Machine learning-assisted sensor array data processing method and system for multiple meningitis

Abstract

The present invention provides a method and system for processing multiple meningitis data using a machine learning-assisted sensor array, belonging to the field of data processing and sensor array technology. The present invention comprises obtaining a sensor element; synthesizing the sensor element using a metal-organic framework, a covalent organic framework, and a hydrogen-bonded organic framework; constructing a sensor array based on the sensor element; obtaining a fluorescence signal based on the sensor array and meningitis cerebrospinal fluid; and obtaining information on the type of meningitis based on the fluorescence signal and a machine learning algorithm. The present invention enables data processing for different types of meningitis infections.

Description

Technical Field

[0001] The present application relates to the field of data processing and sensor array technology, and in particular to a method and system for processing multiple meningitis data using a machine learning-assisted sensor array. Background Art

[0002] Meningitis is a clinical syndrome characterized by inflammation of the meninges and is one of the most common infectious diseases of the central nervous system. Typical symptoms of meningitis include fever, neck stiffness, headache, seizures, and cognitive impairment. Depending on the etiology of the infection, meningitis can be classified as bacterial, tuberculous, viral, cryptococcal, or fungal.

[0003] Despite continuous technological advancements, significant shortcomings and challenges remain in processing data on various types of meningitis. In data analysis and modeling, models are complex: Advanced analytical techniques (such as machine learning and deep learning) can process complex data, but model training and optimization require significant computing resources and data and are susceptible to data bias. Regarding computing resources and efficiency, there are significant computational demands: Processing high-dimensional data (such as imaging and genomic data) requires significant computing resources and storage, which can result in high costs and long processing times. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to provide a method and system for processing multiple meningitis data using a machine learning-assisted sensor array.

[0005] The technical solution adopted by the present invention is:

[0006] In one aspect, an embodiment of the present invention provides a method for processing multiple meningitis data using a machine learning-assisted sensor array, the method comprising the following steps:

[0007] Obtaining a sensing element; wherein the sensing element is synthesized by a metal organic framework, a covalent organic framework and a hydrogen bond organic framework;

[0008] constructing a sensor array based on the sensor elements;

[0009] obtaining a fluorescent signal according to the sensor array and the meningitis cerebrospinal fluid;

[0010] Meningitis type information is obtained based on the fluorescence signal and the machine learning algorithm.

[0011] Furthermore, the obtaining of the sensor element comprises the following steps:

[0012] synthesize metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks using synthetic methods;

[0013] The synthesis methods include one-pot method, post-synthesis method and hydrothermal method;

[0014] In carrying out the synthesis method, a variety of organic ligands and metal salts are used to react in a solvent;

[0015] The metal organic framework, the covalent organic framework and the hydrogen bond organic framework are placed in a 3+ soaked in a solution to obtain a sensing element.

[0016] Furthermore, constructing a sensor array based on the sensor elements includes the following steps:

[0017] A plurality of the sensing elements are used to construct a sensing array.

[0018] Furthermore, obtaining a fluorescent signal based on the sensor array and the meningitis cerebrospinal fluid comprises the following steps:

[0019] Obtaining meningitis cerebrospinal fluid for several types of infection;

[0020] reacting each of the sensing elements in the sensing array with samples of the meningitis cerebrospinal fluid of different infection types to obtain sensing signal information;

[0021] A fluorescence signal is obtained according to the sensing signal information.

[0022] Furthermore, the step of reacting each of the sensor elements in the sensor array with samples of the meningitis cerebrospinal fluid of different infection types to obtain sensor signal information comprises the following steps:

[0023] 20 μL of the meningitis cerebrospinal fluid of different infection types was reacted with 50 μL of each of the sensing elements to obtain sensing signal information.

[0024] Furthermore, obtaining the fluorescence signal according to the sensor signal information includes the following steps:

[0025] The sensing signal information is used to obtain a fluorescence response signal generated by the sensing element when the excitation wavelength is 313 nm;

[0026] According to the fluorescence response signal, Eu 3+ The emission wavelength is 616 nm.

[0027] Furthermore, obtaining meningitis type information based on the fluorescence signal and the machine learning algorithm further includes the following steps:

[0028] Processing and analyzing the collected fluorescence signals using a machine learning algorithm to obtain analysis data;

[0029] Build a classification model;

[0030] According to the analysis data and the classification model, pattern recognition and feature extraction are performed on the fluorescence signal to obtain meningitis type information.

[0031] In another aspect, an embodiment of the present invention provides a machine learning-assisted sensor array multiple meningitis data processing system, the system comprising:

[0032] The first module is used to prepare a sensing element; the sensing element is synthesized by a metal organic framework, a covalent organic framework and a hydrogen bond organic framework;

[0033] A second module is used to construct a sensor array based on the sensor elements;

[0034] a third module, configured to obtain a fluorescent signal based on the sensor array and the meningitis cerebrospinal fluid;

[0035] The fourth module is used to obtain meningitis type information based on the fluorescent signal and the machine learning algorithm.

[0036] On the other hand, an embodiment of the present invention provides a device for processing multiple meningitis data using a machine learning-assisted sensor array, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the device implements the method for processing multiple meningitis data using a machine learning-assisted sensor array as described above.

[0037] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method described above.

[0038] The embodiments of the present application include at least the following beneficial effects: This application provides a method and system for processing multiple types of meningitis data using a machine learning-assisted sensor array. The present invention comprises obtaining sensor elements; synthesizing the sensor elements using metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks; constructing a sensor array based on the sensor elements; obtaining a fluorescence signal based on the sensor array and meningitis cerebrospinal fluid; and obtaining information on the type of meningitis based on the fluorescence signal and a machine learning algorithm. The present invention can process data for different types of meningitis infections and assist in determining the disease type. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a method for processing multiple meningitis data using a machine learning-assisted sensor array according to an embodiment of the present invention;

[0040] Figure 2is a scanning electron microscope (SEM) image of 10 sensor elements in Example 3 provided by the present invention;

[0041] Figure 3 is the X-ray diffraction pattern (XRD) of the 10 sensor elements in Example 3 provided by the present invention;

[0042] Figure 4 is a Fourier transform infrared spectrum of 10 sensor elements in Example 3 provided by the present invention;

[0043] Figure 5 310 nm excitation wavelength before and after the reaction of the representative sensor element with different volumes of cerebrospinal fluid in Example 4 of the present invention;

[0044] Figure 6 This is the fluorescence spectrum before and after the reaction of the sensor element with 20 μL of cerebrospinal fluid at an excitation wavelength of 310 nm in Example 4 provided by the present invention;

[0045] Figure 7 Schematic diagram of the fluorescent signal box plot, correlation, confusion matrix of the classification model, and importance analysis of the sensor elements for detecting TBM cerebrospinal fluid and the reaction characteristics of the sensor array in Experimental Example 1 provided by the present invention;

[0046] Figure 8 The present invention provides a box plot of the fluorescent signal of the cerebrospinal fluid of the non-intracranial infection control group and the reaction characteristic of the sensor array, a loss function curve, and an ROC curve of the TBM cerebrospinal fluid and non-intracranial infection in Test Example 1;

[0047] Figure 9 Schematic diagram of the box plot, correlation, confusion matrix of the classification model and importance analysis of the sensor elements for detecting BM cerebrospinal fluid and characteristic fluorescence signals of the sensor array in Experimental Example 2 provided by the present invention;

[0048] Figure 10 Schematic diagram of the box plot, correlation, confusion matrix of the classification model and importance analysis of the sensor elements for detecting CM cerebrospinal fluid and characteristic fluorescence signals of the sensor array in Experimental Example 3 provided by the present invention;

[0049] Figure 11 Schematic diagram of the box plot, correlation, confusion matrix of the classification model and importance analysis of the sensor elements for detecting VM cerebrospinal fluid and characteristic fluorescence signals of the sensor array in Experimental Example 4 provided by the present invention;

[0050] Figure 12 The LDH graph, confusion matrix of the classification model, sensor element importance analysis, and loss function curve after the reaction of cerebrospinal fluid of different infection types with the sensor array in Experimental Example 5 provided by the present invention are provided;

[0051] Figure 13 Schematic diagram of ROC analysis of BM, CM, VM, and TBM cerebrospinal fluid under the classification model provided by the present invention;

[0052] Figure 14 It is a technical circuit diagram of the test example provided by the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0054] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0055] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0057] Before explaining the embodiments of the present application in detail, some of the nouns and terms involved in the embodiments of the present application are first explained. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0058] 1) MOF (Metal-Organic Frameworks): Metal-organic frameworks refer to three-dimensional porous materials formed by the combination of metal ions or metal clusters and organic ligands through coordination bonds, and are used in applications such as gas storage and catalysis.

[0059] 2) COF (Covalent Organic Frameworks): Covalent organic frameworks are two-dimensional or three-dimensional structures formed by organic molecules connected by covalent bonds. They are mainly used in hydrogen storage, catalysis and separation.

[0060] 3) HOF (Hydrogen-bonded Organic Frameworks): Hydrogen-bonded organic frameworks, organic framework materials self-assembled through hydrogen bonding, used in applications such as gas storage, catalysis and separation.

[0061] 4) Eu 3+ : It is the trivalent europium ion in the praseodymium element and has significant fluorescent properties.

[0062] 5) BM (Bacterial Meningitis): Bacterial meningitis, meningitis caused by bacterial infection.

[0063] 6) CM (Cryptococcal Meningitis): Cryptococcal meningitis, meningitis caused by infection with Cryptococcus neoformans.

[0064] 7) VM (Viral Meningitis): Viral meningitis, meningitis caused by viral infection.

[0065] 8)TBM (Tuberculous Meningitis): Tuberculous meningitis, meningitis caused by infection with Mycobacterium tuberculosis.

[0066] 9) ROC (Receiver Operating Characteristic) analysis: A method used to evaluate the performance of classification models.

[0067] The present invention is based on a comprehensive analysis strategy for meningitis using a machine learning-assisted cross-reactive sensor array. To improve the ability to identify complex samples, the present invention constructs 10 organic framework-doped Eu 3+ The sensor array, composed of 10 sensor elements, cross-reacted with samples of four different types of meningitis infection, producing distinct response patterns. The output signals of 10 sensor elements were used as characteristic signals, and machine learning was used to train and predict characteristic signals to enable data processing for different types of meningitis.

[0068] The present invention's machine learning-assisted sensor array data processing method for various meningitis cases proposes using a sensor array to cross-react with cerebrospinal fluid to generate a specific fluorescence response pattern. Combined with a machine learning algorithm, it demonstrates excellent classification performance and differentiation capabilities, and can perform image recognition and classification to assist in determining the type of disease.

[0069] The embodiments of the present invention are further described below with reference to the accompanying drawings.

[0070] On the one hand, the embodiment of the present invention provides a method for processing multiple meningitis data using a machine learning-assisted sensor array. Specifically, referring to Figure 1 , the method comprises the following steps:

[0071] S100, obtaining a sensing element; the sensing element is synthesized by using a metal organic framework, a covalent organic framework, and a hydrogen bond organic framework;

[0072] S200, constructing a sensor array according to the sensor elements;

[0073] S300, obtaining a fluorescence signal based on the sensor array and the meningitis cerebrospinal fluid;

[0074] S400: Obtain meningitis type information based on the fluorescence signal and the machine learning algorithm.

[0075] The present invention uses machine learning algorithms to analyze and classify these fluorescent signals, which can effectively assist in distinguishing different types of meningitis.

[0076] The embodiment of the present invention discloses that step S100 of acquiring a sensor element includes the following steps:

[0077] S110. Synthesize metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks using synthetic methods;

[0078] S120, Synthesis methods include one-pot method, post-synthesis method, and hydrothermal method;

[0079] S130, in the process of performing the synthesis method, using a plurality of organic ligands and metal salts to react in a solvent;

[0080] S140, placing metal organic frameworks, covalent organic frameworks and hydrogen bonded organic frameworks in a 3+ soaked in a solution to obtain a sensing element.

[0081] As an optional embodiment, the preparation of the metal organic framework (MOF) of the present invention:

[0082] Appropriate metal salts (such as aluminum, zinc, and copper salts) and organic ligands (such as phthalic acid and furanic acid) are selected as reaction materials. The reaction is carried out in a specific solvent (such as water, ethanol, or DMF), and MOF materials are synthesized using a one-pot, post-synthesis, or hydrothermal method. The hydrothermal method is typically performed under high temperature and high pressure, while the one-pot and post-synthesis methods are performed at room temperature and during post-synthesis processing, respectively. The resulting MOF material should be properly washed, dried, and activated to ensure its pore structure and stable properties.

[0083] Preparation of Covalent Organic Frameworks (COFs):

[0084] Select appropriate organic monomers (such as p-phenylenediamine, 2,6-lutidine, etc.) and react with chemical reagents such as acetic acid in an organic solvent (such as chloroform, tetrahydrofuran, etc.) to generate COF.

[0085] COF is prepared using a post-synthesis method to ensure that monomers are successfully connected through covalent bonds to form a two-dimensional or three-dimensional framework structure.

[0086] The synthesized COF material is washed and dried to remove unreacted substances and solvent residues.

[0087] Preparation of hydrogen-bonded organic frameworks (HOFs):

[0088] Select appropriate organic molecules that can self-assemble into framework structures through hydrogen bonds.

[0089] In a specific solvent environment, organic molecules can undergo hydrogen bond self-assembly by adjusting conditions such as temperature and pH value.

[0090] The obtained HOF materials need to undergo appropriate treatments to enhance their stability and performance, including drying and activation steps.

[0091] Eu 3+ Doping:

[0092] The prepared MOF, COF and HOF materials were immersed in a solution containing Eu 3+ solution, or directly add Eu during the synthesis process 3+ .

[0093] Eu can be achieved by suitable reaction conditions (such as temperature, time, pH value, etc.) 3+ Uniform doping in organic frameworks.

[0094] The doped material needs to be washed and dried to remove excess Eu 3+ and solvents.

[0095] The embodiment of the present invention discloses that step S200 constructs a sensor array based on sensor elements, including the following steps:

[0096] S210 , using a plurality of sensor elements to construct a sensor array.

[0097] The embodiment of the present invention discloses that step S300 obtains a fluorescent signal based on the sensor array and the meningitis cerebrospinal fluid, including the following steps:

[0098] S310, obtain cerebrospinal fluid for meningitis of several infection types;

[0099] S320, reacting each sensor element in the sensor array with samples of cerebrospinal fluid of different infection types of meningitis to obtain sensor signal information;

[0100] S330: Obtain a fluorescence signal according to the sensing signal information.

[0101] The embodiment of the present invention discloses that step S320 involves reacting each sensor element in the sensor array with samples of cerebrospinal fluid of different infection types to obtain sensor signal information, including the following steps:

[0102] S321. React 20 μL of cerebrospinal fluid of different infection types with 50 μL of each sensor element to obtain sensor signal information.

[0103] The embodiment of the present invention discloses that step S330 obtains a fluorescence signal based on the sensor signal information, including the following steps:

[0104] S331, sensing signal information, obtaining a fluorescence response signal generated by the sensing element when the excitation wavelength is 313 nm;

[0105] S332, according to the fluorescence response signal, obtain Eu 3+ The emission wavelength is 616 nm.

[0106] As an optional embodiment, the present invention takes 20 μL of cerebrospinal fluid samples from different types of meningitis infection and reacts them with each sensor element in the sensor array, ensuring uniform contact between the sample and the sensor element and controlling the reaction time to ensure data accuracy.

[0107] Fluorescence signal acquisition: Excite the sensor array using a light source with an excitation wavelength of 313 nm. Measure the fluorescence emission intensity of each sensor element at a wavelength of 616 nm using a spectrometer or fluorescence detector. Record the fluorescence response signal of each sensor element in different meningitis CSF samples for subsequent analysis.

[0108] The embodiment of the present invention discloses that step S400 obtains meningitis type information based on the fluorescence signal and the machine learning algorithm, and further includes the following steps:

[0109] S410, processing and analyzing the collected fluorescence signals using a machine learning algorithm to obtain analysis data;

[0110] S420, building a classification model;

[0111] S430. Perform pattern recognition and feature extraction on the fluorescence signal based on the analysis data and the classification model to obtain meningitis type information.

[0112] The data preprocessing of the present invention includes normalizing and standardizing the collected fluorescence signals to eliminate systematic errors and sample differences, and organizing the fluorescence signal data into the input format required by the machine learning algorithm, such as feature vectors or matrices.

[0113] The present invention constructs a classification model by selecting a suitable machine learning algorithm (such as support vector machine, random forest, neural network, etc.) and training it using fluorescence signal data. Through cross-validation and model optimization, the accuracy and robustness of the classification model are ensured.

[0114] The type identification of the present invention: the processed fluorescence signal is input into a trained classification model to predict the infection type of the meningitis sample. The classification results output by the model are used to obtain specific information about different infection types of meningitis.

[0115] As an optional embodiment, the present invention proposes a comprehensive analysis strategy for meningitis based on machine learning-assisted cross-reactive sensor arrays, and constructs 10 organic framework-doped Eu 3+ In this study, the sensor array reacted with potential targets in the cerebrospinal fluid of meningitis with different infection types to generate specific fluorescent response signals, which were then processed using a machine learning algorithm. This demonstrated that the sensor array combined with the machine learning algorithm can effectively distinguish between different types of meningitis infection data.

[0116] In one aspect of the present invention, a sensor array consisting of 10 sensor elements is provided, wherein the sensor elements are made of MOF, COF and HOF doped with Eu. 3+ form.

[0117] The present invention reacts 20 μL of cerebrospinal fluid of different infection types with 50 μL of each sensor element to generate a specific fluorescence response signal when the excitation wavelength is 313 nm. 3+ The strongest emission at 616nm is used as the fluorescence signal output of the sensor element. A classification model is constructed for the fluorescence signal using a machine learning algorithm to realize data processing of different types of meningitis infection and obtain infection type information.

[0118] Another aspect of the present invention provides a method for preparing the sensor array according to the previous aspect of the present invention, comprising the following steps:

[0119] (1) Preparation of MOF, COF and HOF: They can be synthesized by one-pot method, post-synthesis method or hydrothermal method, including the use of different organic ligands and metal salts to react in solvent to generate various MOFs and the formation of HOFs through hydrogen bond self-assembly. COF is produced by post-synthesis method by reacting p-phenylenediamine and 2,6-lutidine with acetic acid in an organic mixed solution.

[0120] (2)Eu 3+ Doping: The synthesized MOF, COF and HOF are doped with Eu 3+ Soak in the solution, or directly add Eu during the reaction 3+ , realizing Eu doping in various organic frameworks 3+ , enhancing its fluorescence properties.

[0121] The present invention proposes the use of MOF, HOF, COF doped with Eu 3+ A sensor array consisting of 10 sensor elements was constructed, and specific fluorescence response signals were generated by reacting with cerebrospinal fluid of different pathogenic types of meningitis. Finally, a classification model was constructed for the fluorescence response signals through a machine learning algorithm, which enabled data processing of different types of infection meningitis and obtained type result information.

[0122] As an optional embodiment, the present invention has the following embodiments:

[0123] Example 1 Materials and Chemical Reagents

[0124] Zirconium chloride octahydrate (ZrOCl2·8H2O), copper chloride hexahydrate (CuCl2·6H2O), terephthalic acid (H2BDC), benzene-1,3,5-tricarboxylic acid (H3BTC), 2,6-pyridinedicarboxylic acid (DPA), isophthalic acid, melamine, 1,2,4,5-benzenetetracarboxylic acid (H4BTEC), copper nitrate trihydrate (Cu(NO3)2·3H2O, zinc nitrate hexahydrate (Zn(NO3)2·6H2O), aluminum chloride hexahydrate (AlCl3·6H2 O), europium nitrate hexahydrate (Eu(NO3)3·6H2O), 2-methylimidazole, 4,4-bipyridine and 2,2′-bipyridine-3,3′-dicarboxylic acid, N,N-dimethylformamide (DMF), anhydrous sodium acetate and anhydrous ethanol, p-phenylenediamine (Pda), m-trimethylbenzene, 2,6-diformylpyridine (Dfp), 1,4-dioxane, acetonitrile and acetic acid can be obtained through conventional commercial channels, and deionized water (18.2 MΩ·cm) can be obtained using a Milli-Q water system.

[0125] Example 2 Instruments and Equipment

[0126] SEM was performed on a scanning electron microscope, and Fourier transform infrared (FT-IR) spectra were recorded on a Tensor 27 FT-IR spectrometer. Fluorescence intensity was recorded on a Fluoromax-4 fluorescence spectrometer. Fluorescence values ​​were recorded on a SpectraMaxR ID3 multi-mode microplate reader.

[0127] Example 3 Synthesis of 10 sensor elements in a sensor array

[0128] In this embodiment, a method for synthesizing a sensor array consisting of 10 sensor elements is provided, which specifically includes the following steps:

[0129] Eu 3+ UiO-66 (EM1): EM1 was synthesized using a one-pot method. First, ZrOCl2·8H2O (84 mg) and H2BDC (180 mg) were dissolved in 12 mL and 4 mL of N,N-dimethylformamide (DMF), respectively. After stirring for 5 minutes, 3 mL of CH3COOH was added while stirring. Eu(NO3)3·6H2O (1 M, 100 μL) was then quickly added to the solution. The solution was then placed in a 50 mL Teflon-lined stainless steel container and reacted at 90°C for 15 hours. The precipitate was collected by centrifugation (8000 rpm, 10 minutes) and washed three times with DMF. Finally, the precipitate was dried at 60°C for 6 hours to obtain EM1.

[0130] Eu 3+ HKUST-1 (EM2): EM2 was synthesized using a post-synthesis method. A methanol solution of H3BTC (430 mg) (50 mL) was slowly added to a methanol solution of Cu(NO3)2·3H2O (900 mg) and PVP (400 mg) (50 mL) and stirred until uniform. After stirring for 10 minutes, the solution was allowed to stand in the dark for one day. The resulting blue solid was collected, centrifuged (9000 rpm, 15 min), washed several times with methanol, and dried in an oven (50°C, 5 h). The resulting product (100 mg) was then immersed in Eu(NO3)3·6H2O (0.1 M, 10 mL) for 12 h. Finally, EM2 was collected and centrifuged (9000 rpm, 15 min), washed several times with deionized water, and dried in an oven (60°C, 10 h) to obtain EM2.

[0131] Eu 3+MIL-53 (EM3): EM3 was synthesized using a one-pot method. Briefly, AlCl₃·6H₂O (45.9 mg), H₂BDC (66.4 mg), and Eu(NO₃)₃·6H₂O (5 mg) were mixed in 30 mL of ethanol, DMF, and deionized water (DMF:H₂O:ethanol = 20:5:5). After stirring for 30 minutes, the solution was heated at 130°C for 8 hours in a 50 mL Teflon-lined stainless steel container. Finally, the solution was centrifuged (8000 rpm, 15 minutes), washed three times with deionized water and ethanol, and then dried in a 60°C oven for 10 hours to obtain EM3.

[0132] Eu-MOF-1 (EM4): 410 mg of Eu(NO3)3·6H2O and 150 mg of anhydrous sodium acetate were ultrasonically dissolved in 45 mL of deionized water to obtain solution A. 200 mg of benzene 1,3,5-tricarboxylic acid (H3BTC) was ultrasonically dissolved in 45 mL of ethanol to obtain solution B. Solution A was then added to solution B. After vigorous stirring at room temperature for 60 min, the precipitate was collected by centrifugation, washed three times with ethanol and water, and then dried in an oven at 60°C for 24 h to obtain EM4.

[0133] Eu-MOF-2 (EM5): In a hydrothermal tube, Eu(NO3)3·6H2O (0.1 mmol, 0.0435 g) and H4btec (0.1 mmol, 0.0254 g) were dissolved in 5 mL of deionized water. The solution was then heated at 120°C for 12 h. The reaction was cooled to room temperature to yield EM5.

[0134] Eu 3+ / Zn-MOF-1 (EM6): Zn(NO3)2·6H2O (0.3 mmol), H3BTC (0.3 mmol), and 4,4-bipyridine (0.15 mmol) were dissolved in 10 mL of DMF. Eu(NO3)3·6H2O (0.1 mmol) was then added to the solution with stirring. The mixture was then reacted at 120°C for 48 hours in a 20 mL Teflon-lined stainless steel container. Finally, the Zn-MOF-1 was collected by centrifugation, washed several times with DMF, and dried in an oven to yield EM6.

[0135] Eu 3+ / COF(EC): Pda (0.5 mmol, 54 mg) was first dissolved in a mixture of xylene (2 mL) and 1,4-dioxane (3 mL). Then, Dfp (0.75 mmol, 101 mg) was also dissolved in a mixture of xylene (2 mL) and 1,4-dioxane (3 mL). Subsequently, one of the mixtures was slowly poured into the other mixture. Acetic acid (0.5 mL, 6 M) was then added dropwise. After the mixture was left at room temperature for two days, a yellow precipitate was obtained by centrifugation. The powder was washed twice with tetrahydrofuran and once with acetone, and then dried at 65 ° C. The obtained COF was stored at room temperature for further use. To load Eu 3+ 60 mg of Eu(NO₃)₃·6H₂O was dissolved in acetonitrile. Then, 10 mg of COF was weighed and added to the terbium nitrate acetonitrile solution. After soaking for one day, the mixture was transferred to a Teflon-lined stainless steel autoclave and heated in an 85°C oven for 24 hours. Centrifugation then yielded a dark red precipitate. The product was washed three times with acetonitrile and dried at 65°C to obtain EC.

[0136] Eu 3+ MA-IPA (EH1): Melamine (MA, 0.1262 g, 0.0010 mol) was added to 30 mL of distilled water while stirring in an oil bath. The MA solution was then heated to 150°C until the MA powder was completely dissolved. The MA solution was then transferred to a 70°C oil bath and stirred for 10 min. Isophthalic acid (0.1662 g, 0.0010 mol) was added. After stirring for 3 h, the transparent mixture was removed and cooled to room temperature. Pure white MA-IPA crystals were isolated from the mixture. The MA-IPA crystals were then dispersed in 50 mL of ethanol containing Eu(NO₃)₃·6H₂O (0.1200 g, 0.2690 mmol). Stirring was continued at room temperature for 6 h, and the white solid was collected by centrifugation to yield EH1.

[0137] Eu 3+ / MA-TPA(EH2): Terephthalic acid (TPA, H2BDC; 0.0498 g, 0.3 mmol) and melamine (MA; 0.0378 g, 0.3 mmol) were stirred in 50 mL of deionized water at room temperature for 30 min. The mixed liquid was then refluxed in an oil bath at 170 °C for 2 h and then heated at room temperature for 20 h. -1 The mixture was cooled at a rate of 100 to obtain colorless layered crystals. The colorless crystals were washed with ethanol and dried in air. MA-TPA (50 mg) was then dispersed in 50 mL of ethanol solution containing Eu(NO3)3·6H2O (4 mg). The mixture was stirred at room temperature for 6 h and the white solid was collected by centrifugation. The product was washed with ethanol 3 times to remove Eu 3+ / MA-TPA surface physically adsorbed excess Eu(NO3)3·6H2O. 3+ / MA-TPA) was dried under vacuum at 80°C overnight to obtain EH2.

[0138] The 10 sensor elements prepared in the examples were characterized.

[0139] (1) The morphology of 10 sensor elements was observed under low-power and high-power microscopes using a HITACHI SU8220 scanning electron microscope to obtain SEM images. The results are as follows: Figure 2 As shown, it can be found that the sensing array consists of 7 Eu 3+ -MOF composition, 2 Eu 3+ -doped HOFs and 1 Eu 3+ -doped COF, where Eu 3+ / UiO-66 was synthesized from ZrOCl2·8H2O and H2BDC. 3+ / HKUST-1 is synthesized from H3BTC and Cu(NO3)2·3H2O. 3+ / MIL-53 is assembled from AlCl3·6H2O and H2BDC. Eu-MOF-1 is synthesized from Eu(NO3)3j6H2O and H3BTC. Eu-MOF-2 is synthesized from Eu(NO3)3·6H2O and H4btec. Zn-MOF-1 is composed of Zn(NO3)2·6H2O, H3BTC and 4,4-bipyridine. Zn-MOF-3 is assembled from Zn(NO3)2·6H2O, 2,2'-bipyridine-3,3'-dicarboxylic acid and 2-methylimidazole. They are represented as EM1, EM2, EM3, EM4, EM5, EM6 and EM7 respectively. Figure 2 The SEM images of AC show that EM1 and EM3 are spherical structures with average sizes of 40±5 and 200±5 nm. EM2 shows fine rods with a length of 1-2 μm and a width of 100-150 nm. The synthesized Eu-MOF-1 has a microsphere structure of 4 μm ( Figure 2 D) in. Figure 2 Figure E is the SEM image of Eu-MOF-2, showing a straw bundle structure. Zn-MOF-2 is an amorphous bulk, but the morphology of nanosheets can still be identified ( Figure 2 F in the Zn-MOF-3, while Zn-MOF-3 presents a flake structure with a size of 300±50nm ( Figure 2 G in Eu 3+ -doped COF is composed of Pda and Dfp, namely EC. The hydrogen bonding interaction between MA and IPA / TPA may drive the two Eu EH1 and EH23+ -doped HOFs. Figure 2 As shown in H, doped Eu 3+ The two-dimensional COFs assembled together and formed flower-like clusters. Eu-HOF-1 was synthesized from MA and IPA, and Eu-HOF-2 was synthesized from MA and PTA. Figure 2 As shown in Figures 1 and 2, the morphologies of the synthesized Eu-HOF-1 and Eu-HOF-2 are flower-like crystals and needle-like crystals, respectively. This type of HOF is a highly ordered two-dimensional layered structure formed by hydrogen bond self-assembly.

[0140] (2) The 9 sensor elements were tested by X-ray diffraction analysis, and the X-ray diffraction (XRD) patterns of the 9 sensor elements were obtained. It can be found that the XRD patterns of 7 EMs ( Figure 3 AG) is consistent with its standard XRD pattern, indicating that Eu 3+ The doping of EH1 and EH2 will not affect the crystal structure of MOFs. Figure 3 HI in the reference), referring to the crystal X-ray diffraction results reported in the previous literature, it shows that the HOF material has been successfully prepared and 3+ The post-synthesis did not destroy the original HOF crystal structure.

[0141] (3) Use Tensor 27 Fourier transform infrared spectrometer to record the Fourier transform infrared (FT-IR) spectra of 10 sensor elements (such as Figure 4 As shown in Figure 2, it can be found that the FT-IR spectra of the 10 sensing materials correspond to their organic ligands, confirming the successful synthesis of the materials. For EM1, the FT-IR spectra at 1581 and 1396 cm -1 ν as (-COO-) and ν s The stretching vibration of (-COO-) belongs to the characteristic band of the carboxylic acid group in the PTA ligand, and the peak of the benzene ring is at 1506 cm -1 For EM2, EM4 and EM6, at 1630cm -1 The absorption band at 1450cm belongs to the stretching vibration of the -COOH group; -1 The peak at 3,600 cm belongs to the stretching vibration of -C=C bond. -1 The peak at 1693 cm-1 should correspond to the Al-O stretching vibration. -1 The disappearance of the absorption band at 3+ -MOFs are formed by carboxyl groups and Eu 3+ ion coordination. EM5 at 3401 and 3486 cm -1 The infrared peak at 1667 cm is attributed to -OH stretching.-1 An infrared peak was observed at 1610 cm -1 , 1540cm -1 , 1495cm -1 and 1379cm -1 For EM7, 1595cm -1 The absorbance at 1620 cm is attributed to the vibration of NH in 2-methylimidazole. -1 ) peak, the greatly attenuated C=O stretching band (1716cm -1 ) indicates that there is a small amount of unreacted aldehyde on the periphery of COF. EH1 at 1570 cm -1 The absorption peak of EH1 is attributed to the antisymmetric stretching vibration of C=O bond (-COO- group). -1 The absorption peaks in the range are attributed to the stretching vibration of the OH bond (OH of the carboxyl group in IPA). EH2 at 1637 cm -1 The C=O vibration band at 3395cm belongs to -COOH. -1 and 3341cm -1 The absorption peaks belong to the antisymmetric stretching vibration and symmetric stretching vibration of NH2 in MA respectively.

[0142] Example 4: Detection of fluorescence signal from sensor element

[0143] (1) In a typical experiment, 50 μL of 10 sensor element solution (2.5 mg mL -1 ) was mixed with 250 μL of deionized water and reacted at room temperature for 20 minutes. The fluorescence spectrum of the sensor element was recorded using a fluorescence spectrometer at Ex / Em (nm) 310:616 nm (scan range: 550-720 nm).

[0144] (2) Fluoromax-4 spectrofluorometer was used to record the fluorescence intensity of different volumes (10, 20, 50, 200 μL) of cerebrospinal fluid (CSF) after reaction with the sensor element ( Figure 5 ), Ex / Em (nm) 310:616nm (scanning range: 550-720nm), determine the optimal cerebrospinal fluid concentration and material concentration to obtain the best fluorescence intensity signal change and improve the signal-to-noise ratio. It can be found that 10μL CSF has little effect on the sensor element, while 200μL CSF cannot detect the signal change through completely quenchable fluorescence. At the same time, considering that cerebrospinal fluid samples are difficult to obtain, 20μL cerebrospinal fluid was selected as the optimal reaction concentration. 20μL cerebrospinal fluid and 50μL sensor element solution (2.5mg·mL -1) were mixed, deionized water was added to keep the total volume at 300 μL, and the fluorescence spectra before (I0) and after (I) addition of cerebrospinal fluid samples were recorded in an 8×10 area in a 96-well plate at Ex / Em (nm) 310:616 nm. Figure 6 ).

[0145] (3) When the excitation wavelength is 310nm, Eu 3+ -MOF, Eu 3+ The spectra of -dopedHOFs show Eu 3+ Strong characteristic fluorescence, with emission peaks at 592, 616, 650 and 698 nm, is the characteristic of Eu 3+ Typical ff transition of Eu 3+ -dopedCOF has almost no fluorescence under excitation due to the 3+ Effectively cooperate to transfer energy to Eu under excitation 3+ , enhance the Eu in COF 3+ Fluorescence, making Eu 3+ When the excitation wavelength is 310nm, Eu 3+ -dopedCOF has the main emission wavelength of Eu 3+ Finally, the strongest emission at 616 nm was selected as the fluorescence signal output of the sensor element.

[0146] Example 5: Machine Learning Algorithm to Build a Classification Model

[0147] (1) Feature selection and machine learning algorithms were implemented using the open-source Python package scikit-learn. A two-sided 5×2 CV f-test was performed using mlextend. A random forest (RF) algorithm was trained using nested stratified k-fold cross-validation and compared with two dummy classifiers (simulating random guessing).

[0148] Test Example 1 Detection and Analysis of Tuberculosis Meningitis

[0149] refer to Figure 7 In this experimental example, the inventors reacted the sensor element with different types of cerebrospinal fluid (tuberculous meningitis and non-intracranial infection control group) and used a machine learning algorithm to construct a classification model to explore the detection and discrimination ability of the sensor element prepared in the above embodiment for tuberculous meningitis.

[0150] A total of 113 cerebrospinal fluid samples were collected for this study, including 56 samples from non-intracranial infection cases and 57 samples from TBM. Each of the 113 cerebrospinal fluid samples reacted with each of the 10 sensor elements six times, and the fluorescence signals were detected. Data were preprocessed by subtracting the fluorescence value after the reaction (I) from the fluorescence value before the reaction (I0). The difference between the two values ​​(ΔI = I0 - I) was used as the signal for the sensor analysis. To eliminate the possibility of interactions between the 10 sensor elements, the Spearman rank correlation coefficient (ρ) was calculated for each of the 10 sensor elements. The correlation between the sensor elements was low, indicating that all 10 sensor elements can provide important characteristic information.

[0151] refer to Figure 8 A machine learning algorithm was used to construct a classification model for TBM and non-intracranial infection based on the sensor analysis signals. To select the simplest model with the best performance, a random forest algorithm was used after testing to construct the classification model. Training used nested stratified cross-validation, using the error rate between prediction and actual results as the loss function, and the classification model with the lowest loss function was selected. The classification model constructed using the random forest algorithm achieved an average prediction accuracy of 96.6% for the sensor analysis signals output by the 10 sensor elements, with a prediction accuracy of 97.1% for TBM samples. Feature importance analysis using random forests was also used to identify the sensor elements with the greatest contribution. Finally, a receiver operating characteristic (ROC) curve (ROC) for TBM and non-intracranial infection was constructed using the random forest algorithm to evaluate the classification performance of the model. The results showed that the area under the curve (AUC) for this model was 0.98, indicating that the sensor array exhibited good detection accuracy and discrimination capabilities. A random forest algorithm was then used to construct a classification model based on the sensor analysis signals of the first five important features (EM1, EM2, EC, EH1, and EH2). The classification model achieved an average prediction accuracy of 95.3%, with a prediction accuracy of 95.9% for TBM samples. Comparing the prediction accuracy of the two models revealed that 10 sensor elements performed better than 5, demonstrating the importance of all ten sensor elements in the sensor array.

[0152] In summary, it can be found that the sensor array combined with the random forest algorithm to construct a classification model has the ability to distinguish TBM from non-intracranial infection, and has high detection accuracy, and has potential value for clinical detection of TBM.

[0153] Test Example 2 Detection and Analysis of Bacterial Meningitis

[0154] refer to Figure 9In this experimental example, the inventors reacted the sensor element with different types of cerebrospinal fluid (bacterial meningitis and non-intracranial infection control group) and used a machine learning algorithm to construct a classification model to explore the detection and discrimination ability of the sensor element prepared in the above embodiment for bacterial meningitis.

[0155] A total of 88 cerebrospinal fluid (CSF) samples were collected for this study, including 45 samples from non-intracranial infection cases and 43 samples from BM. Each of the 88 CSF samples was reacted with 10 sensor elements six times, yielding a total of 528 fluorescence signal acquisitions. The data were preprocessed and analyzed as described above. The correlation coefficients between the 10 sensor elements, assessed using the ρ coefficient, revealed low inter-group correlations, indicating that each sensor element in the sensor array provides significant feature information. A classification model constructed using the random forest algorithm achieved an average prediction accuracy of 97.7%, with a prediction accuracy of 97.3% for BM samples. Significance analysis revealed that EM4, EM5, and EM7 contributed significantly.

[0156] In summary, it can be found that the sensor array combined with the random forest algorithm to construct a classification model for BM and non-intracranial infection also has high detection accuracy and is capable of distinguishing BM and non-intracranial infection. Experimental Example 3 Detection and Analysis of Cryptococcal Meningitis

[0157] refer to Figure 10 In this experimental example, the inventors used the sensor element to react with different types of cerebrospinal fluid (cryptococcal meningitis and non-intracranial infection control group) and applied machine learning algorithms to explore the detection and discrimination capabilities of the sensor element prepared in the above embodiment for cryptococcal meningitis.

[0158] A total of 44 CSF samples were collected for this study, including 21 samples from non-intracranial infection CSF samples and 23 samples from CM CSF samples. Six replicate fluorescence changes at an emission wavelength of 616 nm were recorded using a fluorescence spectrometer. Fluorescence response box plots revealed that CM displayed completely different results, suggesting that CM exhibited distinct fluorescence response patterns from the other three CSF samples (TBM, BM, and non-intracranial infection). Furthermore, the ρ coefficient indicated low intergroup correlation among the 10 sensor elements. A classification model constructed using the random forest algorithm achieved an average prediction accuracy of 98.5%, with a prediction accuracy of 98.6% for CM samples. Significance analysis revealed that the most contributing sensor elements were EM1, EM2, EM3, and EH2, which differed significantly from the most contributing sensor elements in the aforementioned samples.

[0159] In summary, it can be found that the sensor array combined with the random forest algorithm to construct a classification model for CM and non-intracranial infection also has high detection accuracy and is capable of distinguishing CM from non-intracranial infection. Experimental Example 4 Detection and Analysis of Viral Meningitis

[0160] refer to Figure 11 In this experimental example, the inventors used the sensor element to react with different types of cerebrospinal fluid (viral meningitis and non-intracranial infection control group) and applied machine learning algorithms to explore the detection and discrimination capabilities of the sensor element prepared in the above embodiment for viral meningitis.

[0161] A total of 118 cerebrospinal fluid samples were collected for this study, including 56 samples from viral meningitis and 62 samples from non-intracranial infections. Data inclusion and preprocessing, as demonstrated in the aforementioned experiments, revealed that the fluorescence recognition patterns of each sensor element after reaction with VM cerebrospinal fluid differed significantly from those observed before. However, the ρ coefficient indicated relatively high inter-group correlation among the 10 sensor elements in the VM group, suggesting that multiple sensor elements provided similar feature information. A classification model was constructed using a randomized algorithm, achieving an average prediction accuracy of 97.7%, with a prediction accuracy of 97.3% for VM samples. Feature importance analysis revealed that EM1, EM2, EM7, and EH2 were the most contributing sensor components.

[0162] In summary, it can be found that the sensor array combined with the random forest algorithm to construct a classification model also has the ability to distinguish VM from non-intracranial infection.

[0163] Test Example 5 Differential Detection of Meningitis with Different Pathogens

[0164] refer to Figure 12 In this experimental example, the inventors further analyzed four types of meningitis to determine whether the sensor array could be used to specifically distinguish between meningitis caused by different pathogenic infections. First, the inventors used linear discriminant analysis (LDA) to compare the fluorescence response signals of the four CSF samples. They found that, despite some overlap, the four CSF samples with different pathogenic meningitis showed separation. This indicates that the sensor array captured four meningitis-specific signals and was capable of distinguishing CSF samples from different pathogenic infections. This result was also confirmed by constructing a confusion matrix using a random forest algorithm.

[0165] refer to Figure 13The confusion matrix of the classification model shows that the classifier has a high accuracy rate for VM (99.1%), but a lower accuracy rate for CM (92.8%). Furthermore, importance analysis revealed that the sensors EM1, EM2, EM4, and EM5 contribute most to distinguishing the four types of meningitis. Using a random forest algorithm to construct receiver operating characteristic (ROC) curves for CSF samples from the four infection types, the classification model achieved an AUC of 0.92 for BM, 0.95 for CM, 0.96 for VM, and 0.95 for TBM, respectively.

[0166] In summary, it can be found that the sensor array combined with the random forest algorithm to construct a classification model for four types of infection-type meningitis has the ability to distinguish four different types of infection-type meningitis. Among them, the prediction and detection accuracy of VM is higher, while the prediction and detection accuracy of CM is slightly lower. However, the AUCs of the four types of infection-type meningitis are all greater than 0.9, indicating that the model has good detection and discrimination capabilities and has potential value for clinical detection and differential detection.

[0167] Figure 14 This is the technical circuit diagram of the above test example.

[0168] On the other hand, an embodiment of the present invention further provides a machine learning-assisted sensor array multiple meningitis data processing system, the system comprising:

[0169] The first module is used to prepare the sensing element; the sensing element is synthesized through metal organic framework, covalent organic framework and hydrogen bond organic framework;

[0170] The second module is used to construct a sensor array based on the sensor elements;

[0171] a third module, configured to obtain a fluorescence signal based on the sensor array and the meningitis cerebrospinal fluid;

[0172] The fourth module is used to obtain meningitis type information based on fluorescence signals and machine learning algorithms.

[0173] An embodiment of the present invention provides a machine learning-assisted cross-reactive sensor array system for detecting multiple types of meningitis infections. The system includes: ① 10 sensor elements for reacting with cerebrospinal fluid samples and generating specific fluorescence response patterns; ② a machine learning algorithm module for classifying the generated fluorescence response signals to enable differentiation between normal cerebrospinal fluid samples and samples of different types of meningitis.

[0174] Among them, the 10 sensing elements in ① include 7 doped Eu 3+ MOF sensing element, a doped Eu 3+ COF sensing element and 2 Eu 3+ Doped HOF sensing element.

[0175] The machine learning algorithm in ② mainly refers to the random forest algorithm.

[0176] The detection of fluorescence signals requires an excitation wavelength of 310 nm, and includes a fluorescence signal with a maximum emission wavelength of 616 nm.

[0177] The system of the embodiment of the present invention is used to predict different types of meningitis infection.

[0178] As an optional embodiment, 7 Eu doped 3+ The MOF sensing elements were synthesized via a one-pot method, a post-synthesis method, or a hydrothermal method.

[0179] Eu-doped 3+ The COF sensor element was prepared by reacting p-phenylenediamine and 2,6-lutidine with acetic acid in a mixed solution of toluene and 1,4-dioxane, and then doped with Eu in acetonitrile. 3+ Heat treatment synthesis.

[0180] 2 Eu-doped 3+ The HOF sensing element is formed by hydrogen bond self-assembly and doped with Eu in acetonitrile. 3+ .

[0181] On the other hand, an embodiment of the present invention also provides a device for processing multiple meningitis data using a machine learning-assisted sensor array, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the device implements the aforementioned method for processing multiple meningitis data using a machine learning-assisted sensor array.

[0182] The multiple meningitis data processing device of the machine learning-assisted sensor array according to an embodiment of the present invention includes a memory and a processor.

[0183] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), 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.

[0184] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose stored instructions and data even if the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as the permanent storage device. In other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. The system memory may store some or all instructions and data required by the processor during operation. In addition, the memory may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic disks and / or optical disks may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a compact disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0185] The memory stores executable codes, which, when processed by the processor, can enable the processor to execute part or all of the above-mentioned methods.

[0186] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are used to enable a computer to execute the above method.

[0187] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0188] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A machine learning-assisted method for processing multiple meningitis data using a sensor array, characterized in that: The method comprises the following steps: Obtaining a sensing element; wherein the sensing element is synthesized by a metal organic framework, a covalent organic framework and a hydrogen bond organic framework; constructing a sensor array based on the sensor elements; obtaining a fluorescent signal according to the sensor array and the meningitis cerebrospinal fluid; Obtaining meningitis type information based on the fluorescence signal and a machine learning algorithm; The step of obtaining the sensing element comprises the following steps: synthesize metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks using synthetic methods; The synthesis methods include one-pot method, post-synthesis method and hydrothermal method; In carrying out the synthesis method, a variety of organic ligands and metal salts are used to react in a solvent; The metal organic framework, the covalent organic framework and the hydrogen bond organic framework are placed in a 3+ Soaking in a solution to obtain a sensing element; The sensor element includes EC, EH1 and EH2; the EC is made from p-phenylenediamine, isophthalic acid, 1,4-dioxane and 2,6-diformylpyridine; the EH1 is made from melamine and isophthalic acid; the EH2 is made from terephthalic acid and melamine; The machine learning algorithm includes a random forest algorithm; The sensing element also includes EM1, EM2, EM3, EM4, EM5, EM6 and EM7; the EM1 is synthesized from ZrOCl2·8H2O and H2BDC; the EM2 is synthesized from H3BTC and Cu(NO3)2·3H2O; the EM3 is assembled from AlCl3·6H2O and H2BDC; the EM4 is synthesized from Eu(NO3)3·6H2O and H3BTC; the EM5 is synthesized using Eu(NO3)3·6H2O and H4btec as raw materials; the EM6 is composed of Zn(NO3)2·6H2O, H3BTC and 4,4-bipyridine; the EM7 is assembled from Zn(NO3)2·6H2O, 2,2'-bipyridine-3,3'-dicarboxylic acid and 2-methylimidazole.

2. The method according to claim 1, characterized in that The construction of a sensor array based on the sensor elements comprises the following steps: A plurality of the sensing elements are used to construct a sensing array.

3. The method according to claim 1, characterized in that The method of obtaining a fluorescent signal based on the sensor array and the meningitis cerebrospinal fluid comprises the following steps: Obtaining meningitis cerebrospinal fluid for several types of infection; reacting each of the sensing elements in the sensing array with samples of the meningitis cerebrospinal fluid of different infection types to obtain sensing signal information; A fluorescence signal is obtained according to the sensing signal information.

4. The method according to claim 3, characterized in that The step of reacting each of the sensor elements in the sensor array with samples of the meningitis cerebrospinal fluid of different infection types to obtain sensor signal information comprises the following steps: 20 μL of the meningitis cerebrospinal fluid of different infection types was reacted with 50 μL of each of the sensing elements to obtain sensing signal information.

5. The method according to claim 3, characterized in that Obtaining a fluorescent signal according to the sensor signal information comprises the following steps: The sensing signal information is used to obtain a fluorescence response signal generated by the sensing element when the excitation wavelength is 313 nm; According to the fluorescence response signal, Eu 3+ The emission wavelength is 616 nm.

6. The method according to claim 1, characterized in that The method of obtaining meningitis type information according to the fluorescence signal and the machine learning algorithm further comprises the following steps: Processing and analyzing the collected fluorescence signals using a machine learning algorithm to obtain analysis data; Build a classification model; According to the analysis data and the classification model, pattern recognition and feature extraction are performed on the fluorescence signal to obtain meningitis type information.

7. A machine learning-assisted sensor array multi-meningitis data processing system, characterized in that: The system comprises: The first module is used to prepare a sensing element; the sensing element is synthesized by a metal organic framework, a covalent organic framework and a hydrogen bond organic framework; A second module is used to construct a sensor array based on the sensor elements; a third module, configured to obtain a fluorescent signal based on the sensor array and the meningitis cerebrospinal fluid; A fourth module is used to obtain meningitis type information based on the fluorescence signal and a machine learning algorithm; The preparation of the sensing element comprises the following steps: synthesize metal-organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks using synthetic methods; The synthesis methods include one-pot method, post-synthesis method and hydrothermal method; In carrying out the synthesis method, a variety of organic ligands and metal salts are used to react in a solvent; The metal organic framework, the covalent organic framework and the hydrogen bond organic framework are placed in a 3+ Soaking in a solution to obtain a sensing element; The sensor element includes EC, EH1 and EH2; the EC is made from p-phenylenediamine, isophthalic acid, 1,4-dioxane and 2,6-diformylpyridine; the EH1 is made from melamine and isophthalic acid; the EH2 is made from terephthalic acid and melamine; The machine learning algorithm includes a random forest algorithm; The sensing element also includes EM1, EM2, EM3, EM4, EM5, EM6 and EM7; the EM1 is synthesized from ZrOCl2·8H2O and H2BDC; the EM2 is synthesized from H3BTC and Cu(NO3)2·3H2O; the EM3 is assembled from AlCl3·6H2O and H2BDC; the EM4 is synthesized from Eu(NO3)3·6H2O and H3BTC; the EM5 is synthesized using Eu(NO3)3·6H2O and H4btec as raw materials; the EM6 is composed of Zn(NO3)2·6H2O, H3BTC and 4,4-bipyridine; the EM7 is assembled from Zn(NO3)2·6H2O, 2,2'-bipyridine-3,3'-dicarboxylic acid and 2-methylimidazole.

8. A device for processing multiple meningitis data using a machine learning-assisted sensor array, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for processing multiple meningitis data using a machine learning-assisted sensor array as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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