Multi-gas quick response detection and alarm integration method and system

Abstract

The invention relates to the technical field of gas detection, and discloses a multi-gas quick response detection and alarm integration method and system, and the method comprises the steps: collecting a gas sample of a multi-gas region, analyzing the gas absorption fingerprint dynamic state of the multi-gas region, and obtaining gas energy level transition characteristics; carrying out quantum characteristic mapping on the gas energy level transition characteristic, carrying out mixed gas decoupling on the gas sample to obtain an independent gas absorption spectral line, analyzing the independent gas concentration of a multi-gas area, and carrying out first risk analysis on the multi-gas area to obtain a first risk report; analyzing the gas action condition of the multi-gas area to obtain gas action data, and constructing a gas mixing concentration matrix of the multi-gas area; and constructing a gas dynamic diffusion model of the multi-gas area, carrying out second risk analysis on the multi-gas area to obtain a second risk report, and carrying out real-time early warning on the multi-gas area. According to the invention, the detection and alarm accuracy of a complex multi-gas scene can be improved.

Description

Technical Field

[0001] The present invention relates to an integrated method and system for multi-gas rapid response detection and alarm, belonging to the technical field of gas detection. Background Art

[0002] In many fields such as modern industrial production, environmental monitoring, and public safety assurance, the integrated technology of multi-gas rapid response detection and alarm plays a crucial role. From the real-time monitoring of toxic and harmful gases in chemical enterprises, to the accurate control of urban environmental air quality, and then to the prevention of flammable and explosive gases in public places, the quality of this technology is directly related to production safety, environmental quality, and public life and property safety. Detecting multiple gases quickly and accurately and issuing alarms in a timely manner can effectively prevent accidents, reduce potential risks, and improve the overall protection level.

[0003] Currently, the integration of multi-gas rapid response detection and alarm mostly adopts traditional gas sensing technologies. This kind of technology generally relies on various gas sensors to collect gas samples in the environment, and detects gas components and concentrations through the physical or chemical property changes of the sensors. Subsequently, data processing algorithms are used to analyze and interpret the signals output by the sensors, and according to pre-set standards and models, it is judged whether there are dangerous gases and whether the gas concentration exceeds the standard, and then the alarm mechanism is triggered. However, the gas environment in actual application scenarios is extremely complex, often with situations such as the mixing of multiple gases, dynamic changes in concentration, fluctuations in environmental temperature and humidity, and other interference factors. In the face of such a complex environment, due to its fixed detection model and relatively single response method, traditional gas sensing technologies are difficult to flexibly adapt to the changing gas environment, resulting in a significant reduction in the accuracy of detection and alarm for complex multi-gas scenarios. Summary of the Invention

[0004] The present invention provides an integrated method and system for multi-gas rapid response detection and alarm, and its main purpose is to improve the accuracy of detection and alarm for complex multi-gas scenarios.

[0005] To achieve the above object, the integrated method for multi-gas rapid response detection and alarm provided by the present invention includes: Obtaining spatial topology data of a multi-gas region, based on the spatial topology data, deploying a laser detection array for the multi-gas region to obtain a deployed laser detection array, using the deployed laser detection array to collect gas samples of the multi-gas region, and using the gas samples to analyze the dynamic gas absorption fingerprints of the multi-gas region to obtain gas energy level transition characteristics; Perform quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features. Use the enhanced features to decouple the mixed gas of the gas sample to obtain independent gas absorption spectral lines. Use the independent gas absorption spectral lines to analyze the concentration of independent gases in the multi-gas region. Use the concentration of independent gases to perform a first risk analysis on the multi-gas region to obtain a first risk report; Use the trained quantum graph neural network to combine the independent gas absorption spectral lines to analyze the gas interaction status in the multi-gas region to obtain gas interaction data. Use the gas interaction data to construct the gas mixing concentration matrix of the multi-gas region; Construct the neural radiation field of the multi-gas region. Use the neural radiation field and the gas mixing concentration matrix to construct the gas dynamic diffusion model of the multi-gas region. Use the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region to obtain a second risk report. Based on the second risk report and the first risk report, perform real-time warning on the multi-gas region.

[0006] Optionally, the laser detection array deployment on the multi-gas region based on the spatial topology data to obtain the deployed laser detection array includes: Based on the spatial topology data, identify the region size and region shape of the multi-gas region to obtain region pattern information; Based on the region pattern information, perform region network division on the multi-gas region to obtain region grids; Deploy detection devices on the region grids to obtain device deployment regions; Construct the detection array network of the device deployment region; After debugging the devices on the detection array network, obtain the deployed laser detection array.

[0007] Optionally, the use of the gas sample to analyze the gas absorption fingerprint dynamics in the multi-gas region to obtain gas energy level transition characteristics includes: Query the environmental data of the multi-gas region; Based on the environmental data, adjust the instrument parameters of the pre-configured spectrometer in the multi-gas region to obtain the target spectrometer; Use the target spectrometer to detect the absorption spectrum of the gas sample; Perform wavelet transform noise reduction processing on the gas spectrum to obtain the target spectrum; Use the target spectrum to analyze the gas absorption fingerprint dynamics in the multi-gas region to obtain gas energy level transition characteristics.

[0008] Optionally, using the enhanced feature to decouple the mixed gas of the gas sample to obtain independent gas absorption spectral lines includes: Performing quantum state amplitude encoding on the enhanced feature to obtain a quantum encoded feature; Performing a quantum convolution kernel filtering operation on the quantum encoded feature to obtain a convolutional quantum state; Performing a mixed attention modulation on the convolutional quantum state to obtain an enhanced quantum state; Based on the enhanced quantum state, performing variational quantum eigenstate solving on the gas sample to obtain a decoupled eigenstate; Performing sparse recovery on the decoupled eigenstate to obtain independent gas absorption spectral lines.

[0009] Optionally, using the independent gas absorption spectral lines to analyze the independent gas concentrations in the multi-gas region includes: Performing baseline correction on the independent gas absorption spectral lines to obtain baseline-corrected spectral lines; Performing third-order feature extraction on the baseline-corrected spectral lines to obtain feature vectors; Performing a support vector regression operation on the feature vectors to perform concentration mapping on the multi-gas region to obtain predicted gas concentrations; Performing temperature-pressure-humidity coupling correction on the predicted gas concentrations to obtain corrected concentrations; Performing cross-validation on the corrected concentrations to determine the independent gas concentrations in the multi-gas region.

[0010] Optionally, using the independent gas concentrations to perform a first risk analysis on the multi-gas region to obtain a first risk report includes: Constructing a toxicity risk index, a flammable and explosive risk index, and a gas diffusion impact index for the multi-gas region to obtain a multi-dimensional risk index; Using the independent gas concentrations to quantify the multi-dimensional risk index to obtain a quantified index; Based on the quantified index, calculating the comprehensive risk value of the multi-gas region using the following formula: ; Wherein, represents the comprehensive risk value, n represents the number of types of toxic gases in the multi-gas region, m represents the number of types of flammable and explosive gases, represents the toxicity weight coefficient of the i-th type of toxic gas, represents the flammable and explosive weight coefficient of the j-th type of flammable and explosive gas, represents the diffusion impact weight coefficient, represents the toxicity risk index in the quantified index, Represents the flammable and explosive risk index in the quantitative indicators, represents the gas diffusion impact index in the quantitative indicators; Based on the comprehensive risk value, perform a first risk analysis on the multi-gas region to obtain a first risk report.

[0011] Optionally, using the trained quantum graph neural network in combination with the independent gas absorption lines to analyze the gas interaction status in the multi-gas region to obtain gas interaction data, including: Using the trained quantum graph neural network to extract the absorption characteristics of the independent gas absorption lines; Based on the absorption characteristics, using the trained quantum graph neural network to construct a molecular graph of the independent gas absorption lines; Perform quantum encoding on the molecular graph to obtain a quantum graph state; Perform attention adjustment on the sub-graph state to obtain an enhanced quantum graph; Perform multi-physical quantity measurement on the enhanced quantum graph to obtain interaction energy data; Based on the interaction energy data, analyze the gas interaction status in the multi-gas region to obtain gas interaction data.

[0012] Optionally, using the gas interaction data to construct a gas mixing concentration matrix for the multi-gas region, including: Extract the regional geometric data in the gas interaction data; Query the sensor positions in the multi-gas region; Perform spatial discretization operations on the regional geometric data and the sensor positions to obtain a grid index matrix and an adjacency matrix; Perform multi-physical field coupling modeling on the grid index matrix and the adjacency matrix to obtain a differential equation system; Perform discrete processing on the differential equation system to obtain a linear system; Perform iterative solution on the linear system to obtain a spatio-temporal concentration tensor; Construct a gas mixing concentration matrix for the multi-gas region based on the spatio-temporal concentration tensor.

[0013] Optionally, using the neural radiance field and the gas mixing concentration matrix to construct a gas dynamic diffusion model for the multi-gas region, including: Perform spatio-temporal expansion on the neural radiance field to obtain a dynamic neural radiance field model; Perform optical parameter conversion on the gas mixing concentration matrix to obtain a spatio-temporal optical field; Perform physical constraint training on the dynamic neural radiance field model and the spatio-temporal optical field to obtain a trained visual model; Perform real-time rendering on the trained vision model to obtain a gas dynamic diffusion model for the multi-gas region.

[0014] To solve the above problems, the present invention also provides a multi-gas rapid response detection and alarm integration system, which includes: A gas feature analysis module, configured to obtain spatial topology data of a multi-gas region, deploy a laser detection array for the multi-gas region based on the spatial topology data to obtain a deployed laser detection array, collect gas samples of the multi-gas region using the deployed laser detection array, and analyze the dynamic gas absorption fingerprint of the multi-gas region using the gas samples to obtain gas energy level transition characteristics; A first risk analysis module, configured to perform quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features, decouple the mixed gas of the gas samples using the enhanced features to obtain independent gas absorption spectral lines, analyze the independent gas concentrations of the multi-gas region using the independent gas absorption spectral lines, and perform a first risk analysis on the multi-gas region using the independent gas concentrations to obtain a first risk report; A gas mixing analysis module, configured to analyze the gas interaction conditions of the multi-gas region using a trained quantum graph neural network combined with the independent gas absorption spectral lines to obtain gas interaction data, and construct a gas mixing concentration matrix of the multi-gas region using the gas interaction data; A real-time warning module, configured to construct a neural radiation field of the multi-gas region, construct a gas dynamic diffusion model of the multi-gas region using the neural radiation field and the gas mixing concentration matrix, perform a second risk analysis on the multi-gas region using the gas dynamic diffusion model to obtain a second risk report, and perform real-time warning on the multi-gas region based on the second risk report and the first risk report.

[0015] Compared with the problems described in the background art, in the embodiments of the present invention, spatial topological data of a multi-gas region is obtained, and based on this, a laser detection array is deployed to collect gas samples. Then, by analyzing the dynamic gas absorption fingerprints, the gas energy level transition characteristics are obtained, clarifying the spatial structure and gas characteristics of the multi-gas region, and providing basic data for subsequent analysis. Further, through operations such as quantum feature mapping on the gas energy level transition characteristics, decoupling the mixed gas to obtain independent gas absorption spectral lines, analyzing the concentration of independent gases, and performing a first risk analysis to obtain a first risk report, the original features can be enhanced and expanded to more comprehensively and deeply identify the quantum state characteristics of gas molecules, thereby understanding the degree and type of risks faced by the current region, facilitating users to make timely decisions, such as whether to strengthen ventilation, evacuate personnel, take emergency treatment measures, etc., helping to prevent accidents in advance and ensuring the safety of personnel's lives. Further, by analyzing the gas action conditions, a gas mixing concentration matrix is constructed, and then a neural radiance field is constructed and combined with the matrix to construct a gas dynamic diffusion model. Performing a second risk analysis to obtain a second risk report can analyze the interaction conditions between various gases in the multi-gas region, such as collisions, energy transfer, and chemical reactions between gas molecules, thereby understanding the microscopic interaction mechanism between gases and providing a simple and effective mathematical description method for the research of multi-gas regions. Furthermore, by performing real-time early warning on the multi-gas region based on the first and second risk reports, relevant personnel can be timely reminded to take corresponding measures before the risk occurs or intensifies, avoiding the occurrence of accidents or reducing the losses caused by accidents, thereby improving the detection and alarm accuracy in complex multi-gas scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic flowchart of a multi-gas rapid response detection and alarm integration method provided by an embodiment of the present invention; Figure 2 FIG. is a schematic diagram of a module for implementing the multi-gas rapid response detection and alarm integration method provided by an embodiment of the present invention.

[0017] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Embodiments of the present application provide a multi-gas rapid response detection and alarm integration method. The execution subject of the multi-gas rapid response detection and alarm integration method includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the multi-gas rapid response detection and alarm integration method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0020] Embodiment 1: Referring to Figure 1 As shown, it is a schematic flowchart of a multi-gas rapid response detection and alarm integration method provided by an embodiment of the present invention. In this embodiment, the multi-gas rapid response detection and alarm integration method includes: S1. Obtain the spatial topology data of the multi-gas region. Based on the spatial topology data, deploy a laser detection array in the multi-gas region to obtain a deployed laser detection array. Use the deployed laser detection array to collect gas samples in the multi-gas region, and analyze the dynamic gas absorption fingerprint of the multi-gas region using the gas samples to obtain gas energy level transition characteristics.

[0021] Through the step of obtaining the spatial topology data of the multi-gas region in the embodiments of the present invention, the spatial structure information of the multi-gas region can be understood, including the shape, size, internal layout, and possible obstacle distribution of the region, etc., to ensure full coverage of the multi-gas region without dead angles, avoid monitoring blind spots caused by complex spatial structures, and thus improve the comprehensiveness and representativeness of gas sample collection.

[0022] Among them, the spatial topology data refers to an abstract and digital description of the spatial structure of the multi-gas region, such as geometric shape, spatial size, and internal structure information.

[0023] Optionally, the spatial topology data can be obtained by using a three-dimensional laser scanner to scan the multi-gas region to obtain the three-dimensional spatial information of the multi-gas region.

[0024] Furthermore, through the step of deploying a laser detection array in the multi-gas region based on the spatial topology data to obtain a deployed laser detection array in the embodiments of the present invention, laser beams can be emitted into the multi-gas region, and the reflected or transmitted laser after the action of the gas sample can be received, improving the efficiency and quality of gas sample collection.

[0025] Among them, the laser detection array refers to a system composed of multiple laser detection units, usually including multiple transmitting ends and receiving ends. The transmitting ends emit laser beams with specific wavelengths. These laser beams propagate in the multi-gas region and interact with the gases in the region, which can be used for gas sample collection and related analysis of the multi-gas region.

[0026] As an embodiment of the present invention, deploying a laser detection array for the multi-gas region based on the spatial topology data to obtain a deployed laser detection array includes: based on the spatial topology data, identifying the region size and region shape of the multi-gas region to obtain region pattern information; based on the region pattern information, dividing the region network of the multi-gas region to obtain region grids; deploying detection devices for the region grids to obtain device deployment regions; constructing a detection array network for the device deployment regions; and after debugging the devices in the detection array network, obtaining a deployed laser detection array.

[0027] Among them, the detection array network refers to a data transmission and interaction network formed by connecting various laser detection devices distributed in the device deployment region through communication technology.

[0028] Optionally, the region pattern information can be obtained by using geographic information system (GIS) technology to load the spatial topology data, using spatial analysis tools to determine the region size by measuring the boundary coordinate range of the multi-gas region, and identifying the region shape based on the contour characteristics. The region grids can be obtained by using a grid division algorithm to evenly divide the multi-gas region into region grids of appropriate sizes according to its size and shape. The device deployment region is obtained by analyzing the characteristics of each region grid, such as whether it is close to the gas emission source, ventilation conditions, etc., and combining the effective detection range and accuracy requirements of the laser detection devices to determine the device deployment positions in each grid, and then arranging the corresponding devices at the device deployment positions. The detection array network can be obtained by selecting an appropriate communication technology, such as wireless sensor network (WSN) technology, to connect various laser detection devices in the device deployment region.

[0029] In the embodiment of the present invention, by using the deployed laser detection array to collect gas samples in the multi-gas region, rich and accurate gas sample data can be collected, providing an abundant data source for in-depth analysis of gas components and characteristics.

[0030] Among them, the gas sample refers to a gas collection containing various gas components and their concentration information.

[0031] Optionally, the gas sample can be collected by the deployed laser detection array by emitting laser beams with specific wavelengths through the multi-gas region.

[0032] Furthermore, in the embodiment of the present invention, by analyzing the gas absorption fingerprint dynamics of the multi-gas region using the gas sample, the gas energy level transition characteristics can be obtained to accurately identify the types of gases present in the multi-gas region and their concentration changes, which provides a key basis for subsequent accurate analysis and risk assessment of complex multi-gas environments.

[0033] Among them, the gas energy level transition characteristics refer to a series of characteristics exhibited by the internal electrons of gas molecules when they absorb or emit photons of specific energy and transition between different energy levels, such as transition wavelength, transition frequency, and transition probability.

[0034] As an embodiment of the present invention, the method of analyzing the gas absorption fingerprint dynamics of the multi-gas region using the gas sample to obtain the gas energy level transition characteristics includes: querying the environmental data of the multi-gas region, based on the environmental data, adjusting the instrument parameters of the pre-configured spectrometer in the multi-gas region to obtain a target spectrometer, using the target spectrometer to detect the absorption spectrum of the gas sample, performing wavelet transform noise reduction processing on the gas spectrum to obtain a target spectrum, and analyzing the gas absorption fingerprint dynamics of the multi-gas region using the target spectrum to obtain the gas energy level transition characteristics.

[0035] Among them, the pre-configured spectrometer refers to a spectrometer that has been preliminarily set and parameter-adjusted according to the characteristics of the multi-gas region and measurement requirements before gas sample analysis. The absorption spectrum refers to when light with continuous wavelengths passes through a gas medium, gas molecules will selectively absorb certain specific wavelengths of light, thereby forming a series of dark lines or absorption bands on the original continuous spectrum.

[0036] Optionally, the environmental data can be obtained by deploying environmental sensors in the multi-gas region, such as temperature sensors, humidity sensors, etc. The target spectrometer can determine the instrument parameters to be adjusted, such as wavelength range, integration time, and gain, based on the working principle of the spectrometer and environmental impact characteristics by analyzing the acquired environmental data, and then perform the adjustment. The absorption spectrum can be obtained by introducing the gas sample into the measurement chamber of the target spectrometer, ensuring uniform distribution of the gas sample in the measurement chamber, and then starting the target spectrometer to perform scanning measurement on the gas sample according to the set parameters. The wavelet transform noise reduction processing of the gas spectrum to obtain the target spectrum can be achieved by operating the Haar wavelet function. The gas energy level transition characteristics can be determined by extracting characteristic parameters such as the position, intensity, and width of the absorption peaks in the spectrum from the target spectrum to obtain absorption fingerprint information, and then performing information matching between the pre-configured fingerprint database and the absorption fingerprint information.

[0037] S2. Perform quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features. Use the enhanced features to decouple the gas sample into independent gas absorption spectra. Analyze the concentrations of independent gases in the multi-gas region using the independent gas absorption spectra. Use the concentrations of independent gases to perform a first risk analysis on the multi-gas region to obtain a first risk report.

[0038] In the embodiment of the present invention, by performing quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features, the original features can be enhanced and expanded to more comprehensively and deeply identify the quantum state characteristics of gas molecules, making complex mixed gases more distinguishable.

[0039] Optionally, the enhanced features can be obtained by mapping the key information in the gas energy level transition characteristics to the quantum state space through kernel principal component analysis and analyzing and processing the quantum states.

[0040] Furthermore, in the embodiment of the present invention, by using the enhanced features to decouple the gas sample into independent gas absorption spectra, the characteristics of different gases can be clearly distinguished to improve the accuracy and reliability of multi-gas detection.

[0041] Among them, the independent gas absorption spectrum refers to the absorption spectrum that is unique to each gas in the mixed gas and does not overlap or interfere with the absorption spectra of other gases.

[0042] As an embodiment of the present invention, using the enhanced features to decouple the gas sample into independent gas absorption spectra includes: performing quantum state amplitude encoding on the enhanced features to obtain quantum encoded features, performing a quantum convolutional kernel filtering operation on the quantum encoded features to obtain a convolutional quantum state, performing a mixed attention modulation on the convolutional quantum state to obtain an enhanced quantum state, performing variational quantum eigenstate solving on the gas sample based on the enhanced quantum state to obtain a decoupled eigenstate, and performing sparse recovery on the decoupled eigenstate to obtain independent gas absorption spectra.

[0043] Among them, the quantum encoded feature refers to the quantum state representation obtained by encoding the gas energy level transition characteristics through quantum state amplitude encoding. The convolutional quantum state refers to the quantum state obtained by performing a quantum convolutional kernel filtering operation on the quantum encoded feature. The decoupled eigenstate refers to the quantum state obtained by performing variational quantum eigenstate solving after performing a mixed attention modulation on the convolutional quantum state, which is the key intermediate result for decoupling independent gas absorption spectra from mixed gas spectral data.

[0044] Optionally, the quantum coding feature can be obtained by selecting an appropriate number of qubits according to the dimension and numerical range of the enhanced feature, and then using quantum state preparation technology to encode the numerical information of the enhanced feature onto the amplitude of the qubits. The convolutional quantum state is obtained by performing a convolutional operation on the quantum coding feature through quantum gate operations. The enhanced quantum state can be obtained by applying attention weights to the convolutional quantum state through quantum gate operations to enhance the key part information and suppress the interference information. Based on the enhanced quantum state, variational quantum eigenvalue solving is performed on the gas sample, and the decoupled eigenstate can be realized by iteratively adjusting the circuit parameters using a classical optimization algorithm (such as the gradient descent method) to approximate the quantum state to the decoupled eigenstate. The independent gas absorption line can be determined by using the orthogonal matching pursuit algorithm with the decoupled eigenstate data as the input, solving the optimization problem as the output data, and then based on the output.

[0045] Furthermore, in the embodiment of the present invention, analyzing the independent gas concentration in the multi-gas region by using the independent gas absorption line helps with subsequent risk analysis, provides data support for taking targeted measures, and avoids safety accidents caused by inaccurate gas concentration knowledge and the inability to detect potential risks in a timely manner.

[0046] As an embodiment of the present invention, analyzing the independent gas concentration in the multi-gas region by using the independent gas absorption line includes: performing baseline correction on the independent gas absorption line to obtain a baseline-corrected spectrum, performing third-order feature extraction on the baseline-corrected spectrum to obtain a feature vector, performing support vector regression operation on the feature vector to map the concentration of the multi-gas region to obtain a predicted gas concentration, performing temperature-pressure-humidity coupling correction on the predicted gas concentration to obtain a corrected concentration, and performing cross-validation on the corrected concentration to determine the independent gas concentration in the multi-gas region.

[0047] Among them, the baseline-corrected spectrum refers to the baseline shift that may exist in the spectrum during the analysis of the independent gas absorption line due to the characteristics of the instrument itself, environmental factors, etc., which is not caused by gas absorption. The predicted gas concentration refers to the gas concentration value output by the model after processing the feature vector obtained by third-order feature extraction using a support vector regression model.

[0048] Optionally, the baseline-corrected spectrum can be obtained by using the polynomial fitting method to fit the baseline of the independent gas absorption spectrum, determining the mathematical expression of the baseline, and subtracting the baseline influence from the original spectrum based on this. The eigenvector can be obtained by performing a third-order derivative on the baseline-corrected spectrum using the third-order derivative spectroscopy method. The predicted gas concentration can be obtained by performing support vector analysis on the eigenvector using the support vector machine in the machine learning model. The corrected concentration can be obtained by establishing a temperature-pressure-humidity coupling correction model using a deep learning model considering the comprehensive influence of temperature, pressure, and humidity on gas concentration measurement, and then performing temperature-pressure-humidity coupling correction on the predicted gas concentration using the temperature-pressure-humidity coupling correction model. The independent gas concentration can be obtained by performing K-fold cross-validation on the corrected concentration.

[0049] In an embodiment of the present invention, by using the independent gas concentration to perform a first risk analysis on the multi-gas region to obtain a first risk report, the risk level and type faced by the current region can be understood, so as to facilitate the user to make timely decisions, such as whether to strengthen ventilation, evacuate personnel, take emergency treatment measures, etc., which helps to prevent accidents in advance and ensure the safety of personnel's lives.

[0050] Among them, the first risk report refers to an evaluation document generated based on the analysis of the independent gas concentration in the multi-gas region, which summarizes the concentration levels of different gases in the region, and combines safety standards such as the toxicity, flammability and explosiveness characteristics, and exposure limits of each gas to evaluate the risk level of each gas.

[0051] As an embodiment of the present invention, using the independent gas concentration to perform a first risk analysis on the multi-gas region to obtain a first risk report includes: constructing a toxicity risk index, a flammability and explosiveness risk index, and a gas diffusion influence index for the multi-gas region to obtain a multi-dimensional risk index, using the independent gas concentration to quantify the multi-dimensional risk index to obtain a quantified index, and based on the quantified index, calculating the comprehensive risk value of the multi-gas region using the following formula: ; Among them, represents the comprehensive risk value, n represents the number of types of toxic gases in the multi-gas region, m represents the number of types of flammable and explosive gases, represents the toxicity weight coefficient of the i-th toxic gas, represents the flammability and explosiveness weight coefficient of the j-th flammable and explosive gas, represents the diffusion influence weight coefficient, represents the toxicity risk index in the quantified index, represents the flammability and explosiveness risk index in the quantified index, represents the gas diffusion influence index in the quantified index; Based on the comprehensive risk value, perform a first risk analysis on the multi-gas area to obtain a first risk report.

[0052] Among them, the toxicity risk index refers to an index used to measure the potential harm degree of toxic gases in the multi-gas area to human health and the environment. The flammable and explosive risk index refers to an index used to evaluate the possibility and harm degree of dangerous events such as fires or explosions caused by flammable and explosive gases in the multi-gas area. The gas diffusion impact index refers to an index used to describe the gas diffusion behavior in the multi-gas area and its impact on the surrounding environment and personnel.

[0053] Optionally, the multi-dimensional risk index can query the safety standards and professional literature of the multi-gas area, and combine the actual situation of the multi-gas area to determine the specific parameters reflecting gas toxicity, flammability and explosiveness, and diffusion impact. The quantification index can be based on independent gas concentration data, combined with the definitions and calculation methods of each index. For example, for gases with acute toxicity, the exposure limit comparison method can be used to convert the multi-dimensional risk index into specific values. The first risk report can evaluate the risk status of the multi-gas area according to the size of the comprehensive risk value, combined with the pre-set risk level classification standard, and determine the risk level. For example, When it is, it indicates low risk. When it is, it indicates medium risk. At , it indicates high risk.

[0054] It should be explained that the calculation formula of the above comprehensive risk value, by separately considering the contribution of toxic gases to the comprehensive risk, the contribution of flammable and explosive gases to the comprehensive risk, and the impact of gas diffusion factors on the risk, converts the complex risk factors in the multi-gas area into a specific value, which is convenient for intuitively comparing the risk sizes of different multi-gas areas, or evaluating the risks of the same area under different conditions, and can provide a clear basis for risk control. When the comprehensive risk value is relatively high, it indicates that the risk of this area is relatively large and stricter safety measures need to be taken; if the weight corresponding to a certain single index is relatively large and the value is relatively high, it can also prompt that the risk in this aspect needs to be focused on and targeted prevention and control should be carried out.

[0055] It should be further noted that the setting of the weight coefficient is determined according to the importance degree of different factors to the overall risk. For example, if a certain toxic gas has strong toxicity, then its toxicity weight coefficient will be relatively large and the proportion in the calculation of the comprehensive risk value will be higher.

[0056] S3. Use the trained quantum graph neural network to combine the independent gas absorption spectral lines to analyze the gas action status of the multi-gas area to obtain gas action data, and use the gas action data to construct the gas mixing concentration matrix of the multi-gas area.

[0057] In an embodiment of the present invention, by using the trained quantum graph neural network in combination with the independent gas absorption lines, the gas interaction situation in the multi-gas region is analyzed, and gas interaction data can be obtained to analyze the interaction situation among various gases in the multi-gas region, such as collisions, energy transfer, and chemical reactions between gas molecules, etc., thereby understanding the microscopic interaction mechanism between gases.

[0058] Among them, the gas interaction situation refers to the situation where various gases in the multi-gas region influence and interact with each other, such as physical interactions, chemical interactions, and optical interactions, etc. The trained quantum graph neural network refers to a neural network model that has undergone a series of training processes and has the specific ability to process and analyze data related to quantum graphs. Through a large amount of labeled data related to independent gas absorption lines, etc. (such as information on the gas interaction situation corresponding to known absorption characteristics), these data are input into the network, and then during the training process, the network continuously adjusts internal parameters (such as the amplitude and phase of quantum states, parameters of quantum gate operations, etc.) so that the output result of the network (such as the prediction of the gas interaction situation) is infinitely close to the labeled data and then obtained.

[0059] As an embodiment of the present invention, using the trained quantum graph neural network in combination with the independent gas absorption lines to analyze the gas interaction situation in the multi-gas region and obtain gas interaction data includes: using the trained quantum graph neural network to extract the absorption characteristics of the independent gas absorption lines, based on the absorption characteristics, using the trained quantum graph neural network to construct a molecular graph of the independent gas absorption lines, performing quantum encoding on the molecular graph to obtain a quantum graph state, performing attention adjustment on the sub-graph state to obtain an enhanced quantum graph, performing multi-physical quantity measurement on the enhanced quantum graph to obtain interaction energy data, and based on the interaction energy data, analyzing the gas interaction situation in the multi-gas region to obtain gas interaction data.

[0060] Among them, the molecular graph refers to a graphical structure used to describe gas molecules and their mutual relationships in the independent gas absorption lines.

[0061] Optionally, the absorption features can input independent gas absorption spectral line data into the trained quantum graph neural network. By virtue of the superposition and entanglement characteristics of quantum states in the network, key absorption features such as the position, intensity, and shape of absorption peaks in the spectral lines can be efficiently captured. The molecular graph can use the absorption features that can be extracted as input. Let the trained quantum graph neural network associate different absorption features according to the built-in rules and algorithms to construct a graph with nodes representing gas molecules and edges representing the interactions between molecules. The quantum graph state can adopt methods such as quantum state amplitude encoding or phase encoding to map the information of the nodes and edges of the molecular graph to the state of quantum bits, realizing the quantum encoding of the molecular graph. The enhanced quantum graph can use a convolutional model to calculate the attention weights of each part of the information in the quantum graph state. According to the calculated attention weights, use quantum gate operations to adjust the quantum graph state, highlighting important information and suppressing secondary information. The interaction energy data can use quantum measurement techniques to measure multiple physical quantities such as energy, momentum, and angular momentum in the enhanced quantum graph to obtain data related to the interactions of gas molecules. The gas interaction data can combine the principles of quantum mechanics and the knowledge of chemical kinetics to deeply analyze the interaction energy data, infer the types and intensities of physical and chemical interactions between gas molecules in the multi-gas region, and then organize the analysis results into data containing information such as gas diffusion, reaction rate, and equilibrium state.

[0062] Furthermore, in the embodiment of the present invention, by using the gas interaction data to construct the gas mixing concentration matrix of the multi-gas region, the concentration distribution of each gas in the multi-gas region and their mutual influence relationship can be intuitively represented in the form of a matrix, thus providing a simple and effective mathematical description method for the research of the multi-gas region.

[0063] Among them, the gas mixing concentration matrix refers to a matrix used to represent the concentration distribution of different gases in the multi-gas region.

[0064] As an embodiment of the present invention, the use of the gas interaction data to construct the gas mixing concentration matrix of the multi-gas region includes: extracting the regional geometric data in the gas interaction data, querying the sensor positions in the multi-gas region, performing spatial discretization operations on the regional geometric data and the sensor positions to obtain a grid index matrix and an adjacency matrix, performing multi-physical field coupling modeling on the grid index matrix and the adjacency matrix to obtain a differential equation system, discretizing the differential equation system to obtain a linear system, iteratively solving the linear system to obtain a spatio-temporal concentration tensor, and constructing the gas mixing concentration matrix of the multi-gas region based on the spatio-temporal concentration tensor.

[0065] Among them, the regional geometric data refers to a set of information describing the spatial geometric characteristics of the multi-gas region. The grid index matrix refers to a matrix used to record the number and position information of each grid cell after spatial discretization of the multi-gas region. The adjacency matrix refers to a matrix used to represent the adjacent relationship between the discretized grid cells. The differential equation system refers to a set of equations containing unknown functions and their derivatives established based on multi-physics field coupling modeling. The linear system refers to a set of linear equations obtained after discretizing the differential equation system. The spatio-temporal concentration tensor refers to a multi-dimensional data structure containing information about the changes in gas concentration in the multi-gas region in space and time.

[0066] Optionally, the regional geometric data is obtained by screening out information related to the spatial geometry of the multi-gas region from the gas action data, such as data on the shape, boundary coordinates, size, etc. of the region. The sensor positions can be obtained by consulting relevant equipment deployment records or databases to obtain the accurate position coordinate information of each sensor in the multi-gas region. The spatial discretization operation on the regional geometric data and the sensor positions can be implemented by combining the Numpy library of Python with a suitable discretization method (such as the finite element method, finite difference method). The differential equation system can be obtained by comprehensively considering the physical processes of the gas in the multi-gas region (such as diffusion, convection, chemical reaction, etc.) and establishing a mathematical model of multi-physics field coupling. The discretization process of the differential equation system can be realized by using numerical discretization techniques (such as the finite difference method, finite volume method) to transform the continuous differential equation system into a discrete linear system. The iterative solution of the linear system can be achieved by using the conjugate gradient method to solve the linear system. The gas mixture concentration matrix can organize the gas concentration values of each grid cell at different time points obtained by the solution into a spatio-temporal concentration tensor. The dimensions of the tensor include the gas species, spatial position, and time. Then, according to the data in the tensor and the definition and rules of the matrix, the gas mixture concentration matrix is constructed.

[0067] S4. Construct the neural radiance field of the multi-gas region, use the neural radiance field and the gas mixture concentration matrix to construct the gas dynamic diffusion model of the multi-gas region, use the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region to obtain a second risk report, and based on the second risk report and the first risk report, conduct real-time early warning on the multi-gas region.

[0068] In the embodiment of the present invention, by constructing the neural radiance field of the multi-gas region, a more flexible and accurate way can be obtained to describe the multi-gas region.

[0069] Among them, the neural radiance field refers to a tool for representing the appearance and geometric structure of a three-dimensional scene, which can provide a continuous representation of gas-related physical quantities in a multi-gas region, enabling users to query and predict the state of the gas at any position.

[0070] Optionally, the neural radiance field can be constructed by using deep learning technology, based on a multi-layer perceptron (MLP) as the basic neural network structure, combined with data such as gas concentration, temperature, and pressure collected by sensors arranged in the multi-gas region.

[0071] Furthermore, in the embodiment of the present invention, by using the neural radiance field and the gas mixing concentration matrix, constructing the gas dynamic diffusion model of the multi-gas region can simulate the diffusion behavior of the gas in the multi-gas region over time, so as to facilitate users to understand in advance the possible diffusion paths and influence ranges after gas leakage, and predict the change trend of gas concentration under different environmental conditions (such as temperature, wind speed, wind direction, etc.).

[0072] Among them, the dynamic diffusion model refers to a model based on physical principles and mathematical methods for describing the dynamic diffusion behavior of gases in a multi-gas region over time and space.

[0073] As an embodiment of the present invention, constructing the gas dynamic diffusion model of the multi-gas region by using the neural radiance field and the gas mixing concentration matrix includes: performing spatio-temporal expansion on the neural radiance field to obtain a dynamic neural radiance field model, performing optical parameter conversion on the gas mixing concentration matrix to obtain a spatio-temporal optical field, performing physical constraint training on the dynamic neural radiance field model and the spatio-temporal optical field to obtain a trained visual model, and performing real-time rendering on the trained visual model to obtain the gas dynamic diffusion model of the multi-gas region.

[0074] Among them, the dynamic neural radiance field model refers to a model that can learn and capture the dynamic change laws of gas-related characteristics in a multi-gas region over time and space by introducing the time dimension into the neural radiance field and taking time as an additional input variable. The spatio-temporal optical field refers to a physical field that comprehensively considers the time and space dimensions and is obtained by performing optical parameter conversion on the gas mixing concentration matrix.

[0075] Optionally, the dynamic neural radiance field model can introduce a time dimension to the existing neural radiance field model and be obtained by adjusting the model structure using a deep learning framework (such as TensorFlow or PyTorch), for example, by increasing the number of network layers or neurons. The spatio-temporal optical field can be constructed by converting the concentration information in the gas mixture concentration matrix into corresponding optical parameter values according to the optical properties of the gas (such as absorption coefficient, scattering coefficient, etc.) and then combining with the time dimension. The trained visual model can fuse the dynamic neural radiance field model and the spatio-temporal optical field, introduce physical constraint conditions (such as the law of conservation of mass, the law of conservation of energy, etc.) during the training process, and then use a large amount of actual observation data and simulation data to train the fused model. By continuously adjusting the model parameters through an optimization algorithm (such as the stochastic gradient descent method), the output of the model can be made to conform to the actual situation as much as possible. The real-time rendering of the trained visual model can be achieved by operating the ray tracing algorithm.

[0076] In an embodiment of the present invention, by using the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region and obtaining a second risk report, the risk situation of the multi-gas region from a dynamic perspective can be provided, thereby helping users better grasp the development trend of risks, make preparations in advance, reasonably allocate resources, and take effective measures to reduce risks. For example, evacuating people in advance in high-risk areas where the gas may spread and strengthening the operation of ventilation facilities, etc., so as to improve the overall safety and emergency response ability of the multi-gas region.

[0077] Among them, the second risk report refers to a document generated after in-depth analysis of the potential risks of the multi-gas region based on the gas dynamic diffusion model, which details the risk situation faced by the multi-gas region considering the gas dynamic diffusion characteristics.

[0078] As an embodiment of the present invention, using the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region and obtaining a second risk report includes: querying the gas mixture concentration matrix of the multi-gas region, using the gas mixture concentration matrix to construct a risk parameter table for the multi-gas region, performing risk mapping on the risk parameter table and the gas dynamic diffusion model to obtain a multi-channel risk tensor, performing risk grading on the multi-channel risk tensor to obtain a graded risk field, based on the graded risk field, performing a propagation path simulation on the multi-gas region to obtain a propagation simulation path, and using the propagation simulation path to perform a second risk analysis on the multi-gas region to obtain a second risk report.

[0079] Among them, the risk parameter table refers to a table used to organize and present various risk-related parameters in a multi-gas area. The multi-channel risk tensor refers to a high-dimensional data structure used to comprehensively represent the risk information of a multi-gas area in different aspects. The propagation simulation path refers to the possible diffusion and propagation trajectories of gas in the area obtained by simulating the multi-gas area based on the hierarchical risk field.

[0080] Optionally, the risk parameter table can analyze and process the data in the gas mixing concentration matrix according to the properties of the gas (such as toxicity, flammability and explosiveness, etc.) and concentration thresholds. For example, for toxic gases, the risk parameters are determined according to the comparison between its concentration and the safety limit value, and then the relevant risk parameters of each gas (such as concentration, risk level, potential hazard degree, etc.) are organized into a table form. The multi-channel risk tensor can be obtained by using a mathematical mapping method (such as function mapping) to map the data in the risk parameter table to the relevant variables of the gas dynamic diffusion model. The hierarchical risk field can be obtained by formulating risk grading criteria and dividing it into different risk levels (such as low risk, medium risk, high risk, extremely high risk) according to the risk parameter values of each element in the multi-channel risk tensor. The propagation simulation path can use the information in the hierarchical risk field and combine the principle of the gas dynamic diffusion model to determine the diffusion rules and propagation speed of the gas in different risk level areas. The second risk report can conduct a detailed analysis of the propagation simulation path, evaluate the possible impacts of gas diffusion on personnel, facilities and the environment in the multi-gas area, and then combine the risk grading field and the propagation simulation path, and comprehensively consider various factors (such as gas concentration, diffusion speed and influence range, etc.) to construct a risk report.

[0081] By using the second risk report and the first risk report, the embodiment of the present invention can give a real-time warning to the multi-gas area, which can timely remind relevant personnel to take corresponding measures before the risk occurs or intensifies, so as to avoid the occurrence of accidents or reduce the losses caused by accidents.

[0082] Optionally, the real-time warning to the multi-gas area can be realized by comparing the key information such as the risk level, gas diffusion range, potential hazard, etc. in the second risk report and the first risk report, and according to the preset warning threshold and rules, through means such as audible and visual alarm devices, SMS notifications, and pop-up windows on the monitoring system, to give a warning to the risk situation beyond the safe range in the multi-gas area.

[0083] Embodiment 2: As Figure 2 shown, it is the functional module diagram of the multi-gas rapid response detection and alarm integrated system of the present invention.

[0084] The multi-gas rapid response detection and alarm integration system 200 described in the present invention can be installed in an electronic device. According to the functions achieved, the multi-gas rapid response detection and alarm integration system may include a gas characteristic analysis module 201, a first risk analysis module 202, a gas mixing analysis module 203, and a real-time warning module 204. The modules in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0085] In the embodiments of the present invention, the functions of each module / unit are as follows: The gas characteristic analysis module 201 is used to obtain the spatial topology data of the multi-gas region, based on the spatial topology data, deploy a laser detection array for the multi-gas region to obtain a deployed laser detection array, use the deployed laser detection array to collect gas samples in the multi-gas region, and use the gas samples to analyze the gas absorption fingerprint dynamics in the multi-gas region to obtain gas energy level transition characteristics; The first risk analysis module 202 is used to perform quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features, use the enhanced features to decouple the mixed gas of the gas samples to obtain independent gas absorption spectral lines, use the independent gas absorption spectral lines to analyze the independent gas concentration in the multi-gas region, and use the independent gas concentration to perform a first risk analysis on the multi-gas region to obtain a first risk report; The gas mixing analysis module 203 is used to analyze the gas interaction conditions in the multi-gas region by using a trained quantum graph neural network combined with the independent gas absorption spectral lines to obtain gas interaction data, and use the gas interaction data to construct a gas mixing concentration matrix of the multi-gas region; The real-time warning module 204 is used to construct a neural radiation field of the multi-gas region, use the neural radiation field and the gas mixing concentration matrix to construct a gas dynamic diffusion model of the multi-gas region, use the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region to obtain a second risk report, and perform real-time warning on the multi-gas region based on the second risk report and the first risk report.

[0086] Specifically, each module in the multi-gas rapid response detection and alarm integration system 200 in the embodiments of the present invention uses the same technical means as those Figure 1 described in the multi-gas rapid response detection and alarm integration method described above, and can produce the same technical effects, which will not be elaborated here.

[0087] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-gas rapid response detection and alarm integration method, characterized in that: The method comprises: Acquire spatial topological data of a multi-gas region, deploy a laser detection array in the multi-gas region based on the spatial topological data to obtain a deployed laser detection array, collect gas samples in the multi-gas region using the deployed laser detection array, analyze the gas absorption fingerprint dynamics of the multi-gas region using the gas samples, and obtain gas energy level transition characteristics; Performing quantum feature mapping on the gas energy level transition feature to obtain enhanced features, using the enhanced features to perform mixed gas decoupling on the gas sample to obtain independent gas absorption spectra, using the independent gas absorption spectra to analyze independent gas concentrations in the multi-gas region, using the independent gas concentrations to perform a first risk analysis on the multi-gas region to obtain a first risk report; Utilizing the trained quantum graph neural network in combination with the independent gas absorption spectrum to analyze the gas interaction status of the multi-gas region, obtain gas interaction data, and construct a gas mixture concentration matrix of the multi-gas region using the gas interaction data; Construct a neural radiation field for the multi-gas area, construct a gas dynamic diffusion model for the multi-gas area using the neural radiation field and the gas mixture concentration matrix, perform a second risk analysis on the multi-gas area using the gas dynamic diffusion model, obtain a second risk report, and issue a real-time warning to the multi-gas area based on the second risk report and the first risk report.

2. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The step of deploying a laser detection array in the multi-gas region based on the spatial topological data to obtain a deployed laser detection array includes: Based on the spatial topological data, identifying the size and shape of the multi-gas region to obtain regional pattern information; Based on the regional pattern information, the multi-gas region is divided into a regional network to obtain a regional grid; Detecting equipment deployment on the regional grid to obtain an equipment deployment area; Constructing a detection array network in the area where the equipment is deployed; After the detection array network is debugged, a deployed laser detection array is obtained.

3. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The step of analyzing the gas absorption fingerprint dynamics of the multi-gas region using the gas sample to obtain the gas energy level transition characteristics includes: querying environmental data of the multi-gas region; Based on the environmental data, adjusting instrument parameters of a preconfigured spectrometer in the multi-gas region to obtain a target spectrometer; Using the target spectrum measuring instrument to detect the absorption spectrum of the gas sample; Performing wavelet transform noise reduction processing on the gas spectrum to obtain a target spectrum; The target spectrum is used to analyze the gas absorption fingerprint dynamics of the multi-gas region to obtain the gas energy level transition characteristics.

4. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The method of utilizing the enhanced feature to perform mixed gas decoupling on the gas sample to obtain independent gas absorption spectra includes: Performing quantum state amplitude encoding on the enhanced feature to obtain a quantum coded feature; Performing a quantum convolution kernel filtering operation on the quantum coding feature to obtain a convolution quantum state; Performing mixed attention modulation on the convolution quantum state to obtain an enhanced quantum state; Based on the enhanced quantum state, performing a variational quantum eigensolution on the gas sample to obtain a decoupled eigenstate; The decoupled eigenstates are sparsely recovered to obtain independent gas absorption spectral lines.

5. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The analyzing the independent gas concentrations in the multi-gas region by using the independent gas absorption spectra includes: Performing baseline correction on the independent gas absorption spectrum to obtain a baseline correction spectrum; Performing third-order feature extraction on the baseline correction spectrum to obtain a feature vector; Performing a support vector regression operation on the feature vector to perform concentration mapping on the multi-gas region to obtain predicted gas concentration; Performing temperature-pressure-humidity coupling correction on the predicted gas concentration to obtain a corrected concentration; The corrected concentrations are cross-validated to determine the individual gas concentrations for the multiple gas regions.

6. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The step of performing a first risk analysis on the multi-gas region by using the independent gas concentrations to obtain a first risk report includes: Constructing a toxicity risk index, a flammable and explosive risk index, and a gas diffusion impact index for the multi-gas region to obtain a multi-dimensional risk index; Quantifying the multi-dimensional risk index using the independent gas concentration to obtain a quantitative index; Based on the quantitative indicators, the comprehensive risk value of the multi-gas area is calculated using the following formula: ； in, represents the comprehensive risk value, n represents the number of types of toxic gases in the multi-gas area, m represents the number of types of flammable and explosive gases, represents the toxicity weight coefficient of the i-th toxic gas, represents the flammable and explosive weight coefficient of the jth flammable and explosive gas, Expressed as the diffusion influence weight coefficient, Represents the toxicity risk index in the quantitative index, Indicates the flammable and explosive risk index in the quantitative index. Indicates the gas diffusion impact index in the quantitative index; Based on the comprehensive risk value, a first risk analysis is performed on the multi-gas area to obtain a first risk report.

7. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The method of using the trained quantum graph neural network in combination with the independent gas absorption spectrum to analyze the gas interaction status of the multi-gas region to obtain gas interaction data includes: Utilizing the trained quantum graph neural network, extracting the absorption characteristics of the independent gas absorption spectrum lines; Based on the absorption characteristics, using the trained quantum graph neural network, constructing a molecular graph of the independent gas absorption spectrum; Performing quantum encoding on the molecular graph to obtain a quantum graph state; Performing attention adjustment on the sub-graph state to obtain an enhanced quantum graph; Performing multi-physical quantity measurements on the enhanced quantum image to obtain action energy data; Based on the action energy data, the gas action conditions of the multi-gas regions are analyzed to obtain gas action data.

8. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The step of constructing the gas mixture concentration matrix of the multi-gas region by using the gas action data includes: extracting regional geometric data from the gas action data; querying sensor locations of the multi-gas region; Performing a spatial discretization operation on the regional geometric data and the sensor position to obtain a grid index matrix and an adjacency matrix; Performing multi-physics field coupling modeling on the grid index matrix and the adjacency matrix to obtain a differential equation group; Discretizing the differential equations to obtain a linear system; Iteratively solving the linear system to obtain a spatiotemporal concentration tensor; A gas mixture concentration matrix of the multi-gas region is constructed based on the spatiotemporal concentration tensor.

9. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that: The method of constructing a gas dynamic diffusion model of the multi-gas region by using the neural radiation field and the gas mixture concentration matrix includes: Performing spatiotemporal expansion on the neural radiation field to obtain a dynamic neural radiation field model; Performing optical parameter conversion on the gas mixture concentration matrix to obtain a spatiotemporal optical field; Performing physical constraint training on the dynamic neural radiation field model and the spatiotemporal optical field to obtain a trained visual model; The trained visual model is rendered in real time to obtain a gas dynamic diffusion model for the multi-gas region. 10.Multi-gas rapid response detection and alarm integrated system, characterized in that: The system comprises: A gas characteristic analysis module is used to obtain spatial topological data of a multi-gas region, and based on the spatial topological data, to deploy a laser detection array in the multi-gas region to obtain a deployed laser detection array, and to collect gas samples in the multi-gas region using the deployed laser detection array, and to analyze the gas absorption fingerprint dynamics of the multi-gas region using the gas samples to obtain gas energy level transition characteristics; a first risk analysis module, configured to perform quantum feature mapping on the gas energy level transition feature to obtain enhanced features, perform mixed gas decoupling on the gas sample using the enhanced features to obtain independent gas absorption spectra, analyze independent gas concentrations in the multi-gas region using the independent gas absorption spectra, perform a first risk analysis on the multi-gas region using the independent gas concentrations, and obtain a first risk report; A gas mixing analysis module, for analyzing the gas interaction status of the multi-gas region by using the trained quantum graph neural network in combination with the independent gas absorption spectrum, obtaining gas interaction data, and constructing a gas mixing concentration matrix of the multi-gas region by using the gas interaction data; A real-time warning module is used to construct a neural radiation field for the multi-gas area, and to construct a gas dynamic diffusion model for the multi-gas area using the neural radiation field and the gas mixture concentration matrix. The gas dynamic diffusion model is used to perform a second risk analysis on the multi-gas area to obtain a second risk report, and to perform a real-time warning on the multi-gas area based on the second risk report and the first risk report.

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