Easily-mixed bacterium identification method, device and equipment based on chain analysis model and medium

Abstract

The invention discloses an easily-mixed bacterium identification method, device and equipment based on a chain analysis model and a medium, and relates to the technical field of microbiological identification, and the method comprises the following steps: obtaining a protein fingerprint spectrum of a known strain easily becoming mass spectrum easily-mixed bacteria, and carrying out noise treatment on the protein fingerprint spectrum to obtain a treated protein fingerprint spectrum; performing peak value multiple analysis and comparison on different species in the processed protein fingerprint spectrum to obtain special and unique peak values corresponding to the different species, classifying the special and unique peak values by using a preset binary chain analysis model to construct a chain analysis model, training the chain analysis model, and determining the specific and unique peak values of the different species according to the chain analysis model. A target chain analysis model is obtained; and inputting the protein fingerprint spectrum of the to-be-identified miscible bacteria into the target chain analysis model for identification so as to output an identification result of the to-be-identified miscible bacteria. By means of the technical scheme, the dependency on the easily-mixed identification bacterium spectrum can be reduced, and the accuracy and efficiency of easily-mixed bacterium identification are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial identification, and in particular to a method, device, equipment and medium for identifying easily confusable bacteria based on a chain analysis model. Background Art

[0002] At present, mass spectrometry identification of microorganisms is performed by comparing the collected spectra with the spectra in the database, and different similarities are output in the form of scores. Generally, a higher score of 9.0 or 2.0 represents species level identification, a score between 6.0-9.0 or 1.7-2.0 represents genus level identification, and a score below 6.0 or below 1.7 generally means that the result is unreliable. In the process of mass spectrometry identification of microorganisms, due to the limitations of mass spectrometry conditions, some microorganisms with high similarity or close relationship will have multiple species results above 9.0 or 2.0 when identified. This situation is called mixed identification of mass spectrometry results, and such bacteria are called easily mixed identification bacteria. The current problem of mass spectrometry identification of easily confused bacteria can be solved on instruments using a database algorithm of a supermap. There is a method of using a small map database containing easily confused bacteria for secondary matching after the initial supermap matching. This method can solve the problem of identifying individual easily confused bacteria. However, this method is limited in distinguishing species, and the accuracy of distinction is affected by the number of strains in the secondary library. The secondary segmented database needs to include the strains to be distinguished and the number of strains required is large. It is difficult to collect uncommon strains in the first place, and even if a secondary segmented database is built, it cannot ensure accurate identification.

[0003] As can be seen from the above, how to reduce the dependence on the atlas of easily confused bacteria and improve the accuracy and efficiency of easily confused bacteria identification is a problem to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for identifying easily confusable bacteria based on a chain analysis model, which can. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a method for identifying easily confusable bacteria based on a chain analysis model, comprising:

[0006] Obtaining a protein fingerprint of a known strain that is easily confused with a mass spectrometer, and performing noise processing on the protein fingerprint to obtain the processed protein fingerprint;

[0007] Performing multiple peak analysis and comparison on different species in the processed protein fingerprint to obtain special unique peaks corresponding to different species, classifying each special unique peak using a preset binary chain analysis model to construct a chain analysis model, and training the chain analysis model to obtain a target chain analysis model;

[0008] The protein fingerprint of the easily confused bacteria to be identified is input into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

[0009] Optionally, the step of obtaining a protein fingerprint of a known strain that is easily confused with a mass spectrometer and performing noise processing on the protein fingerprint comprises:

[0010] Collecting microbial protein fingerprints, and screening protein fingerprints of known strains that meet preset easy-to-mix bacteria conditions and are easy to become mass spectrometry easy-to-mix bacteria from all the microbial protein fingerprints;

[0011] The protein fingerprint map is subjected to noise processing using a preset typing software.

[0012] Optionally, the noise processing of the protein fingerprint using a preset typing software includes:

[0013] The protein fingerprint spectrum is input into a preset typing software, and noise processing is performed on the protein fingerprint spectrum based on a wavelet transform method; the noise processing includes exponential smoothing elimination processing.

[0014] Optionally, the peak multiple analysis and comparison of different species in the processed protein fingerprint to obtain each special unique peak corresponding to different species includes:

[0015] The processed protein fingerprint map is subjected to multiple analyses using a preset typing software, and multiple peak comparisons are performed on different species in the processed protein fingerprint map to obtain the specific unique peaks corresponding to different species.

[0016] Optionally, the training of the chain analysis model to obtain a target chain analysis model includes:

[0017] Acquire historical species protein fingerprint peaks, and use the historical species protein fingerprint peaks to train the chain analysis model to obtain training results;

[0018] The parameters in the chain analysis model are modified and adjusted based on the training results to obtain a target chain analysis model.

[0019] Optionally, before inputting the protein fingerprint of the easily confusable bacteria to be identified into the target chain analysis model for identification, the method further includes:

[0020] The target chain analysis model is stored modularly and the model startup rules are set.

[0021] Optionally, the step of inputting the protein fingerprint of the easily confusable bacteria to be identified into the target chain analysis model for identification includes:

[0022] Obtaining a protein fingerprint of the easily confused bacteria to be identified, and determining whether the protein fingerprint satisfies the model startup rules;

[0023] If the protein fingerprint meets the model activation rule, the target chain analysis model is activated, and the protein fingerprint is input into the target chain analysis model for identification.

[0024] In a second aspect, the present application discloses a device for identifying easily confusable bacteria based on a chain analysis model, comprising:

[0025] A spectrum acquisition and processing module, used to acquire a protein fingerprint spectrum of a known strain that is easy to be easily confused with a mass spectrum, and perform noise processing on the protein fingerprint spectrum to obtain the processed protein fingerprint spectrum;

[0026] A model building module, used for performing multiple peak analysis and comparison on different species in the processed protein fingerprint map to obtain each special unique peak corresponding to different species, classifying each special unique peak using a preset binary chain analysis model to build a chain analysis model, and training the chain analysis model to obtain a target chain analysis model;

[0027] The easily confused bacteria identification module is used to input the protein fingerprint of the easily confused bacteria to be identified into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

[0028] In a third aspect, the present application discloses an electronic device, comprising:

[0029] Memory, used to store computer programs;

[0030] The processor is used to execute the computer program to implement the aforementioned method for identifying easily confusable bacteria based on the chain analysis model.

[0031] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned method for identifying easily confusable bacteria based on a chain analysis model are implemented.

[0032] It can be seen that the present application provides a method for identifying easily confused bacteria based on a chain analysis model, including obtaining a protein fingerprint spectrum of a known strain that is easily confused with a mass spectrometer, performing noise processing on the protein fingerprint spectrum to obtain the processed protein fingerprint spectrum; performing multiple peak analysis and comparison on different species in the processed protein fingerprint spectrum to obtain each special unique peak corresponding to different species, using a preset binary chain analysis model to classify each of the special unique peaks to construct a chain analysis model, and training the chain analysis model to obtain a target chain analysis model; inputting the protein fingerprint spectrum of the easily confused bacteria to be identified into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified. The present application performs noise processing on the protein fingerprint of known strains that are easy to become mass spectrometry easily confused bacteria, then performs peak multiple analysis and comparison on different species in the processed protein fingerprint, combines a binary chain analysis model to classify the special unique peaks obtained by the peak multiple analysis and comparison, thereby constructing a chain analysis model, reducing the dependence on the easily confused identification bacteria map, and uses the trained target chain analysis model to identify the protein fingerprint map, output the identification results, which can improve the accuracy and efficiency of the identification of easily confused bacteria, and can be applicable not only to the accurate distinction of two-by-two mixed identification bacteria, but also to the accurate identification of multiple mixed bacteria. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] Figure 1 This is a flow chart of a method for identifying easily confusable bacteria based on a chain analysis model disclosed in the present application;

[0035] Figure 2 This is a flow chart of a specific implementation method of the first multiple analysis and comparison disclosed in this application;

[0036] Figure 3 This is a flow chart of a specific implementation method of the second multiple analysis and comparison disclosed in this application;

[0037] Figure 4 This is a schematic diagram of the structure of an easily confusable bacteria identification device based on a chain analysis model disclosed in the present application;

[0038] Figure 5 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] At present, mass spectrometry identification of microorganisms is performed by comparing the collected spectra with the spectra in the database, and different similarities are output in the form of scores. Generally, a higher score of 9.0 or 2.0 represents species level identification, a score between 6.0-9.0 or 1.7-2.0 represents genus level identification, and a score below 6.0 or below 1.7 generally means that the result is unreliable. In the process of mass spectrometry identification of microorganisms, due to the limitations of mass spectrometry conditions, some microorganisms with high similarity or close relationship will have multiple species results above 9.0 or 2.0 when identified. This situation is called mixed identification of mass spectrometry results, and such bacteria are called easily mixed identification bacteria. The current mass spectrometry identification problem of easily confused bacteria is solved on instruments using a database algorithm of a class of database algorithms such as supermaps. There is a method of solving the identification problem of individual easily confused bacteria by using a small map database containing easily confused bacteria for secondary matching after the first supermap match. However, this method is limited in distinguishing species, and the accuracy of distinction is affected by the number of strains in the secondary library. The secondary subdivision database needs to contain the strains to be distinguished and the number of strains required is large. It is difficult to collect uncommon strains in the first place, and even if a secondary subdivision database is built, it cannot ensure accurate identification. As can be seen from the above, how to reduce the dependence on the map of easily confused bacteria and improve the accuracy and efficiency of easily confused bacteria identification is a problem to be solved in this field.

[0041] Mass spectrometry identifies microorganisms by collecting microbial protein fingerprints and comparing them with fingerprints in the database. During identification, protein fingerprints with high abundance and high concentration are used, while fingerprints with medium abundance or relatively low concentration are often ignored. Most of the protein fingerprints with high abundance are the same between similar strains, which makes it impossible to distinguish easily confused bacteria. By effectively utilizing the fingerprint peaks that are ignored when searching the database, accurate identification of easily confused bacteria can be achieved.

[0042] See also Figure 1 As shown, the embodiment of the present invention discloses a method for identifying easily confusable bacteria based on a chain analysis model, which may specifically include:

[0043] Step S11: obtaining a protein fingerprint of a known strain that is easily confused with a mass spectrometer, and performing noise processing on the protein fingerprint to obtain the processed protein fingerprint.

[0044] In this embodiment, microbial protein fingerprints are collected, and protein fingerprints of known strains that are easy to be easily mixed with bacteria by mass spectrometry and meet preset conditions for easy mixing with bacteria are screened from all the microbial protein fingerprints; the protein fingerprints are processed for noise using preset typing software to obtain the processed protein fingerprints.

[0045] The specific noise processing process is: inputting the protein fingerprint spectrum into a preset typing software, and performing noise processing on the protein fingerprint spectrum based on the wavelet transform method; the noise processing includes exponential smoothing elimination processing.

[0046] The present application removes noise in the spectrum through wavelet transform, eliminates burr peaks caused by system errors through exponential smoothing, and reduces unstable interference factors included in the model group.

[0047] Step S12: Perform multiple peak analysis and comparison on different species in the processed protein fingerprint map to obtain special unique peaks corresponding to different species, classify each special unique peak using a preset binary chain analysis model to construct a chain analysis model, and train the chain analysis model to obtain a target chain analysis model.

[0048] In this embodiment, a preset typing software is used to perform multiple analyses on the processed protein fingerprint map, and multiple peak comparisons are performed on different species in the processed protein fingerprint map to obtain special unique peaks corresponding to different species, and a preset binary chain analysis model is used to classify each of the special unique peaks to construct a chain analysis model, and the protein fingerprint peaks of historical species are obtained. The chain analysis model is trained using the protein fingerprint peaks of historical species to obtain training results, and the parameters in the chain analysis model are modified and adjusted based on the training results to obtain a target chain analysis model.

[0049] The specific process of constructing the chain analysis model in the present application is: based on each special unique peak, a single binary analysis model is established in the typing software. When the binary analysis model is established, the peak number, peak matching tolerance, peak abundance, peak abundance tolerance and other parameters should be preliminarily set according to the analysis results of the medium-sized data volume of the protein fingerprint map. Then, the established binary analysis model is cross-correlated to form a chain analysis structure, and a chain analysis model is constructed for the joint analysis of multiple easily confused bacteria.

[0050] The specific implementation methods of multiple analysis and comparison in this application include the following two situations:

[0051] (1) The specific implementation process of the first type of multiple analysis and comparison is as follows Figure 2As shown, an identification result is set as a genus level identification, which includes a common characteristic peak. First, a species with a special unique peak and the peak of the genus level identification are taken to form the first binary judgment. There are two results. One is that it contains a special peak and can be judged as the first species, and the other is that it does not contain a characteristic peak and can only be judged as the name of the genus. At this time, the second species with a characteristic peak is taken to form a second binary judgment with the genus identification, and so on until the analysis tree reaches indivisibility. At this time, the distinction of all easily confused identification bacteria can be completed;

[0052] (2) The specific implementation process of the second multiple analysis and comparison is as follows Figure 3 As shown, firstly, several indistinguishable bacteria are subjected to group peak analysis, and the bacteria are divided into two groups according to the distribution of characteristic peaks, and the difference peak combination between the two groups is used as the first binary branch judgment; then, peak analysis is performed again in the two groups, and the bacteria in the groups are divided into two small bacteria through specific peak combinations, forming the second binary branch judgment; and so on until they are finally indistinguishable.

[0053] In addition, the present application uses known historical species protein fingerprint peaks for model training, and modifies the peak judgment parameters in the model according to the training results until the model accuracy is stable, thereby obtaining the target chain analysis model.

[0054] Step S13: inputting the protein fingerprint of the easily confused bacteria to be identified into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

[0055] In this embodiment, the target chain analysis model is modularly stored, and the model startup rules are set to obtain the protein fingerprint spectrum of the easily confused bacteria to be identified, and determine whether the protein fingerprint spectrum meets the model startup rules. If the protein fingerprint spectrum meets the model startup rules, the target chain analysis model is started, and the protein fingerprint spectrum is input into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

[0056] In other words, the target chain analysis model is stored modularly, and the model activation rules are defined. For example, when mixed identifications at the species level appear in the identification results, and the mixed identifications happen to be species that can be distinguished by the model, the chain model is retrieved from the stored model library and the results are accurately interpreted.

[0057] The innovations and advantages of this application are: (1) differentiation is achieved by establishing a model to reduce map dependence; (2) the easily confused identification strains included in the model can be accurately identified; (3) it is not only applicable to the accurate differentiation of two-to-two mixed identification bacteria, but also to the accurate identification of multiple mixed identification bacteria. This application uses the difference points between strains and groups to connect the easily confused identification species through the difference points in series, and accurately identifies each strain in the easily confused identification strain group.

[0058] In this embodiment, a protein fingerprint of a known strain that is prone to becoming a mass spectrometry easily confused bacterium is obtained, and the protein fingerprint is subjected to noise processing to obtain the processed protein fingerprint; multiple peak analysis and comparison are performed on different species in the processed protein fingerprint to obtain special unique peaks corresponding to different species, and each of the special unique peaks is classified using a preset binary chain analysis model to construct a chain analysis model, and the chain analysis model is trained to obtain a target chain analysis model; the protein fingerprint of the easily confused bacterium to be identified is input into the target chain analysis model for identification to output the identification result of the easily confused bacterium to be identified. The present application performs noise processing on the protein fingerprint of known strains that are easy to become mass spectrometry easily confused bacteria, then performs peak multiple analysis and comparison on different species in the processed protein fingerprint, combines a binary chain analysis model to classify the special unique peaks obtained by the peak multiple analysis and comparison, thereby constructing a chain analysis model, reducing the dependence on the easily confused identification bacteria map, and uses the trained target chain analysis model to identify the protein fingerprint map, output the identification results, which can improve the accuracy and efficiency of the identification of easily confused bacteria, and can be applicable not only to the accurate distinction of two-by-two mixed identification bacteria, but also to the accurate identification of multiple mixed bacteria.

[0059] See also Figure 4 As shown, the embodiment of the present invention discloses a device for identifying easily confusable bacteria based on a chain analysis model, which may specifically include:

[0060] The spectrum acquisition and processing module 11 is used to acquire the protein fingerprint spectrum of the known strains that are easy to be easily confused with the mass spectrum, and perform noise processing on the protein fingerprint spectrum to obtain the processed protein fingerprint spectrum;

[0061] The model building module 12 is used to perform multiple peak analysis and comparison on different species in the processed protein fingerprint map to obtain the special unique peaks corresponding to different species, classify the special unique peaks using a preset binary chain analysis model to build a chain analysis model, and train the chain analysis model to obtain a target chain analysis model;

[0062] The easily confusable bacteria identification module 13 is used to input the protein fingerprint of the easily confusable bacteria to be identified into the target chain analysis model for identification, so as to output the identification result of the easily confusable bacteria to be identified.

[0063] In this embodiment, a protein fingerprint of a known strain that is prone to becoming a mass spectrometry easily confused bacterium is obtained, and the protein fingerprint is subjected to noise processing to obtain the processed protein fingerprint; multiple peak analysis and comparison are performed on different species in the processed protein fingerprint to obtain special unique peaks corresponding to different species, and each of the special unique peaks is classified using a preset binary chain analysis model to construct a chain analysis model, and the chain analysis model is trained to obtain a target chain analysis model; the protein fingerprint of the easily confused bacterium to be identified is input into the target chain analysis model for identification to output the identification result of the easily confused bacterium to be identified. The present application performs noise processing on the protein fingerprint of known strains that are easy to become mass spectrometry easily confused bacteria, then performs peak multiple analysis and comparison on different species in the processed protein fingerprint, combines a binary chain analysis model to classify the special unique peaks obtained by the peak multiple analysis and comparison, thereby constructing a chain analysis model, reducing the dependence on the easily confused identification bacteria map, and uses the trained target chain analysis model to identify the protein fingerprint map, output the identification results, which can improve the accuracy and efficiency of the identification of easily confused bacteria, and can be applicable not only to the accurate distinction of two-by-two mixed identification bacteria, but also to the accurate identification of multiple mixed bacteria.

[0064] In some specific embodiments, the graph acquisition and processing module 11 may specifically include:

[0065] A spectrum collection and screening module is used to collect microbial protein fingerprints and screen the protein fingerprints of known strains that are easy to become mass spectrometry easy-to-mix bacteria and meet the preset easy-to-mix bacteria conditions from all the microbial protein fingerprints;

[0066] The noise processing module is used to perform noise processing on the protein fingerprint map using a preset typing software.

[0067] In some specific embodiments, the graph acquisition and processing module 11 may specifically include:

[0068] The spectrum input module is used to input the protein fingerprint spectrum into a preset typing software, and perform noise processing on the protein fingerprint spectrum based on the wavelet transform method; the noise processing includes exponential smoothing elimination processing.

[0069] In some specific embodiments, the model building module 12 may specifically include:

[0070] The multiple comparison module is used to perform multiple analyses on the processed protein fingerprint map using a preset typing software, and to perform multiple comparisons of peak values ​​of different species in the processed protein fingerprint map to obtain the special unique peak values ​​corresponding to different species.

[0071] In some specific embodiments, the model building module 12 may specifically include:

[0072] A model training module, used to obtain historical species protein fingerprint peaks, and train the chain analysis model using the historical species protein fingerprint peaks to obtain training results;

[0073] The parameter modification and adjustment module is used to modify and adjust the parameters in the chain analysis model based on the training results to obtain a target chain analysis model.

[0074] In some specific embodiments, the easily confusable bacteria identification module 13 may specifically include:

[0075] The modular storage module is used to modularly store the target chain analysis model and set the model startup rules.

[0076] In some specific embodiments, the easily confusable bacteria identification module 13 may specifically include:

[0077] A judgment module is used to obtain a protein fingerprint of the easily confused bacteria to be identified, and to judge whether the protein fingerprint satisfies the model startup rules;

[0078] The identification module is used to start the target chain analysis model if the protein fingerprint meets the model startup rules, and input the protein fingerprint into the target chain analysis model for identification.

[0079] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the method for identifying easily confusable bacteria based on the chain analysis model performed by the electronic device disclosed in any of the aforementioned embodiments.

[0080] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0081] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0082] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the calculation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the easily confusable bacteria identification method based on the chain analysis model performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can also further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from an external device received by the easily confusable bacteria identification device based on the chain analysis model, the data 223 can also include data collected by its own input and output interface 25, etc.

[0083] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0084] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the method for identifying easily confusable bacteria based on the chain analysis model disclosed in any of the aforementioned embodiments are implemented.

[0085] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0086] The above is a detailed introduction to the method, device, equipment and storage medium for identifying easily confusable bacteria based on a chain analysis model provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for identifying easily confusable bacteria based on a chain analysis model, characterized in that: include: Obtaining a protein fingerprint of a known strain that is easily confused with a mass spectrometer, and performing noise processing on the protein fingerprint to obtain the processed protein fingerprint; Performing multiple peak analysis and comparison on different species in the processed protein fingerprint to obtain special unique peaks corresponding to different species, classifying each special unique peak using a preset binary chain analysis model to construct a chain analysis model, and training the chain analysis model to obtain a target chain analysis model; The protein fingerprint of the easily confused bacteria to be identified is input into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

2. The method for identifying easily confusable bacteria based on the chain analysis model according to claim 1, characterized in that: The step of obtaining a protein fingerprint of a known strain that is easily confused with a mass spectrometer and performing noise processing on the protein fingerprint comprises: Collecting microbial protein fingerprints, and screening protein fingerprints of known strains that meet preset easy-to-mix bacteria conditions and are easy to become mass spectrometry easy-to-mix bacteria from all the microbial protein fingerprints; The protein fingerprint map is subjected to noise processing using a preset typing software.

3. The method for identifying easily confusable bacteria based on the chain analysis model according to claim 2, characterized in that: The method of performing noise processing on the protein fingerprint using a preset typing software comprises: The protein fingerprint spectrum is input into a preset typing software, and noise processing is performed on the protein fingerprint spectrum based on a wavelet transform method; the noise processing includes exponential smoothing elimination processing.

4. The method for identifying easily confusable bacteria based on the chain analysis model according to claim 1, characterized in that: The peak multiple analysis and comparison of different species in the processed protein fingerprint map to obtain the special unique peaks corresponding to different species include: The processed protein fingerprint map is subjected to multiple analyses using a preset typing software, and multiple peak comparisons are performed on different species in the processed protein fingerprint map to obtain the specific unique peaks corresponding to different species.

5. The method for identifying easily confusable bacteria based on the chain analysis model according to claim 1, characterized in that: The training of the chain analysis model to obtain a target chain analysis model includes: Acquire historical species protein fingerprint peaks, and use the historical species protein fingerprint peaks to train the chain analysis model to obtain training results; The parameters in the chain analysis model are modified and adjusted based on the training results to obtain a target chain analysis model.

6. The method for identifying easily confusable bacteria based on a chain analysis model according to any one of claims 1 to 5, characterized in that: Before inputting the protein fingerprint of the easily confusable bacteria to be identified into the target chain analysis model for identification, the method further includes: The target chain analysis model is stored modularly and the model startup rules are set.

7. The method for identifying easily confusable bacteria based on the chain analysis model according to claim 6, characterized in that: The step of inputting the protein fingerprint of the easily confusable bacteria to be identified into the target chain analysis model for identification comprises: Obtaining a protein fingerprint of the easily confused bacteria to be identified, and determining whether the protein fingerprint satisfies the model startup rules; If the protein fingerprint meets the model activation rule, the target chain analysis model is activated, and the protein fingerprint is input into the target chain analysis model for identification.

8. A device for identifying easily confusable bacteria based on a chain analysis model, characterized in that: include: A spectrum acquisition and processing module, used to acquire a protein fingerprint spectrum of a known strain that is easy to be easily confused with a mass spectrum, and perform noise processing on the protein fingerprint spectrum to obtain the processed protein fingerprint spectrum; A model building module, used for performing multiple peak analysis and comparison on different species in the processed protein fingerprint map to obtain each special unique peak corresponding to different species, classifying each special unique peak using a preset binary chain analysis model to build a chain analysis model, and training the chain analysis model to obtain a target chain analysis model; The easily confused bacteria identification module is used to input the protein fingerprint of the easily confused bacteria to be identified into the target chain analysis model for identification, so as to output the identification result of the easily confused bacteria to be identified.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the method for identifying easily confusable bacteria based on the chain analysis model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the method for identifying easily confusable bacteria based on a chain analysis model as described in any one of claims 1 to 7 is implemented.

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