Distributed laser-induced spectroscopy analysis apparatus, method, device, and medium

Abstract

The application relates to the field of spectrum technology and provides a distributed laser-induced spectrum analysis device, method, equipment and medium.The device comprises the following: a spectrum detection terminal used for collecting spectrum data of a measured sample; a data server system used for analyzing and processing the spectrum data to obtain a data analysis result; and a result output end used for displaying the data analysis result; wherein the spectrum detection terminal, the data server system and the result output end are deployed in a distributed architecture.The distributed laser-induced spectrum analysis device provided by the application adopts a distributed architecture in which three modules of spectrum collection, data processing and result output are separately deployed, the influence of special environment interference on analysis accuracy can be effectively avoided, the detection personnel can remotely and in real time acquire multi-point detection data, spectrum data collected by multiple nodes can be uniformly summarized and jointly analyzed, overall material distribution evaluation work can be supported, and large-range distributed efficient detection requirements can be met.

Description

Technical Field

[0001] This application relates to the field of spectroscopy technology, and in particular to a distributed laser-induced spectral analysis device, method, equipment, and medium. Background Technology

[0002] Laser-induced spectroscopy (LAS) is an analytical method that uses high-energy laser pulses to excite samples, causing them to produce specific optical responses. The composition of substances is then determined by analyzing the wavelength and intensity of the emitted light. Compared to traditional chemical detection methods, it has significant advantages such as fast detection speed, non-destructive in-situ detection, and the ability to simultaneously identify multiple substances. In recent years, it has been widely used in environmental monitoring, mineral exploration, industrial quality inspection, and other fields.

[0003] Existing typical laser-induced spectroscopy analysis equipment employs a centralized architecture, placing the comparison database, algorithm model, and spectral data of the sample to be analyzed within the same device. All computations and analysis are also performed locally. For a long time, this architecture met routine testing needs. However, using integrated equipment for spectral data acquisition and analysis, especially in special environments such as vacuum, low pressure, high temperature, strong radiation, and underwater, can interfere with the computation and storage modules of the local device, directly affecting the accuracy of the final analysis results. Furthermore, storing all spectral data locally prevents personnel from remotely obtaining results from multiple locations in real time, and hinders the unified aggregation and analysis of data from multiple nodes. This makes it difficult to support overall material distribution assessment in large-scale scenarios and can no longer meet the increasing demand for large-scale distributed testing. Summary of the Invention

[0004] This application provides a distributed laser-induced spectral analysis device, method, equipment, and medium to solve the problem that the centralized architecture of traditional laser-induced spectral analysis equipment can affect the accuracy of the final analysis results under special environments, and cannot meet the needs of large-scale distributed and efficient detection.

[0005] In a first aspect, this application provides a distributed laser-induced spectral analysis device, comprising: A spectral detection terminal is used to collect spectral data of the sample being tested and transmit the spectral data to a data server system. A data server system is used to analyze and process the spectral data, obtain data analysis results, and transmit the data analysis results to the result output terminal; The results output terminal is used to display the data analysis results; The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

[0006] In one embodiment, the spectral detection terminal includes: The laser induction unit is used to output laser light with corresponding parameters according to the needs of different spectral analysis techniques; the laser light is used to excite the characteristic signal light generated by the sample under test. A laser emission and spectral signal collection system is used to emit the laser to the sample under test and to receive the characteristic signal light generated by the sample under test; A spectral signal detection system is used to perform spectral detection processing on the characteristic signal light to obtain the spectral data of the sample under test.

[0007] In one embodiment, the spectral analysis techniques include laser-induced breakdown spectroscopy, laser-induced fluorescence spectroscopy, laser-induced Raman spectroscopy, and laser-induced infrared spectroscopy.

[0008] In one embodiment, the spectral detection terminal further includes: A spectral data preprocessing system is used to perform standardized preprocessing on the spectral data to obtain preprocessed spectral data.

[0009] In one embodiment, the data server system is used to perform data verification on the preprocessed spectral data. If the data verification is successful, the preprocessed spectral data is subjected to sample component analysis to obtain data analysis results.

[0010] In one embodiment, the data server system is further configured to perform terminal-level identity verification on the spectral detection terminal and output-level identity verification on the result output terminal.

[0011] In one embodiment, the distributed laser-induced spectral analysis device is further used for: Spectral data of the sample being tested can be collected in a targeted manner using a single or multiple spectral detection terminals, depending on the requirements of different spectral analysis techniques. By using a single data server or multiple data servers in the data server system, the spectral data are fused and analyzed to obtain the data analysis results corresponding to each spectral data. The data analysis results can be displayed through one or more output terminals in the result output terminal.

[0012] Secondly, this application also provides a distributed laser-induced spectral analysis method, comprising: The spectral data of the sample being tested is collected using a spectral detection terminal. The spectral data is analyzed and processed through a data server system to obtain data analysis results; The data analysis results are displayed through the results output terminal; Among them, the spectral detection terminal, the data server system, and the result output end are deployed using a distributed architecture.

[0013] In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above-mentioned distributed laser-induced spectroscopy analysis methods.

[0014] In a fourth aspect, the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of any of the above-mentioned distributed laser-induced spectroscopy analysis methods.

[0015] The distributed laser-induced spectroscopy analysis device, method, equipment, and medium provided by the present application adopt a distributed architecture in which the three major modules of spectral acquisition, data processing, and result output are separated and deployed, fundamentally solving the pain points of traditional centralized devices. By implementing distributed deployment to separate spectral acquisition and data processing, the influence of special environmental interference on analysis accuracy can be effectively avoided. Moreover, relying on the data server system, multiple spectral detection terminals deployed in a distributed manner can be simultaneously connected. It can not only support the detection personnel to remotely and real-time obtain multi-point detection data, but also perform unified summarization and linkage analysis on the spectral data collected from multiple nodes, effectively supporting the overall material distribution assessment work in a large-scale scenario and meeting the current growing demand for large-scale distributed and efficient detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic structural diagram of the distributed laser-induced spectroscopy analysis device provided by the present application.

[0018] Figure 2 It is a schematic flowchart of data communication transmission provided by the present application.

[0019] Figure 3 It is a schematic diagram of a special case of data communication transmission provided by the present application.

[0020] Figure 4 It is a schematic flowchart of the distributed laser-induced spectroscopy analysis method provided by the present application.

[0021] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein.

[0024] The following is combined Figures 1-5 This application describes the distributed laser-induced spectral analysis apparatus, method, equipment, and media provided.

[0025] Figure 1 This is a schematic diagram of the distributed laser-induced spectral analysis device provided in this application.

[0026] like Figure 1 As shown, the distributed laser-induced spectral analysis device includes: A spectral detection terminal is used to collect spectral data of the sample being tested and transmit the spectral data to a data server system. A data server system is used to analyze and process the spectral data, obtain data analysis results, and transmit the data analysis results to the result output terminal; The results output terminal is used to display the data analysis results; The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

[0027] Specifically, a typical laser-induced spectral analysis device usually includes a spectral detection terminal 1, a data server system 2, and a result output terminal 3. Considering that a single integrated device would be limited by space, sample size, and data processing capabilities in practical applications, this application adopts a distributed architecture to separate the three functional modules and deploy them on independent nodes. These nodes can work collaboratively through a network. Furthermore, depending on different usage requirements, these three functional modules can be distributed in the same geographical location or in different geographical locations. It should be noted that the data server system can interact with the spectral detection terminal and result output terminal at any distance, such as several meters or tens of thousands of kilometers, via Bluetooth, wireless WiFi, network cable, fiber optic, Ethernet, satellite data transmission systems, and various dedicated data transmission methods, as needed.

[0028] The spectral detection terminal outputs laser light with corresponding parameters according to the requirements of spectral analysis technology. By emitting the laser light generated according to the current technical requirements onto the sample under test, the sample is excited to produce corresponding characteristic signal light. Then, the terminal receives and processes the characteristic signal light to obtain the spectral data of the sample. Furthermore, the spectral detection terminal transmits the acquired spectral data of the sample to the data server system via a communication network according to the agreed data encapsulation format.

[0029] The data server system pre-establishes a sample analysis database and configures a spectral analysis algorithm. This database is built upon known sample analysis results. By accessing this database and using the pre-defined spectral analysis algorithm, the data server system performs component analysis on unknown samples. The database is continuously updated based on the latest calculated analysis results, and the spectral analysis algorithm is continuously iterated and optimized based on the accuracy feedback of the analysis results, helping the data server system continuously improve the accuracy of subsequent component analyses. After receiving spectral data uploaded from the spectral detection terminal, the data server system analyzes, calculates, and processes the spectral data in conjunction with the sample analysis database to obtain the data analysis results. Alternatively, it can perform fusion analysis based on multiple spectral data sets to obtain the final analysis results. Furthermore, the data server system transmits the calculated data analysis results of the tested sample to the result output terminal via a communication network, according to the agreed-upon data encapsulation format.

[0030] After receiving the data analysis results of the measured sample uploaded by the data server system, the result output end completes the final display and system control. The result output end can directly visualize the data analysis results, or further perform data processing and statistical analysis, mainly to convert the original analysis data into more valuable information, such as generating data reports, spectral maps, component ratio charts, etc., so that users can more intuitively understand the detection results. At the same time, the data server system also supports decision-making and application control based on the data analysis results, such as issuing warning alerts according to the component analysis results, and feedback on the parameter settings of the spectral detection terminal, the data server system or other devices and processes. It can be understood that the result output end can realize the parameter settings, timing relationship agreements, parameter monitoring and display of the spectral detection terminal and the data server system.

[0031] As can be seen from the functions of the above modules, the data server system is a software system, usually installed on an intelligent analysis chip, a computer or an industrial control computer. The spectral detection terminal is a hardware system, and the result output end is a terminal system combining software and hardware. The analysis results are presented through hardware devices, and application control is achieved through software functions. These three functional modules together constitute a distributed laser-induced spectroscopy analysis device.

[0032] Through a special distributed deployment method, it is possible to remotely collect spectral data, videos or images, electromagnetic fields, rays, and acoustic signals or images. Through the server access method, high-speed data processing and analysis can be achieved. It has technical advantages such as multiple spectral fusion analysis, multiple spectral synchronous analysis, and spectral fusion analysis with the above-mentioned several signals or images. At the same time, it also has the advantages of intensification, intelligence, low overall cost, convenient combination according to needs, and high reliability guarantee.

[0033] The distributed laser-induced spectroscopy analysis device provided by this application adopts a distributed architecture with separate deployment of three major modules: spectral acquisition, data processing, and result output, which fundamentally solves the pain points of traditional centralized devices. By realizing the separation of spectral acquisition and data processing through distributed deployment, the influence of special environmental interference on the analysis accuracy can be effectively avoided. Moreover, relying on the data server system, multiple spectrally distributed detection terminals can be connected simultaneously. It can not only support the detection personnel to remotely and real-time obtain multi-point detection data, but also uniformly summarize and jointly analyze the spectral data collected at multiple nodes, effectively supporting the overall material distribution assessment work in a large-scale scenario and meeting the current growing demand for large-scale distributed efficient detection.

[0034] It should be noted that the distributed laser-induced spectral analysis device provided in this application can meet the needs of different spectral analysis techniques and realize the analysis and processing of different spectral data of the same sample. In one embodiment, the spectral analysis techniques include laser-induced breakdown spectroscopy, laser-induced fluorescence, laser-induced Raman spectroscopy, and laser-induced infrared spectroscopy.

[0035] Specifically, laser-induced breakdown spectroscopy (LIBS) is also known as laser-induced plasma spectroscopy (LIPS). It is an analytical technique based on the plasma emission spectrum generated by the interaction between a laser and a material. This method involves ablating a sample with a laser and then detecting the spectrum of plasma emission to achieve quantitative and qualitative analysis of the material composition. It can also be used for identification and classification.

[0036] Laser-induced fluorescence (LIF) is a technique that uses a laser to excite a substance to produce fluorescence. When a substance is irradiated with light of a specific wavelength, the irradiated substance has a certain probability of absorbing photons, causing the particles to transition to an excited state and then radiatively transition to a lower energy level, emitting photons. The energy of the photons absorbed and emitted by the substance is related to the energy difference between the energy levels of the fluorescent particles, therefore the wavelength range of photons absorbed and emitted by each substance is relatively fixed. Typically, the wavelength of the fluorescent photons is longer than the wavelength of the incident photons.

[0037] Raman spectroscopy (RS) is produced by the Raman scattering effect. The Raman scattering effect refers to the phenomenon where, when light shines on a surface, molecules absorb some energy, vibrate in different ways and to different degrees, and then scatter light of different frequencies. Raman spectroscopy reflects the correlation between the vibrational energy levels (lattice vibrational energy levels) and rotational energy levels of molecules, thus allowing the identification of information about the molecular structure of matter. Laser-induced Raman spectroscopy (LIRS) is an analytical method based on the Raman scattering effect, analyzing the scattered spectra at frequencies different from the incident light to obtain information about the characteristics of molecules. LIRS analysis can be used to analyze and identify the components of both organic and inorganic substances, and is a method for studying the molecular structure of matter.

[0038] Infrared spectroscopy (IR) analysis is a real-time online detection technology that uses a near-infrared light source to illuminate the analyte. The compound molecules vibrate and absorb infrared light of specific wavelengths, producing an absorption spectrum. Analyzing these spectral fingerprints allows for timely acquisition of information on the composition and content of the analyte. Compared to other online detection methods, near-infrared spectroscopy offers advantages such as high safety and reliability, simple operation and maintenance, and high-speed measurement. Laser-induced infrared spectroscopy (LIIR) technology, employing laser-induced excitation, offers significantly improved signal-to-noise ratios, making it easier to detect weak absorption signals; extremely low detection limits, hundreds to thousands of times more sensitive than traditional techniques, reaching the ppb (parts per billion) or even ppt (parts per trillion) level; and extremely fast measurement speeds, enabling real-time online monitoring at the kHz level.

[0039] The aforementioned LIBS, LIF, LIRS, and LIIR technologies can all be included. Figure 1 The various components are as follows. For Raman and infrared spectroscopy techniques that use other types of light sources, the laser-induced light source is simply replaced with another light source, and therefore the method provided in this application is also applicable.

[0040] By combining spectral analysis algorithms, substances can be identified or their components can be quantitatively analyzed. In actual detection processes, different spectral analysis techniques are usually implemented independently. Even when multiple methods are required to detect a sample, the equipment or detection system operates independently.

[0041] In one embodiment, the number of spectral detection terminals is 1 to n, the number of data servers in the data server system is 1 to m, and the number of result output terminals is 1 to a; Spectral data of the sample being tested can be collected in a targeted manner using a single or multiple spectral detection terminals, depending on the requirements of different spectral analysis techniques. By using a single data server or multiple data servers in the data server system, the spectral data are fused and analyzed to obtain the data analysis results corresponding to each spectral data. The data analysis results can be displayed through one or more output terminals in the result output terminal.

[0042] Specifically, in a single spectral detection terminal, one or more identical components can be integrated into a certain functional unit.

[0043] A single spectral detection terminal can also simultaneously perform one or more functions of LIBS, LIF, LIRS, and LIIR.

[0044] One of the functions mentioned can be to acquire signals from different regions, at different times, or at different spectral frequencies through two or more channels. The multiple functions mentioned can be a combination of two or more of LIBS, LIF, LIRS, and LIIR. That is, a single spectral detection terminal can simultaneously fulfill multiple combinations of LIBS, LIF, LIRS, and LIIR functions, or a single spectral detection terminal can be responsible for only one of the LIBS, LIF, LIRS, and LIIR functions, while multiple spectral detection terminals each handle each of the LIBS, LIF, LIRS, and LIIR functions.

[0045] The integration of one or more identical components can be used to achieve the same function or different detection functions in LIBS, LIF, LIRS, and LIIR. For example: (1) In a spectral detection terminal, two laser-induced light sources with different parameters can be included as needed to achieve the LIBS function, but for laser-induced breakdown spectral excitation at different locations; (2) It can also include one laser-induced light source for Raman and three for LIF, which must achieve the acquisition of Raman and fluorescence spectral signals at different time sequences, different levels, and different spatial locations. (3) This is just an example of laser-induced light sources. Other components in the spectral detection terminal can also be combined as needed.

[0046] For a data server system, a single data server can be used to fuse and analyze two or more types of spectral data collected by the spectral detection terminal, obtaining data analysis results for each type of spectral data. These results can then be combined to generate a single data analysis outcome. Alternatively, a single data server can be used to analyze one type of spectral data collected by the spectral detection terminal, while multiple data servers can each handle the analysis of one type of spectral data, obtaining data analysis results for each type of spectral data. These results can then be combined to generate a single data analysis outcome.

[0047] For the output end, all data analysis results or comprehensive data analysis results generated after processing by the data server can be output centrally through a single output end, or single-type data analysis results processed by different data servers can be output separately through multiple output ends.

[0048] When necessary, a master-slave relationship can be set up, for example, placing certain data processing steps on a specific data server system, thereby improving data processing efficiency, reducing data analysis latency, and avoiding data errors.

[0049] Here's an example: 1. Multiple spectral detection terminals can perform coordinated signal acquisition, such as acquiring the first type of spectral data (e.g., LIBS) first, and then acquiring the second type of spectral data (e.g., LIRS). Different acquisition sessions can have preset delay and timing relationships. 2. Data transmission can be sent to different data server systems as needed for categorized data processing, such as LIBS as the first data server, LIRS as the second data server, and LIF as the third data server; or one or more types of data can be processed simultaneously on one or several data servers; and then the necessary logical connections and data communication can be established between the data servers. 3. Finally, the results are collaboratively output to different specific terminals (which can be numbered 1, 2, 3…n) in the output stage. This approach has the advantages of improving data processing efficiency and enabling rapid data integration and comparison.

[0050] A total spectral detection terminal can be constructed from one or more spectral detection terminals (which can be numbered 1, 2, 3…n) that implement LIBS, LIF, LIRS, and LIIR. Testing can be performed by function or for different samples as needed.

[0051] The number of samples can range from 1 to X, and multiple samples can be tested in different locations and environments. For example: 1. Data is collected in different environments, and then the inherent connection is established in the analysis and processing algorithms of the data server system to achieve calibration analysis in special environments such as vacuum, low pressure, high temperature, strong radiation, and underwater. 2. For a particular sample, if the acquired spectral data differs significantly from previous data, it can collaboratively access other collaborative data servers for comprehensive and interconnected data analysis. This also includes accessing data or results obtained from other detection methods (LIBS, LIF, LIRS, LIIR).

[0052] The distributed setup scheme adopted in this application embodiment offers significantly greater flexibility and scalability compared to traditional single-unit integrated laser-induced spectral analysis equipment. If new detection scenarios or expanded detection functions are required in the future, it is not necessary to replace the entire device; only a new spectral detection terminal with the corresponding function or a new data server system with the corresponding computing power is needed, greatly reducing the cost of functional upgrades.

[0053] The following embodiments refer to Figure 2 , Figure 2 This is a schematic diagram of the data communication transmission process provided in this application. Specifically, in the case of only one data server system, the data communication transmission process is as follows: Figure 3 , Figure 3This is a schematic diagram illustrating a specific example of data communication transmission provided in this application.

[0054] In one embodiment, the spectral detection terminal includes: The laser induction unit is used to output laser light with corresponding parameters according to the needs of different spectral analysis techniques; the laser light is used to excite the characteristic signal light generated by the sample under test. A laser emission and spectral signal collection system is used to emit the laser to the sample under test and to receive the characteristic signal light generated by the sample under test; A spectral signal detection system is used to perform spectral detection processing on the characteristic signal light to obtain the spectral data of the sample under test.

[0055] Specifically, the spectral detection terminal 1 includes laser-induced light sources 11, numbered 1 to n1; laser emission and spectral signal collection systems 12, numbered 1 to n2; spectral signal detection systems 13, numbered 1 to n3; spectral data preprocessing systems 14, numbered 1 to n4; and a calibration module 15. As needed, a spectral detection terminal calibration system 16 may be provided, numbered 1 to 10.

[0056] Laser-induced light sources are used to output lasers with corresponding parameters according to the needs of spectral analysis techniques, such as LIBS, LIF, LIRS, and LIIR, to excite corresponding characteristic signal light on the surface of the sample being tested.

[0057] The laser-induced light source is set up as follows: 1. It can be a semiconductor laser, a solid-state laser, or a gas laser, such as, but not limited to, a laser using Nd:YAG as the working medium, or a semiconductor laser with fiber-coupled output, or a carbon dioxide laser; 2. It can be a laser capable of pulsed output or a laser capable of continuous output; pulsed output lasers typically have pulse widths between attoseconds and several seconds, with particularly typical pulse widths ranging from 1 to hundreds of nanoseconds or picoseconds. 3. It can be a laser that can output 2 to 100 pulses with adjustable intervals through power supply or optical modulation to continuously achieve excitation; 4. Alternatively, 2 to 5 lasers can be combined and controlled by a unified timing output device to achieve excitation according to a set time interval; 5. Depending on the excitation requirements, multiple wavelengths of laser light can be output simultaneously or from different light sources to improve the excitation effect of plasma; 6. Excitation enhancement can be achieved through auxiliary systems such as microwaves, sound fields, magnetic fields, and rays.

[0058] The laser emission and spectral signal collection system is used to emit the laser output from the laser induction unit onto the sample under test. When a corresponding characteristic signal light is induced on the surface of the sample under test, the characteristic signal light is collected.

[0059] The laser emission and spectral signal collection system is set up as follows: 1. By using one or more up to 20 lenses, mirrors, or a combination of lenses and mirrors, the direction, divergence angle, and spot diameter of laser emission can be controlled to project a spot of the required size (including convergence to a very small point) at the required distance; by using different or partially identical optical paths, the angle and spot diameter of laser emission can be controlled to collect characteristic signal light and obtain a beam with the required divergence angle and diameter; 2. The emitted laser light is passed through lenses and mirrors, and through one or more Fresnel mirrors or other diffractive optical elements. By utilizing their diffraction and focusing properties, the laser light can be focused and emitted over long distances, and the excitation light signal can be collected. 3. By using diffractive optical devices placed behind mirrors or lens groups, the light beam can be shaped. For example, a circular light spot can be shaped into a thin line with a width of 0.1~5mm and a length of 1~1000mm to achieve large-area scanning and signal acquisition. Similarly, diffractive optical devices can be used to form an array of any number of light spots from 2×2 to 100×100 to perform laser induction and characteristic signal light acquisition at multiple points. 4. A zoom lens group can be set up to adjust the induced laser focusing distance and signal detection efficiency. It can consist of one or more up to 20 lenses or mirrors, or other reflective, transmissive, and diffractive optical devices, or a combination of these. If necessary, an aperture stop can be installed within the optical system to work with the zoom lens group, enabling precise control of the field of view and the intensity of the characteristic signal light, compensating for deviations in laser illumination range and intensity caused by zooming. 5. A dichroic mirror can be set to separate or combine the optical paths of the characteristic signal light and the induced laser while realizing a partial common optical path for the induced laser and the characteristic signal light in the system. 6. Active optical devices can be set to control and optimize the quality of the laser beam and the characteristic signal light.

[0060] The spectral signal detection system is used to perform spectral processing on the received characteristic signal light and detect the intensity information of light signals at different wavelengths to obtain the spectral data of the sample being tested.

[0061] A spectral signal detection system specifically includes a beam splitter and a detector. The beam splitter separates the received characteristic signal light into monochromatic lights of different wavelengths and orders; it can be a grating or other diffractive optical element. The detector digitizes the monochromatic light obtained by the beam splitter after detection by a charge-coupled device (CCD), ultimately yielding spectral data. The detector can be an imaging device such as a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS), or it can be one or more photosensitive devices arranged in a linear array, such as one or more photodiodes or photomultiplier tubes.

[0062] The spectral signal detection system can also be an integrated spectral detection device, including but not limited to spectrometers with Czerny-Turner structure, echelle grating spectrometers, Fourier spectrometers, as well as colorimeters, photometers, and wavelength meters.

[0063] The embodiments of this application can flexibly adapt to the parameter requirements of different spectral analysis technologies through the laser induction unit, and achieve efficient excitation of characteristic signal light of the sample under test in multiple scenarios. Combined with an integrated laser emission and spectral signal collection system, it can reduce optical path transmission loss and improve signal light collection efficiency. Finally, in conjunction with the spectral signal detection system, it can achieve high sensitivity and high accuracy acquisition of spectral data of the sample under test, and can effectively support the rapid analysis needs of various material components.

[0064] In one embodiment, the spectral detection terminal further includes: A spectral data preprocessing system is used to standardize and preprocess spectral data to obtain preprocessed spectral data.

[0065] Specifically, the spectral detection terminal 1 can also be equipped with a spectral data preprocessing system 14.

[0066] The spectral data preprocessing system is used to determine the correctness, usability, and correct the acquired spectral data. It can perform qualitative and quantitative analysis and category identification in real time based on the intensity of the spectral signal, for example: 1. Calibration and correction of spectral wavelength and intensity based on temperature, humidity, air pressure, interference light, atmospheric gas composition, and special environmental parameters such as underwater and vacuum environments; 2. Eliminate background noise interference; 3. Including but not limited to wavelength correction, noise reduction, background removal, and baseline correction of spectral data; 4. Extraction, filtering, and processing using any algorithm; 5. Data processing including but not limited to packaging, encryption, and other methods; 6. Determine whether the obtained signal intensity and spectral characteristics meet the requirements for qualitative and quantitative analysis; 7. Perform intensity correction for specific wavelengths, specific bands, or the entire spectrum; 8. Perform preprocessing for mathematical transformations (such as convolution and wavelet denoising) and artificial intelligence algorithms (such as feature extraction); 9. Depending on the need for data confidentiality, parameters, data, databases, parameters of qualitative and quantitative analysis and preprocessing, algorithms, and externally accessed data can be placed in or combined with other data in the spectral data preprocessing system.

[0067] The spectral detection terminal 1 can also be equipped with a calibration module 15.

[0068] The verification module is used to send the identity information of the spectral detection terminal to the data server system so that the data server system can identify and authenticate the spectral detection terminal.

[0069] Specifically, the verification module sends data packets to the data server system as needed, providing the identity information of the spectral detection terminal. This can be done at specific times, including but not limited to device power-on, after a handshake with the server, during necessary periods in the data acquisition process, or at fixed time intervals (such as every minute, hour, every 24 hours, every week, or at a time specified by the server); it can also be set to respond to power-off commands, parameter setting commands, or active commands from the data server system.

[0070] The identity information provided by the verification module is used to identify and authenticate the spectral detection terminal. This information can be hardware component information or software information, including but not limited to: Internet Protocol (IP) addresses, network location information of public or local area networks; parameters, brand, performance, and other information of any hardware component within the spectral detection terminal, such as the Media Access Control (MAC) address of a wireless router or other communication component; information codes provided or emitted by any component within the spectral detection terminal, such as verification codes emitted by built-in chips, integrated circuit modules, computers, and smart terminals; any signals emitted by any component interface within the spectral detection terminal, such as high and low level signals emitted by RS-232, RS-485, and Universal Serial Bus (USB) interfaces; and location information provided by positioning modules such as BeiDou and Global Positioning System (GPS).

[0071] The spectral detection terminal 1 can also be equipped with a spectral detection terminal calibration system 16 before the spectral data preprocessing system 14 as needed.

[0072] The spectral detection terminal calibration system is used to evaluate, calibrate, and correct the spectral data collected by the spectral detection terminal through calibration equipment. It can communicate with the data server system as needed and is a component that can be set up as required.

[0073] The spectral detection terminal calibration system is set up as follows: 1. It can include any light source used for calibration, such as sodium lamps, mercury lamps, semiconductor lasers, and other devices or apparatuses with stable wavelength light output, the function of which is to correct wavelength drift and intensity changes of spectral signals; 2. It can be achieved through algorithms, as needed, on the spectral detection terminal or data server system to correct wavelength drift, intensity change, spectral line shape, and spectral fitting line shape. 3. It can be an auxiliary device or system used to control, modulate, or enhance the characteristic signal light generated by the laser, with the ultimate goal of improving detection performance.

[0074] In addition, the spectral detection terminal 1 can be equipped with an auxiliary detection system according to the detection requirements. This system is used to collect video or images, electromagnetic fields, radiation, and acoustic signals or images. These data serve as optional auxiliary information, and the additional auxiliary information is synchronously transmitted to the data server system to assist the data server system in analyzing and processing the spectral data.

[0075] This application embodiment uses a spectral data preprocessing system to standardize and correct the acquired raw spectral data, which can effectively filter out the influence of various interference factors on the raw data during the detection process, improve the signal-to-noise ratio and consistency of the spectral data, provide more reliable input data for subsequent material composition analysis, reduce the processing difficulty of subsequent analysis models, and improve the accuracy and stability of detection results.

[0076] In one embodiment, the data server system is used to perform data verification on the preprocessed spectral data. If the data verification is successful, the preprocessed spectral data is subjected to sample component analysis to obtain data analysis results.

[0077] Specifically, the data server system is used to establish a sample analysis database, set up spectral analysis algorithms, analyze, calculate and process spectral data obtained from the spectral detection terminal, and provide the final results to the output terminal.

[0078] The sample analysis database stores any data involved or used during the analysis process. This includes a calibration spectral analysis database based on known samples, and may also include, as needed, parameter settings, algorithms, and comparative analysis databases for techniques such as laser-induced breakdown spectral excitation enhancement and improved qualitative and quantitative analysis performance using auxiliary systems such as microwaves, acoustic fields, magnetic fields, and radiation. The spectral data obtained from the spectral detection terminal is first verified using the data stored in the sample analysis database. Only after successful verification is the spectral data analyzed and processed.

[0079] Spectral analysis algorithms are any data processing procedures involved or used in the analysis process, including any methods for implementing the mapping relationship from spectral data to component content information, such as: 1. Any calibration method for chemical analysis, such as single-line calibration, multi-line calibration, etc.; 2. Any data analysis methods in statistics and machine learning, such as partial least squares (PLS), support vector regression (SVR), and other algorithms; 3. Any artificial intelligence algorithm, such as Random Forest (RF), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and other machine learning or deep learning algorithms.

[0080] It should be noted that, as shown in the above embodiments, the spectral data preprocessing system performs preliminary standardization processing on the collected spectral data, and the data server system performs sample component analysis on the preprocessed spectral data. Based on this processing flow, some or all of the functions of the aforementioned spectral data preprocessing system can be placed in the data server system; alternatively, some functions of the spectral data preprocessing system and the data server system can overlap, complement each other, or both systems can process the data and then compare and verify it, working together to achieve spectral analysis and application functions.

[0081] In addition, depending on the need for data confidentiality, parameters, data, databases, parameters of qualitative and quantitative analysis and preprocessing, algorithms, and external access data can be placed in or combined with data server system components.

[0082] In this embodiment, the preprocessed spectral data is first verified for compliance by a data server system. This effectively eliminates invalid spectral data that does not meet the standards, preventing erroneous data from interfering with the subsequent analysis process. Then, sample component analysis is carried out based on the verified data, which can significantly improve the reliability and accuracy of the component analysis results.

[0083] In one embodiment, the data server system is further configured to perform terminal-level identity verification on the spectral detection terminal and output-level identity verification on the result output terminal.

[0084] Specifically, the data server system can perform verification communication with the spectral detection terminal or result output terminal at any time point as needed to confirm the authenticity of hardware components, equipment information, account information, and working status.

[0085] The most typical approach is to send verification data and determine the authenticity, whether it has been tampered with, and its operating status based on the verification data returned by the terminal.

[0086] For example, but not limited to, verification can be performed at the power-on, power-off, fixed or arbitrary time intervals of the spectral detection terminal or result output terminal, or at any point in time before logging into an account, sending data, or by an event-triggered method.

[0087] Of course, you can also perform verification only on the spectral detection terminal or the result output terminal.

[0088] This application's embodiments employ an identity verification mechanism to ensure the security of the detection process from both ends of the link. By verifying the identity of the spectral detection terminal, the risk of invalid data upload or data tampering caused by unauthorized terminal access can be avoided. By verifying the identity of the result output end, access permissions to the detection results can be strictly controlled to prevent the leakage of sensitive detection data and effectively improve the data security of the entire spectral detection system.

[0089] In summary, based on all the above embodiments, Figure 2 For example, this application has the following workflow mode in the process of spectral data acquisition and transmission: 1. Spectral detection terminal 1 acquires spectral data; 2. The spectral detection terminal 1 sends verification data to the data server system 2 (remote transmission 3). After completing the terminal verification, the data server system 2 returns a correct instruction (remote transmission 4). 3. The spectral detection terminal 1 sends the preprocessed data to the data server system 2 (remote transmission 1). The data server system 2 verifies the data and processes it before sending it to the result output terminal 3 (remote transmission 5) for display, or displays the data after processing. 4. As needed, the data server system 2 can send return information to the spectral detection terminal 1. This includes, but is not limited to, working status information such as "received" and "processed successfully," as well as information such as processing time and server status. Alternatively, the data server system 2 can process the data or evaluate the transmitted data and then provide feedback to correct the parameter settings of the spectral detection terminal 1. 5. As needed, the output terminal can implement system control based on the data sent by the data server system 2; 6. As needed, the result output terminal 3 can feed back the data processing results and control results to the data server system 2 (remote transmission 6). 7. When necessary, the data server system 2 can perform a similar output verification (remote transmission 7) on the result output terminal 3 as the output terminal of the spectral detection terminal 1. Based on the verification data (remote transmission 8) returned by the result output terminal 3, it can determine whether the result output terminal 3 is normal and place this verification before or after the data display step.

[0090] The data transmission mode during measurement is as follows: 1. Steps ① to ⑩ above can be repeated as needed; steps ⑥, ⑦, ⑧, and ⑩ can be omitted as needed, or the steps can be rearranged as described above. 2. As needed, the frequency of remote transmission can be reduced. After the data from multiple measurements is temporarily stored in the spectral detection terminal 1, the data from one or more measurements can be sent at once by the remote transmission step 1. Then, the data server system 2 will process the data together and transmit it to the result output terminal 3 one by one or multiple times after the data processing is completed. 3. During long-term operation, if it is necessary to take multiple measurements and average them, or to perform data calibration at fixed time intervals, the data server system 2 can send correction parameters to the spectral detection terminal 1, thereby realizing the calibration and other functions of the spectral detection terminal 1.

[0091] The following are two specific examples.

[0092] In Example 1, the spectral detection terminal 1 is a detection terminal located at a coal mine industrial site in region A; the data server system 2 is a data workstation located in a data center in region B, transmitting data via a 5G fiber optic network; the result output terminal 3 is located in region C, consisting of an interactive large screen and a desktop server, and is the headquarters of a mining company. The result output terminal 3 is approximately 1200 km away from the data server system 2 and approximately 1500 km away from the spectral detection terminal 1; the distance between the data server system 2 and the spectral detection terminal 1 is approximately 800 km.

[0093] The spectral detection terminal 1 has the following characteristics: 1. The laser-induced light source 11 is a solid-state laser with Nd:YAG as the working material, which can generate laser output with an energy of 100mJ and a pulse width of 10ns. The repetition frequency can be adjusted in the range of 1 to 10Hz as needed. 2. The laser emission and spectral signal collection system 12 includes two parts: a lens with a diameter of 100 mm for collecting signal light, and an optical fiber with a core diameter of 200 μm placed at the focal point of the lens with a diameter of 100 mm for transmitting the signal light to the spectral signal detection system 13. 3. The spectral signal detection system 13 is a Czerny-Turner spectrometer with a 200 line / mm grating. Combined with a 150μm wide narrow slit, the signal light is distributed onto a linear CCD array, enabling spectral signal detection in the 200–600 nm range. 4. The spectral data preprocessing system 14 is implemented by software installed on the industrial control computer inside the spectral detection terminal 1; 5. The verification module 15 is implemented by an industrial control computer installed inside the spectral detection terminal 1 in conjunction with a wireless router. It identifies and authenticates the spectral detection terminal 1 by providing the network card MAC address and IP address. 6. A spectral detection terminal calibration system 16 is provided, including a sodium lamp light source based on wavelength calibration and a correction algorithm for spectral signal fitting.

[0094] When the distributed laser-induced spectral analysis device in this embodiment is running, it performs the following steps in sequence: 1. The spectral detection terminal 1 is powered on and sends verification data to the data server system 2; 2. The data server system 2 uses the MAC address and IP address of the network card provided by the verification module 15 installed inside the spectral detection terminal 1 to identify and authenticate the spectral detection terminal 1; 3. Spectral detection terminal 1 collects spectral data; 4. The spectral detection terminal calibration system 16 emits sodium spectral lines at 589.0 nm and 589.6 nm. The spectral signal detection system 13 times determines the collected spectral wavelengths. The wavelength of the spectral detection terminal 1 is then calibrated based on the deviation. 5. For continuously measured spectral signals, the spectral detection terminal calibration system 16 determines that when the maximum intensity of the acquired spectral signal is lower than 3000, it will issue an alarm indicating that the acquired spectral signal is invalid and mark it to notify the data server system 2 to avoid interfering with the calibration analysis. 6. The spectral detection terminal 1 sends its IP address, device location, and unique coded data to the data server system 2 to initiate terminal verification; 7. Data server system 2 makes a judgment based on the verification result. If the verification result does not meet the requirements, it will issue an alarm and prompt, and store the received data and information; if the verification result meets the requirements, it will return a verification success message. 8. The spectral detection terminal 1 performs preprocessing on the received spectral data. Abnormal noise signals are removed using wavelet transform. 9. Perform intensity linear normalization. The correction algorithm is set as follows: when the maximum intensity of the acquired spectral signal is lower than 6000, the signal at each wavelength is enhanced and corrected by a correction algorithm established based on the plasma emission spectrum and its laws, thereby improving the accuracy of calibration analysis; 10. Obtain curve functions through nonlinear fitting of spectral data; calculate the peak area of ​​fluorescence signals; obtain the center wavelength through integration; 11. Using machine learning algorithms, establish an analytical data model for interference factors, and extract and correct effective spectral information; 12. Deep learning-based algorithms, such as Convolutional Neural Networks (CNNs), extract information from spectral data; and calculate component content information based on Partial Least Squares (PLS) algorithms. 13. Data server system 2 remotely transmits information to result output terminal 3 for data display; 14. Based on the component analysis results, the output terminal will monitor the mining process. When the mined coal contains excessive levels of harmful elements, the output terminal will issue a warning. At the coal mining site where the spectral detection terminal 1 is installed, the parameters of the mining equipment will be adjusted as needed based on this information, and additional coal washing processes will be introduced. 15. Changes to the parameter settings of the mining equipment, as well as additional coal washing process parameters, will be returned to Data Server System 2. Data Server System 2 will record the returned process parameters and use them as a variable in the data model, supporting subsequent analysis iterations and algorithm model improvements, thereby enhancing accuracy.

[0095] In Example 2, the spectral detection terminal 1, data server system 2, and result output terminal 3 are similar to those in Example 1, and will not be described in detail again.

[0096] The difference from Example 1 lies in the data transmission mode during measurement, which is as follows: Data server system 2 performs statistical analysis on the data collected by spectral detection terminal 1, determining the validity of subsequent spectral data based on the characteristic peak intensities of previously collected spectral data. For example, when analyzing the fluorine content in coal, based on the intensity of the emission peak at 685.603 nm of F II measured in the previous 20 measurements and the wavelength drift, an arithmetic mean is taken, with a tolerance range of 10%. If spectral detection terminal 1 determines that the intensity of subsequent spectral signals exceeds the tolerance range, the measurement is considered invalid. If three consecutive spectral acquisitions are uniformly invalid, data server system 2 adjusts the laser operating parameters of spectral detection terminal 1 to obtain a stronger spectrum, ensuring the accuracy of quantitative analysis.

[0097] As can be seen from the above, the device and technology provided in this application have the following functions and advantages: 1. Effectively protects spectral data and prevents data theft. Existing typical laser-induced breakdown spectroscopy detection equipment or systems place the comparison database, algorithm model, and spectral data of the sample to be analyzed in the same device, which poses a serious data security risk. This application, through an architecture that sets up a server at a different location to store the data, can effectively protect the comparison database and algorithm model, preventing theft and leakage. 2. By using methods such as precise positioning of the spectral detection terminal, comparison of device information, hardware identification, and data verification, the spectral detection terminal can be identified, preventing counterfeit devices from accessing the system and protecting the spectral analysis service from being stolen and used by other devices. 3. This application avoids the problems of high data transmission error rates and difficulty in increasing detection frequency caused by system communication delays and instability. Due to factors such as excessive distance between the spectral detection system and the data server or unstable communication systems, remote laser-induced breakdown spectroscopy detection systems struggle to increase detection frequency. Furthermore, issues like network latency and data packet loss lead to high data transmission error rates and system instability. This application, combining the characteristics of laser-induced breakdown spectroscopy technology, proposes an efficient remote data transmission stream structure that solves the problems of high data transmission error rates and system lag during high-frequency testing, achieving remote measurements of up to hundreds of times per second.

[0098] Figure 4 This is a flowchart illustrating the distributed laser-induced spectral analysis method provided in this application.

[0099] like Figure 4 As shown, the distributed laser-induced spectral analysis method includes: Step 401: Collect spectral data of the sample under test using a spectral detection terminal; Step 402: Analyze and process the spectral data through the data server system to obtain data analysis results; Step 403: Display the data analysis result through the result output terminal; Among them, the spectral detection terminal, the data server system, and the result output terminal are deployed using a distributed architecture.

[0100] The distributed laser-induced spectroscopy analysis method provided in this application adopts a distributed architecture with separate deployment of three major modules: spectral acquisition, data processing, and result output, which fundamentally solves the pain points of traditional centralized devices. By implementing distributed deployment to separate spectral acquisition and data processing, it can effectively avoid the influence of special environmental interference on the analysis accuracy. Moreover, relying on the data server system, multiple spectrally detected terminals deployed in a distributed manner can be simultaneously connected. It can not only support the detection personnel to remotely and real-time obtain multi-point detection data, but also perform unified aggregation and linkage analysis on the spectral data collected from multiple nodes, effectively supporting the overall material distribution assessment work in a large-scale scenario and meeting the current growing demand for large-scale distributed and efficient detection.

[0101] It should be noted that the distributed laser-induced spectroscopy analysis method provided in this application can execute the processes described in any of the above embodiments during specific operation, and this embodiment will not be elaborated here.

[0102] Figure 5 is a schematic structural diagram of the electronic device provided in this application. As Figure 5 shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the distributed laser-induced spectroscopy analysis method, which includes: collecting spectral data of the measured sample through a spectral detection terminal; analyzing and processing the spectral data through a data server system to obtain a data analysis result; displaying the data analysis result through a result output terminal; among them, the spectral detection terminal, the data server system, and the result output terminal are deployed using a distributed architecture.

[0103] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] On the other hand, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the distributed laser-induced spectral analysis method provided in the above embodiments. The method includes: acquiring spectral data of a sample to be tested through a spectral detection terminal; analyzing and processing the spectral data through a data server system to obtain data analysis results; and displaying the data analysis results through a result output terminal. The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

[0105] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the distributed laser-induced spectral analysis method provided in the above embodiments. The method includes: acquiring spectral data of a sample to be tested through a spectral detection terminal; analyzing and processing the spectral data through a data server system to obtain data analysis results; and displaying the data analysis results through a result output terminal. The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed laser-induced spectral analysis device, characterized in that, The distributed laser-induced spectral analysis device includes: A spectral detection terminal is used to collect spectral data of the sample being tested and transmit the spectral data to a data server system. A data server system is used to analyze and process the spectral data, obtain data analysis results, and transmit the data analysis results to the result output terminal; The results output terminal is used to display the data analysis results; The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

2. The distributed laser-induced spectral analysis device according to claim 1, characterized in that, The spectral detection terminal includes: The laser induction unit is used to output laser light with corresponding parameters according to the needs of different spectral analysis techniques; the laser light is used to excite the characteristic signal light generated by the sample under test. A laser emission and spectral signal collection system is used to emit the laser to the sample under test and to receive the characteristic signal light generated by the sample under test; A spectral signal detection system is used to perform spectral detection processing on the characteristic signal light to obtain the spectral data of the sample under test.

3. The distributed laser-induced spectral analysis device according to claim 2, characterized in that, The spectral analysis techniques include laser-induced breakdown spectroscopy, laser-induced fluorescence spectroscopy, laser-induced Raman spectroscopy, and laser-induced infrared spectroscopy.

4. The distributed laser-induced spectral analysis device according to claim 1, characterized in that, The spectral detection terminal also includes: A spectral data preprocessing system is used to perform standardized preprocessing on the spectral data to obtain preprocessed spectral data.

5. The distributed laser-induced spectral analysis device according to claim 4, characterized in that, The data server system is used to perform data verification on the preprocessed spectral data. If the data verification is successful, the preprocessed spectral data is subjected to sample component analysis to obtain data analysis results.

6. The distributed laser-induced spectral analysis device according to claim 1, characterized in that, The data server system is also used to perform terminal-level identity verification on the spectral detection terminal and output-level identity verification on the result output terminal.

7. The distributed laser-induced spectral analysis device according to any one of claims 1 to 6, characterized in that, The distributed laser-induced spectral analysis device is also used for: Spectral data of the sample being tested can be collected in a targeted manner using a single or multiple spectral detection terminals, depending on the requirements of different spectral analysis techniques. By using a single data server or multiple data servers in the data server system, the spectral data are fused and analyzed to obtain the data analysis results corresponding to each spectral data. The data analysis results can be displayed through one or more output terminals in the result output terminal.

8. A distributed laser-induced spectral analysis method, characterized in that, The distributed laser-induced spectral analysis method includes: The spectral data of the sample being tested is collected using a spectral detection terminal. The spectral data is analyzed and processed through a data server system to obtain data analysis results; The data analysis results are displayed through the results output terminal; The spectral detection terminal, the data server system, and the result output terminal are deployed in a distributed architecture.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the distributed laser-induced spectral analysis method as described in claim 8.

10. A non-transitory computer-readable storage medium, wherein a computer program is stored on the non-transitory computer-readable storage medium, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed laser-induced spectral analysis method as described in claim 8.

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