Trace material evidence liquid chromatography analysis method and system

Abstract

The invention relates to a liquid chromatographic analysis method and system for trace material evidences. The method comprises the following steps: acquiring chromatographic data, and carrying out data preprocessing to obtain a chromatographic data set; optimizing a numerical simulation process in the initial chromatographic process description model by adopting a concentration-preserving discontinuous finite element method, solving a numerical value by adopting a third-order Runge-Kutta method, constructing a target chromatographic process description model to describe the chromatographic process, and obtaining a component identification result; and performing quantitative and qualitative analysis on the component identification result, generating a liquid chromatogram analysis report, and outputting the liquid chromatogram analysis report. A chromatographic process description model is optimized by adopting a concentration-preserving discontinuous finite element method and a third-order Runge-Kutta method, so that the analysis of a complex sample can be completed in a short time, and the processing speed of liquid chromatographic data of the model can be increased; by performing quantitative and qualitative analysis on the component identification result and outputting a liquid chromatogram analysis report, a user can intuitively view and understand the analysis result, and subsequent decision making is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of compound component identification, and in particular to a trace evidence liquid chromatography analysis method and system. Background Art

[0002] Trace evidence is a fine material that can prove the true situation of a case. Trace evidence is of various types, small in size, and easy to be contaminated or lost. It is often necessary to pay attention to discovery, extraction and fixation. Physical and chemical methods of analyzing material components are mainly used for inspection, especially instrumental analysis methods. In the past few decades, liquid chromatography in separation science has been widely used. Its physical principle is: using liquid as the mobile phase, using a high-pressure infusion system, a single solvent with different polarities or a mixed solvent of different proportions, a buffer solution and other mobile phases are pumped into a chromatographic column equipped with a stationary phase. After the components in the column are separated, they enter the detector for detection, thereby realizing the analysis of the sample. Chromatographic analysis methods also play a key role in a large number of research works that have won the Nobel Prize in Chemistry. Since the common trace evidence in the public security criminal investigation and forensic identification system exists in solid or liquid form, liquid chromatography has therefore become one of the important methods for physical and chemical inspection of trace evidence.

[0003] At present, computational chemists have developed various computational algorithms to solve various liquid chromatography problems or use general software such as COMSOL Multiphysics or MatLab's PDE software package. However, the calculation time of the algorithms based on the software package is very slow, and the accuracy does not meet the application requirements. The algorithms developed by chemists have poor adaptability to model parameters and do not meet the requirements of trace evidence. In addition, the codes of most efficient algorithms are not public, and it is impossible to effectively measure the applicability of these algorithms to the trace evidence liquid chromatography model.

[0004] Therefore, the traditional liquid chromatography analysis method has the problems of low analysis accuracy and large errors. Summary of the invention

[0005] Based on this, in order to solve the above technical problems, a trace evidence liquid chromatography analysis method and system are provided, which can improve the processing speed of liquid chromatography data, ensure the high accuracy of analysis results, and reduce errors.

[0006] A liquid chromatography analysis method for trace evidence, comprising:

[0007] Acquiring chromatographic data, and performing data preprocessing on the chromatographic data to obtain a chromatographic data set;

[0008] Based on the principle of liquid chromatography separation, an initial chromatography process description model is established according to the chromatography data set, and the numerical simulation process in the initial chromatography process description model is optimized by using the concentration-preserving discontinuous finite element method, and the third-order Runge-Kutta method is used to solve the numerical value in the initial chromatography process description model to construct a target chromatography process description model;

[0009] Inputting the chromatographic data set into the target chromatographic process description model, describing the chromatographic process by the target chromatographic process description model, and obtaining a component identification result;

[0010] The component identification results are quantitatively and qualitatively analyzed, and a liquid chromatography analysis report is generated and output.

[0011] In one embodiment, data preprocessing is performed on the chromatographic data to obtain a chromatographic data set, including:

[0012] removing the baseline drift in the chromatographic data to obtain first chromatographic data;

[0013] Using filtering technology to filter the noise in the first chromatogram data to obtain second chromatogram data;

[0014] A chromatogram is generated based on the second chromatogram data, and peaks in the chromatogram are identified to generate a chromatogram data set.

[0015] In one embodiment, based on the liquid chromatography separation principle, an initial chromatography process description model is established according to the chromatography data set, including:

[0016] Determining the concentration data of each component in the trace evidence in the mobile phase and the stationary phase according to the chromatographic data set, and determining the ratio data of the mobile phase and the stationary phase;

[0017] Based on the separation principle of liquid chromatography, a concentration distribution equation of each component in the trace evidence in the test tube is established according to the concentration data and the ratio data;

[0018] An initial chromatographic process description model is established based on the concentration distribution equation.

[0019] In one embodiment, the numerical simulation process in the initial chromatographic process description model is optimized by using a concentration-preserving discontinuous finite element method, including:

[0020] Based on the initial chromatographic process description model, time and space are discretized to determine each discrete time point and space point;

[0021] The finite element method is used to perform interpolation and integration at each of the discrete time points and space points to obtain the numerical solution of the initial chromatographic process description model at each discrete point, thereby completing the optimization of the numerical simulation process.

[0022] In one embodiment, the third-order Runge-Kutta method is used to solve the numerical value in the initial chromatographic process description model, including:

[0023] In the process of running the initial chromatographic process description model for solving, a third-order Runge-Kutta method is used to determine each step length of the solution;

[0024] The slopes corresponding to the step lengths are obtained respectively, and the weighted average of the slopes is performed to complete the numerical solution.

[0025] In one embodiment, constructing a target chromatographic process description model includes:

[0026] Iterating and adjusting parameters of the initial chromatographic process description model, and optimizing the solution result to obtain an optimized solution result;

[0027] The optimized solution result is input into the initial chromatographic process description model after parameter adjustment, the output data is corrected by using the error analysis method, and the initial chromatographic process description model after parameter adjustment is optimized by using the corrected output data to obtain the target chromatographic process description model.

[0028] In one embodiment, the component identification results are quantitatively and qualitatively analyzed, and a liquid chromatography analysis report is generated and outputted, including:

[0029] Performing quantitative and qualitative analysis on the component identification results, and collecting analytical data generated during the analysis process;

[0030] The analysis data and component identification results are written into a liquid chromatography analysis report and output.

[0031] In one embodiment, the method further comprises:

[0032] The user query instruction is collected through the user interface, and the component identification result and the liquid chromatography analysis report are displayed according to the user query instruction.

[0033] A trace evidence liquid chromatography analysis system, the system comprising:

[0034] A data acquisition module, used for acquiring chromatographic data and performing data preprocessing on the chromatographic data to obtain a chromatographic data set;

[0035] A model building module, for establishing an initial chromatographic process description model based on the chromatographic data set based on the principle of liquid chromatography separation, optimizing the numerical simulation process in the initial chromatographic process description model by using the concentration-preserving discontinuous finite element method, solving the numerical value in the initial chromatographic process description model by using the third-order Runge-Kutta method, and building a target chromatographic process description model;

[0036] A component identification module, used for inputting the chromatographic data set into the target chromatographic process description model, describing the chromatographic process by the target chromatographic process description model, and obtaining a component identification result;

[0037] The result analysis module is used to perform quantitative and qualitative analysis on the component identification results, generate a liquid chromatography analysis report and output it.

[0038] The above-mentioned trace evidence liquid chromatography analysis method and system adopts the concentration-preserving discontinuous finite element method and the third-order Runge-Kutta method to optimize the chromatography process description model, which can complete the analysis of complex samples in a short time and improve the processing speed of the model liquid chromatography data; by performing quantitative and qualitative analysis on the component identification results and outputting the liquid chromatography analysis report, the user can intuitively view and understand the analysis results, which is convenient for subsequent decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A diagram showing the application environment of a liquid chromatography analysis method for trace evidence in an embodiment;

[0040] Figure 2 The figure is a schematic diagram of a process of liquid chromatography analysis of trace evidence in one embodiment;

[0041] Figure 3 is a schematic diagram of a user interface in one embodiment;

[0042] Figure 4 A schematic diagram of a user parameter setting interface in one embodiment;

[0043] Figure 5 A schematic diagram of the specific trajectories of various substances in a test tube in one embodiment;

[0044] Figure 6 is a structural block diagram of a trace evidence liquid chromatography analysis system in one embodiment;

[0045] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The trace evidence liquid chromatography analysis method provided in the present application example can be applied to Figure 1 In the application environment shown. Figure 1 As shown, the application environment includes a computer device 110. The computer device 110 can obtain chromatographic data and perform data preprocessing on the chromatographic data to obtain a chromatographic data set; the computer device 110 can establish an initial chromatographic process description model based on the chromatographic data set based on the liquid chromatography separation principle, and use the concentration-preserving discontinuous finite element method to optimize the numerical simulation process in the initial chromatographic process description model, and use the third-order Runge-Kutta method to solve the numerical value in the initial chromatographic process description model to construct a target chromatographic process description model; the computer device 110 can input the chromatographic data set into the target chromatographic process description model, and describe the chromatographic process through the target chromatographic process description model to obtain a component identification result; the computer device 110 can perform quantitative and qualitative analysis on the component identification result, generate a liquid chromatography analysis report and output it. Among them, the computer device 110 can be, but is not limited to, various personal computers, laptops, smart phones, robots, unmanned aerial vehicles, tablet computers and other devices.

[0048] In one embodiment, Figure 2 As shown, a trace evidence liquid chromatography analysis method is provided, comprising the following steps:

[0049] Step 202: Acquire chromatographic data and perform data preprocessing on the chromatographic data to obtain a chromatographic data set.

[0050] The chromatographic data may be data required for liquid chromatography analysis of trace evidence. The computer device may collect the chromatographic data in real time, or may obtain the chromatographic data from a database and then perform data preprocessing operations.

[0051] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include a data preprocessing process, the specific process including: removing the baseline drift in the chromatographic data to obtain the first chromatographic data; using filtering technology to filter the noise in the first chromatographic data to obtain the second chromatographic data; generating a chromatogram based on the second chromatographic data, identifying the peak in the chromatogram, and generating a chromatographic data set.

[0052] The data preprocessing of chromatographic data by computer equipment can include baseline correction, noise filtering, and peak detection. Among them, baseline correction is to remove the baseline drift in the chromatographic data to ensure the accuracy of the data; noise filtering is to use filtering technology to remove the noise in the data and improve the signal-to-noise ratio; peak detection is to identify the peak value in the chromatogram to provide a basis for subsequent analysis.

[0053] Step 204, based on the liquid chromatography separation principle, an initial chromatographic process description model is established according to the chromatographic data set, and the numerical simulation process in the initial chromatographic process description model is optimized by using the concentration-preserving discontinuous finite element method, and the third-order Runge-Kutta method is used to solve the numerical value in the initial chromatographic process description model to construct a target chromatographic process description model.

[0054] In this embodiment, the computer device can establish a mathematical model to describe the chromatography process based on the liquid chromatography separation principle, and then use an optimization algorithm to estimate the model parameters to ensure that the model can accurately describe the actual chromatography data.

[0055] Specifically, in one embodiment, a provided trace evidence liquid chromatography analysis method may also include a process of establishing a mathematical model, the specific process including: determining the concentration data of each component in the trace evidence in the mobile phase and the stationary phase based on the chromatographic data set, and determining the ratio data of the mobile phase and the stationary phase; based on the liquid chromatography separation principle, establishing the concentration distribution equation of each component in the trace evidence in the test tube according to the concentration data and the ratio data; and establishing an initial chromatographic process description model based on the concentration distribution equation.

[0056] Specifically, according to the principle of conservation of mass, we can establish an equation to describe the concentration distribution of each component in the test tube:

[0057]

[0058] Where x is the distance from the entrance of the test tube of length L, t is the time; C i and q i are the concentrations of the i-th substance in the mobile phase and the stationary phase respectively; C represents the i n-dimensional vector composed of; u is the velocity of the mobile phase, F is the ratio of the stationary phase to the mobile phase; μ<<1 represents the diffusion coefficient; g i is the initial concentration of the ith substance in the mobile phase of the test tube, and h i is the injection concentration of the ith substance; if the mixture contains n substances, then i = 1, 2, ..., n, that is, the above concentration distribution equation is composed of n equations.

[0059] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include an optimization process using the concentration-preserving discontinuous finite element method, the specific process including: based on the initial chromatographic process description model, discretizing time and space to determine each discrete time point and space point; using the finite element method to interpolate and integrate at each discrete time point and space point to obtain the numerical solution of the initial chromatographic process description model at each discrete point, thereby completing the optimization of the numerical simulation process.

[0060] In the key step of data simulation, the concentration-preserving discontinuous finite element method is used in this embodiment, and the calculation efficiency is much faster than traditional software such as COMSOL Multiphysics.

[0061] Specifically, taking two components as an example, the discrete format can be defined as:

[0062]

[0063] in:

[0064] Specifically, in this embodiment, it can be defined as follows:

[0065]

[0066] Among them, β0 and β1 are parameters that depend on trace evidence. In this embodiment, the default values ​​are β0=1 and β1=1 / 12; F is the ratio of the stationary phase to the mobile phase; u is the velocity of the mobile phase; C_{i,in} is the injection concentration of the i-th substance; D is the diffusion coefficient; P^k is the set of polynomials of the highest kth order; Z_N={1,2,3,...,N}.

[0067] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include a process of using a third-order Runge-Kutta method to solve a numerical value, and the specific process includes: in the process of running the initial chromatographic process description model for solving, using the third-order Runge-Kutta method to determine each step of the solution; respectively obtaining each slope corresponding to each step, and performing weighted averaging on each slope to complete the numerical solution.

[0068] Specifically, for the time discretization, the third-order Runge-Kutta method can be used, that is:

[0069] For the coefficients of c_h, you can use in:

[0070] Wherein, L is a simplified form of the semi-discrete format in space: (c_h)_t=L(c_h^n), and the third-order Runge-Kutta method is used for this ordinary differential equation; c_i^j is the coefficient of c_h^n represented by the Legendre basis function.

[0071] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include a process of constructing a target chromatographic process description model, the specific process including: iterating and adjusting parameters of the initial chromatographic process description model, and optimizing the solution result to obtain an optimized solution result; inputting the optimized solution result into the initial chromatographic process description model after parameter adjustment, correcting the output data using an error analysis method, and optimizing the initial chromatographic process description model after parameter adjustment using the corrected output data to obtain a target chromatographic process description model.

[0072] By performing multiple iterations and parameter adjustments on the initial chromatographic process description model, the solution results are optimized to ensure the precision and accuracy of the analysis; the results are corrected using error analysis methods to reduce the impact of errors and random errors.

[0073] Step 206: input the chromatographic data set into the target chromatographic process description model, describe the chromatographic process through the target chromatographic process description model, and obtain component identification results.

[0074] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include a process for performing result analysis, the specific process including: performing quantitative and qualitative analysis on component identification results, collecting analysis data generated during the analysis process; writing the analysis data and component identification results into a liquid chromatography analysis report and outputting it.

[0075] The computer device can perform quantitative analysis on the identified components and calculate the concentration of each component; it can also perform qualitative analysis on the components in combination with the chromatographic database and the standard spectrum to determine the specific substance. That is, in this embodiment, the computer device can use an efficient numerical solution algorithm to solve the liquid chromatography positive problem, obtain the component information of the sample, and thus determine the specific substance.

[0076] Step 208, perform quantitative and qualitative analysis on the component identification results, generate a liquid chromatography analysis report and output it.

[0077] In one embodiment, a provided trace evidence liquid chromatography analysis method may also include a data display process, the specific process including: collecting user query instructions through a user interface, and displaying component identification results and liquid chromatography analysis reports according to the user query instructions.

[0078] User interface such as Figure 3As shown, the computer device can display the chromatogram, quantitative and qualitative analysis results through the user interface. Among them, the automatically generated analysis report can include detailed results and analysis process, which is convenient for users to view and save.

[0079] The user can issue a user query command through the user interface, or Figure 4 The interface shown is used to set parameters, which makes it convenient for users to operate, view and analyze results.

[0080] In one embodiment, after specific parameters are given, a liquid chromatography analysis method for trace evidence is provided that can provide the kinetic performance of trace evidence in a solvent, wherein one result is as follows: Figure 5 As shown, Figure 5 Part a is the specific trajectory of substance a in the test tube; part b is the specific trajectory of substance b in the test tube; part c is the specific trajectory of substance c in the test tube; and part d is the specific trajectory of substance d in the test tube.

[0081] It should be understood that, although the various steps in the above-mentioned flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned flow chart may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0082] In one embodiment, Figure 6 As shown, a trace evidence liquid chromatography analysis system is provided, including a data acquisition module 610, a model building module 620, a component identification module 630 and a result analysis module 640, wherein:

[0083] The data acquisition module 610 is used to acquire chromatographic data and perform data preprocessing on the chromatographic data to obtain a chromatographic data set;

[0084] A model building module 620 is used to establish an initial chromatographic process description model based on the chromatographic data set based on the liquid chromatography separation principle, and optimize the numerical simulation process in the initial chromatographic process description model using the concentration-preserving discontinuous finite element method, and solve the numerical value in the initial chromatographic process description model using the third-order Runge-Kutta method to build a target chromatographic process description model;

[0085] The component identification module 630 is used to input the chromatographic data set into the target chromatographic process description model, describe the chromatographic process through the target chromatographic process description model, and obtain the component identification result;

[0086] The result analysis module 640 is used to perform quantitative and qualitative analysis on the component identification results, generate a liquid chromatography analysis report and output it.

[0087] In one embodiment, the data acquisition module 610 is also used to remove the baseline drift in the chromatographic data to obtain the first chromatographic data; use filtering technology to filter the noise in the first chromatographic data to obtain the second chromatographic data; generate a chromatogram based on the second chromatographic data, identify the peak in the chromatogram, and generate a chromatographic data set.

[0088] In one embodiment, the model building module 620 is also used to determine the concentration data of each component in the trace evidence in the mobile phase and the stationary phase based on the chromatographic data set, and determine the ratio data of the mobile phase and the stationary phase; based on the liquid chromatography separation principle, establish the concentration distribution equation of each component in the trace evidence in the test tube according to the concentration data and the ratio data; and establish an initial chromatographic process description model based on the concentration distribution equation.

[0089] In one embodiment, the model building module 620 is also used to discretize time and space based on the initial chromatographic process description model to determine each discrete time point and space point; use the finite element method to interpolate and integrate at each discrete time point and space point to obtain the numerical solution of the initial chromatographic process description model at each discrete point, thereby completing the optimization of the numerical simulation process.

[0090] In one embodiment, the model building module 620 is also used to use the third-order Runge-Kutta method to determine each step of the solution during the process of running the initial chromatographic process description model for solution; obtain each slope corresponding to each step, and perform weighted averaging on each slope to complete the numerical solution.

[0091] In one embodiment, the model building module 620 is also used to iterate and adjust parameters of the initial chromatographic process description model, and optimize the solution results to obtain optimized solution results; the optimized solution results are input into the initial chromatographic process description model after parameter adjustment, the output data is corrected using an error analysis method, and the initial chromatographic process description model after parameter adjustment is optimized using the corrected output data to obtain a target chromatographic process description model.

[0092] In one embodiment, the result analysis module 640 is also used to perform quantitative and qualitative analysis on the component identification results, collect analysis data generated during the analysis process; write the analysis data and component identification results into a liquid chromatography analysis report and output it.

[0093] In one embodiment, the result analysis module 640 is also used to collect user query instructions through the user interface, and display component identification results and liquid chromatography analysis reports according to the user query instructions.

[0094] In this application, advanced trace evidence liquid chromatography analysis methods and system architecture are used to efficiently solve liquid chromatography positive problems, achieving the following beneficial effects:

[0095] Efficient solution: Through the optimized numerical solution algorithm, the processing speed of liquid chromatography data is greatly improved, and the analysis of complex samples can be completed in a short time; the system has real-time data processing capabilities and can output analysis results in real time during sample testing to meet the needs of efficient work.

[0096] Accurate analysis: The use of advanced peak detection and parameter estimation methods ensures high accuracy of the analysis results, and can accurately identify and quantify trace components in the sample; through error correction and result optimization steps, the systematic error and random error are significantly reduced, ensuring the reliability of the analysis results.

[0097] Widely applicable: It is applicable to multiple fields such as criminal investigation, drug analysis, environmental monitoring, etc., and can meet the needs of trace evidence analysis in different application scenarios; the system can handle various types of samples, including complex mixtures and trace components, and has a wide range of applicability.

[0098] User-friendly: The system has an intuitive graphical user interface, which allows users to operate it without complex training, greatly facilitating daily use; through clear chromatograms and detailed analysis reports, users can intuitively view and understand the analysis results, facilitating subsequent decision-making.

[0099] High degree of automation: From sample injection to result output, the system realizes fully automated operation, reduces manual intervention and improves work efficiency; the system automatically generates analysis reports, including detailed results and analysis process, which is easy to record and share.

[0100] High reliability: The system's hardware and software are reasonably designed and operate stably, able to maintain reliable performance in a long-term, high-intensity working environment; the system strictly stores and manages the collected and processed data to ensure the security and integrity of the data.

[0101] Through the implementation of the trace evidence liquid chromatography analysis method and system, efficient and accurate solutions to trace evidence liquid chromatography positive problems have been achieved, which has significantly superior performance and broad application prospects.

[0102] 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 7As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an 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 the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a liquid chromatography analysis method for trace evidence is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0103] Those skilled in the art will understand that Figure 7 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0104] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of a liquid chromatography analysis method for trace evidence when executing the computer program.

[0105] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of a liquid chromatography analysis method for trace evidence are implemented.

[0106] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A liquid chromatography analysis method for trace evidence, characterized in that: The method comprises: Acquiring chromatographic data, and performing data preprocessing on the chromatographic data to obtain a chromatographic data set; Based on the principle of liquid chromatography separation, an initial chromatography process description model is established according to the chromatography data set, and the numerical simulation process in the initial chromatography process description model is optimized by using the concentration-preserving discontinuous finite element method, and the third-order Runge-Kutta method is used to solve the numerical value in the initial chromatography process description model to construct a target chromatography process description model; Inputting the chromatographic data set into the target chromatographic process description model, describing the chromatographic process by the target chromatographic process description model, and obtaining a component identification result; The component identification results are quantitatively and qualitatively analyzed, and a liquid chromatography analysis report is generated and output.

2. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: The chromatographic data is preprocessed to obtain a chromatographic data set, including: removing the baseline drift in the chromatographic data to obtain first chromatographic data; Using filtering technology to filter the noise in the first chromatogram data to obtain second chromatogram data; A chromatogram is generated based on the second chromatogram data, and peaks in the chromatogram are identified to generate a chromatogram data set.

3. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: Based on the liquid chromatography separation principle, an initial chromatography process description model is established according to the chromatography data set, including: Determining the concentration data of each component in the trace evidence in the mobile phase and the stationary phase according to the chromatographic data set, and determining the ratio data of the mobile phase and the stationary phase; Based on the separation principle of liquid chromatography, a concentration distribution equation of each component in the trace evidence in the test tube is established according to the concentration data and the ratio data; An initial chromatographic process description model is established based on the concentration distribution equation.

4. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: The numerical simulation process in the initial chromatographic process description model is optimized by using the concentration-preserving discontinuous finite element method, including: Based on the initial chromatographic process description model, time and space are discretized to determine each discrete time point and space point; The finite element method is used to perform interpolation and integration at each of the discrete time points and space points to obtain the numerical solution of the initial chromatographic process description model at each discrete point, thereby completing the optimization of the numerical simulation process.

5. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: The third-order Runge-Kutta method is used to solve the numerical value in the initial chromatographic process description model, including: In the process of running the initial chromatographic process description model for solving, a third-order Runge-Kutta method is used to determine each step length of the solution; The slopes corresponding to the step lengths are obtained respectively, and the weighted average of the slopes is performed to complete the numerical solution.

6. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: Construct the target chromatographic process description model, including: Iterating and adjusting parameters of the initial chromatographic process description model, and optimizing the solution result to obtain an optimized solution result; The optimized solution result is input into the initial chromatographic process description model after parameter adjustment, the output data is corrected by using the error analysis method, and the initial chromatographic process description model after parameter adjustment is optimized by using the corrected output data to obtain the target chromatographic process description model.

7. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: Perform quantitative and qualitative analysis on the component identification results, generate and output a liquid chromatography analysis report, including: Performing quantitative and qualitative analysis on the component identification results, and collecting analytical data generated during the analysis process; The analysis data and component identification results are written into a liquid chromatography analysis report and output.

8. The trace evidence liquid chromatography analysis method according to claim 1, characterized in that: The method further comprises: The user query instruction is collected through the user interface, and the component identification result and the liquid chromatography analysis report are displayed according to the user query instruction.

9. A trace evidence liquid chromatography analysis system, characterized in that: The system comprises: A data acquisition module, used for acquiring chromatographic data and performing data preprocessing on the chromatographic data to obtain a chromatographic data set; A model building module, for establishing an initial chromatographic process description model based on the chromatographic data set based on the principle of liquid chromatography separation, optimizing the numerical simulation process in the initial chromatographic process description model by using the concentration-preserving discontinuous finite element method, solving the numerical value in the initial chromatographic process description model by using the third-order Runge-Kutta method, and building a target chromatographic process description model; A component identification module, used for inputting the chromatographic data set into the target chromatographic process description model, describing the chromatographic process by the target chromatographic process description model, and obtaining a component identification result; The result analysis module is used to perform quantitative and qualitative analysis on the component identification results, generate a liquid chromatography analysis report and output it.

10. The trace evidence liquid chromatography analysis system according to claim 9, characterized in that: The data acquisition module is also used to: remove the baseline drift in the chromatographic data to obtain first chromatographic data; Using filtering technology to filter the noise in the first chromatogram data to obtain second chromatogram data; A chromatogram is generated based on the second chromatogram data, and peaks in the chromatogram are identified to generate a chromatogram data set.

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