A method, system, device and medium for predicting the rotational mode of interstellar molecules

Abstract

The present application relates to a method, system, device and medium for predicting the rotational mode of interstellar molecules, wherein the method for predicting the rotational mode of interstellar molecules includes: obtaining core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule; inputting the core parameters into a decision tree model for training, during the training process, constructing a node splitting criterion of the decision tree based on the core parameters, gradually splitting the nodes of the decision tree based on the node splitting criterion, and adjusting the depth of the decision tree and the node splitting criterion by cross-validation, selecting the optimal splitting feature of the node splitting criterion by information gain; obtaining a rotational mode prediction model for predicting the rotational mode of the interstellar molecule and the corresponding physical parameters, realizing the prediction of the rotational mode of the interstellar molecule and the explanation of the related physical parameters, improving the accuracy of the prediction through a machine learning algorithm, and providing data support for subsequent interstellar molecule research.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, system, device and medium for predicting the rotational mode of interstellar molecules. Background Art

[0002] With the rapid development of radio astronomy and space exploration technology, scientists have been able to collect unprecedented amounts of data on interstellar molecular structures. This data, not only massive in quantity but also encompassing a wide range of physical and chemical information, is extremely valuable for understanding important scientific questions such as the formation and evolution of molecular clouds and star formation in the universe. The classification of the rotational modes of interstellar molecules is crucial for identifying molecular states and studying the spectral properties of molecular clouds.

[0003] In recent years, machine learning technology has shown great potential in the field of data classification due to its powerful pattern recognition capabilities and automated processing procedures. However, there are currently few studies on the prediction of interstellar molecular rotation patterns, and the prediction accuracy is also low. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, system, equipment and medium for predicting the rotational mode of interstellar molecules in response to the above technical problems.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting the rotational mode of an interstellar molecule, the method comprising:

[0006] Based on the three-dimensional structure of interstellar molecules, the core parameters of the interstellar molecular structure are obtained;

[0007] The core parameters are input into a decision tree model for training to obtain a rotation mode prediction model; during the training process of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters, the nodes of the decision tree are gradually split based on the node splitting criterion, the depth of the decision tree and the node splitting criterion are adjusted using a cross-validation method, and the optimal splitting feature of the node splitting criterion is selected through information gain; the rotation mode prediction model is used to predict the rotation mode of interstellar molecules and the corresponding physical parameters;

[0008] The core parameters of the interstellar molecular structure to be predicted are input into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

[0009] In one embodiment, the core parameters include at least one of the number of atoms, type of atoms, relative molecular mass, molecular homonuclearity, number of isotope substitutions, type of isotope substitutions, conformational isomerism, and non-zero number of rotational constants.

[0010] In one embodiment, the rotation mode includes linear rotation, spherical rotation, symmetrical gyroscope and asymmetrical gyroscope; the physical parameter includes at least one of point group structure, symmetry and moment of inertia.

[0011] In one embodiment, the node splitting criterion for constructing the decision tree based on the core parameters includes:

[0012] determining whether the rotation mode of the interstellar molecule is linear rotation based on the molecular homonuclearity, the conformational isomerism, and the number of atoms;

[0013] determining, based on the relative molecular mass, whether the rotation mode of the interstellar molecule is spherical rotation;

[0014] Based on the non-zero number of the rotation constants, it is determined whether the rotation mode of the interstellar molecule is a symmetric gyro or an asymmetric gyro.

[0015] In one embodiment, the node splitting criterion for constructing the decision tree based on the core parameters further includes:

[0016] If the molecular homonuclearity is 1, the interstellar molecule is of high symmetry; if the molecular homonuclearity is 0, the interstellar molecule is of low symmetry;

[0017] If the conformational isomerism is 1, the interstellar molecule is of low symmetry; if the conformational isomerism is 0, the interstellar molecule is of high symmetry;

[0018] If the atomic type is a light element, the interstellar molecule is of high symmetry; if the atomic type is a heavy element, the interstellar molecule is of low symmetry;

[0019] If the isotope substitution type is a single type, the interstellar molecule is of high symmetry; if the isotope substitution type is multiple types, the interstellar molecule is of low symmetry;

[0020] If the number of isotope substitutions is greater than 0, the interstellar molecule is of low symmetry.

[0021] In one embodiment, obtaining the core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule includes:

[0022] Obtain the chemical formula of interstellar molecules;

[0023] constructing and optimizing the three-dimensional structure of the interstellar molecule based on the chemical formula of the interstellar molecule;

[0024] Based on the three-dimensional structure of the interstellar molecule, core parameters of the interstellar molecular structure are obtained.

[0025] In one embodiment, after obtaining the core parameters of the interstellar molecular structure, the method further includes:

[0026] Perform data cleaning on the core parameters to remove duplicate or missing items;

[0027] Outlier detection is performed on the core parameters based on a set threshold to eliminate abnormal data.

[0028] In a second aspect, an embodiment of the present application further provides a system for predicting the rotational patterns of interstellar molecules, the system comprising an acquisition module, a training module, and a prediction module.

[0029] Acquisition module, used to obtain the core parameters of interstellar molecular structure based on the three-dimensional structure of interstellar molecules;

[0030] A training module is configured to input the core parameters into a decision tree model for training to obtain a rotation mode prediction model; during the training of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters, nodes of the decision tree are gradually split based on the node splitting criterion, the depth of the decision tree and the node splitting criterion are adjusted using a cross-validation method, and an optimal splitting feature of the node splitting criterion is selected through information gain; the rotation mode prediction model is used to predict the rotation mode of interstellar molecules and corresponding physical parameters;

[0031] The prediction module is used to input the core parameters of the interstellar molecular structure to be predicted into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

[0032] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method as described in the first aspect above.

[0033] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0034] The above-mentioned rotation mode prediction method, system, equipment and medium of interstellar molecules obtain the core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule; the core parameters are input into a decision tree model for training to obtain a rotation mode prediction model; during the training process of the decision tree model, the node splitting criterion of the decision tree is constructed based on the core parameters, the nodes of the decision tree are gradually split based on the node splitting criterion, and the depth of the decision tree and the node splitting criterion are adjusted by cross-validation method, and the optimal splitting characteristics of the node splitting criterion are selected by information gain; the rotation mode prediction model is used to predict the rotation mode and corresponding physical parameters of interstellar molecules; the core parameters of the interstellar molecular structure to be predicted are input into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted, thereby realizing the prediction of the rotation mode of the interstellar molecule, and outputting relevant physical parameters for auxiliary explanation, thereby improving the accuracy of the prediction results through machine learning algorithms, and providing reliable data support for subsequent research on interstellar molecules.

[0035] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 is a hardware structure block diagram of a terminal device of a method for predicting the rotational mode of an interstellar molecule in one embodiment;

[0038] Figure 2 is a schematic flow chart of a method for predicting the rotational mode of an interstellar molecule in one embodiment;

[0039] Figure 3 is a decision tree logic diagram in a decision tree model in an embodiment;

[0040] Figure 4 is a structural block diagram of a rotational mode prediction system for interstellar molecules in one embodiment;

[0041] Figure 5 It is a schematic diagram of the structure of a computer device in an embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0043] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0044] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0045] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote quantitative limitations and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0046] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 1 is a block diagram of the hardware structure of the terminal of the method for predicting the rotational mode of interstellar molecules of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown) a processor 102 and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0047] Memory 104 can be used to store computer programs, such as application software programs and modules, such as the computer program corresponding to the interstellar molecule rotational pattern prediction method in this embodiment. Processor 102 executes the computer programs stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some embodiments, memory 104 may further include memory located remotely from processor 102, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0048] The transmission device 106 is used to receive or send data via a network. The network may include a wireless network provided by the terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0049] The present invention provides a method for predicting the rotational mode of interstellar molecules. Figure 1 The terminal in the example is used to illustrate. Figure 2 As shown, the method includes the following steps:

[0050] Step 201: Based on the three-dimensional structure of the interstellar molecule, obtain the core parameters of the interstellar molecular structure.

[0051] The core parameters include the number of atoms (N), atom type (T), relative molecular mass (M), molecular homonuclearity (H), number of isotope substitutions (Ison), isotope substitution type (Isot), conformational isomerism (C), and the number of non-zero rotational constants (B). The core parameters of the interstellar molecular structure serve as input features for the decision tree model, and their characteristic parameters are shown in Table 1.

[0052] Table 1

[0053]

[0054] Step 202: Input the core parameters into a decision tree model for training to obtain a rotation mode prediction model; during the training process of the decision tree model, construct a node splitting criterion of the decision tree based on the core parameters, and gradually split the nodes of the decision tree based on the node splitting criterion. A cross-validation method is used to adjust the depth of the decision tree and the node splitting criterion, and the optimal splitting feature of the node splitting criterion is selected through information gain. The rotation mode prediction model is used to predict the rotation mode of interstellar molecules and the corresponding physical parameters.

[0055] The decision tree is an important machine learning method widely used for classification and regression problems. Its core idea is to divide data into as pure subsets as possible through a series of feature conditional splits, thereby achieving the purpose of classification or prediction. It can summarize decision rules from a series of feature- and label-based data and present these rules in a tree-like structure to solve classification and regression problems. The decision tree algorithm is easy to understand, applicable to various data, and performs well in solving various problems. In particular, various integrated algorithms based on tree models are widely used in various industries and fields. In terms of algorithm selection for decision tree models, there are mainly the following types of decision trees:

[0056] (1) ID3 (Iterative Dichotomiser 3) algorithm

[0057] Core idea: Select the optimal splitting attribute based on information gain. Information gain measures how well an attribute partitions a sample set, and the attribute with the highest information gain is selected as the splitting node. Advantages: Simple and intuitive, suitable for discrete features. Disadvantages: Biased towards multi-valued attributes, prone to overfitting.

[0058] (2) C4.5 algorithm

[0059] Core Concept: Improved from ID3, this method uses information gain (the normalized result of information gain and the number of attribute values) to select the optimal partitioning attribute, thus avoiding bias towards multi-valued attributes. Advantages: Overcomes ID3's bias towards multi-valued attributes and supports partitioning of continuous attributes. Disadvantages: Calculation is complex, and information gain may favor attributes with fewer samples.

[0060] (3) CART (Classification and Regression Tree) algorithm

[0061] Core idea: Use the Gini coefficient or mean squared error to select splitting attributes and generate a binary tree. CART can be used for both classification (classification tree) and regression (regression tree). Advantages: The generated tree has a simple structure and is easy to prune. It supports both continuous and discrete features and is applicable to both classification and regression problems. Disadvantages: Generating a binary tree can result in a large tree depth and high computational complexity.

[0062] (4) CHAID (Chi-squared Automatic Interaction Detection) algorithm

[0063] Core idea: Select split attributes based on the chi-squared test. Applicable to multi-value classification problems, generating a multi-branch tree. Advantages: Applicable to multi-category and multi-value features, and well-handled missing values ​​and imbalanced classifications. Disadvantages: The chi-squared test assumes better performance with large sample sizes, but may perform poorly with small sample sizes.

[0064] In the embodiments of this application, the decision tree model uses the C4.5 algorithm. The reasons and advantages are as follows: (1) Support for multi-way splitting: C4.5 can select the best splitting feature based on the information gain rate and allows multi-way splitting, which is more suitable for complex decision logic. (2) Support for mixed data: It can process both continuous and discrete features. (3) Strong interpretability: The tree structure output by C4.5 is clear, which makes it easy to explain the decision logic of each split node, meeting the needs of patent data cleaning and classification.

[0065] Before model training, the input core parameter features are feature encoded and converted into a form suitable for processing by a decision tree model. During the decision tree model training process, a node splitting criterion for the decision tree is constructed based on the core parameters. The nodes of the decision tree are gradually split based on the node splitting criterion. A cross-validation method is used to adjust the depth of the decision tree and the node splitting criterion. The optimal splitting characteristics of the node splitting criterion are selected using information gain. The rotation mode prediction model is used to predict the rotation mode of interstellar molecules and the corresponding physical parameters.

[0066] The node splitting criterion of the decision tree model uses information gain or the Gini Index as the core indicator for decision tree splitting. The splitting criterion of the decision tree can use information gain or the Gini Index.

[0067] Information gain measures the degree to which a feature reduces the uncertainty of data set classification, and the calculation formula is as follows:

[0068]

[0069] in:

[0070] IG(D,A): Information gain of feature A on data set D, D v : Feature A is a subset of v.

[0071] H(D): Entropy of data set D, defined as:

[0072]

[0073] p i : The probability of category i in the dataset.

[0074] The Gini coefficient measures the degree of impurity of a data set and is calculated as follows:

[0075]

[0076] The splitting of features is based on minimizing the weighted Gini coefficient:

[0077]

[0078] This example uses information gain as a core metric and classifies the predictive power of the target variable based on features. By integrating the characteristic information of the core parameters of interstellar molecules (N, T, M, H, Ison, Isot, C, and B), a node splitting strategy is constructed. Each node is split based on different features (such as the number of atoms, molecular homonuclearity, isotopic substitution, and conformational isomerism) to ensure that all features are fully utilized. A prediction model is obtained through training, ultimately outputting the rotational mode of the interstellar molecule and the corresponding physical parameters. The corresponding physical parameters are shown in Table 2, including the interstellar molecule's point group structure (G), symmetry (S), and moment of inertia (I).

[0079] Table 2

[0080]

[0081] Step 203: Input the core parameters of the interstellar molecular structure to be predicted into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

[0082] In the above steps S201 to S203, the core parameters of the interstellar molecular structure are obtained based on the three-dimensional structure of the interstellar molecule; the core parameters are input into a decision tree model for training; during the training process of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters; the nodes of the decision tree are gradually split based on the node splitting criterion, and the depth of the decision tree and the node splitting criterion are adjusted by cross-validation method; the optimal splitting feature of the node splitting criterion is selected by information gain, and a prediction model for predicting the rotation mode of interstellar molecules and the corresponding physical parameters is obtained, the prediction of the rotation mode of interstellar molecules is realized, and the relevant physical parameters are output for auxiliary explanation, and the accuracy of the prediction results is improved by the machine learning algorithm, providing reliable data support for subsequent research on interstellar molecules.

[0083] In one embodiment, the core parameters include at least one of the number of atoms, type of atoms, relative molecular mass, molecular homonuclearity, number of isotope substitutions, type of isotope substitutions, conformational isomerism, and non-zero number of rotational constants.

[0084] In one embodiment, the rotation mode includes linear rotation, spherical rotation, symmetrical gyroscope and asymmetrical gyroscope; the physical parameter includes at least one of point group structure, symmetry and moment of inertia.

[0085] In one embodiment, obtaining the core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule comprises the following steps:

[0086] Step 301, obtaining the chemical formula of the interstellar molecule.

[0087] Specifically, chemical formulas of interstellar molecules are obtained from astronomical observation data, typically from literature and databases. The accuracy of the obtained formulas is ensured, and the source information is recorded for subsequent verification. Furthermore, preliminary screening of the obtained interstellar molecular formulas is performed to remove duplicates, correct erroneous data, and supplement missing data to ensure data integrity and consistency.

[0088] Step 302: construct and optimize the three-dimensional structure of the interstellar molecule based on the chemical formula of the interstellar molecule.

[0089] Specifically, through 3D structure calculations, using quantitative software packages such as Gaussian, the molecule's 3D structure is constructed and optimized based on its chemical formula. During model construction, appropriate calculation methods and basis sets are selected to ensure that the optimized structure is reasonable and stable. During the optimization process, attention should be paid to geometric parameters and energy convergence criteria to ensure that the final model is free of spurious frequencies and physically meaningful. The optimized structure should be saved in a standard format (such as XYZ or Gaussian input format) for subsequent use and analysis.

[0090] Step 303: Based on the three-dimensional structure of the interstellar molecule, obtain the core parameters of the interstellar molecular structure.

[0091] In one embodiment, the method further includes the following steps after obtaining the core parameters of the interstellar molecular structure: performing data cleaning on the core parameters to remove duplicate or missing items; and performing outlier detection on the core parameters based on a set threshold to eliminate abnormal data.

[0092] Specifically, to ensure the reliability of the data and the accuracy of the results and improve data quality, the original core parameter data is cleaned to remove duplicate or missing items; and the core parameters of the input model are screened for outliers to detect abnormal points that may cause prediction deviations (such as the number of non-zero extreme rotation constants), and abnormal data are filtered or marked according to the set threshold to prevent them from affecting model predictions.

[0093] After completing the above data preprocessing, the decision tree model encodes and extracts the core parameters of the interstellar molecular data, and converts parameters related to the rotation mode, such as nuclearity, number of atoms and their types, conformational isomers, etc., into numerical features or classification features to facilitate processing by the decision tree model.

[0094] In one embodiment, the decision tree construction logic shown in Table 3 and Figure 3 The decision tree logic diagram shown in FIG. 1 includes the following content: the node splitting criteria for constructing the decision tree based on the core parameters mainly explain the core parameters that affect the rotation mode of interstellar molecules.

[0095] Based on the molecular homonuclearity, the conformational isomerism and the number of atoms, it is determined whether the rotation mode of the interstellar molecule is linear rotation.

[0096] The C4.5 decision tree algorithm is used to gradually split the core parameters of interstellar molecules. First, by analyzing the core characteristics of the interstellar molecule (molecular homonuclearity, conformational isomerism, and atomic number), a preliminary determination is made as to whether the molecule is linear. If it is linear, the rotational mode is directly output as linear rotation. If it is nonlinear, further splitting is performed based on other characteristics, further subdividing it into spherical rotation, symmetric gyroscopic rotation, or asymmetric gyroscopic rotation.

[0097] Based on the relative molecular mass, it is determined whether the rotation mode of the interstellar molecule is spherical rotation.

[0098] If the relative molecular mass M is less than 50, spherical rotation is predicted.

[0099] Based on the non-zero number of the rotation constants, it is determined whether the rotation mode of the interstellar molecule is a symmetric gyro or an asymmetric gyro.

[0100] If the non-zero number of the rotation constants B=1, it is predicted to be a symmetrical gyroscope. If the non-zero number of the rotation constants B>1, it is predicted to be an asymmetrical gyroscope.

[0101] In one embodiment, the node splitting criteria of the decision tree also include the following content, which mainly describes the core parameters that affect the symmetry of interstellar molecules.

[0102] If the molecular homonuclearity is 1, the interstellar molecule is of high symmetry; if the molecular homonuclearity is 0, the interstellar molecule is of low symmetry.

[0103] First, a preliminary judgment is made based on the molecular homonuclearity H. If it is a homonuclear molecule (H=1), it enters the high symmetry path; if it is a heteronuclear molecule (H=0), it enters the low symmetry path.

[0104] If the conformational isomerism is 1, the interstellar molecule is of low symmetry; if the conformational isomerism is 0, the interstellar molecule is of high symmetry.

[0105] Next, it is further subdivided based on conformational isomerism C. If there is no conformational isomerism (C=0), the symmetry is maintained and further classification is continued according to the number of atoms N and the atom type T; if there is conformational isomerism (C=1), it is directly predicted to be a low-symmetry molecule.

[0106] If the atomic type is a light element, the interstellar molecule is of high symmetry; if the atomic type is a heavy element, the interstellar molecule is of low symmetry.

[0107] Atoms of heavier elements reduce symmetry.

[0108] If the isotope substitution type is a single type, the interstellar molecule is of high symmetry; if the isotope substitution type is multiple types, the interstellar molecule is of low symmetry.

[0109] Isotopic substitution of multiple types will reduce the symmetry.

[0110] If the number of isotope substitutions is greater than 0, the interstellar molecule is of low symmetry.

[0111] The number of isotope substitutions Ison substitutions will affect the symmetry.

[0112] This embodiment of the present application uses the C4.5 decision tree algorithm to gradually split and classify the core parameters of interstellar molecules. First, by analyzing the core characteristics of the interstellar molecule (such as nuclearity, number of atoms, and conformational isomers), a preliminary judgment is made as to whether the molecule is linear. A preliminary judgment is made based on the molecular homonuclearity H. If it is a homonuclear molecule (H=1), the molecule enters the high symmetry path; if it is a heteronuclear molecule (H=0), the molecule enters the low symmetry path. Next, further subdivision is performed based on conformational isomers C. If there are no conformations (C=0), the current symmetry is maintained, and further splitting is continued based on the number of atoms N and atom type T. If there are conformations (C=1), the molecule is directly predicted to be low symmetry. For the number of atoms N, if N≤2, linear rotation is preferred, and the rotation mode is directly output as linear. If the number of atoms N>2, the molecular mass M and the number of non-zero rotation constants B are further subdivided into spherical rotation, symmetric gyroscopic rotation, or asymmetric gyroscopic rotation. During model training, cross-validation was used to adjust the decision tree depth and splitting criteria. The optimal splitting features were selected based on information gain, ensuring that the model improved prediction accuracy while maintaining good generalization capabilities. The final classification output includes the molecule's rotational mode (linear, spherical, symmetrical gyroscopic, asymmetrical gyroscopic) and related physical parameters (point group structure, moment of inertia, symmetry, etc.).

[0113] When the interstellar molecule is predicted to have high symmetry, the output is 1 (symmetric), and when the interstellar molecule is predicted to have low symmetry, the output is 0 (asymmetric); the calculated moment of inertia I = μr 2 , μ is the reduced mass; r is the bond length between two atoms, and the reduced mass and bond length are obtained by database query; the point group structure includes Cn, Dn, Td, etc., Cn is the axial point group; Dn is the dihedral point group; Td is the regular tetrahedral point group.

[0114] Table 3

[0115]

[0116] The following are the splitting criteria for decision trees:

[0117] Splitting criterion 1: homonuclear H

[0118] Homonuclear molecules H=1: may have higher symmetry.

[0119] Heteronuclear molecules H=0: tend to have lower symmetry.

[0120] Splitting criterion 2: Conformation isomer C / number of isotope substitutions Ison

[0121] Conformation C: Conformation will destroy the symmetry.

[0122] Number of isotope substitutions Ison: Substitution will affect symmetry.

[0123] Splitting criterion three: number of atoms N / atom type T / isotope substitution type Isot

[0124] Number of atoms (N≤2): Linear rotation is predicted.

[0125] Atomic type and isotopic substitution type: Heavy elements or multiple types of substitution will reduce symmetry.

[0126] Splitting criterion 4: Molecular mass M

[0127] Molecular mass M: Molecules with smaller mass are generally more symmetrical, and molecular mass M≤50 are predicted to rotate spherically.

[0128] Splitting criterion 5: the number of non-zero rotation constants B

[0129] B=1: Predicts a symmetrical gyro.

[0130] B>1: predicted to be an asymmetric gyro.

[0131] It should be understood that, although the various steps in the above flow chart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above flow chart may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0132] The present application also provides a system for predicting the rotational patterns of interstellar molecules. Figure 4 As shown, the system includes an acquisition module 10 , a training module 20 , and a prediction module 30 .

[0133] The acquisition module 10 is used to obtain the core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule;

[0134] The training module 20 is used to input the core parameters into a decision tree model for training to obtain a rotation mode prediction model; during the training process of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters, the nodes of the decision tree are gradually split based on the node splitting criterion, and the depth of the decision tree and the node splitting criterion are adjusted using a cross-validation method, and the optimal splitting feature of the node splitting criterion is selected through information gain; the rotation mode prediction model is used to predict the rotation mode of interstellar molecules and the corresponding physical parameters;

[0135] The prediction module 30 is used to input the core parameters of the interstellar molecular structure to be predicted into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

[0136] The system also includes a result output module that outputs the predicted rotation mode and related parameters. This output includes various rotation modes, including linear rotation, spherical rotation, symmetrical gyroscope, and asymmetrical gyroscope, along with auxiliary parameter descriptions (such as rotation constant and moment of inertia). Users can choose to export the results to various formats (such as CSV and JSON) for subsequent analysis. A visual decision path diagram is also generated, showcasing the model's decision path and the impact of key features, helping users understand the model's predictive logic.

[0137] In one embodiment, the core parameters include at least one of the number of atoms, type of atoms, relative molecular mass, molecular homonuclearity, number of isotope substitutions, type of isotope substitutions, conformational isomerism, and non-zero number of rotational constants.

[0138] In one embodiment, the rotation mode includes linear rotation, spherical rotation, symmetrical gyroscope and asymmetrical gyroscope; the physical parameter includes at least one of point group structure, symmetry and moment of inertia.

[0139] In one embodiment, the training module 20 is further used to: determine whether the rotation mode of the interstellar molecule is linear rotation based on the molecular homonuclearity, the conformational isomerism and the number of atoms; determine whether the rotation mode of the interstellar molecule is spherical rotation based on the relative molecular mass; and determine whether the rotation mode of the interstellar molecule is a symmetric gyroscope or an asymmetric gyroscope based on the non-zero number of the rotation constant.

[0140] In one embodiment, the training module 20 is also used to: if the molecular homonuclearity is 1, the interstellar molecule is of high symmetry; if the molecular homonuclearity is 0, the interstellar molecule is of low symmetry; if the conformational isomerism is 1, the interstellar molecule is of low symmetry; if the conformational isomerism is 0, the interstellar molecule is of high symmetry; if the atomic type is a light element, the interstellar molecule is of high symmetry; if the atomic type is a heavy element, the interstellar molecule is of low symmetry; if the isotope substitution type is a single type, the interstellar molecule is of high symmetry; if the isotope substitution type is multiple types, the interstellar molecule is of low symmetry; if the number of isotope substitutions is greater than 0, the interstellar molecule is of low symmetry.

[0141] In one embodiment, the acquisition module 10 is further used to: obtain the chemical formula of the interstellar molecule; construct and optimize the three-dimensional structure of the interstellar molecule based on the chemical formula of the interstellar molecule; and obtain the core parameters of the interstellar molecular structure based on the three-dimensional structure of the interstellar molecule.

[0142] In one embodiment, the system also includes a data preprocessing module configured to: clean the core parameters to remove duplicate or missing entries; and detect outliers based on a set threshold to eliminate abnormal data. If data is missing or abnormal, the system will generate a corresponding prompt and suggest that the user supplement the necessary input conditions.

[0143] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0144] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. Wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for predicting the rotational patterns of interstellar molecules. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covering the display screen, keys, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0145] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned embodiments of the method for predicting the rotation mode of an interstellar molecule are implemented.

[0147] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0148] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0149] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for predicting the rotational mode of an interstellar molecule, characterized in that: The method comprises: Based on the three-dimensional structure of interstellar molecules, the core parameters of the interstellar molecular structure are obtained; The core parameters are input into a decision tree model for training to obtain a rotation mode prediction model; during the training process of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters, the nodes of the decision tree are gradually split based on the node splitting criterion, the depth of the decision tree and the node splitting criterion are adjusted using a cross-validation method, and the optimal splitting feature of the node splitting criterion is selected through information gain; the rotation mode prediction model is used to predict the rotation mode of interstellar molecules and the corresponding physical parameters; The core parameters of the interstellar molecular structure to be predicted are input into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

2. The method according to claim 1, characterized in that The core parameters include at least one of the number of atoms, type of atoms, relative molecular mass, molecular homonuclearity, number of isotope substitutions, type of isotope substitutions, conformational isomerism, and non-zero number of rotational constants.

3. The method according to claim 2, characterized in that The rotation modes include linear rotation, spherical rotation, symmetrical gyroscope and asymmetrical gyroscope; the physical parameters include at least one of point group structure, symmetry and moment of inertia.

4. The method according to claim 3, characterized in that The node splitting criteria for constructing the decision tree based on the core parameters include: determining whether the rotation mode of the interstellar molecule is linear rotation based on the molecular homonuclearity, the conformational isomerism, and the number of atoms; determining, based on the relative molecular mass, whether the rotation mode of the interstellar molecule is spherical rotation; Based on the non-zero number of the rotation constants, it is determined whether the rotation mode of the interstellar molecule is a symmetric gyro or an asymmetric gyro.

5. The method according to claim 4, characterized in that The node splitting criterion for constructing the decision tree based on the core parameters also includes: If the molecular homonuclearity is 1, the interstellar molecule is of high symmetry; if the molecular homonuclearity is 0, the interstellar molecule is of low symmetry; If the conformational isomerism is 1, the interstellar molecule is of low symmetry; if the conformational isomerism is 0, the interstellar molecule is of high symmetry; If the atomic type is a light element, the interstellar molecule is of high symmetry; if the atomic type is a heavy element, the interstellar molecule is of low symmetry; If the isotope substitution type is a single type, the interstellar molecule is of high symmetry; if the isotope substitution type is multiple types, the interstellar molecule is of low symmetry; If the number of isotope substitutions is greater than 0, the interstellar molecule is of low symmetry.

6. The method according to claim 1, characterized in that The core parameters of the interstellar molecular structure obtained based on the three-dimensional structure of the interstellar molecule include: Obtain the chemical formula of interstellar molecules; constructing and optimizing the three-dimensional structure of the interstellar molecule based on the chemical formula of the interstellar molecule; Based on the three-dimensional structure of the interstellar molecule, core parameters of the interstellar molecular structure are obtained.

7. The method according to claim 6, characterized in that After obtaining the core parameters of the interstellar molecular structure, the following steps are also included: Perform data cleaning on the core parameters to remove duplicate or missing items; Outlier detection is performed on the core parameters based on a set threshold to eliminate abnormal data.

8. A system for predicting the rotational patterns of interstellar molecules, characterized in that: The system comprises: Acquisition module, used to obtain the core parameters of interstellar molecular structure based on the three-dimensional structure of interstellar molecules; A training module is configured to input the core parameters into a decision tree model for training to obtain a rotation mode prediction model; during the training of the decision tree model, a node splitting criterion of the decision tree is constructed based on the core parameters, nodes of the decision tree are gradually split based on the node splitting criterion, the depth of the decision tree and the node splitting criterion are adjusted using a cross-validation method, and an optimal splitting feature of the node splitting criterion is selected through information gain; the rotation mode prediction model is used to predict the rotation mode of interstellar molecules and corresponding physical parameters; The prediction module is used to input the core parameters of the interstellar molecular structure to be predicted into the rotation mode prediction model to obtain the rotation mode and corresponding physical parameters of the interstellar molecule to be predicted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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