Interstellar particle data simulation method and device, computer equipment and storage medium

Abstract

The application relates to an interstellar particle data simulation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of reference column density subgraphs obtained by pre-segmentation; respectively performing probability distribution statistics on each reference column density subgraph to determine a first mapping relationship of each reference column density subgraph; performing data conversion on a plurality of groups of first mapping relationships to determine a plurality of groups of second mapping relationships; acquiring a plurality of preset parameters of a to-be-detected particle; performing parallel chemical solving according to the preset parameters of the to-be-detected particle, the first mapping relationship and the second mapping relationship of each reference column density subgraph to determine simulation data of the to-be-detected particle in a corresponding target interstellar cloud. The method can efficiently perform chemical simulation calculation on related parameters of interstellar medium.

Description

Technical Field

[0001] This application relates to the field of interstellar particle simulation technology, and in particular to an interstellar particle data simulation method, apparatus, computer equipment, and storage medium. Background Technology

[0002] The interstellar medium refers to the matter existing between stars, mainly composed of diffuse gas and tiny solid dust particles. Studying the interstellar medium helps us better understand its structure and the process of star formation.

[0003] The relevant technologies generally involve chemical simulation calculations of relevant parameters of the interstellar medium to study it. However, because the amount of data involved in chemical simulation calculations is enormous, the overall efficiency of these technologies is relatively low.

[0004] Therefore, there is an urgent need for a scheme that can efficiently perform chemical simulation calculations on relevant parameters of the interstellar medium. Summary of the Invention

[0005] Therefore, it is necessary to provide an interstellar particle data simulation method, apparatus, computer equipment, and storage medium that can efficiently perform chemical simulation calculations on relevant parameters of the interstellar medium to address the aforementioned technical problems.

[0006] Firstly, this application provides a method for simulating interstellar particle data. The method includes:

[0007] Obtain multiple pre-segmented reference column density sub-maps; the pixel values ​​in the reference column density sub-maps are the column density values ​​of the reference particles, and the reference column density sub-maps are the column density sub-maps of the target interstellar cloud.

[0008] Probability distribution statistics are performed on each of the benchmark column density subplots to determine a first mapping relationship for each benchmark column density subplot; the first mapping relationship is the mapping relationship between column density values ​​and corresponding probability distribution values.

[0009] Data transformation is performed on multiple sets of the first mapping relationship to determine multiple sets of the second mapping relationship; the second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value.

[0010] Multiple sets of preset parameters for the particle to be detected are obtained; each set of preset parameters is determined based on the corresponding visual extinction value and the corresponding preset interstellar environmental conditions.

[0011] Parallel chemical solutions are performed based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0012] In one embodiment, obtaining the pre-segmented multiple reference column density sub-maps includes: obtaining an initial column density map of reference particles; and dividing the initial column density map into a grid based on a preset grid size to determine multiple reference column density sub-maps.

[0013] In one embodiment, the method further includes: for a target reference column density submap, counting the number of first pixels with non-zero column density values ​​in the target reference column density submap; determining the effective pixel ratio corresponding to the target reference column density submap based on the number of first pixels and the total number of pixels corresponding to a preset grid size; if the effective pixel ratio is less than a preset pixel threshold, deleting the target reference column density submap.

[0014] In one embodiment, the step of performing probability distribution statistics on each of the reference column density sub-images to determine the first mapping relationship of each reference column density sub-image includes: for the target reference column density sub-image, statistically analyzing the column density value of each pixel in the target reference column density sub-image; based on the column density value of each pixel, summarizing and statistically analyzing pixels with the same column density value to determine multiple column density values ​​and the corresponding number of pixels; based on the total number of pixels corresponding to the preset grid size and the number of pixels corresponding to each column density value, determining the pixel probability corresponding to each column density value; and based on the multiple column density values ​​and corresponding pixel probabilities of the target reference column density sub-image, determining the first mapping relationship of the target reference column density sub-image.

[0015] In one embodiment, the step of performing data conversion on multiple sets of first mapping relationships to determine multiple sets of second mapping relationships includes: for each first mapping relationship, performing data conversion on the column density value in each first mapping relationship based on a preset conversion coefficient to determine the visual extinction value corresponding to each column density value; and determining the second mapping relationship based on the first mapping relationship and the visual extinction value corresponding to each column density value.

[0016] In one embodiment, the preset parameters include: the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected; the step of determining the simulation data of the particle to be detected in the corresponding target interstellar cloud by performing parallel chemical solving based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship includes: for the target interstellar cloud corresponding to the target reference column density sub-map and the target interstellar environmental conditions, determining the average chemical abundance parameter of the particle to be detected based on the first preset formula, the probability distribution value corresponding to each visual extinction value, the column density value of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value; for the target reference column density sub-map to the target interstellar cloud .... Based on the target interstellar cloud and target interstellar environment conditions, and according to the second preset formula, the probability distribution value corresponding to each visual extinction value, the ambient gas temperature of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value, the average gas temperature parameter of the particle to be detected is determined. For the target interstellar cloud and target interstellar environment conditions corresponding to the target reference column density sub-map, and according to the third preset formula, the probability distribution value corresponding to each visual extinction value, and the celestial brightness temperature of the particle to be detected corresponding to each visual extinction value, the average line intensity parameter of the particle to be detected is determined. Based on the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particle to be detected, the simulation data of the particle to be detected in the corresponding target interstellar cloud is determined.

[0017] In one embodiment, the method further includes: visualizing the simulation data of the particle to be detected in the corresponding target interstellar cloud based on a visualization platform.

[0018] Secondly, this application also provides an interstellar particle data simulation device. The device includes:

[0019] A first acquisition module is used to acquire multiple pre-segmented reference column density sub-maps; the pixel values ​​in the reference column density sub-maps are the column density values ​​of reference particles, and the reference column density sub-maps are column density sub-maps of target interstellar clouds; a statistics module is used to perform probability distribution statistics on each of the reference column density sub-maps to determine a first mapping relationship for each of the reference column density sub-maps; the first mapping relationship is the mapping relationship between column density values ​​and corresponding probability distribution values; a conversion module is used to perform data conversion on multiple sets of the first mapping relationships to determine multiple sets of second mapping relationships; the second mapping relationship is the mapping relationship between visual extinction values ​​and corresponding probability distribution values; a second acquisition module is used to acquire multiple sets of preset parameters for the particle to be detected; each set of preset parameters is determined based on the corresponding visual extinction value and the corresponding preset interstellar environmental conditions; a calculation module is used to perform parallel chemical solving based on the preset parameters of the particle to be detected, the first mapping relationship of each of the reference column density sub-maps, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0020] Thirdly, this application also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement any of the methods in the first aspect above.

[0021] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods in the first aspect described above.

[0022] The aforementioned interstellar particle data simulation method, apparatus, computer equipment, and storage medium acquire multiple pre-segmented reference column density sub-maps and perform probability distribution statistics on each reference column density sub-map to determine a first mapping relationship for each reference column density sub-map; then, perform data transformation on multiple sets of the first mapping relationships to determine multiple sets of second mapping relationships; next, acquire multiple sets of preset parameters for the particle to be detected; and finally, perform parallel chemical solving based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud, thereby achieving efficient chemical simulation calculation of relevant parameters of the interstellar medium. Attached Figure Description

[0023] Figure 1 This is an application environment diagram of the interstellar particle data simulation method in one embodiment;

[0024] Figure 2 This is a flowchart illustrating an interstellar particle data simulation method in one embodiment;

[0025] Figure 3 A schematic diagram illustrating the meshing of the initial column density map for different preset mesh sizes;

[0026] Figure 4 This is a flowchart illustrating the process of removing a baseline column density submap where the effective pixel ratio is less than a preset pixel threshold in one embodiment.

[0027] Figure 5 This is a schematic diagram of a visualization platform in one embodiment;

[0028] Figure 6 This is a schematic diagram of the composition of a target interstellar cloud in one embodiment;

[0029] Figure 7 This is a structural block diagram of an interstellar particle data simulation device in one embodiment;

[0030] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] In the interstellar medium (ISM), parameters such as ultraviolet irradiance, cosmic ray ionization rate, and metallicity have a decisive influence on gas heating and chemical reactions within the interstellar medium. Fitting these parameters using chemical models is an important approach to understanding interstellar cloud structure, star formation, and feedback processes.

[0033] Current techniques typically rely on single-particle column density or visual extinction (AV) inputs for calculation, which fails to comprehensively characterize the statistical features of multi-scale structures in the real interstellar medium. In recent years, using the probability density function (PDF) to describe the statistical distribution of column density or AV has become an effective observational analysis method. However, traditional numerical chemical simulations still face the following challenges:

[0034] Inability to adapt to astronomical image observation data: Most models are limited by preset theoretical initial parameters and cannot directly use real observation data as input, making direct numerical calculations impossible for data obtained from astronomical observations. Low computational efficiency: Because the parameters of the interstellar medium include multiple different dimensions, such as ultraviolet irradiance (FUV), cosmic ray ionization rate (CRIR), and metallicity (Z), the computational workload for calculating the probability density function (PDF) under multiple different parameters is enormous. Large data scale: The calculation results include multiple dimensions of chemical output, such as temperature, dozens of chemical abundances, and the brightness of major radiative cooling lines, leading to complex data management issues. Lack of interactive analysis tools: It is difficult to explore the multidimensional parameter relationships in the calculation results and it is not convenient to match them with observational data.

[0035] Therefore, there is a need for a solution that supports user observation data input, can perform average solutions for large-area sky data, is efficient and parallelizable, and provides data visualization capabilities, in order to process large-scale interplanetary medium (ISM) observation data and quickly invert physical parameters.

[0036] The interstellar particle data simulation method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Server 104 is used to execute the interstellar particle data simulation method. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0037] To address the aforementioned problems, in one embodiment of this application, such as Figure 2 As shown, an interstellar particle data simulation method is provided, including the following steps:

[0038] Step 201: Obtain multiple pre-segmented baseline column density submaps.

[0039] The baseline column density submap is the column density submap of the target interstellar cloud. The baseline column density submap is a column density image obtained by segmenting the initial column density map of a baseline particle in the target interstellar cloud based on pre-defined segmentation rules. The pixel values ​​in the baseline column density submap are the column density values ​​of the baseline particle, which is an actual atom or molecule. In this embodiment, the baseline particle is a hydrogen atom (H), and the initial column density map is the column density image of hydrogen atom H in the target interstellar cloud. An interstellar cloud is a clump of matter in the interstellar medium, formed by local aggregation of matter due to gravity, turbulence, shock wave compression, etc., with a density significantly higher than the background and a relatively independent structure. The target interstellar cloud is the interstellar cloud to be studied. The pixel values ​​in the initial column density map are also the column density values ​​of the baseline particle.

[0040] The column density value is the cumulative total number of a specific particle within a projected column per unit area, based on a preset observation angle. It characterizes the total content of that specific particle along the line of sight in the celestial medium and its spatial variation characteristics.

[0041] After obtaining the initial column density map of the reference particles, the initial column density map of the reference particles in the target interstellar cloud is divided into grids according to a pre-set segmentation rule, thereby obtaining multiple pre-segmented reference column density sub-maps. Each grid corresponds to one reference column density sub-map after the grid division.

[0042] Step 202: Perform probability distribution statistics on each benchmark column density subplot to determine the first mapping relationship of each benchmark column density subplot.

[0043] The first mapping relationship is the mapping relationship between the column density value and the corresponding probability distribution value.

[0044] The pixel values ​​in each reference column density sub-map are statistically analyzed, and based on the total number of pixels in each reference column density sub-map, the probability distribution value corresponding to each column density value in each reference column density sub-map is obtained, and finally the first mapping relationship of each reference column density sub-map is determined.

[0045] The probability distribution value is a parameter that characterizes the frequency of a certain column density value appearing in a certain baseline column density subplot.

[0046] The total number of pixels is the total number of pixels in each baseline bar density submap, and the total number of pixels in each baseline bar density submap is equal.

[0047] For example, the pixel values ​​in any of the reference column density submaps include 200, 201, and 202. There are 2 pixels with a value of 200, 4 pixels with a value of 201, and 4 pixels with a value of 202, for a total of 10 pixels. Therefore, the probability distribution value of pixel value 200 is 2 / 10, which equals 0.2; the probability distribution value of pixel value 201 is 4 / 10, which equals 0.4; and the probability distribution value of pixel value 202 is 4 / 10, which equals 0.4.

[0048] Step 203: Perform data transformation on multiple sets of first mapping relationships to determine multiple sets of second mapping relationships.

[0049] The second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value.

[0050] Visual extinction is a parameter that characterizes the degree to which the interstellar medium weakens starlight in the visible light band. It is used to characterize the dust content in the interstellar medium and its impact on the radiative transmission process.

[0051] Data transformation is performed on all column density values ​​in the first mapping relationship corresponding to each baseline column density sub-map to obtain multiple visual extinction values ​​corresponding to multiple column density values ​​in each group of first mapping relationships. Based on multiple probability distribution values ​​in the first mapping relationship, multiple groups of second mapping relationships are finally determined.

[0052] In this embodiment, the column density values ​​correspond one-to-one with the visual extinction values. The visual extinction value corresponding to each column density value is obtained by multiplying each column density value by a preset conversion coefficient.

[0053] The preset conversion factor is a pre-defined factor that converts column density values ​​into visual extinction values.

[0054] Step 204: Obtain multiple sets of preset parameters for the particles to be detected.

[0055] The preset parameters for each group are determined based on the corresponding visual extinction value, the corresponding target interstellar cloud, and the preset interstellar environmental conditions.

[0056] It should be noted that the preset interstellar environment conditions include several different interstellar environment conditions.

[0057] It should be noted that obtaining multiple sets of preset parameters for the particles to be detected is achieved through the capabilities of the 3D-PDR software itself.

[0058] Wherein, the particle to be detected is an actual atom or molecule. For example, in this embodiment, the particle to be detected is: e - H, H2, C, C + O, O + OH, CH, H2O, HCO+ He, O2, CO, H3 + He + Mg, H2 + CH5 + O2 + CH4 + OH + Mg + CH4, H3O + CO + CH2, H2O + H + CH3 + CH3, CH2 + CH + One or more atoms or molecules in it.

[0059] In this embodiment, the preset parameters include: the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected.

[0060] It should be noted that, under the target interstellar cloud and a certain interstellar environment, a certain visual extinction value corresponds to the column density value of a particle to be detected, the celestial brightness temperature of a particle to be detected, and the ambient gas temperature of a particle to be detected.

[0061] It should be noted that when the target interstellar cloud or a certain interstellar environmental condition changes, even if the value of a certain visual extinction value does not change, the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected will also change when the target interstellar cloud or a certain certain interstellar environmental condition changes.

[0062] Step 205: Perform parallel chemical solution based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0063] The simulation data of the particles to be detected in the corresponding target interstellar cloud are the average chemical abundance parameters, average gas temperature parameters, and average line intensity parameters of the particles to be detected in the corresponding target interstellar cloud.

[0064] The average chemical abundance parameter characterizes the relative abundance of the particle to be detected under the target interstellar environment conditions and within the target interstellar cloud; the average gas temperature parameter characterizes the thermodynamic temperature of the particle to be detected under the target interstellar environment conditions and within the target interstellar cloud; and the average line intensity parameter characterizes the spectral line energy of the particle to be detected under the target interstellar environment conditions and within the target interstellar cloud. The target interstellar environment conditions are any one of the preset interstellar environment conditions.

[0065] In this embodiment, the interstellar environmental conditions are different ultraviolet irradiance, cosmic ray ionization rate, and metallicity.

[0066] Ultraviolet irradiance refers to the total energy flux or photon flux of far-ultraviolet photons received from all directions at a given location per unit time and per unit area. The wavelength of far-ultraviolet photons is typically between 91.2 and 200 nanometers. In this embodiment, the ultraviolet irradiance is... The logarithm is taken within the range, and 61 values ​​are averaged as the ultraviolet irradiance values ​​for different interstellar environmental conditions in this embodiment.

[0067] The cosmic ray ionization rate refers to the probability that an atom or molecule is ionized by cosmic ray particles per unit time. Cosmic ray particles are mainly high-energy protons. In this embodiment, the cosmic ray ionization rate is... The logarithm is taken within the range, and 41 values ​​are averaged as the cosmic ray ionization rate values ​​for different interstellar environmental conditions in this embodiment.

[0068] Metallicity refers to the total relative abundance of all chemical elements other than hydrogen and helium in a celestial body, including stars, gas clouds, and galaxies. In this embodiment, the metallicity values ​​of 0.1, 0.5, 1.0, and 2.0 are preset as values ​​for different interstellar environmental conditions.

[0069] Based on the preset formula, parallel chemical solutions are performed under the target interstellar cloud and target interstellar environment conditions corresponding to each benchmark column density sub-map to obtain multiple average chemical abundance parameters, average gas temperature parameters, and average line intensity parameters, thereby determining the simulation data of the particles to be detected in the corresponding target interstellar cloud.

[0070] In the aforementioned interstellar particle data simulation method, multiple pre-segmented reference column density sub-maps are acquired, and probability distribution statistics are performed on each reference column density sub-map to determine the first mapping relationship for each reference column density sub-map. Then, multiple sets of first mapping relationships are transformed to determine multiple sets of second mapping relationships. Next, multiple sets of preset parameters of the particle to be detected are acquired. Finally, parallel chemical solutions are performed based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud, thereby achieving efficient chemical simulation calculation of relevant parameters of the interstellar medium.

[0071] In other embodiments of this application, obtaining multiple pre-segmented reference column density submaps includes:

[0072] Step 1: Obtain the initial column density map of the reference particles.

[0073] The reference particle is an actual atom or molecule; for example, the reference particle could be a hydrogen atom or a helium atom. The initial column density map is a two-dimensional spatial distribution map of the reference particle, where the pixel values ​​in the initial column density map are the column density values ​​of the reference particle.

[0074] It should be noted that the initial column density map has a spatial resolution of 18″ and a pixel size of 4″.

[0075] It should be noted that in this embodiment, the initial column density map is obtained by observing the target interstellar cloud with an astronomical telescope. In other embodiments of this application, the initial column density map can also be obtained from a database storing the initial column density map; this application does not impose any specific limitations. The target interstellar cloud is the interstellar cloud to be observed. An interstellar cloud is a clump of matter in the interstellar medium that has a significantly higher density than the background and a relatively independent structure, formed by the local aggregation of matter due to gravity, turbulence, shock wave compression, etc.

[0076] Step 2: Based on the preset grid size, divide the initial column density map into grids to determine multiple reference column density sub-maps.

[0077] The preset grid size is a pre-defined grid size used to divide the initial column density map into several reference column density submaps of the preset grid size. For example, in this embodiment, the preset grid size is 33 pixels × 33 pixels. The reference column density submap is the column density submap of the target interstellar cloud, and the pixel values ​​in the reference column density submap are the column density values ​​of the reference particles.

[0078] It should be noted that in this embodiment, 33 pixels × 33 pixels corresponds to a sky physical scale of approximately 0.25 × 0.25 pc. 2 .

[0079] In other embodiments of this application, the preset grid size can also be other grid sizes, as long as they are reasonable. This application does not make specific limitations. For example, Figure 3 As shown, from left to right, schematic diagrams illustrating the meshing of the initial column density map using different preset mesh sizes are presented.

[0080] It should be noted that, in this embodiment, after obtaining the initial column density map of the reference particle, the initial column density map is divided into grids in a simple and efficient manner according to the preset grid size, thereby determining multiple reference column density sub-maps. This lays the foundation for subsequently determining the first mapping relationship and the second mapping relationship of each reference column density sub-map, thereby improving the overall efficiency of the interstellar particle data simulation method provided in this application.

[0081] In other embodiments of this application, such as Figure 4 As shown, the method also includes:

[0082] Step 401: For the target reference column density sub-image, count the number of first pixels with non-zero column density values ​​in the target reference column density sub-image.

[0083] The target reference column density submap is any one of the reference column density submaps. Although in this embodiment, the relevant steps are only performed on the target reference column density submap, in actual scenarios, the relevant steps of this embodiment will be performed on each reference column density submap.

[0084] It should be noted that because interstellar clouds have irregular structures, while images are generally quadrilateral, to accommodate these irregularities, each baseline column density sub-image contains pixels with a column density of zero. These pixels represent content that does not belong to the target interstellar cloud and are considered invalid. Conversely, pixels with non-zero column density values ​​in the baseline column density sub-image are considered valid. The first pixel count is the number of pixels with non-zero column density values ​​in the target baseline column density sub-image.

[0085] Calculate the number of pixels with non-zero column density values ​​in the target baseline column density sub-image, and use this number as the first pixel count.

[0086] Step 402: Determine the effective pixel percentage corresponding to the target baseline column density sub-image based on the number of first pixels and the total number of pixels corresponding to the preset grid size.

[0087] The effective pixel percentage is the ratio of the number of pixels with non-zero column density values ​​in the target baseline column density subplot to the total number of pixels.

[0088] Divide the number of first pixels by the total number of pixels, and use the result of the division as the effective pixel percentage corresponding to the target baseline column density submap.

[0089] For example, in a specific embodiment, the first pixel count of the target reference column density sub-image is 1000, and the total pixel count of the target reference column density sub-image is 1089, so the effective pixel ratio of the target reference column density sub-image is 0.918.

[0090] Step 403: If the percentage of effective pixels is less than the preset pixel threshold, then delete the target baseline column density submap.

[0091] The preset pixel threshold is a preset threshold for the percentage of effective pixels. For example, in one embodiment, the preset pixel threshold is 0.8.

[0092] Compare the effective pixel percentage of the target baseline bar density submap with the preset pixel threshold. If the effective pixel percentage is less than the preset pixel threshold, delete the target baseline bar density submap.

[0093] It should be noted that in this embodiment, by calculating the effective pixel ratio of the target reference column density sub-image and comparing it with a preset pixel threshold, if the effective pixel ratio of the target reference column density sub-image is less than the preset pixel threshold, the target reference column density sub-image is deleted, thereby deleting the reference column density sub-images with a smaller effective pixel ratio in each reference column density sub-image. This prevents the reference column density sub-images with a smaller effective pixel ratio from being used in the subsequent steps of determining the first and second mapping relationships, which would ultimately affect the accuracy of the simulation data of the particle to be detected in the corresponding target interstellar cloud and improve the overall reliability of the interstellar particle data simulation method provided in this application.

[0094] In other embodiments of this application, probability distribution statistics are performed on each reference column density subplot to determine the first mapping relationship of each reference column density subplot, including:

[0095] Step 1: For the target baseline column density submap, calculate the column density value of each pixel in the target baseline column density submap.

[0096] The target reference column density submap is any one of the reference column density submaps. Although in this embodiment, the relevant steps are only performed on the target reference column density submap, in actual scenarios, the relevant steps of this embodiment will be performed on each reference column density submap.

[0097] Determine the column density value of each pixel in the target baseline column density sub-image.

[0098] Step 2: Based on the column density value of each pixel, summarize and count the pixels with the same column density value to determine the number of multiple column density values ​​and the corresponding number of pixels.

[0099] Multiple column density values ​​correspond one-to-one with multiple pixel counts.

[0100] The number of pixels with the same column density value is counted, resulting in multiple column density values ​​and their corresponding number of pixels.

[0101] For example, in a specific embodiment, the number of pixels with a column density value of 200 is 89, that is, the number of pixels corresponding to a column density value of 200 is 89.

[0102] Step 3: Determine the pixel probability corresponding to each column density value based on the total number of pixels corresponding to the preset grid size and the number of pixels corresponding to each column density value.

[0103] The pixel probability is the probability value corresponding to each bar density value. Each bar density value corresponds to a pixel probability.

[0104] Divide the number of pixels corresponding to each column density value by the total number of pixels to obtain the pixel probability corresponding to each column density value.

[0105] For example, if the number of pixels corresponding to the column density value 201 is 89 and the total number of pixels is 1089, then the pixel probability corresponding to the column density value 201 is equal to 89 divided by 1089, which is 0.082.

[0106] Step 4: Determine the first mapping relationship of the target reference column density submap based on the multiple column density values ​​and corresponding pixel probabilities of the target reference column density submap.

[0107] The first mapping relationship is the mapping relationship between the column density value and the corresponding probability distribution value.

[0108] The multiple column density values ​​and corresponding pixel probabilities of the target baseline column density submap are used as the first mapping relationship of the target baseline column density submap.

[0109] It should be noted that pixel probability characterizes the statistical distribution characteristics of the reference particles within the target interstellar cloud, and is used to quantitatively describe the range of variation, concentration, and structural complexity of the reference particles.

[0110] It should be noted that, in this embodiment, after calculating the column density value of each pixel in the target reference column density sub-map, the pixels with the same column density value are summarized and counted according to the column density value of each pixel to determine multiple column density values ​​and the corresponding number of pixels. Then, based on the total number of pixels corresponding to the preset grid size and the number of pixels corresponding to each column density value, the pixel probability corresponding to each column density value is determined, thereby conveniently determining the first mapping relationship of the target reference column density sub-map.

[0111] In other embodiments of this application, data transformation is performed on multiple sets of first mapping relationships to determine multiple sets of second mapping relationships, including:

[0112] Step 1: For each first mapping relationship, the column density value in each first mapping relationship is converted based on a preset conversion coefficient to determine the visual extinction value corresponding to each column density value.

[0113] The preset conversion factor is a pre-defined factor that converts column density values ​​into visual extinction values. Visual extinction values ​​are parameters that characterize the degree to which starlight is attenuated in the visible light band due to the interstellar medium.

[0114] Multiply each column density value by a preset conversion factor to obtain the visual extinction value corresponding to each column density value.

[0115] For example, in this embodiment, the preset conversion factor is .

[0116] Step 2: Determine the second mapping relationship based on the first mapping relationship and the visual extinction value corresponding to each column density value.

[0117] The second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value.

[0118] The second mapping relationship is determined by mapping each visual extinction value corresponding to the column density value to each probability distribution value corresponding to the column density value.

[0119] It should be noted that, in this embodiment, by using preset conversion coefficients based on the column density values ​​in each first mapping relationship, the visual extinction value corresponding to each column density value is determined, and then matched with each probability distribution value corresponding to the column density value, thereby determining the second mapping relationship, laying the foundation for subsequent calculation of simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0120] In other embodiments of this application, the method further includes sorting the second mapping relationship corresponding to each reference column density submap according to the position of each reference column density submap in the initial column density map.

[0121] For example, in this embodiment, the second mapping relationships corresponding to each reference column density subgraph are sorted in order from bottom left to top right.

[0122] In other embodiments of this application, the preset parameters include: the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected; parallel chemical solutions are performed based on the preset parameters of the particle to be detected, the first mapping relationship of each reference column density sub-map, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud, including:

[0123] Step 1: Based on the target interstellar cloud and target interstellar environmental conditions corresponding to the target reference column density submap, determine the average chemical abundance parameter of the particles to be detected according to the first preset formula, the probability distribution value corresponding to each visual extinction value, the column density value of the particles to be detected corresponding to each visual extinction value, and the column density value of the reference particles corresponding to each visual extinction value.

[0124] The probability distribution value corresponding to each visual extinction value is determined according to the second mapping relationship of the target reference column density sub-map. The column density value of the particle to be detected corresponding to each visual extinction value is a part of the preset parameters of the particle to be detected. The column density value of the reference particle corresponding to each visual extinction value is determined according to the first mapping relationship and the second mapping relationship of the target reference column density sub-map.

[0125] The target interstellar environment conditions refer to specific interstellar environmental conditions. These conditions include different ultraviolet irradiance, cosmic ray ionization rates, and metallicity. Ultraviolet irradiance refers to the total energy flow or photon flux of far-ultraviolet photons received per unit time and per unit area at a given location from all directions. The wavelengths of far-ultraviolet photons are typically between 91.2 and 200 nanometers. In this embodiment, the ultraviolet irradiance is taken logarithmically within the range of [value missing], and an average of 61 values ​​is taken as the ultraviolet irradiance values ​​for different interstellar environmental conditions in this embodiment. Cosmic ray ionization rate refers to the probability that an atom or molecule is ionized by cosmic ray particles per unit time. Cosmic ray particles are primarily high-energy protons. In this embodiment, the cosmic ray ionization rate is taken logarithmically within the range of [value missing], and an average of 41 values ​​is taken as the cosmic ray ionization rate values ​​for different interstellar environmental conditions in this embodiment. Metallicity refers to the total relative abundance of all chemical elements other than hydrogen and helium in a celestial body, including stars, gas clouds, and galaxies. In this embodiment, the metallicity is set to preset values ​​of 0.1, 0.5, 1.0 and 2.0 as the metallicity values ​​for different interstellar environmental conditions.

[0126] It should be noted that in this embodiment, the interstellar environmental conditions will be saved as HDF5 files according to the metallicity. That is, metallicity of 0.1, 0.5, 1.0 and 2.0 will each correspond to an HDF5 file. Each HDF5 file includes 61 ultraviolet irradiances and 41 cosmic ray ionization rates, for a total of 61×41 interstellar environmental conditions.

[0127] The average chemical abundance parameter is a parameter that characterizes the relative abundance of the particles to be detected under the target interstellar environmental conditions and in the target interstellar cloud.

[0128] It should be noted that, under the target interstellar cloud and target interstellar environment conditions, one visual extinction value corresponds to one column density value of the particle to be detected.

[0129] Specifically, the first preset formula is: ;

[0130] in, The average chemical abundance parameter, The column density value is the value of the particle to be detected. These are the probability distribution values ​​corresponding to the visual extinction values. is the column density value of the reference particle corresponding to the visual extinction value, and i is the index of the visual extinction value of the target reference column density submap.

[0131] Step 2: Based on the target interstellar cloud cluster and target interstellar environmental conditions corresponding to the target reference column density sub-map, determine the average gas temperature parameter of the particle to be detected according to the second preset formula, the probability distribution value corresponding to each visual extinction value, the ambient gas temperature of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value.

[0132] Among them, the probability distribution value corresponding to each visual extinction value is determined according to the second mapping relationship of the target reference column density sub-map, the ambient gas temperature of the particle to be detected corresponding to each visual extinction value is a part of the preset parameters of the particle to be detected, and the column density value of the reference particle corresponding to each visual extinction value is determined according to the first mapping relationship and the second mapping relationship of the target reference column density sub-map.

[0133] The average gas temperature parameter is a parameter that characterizes the thermodynamic temperature of the particle to be detected under the target interstellar environment conditions and within the target interstellar cloud.

[0134] It should be noted that, under the target interstellar cloud and target interstellar environment conditions, one visual extinction value corresponds to the celestial brightness temperature of a particle to be detected.

[0135] Specifically, the second preset formula is: ;

[0136] in, The average gas temperature parameter, The ambient gas temperature is the temperature of the particles to be detected. These are the probability distribution values ​​corresponding to the visual extinction values. is the column density value of the reference particle corresponding to the visual extinction value, and i is the index of the visual extinction value of the target reference column density submap.

[0137] Step 3: Based on the target interstellar cloud and target interstellar environment conditions corresponding to the target reference column density sub-map, determine the average line intensity parameters of the particles to be detected according to the third preset formula, the probability distribution values ​​corresponding to each visual extinction value, and the celestial brightness temperature of the particles to be detected corresponding to each visual extinction value.

[0138] Among them, the probability distribution value corresponding to each visual extinction value is determined according to the second mapping relationship of the target reference column density sub-map, and the celestial brightness temperature of the particle to be detected corresponding to each visual extinction value is part of the preset parameters of the particle to be detected.

[0139] The average line intensity parameter is a parameter that characterizes the spectral line energy of the particle to be detected under the target interstellar environment conditions and in the target interstellar cloud.

[0140] It should be noted that, under the target interstellar cloud and target interstellar environment conditions, one visual extinction value corresponds to the ambient gas temperature of one particle to be detected.

[0141] Specifically, the third preset formula is: ;

[0142] in, The average line strength parameter, The celestial brightness temperature of the particle to be detected is calculated using well-established radiative transfer processes in astrophysics, based on the energy level population in a pre-calculated dataset. denoted as , where i is the probability distribution value corresponding to the visual extinction value, and i is the index of the visual extinction value of the target baseline column density submap.

[0143] Step 4: Based on the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particle to be detected, determine the simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0144] The simulation data characterizes the chemical simulation data of the particles to be detected in the corresponding target interstellar cloud in the target reference column density subplot.

[0145] The average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particles to be detected in the target reference column density subplot are used as simulation data of the particles to be detected in the corresponding target interstellar cloud.

[0146] It should be noted that the preset formulas used to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud are weighted average algorithms with probability distribution values ​​as weights.

[0147] It should be noted that in other embodiments, the simulation data for determining the particle to be detected in the corresponding target interstellar cloud was not performed using the traditional FOR loop for iterative calculations. Instead, parallel computing was used to accelerate the computation. Furthermore, a multiprocessing pool was created using the multiprocessing.Pool library, which automatically allocates tasks to all CPU cores in the program execution module or supercomputing node. Using the aforementioned interstellar environmental conditions as input, each CPU core performs chemical solution calculations, and the main process monitors the operational status of each CPU core and collects the final results.

[0148] It should be noted that the simulation data obtained in this embodiment will be stored in a table.

[0149] It should be noted that in this embodiment, the relevant steps of this embodiment are performed on the target interstellar cloud clusters and target interstellar environmental conditions corresponding to the target reference column density sub-map. However, in actual scenarios, the relevant steps of this embodiment will be performed on the target interstellar cloud clusters and interstellar environmental conditions corresponding to each reference column density sub-map.

[0150] In other embodiments of this application, the method further includes: visualizing the simulation data of the particle to be detected in the corresponding target interstellar cloud based on a visualization platform.

[0151] Specifically, building a visualization platform includes:

[0152] Server environment preparation: Install the PDFchem software and its dependencies, including the MercuryWeb interface and the Plotly visualization module, on a server with a Python runtime environment. The server can be Linux, macOS, or other systems that support Python.

[0153] Configure access permissions: Assuming the server's independent LAN or public IP address is 1.2.3.4, and you plan to provide services using port 4321, you can edit Mercury's configuration file, for example, located at: ~ / sw / miniconda3 / envs / pdfchem / lib / python3.12 / site-packages / mercury / server / settings.py, and add the server's IP address to the ALLOWED_HOSTS list in that file to allow external access.

[0154] To start the web service: Use the following command to start the Mercury service and keep it running in the background: `mercury run0.0.0.0:4321 &`, where 0.0.0.0 allows access from any internal IP address, and the '&' symbol makes the service run in the background.

[0155] Access method: After the PDFchem backend service is started, the frontend can be accessed from any computer on a local area network. Users can upload PDF data files, adjust parameters such as ultraviolet irradiance, cosmic ray ionization rate, and metallicity on the visualization platform, and call the parallel chemical model on the server side for calculations.

[0156] It should be noted that, in this embodiment, users can adjust the interstellar environmental conditions through a visualization platform, thereby dynamically displaying the changes in the three-dimensional parameter space of the results of various chemical solutions corresponding to different interstellar environmental conditions.

[0157] It should be noted that, in this embodiment, the visualization platform enables multiple users to share the same computing node, submit solution tasks directly through a browser, automatically call the parallelization model of this application, and finally display the simulation data obtained from the calculation in real time on the web page. It also supports cross-platform use and remote cloud use.

[0158] For example, Figure 5 This is a schematic diagram of the visualization platform. The platform uses the Mercury framework to generate a web application, including parameter input boxes for inputting parameters such as FUV, CRIR, and metallicity; a model run button; and a result image display area. It also supports user-uploaded custom PDF files, which include multiple secondary mapping relationships. The result image display area includes: contour plots in the parameter space, two-dimensional contour plots of the line intensity ratios [CII] / CO and [CI] / CO, multi-panel displays of gas temperature and abundance, and an innovative use of ternary plots to display the abundance ratios of C+, C, and CO in the calculation results. Furthermore, when the user hovers the mouse over the image, the corresponding numerical values, parameter points, and probability distribution values ​​are displayed.

[0159] It should be noted that the visualization platform can be deployed on a cloud server, making it easier to use.

[0160] In one specific embodiment of this application, the interstellar particle data simulation method includes:

[0161] Step 1: Interstellar Environment Conditions Mesh Construction Steps: Construct a multidimensional parameter mesh covering ultraviolet irradiance (FUV), cosmic ray ionization rate (CRIR), and metallicity (Z) within a preset range.

[0162] Among them, the ultraviolet irradiance (FUV) is 10 -1 Up to 10 5 Logarithmically uniform sampling between 10, the cosmic ray ionization rate (CRIR) is at 10 -17 Up to 10 -13 s -1 Logarithmically uniform sampling is used, and the metallicity Z includes four values: 0.1, 0.5, 1, and 2.

[0163] Step 2: Based on the 3D-PDR program, input each set of interstellar environmental conditions to perform chemical reaction heat balance calculations and obtain the initial dataset of chemical parameters for all combinations.

[0164] The initial dataset includes parameters such as temperature, column density, abundance, and energy level population.

[0165] Step 3: User Input Data Construction Steps: Based on the column density map or observation data provided by the user, generate multiple baseline column density submaps; and extract the corresponding visual extinction (AV) and the corresponding probability density function (PDF) from each baseline column density submap as input for the chemical solution.

[0166] The visual extinction AV and the corresponding probability density function PDF are extracted from each baseline column density submap by performing histogram statistics on the column density data extracted from each baseline column density submap, and sub-regions with less than 80% integrity are removed.

[0167] Step 4: Parallel Chemical Solution: Based on the Python parallel computing framework, for the interstellar environment condition grid and visual extinction AV and the corresponding probability density function PDF, the chemical model is called in parallel to calculate the corresponding average chemical abundance parameters, average gas temperature parameters, and average line intensity parameters. The average gas temperature parameter is the weighted average gas temperature, the average chemical abundance parameter is the chemical component abundance, and the average line intensity parameter is the brightness of the main cooling line calculated based on the energy level population and radiative transfer process.

[0168] The Python parallel computing framework is the Python library multiprocessing.

[0169] It should be noted that the parallel chemical solution process employs a multi-process or multi-threaded approach to perform chemical solutions on all visual extinction (AV) regions and their corresponding probability density functions (PDFs). The module for parallel chemical solution includes a multi-core CPU parallel framework to simultaneously solve thousands of PDF regions.

[0170] In this embodiment, the visual extinction AV and the corresponding probability density function PDF can be input between users or automatically generated by a program.

[0171] Step 5: Result Storage Step: Store the chemical solution results obtained from parallel computing in HDF5 format to achieve fast read / write and large-scale data management.

[0172] It should be noted that using HDF5 format to store the chemical solution results can speed up the reading of the memory cache, and the caching mechanism can avoid recalculating the same parameter points. It can also support the rapid switching of different input parameters.

[0173] The chemical solution results include: dimensional information of the interstellar environment condition grid, input visual extinction (AV) and corresponding probability density function (PDF) data, average chemical abundance parameters, average gas temperature parameters, and average line intensity parameters.

[0174] Step 6: Visualization and Interaction: The results of the chemical solution obtained by parallel computing are interactively viewed through a web interface, and a visualization output for physical parameter inversion is generated.

[0175] The web interface is the visualization platform mentioned above, which can dynamically display the results of chemical solutions by adjusting the actual parameters of ultraviolet irradiance, cosmic ray ionization rate, and metallicity.

[0176] It should be noted that the visualization platform is implemented based on web technology, supports remote access, and supports Matplotlib and Plotly interfaces for graphical output.

[0177] It should be noted that the aforementioned interstellar particle data simulation method can directly calculate the chemical properties of different sky regions and output weighted average chemical properties. Through pre-calculated datasets, an innovative weighted astrochemical property algorithm, implemented in parallel with the CPU, can rapidly process hydrogen column density images or PDF data from a large number of sky regions, significantly shortening computation time and greatly improving computational efficiency. It supports user-defined PDF input, allowing direct use of real observational data, thus enhancing the flexibility of input data. Multidimensional outputs are stored in HDF5 format, suitable for large-scale model libraries. The constructed interstellar environmental condition grid has a wider range, covering both weak and strong illumination environments. An interactive web software interface is provided for multidimensional parameter exploration and observational data comparison.

[0178] It should be noted that the above-mentioned interstellar particle data simulation method solves the problems of incompatibility with astronomical image observation data, low computational efficiency, large data scale, and lack of interactive analysis tools in related technologies.

[0179] Based on the 3D-PDR program, chemical reaction heat balance calculations are performed for each set of interstellar environmental conditions to obtain the initial dataset of chemical parameters for all combinations.

[0180] In this embodiment, the target interstellar cloud is a cold gas cloud in the interstellar medium with high density and extremely low temperature, where hydrogen mainly exists in the molecular form of H2. A semi-infinite parallel plane model is adopted, assuming that all physicochemical parameters change only along the depth dimension or the visual extinction AV dimension. These physicochemical parameters include density, temperature, and chemical abundance. Furthermore, based on the assumption that ultraviolet radiation and cosmic rays both incident from one side (e.g., the left side), the density of the target interstellar cloud gradually changes exponentially with depth from left to right, and the number density of hydrogen ranges from n = 0.1 to 106 cm⁻¹. -3The correspondence between density *n* and visual extinction *AV* is calculated using an e^(-e) empirical function. The initial abundance ratios of elements such as hydrogen atom (H), hydrogen molecule (H2), helium (He), and oxygen (O) are set to 4:3:1:0.0028. Finally, the 3D-PDR program is used to perform thermal equilibrium calculations for chemical reaction processes under specified ultraviolet irradiance (FUV), cosmic ray ionization rate (CRIR), and metallicity (Z) conditions. The final output is the gas temperature, column density, abundance, and energy level layout number of atoms and molecules corresponding to a set of visual extinction *AV* values. The reaction pathways for specific chemical processes use a subset of the UMIST2012 dataset, including 33 types of atoms or molecules and 330 reaction processes. The 33 types of atoms or molecules are the aforementioned particles to be detected.

[0181] For example, such as Figure 6 The diagram illustrates the matter contained within the target interstellar cloud in this embodiment. This is a semi-infinite parallel plane model where ultraviolet radiation is incident from left to right. Ultraviolet photons attenuate within the matter, affecting the chemical reaction processes of the matter particles at different incident depths.

[0182] This application also provides an interstellar medium chemistry simulation system, comprising:

[0183] Parametric mesh module: Used to generate multidimensional parametric meshes for ultraviolet irradiance, cosmic ray ionization rate, and metallicity.

[0184] Probability density generation module: used to automatically cut and extract visual extinction (AV) and the corresponding probability density function (PDF) from observation data.

[0185] Parallel Chemistry Solving Module: This module is used to input the parametric mesh, visual extinction (AV), and corresponding probability density function (PDF) into the chemical model for parallel chemical solving.

[0186] Data storage module: Used to store the results of parallel chemical solutions in HDF5 format;

[0187] Interactive visualization module: Used to display the results of parallel chemical solutions and support parameter exploration.

[0188] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0189] Based on the same inventive concept, embodiments of this application also provide an interstellar particle data simulation apparatus for implementing the interstellar particle data simulation method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more embodiments of the interstellar particle data simulation apparatus provided below can be found in the limitations of the interstellar particle data simulation method described above, and will not be repeated here.

[0190] In one embodiment of this application, such as Figure 7 As shown, an interstellar particle data simulation device is provided, comprising:

[0191] The first acquisition module 100 is used to acquire multiple pre-segmented reference column density sub-maps; the pixel values ​​in the reference column density sub-maps are the column density values ​​of reference particles, and the reference column density sub-maps are column density sub-maps of the target interstellar cloud.

[0192] The statistics module 200 is used to perform probability distribution statistics on each of the benchmark column density subplots to determine a first mapping relationship for each of the benchmark column density subplots; the first mapping relationship is the mapping relationship between the column density value and the corresponding probability distribution value.

[0193] The conversion module 300 is used to perform data conversion on multiple sets of the first mapping relationship to determine multiple sets of the second mapping relationship; the second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value.

[0194] The second acquisition module 400 is used to acquire multiple sets of preset parameters of the particle to be detected; each set of preset parameters is determined based on the corresponding visual extinction value and the corresponding preset interstellar environmental conditions.

[0195] The calculation module 500 is used to perform parallel chemical solutions based on the preset parameters of the particle to be detected, the first mapping relationship of each of the reference column density sub-maps, and the second mapping relationship, to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud.

[0196] In other embodiments of this application, the first acquisition module 100 is further configured to acquire an initial column density map of reference particles; and to divide the initial column density map into grids based on a preset grid size to determine multiple reference column density sub-maps.

[0197] In other embodiments of this application, the above-mentioned interstellar particle data simulation device further includes a determination module, configured to: count the number of first pixels with non-zero column density values ​​in the target reference column density sub-image; determine the effective pixel ratio corresponding to the target reference column density sub-image based on the number of first pixels and the total number of pixels corresponding to a preset grid size; and delete the target reference column density sub-image if the effective pixel ratio is less than a preset pixel threshold.

[0198] In other embodiments of this application, the statistics module 200 is further configured to: count the column density values ​​of each pixel in the target reference column density submap; summarize and count pixels with the same column density value based on the column density values ​​of each pixel to determine multiple column density values ​​and the corresponding number of pixels; determine the pixel probability corresponding to each column density value based on the total number of pixels corresponding to the preset grid size and the number of pixels corresponding to each column density value; and determine the first mapping relationship of the target reference column density submap based on the multiple column density values ​​and corresponding pixel probabilities of the target reference column density submap.

[0199] In other embodiments of this application, the conversion module 300 is further configured to, for each first mapping relationship, convert the column density value in each first mapping relationship based on a preset conversion coefficient to determine the visual extinction value corresponding to each column density value; and determine a second mapping relationship based on the first mapping relationship and the visual extinction value corresponding to each column density value.

[0200] In other embodiments of this application, the calculation module 500 is further configured to: determine the average chemical abundance parameter of the particle to be detected based on a first preset formula, the probability distribution value corresponding to each visual extinction value, the column density value of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value, for the target interstellar cloud and target interstellar environmental conditions corresponding to the target reference column density submap; determine the average gas temperature parameter of the particle to be detected based on a second preset formula, the probability distribution value corresponding to each visual extinction value, the ambient gas temperature of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value, for the target interstellar cloud and target interstellar environmental conditions corresponding to the target reference column density submap; determine the average line intensity parameter of the particle to be detected based on a third preset formula, the probability distribution value corresponding to each visual extinction value, and the celestial brightness temperature of the particle to be detected corresponding to each visual extinction value; and determine the simulation data of the particle to be detected in the corresponding target interstellar cloud based on the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter.

[0201] In other embodiments of this application, the above-mentioned interstellar particle data simulation device further includes a visualization module, which is used to visualize and display the simulation data of the particle to be detected in the corresponding target interstellar cloud based on a visualization platform.

[0202] Each module in the aforementioned interstellar particle data simulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0203] In one embodiment of this application, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an interstellar particle data simulation method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0204] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0205] In one embodiment of this application, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the interstellar particle data simulation method in the above embodiment.

[0206] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, the computer program being executed by a processor to implement the steps of the interstellar particle data simulation method in the above-described method embodiments.

[0207] In one embodiment of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the interstellar particle data simulation method in the above-described method embodiments.

[0208] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0209] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0210] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0211] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for simulating interstellar particle data, characterized in that, The method includes: Obtain multiple pre-segmented reference column density sub-maps; the pixel values ​​in the reference column density sub-maps are the column density values ​​of the reference particles, and the reference column density sub-maps are the column density sub-maps of the target interstellar cloud. Probability distribution statistics are performed on each of the benchmark column density subplots to determine a first mapping relationship for each benchmark column density subplot; the first mapping relationship is the mapping relationship between column density values ​​and corresponding probability distribution values. Data transformation is performed on multiple sets of the first mapping relationship to determine multiple sets of the second mapping relationship; the second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value. Multiple sets of preset parameters for the particle to be detected are obtained; each set of preset parameters is determined based on the corresponding visual extinction value and the corresponding preset interstellar environmental conditions. Parallel chemical solutions are performed based on the preset parameters of the particle to be detected, the first mapping relationship of each of the reference column density sub-maps, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud. The method further includes: for the target reference column density sub-image, counting the number of first pixels with non-zero column density values ​​in the target reference column density sub-image; determining the effective pixel ratio corresponding to the target reference column density sub-image based on the number of first pixels and the total number of pixels corresponding to the preset grid size; if the effective pixel ratio is less than the preset pixel threshold, deleting the target reference column density sub-image. The preset parameters include: the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected; the simulation data of the particle to be detected in the corresponding target interstellar cloud are the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particle to be detected in the corresponding target interstellar cloud.

2. The interstellar particle data simulation method according to claim 1, characterized in that, The process of obtaining multiple pre-segmented reference column density submaps includes: Obtain the initial column density map of the reference particles; Based on a preset grid size, the initial column density map is divided into grids to determine multiple reference column density sub-maps.

3. The interstellar particle data simulation method according to claim 1, characterized in that, The step of performing probability distribution statistics on each of the benchmark bar density subplots to determine the first mapping relationship for each of the benchmark bar density subplots includes: For the target reference column density submap, the column density value of each pixel in the target reference column density submap is calculated. Based on the column density value of each pixel, pixels with the same column density value are summarized and counted to determine multiple column density values ​​and the corresponding number of pixels; The pixel probability corresponding to each column density value is determined based on the total number of pixels corresponding to the preset grid size and the number of pixels corresponding to each column density value. The first mapping relationship of the target reference column density submap is determined based on multiple column density values ​​and corresponding pixel probabilities.

4. The interstellar particle data simulation method according to claim 3, characterized in that, The step of performing data transformation on multiple sets of the first mapping relationships to determine multiple sets of the second mapping relationships includes: For each first mapping relationship, the column density value in each first mapping relationship is converted based on a preset conversion coefficient to determine the visual extinction value corresponding to each column density value; A second mapping relationship is determined based on the first mapping relationship and the visual extinction value corresponding to each column density value.

5. The interstellar particle data simulation method according to claim 1, characterized in that, The process of determining the simulation data of the particle to be detected in the corresponding target interstellar cloud by performing parallel chemical solutions based on the preset parameters of the particle to be detected, the first mapping relationship of each of the reference column density sub-maps, and the second mapping relationship includes: Based on the target interstellar cloud and target interstellar environmental conditions corresponding to the target baseline column density submap, the average chemical abundance parameter of the particle to be detected is determined according to the first preset formula, the probability distribution value corresponding to each visual extinction value, the column density value of the particle to be detected corresponding to each visual extinction value, and the column density value of the baseline particle corresponding to each visual extinction value. Based on the target interstellar cloud and target interstellar environment conditions corresponding to the target reference column density sub-map, the average gas temperature parameter of the particle to be detected is determined according to the second preset formula, the probability distribution value corresponding to each visual extinction value, the ambient gas temperature of the particle to be detected corresponding to each visual extinction value, and the column density value of the reference particle corresponding to each visual extinction value. Based on the target interstellar cloud and target interstellar environment conditions corresponding to the target reference column density sub-map, the average line intensity parameters of the particles to be detected are determined according to the third preset formula, the probability distribution values ​​corresponding to each visual extinction value, and the celestial brightness temperature of the particles to be detected corresponding to each visual extinction value. Based on the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particle to be detected, the simulation data of the particle to be detected in the corresponding target interstellar cloud are determined.

6. The interstellar particle data simulation method according to claim 1, characterized in that, The method further includes: Based on a visualization platform, the simulation data of the particles to be detected in the corresponding target interstellar cloud are visualized and displayed.

7. An interstellar particle data simulation device, characterized in that, The device includes: The first acquisition module is used to acquire multiple pre-segmented reference column density sub-maps; the pixel values ​​in the reference column density sub-maps are the column density values ​​of reference particles, and the reference column density sub-maps are column density sub-maps of the target interstellar cloud. The statistics module is used to perform probability distribution statistics on each of the benchmark column density subplots to determine a first mapping relationship for each of the benchmark column density subplots; the first mapping relationship is the mapping relationship between the column density value and the corresponding probability distribution value. The conversion module is used to perform data conversion on multiple sets of the first mapping relationship to determine multiple sets of the second mapping relationship; the second mapping relationship is the mapping relationship between the visual extinction value and the corresponding probability distribution value. The second acquisition module is used to acquire multiple sets of preset parameters for the particle to be detected; each set of preset parameters is determined based on the corresponding visual extinction value and the corresponding preset interstellar environmental conditions. The calculation module is used to perform parallel chemical solutions based on the preset parameters of the particle to be detected, the first mapping relationship of each of the reference column density sub-maps, and the second mapping relationship to determine the simulation data of the particle to be detected in the corresponding target interstellar cloud. The determination module is used to count the number of first pixels with non-zero column density values ​​in the target reference column density sub-image; determine the effective pixel ratio corresponding to the target reference column density sub-image based on the number of first pixels and the total number of pixels corresponding to the preset grid size; if the effective pixel ratio is less than the preset pixel threshold, then delete the target reference column density sub-image. The preset parameters include: the column density value of the particle to be detected, the celestial brightness temperature of the particle to be detected, and the ambient gas temperature of the particle to be detected; the simulation data of the particle to be detected in the corresponding target interstellar cloud are the average chemical abundance parameter, average gas temperature parameter, and average line intensity parameter of the particle to be detected in the corresponding target interstellar cloud.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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