Gas collision section determination method and device based on electron group parameters, terminal equipment and storage medium

Abstract

The invention discloses a gas collision section determination method and device based on electron group parameters, terminal equipment and a storage medium. The method comprises the following steps: inputting to-be-solved electron group parameters into a preset section prediction model to obtain a collision section; the training of the section prediction model comprises solving according to a historical collision section to obtain a first electronic group parameter; taking the first electronic group parameter as input of a to-be-trained section prediction model, taking the predicted collision section as output, and repeatedly executing iterative training operation until the loss function converges to obtain the section prediction model; the iterative training operation comprises obtaining a current predicted collision section according to the current first electronic group parameter; calculating a loss function according to the current first electron group parameter and the current second electron group parameter of the current predicted collision section; and generating disturbance data according to the current first electronic group parameter and the current predicted collision section, and updating the current first electronic group parameter. By implementing the method and the device, the mapping relation from the electron group parameters to the collision cross section is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of gas collision cross sections, and in particular to a method, device, terminal equipment and storage medium for determining a gas collision cross section based on electron group parameters. Background Art

[0002] Electron transport models are crucial for predictive control of low-temperature plasma systems. Collision cross sections are usually derived through experimental and theoretical techniques, and verified through electron group experiments to ensure effectiveness. In general, it is difficult to obtain collision cross section sets by calculation and experiment, and they are not self-consistent with the electron group parameters, and need to be manually corrected. Electron group parameters can be measured through electron group experiments, or calculated by solving the Boltzmann equation for the collision cross section. However, existing methods have no direct mapping relationship from electron group data to collision cross sections, and only rely on manual intuition and experience for correction, resulting in inefficient prediction of collision cross sections. Summary of the invention

[0003] The embodiments of the present invention provide a method, apparatus, terminal device and storage medium for determining a gas collision cross section based on electron group parameters, which can effectively solve the problem that the prior art has no direct mapping relationship from electron group data to collision cross section, and only relies on artificial intuition and experience for correction, resulting in low prediction efficiency of the collision cross section.

[0004] An embodiment of the present invention provides a method for determining a gas collision cross section based on an electron group parameter, comprising:

[0005] Obtaining the electron group parameters of the gas to be determined;

[0006] Inputting the electron group parameters into a preset cross-section prediction model for prediction to obtain a collision cross-section of the gas to be determined;

[0007] The training of the cross-section prediction model includes:

[0008] Obtain historical collision cross sections for multiple gases;

[0009] Solving according to the historical collision cross section to obtain a first electron group parameter for representing the historical parameter;

[0010] Taking the first electron group parameter as the input of the cross-section prediction model to be trained and taking the predicted collision cross section as the output, repeatedly performing the iterative training operation until the preset loss function converges, thereby obtaining a trained cross-section prediction model;

[0011] The iterative training operation includes:

[0012] The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter;

[0013] Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters;

[0014] Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function;

[0015] Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

[0016] Furthermore, solving the historical collision cross section to obtain the first electron group parameter for representing the historical parameter includes:

[0017] Determining a first threshold energy corresponding to each historical collision cross section according to the historical collision cross sections;

[0018] Randomly select every two historical collision cross sections as a collision cross section pair, and generate cross section training data according to the collision cross section pair and the corresponding first threshold energy;

[0019] A solution is performed according to the cross-section training data and a preset solver to obtain a first electronic group parameter for representing a historical parameter.

[0020] Further, a collision cross section prediction is performed according to the current first electron group parameter to obtain a current predicted collision cross section, including:

[0021] Normalizing the current first electron group parameter to obtain the target electron group parameter;

[0022] Performing vector conversion according to the target electron group parameter to obtain a current first electron group parameter vector;

[0023] The collision cross section is predicted according to the current first electron group parameter vector to obtain the current predicted collision cross section.

[0024] Furthermore, calculations are performed based on the current first electron group parameters and the current predicted collision cross section to generate disturbance data, including:

[0025] Determining a second threshold energy corresponding to the current predicted collision cross section according to the current predicted collision cross section;

[0026] According to each current first electron group parameter, calculating the similarity between every two current first electron group parameters, and using the similarity as a disturbance judgment value;

[0027] Making a judgment according to the disturbance judgment value and a preset disturbance threshold;

[0028] The current first electronic group parameter corresponding to the disturbance judgment value being greater than the preset disturbance threshold is used as the first target parameter; and the first disturbance data is obtained by performing calculation according to the first target parameter and the corresponding second threshold energy;

[0029] The current first electronic group parameter whose disturbance judgment value is less than or equal to the preset disturbance threshold is used as the second target parameter; and the second disturbance data is obtained by calculating according to the second target parameter and the corresponding second threshold energy;

[0030] Disturbance data is generated according to the first disturbance data and the second disturbance data.

[0031] Further, the current first electron group parameters are updated according to the disturbance data, including:

[0032] Updating the current first electron group parameters according to the first disturbance data;

[0033] Updating the current first electronic group parameters according to the second disturbance data;

[0034] The first disturbance data and the second disturbance data are used to update the current first electron group parameters alternately.

[0035] Furthermore, the cross-section prediction model is a multi-branch neural network model.

[0036] Furthermore, before the electron group parameters are input into a preset cross-section prediction model for prediction, the method further includes: normalizing the electron group parameters to obtain final electron group parameters.

[0037] As an improvement of the above solution, another embodiment of the present invention provides a gas collision cross section determination device based on electron group parameters, comprising:

[0038] A parameter acquisition module, used to acquire the electron group parameters of the gas to be determined;

[0039] A collision cross section determination module, used for inputting the electron group parameters into a preset cross section prediction model for prediction, to obtain a collision cross section of the gas to be determined;

[0040] Among them, the model training module is used for training the cross-section prediction model, including:

[0041] A historical cross section acquisition unit is used to obtain historical collision cross sections of multiple gases;

[0042] An electron group parameter solving unit, used for solving according to the historical collision cross section to obtain a first electron group parameter used to represent the historical parameter;

[0043] An iterative operation unit, used to use the first electron group parameter as the input of the cross-section prediction model to be trained and the predicted collision cross section as the output, and repeatedly perform iterative training operations until a preset loss function converges to obtain a trained cross-section prediction model;

[0044] The iterative training operation includes:

[0045] The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter;

[0046] Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters;

[0047] Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function;

[0048] Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

[0049] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for determining a gas collision cross section based on electron group parameters as described in the above embodiment is implemented.

[0050] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the gas collision cross-section determination method based on electron group parameters described in the above embodiment.

[0051] By implementing the present invention, at least the following beneficial effects are achieved:

[0052] The present invention provides a method, device, terminal device and storage medium for determining gas collision cross sections based on electron group parameters. The method can obtain the electron group parameters of the gas to be determined; input the electron group parameters into a preset cross section prediction model for prediction to obtain the collision cross section of the gas to be determined; wherein the training of the cross section prediction model includes: obtaining the historical collision cross sections of multiple gases; solving according to the historical collision cross sections to obtain a first electron group parameter for representing the historical parameter; using the first electron group parameter as the input of the cross section prediction model to be trained and the predicted collision cross section as the output, and repeatedly performing iterative training operations until the preset collision cross section is reached. The loss function converges to obtain the trained cross-section prediction model; the iterative training operation includes: predicting the collision cross-section according to the current first electron group parameter to obtain the current predicted collision cross-section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter; solving according to the current predicted collision cross-section to obtain the corresponding current second electron group parameter; calculating according to the current first electron group parameter and the current second electron group parameter to obtain the loss function; calculating according to the current first electron group parameter and the current predicted collision cross-section to generate perturbation data; and updating the current first electron group parameter according to the perturbation data. The cross-section prediction model is optimized through iterative training operations, so that the model learns the relationship between the electron group parameter and the collision cross-section, and at the same time, the model is gradually approached to the optimal collision cross-section by adding perturbation data, without the need for manual cross-section correction and model correction, while forming a mapping relationship from the electron group parameter to the collision cross-section, improving the prediction efficiency of the collision cross-section and improving the accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for determining a gas collision cross section based on an electron group parameter provided by an embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of a multi-branch neural network of a method for determining a gas collision cross section based on electron group parameters provided by an embodiment of the present invention;

[0055] Figure 3 It is a schematic diagram of an iterative training operation process provided by an embodiment of the present invention;

[0056] Figure 4 is a schematic diagram of a gas collision cross section provided by an embodiment of the present invention;

[0057] Figure 5 It is a structural schematic diagram of a gas collision cross section determination device based on electron group parameters provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 , is a flow chart of a method for determining a gas collision cross section based on an electron group parameter provided by an embodiment of the present invention, comprising:

[0060] S1. Obtaining the electron group parameters of the gas to be determined;

[0061] S2, inputting the electron group parameters into a preset cross-section prediction model for prediction to obtain a collision cross-section of the gas to be determined;

[0062] The training of the cross-section prediction model includes:

[0063] Obtain historical collision cross sections for multiple gases;

[0064] Solving according to the historical collision cross section to obtain a first electron group parameter for representing the historical parameter;

[0065] Taking the first electron group parameter as the input of the cross-section prediction model to be trained and taking the predicted collision cross section as the output, repeatedly performing the iterative training operation until the preset loss function converges, thereby obtaining a trained cross-section prediction model;

[0066] The iterative training operation includes:

[0067] The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter;

[0068] Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters;

[0069] Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function;

[0070] Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

[0071] Specifically, the electron population parameters include the electron drift velocity W e , density normalized longitudinal electron diffusion coefficient ND L and the effective ionization rate coefficient k effThe collision cross sections include: adsorption collision cross section, ionization collision cross section, vibration excitation collision cross section, electron excitation collision cross section and elastic collision cross section.

[0072] Specifically, the electron group parameters can be obtained by measuring the electron group experiment, such as using the pulse Thomson (PT) experiment to measure the electron group parameters of the gas to be determined, or by solving the Boltzmann equation for the collision cross section. However, there is no direct mapping relationship from the electron group data to the collision cross section. Therefore, this embodiment focuses on determining the collision cross section set from the electron group data, also known as the inverse group problem. At present, there are mainly two numerical techniques for solving the inverse group problem: iterative group technology and the application of artificial neural networks in recent years. However, these two methods have the following significant disadvantages: an important limitation of the solution of the group inverse problem is its pathological nature. Under this limitation, the iterative group technology relies on the intuition and experience of experts, and the trial-and-error nature of the method makes the program inefficient and difficult to replicate; compared with the equivalent network designed in a physical information manner, the traditional artificial neural network limits the ability of the network to reliably and self-consistently simulate multiple independent cross-section regressions; the degeneracy of the inverse group problem limits the prediction of multiple collision cross sections at the same time.

[0073] In a preferred embodiment of the present invention, the electron group parameters of the gas to be determined are first obtained; then the electron group parameters are input into a preset cross-section prediction model for prediction to obtain the collision cross-section of the gas to be determined. Wherein, the training of the cross-section prediction model includes: obtaining the historical collision cross-sections of multiple gases from a preset database, the preset database is the LXCat database; solving according to the historical collision cross-sections to obtain the first electron group parameters used to represent the historical parameters; using the first electron group parameters as the input of the cross-section prediction model to be trained and the predicted collision cross-section as the output, repeatedly performing iterative training operations until the preset loss function converges to obtain a trained cross-section prediction model; the iterative training operation includes: predicting the collision cross-section according to the current first electron group parameters to obtain the current predicted collision cross-section; and the current first electron group parameters of the first iterative training operation are the initial first electron group parameters; solving according to the current predicted collision cross-section to obtain the corresponding current second electron group parameters, and the corresponding electron group parameters can be obtained from the collision cross-section according to the Boltzmann equation; calculating according to the current first electron group parameters and the current second electron group parameters to obtain the loss function, the loss function is an error loss function, x i ,y i They are the first electron group parameter and the second electron group parameter respectively; according to the current first electron group parameter and the current predicted collision cross section, a calculation is performed to generate disturbance data; and according to the disturbance data, the current first electron group parameter is updated.

[0074] Preferably, solving according to the historical collision cross section to obtain the first electron group parameter used to represent the historical parameter includes:

[0075] Determining a first threshold energy corresponding to each historical collision cross section according to the historical collision cross sections;

[0076] Randomly select every two historical collision cross sections as a collision cross section pair, and generate cross section training data according to the collision cross section pair and the corresponding first threshold energy;

[0077] A solution is performed according to the cross-section training data and a preset solver to obtain a first electronic group parameter for representing a historical parameter.

[0078] In a preferred embodiment of the present invention, the first threshold energy corresponding to each historical collision cross section is determined according to the historical collision cross section, and every two historical collision cross sections σ are randomly selected. 1 and σ 2 As a collision cross section pair, according to the collision cross section pair and the corresponding first threshold energy ε 1 and ε 2 , generate cross-section training data, and generate multiple cross-section training data for the same type of cross-sections, such as:

[0079]

[0080] Among them, σs(ε) is the cross-section training data, σ1 and σ2 are collision cross-section pairs, and the corresponding first threshold energy is ε 1 and ε 2 , parameter r' is a random mixing ratio.

[0081] Then, according to the cross-section training data and the preset solver, the Boltzmann equation is used to solve and obtain the first electron group parameter used to represent the historical parameter. The preset solver is the bol sig+ solver. The cross-section training data is input into the solver to obtain the first electron group parameter corresponding to the training data. The first electron parameter represents the historical electron group parameter corresponding to the historical collision cross section. For example, the multi-point Boltzmann equation solver is used to calculate the historical electron group parameter corresponding to the historical collision cross section in the range of 70 to 860Td (1Td = 1Townsend = 10-21 Vm 2 ) of 150 logarithmic spacings under reduced electric field We and ND L and keff.

[0082] Schematically, the collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section, including:

[0083] Normalizing the current first electron group parameter to obtain the target electron group parameter;

[0084] Performing vector conversion according to the target electron group parameter to obtain a current first electron group parameter vector;

[0085] The collision cross section is predicted according to the current first electron group parameter vector to obtain the current predicted collision cross section.

[0086] In a preferred embodiment of the present invention, the electron drift velocity We, the density normalized longitudinal electron diffusion coefficient ND L The effective ionization rate coefficient keff must be normalized. All data except keff are normalized to [-1,1] after taking the logarithm. keff needs special treatment before normalization. 3 / s,10- 25 m 3 / s] is equivalent to 10- 25 m 3 / s, in [-10- 25 m 3 / s,0 m 3 / s) is equivalent to -10- 25 m 3 / s, and then perform the following calculation to obtain the target electron group parameters:

[0087]

[0088] Then, a vector conversion is performed according to the target electron group parameter to obtain the current first electron group parameter vector. and They are respectively the first electron group parameter vectors calculated under several reduced electric fields E / N, and the collision cross section is predicted for the current first electron group parameter vector to obtain the current predicted collision cross section.

[0089] Preferably, the calculation is performed according to the current first electron group parameter and the current predicted collision cross section to generate disturbance data, including:

[0090] Determining a second threshold energy corresponding to the current predicted collision cross section according to the current predicted collision cross section;

[0091] According to each current first electron group parameter, calculating the similarity between every two current first electron group parameters, and using the similarity as a disturbance judgment value;

[0092] Making a judgment according to the disturbance judgment value and a preset disturbance threshold;

[0093] The current first electronic group parameter corresponding to the disturbance judgment value being greater than the preset disturbance threshold is used as the first target parameter; and the first disturbance data is obtained by performing calculation according to the first target parameter and the corresponding second threshold energy;

[0094] The current first electronic group parameter whose disturbance judgment value is less than or equal to the preset disturbance threshold is used as the second target parameter; and the second disturbance data is obtained by calculating according to the second target parameter and the corresponding second threshold energy;

[0095] Disturbance data is generated according to the first disturbance data and the second disturbance data.

[0096] In a preferred embodiment of the present invention, the prediction of a large number of similar sections poses a substantial challenge to the solution of the inverse group problem. The prediction ability of the section prediction model can be improved by limiting the training data to the disturbances around the reference section set. To this end, a perturbation data, i.e., a weighted mixture of the predicted section data, is proposed to train the model, which includes three stages: an initialization stage, an exploration stage, and a refinement stage, such as Figure 3 First, according to the current predicted collision cross section, the second threshold energy ε corresponding to the current predicted collision cross section is determined. s and ε c，i ; ε s and ε c，i represents the threshold energy of the current predicted collision cross section respectively; according to each current first electron group parameter, the similarity r between each two current first electron group parameters is calculated, and the similarity is used as the disturbance judgment value. A value close to 1 will cause the current first electron group parameter σ c and σ c，i Small perturbations around , while values ​​close to 0 lead to large perturbations. Values ​​of r greater than 1 can be used to generate the current first electron group parameter σ c and σ c，i The surrounding enhanced disturbances are used to expand the solution space beyond the available data. The degree of these disturbances defines the network's ability to explore the solution space or improve existing solutions. According to the disturbance judgment value and the preset disturbance threshold, the judgment is made; the current first electron group parameter corresponding to the disturbance judgment value greater than the preset disturbance threshold is used as the first target parameter; and the first target parameter and the corresponding second threshold energy are calculated to obtain the first disturbance data; the current first electron group parameter with a disturbance judgment value less than or equal to the preset disturbance threshold is used as the second target parameter; and the second target parameter and the corresponding second threshold energy are calculated to obtain the second disturbance data; according to the first disturbance data and the second disturbance data, the disturbance data is generated.

[0097] In another preferred embodiment of the present invention, if the training data, i.e., the first electronic group parameter, is restricted to a small perturbation, the solution may fall into a local minimum. On the contrary, a large perturbation may cause the model to be unable to determine a sufficiently accurate set of cross sections. The exploration phase and refinement phase of this embodiment are intended to strike a balance between these two regimes. In the exploration phase, the σ cMake large perturbations to help the network traverse the solution space outside the current fitting range; in the refinement stage, c Small perturbations are made for further refinement. Two iterations are performed during the exploration phase, while five iterations are performed during the refinement phase to help ensure that a particular solution is sufficiently refined after each exploration phase. For high-energy (>10 eV) processes, such as electronic excitation and ionization, r is sampled from the range [0.5, 0.8] in each iteration of the exploration phase, while in the refinement phase, this example sets r = 0.8. For low-energy processes, such as vibrational and elastic processes, r is sampled from the range [0.5, 1.5] during the exploration phase and from the range [0.8, 1.2] during the refinement phase. In order to reduce the impact of non-uniqueness in determining cross-section sets with multiple similar collision processes, the iterative process incrementally explores the solution space by using perturbations.

[0098] Preferably, updating the current first electron group parameters according to the disturbance data includes:

[0099] Updating the current first electron group parameters according to the first disturbance data;

[0100] Updating the current first electronic group parameters according to the second disturbance data;

[0101] The first disturbance data and the second disturbance data are used to update the current first electron group parameters alternately.

[0102] Specifically, the cross-section prediction model is a multi-branch neural network model (multi-branch ANN network model) that bridges the gap between the self-consistency requirement and the independent feature map of each cross-section. That is, for each cross-section, there is an independent dense layer block, and each dense layer block extends from a dense layer block. Each parallel branch is then allowed to develop a feature set specific to a single cross-section, while still ensuring that each regression is performed in the context of a complete cross-section set. This embodiment uses a multi-branch ANN in the following form:

[0103]

[0104] in, is a mapping defined by a dense weight matrix Wi and a bias vector Bi, and mish(x) = xtanh(ln(1+e x )) is a nonlinear activation function, and Form an array of n parallel branches, each of which uses A 2 The output of represents the nth section independently. and Each contains 64 elements, Each output n contains 1 element, while the first two layers contain 256 elements. The size of the weight matrix is ​​corresponding. The architecture diagram of the multi-branch ANN is as follows Figure 2 As shown, for this embodiment, the output end includes 5 neurons (N=5), which respectively represent the adsorption collision cross section, ionization collision cross section, vibration excitation collision cross section, electron excitation collision cross section and elastic collision cross section, and multiple collision cross sections can be predicted simultaneously.

[0105] Preferably, before the electron group parameters are input into a preset cross-section prediction model for prediction, the method further includes: normalizing the electron group parameters to obtain final electron group parameters.

[0106] In a preferred embodiment of the present invention, the gas C 5 F 10 The proposed iterative process is demonstrated by using the predictions of the O-section set. 64 neurons are selected for the hidden layer of each parallel branch. Figure 4 , the collision cross section set obtained for the gas in this embodiment through the above process is shown.

[0107] By implementing this embodiment, the electron group parameters of the gas to be determined are obtained; the electron group parameters are input into a preset cross-section prediction model for prediction to obtain the collision cross-section of the gas to be determined; wherein, the training of the cross-section prediction model includes: obtaining the historical collision cross-sections of multiple gases; solving according to the historical collision cross-sections to obtain the first electron group parameters used to represent the historical parameters; using the first electron group parameters as the input of the cross-section prediction model to be trained and the predicted collision cross-section as the output, repeatedly performing iterative training operations until the preset loss function converges to obtain a trained cross-section prediction model; the iterative training operation includes: predicting the collision cross-section according to the current first electron group parameters to obtain the current predicted collision cross-section; and the current first electron group parameters of the first iterative training operation are the initial first electron group parameters; solving according to the current predicted collision cross-section to obtain the corresponding current second electron group parameters; calculating according to the current first electron group parameters and the current second electron group parameters to obtain the loss function; calculating according to the current first electron group parameters and the current predicted collision cross-section to generate perturbation data; and updating the current first electron group parameters according to the perturbation data. The cross-section prediction model is optimized through iterative training operations, so that the model learns the relationship between the electron group parameters and the collision cross-section. At the same time, by adding perturbation data, the model gradually approaches the optimal collision cross-section. There is no need for manual cross-section correction and model correction. A mapping relationship from the electron group parameters to the collision cross-section is formed, which improves the prediction efficiency of the collision cross-section while also improving the accuracy of the prediction.

[0108] See also Figure 5, is a schematic structural diagram of a gas collision cross section determination device based on electron group parameters provided by an embodiment of the present invention, comprising:

[0109] A parameter acquisition module, used to acquire the electron group parameters of the gas to be determined;

[0110] A collision cross section determination module, used for inputting the electron group parameters into a preset cross section prediction model for prediction, to obtain a collision cross section of the gas to be determined;

[0111] Among them, the model training module is used for training the cross-section prediction model, including:

[0112] A historical cross section acquisition unit is used to obtain historical collision cross sections of multiple gases;

[0113] An electron group parameter solving unit, used for solving according to the historical collision cross section to obtain a first electron group parameter used to represent the historical parameter;

[0114] An iterative operation unit, used to use the first electron group parameter as the input of the cross-section prediction model to be trained and the predicted collision cross section as the output, and repeatedly perform iterative training operations until a preset loss function converges to obtain a trained cross-section prediction model;

[0115] The iterative training operation includes:

[0116] The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter;

[0117] Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters;

[0118] Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function;

[0119] Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

[0120] The present invention provides a gas collision cross section determination device based on electron group parameters, which obtains the electron group parameters of the gas to be determined according to the parameter acquisition module; inputs the electron group parameters into a preset cross section prediction model through the collision cross section determination module to predict and obtain the collision cross section of the gas to be determined; wherein, in the model training module, the cross section prediction model is trained, including: according to the historical cross section acquisition unit, the historical collision cross sections of multiple gases are obtained; in the electron group parameter solving unit, the historical collision cross sections are solved to obtain the first electron group parameter used to represent the historical parameter; through the iterative operation unit, the first electron group parameter is used as the input of the cross section prediction model to be trained to predict the collision cross section of the gas to be determined. The cross section is output, and the iterative training operation is repeatedly performed until the preset loss function converges to obtain a trained cross section prediction model; the iterative training operation includes: predicting the collision cross section according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter; solving according to the current predicted collision cross section to obtain the corresponding current second electron group parameter; calculating according to the current first electron group parameter and the current second electron group parameter to obtain the loss function; calculating according to the current first electron group parameter and the current predicted collision cross section to generate perturbation data; and updating the current first electron group parameter according to the perturbation data. The cross section prediction model is optimized through iterative training operations, so that the model learns the relationship between the electron group parameter and the collision cross section, and at the same time, the model is gradually approached to the optimal collision cross section by adding perturbation data, without the need for manual cross section correction and model correction, while forming a mapping relationship from the electron group parameter to the collision cross section, improving the prediction efficiency of the collision cross section while also improving the prediction accuracy.

[0121] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0122] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0123] Another embodiment of the present invention further provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for determining a gas collision cross section based on an electron group parameter as described in the above embodiment is implemented. The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0124] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and various interfaces and lines are used to connect various parts of the entire terminal device.

[0125] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med i aCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (F l ash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0126] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the gas collision cross-section determination method based on electron group parameters described in the above embodiment.

[0127] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0128] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for determining gas collision cross section based on electron group parameters, characterized in that: include: Obtaining the electron group parameters of the gas to be determined; Inputting the electron group parameters into a preset cross-section prediction model for prediction to obtain a collision cross-section of the gas to be determined; The training of the cross-section prediction model includes: Obtain historical collision cross sections for multiple gases; Solving according to the historical collision cross section to obtain a first electron group parameter for representing the historical parameter; Taking the first electron group parameter as the input of the cross-section prediction model to be trained and taking the predicted collision cross section as the output, repeatedly performing the iterative training operation until the preset loss function converges, thereby obtaining a trained cross-section prediction model; The iterative training operation includes: The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter; Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters; Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function; Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

2. A method for determining a gas collision cross section based on an electron group parameter according to claim 1, characterized in that: Solving according to the historical collision cross section, the first electron group parameter used to represent the historical parameter is obtained, including: Determining a first threshold energy corresponding to each historical collision cross section according to the historical collision cross sections; Randomly select every two historical collision cross sections as a collision cross section pair, and generate cross section training data according to the collision cross section pair and the corresponding first threshold energy; A solution is performed according to the cross-section training data and a preset solver to obtain a first electronic group parameter for representing a historical parameter.

3. A method for determining a gas collision cross section based on an electron group parameter according to claim 1, characterized in that: The collision cross section is predicted based on the current first electron group parameters to obtain the current predicted collision cross section, including: Normalizing the current first electron group parameter to obtain the target electron group parameter; Performing vector conversion according to the target electron group parameter to obtain a current first electron group parameter vector; The collision cross section is predicted according to the current first electron group parameter vector to obtain the current predicted collision cross section.

4. A method for determining a gas collision cross section based on an electron group parameter according to claim 1, characterized in that: Calculate based on the current first electron group parameters and the current predicted collision cross section to generate disturbance data, including: Determining a second threshold energy corresponding to the current predicted collision cross section according to the current predicted collision cross section; According to each current first electron group parameter, calculating the similarity between every two current first electron group parameters, and using the similarity as a disturbance judgment value; Making a judgment according to the disturbance judgment value and a preset disturbance threshold; The current first electronic group parameter corresponding to the disturbance judgment value being greater than the preset disturbance threshold is used as the first target parameter; and the first disturbance data is obtained by performing calculation according to the first target parameter and the corresponding second threshold energy; The current first electronic group parameter whose disturbance judgment value is less than or equal to the preset disturbance threshold is used as the second target parameter; and the second disturbance data is obtained by calculating according to the second target parameter and the corresponding second threshold energy; Disturbance data is generated according to the first disturbance data and the second disturbance data.

5. A method for determining a gas collision cross section based on an electron group parameter according to claim 4, characterized in that: The current first electron group parameters are updated according to the disturbance data, including: Updating the current first electron group parameters according to the first disturbance data; Updating the current first electronic group parameters according to the second disturbance data; The first disturbance data and the second disturbance data are used to update the current first electron group parameters alternately.

6. A method for determining a gas collision cross section based on an electron group parameter according to claim 1, characterized in that: The cross-section prediction model is a multi-branch neural network model.

7. A method for determining a gas collision cross section based on an electron group parameter according to claim 1, characterized in that: Before the electron group parameters are input into a preset cross-section prediction model for prediction, the method further includes: normalizing the electron group parameters to obtain final electron group parameters.

8. A gas collision cross section determination device based on electron group parameters, characterized in that: include: A parameter acquisition module, used to acquire the electron group parameters of the gas to be determined; A collision cross section determination module, used for inputting the electron group parameters into a preset cross section prediction model for prediction, to obtain a collision cross section of the gas to be determined; Among them, the model training module is used for training the cross-section prediction model, including: A historical cross section acquisition unit is used to obtain historical collision cross sections of multiple gases; An electron group parameter solving unit, used for solving according to the historical collision cross section to obtain a first electron group parameter used to represent the historical parameter; An iterative operation unit, used to use the first electron group parameter as the input of the cross-section prediction model to be trained and the predicted collision cross section as the output, and repeatedly perform iterative training operations until a preset loss function converges to obtain a trained cross-section prediction model; The iterative training operation includes: The collision cross section is predicted according to the current first electron group parameter to obtain the current predicted collision cross section; and the current first electron group parameter of the first iterative training operation is the initial first electron group parameter; Solve according to the current predicted collision cross section to obtain the corresponding current second electron group parameters; Calculate according to the current first electron group parameter and the current second electron group parameter to obtain a loss function; Calculation is performed according to the current first electron group parameters and the current predicted collision cross section to generate disturbance data; and the current first electron group parameters are updated according to the disturbance data.

9. A terminal device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for determining a gas collision cross section based on an electron group parameter as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for determining a gas collision cross section based on electron group parameters as described in any one of claims 1 to 7.

Images