Tunnel toxic gas detection method, device, equipment, system and storage medium

Abstract

The invention relates to a tunnel toxic gas detection method, device, equipment and system and a storage medium, and belongs to the technical field of information detection.The method comprises the steps that concentration measured values of multiple toxic gases collected by multiple gas sensor nodes in a tunnel are obtained; inputting the concentration measurement values of the various toxic gases into a linear minimum mean square error estimation model, and outputting the position of a gas leakage source and an estimated value of a gas release rate; inputting the estimated value of the gas leakage source parameter into a gas diffusion model, and outputting a gas diffusion parameter; the gas diffusion model is a static smoke plume gas diffusion model based on turbulence diffusion, and the gas diffusion parameters comprise the diffusivity of a gas leakage source; and inputting the estimated value of the gas leakage source parameter, the gas diffusion parameter and the standard-exceeding concentration threshold value of the toxic gas into a finite element prediction model, and outputting a toxic gas standard-exceeding prediction result. According to the invention, continuous monitoring and dynamic early warning of the toxic gas in the tunnel can be realized.

Description

Technical Field

[0001] The present application relates to the field of information detection technology, and in particular to a method, device, equipment, system and storage medium for detecting toxic gases in tunnels. Background Art

[0002] In the construction and operation of modern transportation networks, tunnels are key engineering structures that connect regions and shorten time and space distances. Their safety and reliability have always been the focus of attention in the engineering and academic circles. With the continuous advancement of tunnel engineering technology, especially in the construction and operation of long-distance and large-section tunnels under complex geological conditions, the detection, monitoring and management of toxic gases have become a key issue that needs to be solved urgently. In tunnels under construction and completed tunnels, due to the complexity of geological structures, high energy consumption of construction machinery, and the accumulation of exhaust emissions when vehicles pass through, a series of toxic and harmful gases are often produced, such as carbon monoxide (CO), hydrogen sulfide (H2S), nitrogen oxides (NOx), sulfur dioxide (SO2), methane (CH4), carbon dioxide (CO2), ammonia (NH3), etc. The presence of these toxic gases, on the one hand, seriously threatens the life and health of construction workers, and on the other hand, poses a potential risk to the long-term stability of tunnel structures and the safety of vehicle traffic during operation.

[0003] Traditional methods of detecting toxic gases mostly rely on inspectors to use handheld detectors to sample specific areas in the tunnel, and then combine them with laboratory analysis. This method is not only time-consuming and laborious, but also requires high safety of the inspectors, and it is difficult to achieve continuous monitoring and dynamic early warning of toxic gases. Summary of the invention

[0004] In order to achieve continuous monitoring and dynamic early warning of toxic gases in tunnels, the present application provides a tunnel toxic gas detection method, device, equipment, system and storage medium.

[0005] In the first aspect, the present application provides a method for detecting poisonous gases in a tunnel, which adopts the following technical solution: A method for detecting poisonous gas in a tunnel, comprising: Obtaining concentration measurements of multiple toxic gases collected by multiple gas sensor nodes in the tunnel; Inputting the concentration measurement values ​​of the multiple toxic gases into a preset linear minimum mean square error estimation model, and outputting estimated values ​​of gas leakage source parameters; wherein the gas leakage source parameters include the location of the gas leakage source and the gas release rate; Inputting the estimated value of the gas leakage source parameter into a preset gas diffusion model, and outputting the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; The estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas are input into a preset finite element prediction model, and the prediction result of the excessive concentration of the toxic gas is output.

[0006] Optionally, the formula of the linear minimum mean square error estimation model is: ; The performance of the estimator is given by the error vector Determination, Error Vector The mean of the covariance matrix is ​​0. for: ; In the formula, represents a vector to be estimated, the vector to be estimated is composed of the position of the gas leakage source and the gas release rate. is a real matrix with dimension p×1, that is ; Represents the vector to be estimated An estimated value of Represents the vector to be estimated The mean of Represents the vector to be estimated The covariance matrix of z represents the observation vector, the observation vector z For a dimension N ×1 real matrix, that is ; D represents the observation matrix, the observation matrix D For a dimension N ×p real matrix, that is ; Represents the observation matrix D The transpose of Indicates that given an observation vector z and the vector to be estimated Under the condition that The expected value of the outer product of represents the error vector The transpose of v represents a zero mean, and the covariance matrix is The noise vector v is a one-dimensional noise vector. N ×1 real matrix, that is , and v andθ Uncorrelated, jointly distributed Any; c represents a random variable.

[0007] Optionally, the formula of the gas diffusion model is: ; In the formula, , represents the coordinate position of the gas sensor node; , represents the coordinate position of the gas leakage source; Represents the gas sensor node The gas concentration value at time t; Represents the gas sensor node The gas leak source The Euclidean distance between Indicates the initial time when the gas leakage source releases gas; , represents the wind speed vector; Indicates the diffusion rate of the gas leakage source; Represents an estimate of the gas release rate from the gas leak source.

[0008] Optionally, the inputting the estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas into a preset finite element prediction model, and outputting the prediction result of the excessive concentration of the toxic gas includes: Based on the estimated values ​​of the gas leakage source parameters, the tunnel space is divided into a plurality of grid cells; Constructing a control equation of the finite element prediction model based on the gas diffusion parameter and the wind speed vector in the tunnel; Based on the estimated values ​​of the gas leakage source parameters, setting the initial conditions and boundary conditions of the finite element prediction model; Based on the initial conditions and the boundary conditions, solving the control equation on each grid unit to obtain the gas concentration distribution of the poisonous gas in the tunnel; The prediction result of the toxic gas exceeding the standard is determined based on the gas concentration distribution of the toxic gas in the tunnel and the exceeding concentration threshold of the toxic gas.

[0009] Optionally, the control equation is: ; In the formula, , represents the wind speed vector in the tunnel; Indicates the diffusion rate of the gas leakage source; Indicates the coordinates in the tunnel The gas concentration value at time t; Represents the concentration gradient.

[0010] Optionally, the determining the prediction result of the toxic gas exceeding the standard based on the gas concentration distribution of the toxic gas in the tunnel and the exceeding concentration threshold of the toxic gas includes: Compare all gas concentration values ​​with the excessive concentration threshold, and screen out all excessive points where the gas concentration value is higher than the excessive concentration threshold; For a non-exceeding point whose gas concentration value is not higher than the exceeding concentration threshold, using an interpolation method to calculate the gas concentration value of the non-exceeding point; Draw a three-dimensional isoconcentration map based on the gas concentration values ​​of all exceeding points and all non-exceeding points; The leakage level of the poisonous gas is determined based on the three-dimensional isoconcentration line map.

[0011] In the second aspect, the present application provides a tunnel toxic gas detection device, which adopts the following technical solution: A tunnel poisonous gas detection device, comprising: A concentration acquisition module is used to obtain the concentration measurement values ​​of various poisonous gases collected by multiple gas sensor nodes in the tunnel; A first model module, used to input the concentration measurement values ​​of the multiple toxic gases into a preset linear minimum mean square error estimation model, and output an estimated value of a gas leakage source parameter; wherein the gas leakage source parameter includes the location of the gas leakage source and the gas release rate; A second model module is used to input the estimated value of the gas leakage source parameter into a preset gas diffusion model and output the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; The third model module is used to input the estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas into a preset finite element prediction model, and output the prediction result of the excessive concentration of the toxic gas.

[0012] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory and a processor; the memory stores a computer program that can be loaded by the processor and executes the method described in any one of the first aspect or the second aspect.

[0013] In a fourth aspect, the present application provides a tunnel toxic gas detection system, which adopts the following technical solution: A tunnel poisonous gas detection system, comprising the plurality of gas sensor nodes and the electronic device according to the third aspect; Wherein, the plurality of gas sensor nodes include semiconductor sensor nodes, optical sensor nodes and electrochemical sensor nodes; The semiconductor sensor node is arranged on the inner surface layer of the lining concrete of the tunnel; The optical sensor node is arranged on the outer surface of the lining concrete of the tunnel; The electrochemical sensor nodes are arranged at the cable trench positions on both sides of the tunnel.

[0014] In a fifth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method described in any one of the first aspect or the second aspect.

[0015] By adopting the above technical scheme and combining it with multiple calculation models such as LMMSE model, turbulent diffusion model, finite element prediction model, etc., it is possible to achieve high-precision parameter estimation of gas leakage source in tunnels, gas diffusion simulation and prediction of excessive levels of toxic gases, thereby achieving continuous monitoring and dynamic early warning of toxic gases in tunnels. It is suitable for complex flow environments and has high practicality and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for detecting poisonous gas in a tunnel according to an embodiment of the present application.

[0017] Figure 2 It is a flowchart of the sub-steps of step S104 in a tunnel toxic gas detection method according to an embodiment of the present application.

[0018] Figure 3 It is a flowchart of the sub-steps of step S205 in a tunnel toxic gas detection method according to an embodiment of the present application.

[0019] Figure 4 It is a structural block diagram of a tunnel poisonous gas detection device according to an embodiment of the present application.

[0020] Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present application.

[0021] Figure 6 It is a main schematic diagram of the tunnel cross-section layout of gas sensor nodes in a tunnel toxic gas detection system in an embodiment of the present application.

[0022] Figure 7 It is a side view schematic diagram of the tunnel cross-section layout of gas sensor nodes in a tunnel toxic gas detection system in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0024] Figure 1 A flow chart of a method for detecting poisonous gas in a tunnel provided in this embodiment. The method can be applied to a server or a terminal device. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services; the terminal device can be a smart phone, a tablet computer, a desktop computer, etc., which is not specifically limited in this embodiment.

[0025] like Figure 1 As shown, the main process of the method is described as follows (steps S101 to S104): Step S101, obtaining concentration measurement values ​​of multiple poisonous gases collected by multiple gas sensor nodes in the tunnel; Step S102, inputting the concentration measurement values ​​of the multiple toxic gases into a preset linear minimum mean square error estimation model, and outputting the estimated values ​​of the gas leakage source parameters; wherein the gas leakage source parameters include the location of the gas leakage source and the gas release rate; Step S103, inputting the estimated value of the gas leakage source parameter into a preset gas diffusion model, and outputting the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; Step S104, inputting the estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas into a preset finite element prediction model, and outputting the prediction result of the excessive concentration of the toxic gas.

[0026] In this embodiment, different gas sensor nodes can collect gas concentration information at different locations and transmit it to the data information processing center synchronously, and then estimate the state parameters such as the location of the gas leakage source and the gas release rate through the sensor network collaborative information processing technology. The sensor network collaborative information processing technology is generally implemented using a distributed estimation algorithm. For the detection and positioning of the toxic gas leakage source, the design of the distributed estimation algorithm is the key to the efficient estimation of the state parameters of the gas leakage source by the entire sensor network. This embodiment adopts the linear minimum mean square error estimation model (LMMSE) in the distributed estimation algorithm designed based on the probability estimation theory to estimate the state parameters such as the location of the toxic gas leakage source and the gas release rate.

[0027] In some optional embodiments, for linear minimum mean square error estimation, if the observation vector z satisfies the following Bayesian linear model z=Dθ+v, where, ; θ represents the vector to be estimated, which is composed of the location of the gas leakage source and the gas release rate. The vector to be estimated θ is a real number matrix with a dimension of p×1, that is, ; D represents the observation matrix, which is a real number matrix with a dimension of N×p, that is, ; v represents a zero mean, and the covariance matrix (i.e., the covariance matrix (Covariance Matrix), a matrix that describes the linear relationship between the components of a random vector) is The noise vector v is a real number matrix with a dimension of N×1, that is, , and v and θ are uncorrelated, the joint distribution p(v,θ) can be arbitrary as long as their marginal distributions satisfy the uncorrelated condition. p(v,θ) can be decomposed into the product of marginal distributions, that is, p(v,θ)=p(v)p(θ).

[0028] The formula of the linear minimum mean square error estimation model is: ; The performance of the estimator is given by the error vector The error vector ε has a mean of 0 and the covariance matrix for:

[0029] In the formula, Represents the estimated value of the vector to be estimated; E(θ) represents the mean of the vector θ to be estimated; Represents the covariance matrix of the vector θ to be estimated; z represents the observation vector, which is a real number matrix with a dimension of N×1, that is, ; represents the transpose of the observation matrix D; It represents the expected value of the outer product of the error vector ε under the given observation vector z and the vector to be estimated θ; represents the transpose of the error ε; c represents a random variable.

[0030] It should be noted that in dimension N×1, N represents the length of the vector, that is, there are N elements in the vector. In dimension N×p, it means there are N rows and p columns, where each row corresponds to an observation value and each column corresponds to a parameter.

[0031] This embodiment adopts the linear minimum mean square error estimation (LMMSE) in the distributed estimation algorithm designed based on the probability estimation theory, which can realize the accurate and efficient positioning of the leakage source of the toxic gas in the tunnel and improve the response speed; the distributed design enhances the robustness and scalability of the system; the LMMSE probability estimation can cope with complex environments and improve the detection reliability; the distributed estimation algorithm can optimize the parameters according to the actual situation of the tunnel to achieve the best performance.

[0032] In some optional embodiments, a static smoke plume gas diffusion model based on turbulent diffusion theory is used to describe the diffusion state of the tunnel poisonous gas. Considering that the propagation of gas in the air is often affected by wind in addition to its own diffusion, a gas diffusion model in the wind is used.

[0033] Assume that in a homogeneous and uniform wind field (wind speed and wind direction are exactly the same at any position in space and do not change), the gas leakage source continuously releases gas into the air at a constant release rate s, and the released substance diffuses in the tunnel environment at a certain rate Spread to all directions. Considering the influence of wind speed, according to Philkes' law, we can get: ; The formula of the gas diffusion model can be derived from the above formula: ; In the formula, , represents the coordinate position of the gas sensor node; , represents the coordinate position of the gas leakage source; Represents the gas sensor node The gas concentration value at time t; Represents the gas sensor node The gas leak source The Euclidean distance between Indicates the initial time when the gas leakage source releases gas; , represents the wind speed vector; Indicates the diffusion rate of the gas leakage source; an estimate of the gas release rate from the gas leak source; δ represents the small change in instantaneous point source diffusion; exp() represents an exponential function with the natural constant e (approximately equal to 2.71828) as the base; erfc() stands for complementary error function.

[0034] When t≤ hour, , when t→+∞, Indicates that the equilibrium state has been reached, and the formula of the gas diffusion model is: ; when When there is wind only in the x direction, the formula of the gas diffusion model is transformed into: .

[0035] This embodiment adopts a static smoke plume gas diffusion model based on turbulent diffusion theory to simulate the diffusion state of toxic gases in tunnels under construction and completed tunnels. It innovatively combines turbulent diffusion theory with static models to significantly improve simulation accuracy. The calculation is efficient and fast, and simulation results can be provided quickly. It takes up less resources, reducing the cost and threshold of simulation. The model structure design is simple and easy to program and promote. It has strong predictive ability and can accurately reflect the diffusion trend of toxic gases and potential danger areas. It has a solid theoretical foundation to ensure the accuracy and reliability of simulation results.

[0036] In this embodiment, the prediction result of the excess of toxic gases includes the excess range and excess level of toxic gas concentration in the tunnel, so that the corresponding detection personnel can timely discover and eliminate risks. Therefore, it is necessary to screen the types of common high-risk toxic gases and the corresponding excess concentration thresholds. Specifically, the types of common high-risk toxic gases in tunnels and the corresponding excess concentration thresholds can be screened in combination with national industry-related standards and specifications such as "Occupational Exposure Limits for Hazardous Factors in the Workplace" (GBZ 2.1-2019), "Technical Specifications for Highway Tunnel Construction" (JTG / T 3660-2020), "Technical Specifications for Environmental Health Risk Monitoring" (T / CSES53-2022), and "List of Priority Controlled Chemicals".

[0037] The diversified screening method of standard reference-gas identification-threshold concentration is used to screen out common high-risk toxic gases step by step. First, it ensures the scientificity and rigor of the screening process. By following the nationally recognized standards and common chemical classifications, combined with precise threshold concentration settings, the accuracy and reliability of the screening results are effectively improved; second, it enhances the comprehensiveness and systematicness of the screening. Through diversified screening indicators and step-by-step screening processes, it can comprehensively cover and effectively screen common high-risk toxic gases; third, it improves the flexibility and adaptability of screening. This method can be flexibly adjusted according to the actual needs of tunnel toxic gas detection; fourth, it significantly improves the efficiency of screening work. Through scientific methods and process design, unnecessary duplication of work and waste of resources are reduced; fifth, it strengthens the application value of the screening results. The high-risk toxic gas information screened out provides an important basis for subsequent monitoring, early warning and prevention and control work, which helps to reduce the potential risks of toxic gases to the environment and human body.

[0038] In this embodiment, toxic gases such as methane, carbon monoxide, carbon dioxide, hydrogen sulfide, sulfur dioxide, ammonia, nitrogen dioxide, nitrogen, chlorine, phosgene (COCl2), diphosgene (C2Cl4O2), formaldehyde, ozone, hydrogen cyanide (HCN), ethane, ethylene, propylene, propane, benzene, and toluene are screened out.

[0039] In some optional embodiments, such as Figure 2 As shown, step S104 includes the following sub-steps: Step S201, dividing the tunnel space into a plurality of grid units based on the estimated values ​​of the gas leakage source parameters; In step S201, the grid cells are reasonably divided according to the location and diffusion range of the leakage source. The grid density should be high enough to capture the details of the concentration changes, especially near the leakage source and in the high concentration area. Of course, adaptive grid technology can be used to dynamically adjust the grid density.

[0040] Step S202, constructing a control equation of a finite element prediction model based on gas diffusion parameters and the wind speed vector in the tunnel; Optionally, for gas leakage problems, the governing equation describes the variation of the gas concentration φ(x,y,z,t) in three-dimensional space. This governing equation takes into account the diffusion and convection effects of the gas and is the basis of the finite element model. In practical applications, this continuous partial differential equation needs to be discretized, that is, it is converted into a system of algebraic equations through the finite element method. Then, by solving this system of algebraic equations, the approximate values ​​of the gas concentration at discrete points (finite element grid points) can be obtained. These approximate values ​​can be used to predict the distribution of gas concentration in space.

[0041] The control equation can be designed as follows: ; In the formula, , represents the wind speed vector in the tunnel; Indicates the diffusion rate of the gas leak source; φ(x,y,z,t) represents the gas concentration value at the coordinate (x,y,z) in the tunnel at time t; ∇φ represents the concentration gradient, which is a vector whose components are the partial derivatives of the gas concentration values ​​in the x, y, and z directions. In the process of gas diffusion, the concentration gradient is an important factor affecting the direction and rate of movement of gas molecules. Gas molecules will diffuse from high-concentration areas to low-concentration areas until the concentration is evenly distributed.

[0042] Step S203, setting initial conditions and boundary conditions of the finite element prediction model based on the estimated values ​​of the gas leakage source parameters; In step S203, the initial condition is that at the gas leakage source location at the initial time The gas concentration value is φ(x,y,z, )=Q. The boundary condition is that the gas concentration value on the boundary of the calculation domain is zero or satisfies certain diffusion conditions.

[0043] Step S204, based on the initial conditions and boundary conditions, solving the control equation on each grid unit to obtain the gas concentration distribution of the poisonous gas in the tunnel; Step S205, determining a prediction result of excessive concentration of toxic gases based on the gas concentration distribution of the toxic gases in the tunnel and the excessive concentration threshold of the toxic gases.

[0044] If the gas concentration of the toxic gas at a certain location in the tunnel exceeds the excessive concentration threshold of the toxic gas, it means that the toxic gas at that location exceeds the standard.

[0045] Further, for step S205, if Figure 3 As shown, the specific implementation can be carried out according to the following steps: Step S301, comparing all gas concentration values ​​with the excessive concentration threshold, and screening out all excessive points where the gas concentration value is higher than the excessive concentration threshold; Step S302, for non-exceeding points whose gas concentration values ​​are not higher than the exceeding concentration threshold, use an interpolation method to calculate the gas concentration values ​​of the non-exceeding points; Optionally, interpolation methods such as linear interpolation, Kriging interpolation and Lagrange interpolation can be used. The three-dimensional isoconcentration map needs to show the continuous distribution of concentration, not just the concentration values ​​of discrete points. A continuous concentration field can be generated by interpolation, so that the isoconcentration lines can smoothly connect the known points. Although the concentration values ​​of non-exceeding points are lower than the threshold, they are still important for the overall risk assessment, and can more clearly show the concentration gradient and diffusion trend, and can more comprehensively evaluate the impact range of the leak, which is very important for understanding the dynamic changes and impact range of gas leaks.

[0046] Step S303, drawing a three-dimensional iso-concentration line diagram based on the gas concentration values ​​of all exceeding points and all non-exceeding points; Optionally, contour3 function provided by MATLAB, Surfer visualization software, Axes3D.contour or Axes3D.contourf function of Python, etc. can be used to draw contour map in three-dimensional space, visualize the gas concentration data in three-dimensional coordinate system, and intuitively display the concentration distribution.

[0047] Step S304, determining the leakage level of the poisonous gas based on the three-dimensional isoconcentration line map.

[0048] The larger the area where the concentration exceeds the standard in the isoconcentration line map, the higher the leakage level; the range and position of the high-concentration area in the isoconcentration line map can reflect the intensity and diffusion direction of the leakage source. By dynamically simulating the isoconcentration line map, the gas diffusion rate can be observed. The faster the diffusion rate, the higher the leakage level.

[0049] Different warning signals can be used for different leakage levels. For example, five types of lights, green (normal signal), blue (general alarm signal), yellow (heavy alarm signal), red (serious alarm signal), and orange (extremely heavy alarm signal), can be used for alarm prompts. In this way, the degree of gas leakage hazard can be accurately divided, and the colors are intuitive and easy to identify, which can enhance the vigilance of staff, quickly trigger corresponding emergency measures, simplify the decision-making process, and improve the efficiency of tunnel safety management.

[0050] Based on the same inventive concept, an embodiment of the present invention provides a tunnel poisonous gas detection device. Figure 4 This is a structural block diagram of a tunnel poisonous gas detection device 400 provided in an embodiment of the present application. Figure 4 As shown, the tunnel poisonous gas detection device 400 mainly includes: The concentration acquisition module 401 is used to obtain the concentration measurement values ​​of multiple poisonous gases collected by multiple gas sensor nodes in the tunnel; The first model module 402 is used to input the concentration measurement values ​​of multiple toxic gases into a preset linear minimum mean square error estimation model, and output the estimated value of the gas leakage source parameter; wherein the gas leakage source parameter includes the location of the gas leakage source and the gas release rate; The second model module 403 is used to input the estimated value of the gas leakage source parameter into a preset gas diffusion model and output the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; The third model module 404 is used to input the estimated values ​​of the gas leakage source parameters, the gas diffusion parameters and the excessive concentration threshold of the toxic gas into a preset finite element prediction model, and output the prediction result of the excessive concentration of the toxic gas.

[0051] In some optional embodiments, the formula of the linear minimum mean square error estimation model in the first model module 402 is: ; The performance of the estimator is given by the error vector Determination, Error Vector The mean of the covariance matrix is ​​0. for: ; In the formula, Represents the vector to be estimated, which is composed of the location of the gas leakage source and the gas release rate. is a real matrix with dimension p×1, that is ; Represents the vector to be estimated An estimated value of Represents the vector to be estimated The mean of Represents the vector to be estimated The covariance matrix of z represents the observation vector, the observation vector z For a dimension N ×1 real matrix, that is ; D represents the observation matrix, the observation matrix D For a dimension N ×p real matrix, that is ; Represents the observation matrix D The transpose of Indicates that given an observation vector z and the vector to be estimated Under the condition that The expected value of the outer product of represents the error vector The transpose of v represents a zero mean, and the covariance matrix is The noise vector v is a one-dimensional noise vector. N ×1 real matrix, that is , and v and θ Uncorrelated, jointly distributed Any; c represents a random variable.

[0052] In some optional embodiments, the formula of the gas diffusion model in the second model module 403 is: ; In the formula, , represents the coordinate position of the gas sensor node; , represents the coordinate position of the gas leakage source; Represents a gas sensor node The gas concentration value at time t; Represents a gas sensor node Gas leak source The Euclidean distance between Indicates the initial moment when the gas leak source releases gas; , represents the wind speed vector; Indicates the diffusion rate of the gas leak source; Represents an estimate of the gas release rate from a gas leak source.

[0053] In some optional embodiments, the third model module 404 includes: A grid division module, for dividing the tunnel space into a plurality of grid cells based on the estimated values ​​of the gas leakage source parameters; An equation building module for building the governing equations of the finite element prediction model based on the gas diffusion parameters and the wind speed vector in the tunnel; A condition setting module, used to set initial conditions and boundary conditions of the finite element prediction model based on the estimated values ​​of the gas leakage source parameters; An equation solving module is used to solve the control equation on each grid unit based on initial conditions and boundary conditions to obtain the gas concentration distribution of the poisonous gas in the tunnel; The determination module is used to determine the prediction result of the excess concentration of toxic gases based on the gas concentration distribution of the toxic gases in the tunnel and the excess concentration threshold of the toxic gases.

[0054] Furthermore, the control equation is: ; In the formula, , represents the wind speed vector in the tunnel; Indicates the diffusion rate of the gas leak source; Indicates the coordinates in the tunnel The gas concentration value at time t; Represents the concentration gradient.

[0055] In some optional embodiments, the determination module is specifically used to compare all gas concentration values ​​with the excess concentration threshold, and screen out all excess points whose gas concentration values ​​are higher than the excess concentration threshold; for non-exceeding points whose gas concentration values ​​are not higher than the excess concentration threshold, use the interpolation method to calculate the gas concentration value of the non-exceeding point; based on the gas concentration values ​​of all exceeding points and all non-exceeding points, draw a three-dimensional isoconcentration line map; and determine the leakage level of the toxic gas based on the three-dimensional isoconcentration line map.

[0056] It should be noted that the functional modules in the embodiments of the present application can be integrated together to form an independent unit, for example, integrated in a processing unit, or each module can exist physically alone, or two or more modules can be integrated to form an independent unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, a server or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a disk or an optical disk.

[0057] The various variations and specific examples in the method provided in the embodiment of the present application are also applicable to a tunnel toxic gas detection device provided in the present embodiment. Through the above detailed description of the tunnel toxic gas detection method, those skilled in the art can clearly know the implementation method of the tunnel toxic gas detection device in the present embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0058] Figure 5 This is a structural block diagram of an electronic device 500 provided in an embodiment of the present application. Figure 5 As shown, the electronic device 500 includes a memory 501 , a processor 502 , and a communication bus 503 ; the memory 501 and the processor 502 are connected via the communication bus 503 .

[0059] The memory 501 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 501 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the tunnel toxic gas detection method provided in the above embodiment, etc.; the data storage area may store data involved in a tunnel toxic gas detection method provided in the above embodiment, etc.

[0060] The processor 502 may include one or more processing cores. The processor 502 calls the data stored in the memory 501 by running or executing the instructions, programs, code sets or instruction sets stored in the memory 501, and performs various functions and processes data of the present application. The processor 502 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor function can also be other, and the embodiments of the present application are not specifically limited.

[0061] The communication bus 503 may include a path to transmit information between the above components. The communication bus 503 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 503 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one double arrow is used in the diagram, but it does not mean that there is only one bus or one type of bus. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] The embodiment of the present application provides a tunnel poisonous gas detection system, the system comprises the plurality of gas sensor nodes and Figure 5 The electronic device shown. Figure 6 As shown, the plurality of gas sensor nodes include semiconductor sensor nodes 601, optical sensor nodes 602, and electrochemical sensor nodes 603. The following specifically describes the locations and types of various gas sensor nodes.

[0063] like Figure 6 , Figure 7 As shown, the semiconductor sensor node 601 is arranged on the inner surface layer of the tunnel lining concrete 604; a gas sensor based on SnO2 nanomaterials can be used, which responds to 50ppm methane (CH4), carbon monoxide (CO), and hydrogen sulfide (H2S) gas at an optimal working temperature of 0~40℃, with a response and recovery time of 10s and 22s respectively, an experimental detection limit of 50ppb, and a theoretical detection limit of 0.34ppb. It can achieve effective detection of 0.05-500ppm methane (CH4) and carbon monoxide (CO) gases. The sensor has high responsiveness, good selectivity and stability, low detection limit, and strong moisture resistance.

[0064] like Figure 6 , Figure 7 As shown, the optical sensor node 602 is arranged on the outer surface of the tunnel lining concrete 604, namely, the tunnel vault, the spandrels on both sides and the waist positions on both sides; an NDIR infrared gas sensor can be used, which can accurately detect carbon dioxide (CO2), sulfur dioxide (SO2), ammonia (NH3), and nitrogen dioxide (NO2) gases with a volume fraction of 0-5% at a temperature of 0-40°C. The maximum measurement error of the detection is less than ±0.12%, and the detection accuracy can reach 3%. It has the advantages of high precision, wide range, miniaturization, and good stability.

[0065] like Figure 6 , Figure 7 As shown, the electrochemical sensor node 603 is arranged in the cable trench 605 on any one side or both sides of the tunnel; a constant potential electrolytic gas sensor can be used, which can accurately detect hydrogen sulfide (H2S), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and nitrogen (N2) at room temperature, with a detection error of no more than ±0.1%, a gas response value of 256.3, and a response time distribution in the range of 5 to 50s. It has strong portability, good selectivity, high accuracy, fast measurement speed, long life, and strong environmental adaptability.

[0066] In this embodiment, the gas concentration data collected by each gas sensor node can be directly read and collected and converted by the cloud collection unit and the photoelectric conversion unit, and then the collected data information is stored in the high-speed storage unit.

[0067] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute a tunnel toxic gas detection method as provided in the above embodiment.

[0068] In this embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0069] The computer program in this embodiment includes a computer program for executing Figure 1 The program code of the method shown in the embodiment may include instructions corresponding to the steps of the method provided in the above embodiment. The computer program may be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network (such as the Internet, a local area network, a wide area network and / or a wireless network). The computer program may be executed entirely on the user's computer or as an independent software package.

[0070] In the embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0071] In addition, it is to be understood that relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprises," "comprising," or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or device.

[0072] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting poisonous gases in a tunnel, characterized in that: include: Obtaining concentration measurements of multiple toxic gases collected by multiple gas sensor nodes in the tunnel; Inputting the concentration measurement values ​​of the multiple toxic gases into a preset linear minimum mean square error estimation model, and outputting estimated values ​​of gas leakage source parameters; wherein the gas leakage source parameters include the location of the gas leakage source and the gas release rate; Inputting the estimated value of the gas leakage source parameter into a preset gas diffusion model, and outputting the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; The estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas are input into a preset finite element prediction model, and the prediction result of the excessive concentration of the toxic gas is output.

2. The method for detecting poisonous gas in a tunnel according to claim 1, characterized in that: The formula of the linear minimum mean square error estimation model is: ； The performance of the estimator is given by the error vector Determination, Error Vector The mean of the covariance matrix is ​​0. for: ； In the formula, represents a vector to be estimated, the vector to be estimated is composed of the position of the gas leakage source and the gas release rate. is a real matrix with dimension p×1, that is ; Represents the vector to be estimated An estimated value of Represents the vector to be estimated The mean of Represents the vector to be estimated The covariance matrix of z represents the observation vector, the observation vector z For a dimension N ×1 real matrix, that is ; D represents the observation matrix, the observation matrix D For a dimension N ×p real matrix, that is ; Represents the observation matrix D The transpose of Indicates that given an observation vector z and the vector to be estimated Under the condition that The expected value of the outer product of represents the error vector The transpose of v represents a zero mean, and the covariance matrix is The noise vector v is a one-dimensional N ×1 real matrix, that is , and v and θ Uncorrelated, jointly distributed Any; c represents a random variable.

3. The tunnel poisonous gas detection method according to claim 1 or 2, characterized in that: The formula of the gas diffusion model is: ； In the formula, , represents the coordinate position of the gas sensor node; , represents the coordinate position of the gas leakage source; Represents the gas sensor node The gas concentration value at time t; Represents the gas sensor node The gas leak source The Euclidean distance between Indicates the initial time when the gas leakage source releases gas; , represents the wind speed vector; Indicates the diffusion rate of the gas leakage source; Represents an estimate of the gas release rate from the gas leak source.

4. The tunnel poisonous gas detection method according to claim 1 or 2, characterized in that: The step of inputting the estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas into a preset finite element prediction model and outputting the prediction result of the excessive concentration of the toxic gas comprises: Based on the estimated values ​​of the gas leakage source parameters, the tunnel space is divided into a plurality of grid cells; Constructing a control equation of the finite element prediction model based on the gas diffusion parameter and the wind speed vector in the tunnel; Based on the estimated values ​​of the gas leakage source parameters, setting the initial conditions and boundary conditions of the finite element prediction model; Based on the initial conditions and the boundary conditions, solving the control equation on each grid unit to obtain the gas concentration distribution of the poisonous gas in the tunnel; The prediction result of the toxic gas exceeding the standard is determined based on the gas concentration distribution of the toxic gas in the tunnel and the exceeding concentration threshold of the toxic gas.

5. The tunnel poisonous gas detection method according to claim 4, characterized in that: The control equation is: ； In the formula, , represents the wind speed vector in the tunnel; Indicates the diffusion rate of the gas leakage source; Indicates the coordinates in the tunnel The gas concentration value at time t; Represents the concentration gradient.

6. The method for detecting poisonous gas in a tunnel according to claim 4, characterized in that: The determining of the prediction result of the toxic gas exceeding the standard based on the gas concentration distribution of the toxic gas in the tunnel and the exceeding concentration threshold of the toxic gas includes: Compare all gas concentration values ​​with the excessive concentration threshold, and screen out all excessive points where the gas concentration value is higher than the excessive concentration threshold; For a non-exceeding point whose gas concentration value is not higher than the exceeding concentration threshold, using an interpolation method to calculate the gas concentration value of the non-exceeding point; Draw a three-dimensional isoconcentration map based on the gas concentration values ​​of all exceeding points and all non-exceeding points; The leakage level of the poisonous gas is determined based on the three-dimensional isoconcentration line map.

7. A tunnel poisonous gas detection device, characterized in that: include: A concentration acquisition module is used to obtain the concentration measurement values ​​of various poisonous gases collected by multiple gas sensor nodes in the tunnel; A first model module, used to input the concentration measurement values ​​of the multiple toxic gases into a preset linear minimum mean square error estimation model, and output an estimated value of a gas leakage source parameter; wherein the gas leakage source parameter includes the location of the gas leakage source and the gas release rate; A second model module is used to input the estimated value of the gas leakage source parameter into a preset gas diffusion model and output the gas diffusion parameter; wherein the gas diffusion model is a static plume gas diffusion model based on turbulent diffusion, and the gas diffusion parameter includes the diffusion rate of the gas leakage source; The third model module is used to input the estimated value of the gas leakage source parameter, the gas diffusion parameter and the excessive concentration threshold of the toxic gas into a preset finite element prediction model, and output the prediction result of the excessive concentration of the toxic gas.

8. An electronic device, characterized in that: It comprises a memory and a processor; the memory stores a computer program that can be loaded by the processor and executes the tunnel toxic gas detection method as claimed in any one of claims 1 to 6.

9. A tunnel poisonous gas detection system, characterized in that: An electronic device comprising the plurality of gas sensor nodes and claim 8; Wherein, the plurality of gas sensor nodes include semiconductor sensor nodes, optical sensor nodes and electrochemical sensor nodes; The semiconductor sensor node is arranged on the inner surface layer of the lining concrete of the tunnel; The optical sensor node is arranged on the outer surface of the lining concrete of the tunnel; The electrochemical sensor nodes are arranged at the cable trench positions on both sides of the tunnel.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the tunnel poisonous gas detection method as claimed in any one of claims 1 to 6.

Images