshold shall be taken as the starting city.

[0014] According to a second aspect of the embodiments of this specification, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0015] According to a third aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer instructions, which, when executed by a processor, implement the steps of the method described above.

[0016] The technical solutions provided in the embodiments of this specification may include the following beneficial effects: In this embodiment, the geographical coordinates of multiple cities are first obtained, and a city coordinate network is constructed based on these coordinates to generate an inter-city path attribute table. This table includes the starting city, the ending city, the distance, and the azimuth. Then, air quality data for multiple cities is obtained, and cities whose air quality data meets a preset air quality threshold are identified as experiencing sandstorm events. Further, the city where the sandstorm event occurred in the earliest time period is used as the starting city. The distances between adjacent cities of the starting city are obtained based on the inter-city path attribute table, and the sandstorm movement speed is estimated. The arrival time window for sandstorms in adjacent cities is calculated based on the distances between adjacent cities and the sandstorm movement speed. If the time of a sandstorm event in an adjacent city falls within the time window, it is determined to be the same sandstorm event, and this process is recursively extended to its adjacent cities, and so on, until no cities experiencing sandstorm events are identified. Finally, a sandstorm event table is output, which includes a list of cities experiencing sandstorm events and their start and end times.

[0017] This specification describes a method for determining whether dust storms have occurred in different cities based on air quality data from multiple cities, rather than data from a single city or a specific monitoring point. This improves the comprehensiveness and accuracy of dust storm identification. After confirming that a city has experienced a dust storm, it further combines the positional relationships between cities in a city coordinate network to perform spatiotemporal reasoning between different cities (mainly adjacent cities) to identify dust storm conditions in other cities. By merging dust storm events with spatiotemporal connections, it is possible to identify large-scale dust storm events based on the changing trends of dust storms.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification.

[0020] Figure 1 This is a flowchart illustrating a method for identifying sandstorm weather according to an exemplary embodiment of this specification.

[0021] Figure 2 This is a flowchart illustrating another method for identifying dusty weather according to an exemplary embodiment of this specification.

[0022] Figure 3 This is a flowchart illustrating yet another method for identifying dusty weather according to an exemplary embodiment of this specification.

[0023] Figure 4 This is a schematic diagram of the structure of a triangular mesh illustrated in this specification according to an exemplary embodiment.

[0024] Figure 5 This is a schematic diagram illustrating a large-scale sandstorm weather event according to an exemplary embodiment of this specification.

[0025] Figure 6 This is a block diagram illustrating a computer device according to an exemplary embodiment. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0027] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0029] The embodiments described in this specification will now be described in detail.

[0030] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for identifying sandstorm weather according to an exemplary embodiment, comprising the following steps: Step S1: Obtain the geographical coordinates of multiple cities, construct a city coordinate network based on the geographical coordinates of the cities, and generate a path attribute table between cities, including the starting city, the ending city, the distance, and the azimuth.

[0031] Step S2: Obtain air quality data from multiple cities, and determine cities whose air quality data meets the preset air quality threshold as having experienced a sandstorm weather event.

[0032] Step S3: Take the city where the sandstorm weather event occurred in the earliest time period as the starting city, obtain the distance between the starting city and its neighboring cities according to the inter-city path attribute table, estimate the sandstorm movement speed, and calculate the sandstorm arrival time window of the neighboring cities based on the distance between neighboring cities and the sandstorm movement speed.

[0033] Step S4: If the time of a sandstorm event in a neighboring city falls within the time window, it is determined to be the same sandstorm event, and the process is recursively extended to its neighboring cities, and so on, until no city where a sandstorm event has occurred is identified.

[0034] Step S5: Output the dust storm weather event table, which contains a list of cities where dust storm weather events occurred and their start and end times.

[0035] This application constructs a city coordinate network using the geographical coordinates of multiple cities, enabling the network to identify the geographical locations of multiple cities and providing a geographic information foundation for identifying dust storms in multiple cities. By identifying air quality data from multiple cities, rather than data from a single city or a specific monitoring point, it provides an air data foundation for subsequent identification of large-scale dust storm events, improving the comprehensiveness and accuracy of dust storm identification. When a dust storm event is identified, the city where the dust storm occurred in the earliest time period is used as the starting city. The arrival time windows of dust storms in adjacent cities are calculated based on the distance to neighboring cities and the dust movement speed. Obtaining the dust storm arrival time windows of adjacent cities allows for the identification of dust storms in those cities, thus more effectively identifying large-scale dust storm events. By recursively expanding the identification of dust storm events in adjacent cities, dust storm events from multiple cities can be merged into a single dust storm event, thereby effectively identifying large-scale dust storm events. Finally, the output dust storm event table provides the source city and the timeline of large-scale dust storm events.

[0036] In other words, this application utilizes air quality data from multiple cities, combined with the spatial relationships between cities in a city coordinate network, to perform spatiotemporal reasoning on dust weather events between different cities, and merges dust weather events in cities with spatiotemporal connections, thereby enabling the identification of large-scale dust weather events based on the changing trends of dust weather.

[0037] A city's geographic coordinate system is a system that precisely describes the location of spatial elements such as points, lines, and areas within a city. This application primarily uses latitude and longitude to represent the geographic coordinates of each city. Constructing a city coordinate network based on these coordinates involves building a network of coordinates between cities using their latitude and longitude information, providing data for subsequent extraction of city locations. City latitude and longitude can be obtained through electronic maps, online map tools, or publicly available datasets. After acquisition, the accuracy of the correspondence between city names and location information can be verified. City latitude and longitude information can be obtained using technologies such as GNSS (Global Navigation Satellite System; satellite navigation, such as BeiDou) and RTK (Real-Time Kinematic) to ensure centimeter-level accuracy. A path attribute table between cities is generated based on the city coordinate network. This table includes the starting city, ending city, distance, and azimuth. The path attribute table records the starting and ending cities between adjacent cities and calculates the distance between them based on their geographic coordinates, providing a basis for the propagation of dust storms. The azimuth precisely indicates the direction from the starting city to the ending city. The calculation of azimuth takes into account the curvature of the Earth and the influence of the geographic coordinate system to ensure its accuracy.

[0038] The city's air quality data can include at least one air quality data point related to dust storms, such as PM10 concentration, PM2.5 concentration, nitrous oxide, and sulfur dioxide. This air quality data can be obtained from the national environmental monitoring station in text format, covering almost all prefecture-level administrative divisions and county-level administrative divisions directly under provincial jurisdiction. The time resolution of the air quality data is hourly. Obtaining air quality data from multiple cities provides more comprehensive data for identifying dust storm events. When acquiring air quality data, the initial data needs to be filtered to remove missing records and outliers. Further analysis of the air quality data identifies cities with air quality data that meet preset air quality thresholds as having experienced dust storm events. These preset air quality thresholds can be obtained through statistical analysis of historical dust storm event data; for example, a PM10 concentration exceeding a certain value can be used to define a dust storm event. It is important to note that when connecting city air quality data with their corresponding city geographic coordinates, the sequence number or city name must be correctly matched.

[0039] After identifying the cities where dust storms occurred, the number of cities and the timing of the dust storms are further determined, with the city where the dust storm occurred in the earliest time period serving as the starting city. Specifically, if two or more cities experienced dust storms, the times of the dust storms in those cities are obtained. If only one city is identified as experiencing a dust storm within a preset time threshold (e.g., within a few hours), that city is used as the starting city for dust storm identification. If two or more non-adjacent cities are identified as experiencing dust storms within the preset time threshold, each of these cities is used as the starting city for identification. After identification, the dust storm event tables for these starting cities are compared. If the duplication rate of two or more dust storm event tables is higher than a preset duplication threshold (e.g., higher than 50%), they are merged into a single large-scale dust storm event, i.e., the dust storm event tables are merged, and duplicate records are deleted. If no duplicate dust storm events are found in any of the dust storm event tables, they are considered independent dust storm events.

[0040] The specific scheme for identifying large-scale dust storm events based on the originating city is as follows: The distances between adjacent cities of the originating city are obtained based on the inter-city path attribute table, and the dust movement speed is estimated. The arrival time window of the dust in adjacent cities is calculated based on the distances and dust movement speeds. If the time of a dust storm event in an adjacent city falls within the time window, it is determined to be the same dust storm event, and this process is recursively extended to its adjacent cities, and so on, until no more cities with dust storm events are identified. In other words, firstly, the dust arrival time window of cities adjacent to the originating city is calculated using the ratio of distance to speed. Then, air quality data of the adjacent city is obtained to identify whether a dust storm event has occurred. If a dust storm event has occurred in the adjacent city, the time of the dust storm event is obtained, and it is determined whether it falls within the time window. If the result is yes, the dust storm event in the adjacent city is identified as being caused by dust propagation from the originating city, and the dust storm events in the two adjacent cities are determined to be the same dust storm event. Then, using the neighboring city as the starting city, the above scheme is repeated and extended to the identification of dust weather events in its neighboring cities until no cities experiencing dust weather events are identified. By continuously identifying dust weather events in neighboring cities, they can ultimately be summarized into the same large-scale dust weather event, thus achieving the goal of identifying large-scale dust weather events.

[0041] It is worth noting that if the cities adjacent to the originating city of the earliest dust storm event do not identify dust storm events within the time window, the dust storm event in the originating city is considered an isolated dust storm event.

[0042] To make it easier to understand, examples will be given below.

[0043] The process involves iterating through PM10 concentration data for each city to identify the first city (M) where a dust storm event occurred and its occurrence time (T0). The path attribute table for cities is then used to filter path records originating from city M. The time required for dust to move along these paths originating from city M is calculated. Generally, dust storms move at speeds of 40-100 km / h, a value that can be adjusted based on actual measurements or simulations. Dividing the distance between cities by this speed yields the possible time windows (T1-T2) for dust to reach neighboring cities of city M. Next, dust storm events are identified in neighboring cities of city M. If a dust storm event in city N occurs within this time window, it is considered the same event, and the two events are merged to form a dust storm event table. This table can include attributes such as city, start and end times of the dust storm event, and average PM10 concentration. Continue performing the above operations on the neighboring cities of N, and continue to expand, adding the identified cities that meet the criteria to the dust storm event table, until no new cities experiencing dust storm events can be found. At this point, the dust storm event identification process ends. If no city experiencing a dust storm event within the time window can be found among the neighboring cities of the starting city M, then the dust storm event in the starting city M is determined to be an isolated dust storm event.

[0044] The above schemes all assume an ideal dust storm propagation scenario for identifying dust weather events. However, in real-world environments, the direction of dust movement is related to the direction of upper-level airflow, and its direction is unlikely to completely coincide with the path between two adjacent cities. Therefore, when upper-level wind direction monitoring is available, it can be used as an influencing factor to identify the propagation speed of dust. Please refer to [link / reference needed] for details. Figure 2 This includes the following steps: Step S31: Obtain the cosine of the angle between the azimuth and wind direction of the line connecting the city where the sandstorm event occurred and its neighboring cities.

[0045] Step S32: Obtain the velocity component of the sand and dust movement based on the preset sand and dust movement speed and cosine value.

[0046] Multiplying the cosine value by the dust movement yields the velocity component of the path between cities, providing a more accurate calculation. If upper-level wind direction observation data is unavailable, calculations can be performed based on the seasonal prevailing wind direction.

[0047] In one embodiment, the step S1 described above, "constructing a city coordinate network based on the city's geographical coordinates," can specifically refer to constructing a triangular network based on the city's geographical coordinates. This triangular network can be a Delauney triangulation. A Delauney triangulation connects discrete points in space (such as the geographical coordinates of a city) into a series of non-overlapping triangles, which can be used to express the spatial relationship between adjacent discrete points. Delauney triangulations have the property of an empty circumcircle, meaning that the circumcircle of any triangle will not contain a fourth point. This property ensures that Delauney triangulations avoid generating sharp triangles and have the property that the three points generating the triangle are closest, and the same set of points can only generate a unique result, making it more suitable for expressing the spatial relationship between cities.

[0048] The Delaunay triangulation construction process ensures that the connections between adjacent cities conform as closely as possible to actual geographical conditions. After construction, the network of city coordinates allows for rapid determination of whether any two cities are adjacent, thus effectively enabling subsequent dust storm event identification and merging operations. This method not only improves the efficiency of dust storm event identification but also ensures the accuracy of the results. Through the Delaunay triangulation, the propagation and evolution of dust storm events across different cities can be understood and analyzed more intuitively.

[0049] Please see Figure 3 Constructing a triangular network based on the city's geographical coordinates includes the following steps: Step S11: Generate a super triangle containing the geographic coordinates of all cities.

[0050] Step S12: Insert the city's geographical coordinates point by point and reconstruct the triangular network according to the empty circumcircle criterion.

[0051] Step S13: After deleting the triangles associated with the super triangle, a triangular network is generated.

[0052] Specifically, in the step of generating a super triangle containing the geographic coordinates of all cities, a sufficiently large triangle is first selected so that it completely encompasses the geographic coordinates of all cities. This triangle is called the super triangle. To ensure the super triangle is large enough, the maximum and minimum X-values, maximum and minimum Y-values ​​among the geographic coordinates of all cities can be selected, and then a sufficiently large triangle is constructed based on these four extreme points. The construction of the super triangle is the foundation for subsequent steps; it ensures that the geographic coordinates of all cities are located within the triangular network, thus facilitating the subsequent generation and processing of the triangulation network.

[0053] In the step of inserting the city's geographic coordinates point by point and reconstructing the triangulation according to the empty circumcircle criterion, discrete geographic coordinates are added sequentially. It is then determined whether the circumcircle of an existing triangle contains the geographic coordinates of the city being added. Common edges of all triangles whose circumcircles contain the geographic coordinates of the city being added are deleted. Finally, the geographic coordinates of the city being inserted are connected to the vertices of all triangles whose common edges have been deleted. The algorithm for determining whether a city's geographic coordinates are within the circumcircle of a triangle is as follows: in , , , , , Each of the existing The coordinates of the three vertices, and Let p be the geographic coordinates of the newly inserted city. If the value of this determinant is greater than 0, then the geographic coordinates p of the city are in... If the determinant of the matrix is ​​less than 0 inside the circumcircle, then the city's geographical coordinates p are within the matrix. The outside of the circumcircle.

[0054] Furthermore, local optimization is performed on the newly generated triangles. Two adjacent triangles are treated as a single quadrilateral. It is then determined whether the fourth vertex of this quadrilateral lies inside the circumcircle of the triangle. If it does, the two triangles are divided inside the quadrilateral by another diagonal. This operation is performed on all newly generated triangles.

[0055] The process of repeatedly inserting the geographic coordinates of cities and performing local optimization on the newly generated triangles continues until the geographic coordinates of all discrete cities are included in the graph.

[0056] Finally, delete all triangles that contain vertices of the super triangle to obtain the final triangulation network.

[0057] As mentioned above, triangulation networks (such as the Delauney triangulation network) have the characteristic that the three points that generate triangles are closest to each other, which is beneficial for representing the spatial relationship between adjacent cities. After forming the triangulation network, a bidirectional boundary list is generated based on the network, that is, an edge ab is recorded once from a to b and once from b to a, and the side lengths and azimuths of ab and ab are calculated simultaneously. The methods for calculating the side lengths and azimuths differ under different coordinate systems. This application uses a geographic coordinate system (latitude and longitude) as an example to calculate the distance and azimuth between cities. Specifically, the distance between cities is obtained based on the Earth's radius and the central angle, where the central angle is related to the longitude and latitude between two adjacent cities. In a specific example, the distance can be determined according to the following formula: Where d is the distance between two adjacent cities, R is the Earth's radius, and c is the central angle. and This represents the difference in latitude and longitude between two adjacent cities. and These represent the latitudes of two adjacent cities. Since the movement of dust is closely related to topography and atmospheric conditions, the distance between cities can be adjusted appropriately based on these factors.

[0058] Furthermore, the north and east components can be obtained from the latitudes of two adjacent cities, and the azimuth can be derived from these components. In a specific example, the azimuth can be determined using the following formula: in, This is the azimuth (0° for true north, clockwise). The component representing true north. The due east component, This is the remainder symbol.

[0059] In one embodiment, the scheme of "determining cities whose air quality data meets a preset air quality threshold as having experienced a sandstorm weather event" in step S2 above can be implemented through the following detailed scheme: If the ratio of the current PM10 concentration to the PM10 concentration at a preset time is greater than a preset ratio threshold, and the current PM10 concentration is greater than a preset concentration threshold, then it is determined that a sandstorm weather event has occurred. For example, if the current hourly PM10 concentration of a city is greater than twice the average of the previous six hours, and the current hourly PM10 concentration is greater than 150 μg / m³, then a sandstorm weather event is determined to have occurred. 3 If the PM10 concentration drops below 1.1 times the average of the six hours preceding the dust storm, it is considered a dust storm event. Furthermore, the criterion for the end of a dust storm is that the current hourly PM10 concentration first drops to within 1.1 times the average of the six hours preceding the dust storm. This allows for the identification of the start and end times of dust storms in individual cities. Additionally, the start and end of dust storms can also be monitored using the ratio of PM2.5 concentration to PM10 concentration, which will not be elaborated upon here.

[0060] To facilitate understanding, the identification method for sandstorm weather will be explained through the following examples.

[0061] Ten cities were selected for sandstorm weather identification, and the geographical coordinates of the cities are shown in Table 1.

[0062] Table 1 shows the city's geographical coordinates. A triangulation network was established for the 10 cities in Table 1, and an inter-city path attribute table containing bidirectional distance and azimuth information between adjacent cities was generated. The established triangulation network is as follows: Figure 4 As shown in Table 2, the path attributes between cities are listed below.

[0063] Table 2 shows the intercity route attributes. Historical PM10 concentration data for 10 cities were analyzed using time series analysis. The quantitative relationship between the PM10 concentration at a given time and the average concentration of the preceding 6 hours was examined. If the PM10 concentration at a given time was more than twice the average concentration of the preceding 6 hours, the absolute value of the PM10 concentration was then checked to see if it exceeded 150 μg / m³. 3 If both tests pass, a sandstorm weather event is determined to have occurred. Based on the tests, a sandstorm weather event occurred in city C at 14:00 on a certain day; city C is designated as the starting city.

[0064] By consulting the inter-city path attribute table (Table 2), there are 5 paths originating from city C, with destinations in cities A, D, E, G, and I, respectively, at distances of 120, 107, 128, 70, and 145 km from city C. Assuming a dust storm speed of 40-100 km / h, if the dust storm travels along these inter-city paths, the time required to reach the five cities would be 1.2-3 h, 1.1-2.7 h, 1.3-3.2 h, 0.7-1.75 h, and 1.5-3.6 h, respectively. The table then identifies the dust storm occurrence time in city C (14:00 on a certain day), and details the dust storm occurrences in the five adjacent cities (A, D, E, G, and I). The identification results show that dust storms occurred in city G one hour later, and in cities E and I two hours later, which matches the time required for dust storm movement. No dust storms occurred in the remaining cities within the time period. Therefore, the dust storms in cities C, G, E, and I can be grouped into a large-scale dust storm event and added to the dust storm event table. This process continues, expanding outwards to the cities adjacent to the newly identified cities G, E, and I, until no more cities with dust storm events can be identified, forming the final dust storm event table. Based on this table, a graph can be drawn to represent this large-scale dust storm event. Figure 5As shown in the figure, the identification results of the large-scale dust storm event indicate that it covered cities B, C, E, F, G, and I. Its trajectory was approximately from northeast to southwest, moving from its origin city C to city F within a three-hour period. Combining this with information such as PM10 concentrations and duration of the dust storm in these cities allows for a more in-depth analysis of the event. Analyzing multiple large-scale dust storm events over a long period allows for research into the characteristics and evolution patterns of dust movement in the region.

[0065] Corresponding to the embodiments of the foregoing methods, this specification also provides embodiments of the computer devices to which they are applied. Please refer to [link / reference needed] for details. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0066] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected via a system bus. It should be noted that only the computer device 6 with components 61-63 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0067] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0068] The memory 61 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit and its external storage device of the computer device 6. In this embodiment, the memory 61 is typically used to store the operating system and various information management operating systems installed on the computer device 6, such as computer-readable instructions for identifying sandstorm weather. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or will be output.

[0069] In some embodiments, the processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions stored in the memory 61 or to process data, such as computer-readable instructions for running a method for identifying sandstorm weather.

[0070] The network interface 63 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 6 and other electronic devices.

[0071] This application also provides another embodiment, namely, a computer program product storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the sandstorm weather identification method described above.

[0072] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0073] Other embodiments of this specification will readily occur to those skilled in the art upon consideration of the specification and practice of the invention claimed herein. This specification is intended to cover any variations, uses, or adaptations that follow the general principles of this specification and include common knowledge or customary techniques in the art not claimed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this specification are indicated by the following claims.

[0074] It should be understood that this specification is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this specification is limited only by the appended claims.

[0075] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

Claims

1. A method for identifying sandstorm weather, characterized in that, include: Obtain the geographic coordinates of multiple cities, construct a city coordinate network based on the geographic coordinates of the cities, and generate an inter-city path attribute table, which includes the starting city, the ending city, the distance, and the azimuth. The air quality data of the multiple cities is obtained, and cities whose air quality data meets the preset air quality threshold are identified as experiencing sandstorm weather events. The city where the sandstorm event occurred in the earliest time period is taken as the starting city. The distance between the starting city and its neighboring cities is obtained according to the path attribute table between cities. The movement speed of the sandstorm is estimated. The arrival time window of the sandstorm in the neighboring cities is calculated based on the distance between the neighboring cities and the movement speed of the sandstorm. If the time of a sandstorm event in a neighboring city falls within the time window, it is determined to be the same sandstorm event, and this is recursively extended to its neighboring cities, and so on, until no city where a sandstorm event has occurred is identified. Output a dust storm weather event table, which includes a list of cities where dust storm weather events occurred and their start and end times.

2. The method for identifying sandstorm weather according to claim 1, characterized in that, The step of constructing a city coordinate network based on the city's geographical location coordinates further includes: A triangular network is constructed based on the geographical coordinates of the city.

3. The method for identifying sandstorm weather according to claim 2, characterized in that, The step of constructing a triangular network based on the geographical coordinates of the city further includes: Generate a super triangle containing the geographic coordinates of all cities; Insert the city's geographical coordinates point by point and reconstruct the triangulation network according to the criterion of the empty circumcircle; The triangular network is generated after deleting triangles associated with the super triangle.

4. The method for identifying sandstorm weather according to claim 1, characterized in that, The step of generating the inter-city path attribute table further includes: The distance is obtained based on the Earth's radius and the central angle, wherein the central angle is related to the longitude and latitude between two adjacent cities; The north and east components are obtained based on the latitudes of two adjacent cities, and the azimuth is obtained based on the north and east components.

5. The method for identifying sandstorm weather according to claim 1, characterized in that, The step of estimating the speed of sand and dust movement also includes: Obtain the cosine of the angle between the azimuth and wind direction of the line connecting the city where the sandstorm event occurred and its neighboring cities; The velocity component of the sand and dust movement is obtained based on the preset sand and dust movement speed and the cosine value.

6. The method for identifying sandstorm weather according to claim 1, characterized in that, The identification method further includes: If no dust storm event occurs in the adjacent city within the time window, then the dust storm event in the starting city is an isolated dust storm event. If the duplication rate of at least two dust storm event tables is higher than the preset duplication rate threshold, then the dust storm event tables will be merged and the duplicate records will be deleted.

7. The method for identifying sandstorm weather according to claim 1, characterized in that, The air quality data includes PM10 concentration; The step of determining a city whose air quality data meets a preset air quality threshold as having experienced a sandstorm weather event further includes: If the ratio of the current PM10 concentration to the PM10 concentration at a preset time is greater than a preset ratio threshold, and the current PM10 concentration is greater than a preset concentration threshold, then a sandstorm weather event is determined to have occurred.

8. The method for identifying sandstorm weather according to claim 1, characterized in that, The step of using the city where the sandstorm event occurred earliest as the starting city also includes: If two or more non-adjacent cities are identified as having experienced sandstorm weather events within a preset time threshold, then the cities where the sandstorm weather events occurred within the preset time threshold shall be taken as the starting cities.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The device stores computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-8.

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