A method for gas analysis based on optical technology

Abstract

The application discloses a kind of gas analysis methods based on optical technology, belong to optical gas detection technical field, this method includes: three-dimensional modeling to target area and division grid unit, initial concentration and environmental parameter are collected, diffusion equation is constructed to solve concentration at each time, generate prediction curve and calculate concentration evaluation value grading, dynamically allocate monitoring resources;The proportion of grid concentration grade duration is counted, high-risk unit and high-concentration core node are marked, and are integrated into high-concentration aggregation area, and potential area of diffusion source is screened and diffusion source unit is determined;High-concentration exposure unit is marked, the diffusion influence distance, source point and area average evaluation value are calculated, the risk evaluation value is obtained and graded early warning is carried out;The application realizes the global monitoring of gas concentration, accurate positioning of diffusion source and risk grading prevention and control, and improves the detection efficiency and control ability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optical gas detection, and particularly relates to a gas analysis method based on optical technology. BACKGROUND

[0002] Traditional gas detection adopts single light path or fixed multi-light path mode, can only reflect local concentration, and is difficult to accurately depict the concentration distribution of the target space; the positioning of the diffusion source depends on experience, and the error is large; the risk warning lacks correlation with the concentration dynamics and source characteristics, and the response is lagging, thereby the following technical problems are proposed:

[0003] Single detection point data is limited, and the traditional method can only obtain local concentration and cannot reflect the global distribution of the gas in the three-dimensional space, resulting in missed detection or misjudgment;

[0004] It is difficult to locate the diffusion source only through local concentration data, and the aggregation analysis based on the spatial and temporal characteristics of the grid concentration level is lacking, and the tracing accuracy is insufficient;

[0005] The risk warning does not integrate the diffusion influence range, source intensity and regional overall concentration characteristics, and it is difficult to quantify the global risk and realize the graded response, and therefore the application provides a gas analysis method based on optical technology. SUMMARY

[0006] The purpose of the application is to provide a gas analysis method based on optical technology to solve the problems proposed in the background.

[0007] To achieve the above purpose, the application provides the following technical scheme: a gas analysis method based on optical technology, comprising the following steps:

[0008] Step 1: three-dimensional modeling of the target area and division of grid units, collection of initial concentration and environmental parameters of the grid units, construction of diffusion equation, solution of grid unit concentration at each time, construction of grid unit gas concentration prediction change curve, calculation of concentration evaluation value and grading, and dynamic allocation of monitoring resources according to the concentration level;

[0009] Step 2: statistics of grid concentration level time length proportion, marking of high-risk units according to high concentration level proportion; building a sphere with the high-risk unit as the center, marking high concentration core nodes according to the risk aggregation number, and integrating the connected nodes into a high concentration aggregation area; calculating the regional aggregation intensity value, screening the potential area of the diffusion source, calculating the source point matching value of each grid unit in the potential area of the diffusion source, and determining the diffusion source unit;

[0010] Step 3: marking high concentration exposure units, calculating diffusion influence distance; calculating the average concentration evaluation value of the diffusion source and the target area, and comprehensively analyzing the risk evaluation value according to the above parameters, and performing warning according to the risk evaluation value.

[0011] Preferably, in step one, the specific process of solving the grid cell concentration at each time point is as follows:

[0012] Construct a three-dimensional space of the target region and divide the three-dimensional space of the target region into several cubic grid cells; collect the initial target gas concentration of each grid cell;

[0013] Monitor the environmental parameters of each grid cell;

[0014] Based on Fick's diffusion law and the continuity equation, the gas diffusion equation for each grid cell is constructed as follows: ;

[0015] in, This represents the rate of change of the target gas concentration over time. Represents the molecular diffusion term; Source and sink terms that vary with spatial location and time; This is the concentration gradient vector;

[0016] The symbol is a partial differential; c is the gas concentration; D is the gas diffusion coefficient; S is the Laplace operator; S is the gas source term; V is the wind speed vector; and V is the concentration gradient vector. t represents the three-dimensional spatial coordinates of the grid cell; t is the time variable.

[0017] Treating each grid cell as a computational node, the diffusion equations are transformed into a system of algebraic equations using the Galerkin method: ;

[0018] in, This is the quality matrix; The vector of the first derivative of concentration with respect to time; Here is the stiffness matrix; It is a concentration vector; Load vector;

[0019] The equation system is solved by explicit time integration, and the time step is preset to obtain the concentration value c(x,y,z,t) of each grid cell at each time step.

[0020] Preferably, in step one, the specific process for calculating the concentration evaluation value of the grid cell is as follows:

[0021] Set a preset prediction duration, and for each network unit, construct a two-dimensional rectangular coordinate system with the prediction duration as the horizontal axis and the target gas concentration as the vertical axis;

[0022] In the prediction duration, the target gas concentration values corresponding to each time step of the network cell are substituted into the rectangular coordinate system to obtain a plurality of time-target gas concentration data points, and the target gas concentration prediction change curve is obtained by connecting the data points in sequence according to the time sequence.

[0023] From the gas concentration prediction change curve, the concentration exceeding duration CT, the peak concentration CF, the gas concentration average CJ, the concentration increase rate CV, and the curve fluctuation value CB are extracted, and after normalization and dimensionless processing, the concentration evaluation value CP is obtained by using the formula: CP = CT × a1 + CF × a2 + CJ × a3 + CV × a4 + CB × a5, wherein a1, a2, a3, a4, and a5 are preset weight coefficients.

[0024] Preferably, in step one, the specific process of grading according to the concentration evaluation value and dynamically allocating monitoring resources according to the concentration level is as follows:

[0025] The gas concentration level is divided into high, medium, and low concentration levels, and each concentration level corresponds to a concentration evaluation value interval.

[0026] By matching the concentration evaluation value of the network unit in the prediction duration with the concentration evaluation value interval corresponding to each concentration level, the corresponding concentration level is output, and this concentration level is taken as the concentration level of the grid unit in the prediction duration.

[0027] Based on the concentration level of the grid unit, a differentiated resource allocation rule is formulated, and is arranged into a monitoring resource allocation rule table.

[0028] By substituting the concentration level of each grid unit into the monitoring resource allocation table for matching, the corresponding execution rule is output, and if the concentration level of the grid unit is updated, the original execution rule is immediately terminated, and the new rule is matched by substituting into the monitoring resource allocation table and executed.

[0029] Preferably, the specific rules in the monitoring resource allocation rule table are as follows:

[0030] High concentration level grid unit: the light path scanning period is controlled to be x1 times the initial period, and the light beam density degree is controlled to be y1 times the initial density; wherein x1 and y1 are preset control coefficients, x1 is less than 1, and y1 is greater than 1.

[0031] Medium concentration level grid unit: keep the normal detection mode;

[0032] Low concentration level grid unit: the light path scanning period is controlled to be x2 times the initial period, and the light beam density degree is controlled to be y2 times the initial density; wherein x2 and y2 are preset control coefficients, x2 is greater than 1, and y2 is less than 1.

[0033] Preferably, in step two, the specific process of marking the high-concentration core node is as follows:

[0034] A monitoring period of the target gas in the target area is preset;

[0035] For each grid cell, the concentration level of each monitoring in the monitoring period is recorded, and the cumulative duration of each level is counted;

[0036] A circular analysis diagram is constructed with the total duration of the monitoring period as the radius. In the diagram, three fan-shaped areas are divided according to the high, medium and low target gas concentration levels, respectively, to represent the proportion of the duration of the grid cell in the corresponding concentration level;

[0037] The high-concentration level proportion is extracted from the circular analysis diagram. If the high-concentration level proportion of the grid cell is greater than the corresponding preset threshold, the grid cell is marked as a high-risk cell;

[0038] For each high-risk cell, a high-risk sphere is constructed with the center point as the sphere center and a preset sphere radius. The number of high-risk cells in all grid cells within the sphere is counted and recorded as the risk aggregation number;

[0039] If the risk aggregation number of the high-risk cell is greater than the corresponding threshold, the risk cell is marked as a high-concentration core node.

[0040] Preferably, in step two, the specific process of screening the potential area of the diffusion source is as follows:

[0041] The grid cells corresponding to the high-concentration core nodes that are connected in space are integrated into a continuous spatial area, which is recorded as a high-concentration aggregation area;

[0042] For each high-concentration aggregation area, the following parameters are extracted:

[0043] The total number of high-concentration core nodes GN;

[0044] The average high-concentration proportion GB of all grids;

[0045] The proportion GS of the area to the total area of the target area;

[0046] After normalizing and de-dimensioning the above parameters, the aggregation intensity value IP is obtained using the formula: IP=GN×b1+GB×b2+GS×b3, where b1, b2 and b3 are preset weight coefficients;

[0047] From all high-concentration aggregation areas, the area with the aggregation intensity value is selected and recorded as the potential area of the diffusion source.

[0048] Preferably, in step two, the specific process of determining the diffusion source cell is as follows:

[0049] For each grid cell in the potential area of the diffusion source, the following parameters are extracted:

[0050] The time GT at which the concentration first reaches the high level during the monitoring period;

[0051] The average increase GV of the concentration from the low level to the high level;

[0052] After the above parameters are normalized and de-dimensioned, the source matching value IM is obtained by using the formula: IM = (1 / GT) x c1 + GV x c2, wherein c1 and c2 are preset weight coefficients;

[0053] The grid cell with the maximum source matching value is selected from the potential area of the diffusion source, and is recorded as the diffusion source cell.

[0054] Preferably, in step three, the specific process of calculating the risk evaluation value is as follows:

[0055] The grid cell that has appeared the high concentration level during the monitoring period is marked as a high concentration exposure cell; the three-dimensional Euclidean distance between each high concentration exposure cell and the diffusion source cell is calculated, and the mean value is calculated to obtain the diffusion influence distance KZ;

[0056] The average concentration evaluation value of the diffusion source cell during the monitoring period is calculated, and is recorded as the source average evaluation value YP;

[0057] The average concentration evaluation value of each network cell during the monitoring period is calculated, and then the average concentration evaluation value of each network cell is calculated to obtain the regional average evaluation value QP;

[0058] After the diffusion influence distance KZ, the source average evaluation value YP and the regional average evaluation value QP of the target area during the monitoring period are normalized and de-dimensioned, the risk evaluation value FPZ is obtained by using the formula: FPZ = KZ x f1 + YP x f2 + QP x f3, wherein f1, f2 and f3 are preset weight coefficients.

[0059] Preferably, in step three, the specific process of performing early warning according to the risk evaluation value is as follows:

[0060] If the risk evaluation value corresponding to the target area during the monitoring period is greater than the corresponding early warning threshold, a warning response is performed;

[0061] A plurality of risk warning levels are set, and each risk warning level corresponds to a risk evaluation value interval;

[0062] The risk evaluation value of the target area during the monitoring period is compared with the risk warning level corresponding to each risk warning level, and a corresponding warning response is performed.

[0063] Compared with the prior art, the present application has the following advantages:

[0064] (1) The gas analysis method based on optical technology realizes accurate characterization of the global spatio-temporal distribution of gas concentration by three-dimensional grid modeling and diffusion equation solving, combined with concentration evaluation value grading and dynamic resource allocation, solves the problem of "partial data one-sidedness" of traditional detection, and improves the detection accuracy of high-risk areas and the overall efficiency of the system through differentiated scheduling of optical path resources.

[0065] (2) The gas analysis method based on optical technology locates the diffusion source unit through multi-parameter comprehensive analysis based on the grid concentration level, including: high concentration level proportion, risk aggregation number, time when the high concentration level is first reached, etc., breaks through the error limitation of traditional experience tracing, realizes accurate positioning of the diffusion source, and provides a scientific basis for leakage point investigation.

[0066] (3) The gas analysis method based on optical technology calculates the risk evaluation value by integrating the diffusion influence distance, the average evaluation value of the source point and the average evaluation value of the region, constructs a multi-level early warning and linkage response mechanism, solves the problem of "monitoring and disposal disconnection", realizes the upgrade from "passive monitoring" to "active graded prevention and control", and greatly improves the control ability of gas leakage risk. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0069] Embodiment one;

[0070] Please refer to Figure 1 The present application provides a gas analysis method based on optical technology, comprising the following steps:

[0071] Step one: three-dimensional modeling of the target area and division of grid units, collection of initial concentration and environmental parameters of the grid units, establishment of diffusion equation to solve the concentration of each time grid unit, construction of grid unit gas concentration prediction change curve, calculation of concentration evaluation value and grading, dynamic allocation of monitoring resources according to the grade, the specific process is as follows:

[0072] The target area is scanned in three dimensions using a 3D scanner to obtain the three-dimensional coordinate data of each location in the target area, and the three-dimensional space of the target area is constructed based on this data; the target area is the area where gas monitoring is performed.

[0073] The three-dimensional space of the target area is uniformly divided into several cubic grid units; the initial gas concentration of each grid unit is collected through a distributed optical sensor array.

[0074] Huotong deployed miniature weather stations in the target area to monitor environmental parameters in each grid unit, including wind speed, wind direction, temperature, and air pressure.

[0075] Based on Fick's diffusion law and the continuity equation, the gas diffusion equation for each grid cell is constructed as follows: ;

[0076] in, This represents the rate of change of the target gas concentration over time. Represents the molecular diffusion term; Source and sink terms that vary with spatial location and time; This is the concentration gradient vector;

[0077] The symbol is a partial differential; c is the gas concentration; D is the gas diffusion coefficient; S is the Laplace operator; S is the gas source term; V is the wind speed vector; and V is the concentration gradient vector. t represents the three-dimensional spatial coordinates of the grid cell; t is the time variable.

[0078] Treating each grid cell as a computational node, the diffusion equations are transformed into a system of algebraic equations using the Galerkin method: ;

[0079] in, This is the quality matrix; The vector of the first derivative of concentration with respect to time; Here is the stiffness matrix; It is a concentration vector; Load vector;

[0080] The equation system is solved by explicit time integration and the time step is preset to obtain the concentration value c(x,y,z,t) of each grid cell at each time step.

[0081] Set a preset prediction duration, and for each network unit, construct a two-dimensional rectangular coordinate system with the prediction duration as the horizontal axis and the target gas concentration as the vertical axis;

[0082] In the prediction duration, the target gas concentration values corresponding to each time step of the network cell are substituted into the rectangular coordinate system to obtain a plurality of time-target gas concentration data points, and the data points are sequentially connected by a curve according to the time sequence to obtain a target gas concentration prediction change curve;

[0083] The length of the prediction duration is the interval duration of each monitoring of the target gas concentration in the target area by the distributed optical sensor array.

[0084] From the gas concentration prediction change curve, the concentration exceeding duration CT, the peak concentration CF, the gas concentration average CJ, the concentration rate CV, and the curve fluctuation value CB are extracted, and after normalization and dimensionless processing, the concentration evaluation value CP is obtained by the formula: CP=CT×a1+CF×a2+CJ×a3+CV×a4+CB×a5, wherein a1, a2, a3, a4, and a5 are preset weight coefficients.

[0085] Further, the concentration exceeding duration, the concentration rate, and the curve fluctuation value are obtained as follows:

[0086] In the target gas concentration prediction change curve, a preset concentration safety threshold line is drawn, and the total duration of the target gas concentration prediction change curve exceeding the corresponding threshold line is calculated, which is recorded as the concentration exceeding duration.

[0087] The peak concentration in the target gas concentration prediction change curve is obtained, and the average rate of increase of the gas concentration from the initial stage to the peak concentration is calculated to obtain the concentration rate.

[0088] The standard deviation value of the target gas concentration prediction change curve is calculated to obtain the curve fluctuation value.

[0089] The gas concentration level is divided into high, medium, and low concentration levels, and each concentration level corresponds to a concentration evaluation value interval.

[0090] The concentration evaluation value of the network unit in the prediction duration is matched with the concentration evaluation value interval corresponding to each concentration level, and the corresponding concentration level is output, and this concentration level is taken as the concentration level of the grid unit in the prediction duration.

[0091] Based on the concentration level of the grid unit, a differentiated resource allocation rule is formulated, and is arranged into a monitoring resource allocation rule table.

[0092] The concentration level of each grid unit is substituted into the monitoring resource allocation table for matching, and the corresponding execution rule is output. If the concentration level of the grid unit is updated, the original execution rule is terminated immediately, and the new rule is matched and executed by substituting into the monitoring resource allocation table.

[0093] Further, the specific rules in the monitoring resource allocation rule table are as follows:

[0094] High-concentration level grid unit: the optical path scanning period is regulated to x1 times of the initial period, and the light beam density degree is regulated to y1 times of the initial density; wherein x1 and y1 are preset regulation coefficients, x1 is less than 1, and y1 is greater than 1;

[0095] Medium-concentration level grid unit: keep the regular detection mode;

[0096] Low-concentration level grid unit: the optical path scanning period is regulated to x2 times of the initial period, and the light beam density degree is regulated to y2 times of the initial density; wherein x2 and y2 are preset regulation coefficients, x2 is greater than 1, and y2 is less than 1.

[0097] It should be noted that by three-dimensional modeling and grid division, the target area is discretized into quantifiable units, combined with the data of the distributed optical sensor and the micro weather station, the accurate correlation of gas concentration and environmental parameters is realized, the problem of "local data cannot reflect the global distribution" in traditional detection is solved, and fine-grained basic data is provided for subsequent diffusion analysis;

[0098] The diffusion equation constructed based on Fick's diffusion law, combined with the Galerkin method and explicit time integration, can dynamically predict the grid concentration at each time and generate a change curve, breaking through the limitation of "static detection cannot capture the spatio-temporal variation of concentration", and can predict the migration trend of high-concentration areas in advance, providing a theoretical basis for active prevention and control;

[0099] Through concentration evaluation value, the level is divided, and the optical path resources are dynamically allocated according to the level, which not only ensures the detection accuracy of high-risk areas, but also avoids the waste of resources in low-risk areas, solves the contradiction between "optical path scheduling and actual risk", and improves the overall efficiency of the system; At the same time, the dynamic updating mechanism of the monitoring resource allocation rule table can quickly adapt to the non-steady-state characteristics of gas diffusion, avoid detection misplacement caused by mechanical delay, ensure that the optical path coverage is always synchronized with the high-concentration area, and improve the detection accuracy.

[0100] Step two: calculate the grid concentration level duration ratio, mark the high-risk unit according to the high-concentration level ratio, build a sphere with the high-risk unit as the center, mark the high-concentration core node according to the risk aggregation number, integrate the connected nodes into a high-concentration aggregation area; calculate the area aggregation intensity value, screen the potential area of the diffusion source, calculate the source point matching value of each grid unit in the potential area of the diffusion source, and determine the diffusion source unit, the specific process is as follows:

[0101] The preset monitoring period of the target gas in the target area includes a plurality of interval time lengths for monitoring the target gas concentration in the target area by the distributed optical sensor array;

[0102] For each grid cell, record the concentration level of each monitoring in the monitoring duration, and count the cumulative duration of each level;

[0103] With the total duration of the monitoring period as the radius, a circular analysis diagram is constructed. In the diagram, according to the high, medium and low target gas concentration levels, three sector regions are divided, respectively, to represent the proportion of the duration of the grid cell in the corresponding concentration level;

[0104] From the circular analysis diagram, the high concentration level proportion is extracted, and a preset high concentration level proportion threshold is set. If the high concentration level proportion of the grid cell is greater than the corresponding preset threshold, the grid cell is marked as a high-risk cell;

[0105] For each high-risk cell, a preset spherical radius is constructed with the center point as the spherical center to construct a high-risk sphere. The number of high-risk cells in all grid cells within the sphere is counted and recorded as the risk aggregation number;

[0106] A risk aggregation number threshold is preset. If the risk aggregation number of the high-risk cell is greater than the corresponding threshold, the risk cell is marked as a high-concentration core node;

[0107] The grid cells corresponding to the high-concentration core nodes connected in space are integrated into a continuous spatial area, which is recorded as a high-concentration aggregation area;

[0108] For each high-concentration aggregation area, the following parameters are extracted:

[0109] The total number of high-concentration core nodes GN reflects the aggregation size;

[0110] The average high-concentration proportion GB of all grids reflects the overall concentration level;

[0111] The proportion GS of the area of the region to the total area of the target region reflects the diffusion range;

[0112] After normalizing and de-dimensioning the above parameters, the aggregation intensity value IP is obtained using the formula: IP=GN×b1+GB×b2+GS×b3, where b1, b2, and b3 are preset weight coefficients;

[0113] From all high-concentration aggregation areas, the area with the aggregation intensity value is selected and recorded as the potential diffusion source area;

[0114] For each grid cell in the potential diffusion source area, the following parameters are extracted:

[0115] The time GT when the high concentration level is first reached in the monitoring period; The shorter the time to reach the high concentration level, the closer to the diffusion source;

[0116] The average speed GV of the concentration increasing from the low level to the high level; The greater the release intensity, the faster the speed;

[0117] After the above parameters are normalized and de-dimensioned, a source point matching value IM is obtained by using the formula: IM = (1 / GT) x c1 + GV x c2, where c1 and c2 are preset weight coefficients;

[0118] A grid cell with the largest source point matching value is selected from the potential diffusion source area as a diffusion source cell.

[0119] It should be noted that by statistically analyzing the grid concentration level duration ratio and constructing a circular analysis diagram, the abstract concentration level is converted into an intuitive sector ratio, achieving visualized assessment of the grid risk, which is easier to identify the grid in a high concentration state for a long time than a simple numerical judgment, and provides a clear basis for marking high-risk cells.

[0120] A sphere is constructed with the high-risk cell as the center, and the risk aggregation number is counted, and a high-concentration core node is screened through spatial aggregation, solving the problem of "difficulty in locating the source when a single high-risk cell exists in isolation", and using the spatial correlation of gas diffusion to lock the potential leakage area, improving the pertinence of diffusion source positioning;

[0121] The connected core nodes are integrated to form a high-concentration aggregation area, and the aggregation intensity value is used to quantify the area characteristics, achieving an upgrade from "dispersed grid" to "continuous area" analysis, avoiding misjudgment of isolated high-concentration points, and narrowing the diffusion source investigation range;

[0122] In the potential area, the source point matching value is calculated in combination with the time when the high concentration is first reached and the concentration increase rate, and the diffusion rule that "the closer to the source, the earlier the high concentration appears and the faster the increase" is used to accurately locate the diffusion source cell, which is especially suitable for gas leakage tracing in complex environments in industrial scenarios, and is convenient for subsequent quantitative risk concentration of the target area.

[0123] Step three: mark high-concentration exposure cells and calculate diffusion influence distance; calculate the average concentration evaluation value of the diffusion source and the target area, and obtain the risk evaluation value based on the comprehensive analysis of the above parameters, and give a warning according to the risk evaluation value, the specific process is as follows:

[0124] Grid cells that have appeared in high concentration levels during the monitoring period are marked as high-concentration exposure cells; the three-dimensional Euclidean distance between each high-concentration exposure cell and the diffusion source cell is calculated, and the average value is calculated to obtain the diffusion influence distance KZ; wherein the diffusion influence distance reflects the average range of the spread of high-concentration gas from the diffusion source, and the larger the value, the wider the diffusion influence, and the larger the potential risk coverage area;

[0125] Calculate the average concentration evaluation value of the diffusion source unit in the monitoring period, denoted as the source point average evaluation value YP; wherein the source point average evaluation value reflects the average risk level of the diffusion source unit in the entire monitoring period, and the higher the value, the higher the comprehensive risk of the concentration characteristics of the diffusion source itself;

[0126] Calculate the average concentration evaluation value of each network unit in the monitoring period, and then calculate the average concentration evaluation value of each network unit by mean value to obtain the regional average evaluation value QP; wherein the average risk level of the target region as a whole, and the higher the value, the higher the comprehensive concentration risk of each grid in the region;

[0127] By normalizing and de-dimensioning the corresponding diffusion influence distance KZ, the source point average evaluation value YP and the regional average evaluation value QP of the target region in the monitoring period, the risk evaluation value FPZ is obtained by using the formula: FPZ=KZ×f1+YP×f2+QP×f3, wherein f1, f2 and f3 are preset weight coefficients;

[0128] A preset warning risk evaluation threshold is set, and if the corresponding risk evaluation value of the target region in the monitoring period is greater than the corresponding warning threshold, a warning response is executed;

[0129] A plurality of risk warning levels are set, and each risk warning level corresponds to a risk evaluation value interval;

[0130] By comparing the risk evaluation value of the target region in the monitoring period with the risk warning level corresponding to each risk warning level, a corresponding warning response is executed.

[0131] It should be noted that by marking the high concentration exposure unit and calculating the diffusion influence distance, the spread range of high concentration gas is quantified; the source point average evaluation value and the regional average evaluation value evaluate the concentration characteristics from two dimensions of "diffusion source risk" and "global overall risk"; the risk evaluation value calculated based on the above parameters quantifies the risk degree of the target region;

[0132] Based on the risk evaluation value, a plurality of warning levels are divided and corresponding responses are executed, realizing a closed loop of "risk grading-precise disposal", solving the problem of resource waste or insufficient disposal caused by "one-size-fits-all" response in traditional warning, and improving the efficiency of risk control;

[0133] The entire warning process and steps are deeply linked with the concentration evaluation of step one and the diffusion source positioning of step two: the calculation of the risk evaluation value depends on the concentration evaluation value of step one and the diffusion source unit determined in step two, ensuring the coherence and scientificity of the warning parameters, making the risk assessment upgrade from "isolated data" to "full-process correlation analysis", and greatly improving the reliability of the warning.

[0134] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method of gas analysis based on optical techniques, characterized in that: The method comprises the following steps: Step one: three-dimensional modeling of the target area and division of grid cells, collection of initial concentration and environmental parameters of the grid cells, construction of diffusion equation, solution of grid cell concentration at each time, construction of grid cell gas concentration prediction change curve, calculation of concentration evaluation value and grading, dynamic allocation of monitoring resources according to the concentration grade; The specific process of solving the concentration of each grid cell at each time is as follows: Construct a three-dimensional space of the target area, and uniformly divide the three-dimensional space of the target area into a plurality of cubic grid cells; collect the initial time target gas concentration of each grid cell; Monitor the environmental parameters of each grid cell; Based on Fick's diffusion law and continuity equation, the gas diffusion equation of each grid element is constructed: ; wherein, represents the rate of change of the target gas concentration with time; represents the molecular diffusion term; is a source / sink term that varies with spatial location and time; is the concentration gradient vector; is the partial differential symbol; c is the gas concentration; D is the gas diffusion coefficient; is the Laplace operator; S is the gas source term; V is the wind velocity vector; is the three-dimensional spatial coordinate of the grid cell; t is the time variable; Each grid cell is taken as a computing node, and the diffusion equation is converted into algebraic equations by the Galerkin method: ; wherein, is a mass matrix; is a first derivative vector of concentration with respect to time; is a stiffness matrix; is a concentration vector; is a load vector; Solve the equation set by using explicit time integration method, and preset the time step to obtain the concentration value c(x, y, z, t) of each grid cell at each time step corresponding to the time step; Step two: statistics of grid concentration grade time length proportion, marking of high-risk units according to high concentration grade proportion, construction of spheres with high-risk units as centers, marking of high concentration core nodes according to risk aggregation number, integration of connected nodes into high concentration aggregation area, calculation of regional aggregation intensity value, screening of potential diffusion source area, calculation of source point matching value of each grid cell in the potential diffusion source area, and determination of diffusion source unit; Step three: marking of high concentration exposure unit, calculation of diffusion influence distance, calculation of average concentration evaluation value of diffusion source and target area, analysis based on diffusion influence distance and average concentration evaluation value, obtaining of risk evaluation value, and prewarning according to the risk evaluation value; The specific process of calculating the risk evaluation value is as follows: Mark the grid cells that appear in the high concentration grade in the monitoring period as high concentration exposure units; calculate the three-dimensional Euclidean distance between each high concentration exposure unit and the diffusion source unit, and perform mean value calculation to obtain the diffusion influence distance KZ; Calculate the average concentration evaluation value of the diffusion source unit in the monitoring period, denoted as the source point average evaluation value YP; Calculate the average concentration evaluation value of each network unit in the monitoring period, and then perform mean value calculation on the average concentration evaluation value of each network unit to obtain the regional average evaluation value QP; After normalizing and de-dimensioning the diffusion influence distance KZ, the source point average evaluation value YP and the regional average evaluation value QP of the target area in the monitoring period, the risk evaluation value FPZ is obtained by using the formula: FPZ=KZ×f1+YP×f2+QP×f3, wherein f1, f2 and f3 are preset weight coefficients.

2. The method for gas analysis based on optical technology according to claim 1, characterized in that: In step one, the specific process of calculating the concentration evaluation value of the grid cell is as follows: Set a preset prediction time, and for each network unit, construct a two-dimensional rectangular coordinate system with the prediction time as the horizontal coordinate and the target gas concentration as the vertical coordinate; In the prediction time, the target gas concentration value of each time step of the network unit is substituted into the rectangular coordinate system to obtain a plurality of time-target gas concentration data points, and the target gas concentration prediction change curve is obtained by sequentially connecting the data points in time order; From the gas concentration prediction change curve, the concentration exceeding time CT, the peak concentration CF, the gas concentration average CJ, the concentration increase rate CV and the curve fluctuation value CB are extracted. After normalization and de-dimensioning, the concentration evaluation value CP is obtained by using the formula: CP=CT×a1+CF×a2+CJ×a3+CV×a4+CB×a5, wherein a1, a2, a3, a4, and a5 are preset weight coefficients.

3. The method of gas analysis based on optical technology according to claim 2, characterized in that: In step one, the specific process of grading according to the concentration evaluation value and dynamically allocating monitoring resources according to the concentration level is as follows: The gas concentration level is divided into three concentration levels: high, medium, and low, and each concentration level corresponds to a concentration evaluation value interval. The concentration evaluation value of the network unit in the prediction duration is matched with the concentration evaluation value interval corresponding to each concentration level, and the corresponding concentration level is output, which is taken as the concentration level of the grid unit in the prediction duration. Based on the concentration level of the grid unit, a differentiated resource allocation rule is formulated, which is arranged into a monitoring resource allocation rule table. The concentration level of each grid unit is substituted into the monitoring resource allocation rule table for matching, and the corresponding execution rule is output. If the concentration level of the grid unit is updated, the original execution rule is terminated immediately, and the new rule is matched and executed by substituting it into the monitoring resource allocation rule table.

4. The method of gas analysis based on optical technology according to claim 3, characterized in that: The specific rules in the monitoring resource allocation rule table are as follows: For a high-concentration-level grid unit, the optical path scanning period is controlled to be x1 times the initial period, and the beam density degree is controlled to be y1 times the initial density, wherein x1 and y1 are preset control coefficients, x1 is less than 1, and y1 is greater than 1. For a medium-concentration-level grid unit, the normal detection mode is maintained. For a low-concentration-level grid unit, the optical path scanning period is controlled to be x2 times the initial period, and the beam density degree is controlled to be y2 times the initial density, wherein x2 and y2 are preset control coefficients, x2 is greater than 1, and y2 is less than 1.

5. The method of gas analysis based on optical technology according to claim 4, characterized in that: In step two, the specific process of marking high-concentration core nodes is as follows: A monitoring period of a target gas in a target area is preset. For each grid unit, the concentration level of each monitoring is recorded within the monitoring period, and the cumulative duration of each level is counted. A circular analysis diagram is constructed with the total duration of the monitoring period as the radius. The diagram is divided into three sectors according to the high, medium, and low concentration levels of the target gas, representing the proportion of the duration of the grid unit in the corresponding concentration level. From the circular analysis diagram, the high-concentration-level proportion is extracted. If the high-concentration-level proportion of a grid unit is greater than a corresponding preset threshold, the grid unit is marked as a high-risk unit. For each high-risk unit, a high-risk sphere is constructed with the center point as the sphere center and a preset sphere radius. The number of high-risk units in all grid units within the sphere is counted and recorded as the risk aggregation number. If the risk aggregation number of a high-risk unit is greater than a corresponding threshold, the high-risk unit is marked as a high-concentration core node.

6. The method of gas analysis based on optical technology according to claim 5, characterized in that: In step two, the specific process of screening potential areas of diffusion sources is as follows: The grid units corresponding to the high-concentration core nodes that are connected in space are integrated into continuous spatial areas, which are recorded as high-concentration aggregation areas. For each high-concentration aggregation area, the following parameters are extracted: The total number of high-concentration core nodes GN; The average high-concentration proportion GB of all grids; The proportion GS of the area to the total area of the target area; After the above parameters are normalized and de-dimensioned, the aggregation intensity value IP is obtained by using the formula: IP=GN×b1+GB×b2+GS×b3, wherein b1, b2 and b3 are preset weight coefficients; A region with the maximum aggregation intensity value is selected from all high-concentration aggregation regions as a potential diffusion source region.

7. The method of gas analysis based on optical technology according to claim 6, characterized in that: In step two, the specific process of determining the diffusion source unit is as follows: For each grid unit in the potential diffusion source region, the following parameters are extracted: the time GT at which the concentration first reaches the high-concentration level during the monitoring period; the average increasing speed GV of the concentration from the low level to the high level; After the above parameters are normalized and de-dimensioned, the source point matching value IM is obtained by using the formula: IM=(1 / GT)×c1+GV×c2, wherein c1 and c2 are preset weight coefficients; A grid unit with the maximum source point matching value is selected from the potential diffusion source region as the diffusion source unit.

8. The method of gas analysis based on optical technology according to claim 7, characterized in that: In step three, the specific process of performing early warning according to the risk evaluation value is as follows: If the risk evaluation value of the target region corresponding to the monitoring period is greater than the corresponding early warning threshold, a warning response is performed; a plurality of risk early warning levels are set, each risk early warning level corresponding to a risk evaluation value interval; the risk evaluation value of the target region corresponding to the monitoring period is compared with the risk early warning levels corresponding to the risk early warning levels, and a corresponding early warning response is performed.

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