CN109916989B - Organic matter detection method, device and system based on artificial intelligence - Google Patents
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Abstract
The invention discloses an organic matter detection method, device and system based on artificial intelligence, wherein the method comprises the following steps: receiving an original detection data file, analyzing the file and exporting the file according to a set peak height threshold value as a boundary to obtain first detection data; determining a first set category to which the first detection data belong, and eliminating signal-to-noise ratio interference of the first detection data according to the first set category to obtain second detection data; and determining a second set type to which the second detection data belongs, processing the detection data excluding signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and performing organic matter detection on the third detection data.
Description
Technical Field
The disclosure belongs to the technical field of organic matter detection, and relates to an organic matter detection method, device and system based on artificial intelligence.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Organic substance detection is also called organic compound detection, such as detection of organic compounds such as pesticides, drugs, easily-made drugs, antibiotics and the like. The traditional organic matter detection is realized in a laboratory by means of instruments such as a liquid chromatograph or a gas chromatograph, the detection sensitivity and the result accuracy are ensured, and a sample to be detected obtained by sampling needs to be subjected to complex pretreatment and then enters a mass spectrometer for detection. However, the pretreatment and analysis processes of the sample to be detected in the detection mode need several hours, the procedure is complex, the process is complicated, the time is long, the efficiency is low, and the working intensity of an operator is high.
With the development of the technology, in order to solve the problems, the company and the cooperative unit of the inventor research and develop a system capable of detecting most organic matters, for example, a rapid pesticide residue detector based on a mass spectrometry technology and a mass spectrometry rapid screening detection system for vehicle-mounted pesticide residues and harmful substances in food and drug, the rapid pesticide residue detector based on the mass spectrometry technology improves the detection accuracy and the detection efficiency of a thermal desorption-electrospray ion source technology, and greatly shortens the analysis time and the detection time of the pesticide residues to be detected; thermal desorption-electrospray mass spectrum fast screening combination instrument provided by the fast screening detection system for harmful mass spectra in the vehicle-mounted pesticide residue and food medicine can detect most organic matters, is not limited by volatility and thermal stability of a sample, can rapidly go to a destination, and realizes fast screening of the harmful mass spectra in crops and food medicine.
However, the inventor finds that, in the research process, although the conventional organic compound detection system based on the mass spectrometry technology improves the speed and accuracy of the traditional organic compound detection, the system directly analyzes the original detection data file after acquiring the data to be detected, and searches for the organic compound from the original detection data file, and the noise in the detection data influences the accuracy of the organic compound detection. At present, a method which has high accuracy and high speed and can be applied to the detection and analysis of all organic compounds is urgently needed.
Disclosure of Invention
Aiming at the defects in the prior art, one or more embodiments of the disclosure provide an organic matter detection method, device and system based on artificial intelligence, original detection data of a mass spectrometry mobile platform is rapidly screened for organic compounds, interference factors influencing result judgment are identified and eliminated by artificial intelligence, and the accuracy of organic compound detection results is effectively improved.
In accordance with one aspect of one or more embodiments of the present disclosure, there is provided an artificial intelligence based organic matter detection method.
An organic matter detection method based on artificial intelligence, which comprises the following steps:
receiving an original detection data file, analyzing the file and exporting the file according to a set peak height threshold value as a boundary to obtain first detection data;
determining a first set category to which the first detection data belong, and eliminating signal-to-noise ratio interference of the first detection data according to the first set category to obtain second detection data;
and determining a second set type to which the second detection data belongs, processing the detection data excluding signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and performing organic matter detection on the third detection data.
Further, in the method, the specific step of obtaining the first detection data includes:
decompressing the original test data file;
analyzing the decompressed original detection data file through a mass spectrometry tool;
exporting the decompressed original detection data file according to the set peak height threshold as a boundary;
and obtaining detection data.
Further, in the method, the set categories of the first detection data include conventional fast screening organic compounds and high hazard limiting and response low organic compounds.
Further, in the method, the specific method for performing signal-to-noise interference rejection on the first detection data according to the set first set category includes:
the detection limit of the conventional fast screening organic compound is larger than a first signal-to-noise ratio threshold value, and signal-to-noise ratio interference elimination is carried out;
and the high-risk limit and the detection limit of the response low-class organic compound take the value larger than a second signal-to-noise ratio threshold value as a limit, and signal-to-noise ratio interference elimination is carried out.
The first signal-to-noise ratio threshold is greater than a second signal-to-noise ratio threshold.
Further, in the method, the specific method for performing signal-to-noise interference rejection on the first detection data according to the first set category includes:
receiving the conventional fast screening organic compound data set, and training a signal-to-noise ratio interference elimination model of the conventional fast screening organic compound based on a neural network;
receiving the high-risk limit and response low-class organic compound data set, and training a signal-to-noise ratio interference elimination model of the high-risk limit and response low-class organic compound based on a neural network;
performing signal-to-noise interference elimination on the conventional fast screening organic compound in the detection data according to a signal-to-noise interference elimination model corresponding to the conventional fast screening organic compound;
and carrying out signal-to-noise interference elimination on the high-risk-limit and response low-class organic compounds in the detection data according to the corresponding signal-to-noise interference elimination models.
Further, in the method, the second set of categories includes a detected tail-like organic compound and a non-detected tail-like organic compound;
the limit of the detection time for detecting the smear-like organic compound is relaxed on the basis of the limit of the detection time for not detecting the smear-like organic compound.
Further, in the method, the specific method steps for detecting the third organic substance include:
searching organic compounds in the third detection data, and deriving standard chart data and detection result data of each compound;
and comparing the detection result data with the corresponding standard chart data to determine an effective value, and analyzing the organic compound according to a judgment standard.
Further, the method further comprises: and (3) carrying out false positive screening on the detected organic compound to exclude the false positive organic compound, wherein the false positive organic compound is an organic compound of which the ratio of quantitative ions to qualitative ions exceeds the fixed value of the detection result of the standard sample in the actual sample detection.
According to an aspect of one or more embodiments of the present disclosure, there is provided a computer-readable storage medium.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method for artificial intelligence based organic matter detection.
According to an aspect of one or more embodiments of the present disclosure, there is provided a terminal device.
A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium stores instructions adapted to be loaded by a processor and to perform the artificial intelligence based organic matter detection method.
In accordance with one aspect of one or more embodiments of the present disclosure, an artificial intelligence based organic matter detection apparatus is provided.
An organic matter detection device based on artificial intelligence, the device is based on the organic matter detection method based on artificial intelligence, comprising:
the data acquisition module is configured to receive an original detection data file, analyze the file and export the file according to a set peak height threshold value as a boundary to obtain first detection data;
the signal-to-noise interference elimination module is configured to determine a first set category to which the first detection data belongs, and perform signal-to-noise interference elimination on the first detection data according to the first set category to obtain second detection data;
and the organic matter detection module is configured to determine a second set type to which the second detection data belongs, process the detection data excluding the signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and perform organic matter detection on the third detection data.
In accordance with one aspect of one or more embodiments of the present disclosure, an artificial intelligence based organic matter detection system is provided.
An organic matter detection system based on artificial intelligence, the system comprising: the organic matter detection device based on artificial intelligence and the organic compound detector connected with the organic matter detection device;
the organic compound detector is used for collecting original detection data and sending the original detection data to the organic compound detection device based on artificial intelligence.
The beneficial effect of this disclosure:
according to the organic matter detection method, device and system based on artificial intelligence, the original detection data of the mass spectrum mobile platform are rapidly screened for organic compounds, interference factors influencing result judgment are identified and eliminated by the artificial intelligence, signal-to-noise ratio interference, detection time interference and pseudo-positive organic compounds are respectively eliminated, and the accuracy of organic compound detection results is effectively improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow diagram of a method for artificial intelligence based organic detection in accordance with one or more embodiments.
Detailed Description
Technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in one or more embodiments of the present disclosure, and it is apparent that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art based on one or more embodiments of the disclosure without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Without conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other, and the present disclosure will be further described with reference to the drawings and the embodiments.
As shown in fig. 1, according to an aspect of one or more embodiments of the present disclosure, there is provided an artificial intelligence based organic matter detection method.
An organic matter detection method based on artificial intelligence, which comprises the following steps:
s101, receiving an original detection data file, analyzing the file and exporting the file according to a set peak height threshold as a limit to obtain first detection data;
s102, determining a first set type to which the first detection data belong, and eliminating signal-to-noise ratio interference of the first detection data according to the first set type to obtain second detection data;
s103, determining a second set type to which the second detection data belong, processing the detection data excluding signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and performing organic matter detection on the third detection data.
In step S101 of this embodiment, the specific step of obtaining the first detection data includes:
decompressing the original test data file;
analyzing the decompressed original detection data file through a mass spectrometry tool;
exporting the decompressed original detection data file according to the set peak height threshold as a boundary;
and obtaining detection data.
In step S102 of this embodiment, the set categories of the first detection data include conventional fast screening organic compounds and high-risk limit and response low-category organic compounds.
In the present embodiment, the set categories of the first detection data include "normal fast screening" and "highly toxic prohibited and low-response pesticide", taking the analysis of pesticide residue in agricultural products as an example.
In step S102 of this embodiment, the specific method for performing signal-to-noise interference rejection on the first detection data according to the first setting category includes:
the detection limit of the conventional fast screening organic compound is larger than a first signal-to-noise ratio threshold value, and signal-to-noise ratio interference elimination is carried out;
and the high-risk limit and the detection limit of the response low-class organic compound take the value larger than a second signal-to-noise ratio threshold value as a limit, and signal-to-noise ratio interference elimination is carried out.
The first signal-to-noise ratio threshold is greater than a second signal-to-noise ratio threshold.
In this embodiment, taking pesticide residue analysis in agricultural products as an example, the confirmation data derivation is defined by a peak height of 50, and the derived data are respectively determined according to classification ("conventional fast screening" and "highly toxic prohibited and low-response pesticide"): the detection limit of the conventional fast screening pesticide is defined by the signal-to-noise ratio of more than 10; in addition, the pesticide has a signal-to-noise ratio greater than 3.
In step S102 of this embodiment, signal-to-noise interference rejection may also be performed by establishing a signal-to-noise interference rejection model. The specific method for eliminating the signal-to-noise ratio interference of the first detection data according to the first set category comprises the following steps:
receiving the conventional fast screening organic compound data set, and training a signal-to-noise ratio interference elimination model of the conventional fast screening organic compound based on a neural network;
receiving the high-risk limit and response low-class organic compound data set, and training a signal-to-noise ratio interference elimination model of the high-risk limit and response low-class organic compound based on a neural network;
performing signal-to-noise interference elimination on the conventional fast screening organic compound in the detection data according to a signal-to-noise interference elimination model corresponding to the conventional fast screening organic compound;
and carrying out signal-to-noise interference elimination on the high-risk-limit and response low-class organic compounds in the detection data according to the corresponding signal-to-noise interference elimination models.
In step S103 of the present embodiment, the second setting category includes a detected tail organic compound and a non-detected tail organic compound;
the limit of the detection time for detecting the smear-like organic compound is relaxed on the basis of the limit of the detection time for not detecting the smear-like organic compound.
In this example, the limit of the detection time is appropriately widened for a pesticide such as carbendazim which is liable to be smeared, taking the analysis of the pesticide residue in agricultural products as an example.
For pesticides with sharp peaks, although both pairs of ions peak within a standard time, the peaks will deviate, which theoretically should not be detected. To avoid this, a fixed detection time limit of 80% -120% is set for different pesticides, but the receiving server manually modifies the instruction and selects 70% -130% manually.
In step S103 of this embodiment, the specific method for detecting the third organic substance includes:
s1031: searching organic compounds in the third detection data, and deriving standard chart data and detection result data of each compound;
obtaining standard graph data and detection result data of each compound through a mass spectrometry tool, and specifically comprises the following steps:
searching for organic compounds in the detection data by a mass spectrometry tool;
integrating the searched organic compounds with a standard diagram to derive standard diagram data;
and circularly deriving detection result data of each organic compound corresponding to the standard chart data.
In the present embodiment, for raw data processing:
and the analysis of the original detection data by the Qualitative analysis is realized based on the user simulation operation of the key sprite. Including file import, method editor setting, compound lookup, data export, etc.
S1032: and comparing the detection result data with the corresponding standard chart data to determine an effective value, and analyzing the organic compound according to a judgment standard.
The specific steps for analyzing the organic compounds comprise:
acquiring standard chart data and establishing a standard chart time section;
acquiring detection result data of each organic compound corresponding to standard chart data of one organic compound;
comparing ions according to the sampling time of the detection result data and the time section of the standard graph to obtain a judgment result;
and when the two pairs of ion pair data exist and the peak value is higher than a preset judgment standard, judging that the organic compound exists.
Further, in the method, the sample injection time of the detection result data is widened by 20% on the basis of the time section of the standard graph.
In this embodiment, the detection result determination is a result of a quantitative analysis, and the detection result is determined by performing an algorithm of a design criterion of a binding time, a peak area, and a peak height on the basis of a standard chart and various substance analysis charts.
Further, the method further comprises: and (3) carrying out false positive screening on the detected organic compound to exclude the false positive organic compound, wherein the false positive organic compound is an organic compound of which the ratio of quantitative ions to qualitative ions exceeds the fixed value of the detection result of the standard sample in the actual sample detection.
In this embodiment, taking pesticide residue analysis in agricultural products as an example, the peak of pesticides such as phosphamidon, monocrotophos, and bermudophosphorus completely meets the detection condition, but the ratio of two pairs of ions (the ratio of quantitative ions to qualitative ions has a fixed value in the detection result of the standard) far exceeds the fixed value in the actual sample detection, and therefore, the actual sample is considered as a false positive and needs to be excluded.
In this embodiment, taking the analysis of pesticide residue in agricultural products as an example, the qualitative result determination of the content of a single material (pesticide) is automatically analyzed and determined according to the basic data of the detection result. The decision logic is as follows:
1. acquiring standard chart data and establishing a standard chart time section;
2. acquiring single material (pesticide) data, comparing the sample introduction time with a time section of a standard graph, wherein the time range can be widened by 20% (for example, the time of 10ms is counted in a standard time zone of 1.000-1.100, the sample introduction time section can be widened to 0.980-1.120 time period), and if the single material (pesticide) data exists in the time period (both pairs of ions exist in the data file) and the peak value is higher than 50 (configurable), judging that the material (pesticide) exists;
3. storing the judgment result into a database, and taking the maximum value of the peak area;
further, in the method, the detection result data and the judgment result are stored in a database, corresponding processing suggestions corresponding to the judgment result are also stored in the database, and the processing suggestions are sent to a client for displaying when a user inquires;
the display content comprises: the method comprises the following steps of detecting a serial number, generating time of a result, an organic compound name, a maximum peak area, a single judgment result and a processing suggestion.
Example two
According to an aspect of one or more embodiments of the present disclosure, there is provided a computer-readable storage medium.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute a method for artificial intelligence based organic matter detection.
EXAMPLE III
According to an aspect of one or more embodiments of the present disclosure, there is provided a terminal device.
A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium stores instructions adapted to be loaded by a processor and to perform the artificial intelligence based organic matter detection method.
These computer-executable instructions, when executed in a device, cause the device to perform methods or processes described in accordance with various embodiments of the present disclosure.
In the present embodiments, a computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for performing various aspects of the present disclosure. The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present disclosure by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Example four
In accordance with one aspect of one or more embodiments of the present disclosure, an artificial intelligence based organic matter detection apparatus is provided.
An organic matter detection device based on artificial intelligence, the device is based on the organic matter detection method based on artificial intelligence, comprising:
the data acquisition module is configured to receive an original detection data file, analyze the file and export the file according to a set peak height threshold value as a boundary to obtain first detection data;
the signal-to-noise interference elimination module is configured to determine a first set category to which the first detection data belongs, and perform signal-to-noise interference elimination on the first detection data according to the first set category to obtain second detection data;
and the organic matter detection module is configured to determine a second set type to which the second detection data belongs, process the detection data excluding the signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and perform organic matter detection on the third detection data.
It should be noted that although several modules or sub-modules of the device are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
EXAMPLE five
In accordance with one aspect of one or more embodiments of the present disclosure, an artificial intelligence based organic matter detection system is provided. In the embodiment, the detection of pesticide residues in agricultural products is taken as an example, and an artificial intelligence-based organic matter detection system is constructed by combining the current characteristics of a rapid pesticide residue screening mass spectrum mobile platform and the conditions of software and hardware of equipment.
An organic matter detection system based on artificial intelligence, the system comprising: the organic matter detection device based on artificial intelligence and the organic compound detector connected with the organic matter detection device;
the organic compound detector is used for collecting original detection data and sending the original detection data to the organic compound detection device based on artificial intelligence.
The system also includes
The detection machine is used for receiving original detection data collected by the organic compound detection machine and sending the original detection data to the board jumping machine;
and the board skipping machine is used for forwarding the original detection data sent by the detection machine to the data processing and analyzing server.
In order to ensure the network safety of the detection machine, a trigger is added to the public network without directly connecting the detection machine with the public network, and the data of the detection machine is automatically uploaded to a public network server for analysis through a program after being acquired.
The system also designs component multiplexing, and components for transferring original data files to the detection machine and uploading files to the cloud server by the springboard machine can be multiplexed. And realizing data transfer logic for uploading the local file to other servers.
In this embodiment, the server is used in the organic matter detection device based on artificial intelligence, and includes:
the FTP server is used for analyzing the files to obtain detection data and is connected with the data analyzers;
the data analyzer is used for downloading the detection data in the FTP server, executing the organic matter detection method based on artificial intelligence and storing the analysis result in a database server connected with the data analyzer;
the database server is used for storing the analysis result obtained by the data analysis machine and is connected with the application server;
and the application server is used for acquiring the analysis result stored by the database server and displaying the analysis result.
In the embodiment, the system adopts a B/S architecture and can support mainstream browsers such as IE10+, chrome, firefox and the like. The accuracy and rules of all the data integrated depend on the data presented by the source system.
The system adopts a J2EE architecture, has strong expansibility, and can support a cloud architecture, a distributed framework and metadata driving. Meanwhile, aiming at the characteristics of flexible and changeable service, multiple processes, complex report forms and the like, a rapid development platform is selected, and project requirements can be rapidly supported.
One or more embodiments of the present disclosure may also be used in other organic compound assays, such as assays for detecting organic compounds such as icine, morphine in drugs, and antibiotics in sea mud.
The beneficial effect of this disclosure:
according to the organic matter detection method, device and system based on artificial intelligence, the original detection data of the mass spectrum mobile platform are rapidly screened for organic compounds, interference factors influencing result judgment are identified and eliminated by the artificial intelligence, signal-to-noise ratio interference, detection time interference and pseudo-positive organic compounds are respectively eliminated, and the accuracy of organic compound detection results is effectively improved.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An organic matter detection method based on artificial intelligence is characterized by comprising the following steps:
receiving an original detection data file, analyzing the file and exporting the file according to a set peak height threshold value as a boundary to obtain first detection data;
determining a first set category to which the first detection data belong, and eliminating signal-to-noise ratio interference of the first detection data according to the first set category to obtain second detection data;
determining a second set type to which the second detection data belongs, processing the detection data excluding signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and performing organic matter detection on the third detection data;
the set types of the first detection data comprise conventional fast screening organic compounds and high-risk limit and response low-class organic compounds;
the specific method for eliminating signal-to-noise interference of the first detection data according to the set first set category comprises the following steps:
the detection limit of the conventional fast screening organic compound is larger than a first signal-to-noise ratio threshold value, and signal-to-noise ratio interference elimination is carried out;
the high-risk limit and response low-class organic compound detection limit is larger than a second signal-to-noise ratio threshold value and signal-to-noise ratio interference elimination is carried out;
the first signal-to-noise ratio threshold is greater than a second signal-to-noise ratio threshold;
or, in the method, the specific method for performing signal-to-noise interference rejection on the first detection data according to the first set category includes:
receiving the conventional fast screening organic compound data set, and training a signal-to-noise ratio interference elimination model of the conventional fast screening organic compound based on a neural network;
receiving the high-risk limit and response low-class organic compound data set, and training a signal-to-noise ratio interference elimination model of the high-risk limit and response low-class organic compound based on a neural network;
performing signal-to-noise interference elimination on the conventional fast screening organic compound in the detection data according to a signal-to-noise interference elimination model corresponding to the conventional fast screening organic compound;
and carrying out signal-to-noise interference elimination on the high-risk-limit and response low-class organic compounds in the detection data according to the corresponding signal-to-noise interference elimination models.
2. The method as claimed in claim 1, wherein the step of obtaining the first detection data comprises:
decompressing the original test data file;
analyzing the decompressed original detection data file through a mass spectrometry tool;
exporting the decompressed original detection data file according to the set peak height threshold as a boundary;
and obtaining detection data.
3. The method according to claim 1, wherein the second set category comprises a detection tail organic compound and a non-detection tail organic compound;
the limit of the detection time for detecting the smear-like organic compound is relaxed on the basis of the limit of the detection time for not detecting the smear-like organic compound.
4. The method of claim 1, further comprising: and (3) carrying out false positive screening on the detected organic compound to exclude the false positive organic compound, wherein the false positive organic compound is an organic compound of which the ratio of quantitative ions to qualitative ions exceeds the fixed value of the detection result of the standard sample in the actual sample detection.
5. A computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform a method for artificial intelligence based organic matter detection according to any of claims 1-4.
6. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; a computer-readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform an artificial intelligence based organic matter detection method according to any one of claims 1-4.
7. An artificial intelligence based organic matter detection apparatus based on an artificial intelligence based organic matter detection method according to any one of claims 1 to 4, comprising:
the data acquisition module is configured to receive an original detection data file, analyze the file and export the file according to a set peak height threshold value as a boundary to obtain first detection data;
the signal-to-noise interference elimination module is configured to determine a first set category to which the first detection data belongs, and perform signal-to-noise interference elimination on the first detection data according to the first set category to obtain second detection data;
and the organic matter detection module is configured to determine a second set type to which the second detection data belongs, process the detection data excluding the signal-to-noise interference according to a detection time limit corresponding to the second set type to obtain third detection data, and perform organic matter detection on the third detection data.
8. An organic matter detection system based on artificial intelligence, the system comprising: an artificial intelligence based organic matter detecting device as claimed in any one of claims 1 to 4 and an organic compound detector connected thereto;
the organic compound detector is used for collecting original detection data and sending the original detection data to the organic compound detection device based on artificial intelligence.
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