Dynamic operation identification method, device and equipment for easily mixed bacteria and medium

Abstract

The invention discloses a dynamic operation identification method, device and equipment for easily-mixed bacteria and a medium, and relates to the technical field of microbial identification, a mass spectrum to be matched is matched with a mass spectrum database, and an initial matching result, an initial peak matching condition and a database map corresponding to an initial tolerance value are obtained; judging whether the initial tolerance value meets a convergence condition or not by utilizing the initial matching result and the initial peak matching condition, and if the initial tolerance value meets the convergence condition, taking the initial matching result as a dynamic operation identification result of the miscible bacteria; if not, calculating the initial matching result and the initial peak matching condition to obtain an optimized tolerance value; matching the database atlas based on the optimized tolerance value to obtain a current matching result and a current peak matching condition, repeating a convergence condition judgment process until a convergence condition is met, and taking the current matching result as a dynamic operation identification result of the easily-mixed bacteria, so that the dynamic operation identification complexity of the easily-mixed bacteria can be reduced, and the identification efficiency of the easily-mixed bacteria is improved. The efficiency and the accuracy of the mass spectrum dynamic operation identification of the easily-mixed bacteria are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial identification, and in particular to a dynamic calculation identification method, device, equipment and medium for easily mixed bacteria. Background Art

[0002] At present, mass spectrometry identification of microorganisms is carried out by comparing the collected spectrum with the spectrum in the database, and the different similarities are output in the form of scores. Generally, a high score of 9.0 or above represents species level identification, a score between 6.0 and 9.0 represents genus level identification, and a score below 6.0 generally represents unreliable results. In the process of mass spectrometry identification of microorganisms, due to the limitation of mass spectrometry conditions, some microorganisms with high similarity or close relationship will have more than 9.0 points when identified, and multiple species results are clear. Such bacteria are called easily confused identification bacteria. At present, on instruments using super-map database algorithms and a type of database algorithm, there is a method of using a small-scale map database containing easily confused identification bacteria for secondary matching after the first super-map matching, which can solve the identification problem of individual easily confused identification bacteria. However, this method is limited in distinguishing species, and a small-scale segmented database needs to be constructed separately. A type of easily confused identification bacteria needs to be constructed separately. The workload is large, the number of samples required is large, and most uncommon easily confused identification bacteria are difficult to collect, and the integrity and validity of the database are limited.

[0003] As can be seen from the above, how to reduce the complexity of dynamic calculation identification of easily mixed bacteria and improve the efficiency and accuracy of dynamic calculation identification of easily mixed bacteria 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 dynamic calculation identification of easily mixed bacteria, which can reduce the complexity of dynamic calculation identification of easily mixed bacteria and improve the efficiency and accuracy of dynamic calculation identification of easily mixed bacteria. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a dynamic computational identification method for easily confusable bacteria, comprising:

[0006] Setting an initial tolerance value for mass spectrum matching, and matching the mass spectrum of easily mixed bacteria to be matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation, and a database spectrum corresponding to the initial tolerance value;

[0007] Using the initial matching result and the initial peak matching situation, judging whether the initial tolerance value satisfies a preset convergence condition, and if the initial tolerance value satisfies the preset convergence condition, taking the initial matching result as the dynamic calculation identification result of the easily confusable bacteria;

[0008] If the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching condition are calculated to obtain an optimized tolerance value;

[0009] The database spectrum is matched based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, and the process of judging the convergence condition is repeatedly executed until the optimized tolerance value satisfies the convergence condition, and the current matching result is used as the dynamic calculation identification result of the easily confused bacteria.

[0010] Optionally, the setting of an initial tolerance value for mass spectrum matching, and matching the mass spectrum of easily mixed bacteria to be matched with a mass spectrum database to obtain an initial matching result, an initial peak matching situation, and a database spectrum corresponding to the initial tolerance value, includes:

[0011] Adaptively set the initial tolerance value for mass spectrum matching;

[0012] The mass spectrum to be matched of the easily mixed bacteria is matched with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​of all matching peaks in the mass spectrum database and the mass spectrum peaks to be matched of the mass spectrum to be matched.

[0013] Optionally, the using the initial matching result and the initial peak matching situation to judge whether the initial tolerance value satisfies a preset convergence condition includes:

[0014] A termination equation is set, the initial matching result and the initial peak matching condition are input into the termination equation, and it is determined whether the initial tolerance value meets the preset convergence condition; the termination equation is:

[0015] ;

[0016] in, is the initial matching result, This is the initial peak matching situation.

[0017] Optionally, the determining whether the initial tolerance value satisfies a preset convergence condition, if the initial tolerance value satisfies the preset convergence condition, includes:

[0018] Determine whether all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value;

[0019] If all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value, the initial peak matching situation is uniquely identified. If the uniqueness identification passes, it is determined that the initial tolerance value meets the preset convergence condition.

[0020] Optionally, before calculating the initial matching result and the initial peak matching situation, the method further includes:

[0021] Set the intermediate variable matching results and intermediate variable peak matching conditions;

[0022] The values ​​of the initial matching result and the initial peak matching situation are assigned to the intermediate variable matching result and the intermediate variable peak matching situation.

[0023] Optionally, the calculating the initial matching result and the initial peak matching condition to obtain an optimized tolerance value includes:

[0024] A tolerance adjustment equation is set, and the intermediate variable matching result and the intermediate variable peak matching situation are input into the tolerance adjustment equation for calculation to obtain an optimized tolerance value; the tolerance adjustment equation is:

[0025] ;

[0026] in, To optimize the tolerance value, is the intermediate variable matching result, This is the peak matching situation of the intermediate variable.

[0027] Optionally, before setting the initial tolerance value for mass spectrum matching, the method further includes:

[0028] Build dynamic operation startup rules based on business needs and in accordance with the preset rule building method;

[0029] Each of the dynamic operation activation rules is saved in the preset dynamic operation activation rule library.

[0030] Determine whether there is a target dynamic operation start rule corresponding to each of the dynamic operation start rules in the dynamic operation start rule base locally;

[0031] If there is a target dynamic operation activation rule corresponding to each of the dynamic operation activation rules in the dynamic operation activation rule library locally, the target dynamic operation activation rule is activated, and the process of dynamic operation identification of easily mixed bacteria is executed.

[0032] In a second aspect, the present application discloses a dynamic computing identification device for easily mixed bacteria, comprising:

[0033] A matching module, used to set an initial tolerance value for mass spectrum matching, and match the mass spectrum of easily mixed bacteria to be matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value;

[0034] A first operation identification result determination module is used to determine whether the initial tolerance value meets a preset convergence condition by using the initial matching result and the initial peak matching condition, and if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria;

[0035] A calculation module, configured to calculate the initial matching result and the initial peak matching condition to obtain an optimized tolerance value if the initial tolerance value does not meet a preset convergence condition;

[0036] The second operation identification result determination module is used to match the database spectrum based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, repeatedly execute the convergence condition judgment process until the optimized tolerance value meets the convergence condition, and use the current matching result as the dynamic operation identification result of the easily confused bacteria.

[0037] Optionally, the matching module includes:

[0038] An initial tolerance value setting module, used for adaptively setting the initial tolerance value of mass spectrum matching;

[0039] The spectrum matching module is used to match the mass spectrum to be matched of easily mixed bacteria with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​of all matching peaks in the mass spectrum database and the mass spectrum peak to be matched of the mass spectrum to be matched.

[0040] In a third aspect, the present application discloses an electronic device, comprising:

[0041] Memory, used to store computer programs;

[0042] The processor is used to execute the computer program to implement the above-mentioned dynamic calculation identification method of easily mixed bacteria.

[0043] 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 dynamic calculation identification method for easily confusable bacteria disclosed above are implemented.

[0044] It can be seen that the present application provides a dynamic operation and identification method for easily confused bacteria, including setting an initial tolerance value for mass spectrum matching, and matching the mass spectrum to be matched of the easily confused bacteria with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; using the initial matching result and the initial peak matching situation, judging whether the initial tolerance value meets the preset convergence condition, if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation and identification result of the easily confused bacteria; if the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching situation are calculated to obtain an optimized tolerance value; based on the optimized tolerance value, the database spectrum is matched to obtain a current matching result and a current peak matching situation, and the convergence condition judgment process is repeated until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation and identification result of the easily confused bacteria. The present application uses the set initial tolerance value to match the mass spectrum to be matched with the mass spectrum database, and determines whether the initial tolerance value meets the preset convergence condition. If it does, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria. If it does not, the optimized tolerance value is calculated, and the matching is performed again until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation identification result of the easily mixed bacteria. The present application can realize the dynamic operation identification of easily mixed bacteria through the dynamic matching between the mass spectrum to be matched and the mass spectrum database and the convergence judgment of the tolerance value without adding an additional database, reduce the workload of the dynamic operation identification of the mixed bacteria mass spectrum, and is applicable to the identification problems of different easily mixed identification bacteria, reduce the complexity of the dynamic operation identification of easily mixed bacteria, and improve the efficiency and accuracy of the dynamic operation identification of easily mixed bacteria. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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.

[0046] Figure 1 This is a flow chart of a dynamic calculation identification method for easily mixed bacteria disclosed in this application;

[0047] Figure 2 A specific flow chart of dynamic calculation identification disclosed in this application;

[0048] Figure 3 This is a schematic diagram of the structure of a dynamic computing identification device for easily mixed bacteria disclosed in this application;

[0049] Figure 4 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0050] 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.

[0051] At present, mass spectrometry identification of microorganisms is carried out by comparing the collected spectrum with the spectrum in the database, and the different similarities are output in the form of scores. Generally, a high score of 9.0 or above represents species level identification, a score between 6.0 and 9.0 represents genus level identification, and a score below 6.0 generally represents unreliable results. In the process of mass spectrometry identification of microorganisms, due to the limitation of mass spectrometry conditions, some microorganisms with high similarity or close relationship will have more than 9.0 points when identified, and multiple species results are clear. Such bacteria are called easily confused identification bacteria. At present, on instruments using super-map database algorithms and a type of database algorithm, there is a method of using a small-scale map database containing easily confused identification bacteria for secondary matching after the first super-map matching, which can solve the identification problem of individual easily confused identification bacteria. However, this method is limited in distinguishing species, and a small-scale segmented database needs to be constructed separately. A type of easily confused identification bacteria needs to be constructed separately. The workload is large, the number of samples required is large, and most uncommon easily confused identification bacteria are difficult to collect, and the integrity and validity of the database are limited. As can be seen from the above, how to reduce the complexity of dynamic calculation identification of easily mixed bacteria and improve the efficiency and accuracy of dynamic calculation identification of easily mixed bacteria is a problem to be solved in this field.

[0052] See also Figure 1 As shown, the embodiment of the present invention discloses a dynamic calculation identification method for easily mixed bacteria, which may specifically include:

[0053] Step S11: setting an initial tolerance value for mass spectrum matching, and matching the mass spectrum of easily confusable bacteria to be matched with the mass spectrum database to obtain an initial matching result, initial peak matching status and database spectrum corresponding to the initial tolerance value.

[0054] In the process of mass spectrometry matching and identification of microorganisms, this application introduces a tolerance value t when matching mass spectrum peaks in order to correct the mass number error generated during instrument detection. Tolerance value: refers to the selection range when matching peaks. The larger the tolerance, the larger the range selected when matching peaks. The purpose of the tolerance is that under certain authorizations, small changes in the number of peaks will not interfere with the accuracy of the overall matching results. Usually t is an appropriate constant to ensure that the mass spectrum peak is not misjudged due to t being too large, and the mass spectrum peak is not missed due to t being too small. However, due to certain inter-instrument differences on the instrument, and different differences in fingerprint spectra of different microorganisms or different strains of microorganisms, the pre-set tolerance value t may have a certain impact on the correctness of the actual matching results. This application proposes a quantitative method of adaptive t, so that the tolerance value t can be automatically optimized during the matching process, and finally a suitable value is used for matching judgment.

[0055] In this embodiment, the initial tolerance value for mass spectrum matching is adaptively set; the mass spectrum to be matched of easily confused bacteria is matched with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation, and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​between all matching peaks in the mass spectrum database and the mass spectrum peak to be matched of the mass spectrum to be matched.

[0056] Specifically, use a larger tolerance value As the initial value, get the initial tolerance value , the mass spectrum to be matched Spectra in the mass spectrometry database Perform matching to obtain the initial sorted matching results ,and Corresponding database map , and the initial peak matching (in, Including the error values ​​between all mass spectrum database matching peaks and the mass spectrum peaks to be matched).

[0057] Step S12: using the initial matching result and the initial peak matching situation, judging whether the initial tolerance value satisfies a preset convergence condition; if the initial tolerance value satisfies the preset convergence condition, taking the initial matching result as the dynamic calculation identification result of the easily confusable bacteria.

[0058] In this embodiment, a termination equation is set, the initial matching result and the initial peak matching situation are input into the termination equation, and it is determined whether the initial tolerance value satisfies the preset convergence condition. If the initial tolerance value satisfies the preset convergence condition, the initial matching result is used as the dynamic calculation identification result of the easily confusable bacteria; the termination equation is:

[0059] ;

[0060] in, is the initial matching result, This is the initial peak matching situation.

[0061] Among them, the convergence condition judgment process is as follows: judge whether all the mass spectrum peak error values ​​in the initial matching results are not greater than the initial tolerance value; if all the mass spectrum peak error values ​​in the initial matching results are not greater than the initial tolerance value, then the initial peak matching situation is uniquely identified, and if the uniqueness identification passes, it is determined that the initial tolerance value meets the preset convergence condition.

[0062] Specifically, a termination equation is set to determine the initial tolerance value Whether it has converged (i.e., judging whether is the best tolerance), and use this termination equation to Matching with the initial peak Determine the initial tolerance Whether the convergence condition has been reached. Matching with the initial peak Substitute into the termination equation and judge the convergence condition. If the tolerance is When the variable All mass spectrometry peak error values ​​are less than or equal to In this case, the matching result If the uniqueness is passed, it means that the tolerance has converged, that is, the initial tolerance value meets the preset convergence condition. Assigned to the tolerance variable after convergence ,Right now ,Will As the result of dynamic calculation identification of easily mixed bacteria , .

[0063] Step S13: If the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching condition are calculated to obtain an optimized tolerance value.

[0064] In this embodiment, if the initial tolerance value does not meet the preset convergence condition, the intermediate variable matching result and the intermediate variable peak matching situation are set, the values ​​of the initial matching result and the initial peak matching situation are assigned to the intermediate variable matching result and the intermediate variable peak matching situation, and the tolerance adjustment equation is set. The intermediate variable matching result and the intermediate variable peak matching situation are input into the tolerance adjustment equation for calculation to obtain the optimized tolerance value; the tolerance adjustment equation is:

[0065] ;

[0066] in, To optimize the tolerance value, is the intermediate variable matching result, This is the peak matching situation of the intermediate variable.

[0067] Specifically, if the initial tolerance value does not meet the preset convergence condition, then set the intermediate variable, including the intermediate variable matching result Matching with intermediate variable peaks , receive the initial match to obtain the initial match result Matching with the initial peak ,make , , then set the tolerance adjustment equation, the intermediate variable matches the result Matching with intermediate variable peaks Substitute into the tolerance adjustment equation to get the optimized tolerance value .

[0068] Step S14: Match the database spectrum based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, repeat the process of convergence condition judgment until the optimized tolerance value meets the convergence condition, and use the current matching result as the dynamic calculation identification result of the easily confused bacteria.

[0069] In this embodiment, the optimized tolerance value is used Database Graph Match again to get the current matching result ,and Corresponding current database map Matching with current peak , again using the termination equation, Substitute into the termination equation for calculation and judge Whether it has converged. If it has converged, the corresponding current matching result will be As the result of dynamic calculation identification of easily mixed bacteria.

[0070] The specific process of implementing dynamic computational identification in this application is as follows: Figure 2As shown, (1) the mass spectrum to be matched of the easily mixed bacteria is collected; (2) the initial tolerance value of the mass spectrum matching is set, and the mass spectrum to be matched of the easily mixed bacteria is matched with the mass spectrum database to obtain the initial matching result, initial peak matching situation and database spectrum corresponding to the initial tolerance value; (3) it is judged whether the initial tolerance value meets the preset convergence condition. If the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria; (4) if the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching situation are calculated to obtain the optimized tolerance value, and then the database spectrum is matched based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, and the process of executing the convergence condition judgment is repeated. If the optimized tolerance value does not meet the preset convergence condition, the optimized tolerance value is continued to be calculated until the preset convergence condition is met. For example, in the nth cycle, the optimized tolerance value meets the preset convergence condition, and the corresponding nth matching result is used as the dynamic operation identification result of the easily mixed bacteria.

[0071] In addition, before setting the initial tolerance value for mass spectrum matching, it also includes: constructing each dynamic operation startup rule based on business needs and according to a preset rule construction method; and saving each of the dynamic operation startup rules to a preset dynamic operation startup rule library.

[0072] Therefore, the prerequisite for the process of dynamic operation identification of easily confused bacteria is: first, determine whether there is a target dynamic operation startup rule corresponding to each of the dynamic operation startup rules in the dynamic operation startup rule base locally; if there is a target dynamic operation startup rule corresponding to each of the dynamic operation startup rules in the dynamic operation startup rule base locally, then start the target dynamic operation startup rule and execute the process of dynamic operation identification of easily confused bacteria proposed in this application.

[0073] In this embodiment, an initial tolerance value for mass spectrum matching is set, and the mass spectrum to be matched of the easily confused bacteria is matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; using the initial matching result and the initial peak matching situation, it is judged whether the initial tolerance value meets the preset convergence condition; if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily confused bacteria; if the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching situation are calculated to obtain an optimized tolerance value; based on the optimized tolerance value, the database spectrum is matched to obtain a current matching result and a current peak matching situation, and the process of judging the convergence condition is repeated until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation identification result of the easily confused bacteria. The present application uses the set initial tolerance value to match the mass spectrum to be matched with the mass spectrum database, and determines whether the initial tolerance value meets the preset convergence condition. If it does, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria. If it does not, the optimized tolerance value is calculated, and the matching is performed again until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation identification result of the easily mixed bacteria. The present application can realize the dynamic operation identification of easily mixed bacteria through the dynamic matching between the mass spectrum to be matched and the mass spectrum database and the convergence judgment of the tolerance value without adding an additional database, reduce the workload of the dynamic operation identification of the mixed bacteria mass spectrum, and is applicable to the identification problems of different easily mixed identification bacteria, reduce the complexity of the dynamic operation identification of easily mixed bacteria, and improve the efficiency and accuracy of the dynamic operation identification of easily mixed bacteria.

[0074] See also Figure 3 As shown, the embodiment of the present invention discloses a dynamic computing identification device for easily mixed bacteria, which may specifically include:

[0075] The matching module 11 is used to set an initial tolerance value for mass spectrum matching, and match the mass spectrum of easily mixed bacteria to be matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value;

[0076] A first operation identification result determination module 12 is used to determine whether the initial tolerance value meets a preset convergence condition by using the initial matching result and the initial peak matching condition, and if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria;

[0077] A calculation module 13, configured to calculate the initial matching result and the initial peak matching condition to obtain an optimized tolerance value if the initial tolerance value does not meet a preset convergence condition;

[0078] The second operation identification result determination module 14 is used to match the database spectrum based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, and repeatedly execute the convergence condition judgment process until the optimized tolerance value meets the convergence condition, and use the current matching result as the dynamic operation identification result of the easily confused bacteria.

[0079] In this embodiment, an initial tolerance value for mass spectrum matching is set, and the mass spectrum to be matched of the easily confused bacteria is matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; using the initial matching result and the initial peak matching situation, it is judged whether the initial tolerance value meets the preset convergence condition; if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily confused bacteria; if the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching situation are calculated to obtain an optimized tolerance value; based on the optimized tolerance value, the database spectrum is matched to obtain a current matching result and a current peak matching situation, and the process of judging the convergence condition is repeated until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation identification result of the easily confused bacteria. The present application uses the set initial tolerance value to match the mass spectrum to be matched with the mass spectrum database, and determines whether the initial tolerance value meets the preset convergence condition. If it does, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria. If it does not, the optimized tolerance value is calculated, and the matching is performed again until the optimized tolerance value meets the convergence condition, and the current matching result is used as the dynamic operation identification result of the easily mixed bacteria. The present application can realize the dynamic operation identification of easily mixed bacteria through the dynamic matching between the mass spectrum to be matched and the mass spectrum database and the convergence judgment of the tolerance value without adding an additional database, reduce the workload of the dynamic operation identification of the mixed bacteria mass spectrum, and is applicable to the identification problems of different easily mixed identification bacteria, reduce the complexity of the dynamic operation identification of easily mixed bacteria, and improve the efficiency and accuracy of the dynamic operation identification of easily mixed bacteria.

[0080] In some specific embodiments, the matching module 11 may specifically include:

[0081] An initial tolerance value setting module, used for adaptively setting the initial tolerance value of mass spectrum matching;

[0082] The spectrum matching module is used to match the mass spectrum to be matched of easily mixed bacteria with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​of all matching peaks in the mass spectrum database and the mass spectrum peak to be matched of the mass spectrum to be matched.

[0083] In some specific embodiments, the first operation identification result determination module 12 may specifically include:

[0084] The convergence judgment module is used to set a termination equation, input the initial matching result and the initial peak matching situation into the termination equation, and judge whether the initial tolerance value meets the preset convergence condition; the termination equation is:

[0085] ;

[0086] in, is the initial matching result, This is the initial peak matching situation.

[0087] In some specific embodiments, the first operation identification result determination module 12 may specifically include:

[0088] An error value judgment module is used to judge whether all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value;

[0089] The uniqueness identification module is used to perform uniqueness identification on the initial peak matching situation if all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value, and if the uniqueness identification passes, it is determined that the initial tolerance value meets the preset convergence condition.

[0090] In some specific embodiments, the calculation module 13 may specifically include:

[0091] An intermediate variable setting module is used to set the intermediate variable matching results and intermediate variable peak matching conditions;

[0092] An assignment module is used to assign the numerical values ​​of the initial matching result and the initial peak matching situation to the intermediate variable matching result and the intermediate variable peak matching situation.

[0093] In some specific embodiments, the calculation module 13 may specifically include:

[0094] The tolerance value calculation module is used to set the tolerance adjustment equation, input the intermediate variable matching result and the intermediate variable peak matching situation into the tolerance adjustment equation for calculation to obtain the optimized tolerance value; the tolerance adjustment equation is:

[0095] ;

[0096] in, To optimize the tolerance value, is the intermediate variable matching result, This is the peak matching situation of the intermediate variable.

[0097] In some specific embodiments, the matching module 11 may further include:

[0098] A rule building module is used to build various dynamic operation startup rules based on business needs and in accordance with a preset rule building method;

[0099] The rule saving module is used to save each of the dynamic operation startup rules to the preset dynamic operation startup rule library.

[0100] In some specific embodiments, the dynamic computing identification device for easily mixed bacteria may further include:

[0101] A judgment module, used for judging whether there is a target dynamic operation start rule corresponding to each of the dynamic operation start rules in the dynamic operation start rule base locally;

[0102] The rule activation module is used to activate the target dynamic operation activation rule if there is a target dynamic operation activation rule corresponding to each of the dynamic operation activation rules in the dynamic operation activation rule library locally, and execute the process of dynamic operation identification of easily mixed bacteria.

[0103] Figure 4 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 dynamic calculation identification method of easily confusable bacteria performed by the electronic device disclosed in any of the aforementioned embodiments.

[0104] 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.

[0105] 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.

[0106] 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 operation 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 dynamic operation and identification method of easily mixed bacteria performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can 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 dynamic operation and identification device of easily mixed bacteria, the data 223 can also include data collected by its own input and output interface 25, etc.

[0107] 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.

[0108] 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 dynamic calculation identification method of easily confused bacteria disclosed in any of the aforementioned embodiments are implemented.

[0109] 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.

[0110] The above is a detailed introduction to the dynamic calculation identification method, device, equipment and storage medium for easily confused bacteria 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 dynamic calculation identification method for easily mixed bacteria, characterized in that: include: Setting an initial tolerance value for mass spectrum matching, and matching the mass spectrum of easily mixed bacteria to be matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation, and a database spectrum corresponding to the initial tolerance value; Using the initial matching result and the initial peak matching situation, judging whether the initial tolerance value satisfies a preset convergence condition, and if the initial tolerance value satisfies the preset convergence condition, taking the initial matching result as the dynamic calculation identification result of the easily confusable bacteria; If the initial tolerance value does not meet the preset convergence condition, the initial matching result and the initial peak matching condition are calculated to obtain an optimized tolerance value; The database spectrum is matched based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, and the process of judging the convergence condition is repeatedly executed until the optimized tolerance value satisfies the convergence condition, and the current matching result is used as the dynamic calculation identification result of the easily confused bacteria.

2. The dynamic calculation identification method of easily mixed bacteria according to claim 1 is characterized in that: The initial tolerance value for mass spectrum matching is set, and the mass spectrum to be matched of easily mixed bacteria is matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value, including: Adaptively set the initial tolerance value for mass spectrum matching; The mass spectrum to be matched of the easily mixed bacteria is matched with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​of all matching peaks in the mass spectrum database and the mass spectrum peaks to be matched of the mass spectrum to be matched.

3. The dynamic calculation identification method of easily mixed bacteria according to claim 1 is characterized in that: The using the initial matching result and the initial peak matching situation to judge whether the initial tolerance value meets a preset convergence condition includes: A termination equation is set, the initial matching result and the initial peak matching condition are input into the termination equation, and it is determined whether the initial tolerance value meets the preset convergence condition; the termination equation is: ； in, is the initial matching result, This is the initial peak matching situation.

4. The dynamic calculation identification method of easily mixed bacteria according to claim 1 is characterized in that: The determining whether the initial tolerance value satisfies a preset convergence condition, if the initial tolerance value satisfies the preset convergence condition, includes: Determine whether all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value; If all mass spectrum peak error values ​​in the initial matching result are not greater than the initial tolerance value, the initial peak matching situation is uniquely identified. If the uniqueness identification passes, it is determined that the initial tolerance value meets the preset convergence condition.

5. The dynamic calculation identification method for easily mixed bacteria according to any one of claims 1 to 4, characterized in that: Before calculating the initial matching result and the initial peak matching situation, the method further includes: Set the intermediate variable matching results and intermediate variable peak matching conditions; The values ​​of the initial matching result and the initial peak matching situation are assigned to the intermediate variable matching result and the intermediate variable peak matching situation.

6. The dynamic calculation identification method of easily mixed bacteria according to claim 5 is characterized in that: The calculating the initial matching result and the initial peak matching condition to obtain an optimized tolerance value includes: A tolerance adjustment equation is set, and the intermediate variable matching result and the intermediate variable peak matching situation are input into the tolerance adjustment equation for calculation to obtain an optimized tolerance value; the tolerance adjustment equation is: ； in, To optimize the tolerance value, is the intermediate variable matching result, This is the peak matching situation of the intermediate variable.

7. A dynamic calculation identification method for easily mixed bacteria, characterized in that: Before setting the initial tolerance value for mass spectrum matching, the method further includes: Build dynamic operation startup rules based on business needs and in accordance with the preset rule building method; Saving each of the dynamic operation activation rules to a preset dynamic operation activation rule library; Determine whether there is a target dynamic operation start rule corresponding to each of the dynamic operation start rules in the dynamic operation start rule base locally; If there is a target dynamic operation activation rule corresponding to each of the dynamic operation activation rules in the dynamic operation activation rule library locally, the target dynamic operation activation rule is activated, and the process of dynamic operation identification of easily mixed bacteria is executed.

8. A dynamic computing identification device for easily mixed bacteria, characterized in that: include: A matching module, used to set an initial tolerance value for mass spectrum matching, and match the mass spectrum of easily mixed bacteria to be matched with the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; A first operation identification result determination module is used to determine whether the initial tolerance value meets a preset convergence condition by using the initial matching result and the initial peak matching condition, and if the initial tolerance value meets the preset convergence condition, the initial matching result is used as the dynamic operation identification result of the easily mixed bacteria; A calculation module, configured to calculate the initial matching result and the initial peak matching condition to obtain an optimized tolerance value if the initial tolerance value does not meet a preset convergence condition; The second operation identification result determination module is used to match the database spectrum based on the optimized tolerance value to obtain the current matching result and the current peak matching situation, repeatedly execute the convergence condition judgment process until the optimized tolerance value meets the convergence condition, and use the current matching result as the dynamic operation identification result of the easily confused bacteria.

9. The dynamic calculation identification device for easily mixed bacteria according to claim 8, characterized in that: The matching module comprises: An initial tolerance value setting module, used for adaptively setting the initial tolerance value of mass spectrum matching; The spectrum matching module is used to match the mass spectrum to be matched of easily mixed bacteria with all spectra in the mass spectrum database to obtain an initial matching result, an initial peak matching situation and a database spectrum corresponding to the initial tolerance value; wherein the initial peak matching situation includes the error values ​​of all matching peaks in the mass spectrum database and the mass spectrum peak to be matched of the mass spectrum to be matched.

10. 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 dynamic calculation identification method of easily confusable bacteria as described in any one of claims 1 to 7.

11. 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 dynamic calculation identification method for easily confusable bacteria as described in any one of claims 1 to 7 is implemented.

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