Integrated Method and System for Multi-Gas Fast Response Detection and Alarm

Abstract

The present invention relates to the field of gas detection technology, and discloses a multi-gas rapid response detection and alarm integration method and system, including: collecting gas samples in a multi-gas area, analyzing the dynamic gas absorption fingerprints in the multi-gas area to obtain gas energy level transition characteristics; performing quantum feature mapping on the gas energy level transition characteristics, decoupling the gas samples for mixed gases to obtain independent gas absorption spectral lines, analyzing the concentrations of independent gases in the multi-gas area, performing a first risk analysis on the multi-gas area to obtain a first risk report; analyzing the gas action conditions in the multi-gas area to obtain gas action data, and constructing a gas mixed concentration matrix for the multi-gas area; constructing a gas dynamic diffusion model for the multi-gas area, performing a second risk analysis on the multi-gas area to obtain a second risk report, and performing real-time early warning on the multi-gas area. The present invention can improve the detection and alarm accuracy in complex multi-gas scenarios.

Description

Technical Field

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

[0002] In many fields such as modern industrial production, environmental monitoring, and public safety guarantee, 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, and there are often 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 detecting and alarming 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 detecting and alarming 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:

[0006] 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 dynamic gas absorption fingerprints in the multi-gas region to obtain gas energy level transition characteristics;

[0007] Perform quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features. Using the enhanced features, decouple the mixed gas of the gas sample to obtain independent gas absorption spectral lines. Using the independent gas absorption spectral lines, analyze the independent gas concentrations in the multi-gas region. Using the independent gas concentrations, perform a first risk analysis on the multi-gas region to obtain a first risk report;

[0008] Using the trained quantum graph neural network and the independent gas absorption spectral lines, analyze the gas interaction conditions in the multi-gas region to obtain gas interaction data. Using the gas interaction data, construct the gas mixing concentration matrix of the multi-gas region;

[0009] Construct the neural radiation field of the multi-gas region. Using the neural radiation field and the gas mixing concentration matrix, construct the gas dynamic diffusion model of the multi-gas region. Using the gas dynamic diffusion model, 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.

[0010] 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:

[0011] Based on the spatial topology data, identify the region size and region shape of the multi-gas region to obtain region pattern information;

[0012] Based on the region pattern information, perform region network division on the multi-gas region to obtain region grids;

[0013] Deploy detection devices on the region grids to obtain device deployment areas;

[0014] Construct the detection array network of the device deployment area;

[0015] After device debugging of the detection array network, obtain the deployed laser detection array.

[0016] Optionally, the analysis of the gas absorption fingerprint dynamics in the multi-gas region using the gas sample to obtain the gas energy level transition characteristics includes:

[0017] Query the environmental data of the multi-gas region;

[0018] Based on the environmental data, adjust the instrument parameters of the pre-configured spectrometer in the multi-gas region to obtain the target spectrometer;

[0019] Use the target spectrometer to detect the absorption spectrum of the gas sample;

[0020] Perform wavelet transform denoising on the gas spectrum to obtain a target spectrum;

[0021] Analyze the gas absorption fingerprint dynamics in the multi-gas region using the target spectrum to obtain gas energy level transition characteristics.

[0022] Optionally, the using the enhanced features to decouple the gas sample into independent gas absorption spectral lines includes:

[0023] Perform quantum state amplitude encoding on the enhanced features to obtain quantum encoded features;

[0024] Perform a quantum convolutional kernel filtering operation on the quantum encoded features to obtain a convolutional quantum state;

[0025] Perform a hybrid attention modulation on the convolutional quantum state to obtain an enhanced quantum state;

[0026] Based on the enhanced quantum state, perform variational quantum eigenstate solving on the gas sample to obtain a decoupled eigenstate;

[0027] Perform sparse recovery on the decoupled eigenstate to obtain independent gas absorption spectral lines.

[0028] Optionally, the using the independent gas absorption spectral lines to analyze the independent gas concentrations in the multi-gas region includes:

[0029] Perform baseline correction on the independent gas absorption spectral lines to obtain baseline-corrected spectral lines;

[0030] Perform third-order feature extraction on the baseline-corrected spectral lines to obtain feature vectors;

[0031] Perform a support vector regression operation on the feature vectors to map the concentrations in the multi-gas region to obtain predicted gas concentrations;

[0032] Perform temperature-pressure-humidity coupling correction on the predicted gas concentrations to obtain corrected concentrations;

[0033] Perform cross-validation on the corrected concentrations to determine the independent gas concentrations in the multi-gas region.

[0034] Optionally, the using the independent gas concentrations to perform a first risk analysis on the multi-gas region to obtain a first risk report includes:

[0035] Construct toxicity risk indicators, flammable and explosive risk indicators, and gas diffusion impact indicators for the multi-gas region to obtain multi-dimensional risk indicators;

[0036] Use the independent gas concentrations to quantify the multi-dimensional risk indicators to obtain quantified indicators;

[0037] Based on the above quantization metrics, calculate the comprehensive risk value of the multi-gas area using the following formula:

[0038] ;

[0039] where, 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 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 quantization metrics, represents the flammable and explosive risk index in the quantization metrics, represents the gas diffusion impact index in the quantization metrics;

[0040] Based on the comprehensive risk value, conduct a first risk analysis on the multi-gas area to obtain a first risk report.

[0041] Optionally, use the trained quantum graph neural network to combine with the independent gas absorption lines to analyze the gas interaction situation in the multi-gas area, and obtain gas interaction data, including:

[0042] Use the trained quantum graph neural network to extract the absorption characteristics of the independent gas absorption lines;

[0043] Based on the absorption characteristics, use the trained quantum graph neural network to construct the molecular graph of the independent gas absorption lines;

[0044] Perform quantum encoding on the molecular graph to obtain a quantum graph state;

[0045] Perform attention adjustment on the sub-graph state to obtain an enhanced quantum graph;

[0046] Perform multi-physical quantity measurement on the enhanced quantum graph to obtain interaction energy data;

[0047] Based on the interaction energy data, analyze the gas interaction situation in the multi-gas area to obtain gas interaction data.

[0048] Optionally, use the gas interaction data to construct the gas mixing concentration matrix of the multi-gas area, including:

[0049] Extract the regional geometric data in the gas interaction data;

[0050] Query the sensor positions in the multi-gas area;

[0051] Perform a spatial discretization operation on the regional geometric data and the sensor positions to obtain a grid index matrix and an adjacency matrix;

[0052] Perform a multi-physics field coupling modeling on the grid index matrix and the adjacency matrix to obtain a system of differential equations;

[0053] Perform a discretization process on the system of differential equations to obtain a linear system;

[0054] Perform an iterative solution on the linear system to obtain a spatio-temporal concentration tensor;

[0055] Construct a gas mixing concentration matrix for the multi-gas region based on the spatio-temporal concentration tensor.

[0056] Optionally, the constructing of the gas dynamic diffusion model for the multi-gas region by using the neural radiance field and the gas mixing concentration matrix includes:

[0057] Perform a spatio-temporal expansion on the neural radiance field to obtain a dynamic neural radiance field model;

[0058] Perform an optical parameter conversion on the gas mixing concentration matrix to obtain a spatio-temporal optical field;

[0059] Perform a physical constraint training on the dynamic neural radiance field model and the spatio-temporal optical field to obtain a trained visual model;

[0060] Perform a real-time rendering on the trained visual model to obtain a gas dynamic diffusion model for the multi-gas region.

[0061] To solve the above problems, the present invention also provides a multi-gas rapid response detection and alarm integration system, and the system includes:

[0062] 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 by using the deployed laser detection array, and analyze the dynamic gas absorption fingerprint of the multi-gas region by using the gas samples to obtain gas energy level transition characteristics;

[0063] A first risk analysis module, configured to perform a quantum feature mapping on the gas energy level transition characteristics to obtain enhanced features, decouple the gas samples into independent gas absorption spectra by using the enhanced features, analyze the independent gas concentrations of the multi-gas region by using the independent gas absorption spectra, and perform a first risk analysis on the multi-gas region by using the independent gas concentrations to obtain a first risk report;

[0064] A gas mixing analysis module, which is used to analyze the gas interaction condition in the multi-gas region by using the trained quantum graph neural network in combination with the independent gas absorption spectral lines, obtain gas interaction data, and construct a gas mixing concentration matrix for the multi-gas region by using the gas interaction data;

[0065] A real-time early warning module, which is used to construct the neural radiation field of the multi-gas region, construct a gas dynamic diffusion model for the multi-gas region by using the neural radiation field and the gas mixing concentration matrix, perform a second risk analysis on the multi-gas region by using the gas dynamic diffusion model, obtain a second risk report, and perform real-time early warning on the multi-gas region based on the second risk report and the first risk report.

[0066] Compared with the problems in the background art, in the embodiments of the present invention, by obtaining the spatial topology data of the multi-gas region, a laser detection array is deployed based on this to collect gas samples, and then the gas absorption fingerprint dynamics is analyzed to obtain the gas energy level transition characteristics, clarifying the spatial structure and gas characteristics of the multi-gas region, and providing basic data for subsequent analysis; further, the present invention performs operations such as quantum feature mapping on the gas energy level transition characteristics, decouples the mixed gas to obtain independent gas absorption spectral lines, analyzes the independent gas concentration and performs a first risk analysis to obtain a first risk report, which can enhance and expand the original features to more comprehensively and deeply identify the quantum state characteristics of gas molecules, so as to understand the degree and type of risks faced by the current region, so as to facilitate users 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; further, the present invention analyzes the gas interaction condition to construct a gas mixing concentration matrix, and then constructs a neural radiation field and combines it with the matrix to construct a gas dynamic diffusion model, and performs a second risk analysis to obtain a second risk report, which can analyze the interaction condition between various gases in the multi-gas region, such as collisions, energy transfer and chemical reactions between gas molecules, etc., and then understand the microscopic interaction mechanism between gases, and provides a simple and effective mathematical description method for the research of the multi-gas region; furthermore, the present invention performs real-time early warning on the multi-gas region based on the first and second risk reports, which can timely remind relevant personnel to take corresponding measures before the risk occurs or intensifies, avoid the occurrence of accidents or reduce the losses caused by accidents, thereby improving the detection and alarm accuracy of complex multi-gas scenarios. Description of the Drawings

[0067] Figure 1 It is a schematic flowchart of a multi-gas rapid response detection and alarm integration method provided by an embodiment of the present invention;

[0068] Figure 2It 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.

[0069] The implementation, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. Detailed implementation manners

[0070] 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.

[0071] The 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.

[0072] Embodiment 1:

[0073] Referring to Figure 1 As shown, it is a flowchart of the 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:

[0074] S1. Obtain the spatial topology data of the multi-gas area, deploy a laser detection array for the multi-gas area based on the spatial topology data to obtain a deployed laser detection array, collect gas samples of the multi-gas area by using the deployed laser detection array, and analyze the dynamic gas absorption fingerprint of the multi-gas area by using the gas samples to obtain gas energy level transition characteristics.

[0075] Through the acquisition of the spatial topology data of the multi-gas area in the embodiment of the present invention, the spatial structure information of the multi-gas area can be understood, including the shape, size, internal layout of the area, and the distribution of possible obstacles, etc., to ensure full coverage of the multi-gas area without dead corners, avoid monitoring blind spots caused by complex spatial structures, and thus improve the comprehensiveness and representativeness of gas sample collection.

[0076] Among them, the spatial topology data refers to an abstract and digital description of the spatial structure of the multi-gas area, such as geometric shape, spatial size, and internal structure information.

[0077] Optionally, the spatial topology data can be obtained by scanning the multi-gas area with a three-dimensional laser scanner to obtain the three-dimensional spatial information of the multi-gas area.

[0078] Furthermore, in the embodiment of the present invention, by deploying a laser detection array for the multi-gas region based on the spatial topology data, the deployed laser detection array can emit laser beams towards the multi-gas region and receive the reflected or transmitted laser after the action of the gas sample, thereby improving the efficiency and quality of gas sample collection.

[0079] 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, which propagate in the multi-gas region and interact with the gases in the region, and can be used for gas sample collection and related analysis in the multi-gas region.

[0080] As an embodiment of the present invention, the deployment of the laser detection array for the multi-gas region based on the spatial topology data to obtain the deployed laser detection array includes: identifying the region size and region shape of the multi-gas region based on the spatial topology data to obtain region pattern information, dividing the region network of the multi-gas region based on the region pattern information 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 obtaining the deployed laser detection array after debugging the devices in the detection array network.

[0081] 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.

[0082] 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 device to determine the device deployment position 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.

[0083] 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.

[0084] Among them, the gas sample refers to a gas collection containing various gas components and their concentration information.

[0085] Optionally, the gas sample can be obtained by deploying a laser detection array to emit a laser beam with a specific wavelength through a multi-gas region.

[0086] Furthermore, in the embodiment of the present invention, by analyzing the dynamic gas absorption fingerprint of the multi-gas region using the gas sample, the gas energy level transition characteristics can be obtained to accurately identify the gas types and their concentration changes in the multi-gas region, which provides a key basis for subsequent accurate analysis and risk assessment of a complex multi-gas environment.

[0087] 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 with specific energies and transition between different energy levels, such as transition wavelength, transition frequency, and transition probability.

[0088] As an embodiment of the present invention, the method of analyzing the dynamic gas absorption fingerprint 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 a 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 dynamic gas absorption fingerprint of the multi-gas region using the target spectrum to obtain the gas energy level transition characteristics.

[0089] 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 selectively absorb certain specific wavelengths of light, thus forming a series of dark lines or absorption bands on the original continuous spectrum.

[0090] Optionally, the environmental data can be obtained by deploying environmental sensors in a multi-gas area, such as temperature sensors, humidity sensors, etc. The target spectrometer can determine the instrument parameters that need to be adjusted, such as wavelength range, integration time, and gain, etc., by analyzing the acquired environmental data according to the working principle and environmental impact characteristics of the spectrometer, and then make adjustments. The absorption spectrum can be obtained by introducing the gas sample into the measurement chamber of the target spectrometer, ensuring that the gas sample is evenly distributed in the measurement chamber, and then starting the target spectrometer to scan and measure the gas sample according to the set parameters. The denoising process of wavelet transform on the gas spectrum to obtain the target spectrum can be achieved by operating the Haar wavelet function. The gas energy level transition characteristics can extract the characteristic parameters of the absorption peak position, intensity, and width, etc. in the spectrum from the target spectrum to obtain the absorption fingerprint information, and then determine it by matching the pre-configured fingerprint database with the absorption fingerprint information.

[0091] S2. 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 lines. Use the independent gas absorption lines to analyze the concentration of independent gases in the multi-gas area. Use the concentration of independent gases to perform a first risk analysis on the multi-gas area to obtain a first risk report.

[0092] 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 the complex mixed gas more distinguishable.

[0093] 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 state.

[0094] Furthermore, in the embodiment of the present invention, by using the enhanced features to decouple the mixed gas of the gas sample to obtain independent gas absorption lines, the characteristics of different gases can be clearly distinguished to improve the accuracy and reliability of multi-gas detection.

[0095] Among them, the independent gas absorption line refers to the absorption line that is unique to each gas in the mixed gas and does not overlap or interfere with the absorption lines of other gases.

[0096] As an embodiment of the present invention, the utilization of 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 hybrid attention modulation on the convolutional quantum state to obtain an enhanced quantum state, based on the enhanced quantum state, performing variational quantum eigenvalue solving on the gas sample to obtain a decoupled eigenstate, and performing sparse recovery on the decoupled eigenstate to obtain independent gas absorption spectral lines.

[0097] Among them, the quantum encoded feature refers to the quantum state representation obtained by encoding the gas energy level transition feature through quantum state amplitude encoding. The convolutional quantum state refers to the quantum state obtained by performing a quantum convolution kernel filtering operation on the quantum encoded feature. The decoupled eigenstate refers to the quantum state obtained by performing a hybrid attention modulation on the convolutional quantum state and then solving through variational quantum eigenvalue, which is the key intermediate result for realizing the decoupling of independent gas absorption spectral lines from the mixed gas spectral data.

[0098] Optionally, the quantum encoded 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 the 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 convolution operation on the quantum encoded 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. The variational quantum eigenvalue solving of the gas sample based on the enhanced quantum state to obtain the decoupled eigenstate can be realized by iteratively adjusting the circuit parameters through a classical optimization algorithm (such as the gradient descent method) to make the quantum state approach the decoupled eigenstate. The independent gas absorption spectral lines 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.

[0099] Furthermore, in the embodiment of the present invention, analyzing the independent gas concentrations in the multi-gas region by using the independent gas absorption spectral lines is helpful for subsequent risk analysis, provides data support for taking targeted measures, and avoids safety accidents caused by inaccurate grasp of gas concentrations and inability to detect potential risks in a timely manner.

[0100] As an embodiment of the present invention, the analysis of the independent gas concentration in the multi-gas region by using the independent gas absorption lines includes: performing baseline correction on the independent gas absorption lines to obtain a baseline-corrected spectrum line, performing third-order feature extraction on the baseline-corrected spectrum line 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 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.

[0101] Among them, the baseline-corrected spectrum line refers to that when analyzing the independent gas absorption lines, due to the characteristics of the instrument itself, environmental factors, etc., there may be a baseline shift in the spectrum line that 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.

[0102] Optionally, the baseline-corrected spectrum line can be obtained by using a polynomial fitting method to fit the baseline of the independent gas absorption lines, determining the mathematical expression of the baseline, and subtracting the baseline influence from the original spectrum line based on this. The feature vector can be obtained by performing third-order differentiation on the baseline-corrected spectrum line using the third-order derivative spectroscopy method. The predicted gas concentration can be obtained by performing support vector analysis on the feature vector using a support vector machine in a machine learning model. The corrected concentration can be obtained by considering the comprehensive influence of temperature, pressure, and humidity on gas concentration measurement, establishing a temperature-pressure-humidity coupling correction model using a deep learning model, 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.

[0103] In the embodiment of the present invention, by using the independent gas concentration to perform a first risk analysis on the multi-gas region and obtaining a first risk report, the risk level and type faced by the current region can be understood, which is convenient for users to make decisions in a timely manner, such as whether to strengthen ventilation, evacuate personnel, take emergency treatment measures, etc., and helps to prevent accidents in advance and ensure the safety of personnel's lives.

[0104] 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. It summarizes the concentration levels of different gases in the region and evaluates the risk level of each gas by combining safety standards such as the toxicity, flammability, explosiveness characteristics, and exposure limits of each gas.

[0105] As an embodiment of the present invention, the first risk analysis of the multi-gas region is performed using the independent gas concentration to obtain a first risk report, including: 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 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:

[0106] ;

[0107] 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 toxic gas, represents the flammable and explosive weight coefficient of the j-th 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 quantified index, represents the gas diffusion impact index in the quantified index;

[0108] Based on the comprehensive risk value, the first risk analysis of the multi-gas region is performed to obtain a first risk report.

[0109] Wherein, the toxicity risk index refers to an index used to measure the potential harm degree of toxic gases in the multi-gas region 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 fire or explosion caused by flammable and explosive gases in the multi-gas region, and the gas diffusion impact index refers to an index used to describe the gas diffusion behavior in the multi-gas region and its impact on the surrounding environment and personnel.

[0110] Optionally, the multi-dimensional risk index can be obtained by querying the safety standards and professional literature of the multi-gas region and combining the actual situation of the multi-gas region to determine the specific parameters reflecting gas toxicity, flammability and explosiveness, and diffusion impact. The quantified index can be based on the independent gas concentration data and 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 region according to the size of the comprehensive risk value and in combination with the pre-set risk level classification standard to determine the risk level. For example, when it is, it represents low risk, when it is, it represents medium risk, and when it is in , it represents high risk.

[0111] It should be explained that in the above formula for calculating the 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, the complex risk factors in the multi-gas area are transformed into a specific value, which is convenient for intuitively comparing the risk levels of different multi-gas areas or evaluating the risks of the same area under different conditions. It can provide a clear basis for risk control. When the comprehensive risk value is relatively high, it indicates that the risk in this area is relatively large and stricter safety measures need to be taken; if the weight corresponding to a certain single indicator 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.

[0112] It should be further noted that the setting of the weight coefficient is determined according to the importance of different factors to the overall risk. For example, if a certain toxic gas has a very strong toxicity, then its toxicity weight coefficient will be relatively large and the proportion it occupies in the calculation of the comprehensive risk value will be higher.

[0113] S3. Using the trained quantum graph neural network in combination with the independent gas absorption lines, analyze the gas interaction conditions in the multi-gas area to obtain gas interaction data, and use the gas interaction data to construct the gas mixing concentration matrix of the multi-gas area.

[0114] In the embodiment of the present invention, by using the trained quantum graph neural network in combination with the independent gas absorption lines to analyze the gas interaction conditions in the multi-gas area and obtain gas interaction data, the interaction conditions between various gases in the multi-gas area can be analyzed, such as collisions, energy transfer, and chemical reactions between gas molecules, etc., so as to understand the microscopic interaction mechanism between gases.

[0115] Among them, the gas interaction conditions refer to the situation where various gases in the multi-gas area affect and interact with each other, such as physical effects, chemical effects, and optical effects, etc. The trained quantum graph neural network refers to a neural network model that has gone through a series of training processes and has the specific ability to process and analyze data related to quantum graphs. It can input a large amount of labeled data related to independent gas absorption lines, etc. (such as information on gas interaction conditions corresponding to known absorption characteristics), and then in 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 gas interaction conditions) is infinitely close to the labeled data and then obtained.

[0116] As an embodiment of the present invention, the method of analyzing the gas interaction status in the multi-gas region by using the trained quantum graph neural network in combination with the independent gas absorption lines to obtain gas interaction data includes: using the trained quantum graph neural network to extract the absorption characteristics of the independent gas absorption lines, and 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 status in the multi-gas region to obtain gas interaction data.

[0117] 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.

[0118] Optionally, the absorption characteristics can be obtained by inputting the independent gas absorption line data into the trained quantum graph neural network and efficiently capturing key absorption characteristics such as the position, intensity, and shape of absorption peaks in the spectrum by virtue of the superposition and entanglement characteristics of quantum states in the network. The molecular graph can take the extracted absorption characteristics as input, and let the trained quantum graph neural network associate different absorption characteristics 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 be obtained by using 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 to achieve the quantum encoding of the molecular graph. The enhanced quantum graph can calculate the attention weights of each part of the information in the quantum graph state by using a convolutional model, and based on the calculated attention weights, use quantum gate operations to adjust the quantum graph state to highlight important information and suppress secondary information. The interaction energy data can be obtained by using quantum measurement techniques to measure multi-physical quantities such as energy, momentum, and angular momentum in the enhanced quantum graph to obtain data related to the interaction of gas molecules. The gas interaction data can be obtained by combining 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.

[0119] Furthermore, by using the gas interaction data to construct the gas mixing concentration matrix of the multi-gas region, the embodiment of the present invention can intuitively represent the concentration distribution of each gas in the multi-gas region and their mutual influence relationship in the form of a matrix, thereby providing a simple and effective mathematical description method for the research of the multi-gas region.

[0120] Among them, the gas mixing concentration matrix refers to a matrix used to represent the distribution of different gas concentrations in a multi-gas region.

[0121] As an embodiment of the present invention, constructing the gas mixing concentration matrix of the multi-gas region by using the gas action data includes: extracting the regional geometric data in the gas action data, querying the sensor positions in the multi-gas region, performing a spatial discretization operation on the regional geometric data and the sensor positions to obtain a grid index matrix and an adjacency matrix, performing a multi-physical field coupling modeling on the grid index matrix and the adjacency matrix to obtain a differential equation system, performing a discretization process on the differential equation system to obtain a linear system, performing an iterative solution on 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.

[0122] 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-physical field coupling modeling, the linear system refers to a set of linear equations obtained after discretizing the differential equation system, and the spatio-temporal concentration tensor refers to a multi-dimensional data structure containing information on the change of gas concentration in space and time in the multi-gas region.

[0123] 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 referring to relevant equipment deployment records or databases to obtain the precise position coordinate information of each sensor within 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 within the multi-gas region (such as diffusion, convection, chemical reactions, etc.) and establishing a mathematical model of multi-physical field coupling. The discretization process of the differential equation system can be achieved 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 realized by using the conjugate gradient method to solve the linear system. The gas mixing 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 gas types, spatial positions, and time. Then, according to the data in the tensor and the definitions and rules of the matrix, the gas mixing concentration matrix is constructed.

[0124] S4. Construct the neural radiance field of the multi-gas region, use the neural radiance 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 conduct 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.

[0125] 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.

[0126] 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 users with a continuous representation of gas-related physical quantities within the multi-gas region, enabling users to query and predict the gas state at any position.

[0127] Optionally, the neural radiance field can be constructed by applying deep learning techniques, based on the multi-layer perceptron (MLP) as the basic neural network structure, and combining data such as gas concentration, temperature, and pressure collected by sensors arranged in the multi-gas region.

[0128] Furthermore, in the embodiment of the present invention, by using the neural radiance field and the gas mixing concentration matrix to construct the gas dynamic diffusion model of the multi-gas region, the diffusion behavior of gas in the multi-gas region over time can be simulated, so that users can 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.).

[0129] Among them, the dynamic diffusion model refers to a model based on physical principles and mathematical methods for describing the dynamic diffusion behavior of gas in a multi-gas region over time and space.

[0130] As an embodiment of the present invention, using the neural radiance field and the gas mixing concentration matrix to construct the gas dynamic diffusion model of the multi-gas region 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.

[0131] Among them, the dynamic neural radiance field model refers to a model that introduces the time dimension into the neural radiance field, takes time as an additional input variable, and enables the model to learn and capture the dynamic change laws of gas-related characteristics in the multi-gas region over time and space. 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.

[0132] Optionally, the dynamic neural radiance field model can introduce the time dimension into the existing neural radiance field model and adjust the model structure by increasing the number of network layers or neurons using a deep learning framework (such as TensorFlow or PyTorch). The spatio-temporal optical field can convert the concentration information in the gas mixing concentration matrix into corresponding optical parameter values according to the optical characteristics of the gas (such as absorption coefficient, scattering coefficient, etc.) and then construct it in combination 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. The model parameters are continuously adjusted through an optimization algorithm (such as the stochastic gradient descent method) to make the output of the model as consistent with the actual situation as possible and then obtained. The real-time rendering of the trained visual model can be achieved by operating the ray tracing algorithm.

[0133] 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, a second risk report can be obtained, which can provide the risk situation of the multi-gas region from a dynamic perspective, 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, strengthening the operation of ventilation facilities, etc., so as to improve the overall safety and emergency response ability of the multi-gas region.

[0134] 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.

[0135] As an embodiment of the present invention, using the gas dynamic diffusion model to perform a second risk analysis on the multi-gas region to obtain a second risk report includes: querying the gas mixing concentration matrix of the multi-gas region, using the gas mixing 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.

[0136] Among them, the risk parameter table refers to a table used to organize and present various risk-related parameters in the multi-gas region, the multi-channel risk tensor refers to a high-dimensional data structure used to comprehensively represent the risk information of the multi-gas region in different aspects, and the propagation simulation path refers to the possible diffusion and propagation trajectory of the gas in the region obtained by simulating the multi-gas region based on the graded risk field.

[0137] 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, explosiveness, etc.) and concentration thresholds. For example, for toxic gases, the risk parameters are determined based on the comparison of their concentrations with the safety limits, and then the relevant risk parameters of each gas (such as concentration, risk level, potential hazard degree, etc.) are sorted 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 graded 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 the elements in the multi-channel risk tensor. The propagation simulation path can use the information in the graded 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 regions. 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, considering various factors (such as gas concentration, diffusion speed, and influence range, etc.) to construct a risk report.

[0138] In the embodiment of the present invention, by using the second risk report and the first risk report to conduct real-time early warning on the multi-gas area, relevant personnel can be timely reminded 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.

[0139] Optionally, the real-time early warning on the multi-gas area can be realized by comparing the key information such as risk levels, gas diffusion ranges, and potential hazards in the second risk report and the first risk report, and according to the preset early warning thresholds and rules, through means such as audible and visual alarm devices, SMS notifications, and pop-up windows on the monitoring system, to give early warning of the risk situation exceeding the safe range in the multi-gas area.

[0140] Embodiment 2:

[0141] As Figure 2 shown, it is the functional module diagram of the multi-gas rapid response detection and alarm integration system of the present invention.

[0142] The multi-gas rapid response detection and alarm integration system 200 of the present invention can be installed in an electronic device. According to the functions to be realized, the multi-gas rapid response detection and alarm integration system can include a gas characteristic analysis module 201, a first risk analysis module 202, a gas mixing analysis module 203, and a real-time early warning module 204. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0143] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0144] The gas characteristic analysis module 201 is configured to obtain the spatial topology data of the 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 by using the deployed laser detection array, analyze the dynamic gas absorption fingerprint of the multi-gas region by using the gas samples, and obtain gas energy level transition characteristics;

[0145] The first risk analysis module 202 is configured to perform quantum characteristic mapping on the gas energy level transition characteristics to obtain enhanced characteristics, decouple the mixed gas of the gas samples by using the enhanced characteristics to obtain independent gas absorption spectral lines, analyze the independent gas concentration of the multi-gas region by using the independent gas absorption spectral lines, and perform a first risk analysis on the multi-gas region by using the independent gas concentration to obtain a first risk report;

[0146] The gas mixing analysis module 203 is configured to analyze the gas interaction condition of the multi-gas region by using the trained quantum graph neural network in combination with the independent gas absorption spectral lines to obtain gas interaction data, and construct a gas mixing concentration matrix of the multi-gas region by using the gas interaction data;

[0147] The real-time warning module 204 is configured to construct a neural radiation field of the multi-gas region, construct a gas dynamic diffusion model of the multi-gas region by using the neural radiation field and the gas mixing concentration matrix, perform a second risk analysis on the multi-gas region by 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.

[0148] Specifically, each module in the multi-gas rapid response detection and alarm integration system 200 in the embodiments of the present invention adopts the same technical means as those in the Figure 1 multi-gas rapid response detection and alarm integration method described above, and can produce the same technical effects, which will not be elaborated here.

[0149] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0150] 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 includes: Obtain the spatial topological data of the multi-gas region. Based on the spatial topological 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 use the gas samples to analyze the dynamic gas absorption fingerprint in 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 in the gas samples 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, and 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 with the independent gas absorption spectral lines to analyze the gas interaction status in the multi-gas region to obtain gas interaction data, and use the gas interaction data to construct a gas mixing concentration matrix for the multi-gas region; Construct a neural radiance field for the multi-gas region. Use the neural radiance field and the gas mixing concentration matrix to construct a gas dynamic diffusion model for 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, conduct real-time warning on the multi-gas region.

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, identify the region size and region shape of the multi-gas region to obtain region pattern information; Based on the region pattern information, divide the multi-gas region into regional networks to obtain regional grids; Deploy detection devices in the regional grids to obtain device deployment regions; Construct a detection array network for the device deployment regions; After debugging the devices in the detection array network, obtain a deployed laser detection array.

3. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that The step of using the gas samples to analyze the dynamic gas absorption fingerprint 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 a target spectrometer; Use the target spectrometer to detect the absorption spectrum of the gas samples; Perform wavelet transform noise reduction processing on the absorption spectrum to obtain a target spectrum; Use the target spectrum to analyze the dynamic gas absorption fingerprint in the multi-gas region to obtain gas energy level transition characteristics.

4. The multi-gas rapid response detection and alarm integration method according to claim 1, wherein, The step of using the enhanced features to decouple the mixed gas in the gas samples to obtain independent gas absorption spectral lines includes: Perform quantum state amplitude encoding on the enhanced features to obtain quantum encoded features; Perform quantum convolutional kernel filtering operation on the quantum encoded features to obtain convolutional quantum states; Perform mixed attention modulation on the convolutional quantum states to obtain enhanced quantum states; Based on the enhanced quantum state, perform variational quantum eigensolution on the gas sample to obtain decoupled eigenstates; Perform sparse recovery on the decoupled eigenstates 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 Analyze the independent gas concentrations in the multi-gas region by using the independent gas absorption spectral lines, including: Perform baseline correction on the independent gas absorption spectral lines to obtain baseline-corrected spectral lines; Perform third-order feature extraction on the baseline-corrected spectral lines to obtain feature vectors; Perform support vector regression operation on the feature vectors to map the concentration of the multi-gas region and obtain predicted gas concentrations; Perform temperature-pressure-humidity coupling correction on the predicted gas concentrations to obtain corrected concentrations; Perform cross-validation on the corrected concentrations to determine the independent gas concentrations in the multi-gas region.

6. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that Perform a first risk analysis on the multi-gas region by using the independent gas concentrations to obtain a first risk report, including: Construct toxicity risk indicators, flammable and explosive risk indicators, and gas diffusion impact indicators for the multi-gas region to obtain multi-dimensional risk indicators; Quantify the multi-dimensional risk indicators by using the independent gas concentrations to obtain quantified indicators; Based on the quantified indicators, calculate the comprehensive risk value of the multi-gas region by using the following formula: ； Among them, represents the comprehensive risk value, n represents the number of types of toxic gases in the multi-gas area, and 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 influence weight coefficient, represents the toxicity risk index in the quantification index, represents the flammable and explosive risk index in the quantification index, represents the gas diffusion influence index in the quantification index; Based on the comprehensive risk value, perform a first risk analysis on the multi-gas region to obtain a first risk report.

7. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that Analyze the gas interaction conditions in the multi-gas region by using the trained quantum graph neural network combined with the independent gas absorption spectral lines to obtain gas interaction data, including: Use the trained quantum graph neural network to extract the absorption characteristics of the independent gas absorption spectral lines; Based on the absorption characteristics, use the trained quantum graph neural network to construct a molecular graph of the independent gas absorption spectral lines; Perform quantum encoding on the molecular graph to obtain a quantum graph state; Perform attention adjustment on the subgraph 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 conditions in the multi-gas region to obtain gas interaction data.

8. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that Construct the gas mixing concentration matrix of the multi-gas region by using the gas interaction data, including: Extract the regional geometric data from 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 the gas mixing concentration matrix of the multi-gas region based on the spatio-temporal concentration tensor.

9. The multi-gas rapid response detection and alarm integration method according to claim 1, characterized in that Construct the gas dynamic diffusion model of the multi-gas region by using the neural radiance field and the gas mixing concentration matrix, 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; Physically constrain the training of 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 visual model to obtain a gas dynamic diffusion model for the multi-gas region.

10. Multi-gas rapid response detection and alarm integration system, characterized in that, The system 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, use the enhanced features to decouple the mixed gas of the gas samples 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 status of the multi-gas region using a trained quantum graph neural network in combination 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 radiance field of the multi-gas region, construct a gas dynamic diffusion model of the multi-gas region using the neural radiance 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.

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