Regional risk prediction method and system based on toxic gas diffusion analysis

Abstract

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

Description

Technical Field

[0001] The present invention relates to the field of toxic gas risk prediction, and in particular to a regional risk prediction method and system based on toxic gas diffusion analysis. Background Art

[0002] The regional risk prediction method based on toxic gas diffusion analysis mainly simulates the diffusion process after the toxic gas leak, calculates the gas concentration in each region, and then predicts the risk level. This method focuses on quantitative analysis, inputs parameters such as accident source intensity, meteorological data, topography and geomorphology into the atmospheric diffusion model, predicts the gas diffusion range and concentration distribution, and combines factors such as population density and environmental sensitive points to comprehensively predict the toxic gas exposure risks faced by each region, providing a scientific basis for the formulation of targeted risk prevention and control measures and emergency plans, ensuring that risks are effectively reduced in sudden environmental events and protecting people's lives and property.

[0003] The existing regional risk prediction methods based on toxic gas diffusion analysis have certain limitations. For example, the initial parameters of the diffusion model for actual scenarios cannot be directly measured and obtained; the real-time data integration capability is weak, which affects the timeliness of the prediction results; it is highly dependent on the initial parameters of the gas leakage source, and parameter errors can easily amplify prediction uncertainties; in addition, the prediction results lack specificity in guiding specific emergency measures and are difficult to meet diverse emergency needs. It is urgent to optimize the methods and improve the accuracy in combination with actual application scenarios. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for regional risk prediction based on toxic gas diffusion analysis to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, one aspect of the present invention provides a regional risk prediction method based on toxic gas diffusion analysis, which adopts a method for predicting the full spatiotemporal impact range of leakage based on an inversion algorithm when there are enough sensors monitoring the concentration of toxic gases, and comprises the following steps:

[0006] Step S1, obtaining real-time gas concentration data;

[0007] Step S2, inputting real-time meteorological parameters and using a gas diffusion model to calculate the predicted concentration value at the location of the gas sensor;

[0008] Step S3, using the Grey Wolf Optimization Algorithm to calculate source item parameters, repeatedly iterating steps S2 and S3 until convergence conditions are met; the source item parameters include leakage three-dimensional position, leakage rate and leakage time;

[0009] Step S4, determining the impact range after 1 hour based on the gas diffusion model and the toxicity threshold of hazardous chemicals;

[0010] Step S5, according to the impact range, identify the leakage disaster risk source, mainly including the number of people in the affected area after 1 hour, the road construction level in the affected area, the road congestion in the affected area, the number of emergency shelters in the affected area, and the number of major hazard sources in the affected area;

[0011] Step S6, based on the risk source identification results and combined with expert knowledge, establish a regional hazardous chemical leakage disaster risk prediction index system;

[0012] Step S7, experts weight and score the regional hazardous chemicals leakage disaster dynamic risk prediction index system, and the system constructs a judgment matrix based on the weight calculation to build an AHP risk prediction model;

[0013] Step S8, assigning a score to each prediction indicator according to the influence range of the gas diffusion model;

[0014] Step S9, calculating the risk values ​​of different time periods in the future according to the AHP risk prediction model and the integrated gas diffusion model;

[0015] Step S10, based on the real-time predicted risk value of each item, analyze the main risks in different time periods and propose disposal measures.

[0016] Furthermore, the meteorological parameters in step S1 include emission parameters, expansion parameters, and wind speed, among which the horizontal diffusion parameter The calculation formula is as follows:

[0017] ;

[0018] The vertical diffusion parameters are calculated based on the Briggs equation according to the atmospheric stability and the downwind distance x. , the formula is as follows:

[0019] ;

[0020] Where x is the downwind distance, t is the time, and Γ is the vertical temperature lapse rate. The calculation formula for Γ is as follows:

[0021] ;

[0022] Where g is the acceleration due to gravity, is the specific heat capacity of air at constant pressure, The temperature is decreasing. It's the height difference.

[0023] Furthermore, the gas diffusion model is a Gaussian plume model, and the formula is:

[0024] ;

[0025] Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, z0 is the height of the leakage location, is the horizontal diffusion parameter, is the vertical diffusion parameter, and C(x, y, z, t) is the toxic gas concentration at each location at each time.

[0026] Furthermore, the objective function of the gray wolf optimization algorithm in step S3 is expressed as follows:

[0027] ;

[0028] in is the calculated value, is the observed value, M is the total number of monitored sensors, x0 is the horizontal coordinate of the leak source location, y0 is the vertical coordinate of the leak source location, z0 is the height coordinate of the leak source location, Q is the leak rate, t is the time, and the tracing goal is to use the gray wolf optimization algorithm to find the leak source. Smallest The goal of tracing is to use the gray wolf optimization algorithm to find Smallest , the gray wolf optimization algorithm comprises the following steps:

[0029] Step S301, initialize the population number and randomly initialize the position of the wolf pack, ,in , N is the size of the wolf pack;

[0030] Step S302, for each wolf's position , calculate its fitness value ;

[0031] Step S303, according to the fitness value, select the best three solutions as alpha, beta and delta wolves, where is the optimal solution location, is the position of the second best solution, is the location of the third optimal solution;

[0032] Step S304, calculate coefficients A and C, the formula is as follows:

[0033] ;

[0034] ;

[0035] where a decreases linearly from 2 to 0, is a random number in [0, 1], is a random number in [0, 1];

[0036] Step S305: for each wolf , and update its position according to the following formula:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] in , and is the random value of coefficient A in step S304, , and is the random value of coefficient C in step S304;

[0045] Step S306, check the boundary to ensure the updated wolf position Do not exceed the definition domain of the problem;

[0046] Step S307, check whether the maximum number of iterations is reached or the predetermined fitness threshold is reached to obtain the global minimum value of the objective function; otherwise, return to step S302 and repeat the above steps S302 to S307.

[0047] Furthermore, step S4 includes the following steps:

[0048] Step S401, determining the third-level toxicity threshold of the leaked hazardous chemicals;

[0049] Step S402, calculating the three-level threshold concentration contour lines;

[0050] Step S403, determining the geodetic coordinates of the leakage source;

[0051] Step S404, mapping the three-level concentration contour lines to the geodetic coordinate system in combination with the wind direction information;

[0052] Step S405: determine the impact range on the map.

[0053] Furthermore, the calculation formula of the three-level threshold concentration contour line in step S402 is:

[0054] ;

[0055] Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, is the horizontal diffusion parameter, is the vertical diffusion parameter .

[0056] Furthermore, the regional hazardous chemicals leakage disaster risk factors in the regional hazardous chemicals leakage disaster risk prediction index system include source factors, environmental factors, social factors and emergency response capabilities. Combined with the identification and cause analysis of safety factors, secondary indicators are set up to establish a safety evaluation hierarchy.

[0057] Further, step S7 includes the following steps:

[0058] Step S701, construct a judgment matrix ;

[0059] Step S702, performing hierarchical single sorting and consistency check;

[0060] The weight value is calculated according to the square root method. The calculation formula is as follows:

[0061] Calculate the product of each row of the matrix using the following formula:

[0062] ;

[0063] where a ij is the element of the judgment matrix in step S701;

[0064] calculate The nth root of is as follows:

[0065] ;

[0066] Calculate vector normalization, the formula is as follows:

[0067] ;

[0068] W i =[W1,W2,…,W n ] T is the eigenvector, and the maximum eigenroot is calculated based on the eigenvector. The calculation is as follows:

[0069] ;

[0070] Check the consistency of the judgment matrix. The formula is as follows:

[0071] ;

[0072] When CI = 0, it means that the matrix is ​​completely consistent; the larger the CI, the worse the consistency of the matrix; the random consistency ratio CR of the judgment matrix is ​​used to indicate whether the judgment matrix has a satisfactory consistency result, which is calculated as follows:

[0073] ;

[0074] When CR < 0.10, the judgment matrix result is good and the consistency is strong; when CR > 0.10, the matrix needs to be adjusted until the consistency result is satisfactory;

[0075] Step S703, performing total sorting and consistency check on the levels;

[0076] Step S704, determining the evaluation level of each indicator to form a risk evaluation set;

[0077] Step S705, calculate the degree of membership of each factor to its upper parent node, and obtain the fuzzy relationship matrix R between the risk assessment set and the factor index set, which is expressed as the following formula:

[0078] ;

[0079] Step S706, calculate the multi-level comprehensive evaluation set B, the calculation method is to transform the feature vector W i The calculation formula is as follows:

[0080] ;

[0081] in is an element of the multi-level comprehensive evaluation set B.

[0082] Furthermore, in the case of continuous leakage, it is believed that the scope of influence of the leakage diffusion has entered a dynamic stable state, and its scope of influence will not change significantly over time. The spatial position concentration calculation formula of the method is:

[0083] ;

[0084] Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, z0 is the height of the leakage location, is the horizontal diffusion parameter, is the vertical diffusion parameter.

[0085] The source item parameters include the two-dimensional leakage position and leakage rate. The plane coordinates of the leakage position and the leakage rate calculation formula are:

[0086] ;

[0087] in is the calculated value, is the observed value, M is the total number of monitored sensors, x0 is the horizontal coordinate of the leakage source position, y0 is the vertical coordinate of the leakage source position, and Q is the leakage rate.

[0088] Another aspect of the present invention provides a regional risk prediction system based on toxic gas diffusion analysis, including a gas concentration module, a meteorological parameter module, a source parameter module, an impact range module, a risk source identification module, a prediction index module, a prediction model module, a scoring assignment module, a risk calculation module, and a disposal suggestion module, wherein:

[0089] The gas concentration module is used to obtain real-time gas concentration data;

[0090] The meteorological parameter module is used to input real-time meteorological parameters and calculate the predicted concentration value at the location of the gas sensor using the gas diffusion model formula;

[0091] The source item parameter module is used to calculate the source item parameters by iterative convergence using the Grey Wolf optimization algorithm; the source item parameters include the leakage three-dimensional position, leakage rate and leakage time;

[0092] The impact range module is used to determine the impact range after one hour based on the gas diffusion model and the toxicity threshold of hazardous chemicals;

[0093] The risk source identification module is used to identify the risk sources of leakage disasters according to the impact range, mainly including the number of people in the affected area after 1 hour, the road construction level in the affected area, the road congestion in the affected area, the number of emergency shelters in the affected area, and the number of major hazard sources in the affected area;

[0094] The prediction index module is used to establish a regional hazardous chemical leakage disaster risk prediction index system based on risk source identification results and expert knowledge;

[0095] The prediction model module is used by experts to weight and score the dynamic risk prediction index system of regional hazardous chemical leakage disasters. The system constructs a judgment matrix based on the weight calculation and builds an AHP risk prediction model.

[0096] The scoring module is used to score and assign values ​​to each prediction indicator according to the influence range of the gas diffusion model;

[0097] The risk calculation module is used to calculate the risk values ​​of different time periods in the future based on the AHP risk prediction model and the integrated gas diffusion model;

[0098] The disposal suggestion module is used to analyze the main risks in different time periods and propose disposal measures based on the real-time predicted risk value of each item.

[0099] Compared with the prior art, the present system and method have the following advantages:

[0100] 1. In order to improve the calculation accuracy of the leakage diffusion model, the present invention adopts the Gray Wolf optimization algorithm based on the real-time gas sensor and meteorological sensor monitoring data to inversely trace the source item parameters of the leakage source. The Gray Wolf optimization algorithm is a meta-heuristic algorithm with strong global search capabilities, low computational complexity, fast convergence speed, and can quickly and accurately calculate the tracing results, thereby improving the efficiency of emergency rescue.

[0101] 2. On the basis of the source tracing prediction of the leakage source, a regional risk prediction method based on toxic gas diffusion analysis is proposed. Based on the tracing calculation results, the gas diffusion model is used to calculate the spatiotemporal impact range of the toxic gas diffusion evolution by comprehensively considering factors such as gas properties, meteorological conditions, and terrain characteristics. The AHP method is used to predict and evaluate risks. The AHP method uses hierarchical analysis to comprehensively consider multiple factors and multiple standards in risk prediction and evaluation, making the prediction and evaluation results more comprehensive and objective. The AHP method is easy to operate, and the results are intuitive, which is easy for decision makers to understand and adopt, thereby improving the accuracy of prediction and evaluation, providing a scientific basis for risk management, and ultimately realizing the practicality and popularization of the prediction system.

[0102] 3. The present invention improves prediction accuracy by comprehensively considering factors such as gas properties, meteorological conditions, and terrain characteristics, provides a scientific basis for risk management, and ultimately realizes the practicality and popularization of the prediction system. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 This is a flow chart of a regional risk prediction method based on toxic gas diffusion analysis.

[0104] Figure 2 This is a schematic diagram of the calculation process of the full-time and space impact range of the leakage based on the inversion algorithm.

[0105] Figure 3 This is a schematic diagram of the calculation process of the continuous leakage impact range based on the inversion algorithm. DETAILED DESCRIPTION

[0106] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0107] like Figure 1 Shown is a flow chart of the method of the present invention. An embodiment of the present invention provides a regional risk prediction method based on toxic gas diffusion analysis.

[0108] When there are enough effective monitoring field sensors deployed, that is, when there are enough sensors that can detect the concentration of toxic gases, the method of leak full-time and space impact range based on inversion algorithm can be used. The specific steps are as follows:

[0109] Step S1, access real-time meteorological sensor data to obtain real-time gas concentration data.

[0110] The sensor data accessed include:

[0111] (a) Gas concentration data , PID and electrochemical sensors can be used.

[0112] (b) Meteorological data, including wind speed U and wind direction WD.

[0113] (c) Positioning data and timestamp: It is necessary to know the GPS location data of each monitoring sensor.

[0114] Sensors can access transmission networks using ZigBee, Wi-Fi, Bluetooth, LoRa, and NB-IoT. The ZigBee protocol is suitable for low-power, low-data-transmission-rate sensor networks and is suitable for large-scale deployment. LoRa and NB-IoT are suitable for long-distance, low-power IoT applications, especially in wide-area network coverage scenarios. Sensor data formats support OPC, ModBus, MQTT, and WebSocket.

[0115] Determine the emission parameters of the leakage source, including the toxic gas emission rate Q of the pollution source, in kg / s, and the effective emission height z0 of the pollution source, in m.

[0116] According to the atmospheric stability, the vertical temperature lapse rate is calculated. First, the vertical temperature lapse rate Γ needs to be determined. Γ is a coefficient related to the atmospheric stability. The calculation formula is as follows:

[0117] ;

[0118] Where g is the acceleration due to gravity, is the specific heat capacity of air at constant pressure, The temperature is decreasing. is the height difference, Γ, which typically ranges from 0.75 (very unstable) to 0.15 (very stable).

[0119] Then calculate the horizontal diffusion parameter and vertical diffusion parameter. According to the atmospheric stability and wind speed U, the Briggs equation is used to calculate. As time goes by, the diffusion range of pollutants will increase, so the horizontal diffusion parameter The calculation formula is as follows

[0120] ;

[0121] x is the downwind distance in kilometers. t is the time in hours.

[0122] The vertical diffusion parameters are calculated based on the Briggs equation according to the atmospheric stability and the downwind distance x. , unit is m.

[0123] ;

[0124] x is the downwind distance in kilometers. t is the time in hours.

[0125] Step S2, input real-time meteorological parameters, and use the Gaussian plume model to calculate the predicted concentration value at the location of the gas sensor.

[0126] Substitute the emission parameters, diffusion parameters, wind speed and calculation point coordinates determined above into the following Gaussian plume model equation:

[0127] ;

[0128] Calculate the toxic gas concentration C(x,y,z) at each location in μg / m 3 X is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the elevation coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, and z0 is the height of the leakage source.

[0129] Step S3, using the Grey Wolf optimization algorithm to perform source parameter inversion calculation. The source parameter includes the leakage three-dimensional position, leakage rate, and leakage time. Repeat steps S2 and S3 until the convergence condition is met.

[0130] The method for tracing the source of poisonous gas leakage based on the optimization algorithm and the gas diffusion model first needs to construct the objective function. and calculated values The variance of is constructed as the objective function of the optimization problem. Assuming the number of monitoring points is M, the objective function is expressed as follows:

[0131] .

[0132] Calculated Value The observed value can be calculated using the Gaussian plume model equation according to the coordinates of the monitoring point (x, y, z, t). The gas diffusion model simulation method is adopted, and the sensor accuracy and Gaussian white noise constraints are increased to be closer to the real data. M is the total number of monitored sensors, x0 is the horizontal coordinate of the leakage source position, y0 is the vertical coordinate of the leakage source position, z0 is the height coordinate of the leakage source position, Q is the leakage rate, and t is the time.

[0133] Input parameters of the objective function is a 5-dimensional vector. The tracing goal is to use the gray wolf optimization algorithm to find Smallest The specific steps are as follows:

[0134] Step S301, randomly initialize the position of the wolf pack, ,in , N is the size of the wolf pack.

[0135] Step S302, for each wolf's position , calculate its fitness value .

[0136] Step S303, updating alpha, beta and delta wolves.

[0137] According to the fitness value, the best three solutions are selected as alpha, beta and delta wolves. is the optimal solution location, is the location of the second best solution, It is the location of the third optimal solution.

[0138] Step S304, calculate coefficients A and C using the following formula:

[0139] ;

[0140] ;

[0141] where a decreases linearly from 2 to 0, is a random number in [0, 1], is a random number in [0, 1].

[0142] Step S305: for each wolf (except alpha, beta and delta), update their positions according to the following formula:

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] in , and is a random value of coefficient A, , and is a random value of coefficient C.

[0151] Step S306, boundary check to ensure the updated wolf position Do not exceed the definition domain of the problem.

[0152] Step S307, check whether the maximum number of iterations is reached or the predetermined fitness threshold is reached to obtain the global minimum value of the objective function; otherwise, return to step S302 and repeat the above steps S302 to S307.

[0153] Step S4, based on the toxic gas Gaussian plume model and the toxicity threshold of hazardous chemicals, determine the impact range after 1 hour. The specific process is as follows:

[0154] Step S401, determining the third-level toxicity threshold of the leaked hazardous chemicals.

[0155] Step S402, calculating the third-level threshold concentration contour, ie, the influence range.

[0156] The Gaussian plume model is a mathematical model used to describe the diffusion of pollutants under stable atmospheric conditions. The model assumes that the distribution of pollutant concentrations conforms to the Gaussian distribution. Assuming that the three-level toxic thresholds of toxic gases are C1, C2, and C3, the contour map will show areas that meet the following conditions:

[0157] ;

[0158] ;

[0159] .

[0160] In practical applications, it is usually assumed that the concentration of pollutants is the highest at the emission source height z0, so the model can be simplified to only consider the case of z = z0. In this way, the above formula can be simplified to:

[0161] ;

[0162] Get the basic calculation formula for drawing contour maps.

[0163] Step S403, determining the geodetic coordinates of the leakage source.

[0164] Step S404, mapping the three-level concentration contour lines to the geodetic coordinate system in combination with the wind direction information.

[0165] Use the geographic coordinates of the emission source as a reference point. Convert the calculated horizontal distance into displacements in the east-west and north-south directions based on the average wind speed direction (wind direction). Convert the displacements into changes in longitude and latitude using the Earth's geographic coordinate system. This usually involves spherical trigonometry, using the following formula:

[0166] ;

[0167] ;

[0168] in, is the change in longitude, is the change in latitude, Δx and Δy are the displacements in the east-west and north-south directions, R is the radius of the Earth, is the latitude of the reference point.

[0169] Step S405: determine the impact range on the map.

[0170] Step S5, based on the impact range, identify the leakage disaster risk sources, mainly including the number of people in the affected area after 1 hour, the road construction level in the affected area, the road congestion in the affected area, the number of emergency shelters in the affected area and the number of major hazard sources in the affected area.

[0171] Steps S1 to S5 are the calculation process of the full-time and space impact range of the leakage based on the inversion algorithm, and the flow chart is as follows: Figure 2 shown.

[0172] Step S6, based on the risk source identification results and combined with expert knowledge, a regional hazardous chemicals leakage disaster dynamic risk prediction index system is established. The main index system is shown in Table 1.

[0173] Determine the influencing factors of the things to be evaluated, establish the factor index set U, and the risk prediction index set U is a multi-level tree structure. The risk factors of regional hazardous chemical leakage disasters include source factors, environmental factors, social factors, and emergency response capabilities. The prediction index system is a system used to predict the risk level of regional hazardous chemical leakage disasters, including indicators in multiple aspects. Further combined with the identification and cause analysis of safety factors, the first-level indicators are deeply analyzed and the second-level indicators are set. A safety evaluation hierarchy is established. Compared with traditional risk prediction, the risk of hazardous chemical leakage disasters evolves over time, and its risks change dynamically. The various parameters in the prediction system change based on the scope of influence of the toxic gas diffusion model.

[0174] Table 1:

[0175]

[0176] In step S7, experts weight and score the regional hazardous chemical leakage disaster dynamic risk prediction index system. The system constructs a judgment matrix based on the specific weight calculation and constructs an AHP risk prediction model. First, perform hierarchical single sorting and inspection, and use the analytic hierarchy process to calculate the weight of the evaluation index of the evaluation unit. Construct a matrix for each single factor indicator and calculate its consistency ratio. The consistency ratio CR<0.10 indicates that the matrix between the constructed indicators can achieve satisfactory results, and the total hierarchical sorting and consistency ratio can be calculated. Establish a judgment matrix to calculate the weight of AHP, and the weight calculation value is shown in the following table. Then consult the expert group to determine the relative importance of each vertical indicator. The scale is based on the 1-9 scale method, establish a pairwise comparison matrix, and calculate the weight value of each indicator.

[0177] Step S701, constructing a judgment matrix.

[0178] The judgment matrix is ​​used to determine the relative importance of the elements in the previous layer of the hierarchy. Assuming there are n elements, the judgment matrix can be obtained by comparing the elements of each layer in pairs according to the expert scoring method. , as shown in Table 2:

[0179] Table 2 AHP judgment matrix

[0180] Each element According to the 1-9 scale rule and its reciprocal as the judgment scale, as shown in Table 3:

[0181] Table 3 AHP judgment scale values

[0182]

[0183] Step S702, perform hierarchical single sorting and consistency check.

[0184] Hierarchical single sorting is to determine the weight value of each element in a certain level to the importance of an element in the previous level. The weight value is calculated according to the square root method.

[0185] First, calculate the product of each row of the matrix. The calculation formula is as follows:

[0186] ;

[0187] Recalculate The nth root of is as follows:

[0188] ;

[0189] The vector normalization calculation formula is as follows:

[0190] ;

[0191] Wi=[W1,W2,…,Wn] T That is, the eigenvector. The maximum eigenroot is calculated based on the eigenvector. The calculation formula is as follows:

[0192] ;

[0193] Check the consistency of the judgment matrix. The calculation formula is as follows:

[0194] ;

[0195] When CI = 0, it means that the matrix is ​​completely consistent; the larger the CI, the worse the consistency of the matrix. To determine whether the matrix has a satisfactory consistency result, the random consistency ratio CR of the judgment matrix is ​​often used. The calculation formula is as follows:

[0196] ;

[0197] When CR < 0.10, the judgment matrix result is good and the consistency is strong; when CR > 0.10, the matrix needs to be adjusted until the consistency result is satisfactory. The average random index RI value is shown in Table 4.

[0198]

[0199] Step S703, perform total hierarchical sorting and consistency check.

[0200] The total hierarchical ranking is the weight value of the importance index of all elements in this level with respect to the previous level. After the consistency check of the hierarchical single ranking, there may be inconsistency between the levels. Therefore, the consistency check should be carried out on the result of the total hierarchical ranking.

[0201] Step S704: determine the risk assessment set.

[0202] For each indicator, a score is given with V=(0, 1, 2, 3, 4, 5) as the comment set to form a risk assessment set. Among them, 5 points is the highest weight, indicating the greatest impact; 0 means no impact.

[0203] Step S705, calculating the membership matrix.

[0204] After the comment set V and the indicator set U are determined, the degree of membership of each factor to its upper parent node is calculated according to fuzzy mathematics theory, and the fuzzy relationship matrix between the risk assessment set and the factor indicator set is obtained. This matrix is ​​the membership matrix. First, a fuzzy mapping f:U→F(V) is constructed, where F(V) represents the entire fuzzy set on the comment set V, represented by the matrix R:

[0205] .

[0206] Step S706, calculating a comprehensive evaluation set.

[0207] The calculation of the multi-level comprehensive evaluation set B is to transform the feature vector W i A ranking that is mutually operated with the evaluation membership matrix R of each indicator. The calculation process is as follows:

[0208] ;

[0209] In the formula, first and Take the product and then sum it up to get the comment set elements The advantage of the weighted average model is that it comprehensively considers the influence of all indicators on the basis of retaining the evaluation information of single indicators, effectively integrates the comprehensive evaluation of indicators, and realizes a holistic comprehensive evaluation.

[0210] Step S9: assigning a score to each prediction index according to the influence range of the gas diffusion model.

[0211] Step S10, calculating the risk values ​​of different time periods in the future according to the AHP risk prediction model and the integrated gas diffusion model.

[0212] In step S11, the system analyzes the main risks in different time periods and proposes treatment measures based on the real-time predicted risk value of each item.

[0213] For continuous leakage, the leakage diffusion impact range has entered a dynamic stable state, and its impact range will not change significantly over time. The differences between the calculation method of the toxic gas diffusion impact range based on the inversion algorithm and the leakage full-time and space impact range method include the following:

[0214] In step S2, the Gaussian plume formula is simplified, and then the predicted concentration value at the location of the gas sensor is calculated. The simplified spatial location concentration calculation formula becomes:

[0215] .

[0216] In step S3, due to the small number of effective monitoring sensors, in order to ensure the effective convergence of the source tracing algorithm, it is necessary to simplify the source term parameters of the inversion calculation. The simplified source term parameters only calculate These three dimensions, namely the plane coordinates of the leak location and the leak rate, simplify the objective function as follows:

[0217] .

[0218] like Figure 3 The figure shows a schematic diagram of the calculation process of the continuous leakage impact range based on the inversion algorithm.

[0219] Based on the source tracing prediction of leakage sources, the present invention proposes a regional risk prediction method based on toxic gas diffusion analysis, which mainly involves developing and improving gas diffusion models, data collection and processing, building a risk prediction system, conducting uncertainty analysis, studying emergency response strategies, case analysis and verification, and system integration and demonstration application. By comprehensively considering factors such as gas properties, meteorological conditions, and terrain characteristics, the prediction accuracy is improved, a scientific basis is provided for risk management, and ultimately the practical application and popularization of the prediction system are realized.

[0220] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional risk prediction method based on toxic gas diffusion analysis, characterized in that: In the case where there are enough sensors monitoring the concentration of toxic gases, the method of leak full-time and space impact range based on inversion algorithm is adopted, which includes the following steps: Step S1, obtaining real-time gas concentration data and meteorological parameters; Step S2, using a gas diffusion model to calculate a predicted concentration value at a location where the gas sensor is located; Step S3, using the Grey Wolf Optimization Algorithm to calculate source item parameters, repeatedly iterating steps S2 and S3 until convergence conditions are met; the source item parameters include leakage three-dimensional position, leakage rate and leakage time; Step S4, determining the impact range after 1 hour based on the gas diffusion model and the toxicity threshold of hazardous chemicals; Step S5, according to the impact range, identify the leakage disaster risk source, mainly including the number of people in the affected area after 1 hour, the road construction level in the affected area, the road congestion in the affected area, the number of emergency shelters in the affected area, and the number of major hazard sources in the affected area; Step S6, based on the risk source identification results and combined with expert knowledge, establish a regional hazardous chemical leakage disaster risk prediction index system; Step S7, experts weight and score the regional hazardous chemicals leakage disaster dynamic risk prediction index system, and the system constructs a judgment matrix based on the weight calculation to build an AHP risk prediction model; Step S8, assigning a score to each prediction indicator according to the influence range of the gas diffusion model; Step S9, calculating the risk values ​​of different time periods in the future according to the AHP risk prediction model and the integrated gas diffusion model; Step S10, based on the real-time predicted risk value of each item, analyze the main risks in different time periods and propose disposal measures.

2. The method for regional risk prediction based on toxic gas diffusion analysis according to claim 1 is characterized in that: The meteorological parameters in step S1 include emission parameters, expansion parameters, and wind speed, among which the horizontal diffusion parameter The calculation formula is as follows: ； The vertical diffusion parameters are calculated based on the Briggs equation according to the atmospheric stability and the downwind distance x. , the formula is as follows: ； Where x is the downwind distance, t is the time, and Γ is the vertical temperature lapse rate. The calculation formula for Γ is as follows: ； Where g is the acceleration due to gravity, is the specific heat capacity of air at constant pressure, The temperature is decreasing. It's the height difference.

3. The regional risk prediction method based on toxic gas diffusion analysis according to claim 2 is characterized in that: The gas diffusion model is a Gaussian plume model, and the formula is: ； Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, z0 is the height of the leakage location, is the horizontal diffusion parameter, is the vertical diffusion parameter, and C(x, y, z, t) is the toxic gas concentration at each location at each time.

4. The regional risk prediction method based on toxic gas diffusion analysis according to claim 1 is characterized in that: The objective function of the gray wolf optimization algorithm in step S3 is expressed as follows: ； in is the calculated value, is the observed value, M is the total number of monitored sensors, x0 is the horizontal coordinate of the leak source location, y0 is the vertical coordinate of the leak source location, z0 is the height coordinate of the leak source location, Q is the leak rate, t is the time, and the tracing goal is to use the gray wolf optimization algorithm to find the leak source. Smallest , the gray wolf optimization algorithm comprises the following steps: Step S301, initialize the population number and randomly initialize the position of the wolf pack, ,in , N is the size of the wolf pack; Step S302: for each wolf's position , calculate its fitness value ; Step S303, according to the fitness value, select the best three solutions as alpha, beta and delta wolves, where is the optimal solution location, is the position of the second best solution, is the location of the third optimal solution; Step S304, calculate coefficients A and C, the calculation formula is as follows: ； ； where a decreases linearly from 2 to 0, is a random number in [0, 1], is a random number in [0, 1]; Step S305: for each wolf , and update its position according to the following formula: ； ； ； ； ； ； ； in , and is the random value of coefficient A in step S304, , and is the random value of coefficient C in step S304; Step S306, check the boundary to ensure the updated wolf position Do not exceed the definition domain of the problem; Step S307, check whether the maximum number of iterations is reached or the predetermined fitness threshold is reached to obtain the global minimum value of the objective function; otherwise, return to step S302 and repeat the above steps S302 to S307.

5. The regional risk prediction method based on toxic gas diffusion analysis according to claim 2 is characterized in that: Step S4 includes the following steps: Step S401, determining the third-level toxicity threshold of the leaked hazardous chemicals; Step S402, calculating the three-level threshold concentration contour lines; Step S403, determining the geodetic coordinates of the leakage source; Step S404, mapping the three-level concentration contour lines to the geodetic coordinate system in combination with the wind direction information; Step S405: determine the impact range on the map.

6. A regional risk prediction method based on toxic gas diffusion analysis according to claim 5, characterized in that: The calculation formula for the three-level threshold concentration contour line in step S402 is: ； Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, is the horizontal diffusion parameter, is the vertical diffusion parameter .

7. The regional risk prediction method based on toxic gas diffusion analysis according to claim 1 is characterized in that: The regional hazardous chemicals leakage disaster risk factors in the regional hazardous chemicals leakage disaster risk prediction index system include source factors, environmental factors, social factors and emergency response capabilities. Combined with the identification and cause analysis of safety factors, secondary indicators are set up to establish a safety evaluation hierarchy.

8. The regional risk prediction method based on toxic gas diffusion analysis according to claim 1 is characterized in that: Step S7 includes the following steps: Step S701, construct a judgment matrix ; Step S702, performing hierarchical single sorting and consistency check; The weight value is calculated according to the square root method. The calculation formula is as follows: Calculate the product of each row of the matrix using the following formula: ； where a ij is the element of the judgment matrix in step S701; calculate The nth root of is as follows: ； Calculate vector normalization, the formula is as follows: ； W i =[W1,W2,…,W n ] T is the eigenvector, and the maximum eigenroot is calculated based on the eigenvector. The calculation is as follows: ； Check the consistency of the judgment matrix. The formula is as follows: ； When CI = 0, it means that the matrix is ​​completely consistent; the larger the CI, the worse the consistency of the matrix; the random consistency ratio CR of the judgment matrix is ​​used to indicate whether the judgment matrix has a satisfactory consistency result, which is calculated as follows: ； When CR < 0.10, the judgment matrix result is good and the consistency is strong; when CR > 0.10, the matrix needs to be adjusted until the consistency result is satisfactory; Step S703, performing total sorting and consistency check on the levels; Step S704, determining the evaluation level of each indicator to form a risk evaluation set; Step S705, calculate the degree of membership of each factor to its upper parent node, and obtain the fuzzy relationship matrix R between the risk assessment set and the factor index set, which is expressed as the following formula: ； Step S706, calculate the multi-level comprehensive evaluation set B, the calculation method is to transform the feature vector W i The calculation formula is as follows: ； in is an element of the multi-level comprehensive evaluation set B.

9. The regional risk prediction method based on toxic gas diffusion analysis according to claim 2 is characterized in that: In the case of continuous leakage, it is believed that the scope of leakage diffusion has entered a dynamic stable state, and its scope of influence will not change significantly over time. The spatial position concentration calculation formula of the method is: ； Where x is the horizontal coordinate, corresponding to the longitude of the geodetic coordinate system after conversion, y is the vertical coordinate, corresponding to the latitude of the geodetic coordinate system after conversion, z is the height coordinate, corresponding to the height coordinate converted into the geodetic coordinate system, Q is the leakage rate, U is the wind speed, z0 is the height of the leakage location, is the horizontal diffusion parameter, is the vertical diffusion parameter; The source item parameters include the two-dimensional leakage position and leakage rate. The plane coordinates of the leakage position and the leakage rate calculation formula are: ； in is the calculated value, is the observed value, M is the total number of monitored sensors, x0 is the horizontal coordinate of the leakage source position, y0 is the vertical coordinate of the leakage source position, and Q is the leakage rate.

10. A regional risk prediction system based on toxic gas diffusion analysis, characterized in that: It includes gas concentration module, meteorological parameter module, source parameter module, impact range module, risk source identification module, prediction index module, prediction model module, scoring module, risk calculation module and disposal suggestion module, among which: The gas concentration module is used to obtain real-time gas concentration data; The meteorological parameter module is used to input real-time meteorological parameters and calculate the predicted concentration value at the location of the gas sensor using the gas diffusion model formula; The source item parameter module is used to calculate the source item parameters by iterative convergence using the Grey Wolf optimization algorithm; the source item parameters include the leakage three-dimensional position, leakage rate and leakage time; The impact range module is used to determine the impact range after one hour based on the gas diffusion model and the toxicity threshold of hazardous chemicals; The risk source identification module is used to identify the risk sources of leakage disasters according to the impact range, mainly including the number of people in the affected area after 1 hour, the road construction level in the affected area, the road congestion in the affected area, the number of emergency shelters in the affected area, and the number of major hazard sources in the affected area; The prediction index module is used to establish a regional hazardous chemical leakage disaster risk prediction index system based on risk source identification results and expert knowledge; The prediction model module is used by experts to weight and score the dynamic risk prediction index system of regional hazardous chemical leakage disasters. The system constructs a judgment matrix based on the weight calculation and builds an AHP risk prediction model. The scoring module is used to score and assign values ​​to each prediction indicator according to the influence range of the gas diffusion model; The risk calculation module is used to calculate the risk values ​​of different time periods in the future based on the AHP risk prediction model and the integrated gas diffusion model; The disposal suggestion module is used to analyze the main risks in different time periods and propose disposal measures based on the real-time predicted risk value of each item.

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