A method and system for simulating biochemical damage query based on Google S2 grid

Abstract

The application discloses a simulation biochemical damage query method and system based on Google S2 grid, relates to data processing and simulation technology, and comprises the following steps: constructing a Gaussian plume model based on the S2 grid; constructing a time-space index; encoding time information into a first time code, combining the S2 grid code representing a spatial position, and forming a time-space integrated code; performing simulation terminal damage query; receiving time-space information uploaded by a simulation terminal, converting the time-space information into a time-space integrated code to be queried; querying pollution concentration data associated with the time-space integrated code to be queried based on a joint index; and determining whether damage occurs to the simulation terminal according to the queried pollution concentration data and the type of the simulation terminal. The application is used for solving the problem that the existing simulation training system has poor flexibility of a biochemical coordinate system and it is difficult to query pollution concentration in real time.

Description

Technical Field

[0001] This application relates to the fields of data processing and simulation technology, and in particular to a method and system for simulating biochemical damage query based on Google S2 grid. Background Technology

[0002] In current simulation training, the methods for simulating and representing biochemical contamination areas are relatively basic. Typically, these areas are drawn as vector graphics, often with circular or elliptical shapes. On the situation map, to illustrate the degree of biochemical contamination, a color gradient from the center to the edge is used to simulate changes in pollution concentration at different locations within the area. However, real biochemical contamination areas exhibit significant irregularity and dynamism, their formation influenced by a combination of complex factors, including the type and dosage of biochemical substances, as well as local meteorological conditions such as wind speed and direction. These factors interact to collectively determine the shape, extent, and concentration distribution of the biochemical contamination area.

[0003] Numerical analysis of biochemical contamination diffusion can be performed using the Gaussian Puff model. This algorithm exhibits high stability and excellent computational efficiency under complex meteorological conditions, especially in low wind speeds or variable wind directions. The basic principle of the Gaussian Puff model is to divide a specified area into an n×n regular grid matrix, with each grid cell independently performing Gaussian diffusion calculations. While this gridded approach theoretically enables refined modeling of spatial pollution concentrations, a major challenge in practical application is the need to construct a specific coordinate system for the simulation area. This coordinate system lacks flexibility and is limited to the current scenario. Summary of the Invention

[0004] This application provides a method and system for simulating biochemical damage query based on Google S2 grid, which solves the problem of insufficient flexibility of biochemical coordinate system in existing simulation training systems, making it difficult to query pollution concentration in real time.

[0005] This application provides a method for simulating biochemical damage query based on Google S2 grid, including: Constructing a Gaussian plume model based on the S2 grid: Based on a preset rectangular window and biochemical diffusion parameters, the Gaussian plume model is calculated on the S2 geospatial grid to determine the pollution concentration of each S2 grid cell within the window, thus forming pollution area data; Constructing a spatiotemporal index: Encode time information into a first time code, combine it with an S2 grid code representing spatial location to form a spatiotemporal integrated code, and establish a joint index based on the spatiotemporal integrated code, the joint index containing time and spatial dimensions; Perform a simulated terminal damage query: Receive the spatiotemporal information uploaded by the simulated terminal and convert it into a spatiotemporal integrated code to be queried; based on the joint index, query the pollution concentration data associated with the spatiotemporal integrated code to be queried; and determine whether the simulated terminal has been damaged based on the queried pollution concentration data and the type of the simulated terminal.

[0006] This application provides a simulated biochemical damage query system based on Google S2 grid, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the simulated biochemical damage query method based on Google S2 grid as described above.

[0007] Based on the highly stable Gaussian plume model, this embodiment of the application uses an S2 grid as the underlying computational unit and ensures that the calculation is always performed within a specific window by moving the window range. This avoids the problem that the diffusion unit exceeds the window and cannot be calculated, thus solving the problem of insufficient flexibility of the biochemical coordinate system in the existing simulation training system and the difficulty in querying the pollution concentration in real time.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of the data link for the simulated biochemical damage query method based on Google S2 grid in an embodiment of this application; Figure 2 This is a schematic diagram of the binary S2 grid encoding bit distribution of the simulated biochemical damage query method based on Google S2 grid according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the effect of the situation display of biochemical contamination areas in the simulated biochemical damage query method based on Google S2 grid according to an embodiment of this application. Figure 4 This is a schematic diagram of the spatiotemporal index structure of the memory database for the simulated biochemical damage query method based on Google S2 grid according to an embodiment of this application; Figure 5This is a schematic diagram of the spatiotemporal index structure of a relational database for a Google S2 grid-based simulated biochemical damage query method, as described in an embodiment of this application. Detailed Implementation

[0010] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0011] Google S2, as a discrete global grid system (DGGS), possesses continuous hierarchical partitioning. Its nested parent-child cell structure establishes clear nesting relationships between grid cells at different levels, facilitating spatial data organization and management. Grid coding continuity ensures that each grid cell has a unique and continuous code, simplifying data storage, retrieval, and analysis. Furthermore, the S2 grid guarantees that the maximum area difference between grid cells at the same level does not exceed twice the area of ​​the grid cell, making it suitable for representing the overall pollution concentration of the grid using the concentration calculated from the coordinates of the S2 grid's center point. These characteristics give the Google S2 grid great potential for biochemical modeling calculations.

[0012] This application addresses the data latency issue in simulation training by assigning a time attribute to the output contamination concentration grid, generating a spatiotemporal grid index to ensure query consistency and training accuracy. The biochemical damage query method based on the Google S2 grid in this application performs simulated calculations of the contaminated area based on parameters such as the type, impact point, and dose of the virtual biochemical attack, queries the contamination values ​​of various targets within the range, and sends the contamination concentration to the corresponding simulation terminal. The simulation terminal determines whether damage has occurred based on the contamination concentration and its own type. This application proposes a simulated biochemical damage query method based on the Google S2 grid, such as... Figure 1 As shown, it includes the following steps: In step S101, a Gaussian plume model based on an S2 grid is constructed: based on a preset rectangular window and biochemical diffusion parameters, the Gaussian plume model is calculated on the S2 geospatial grid to determine the pollution concentration of each S2 grid cell within the window, forming pollution area data. In a specific example of this application, several typical scenarios with precise parameterization are constructed, covering biochemical pollution models. Interface parameters include average wind speed, release height, assessment time, wind direction, and atmospheric stability. The parameter values ​​are imported into the Gaussian plume model, and its core calculation formula is as follows: For any receiver point (x, y) on the ground (z=0), the concentration at time t after the source term is released is: in, C: Pollutant concentration (unit: Bq / ) ,kg / ,particles / wait); : Total amount of source items released instantaneously (unit: Bq, kg, number, etc.); u: Average wind speed (m / s) along the path of the smoke plume; H: Effective release height of the source (m); t: Time elapsed after release (s); x, y, z: coordinates (m) in the downwind, crosswind, and vertical directions, respectively; , , : These are the diffusion coefficients (m) in the downwind, crosswind, and vertical directions, respectively, and are key parameters representing the size of the smoke plume.

[0013] In step S102, a spatiotemporal index is constructed: time information is encoded into a first time code, which is combined with the S2 grid code representing spatial location to form a spatiotemporal integrated code. A joint index is then established based on this spatiotemporal integrated code, and the joint index includes both time and spatial dimensions. In this specific example, the spatiotemporal encoding uses a method where time encoding precedes spatial encoding. For time partitioning, an extended binary search method is used. The time granularity in this example is set to 1 minute based on the model evaluation time and data size. The spatial granularity uses a multi-scale approach, varying the window grid levels corresponding to different typical scenario types.

[0014] In step S103, a simulated terminal damage query is performed: The spatiotemporal information uploaded by the simulated terminal is received and converted into a spatiotemporal integrated code to be queried; based on the joint index, pollution concentration data associated with the spatiotemporal integrated code to be queried is queried; and based on the queried pollution concentration data and the type of the simulated terminal, it is determined whether the simulated terminal has been damaged. The core of the simulated equipment damage calculation is to query the pollution concentration at a certain time and place through the database index, and then send the pollution information to the simulated terminal. The simulated terminal judges the damage effect based on the magnitude of the pollution concentration and its own type. In this application example, multiple parent node corresponding codes are obtained based on the tree structure of the quadtree and combined with time information. A joint index equal-value query method is used to query whether the database contains pollution information at that time and place. The simulated terminal judges whether it has reached its own damage threshold based on the pollution value.

[0015] Based on the highly stable Gaussian plume model, this embodiment of the application uses an S2 grid as the underlying computational unit and ensures that the calculation is always performed within a specific window by moving the window range. This avoids the problem that the diffusion unit exceeds the window and cannot be calculated, thus solving the problem of insufficient flexibility of the biochemical coordinate system in the existing simulation training system and the difficulty in querying the pollution concentration in real time.

[0016] In some embodiments, constructing a Gaussian smoke cloud model based on an S2 grid includes: The basic S2 grid hierarchy is determined based on typical scenario types, and an N*N S2 grid calculation window is defined centered on the release point. In a specific example, each grid participates in the simulation calculation through pre-set N*N rectangular grid windows, and the single-scale hierarchy of the S2 grid is calculated based on the pre-set N value and the window range.

[0017] Further define typical scenario types, and obtain the minimum s2 grid of the scenario type based on the window range size. The grid cell satisfies the quadtree partitioning condition of the basic grid. Then, perform quadtree partitioning on the basic grid according to the window cell to obtain the hierarchical grid that meets the condition.

[0018] When performing the first Gaussian smoke model calculation, this task information (hshwrb) is added to the database. The task information is shown in Table 1.

[0019] Table 1 Database Tables - Virtual Biochemistry Task Table (hshwrb) Starting from the grid at a specified angular position within the calculation window, all S2 grid cells within the window are traversed in a set order; for example, based on the latitude and longitude data of the release point, S2 grid encoding conversion is performed to calculate the row and column numbers of the release point in its respective hexahedral coordinate system. , Then, calculate the row and column coordinates of the bottom left corner based on the window range. , .

[0020] For each S2 grid cell that is traversed, calculate the great circle distance components (word_x, word_y) between it and the grid cell where the release point is located. In the specific example, the calculation starts from the lower left corner of the window. Calculate the great circle distance between the row where the current grid cell is located and the row where the center grid cell is located, and obtain the positive and negative distance values ​​word_x. Calculate the great circle distance between the column where the current grid cell is located and the column where the center grid cell is located, and obtain the positive and negative distance values ​​word_y.

[0021] The large circular arc distance components (word_x, word_y) and associated parameters are input into the Gaussian plume model to calculate the pollution concentration of the grid cell. In some embodiments, the associated parameters input into the Gaussian plume model include wind speed, wind direction, and total source term.

[0022] Specifically, wind speed, wind direction, total source term, release height, atmospheric stability, and calculated word_x and word_y are substituted into the Gaussian plume model to obtain the pollution concentration c of the grid, and compared with the maximum pollution concentration. Compare and determine whether to assign Then, the grid and pollution concentration (GWND, see Table 2) are stored in a temporary container temp.

[0023] Table 2. Grid Pollution Concentration Structure (GWND) Traverse all grid cells within the window area from left to right and from bottom to top, following the steps described above. In some embodiments, it further includes: The calculated pollution concentration of each grid cell is compared with multiple preset concentration threshold intervals to distinguish different levels of pollution areas. For example, the maximum pollution concentration is divided into four equal parts: extremely heavy pollution concentration, heavy pollution concentration, moderate pollution, and light pollution. The temporary variable temp is traversed to determine the degree of pollution of the polluted grid area and stored in the corresponding containers m_RedDatas, m_OrangeDatas, m_AmberDatas, and m_TealDatas.

[0024] Calculate the minimum bounding rectangle of the polluted area, calculate the grid cells of the minimum bounding rectangle of the polluted area set data, and store the basic information of this calculation in the database table hshgsfa. The basic information is detailed in Table 3.

[0025] Table 3 Database Tables - Virtual Biochemistry Scheme at Different Time Points (hshgsfa) When performing the next model calculation, the S2 grid where the center point of the smallest outer rectangle of the previous contaminated area is located is used as the center point of the new calculation window.

[0026] The Gaussian plume model in this embodiment adopts a timed refresh strategy, starting full-scale model calculation every certain period of time, for example, every 1 minute. The center grid of the smallest bounding rectangle of the previous pollution area is used as the center grid for this calculation. The center grid of the window is moved to this point, and then the grids within the window are traversed and calculated in the order from left to right and from bottom to top. The current s2 grid is calculated, and the distance (in meters) between the current grid and the release point is calculated using the geometry library of s2. Then, it is substituted into the Gaussian plume model to obtain the pollution concentration. Finally, the algorithm automatically stops the calculation when the time threshold or the window boundary is reached.

[0027] The system software corresponding to the method in this application also includes a situation display module, which uses spatial grid encoding. The Google S2 grid employs the Hilbert algorithm with filled curves, connecting spheres into a continuous curve according to a specific connection order. It is represented using a combination of hexahedral numbers, column numbers, and hierarchical marker bits. The binary encoded bitmap is shown below. Figure 2 As shown, the data type is a 64-bit unsigned integer. The purple bits store the binary representation of the hexahedral number, the flag bit "1" (blue bit) indicates the start of the grid encoding, and the green binary bits represent the Hilbert code corresponding to the row and column numbers of each member.

[0028] In data transmission, a single grid cell of the biochemically contaminated area includes S2 grid encoding and contamination concentration, requiring only 12 bytes. The information needed for situational awareness is transmitted to the situational awareness software via a TCP protocol signal transmission channel. The software decodes the S2 grid to obtain four fixed-point coordinates. Each grid cell is colored based on its contamination concentration and the maximum contamination concentration, and node merging is used to accelerate rendering efficiency. By pushing real-time calculation results to the situational awareness software, dynamic visualization rendering of the contaminated area is achieved. See the contaminated area rendering images at different assessment times. Figure 3 As shown.

[0029] The specific steps for the situation display module are as follows: Pass parameters to the biochemical diffusion simulation middleware (SB_TSXS, see Table 4 for details).

[0030] Table 4. Situation Display Command Description Table (SB_TSXS) Based on the received biochemical grid information, the situational awareness software decodes the coordinates of four fixed points through the S2 grid, divides the maximum pollution concentration into four equal pollution areas, and draws quadrilaterals of different colors on the situational awareness map.

[0031] In-memory databases use B-tree or B+ tree indexes for time encoding, while spatial and pollution concentration information is stored as hash keys. Relational databases are stored according to the same data storage method. Figure 4 , 5 As shown, time and S2 grid encoding are used as a combined primary key, and the data is stored in a relational database using a B+ tree. In some embodiments, constructing a spatiotemporal index includes: Get the current system time and convert the timestamp into a first time code with a specified time granularity. For example, arrange the timestamp into a time string as "yyyyMMddhhmm" and convert it into a 32-bit unsigned integer time code using the extended binary search method, with a time granularity of 1 minute.

[0032] The first time code and the S2 grid code set containing pollution concentration information are stored in the memory database in the form of key-value pairs. The memory database determines every minute whether the key-value pair contains the time code corresponding to the current time.

[0033] The first time code is concatenated with the S2 grid code to form a spatiotemporal integrated code, which is used as a composite primary key and stored together with the pollution concentration information based on a B+ tree index. In a specific example, a spatiotemporal integrated code can be used with the time code first and the S2 code second, storing the 32-bit integer TimeCode and the 64-bit integer CellId as a composite primary key in a relational database using a B+ tree.

[0034] In the specific examples, both the in-memory database and the relational database use B+ trees to build indexes, and the storage information is shown in Table 5.

[0035] Table 5. Database Table - Spatiotemporal Information Table of Biochemical Area Pollution Concentration (hcbsxb) In some embodiments, the spatiotemporal integrated coding is configured such that the first time coding is placed in the high bit and the S2 grid coding is placed in the low bit.

[0036] The core of the simulation equipment damage calculation in this embodiment is to query the pollution concentration at a certain time and place through a database index, and then send the pollution information to the simulation terminal. The simulation terminal judges the damage effect based on the magnitude of the pollution concentration and its own type. The principle is to calculate the corresponding codes of multiple parent nodes based on the tree structure of a quadtree, combine them with time information, and use a joint index equal-value query method to query whether the database contains pollution information for that time and place. The simulation terminal judges whether it has reached its own damage threshold based on the pollution value. In some embodiments, performing the simulation terminal damage query includes: The spatial coordinates uploaded by the simulated terminal are converted into the highest-level S2 grid code. First, it is determined whether the spatial information uploaded by the simulated terminal is within the minimum bounding rectangle of the contaminated area in a certain time period. If not, it is determined whether the simulated terminal is in the biochemical contaminated area. If it is in the biochemical contaminated area, the simulated terminal is ordered to leave the biochemical area.

[0037] Based on the S2 grid hierarchy corresponding to the typical scenario type, starting from the highest-level S2 grid code, the encoding is traced back level by level to obtain multiple parent node S2 grid codes at different levels. Specifically, the spatiotemporal information uploaded by the simulated terminal is converted into spatiotemporal codes. The temporal code uses an extended binary search method, and the spatial information generates codes with a maximum of 30 levels. According to the encoding hierarchy corresponding to the typical scenario type, multiple spatial S2 codes at different scales are generated from one code, and these are combined with the temporal codes to form a set STCodes.

[0038] The S2 grid codes of the multiple parent nodes are combined with the second time code converted from the query time to form a set of spatiotemporal integrated codes to be queried. It is determined whether the system time code is greater than the simulated terminal's TimeCode. If not, the STCodes set is traversed, the memory database is queried to obtain the pollution concentration information and biochemical entity ID, and the biochemical type is obtained from the database. Then, it is determined whether the simulated terminal is in a biochemical contamination area. If not, an entry command into the biochemical area is issued to the simulated terminal, which then determines whether it is damaged based on the concentration and its own type.

[0039] In some embodiments, performing a simulated terminal damage query further includes: querying the spatiotemporal integrated coding set based on the memory database; if no match is found, querying based on a composite index.

[0040] If the query returns an empty result, continue querying the relational database and execute a composite index query on the database table.

[0041] (1) If the pollution concentration information and biochemical entity ID are returned, then query the database to obtain the biochemical type.

[0042] (2) If the returned result is empty, then determine whether the simulated terminal is in the biochemical contamination area. If it is, then send the simulated terminal to leave the biochemical area.

[0043] The method in this application addresses the problem that biochemical simulation diffusion models are limited to specific local coordinate systems. It designs a Gaussian smoke model method based on Google S2 grid. On the basis of the highly stable Gaussian smoke model, it uses S2 grid as the underlying calculation unit and ensures that the calculation is always carried out within a specific window by moving the window range, thus avoiding the problem that the diffusion unit exceeds the window and cannot be calculated.

[0044] To address the issue of slow query efficiency in biochemical damage analysis using existing technologies, a spatiotemporal composite index query method is proposed. The in-memory database uses key-value storage for spatiotemporal codes, and the relational database uses a composite index to store spatiotemporal codes. Furthermore, S2 multi-scale nodes are used in conjunction with time codes to retrieve pollution concentration data, thereby improving the retrieval efficiency and enabling rapid determination of whether damage has been performed.

[0045] The embodiment also proposes a simulated biochemical damage query system based on Google S2 grid, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the simulated biochemical damage query method based on Google S2 grid as described above.

[0046] It should be noted that, in the embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0047] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0048] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0049] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims. All of these forms are within the protection scope of this application.

Claims

1. A method for simulating biochemical damage query based on Google S2 grid, characterized in that, include: Constructing a Gaussian plume model based on the S2 grid: Based on a preset rectangular window and biochemical diffusion parameters, the Gaussian plume model is calculated on the S2 geospatial grid to determine the pollution concentration of each S2 grid cell within the window, thus forming pollution area data; Constructing a spatiotemporal index: Encode time information into a first time code, combine it with an S2 grid code representing spatial location to form a spatiotemporal integrated code, and establish a joint index based on the spatiotemporal integrated code, the joint index containing time and spatial dimensions; Perform simulated terminal damage query: Receive spatiotemporal information uploaded by the simulated terminal and convert it into a spatiotemporal integrated code to be queried; Based on the composite index, pollution concentration data associated with the spatiotemporal integrated code to be queried is queried. Based on the queried pollution concentration data and the type of the simulated terminal, it is determined whether the simulated terminal has been damaged.

2. The method for simulating biochemical damage query based on Google S2 grid as described in claim 1, characterized in that, Constructing a Gaussian smoke cloud model based on an S2 grid includes: The basic S2 grid level is determined based on the typical scenario type, and the N*N S2 grid calculation window is determined with the release point as the center. Starting from the grid at a specified corner position of the calculation window, traverse all S2 grid cells within the window in a set order; For each S2 grid cell that is traversed, calculate the great circle distance component (word_x, word_y) between it and the grid cell where the release point is located. The pollution concentration of the grid cell is calculated by inputting the large circular arc distance components (word_x, word_y) and associated parameters into the Gaussian smoke cloud model.

3. The method for simulating biochemical damage query based on Google S2 grid as described in claim 2, characterized in that, The correlation parameters input to the Gaussian smoke cloud model include wind speed, wind direction, and total source term.

4. The method for simulating biochemical damage query based on Google S2 grid as described in claim 1, characterized in that, Also includes: The calculated pollution concentration of each grid cell is compared with multiple preset concentration threshold ranges to distinguish different levels of pollution areas; Calculate the minimum bounding rectangle of the contaminated area; When performing the next model calculation, the S2 grid where the center point of the smallest outer rectangle of the previous contaminated area is located is used as the center point of the new calculation window.

5. The method for simulating biochemical damage query based on Google S2 grid as described in claim 1, characterized in that, Constructing a spatiotemporal index includes: Get the current system time and convert the timestamp into first time encoding with a specified time granularity; The first time code and the S2 grid code set containing pollution concentration information are stored in the memory database in the form of key-value pairs; The first time code is concatenated with the S2 grid code to form a spatiotemporal integrated code, which is stored together with the pollution concentration information based on a B+ tree index as a joint primary key.

6. The simulated biochemical damage query method based on Google S2 grid as described in claim 5, characterized in that, The spatiotemporal integrated coding is configured such that the first time code is placed in the high bit and the S2 grid code is placed in the low bit.

7. The method for simulating biochemical damage query based on Google S2 grid as described in claim 1, characterized in that, Executing a simulated terminal damage query includes: Convert the spatial coordinates uploaded by the simulated terminal into the highest level S2 grid encoding; Based on the S2 grid level corresponding to the typical scenario type, starting from the highest level S2 grid code, backtracking level by level, we obtain the parent node S2 grid codes of multiple different levels. The S2 grid codes of the multiple parent nodes are combined with the second time codes converted from the query time to form a set of spatiotemporal integrated codes to be queried.

8. The method for simulating biochemical damage based on Google S2 grid as described in claim 7, characterized in that, Executing a simulated terminal damage query also includes: If the query for the spatiotemporal integrated encoding set is not found in the in-memory database, a query is performed based on the composite index.

9. A simulated biochemical damage query system based on Google S2 grid, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the steps of the simulated biochemical damage query method based on the Google S2 grid as described in any one of claims 1 to 8.

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