A risk warning method and system for safety prevention of hazardous chemicals

Abstract

The present invention relates to the technical field of hazardous chemicals safety prevention, and discloses a hazardous chemicals safety prevention risk warning method and system, the method comprising: constructing a hazardous chemicals risk assessment index system, and collecting and preprocessing risk assessment index data corresponding to the hazardous chemicals risk assessment index; constructing a hazardous chemicals leakage warning model, and performing hazardous chemicals leakage risk warning on the preprocessed risk assessment index data. The present invention extracts the storage risk degree of hazardous chemicals themselves and the safety factor of the hazardous chemicals storage environment, calculates the neighbor weight information of the neighbor local data according to the linear coefficient between the neighbor local data, performs dimensionality reduction processing on the gas concentration sequence data while ensuring that the neighbor weight information remains unchanged, performs hazardous chemicals leakage risk warning on the data after dimensionality reduction processing, obtains hazardous chemicals leakage warning results that characterize the hazardous chemicals leakage concentration and the hazard to the storage environment, and realizes hazardous chemicals safety prevention risk warning.

Description

Technical Field

[0001] The present invention relates to the field of safety prevention of hazardous chemicals, and in particular to a risk early warning method and system for safety prevention of hazardous chemicals. Background Art

[0002] Hazardous chemicals are widely used in many fields such as industry, agriculture, medicine, and construction. While promoting economic development, they are also accompanied by potential safety risks. The occurrence of accidents poses a serious threat to society and the environment. First, the existing safety management system often focuses on post-event processing and lacks an effective risk warning mechanism. Secondly, many companies have problems such as weak safety awareness, insufficient training, and imperfect emergency plans in the management of hazardous chemicals, which leads to the inability to effectively respond when accidents occur. In addition, hazardous chemicals are of various types and complex natures, and traditional risk assessment methods are difficult to adapt to the safety management needs under the new situation. Summary of the invention

[0003] In view of this, the present invention proposes a risk warning method for safety prevention of hazardous chemicals. By analyzing massive multi-dimensional perception data, potential risk factors are identified, a risk assessment model is established, and dynamic monitoring and early warning of hazardous chemicals safety are achieved.

[0004] To achieve the above object, the present invention provides a dangerous chemicals safety risk warning method, comprising the following steps:

[0005] S1: Construct a risk assessment indicator system for hazardous chemicals and collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals;

[0006] S2: preprocessing the collected risk assessment indicator data to obtain preprocessed risk assessment indicator data;

[0007] S3: constructing a hazardous chemical leakage warning model structure, and optimizing and solving the model parameters of the hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and the hazardous chemical leakage warning result as output, wherein the noise-robust support vector machine is an implementation method for constructing the hazardous chemical leakage warning model structure, and wherein the improved population optimization is an implementation method for optimizing and solving the model parameters;

[0008] S4: Based on the hazardous chemicals leakage warning model structure and the model parameters obtained by optimization, a hazardous chemicals leakage warning model is constructed, and hazardous chemicals leakage risk warning is performed on the preprocessed risk assessment indicator data.

[0009] As a further improvement method of the present invention:

[0010] Optionally, the hazardous chemicals risk assessment index system is constructed in step S1, including:

[0011] Construct a risk assessment index system for hazardous chemicals, which includes a risk assessment index system for hazardous chemicals at different levels, including a physical and chemical property assessment index system for hazardous chemicals. 1 , Toxicity Assessment Index System P 2 、Environmental impact assessment indicator system 3 , Emergency Response Evaluation Index System 4 And the leakage gas concentration assessment index system P 5 , and based on the hazardous chemicals risk assessment indicator system, a set of hazardous chemicals risk assessment indicators is obtained:

[0012]

[0013] in:

[0014] Represents the risk assessment index system P of hazardous chemicals at the i-th level i The risk assessment index of the jth hazardous chemical; among which the physical and chemical property assessment index system P 1 The 1st to 4th hazardous chemicals risk assessment indicators are the flash point of hazardous chemicals, the ignition point of hazardous chemicals, the spontaneous combustion point of hazardous chemicals and the boiling point of hazardous chemicals;

[0015] Toxicity assessment index system P 2 The risk assessment indicators for hazardous chemicals of types 1 to 4 are acute toxicity, reproductive toxicity, carcinogenicity and mutagenicity;

[0016] Environmental Impact Assessment Index System 3 The risk assessment indicators for the first four hazardous chemicals are environmental biodegradability, environmental bioaccumulation, environmental persistence, and storage environment hazard level;

[0017] Emergency response evaluation index system 4 The 1st to 4th hazardous chemicals risk assessment indicators are, in order, whether there are hazardous chemicals safety operation regulations and procedures, whether there are hazardous chemicals leakage treatment measures, whether there are first aid measures, and whether there are fire-fighting measures;

[0018] Leakage gas concentration assessment index system P 5 The risk assessment indicators for types 1 to 4 of hazardous chemicals are ammonia concentration, chlorine concentration, hydrogen sulfide concentration and carbon monoxide concentration.

[0019] Optionally, the collecting of risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals includes:

[0020] Collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals. The collection process of risk assessment indicator data is as follows:

[0021] S11: Evaluation index system based on physicochemical properties P 1 And the toxicity assessment index system P 2 The risk assessment indicators of hazardous chemicals in the collection of their own characteristics constitute risk assessment indicator data; among them, the risk assessment indicators of hazardous chemicals

[0022] The corresponding risk assessment indicator data are as follows: Hazardous chemicals risk assessment indicators The corresponding risk assessment indicator data are as follows:

[0023] S12: Based on the environmental impact assessment indicator system P 3 Hazardous chemicals risk assessment indicators Calculate and test the storage environment of hazardous chemicals to obtain the oxygen consumption of microorganisms required for the complete degradation of hazardous chemicals in the storage environment After the leakage of hazardous chemicals, the ratio of the concentration of hazardous chemicals in the organism to the concentration of hazardous chemicals in the storage environment After a hazardous chemical leaks, the time required for the concentration of hazardous chemicals in the storage environment to be reduced by half And the degree of danger of the storage environment

[0024]

[0025] in:

[0026] Sum represents the total number of people near the hazardous chemicals storage environment, ∈ represents the preset personnel correction coefficient threshold;

[0027] num represents the total amount of hazardous chemicals stored, and NUM represents the critical amount of hazardous chemicals stored;

[0028] S13: Construct an emergency response evaluation index system P 4 The risk assessment indicator data corresponding to the 1st to 4th hazardous chemicals risk assessment indicators in in If the storage environment of hazardous chemicals has hazardous chemicals risk assessment indicators The indicator description is otherwise

[0029] S14: Based on the leak gas concentration evaluation index system P 5 , collect sequence data of different gas concentrations in the risk assessment indicators of hazardous chemicals as the risk assessment indicators of hazardous chemicals Corresponding risk assessment indicator data in in Indicates the risk assessment index of hazardous chemicals The sequence data of the associated gas, Indicates the gas concentration monitoring of the hazardous chemicals storage environment, and the hazardous chemicals risk assessment indicators monitored at N gas concentration monitoring moments The gas concentration value of the associated gas, Indicates the risk assessment index of hazardous chemicals monitored at the nth gas concentration monitoring moment The gas concentration value of the associated gas, n∈[1,N];

[0030] S15: Construct the risk assessment indicator data set x corresponding to the risk assessment indicators of hazardous chemicals:

[0031]

[0032] in:

[0033] Indicates the risk assessment index of hazardous chemicals Corresponding risk assessment indicator data.

[0034] Optionally, in step S2, preprocessing the collected risk assessment indicator data includes:

[0035] The collected risk assessment indicator data are preprocessed to obtain preprocessed risk assessment indicator data, wherein the preprocessing process of the risk assessment indicator data is as follows:

[0036] S21: Extract the risk assessment index data representing the storage risk level of hazardous chemicals from the risk assessment index data set x, and calculate the storage risk level of hazardous chemicals α 1 ;

[0037] S22: Extract the risk assessment indicator data representing the safety factor of the hazardous chemicals storage environment from the risk assessment indicator data set x, and calculate the safety factor α of the hazardous chemicals storage environment 2 ; Among them, the safety factor of hazardous chemicals storage environment is α 2 The calculation formula is:

[0038]

[0039] in:

[0040] Indicates the risk assessment index of hazardous chemicals The corresponding standard assessment data value; the standard assessment data value is the risk assessment index of most hazardous chemicals. The data value below which it falls;

[0041] exp(·) represents an exponential function with a natural constant as the base;

[0042] S23: Extract risk assessment indicator data And the risk assessment indicator data Perform data dimensionality reduction to obtain the risk assessment index data y after dimensionality reduction, where the risk assessment index data The data dimension reduction process is as follows:

[0043] S231: Risk assessment indicator data Convert to multi-dimensional time series indicator data x:

[0044] x=(x 1 ,x 2 ,...,x n ,...,x n );

[0045]

[0046] in:

[0047] x n Represents the multi-dimensional index data obtained from monitoring the nth gas concentration at the monitoring moment;

[0048] S232: Calculate the distance between different multidimensional indicator data in the multidimensional time series indicator data x, where the multidimensional indicator data x n With multidimensional indicator data x p The distance between them is dis(x n ,x p )=||x n -x p || 2 ,||·|| 2 is the L2 norm, p∈[1,N],p≠n;

[0049] S233: Traverse to obtain K nearest neighbor index data of any multidimensional index data in the multidimensional time series index data x, where the multidimensional index data x n The K nearest neighbor index data is (x n (1),x n (2),...,x n (K)), multidimensional indicator data x n The K nearest neighbor index data is the distance multidimensional index data x n The most recent K multi-dimensional indicator data, x n (K) represents the distance multidimensional index data x n The K-th nearest neighbor indicator data;

[0050] S234: Calculate the neighbor weight information of each neighbor index data in the K nearest neighbor index data of the multi-dimensional index data, where the neighbor index data x n The neighbor weight information of (k) is:

[0051]

[0052] I K =[1,1,...,1] T ;

[0053] in:

[0054] Represents the neighbor index data x n (k) neighbor weight information;

[0055] Represents the neighbor index data x n (k)’s nearest neighbor difference sequence;

[0056] I K Represents a vector with a length of K and all element values ​​are 1;

[0057] T stands for transpose;

[0058] S235: Constructing a target function for solving the dimension reduction index data corresponding to the multidimensional index data, where the multidimensional index data x n The corresponding dimension reduction index data y n The objective function to be solved is:

[0059]

[0060] E n =e n (e n ) T ;

[0061]

[0062] in:

[0063] Tr(·) represents the calculation function of the matrix trace;

[0064] e n Represents multidimensional indicator data x n The neighbor weight information vector of ;

[0065] I K (k) represents a vector of length K, where I K The element value of the kth element in (k) is 1, and the element values ​​of other elements are 0;

[0066] To solve the objective function Solve it and get Tr(y n E n (y n ) T) to achieve the minimum dimension reduction index data y n ;

[0067] S236: Construct the risk assessment index data y after dimensionality reduction = (y 1 ,y 2 ,...,y n ,...,y N );

[0068] S24: Constitutes the risk assessment index data after preprocessing: [y,α 1 ,α 2 ].

[0069] Optionally, in step S21, risk assessment index data characterizing the storage risk level of hazardous chemicals themselves are extracted from the risk assessment index data set x, and the storage risk level of hazardous chemicals themselves is calculated. 1 ,include:

[0070] S211: Extract risk assessment indicator data that characterizes the storage risk level of hazardous chemicals:

[0071]

[0072] S212: Obtain temperature information tem of the hazardous chemicals storage environment;

[0073] S213: Calculate the storage risk level of hazardous chemicals 1 :

[0074]

[0075]

[0076] in:

[0077] exp(·) represents an exponential function with a natural constant as the base;

[0078] Represents risk assessment indicator data The weight of the indicator.

[0079] Optionally, constructing a hazardous chemicals leakage warning model structure in step S3 includes:

[0080] Constructing a hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and takes the hazardous chemical leakage warning result as output, wherein a noise-robust support vector machine is an implementation method for constructing the hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model structure includes an input layer, a risk degree mapping layer, and a risk warning layer;

[0081] The input layer is used to receive the pre-processed risk indicator data;

[0082] The risk level mapping layer is used to map the storage risk level of hazardous chemicals themselves and the safety factor of the hazardous chemicals storage environment to the risk assessment index data after dimensionality reduction, and obtain the risk assessment index mapping data;

[0083] The risk warning layer is a support vector machine structure. It uses a noise-robust method to construct the training loss function of the support vector machine and solve the model parameters of the support vector machine. The risk warning layer is used to receive risk assessment indicator mapping data and output hazardous chemical leakage warning results.

[0084] Get the D group of training data to form the training data set data for the hazardous chemicals leakage warning model:

[0085] data={(Y d ,S d )|d∈[1,D]};

[0086] in:

[0087] (Y d ,S d ) represents the obtained d-th group of training data, Y d represents the risk assessment index mapping data of the dth group of hazardous chemicals obtained, S d Represents risk assessment indicator mapping data Y d The corresponding degree of leakage of hazardous chemicals, S d ∈[0,1];

[0088] Use the training data set data to construct the training loss function of the risk warning layer:

[0089]

[0090] in:

[0091] ||·|| represents the L1 norm;

[0092] F(w,b) represents the training loss function of the risk warning layer, w,b represents the model parameters in the risk warning layer;

[0093] L w,b (Y d ,S d ) represents the risk warning layer constructed with model parameters w, b, for the dth group of training data (Y d ,S d ), where the smaller the training loss, the closer the hazardous chemical leakage warning result output by the risk warning layer is to the hazardous chemical leakage degree in the training data, and the higher the accuracy of the hazardous chemical leakage warning result;

[0094] ρ represents the target quantile, ρ∈[-1,1];

[0095] The model parameters of the hazardous chemicals leakage warning model are optimized based on the training loss function.

[0096] Optionally, the optimizing and solving the model parameters of the hazardous chemicals leakage warning model based on the training loss function includes:

[0097] S31: Initialize and generate U groups of model parameters, where the uth group of model parameters generated by initialization is in They correspond to the model parameters w, b, u∈[1,U] in the risk warning layer respectively, and set the iteration upper limit θ of the model parameters up and the iterative lower limit θ down ;

[0098] S32: Set the current iteration number of the model parameters to z, the initial value of z is 0, and the maximum value is Max. Then the zth iteration result of the uth group of model parameters is

[0099] S33: Calculate the population disturbance value of each group of model parameters, where the model parameters The population perturbation value of is:

[0100]

[0101] in:

[0102] Represents model parameters The population disturbance value of

[0103] S34: Using the U groups of model parameters obtained in the z-th iteration as the input value of the training loss function, obtaining the training loss function value of each group of model parameters, and selecting the model parameter with the smallest training loss function value as the optimal model parameter obtained in the z-th iteration

[0104] S35: Iterate each set of model parameters, where the model parameters The iteration formula is:

[0105]

[0106] in:

[0107] rand(0,1) represents a random number between 0 and 1, and rand(1,2) represents a random number between 1 and 2;

[0108] represents the random model parameters in the U group of model parameters obtained in the z-th iteration;

[0109] Represents model parameters The population-optimized random value of ;

[0110] S36: Let z=z+1, return to step S33, until the maximum number of iterations is reached, and select the optimal model parameters obtained by the iteration at this time as the model parameter optimization solution result θ * =(w * ,b * ).

[0111] Optionally, in step S4, a hazardous chemical leakage warning model is constructed, and a hazardous chemical leakage risk warning is performed on the pre-processed risk assessment indicator data, including:

[0112] Based on the structure of the hazardous chemicals leakage warning model and the model parameters (w * ,b * ), build a hazardous chemical leakage warning model, and conduct hazardous chemical leakage risk warning for the pre-processed risk assessment index data. The hazardous chemical leakage risk warning process is as follows:

[0113] S41: The input layer receives the pre-processed risk indicator data and splits it into the storage risk level of hazardous chemicals α 1 , Hazardous chemicals storage environment safety factor α 2 And the risk assessment index data y after dimension reduction;

[0114] S42: The risk level mapping layer converts the storage risk level of hazardous chemicals itself into α 1 And the safety factor α of hazardous chemicals storage environment 2 Mapped to the risk assessment indicator data y after dimensionality reduction, the risk assessment indicator mapping data Y is obtained:

[0115]

[0116] in:

[0117] Q 1 The control parameter representing the storage risk level of hazardous chemicals, Q 2 Represents the control parameter of the safety factor of the hazardous chemicals storage environment;

[0118] Q represents the mapping processing matrix;

[0119] S43: The risk warning layer receives the risk assessment index mapping data Y and outputs the hazardous chemical leakage warning result:

[0120] S=w * Y+b * ;

[0121] in:

[0122] S represents the hazardous chemical leakage warning result. If S is higher than the preset leakage threshold, it means that there is a hazardous chemical leakage and it will cause serious harm to the storage environment, and a safety alarm will be issued immediately.

[0123] In order to solve the above problems, the present invention provides a dangerous chemicals safety prevention risk warning system, characterized in that the system comprises:

[0124] The data collection module is used to build a risk assessment indicator system for hazardous chemicals and collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals;

[0125] A data processing module is used to preprocess the collected risk assessment indicator data to obtain preprocessed risk assessment indicator data;

[0126] The safety warning device is used to construct a hazardous chemical leakage warning model based on the hazardous chemical leakage warning model structure and the model parameters obtained by optimization, and to issue a hazardous chemical leakage risk warning for the pre-processed risk assessment indicator data.

[0127] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0128] A memory storing at least one instruction;

[0129] Communication interface, enabling electronic equipment to communicate; and

[0130] The processor executes the instructions stored in the memory to implement the above-mentioned hazardous chemicals safety prevention risk warning method.

[0131] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned hazardous chemicals safety prevention risk warning method.

[0132] Compared with the prior art, the present invention proposes a risk warning method for safety prevention of hazardous chemicals, which has the following advantages:

[0133] First, this scheme collects risk assessment index data that characterize the risks of hazardous chemicals themselves and the risks of the storage environment, and extracts the storage risk degree of hazardous chemicals themselves and the safety factor of the hazardous chemical storage environment from them, and quantifies the risks of hazardous chemicals themselves and the storage environment, so that the hazardous chemical leakage warning results can characterize the degree of harm to the storage environment. The gas concentration sequence data of the hazardous chemical storage environment is collected in real time, and the neighbor weight information of the neighbor local data is calculated according to the linear coefficient between the neighbor local data. While ensuring that the neighbor weight information remains unchanged, the gas concentration sequence data is subjected to dimensionality reduction processing to obtain the reduced dimensionality data with unchanged neighbor information, thereby reducing the computing resources required for subsequent safety warnings and improving the efficiency of safety risk warnings.

[0134] At the same time, this scheme constructs a multi-objective quantile training loss function so that the training loss function pays more attention to different parts of the data distribution and is insensitive to outliers, thereby reducing the impact of outliers on model parameters and improving the robustness of the hazardous chemicals leakage warning model. In the process of solving the model parameters, the iterative information of all model parameters is combined to generate group perturbation values ​​and group optimized random values ​​respectively. The group perturbation values ​​are used to generate new perturbation individuals, so that they are separated from the previously aggregated optimization direction, improving the algorithm's search ability and increasing diversity. The group optimized random values ​​are used to realize information exchange between different groups of model parameters, so that the model parameters will approach the global optimal model parameters according to the interactive information, avoiding blind search and improving the overall solution accuracy of the algorithm. Then, the trained hazardous chemicals leakage warning model is used to perform hazardous chemicals leakage risk warning on the preprocessed risk assessment indicator data, and obtain hazardous chemicals leakage warning results that characterize the hazardous chemicals leakage concentration and the hazards to the storage environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0135] Figure 1 A schematic diagram of a process for early warning of safety risks of hazardous chemicals provided by an embodiment of the present invention;

[0136] Figure 2 A functional module diagram of a hazardous chemicals safety risk warning system provided by an embodiment of the present invention;

[0137] Figure 2 In: 100 hazardous chemicals safety risk warning system, 101 data acquisition module, 102 data processing module, 103 safety warning device;

[0138] Figure 3 A schematic diagram of the structure of an electronic device for implementing a hazardous chemicals safety risk warning method provided by an embodiment of the present invention.

[0139] Figure 3 In: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface;

[0140] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0141] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0142] The embodiment of the present application provides a method for early warning of safety risks of hazardous chemicals. The execution subject of the method for early warning of safety risks of hazardous chemicals includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for early warning of safety risks of hazardous chemicals can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0143] Embodiment 1:

[0144] S1: Construct a risk assessment indicator system for hazardous chemicals and collect risk assessment indicator data corresponding to the risk assessment indicators for hazardous chemicals.

[0145] The hazardous chemicals risk assessment indicator system is constructed in step S1, including:

[0146] Construct a risk assessment index system for hazardous chemicals, which includes a risk assessment index system for hazardous chemicals at different levels, including a physical and chemical property assessment index system for hazardous chemicals. 1 , Toxicity Assessment Index System P 2 、Environmental impact assessment indicator system 3 , Emergency Response Evaluation Index System 4 And the leakage gas concentration assessment index system P 5 , and based on the hazardous chemicals risk assessment indicator system, a set of hazardous chemicals risk assessment indicators is obtained:

[0147]

[0148] in:

[0149] Represents the risk assessment index system P of hazardous chemicals at the i-th level i The risk assessment index of the jth hazardous chemical; among which the physical and chemical property assessment index system P 1 The 1st to 4th hazardous chemicals risk assessment indicators are the flash point of hazardous chemicals, the ignition point of hazardous chemicals, the spontaneous combustion point of hazardous chemicals and the boiling point of hazardous chemicals;

[0150] Toxicity assessment index system P 2The risk assessment indicators for hazardous chemicals of types 1 to 4 are acute toxicity, reproductive toxicity, carcinogenicity and mutagenicity;

[0151] Environmental Impact Assessment Index System 3 The risk assessment indicators for the first four hazardous chemicals are environmental biodegradability, environmental bioaccumulation, environmental persistence, and storage environment hazard level;

[0152] Emergency response evaluation index system 4 The 1st to 4th hazardous chemicals risk assessment indicators are, in order, whether there are hazardous chemicals safety operation regulations and procedures, whether there are hazardous chemicals leakage treatment measures, whether there are first aid measures, and whether there are fire-fighting measures;

[0153] Leakage gas concentration assessment index system P 5 The risk assessment indicators for types 1 to 4 of hazardous chemicals are ammonia concentration, chlorine concentration, hydrogen sulfide concentration and carbon monoxide concentration.

[0154] The risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals collected include:

[0155] Collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals. The collection process of risk assessment indicator data is as follows:

[0156] S11: Evaluation index system based on physicochemical properties P 1 And the toxicity assessment index system P 2 The risk assessment indicators of hazardous chemicals in the collection of their own characteristics constitute risk assessment indicator data; among them, the risk assessment indicators of hazardous chemicals

[0157] The corresponding risk assessment indicator data are as follows: Hazardous chemicals risk assessment indicators The corresponding risk assessment indicator data are as follows:

[0158] S12: Based on the environmental impact assessment indicator system P 3 Hazardous chemicals risk assessment indicators Calculate and test the storage environment of hazardous chemicals to obtain the oxygen consumption of microorganisms required for the complete degradation of hazardous chemicals in the storage environment After the leakage of hazardous chemicals, the ratio of the concentration of hazardous chemicals in the organism to the concentration of hazardous chemicals in the storage environment After a hazardous chemical leaks, the time required for the concentration of hazardous chemicals in the storage environment to be reduced by half And the degree of danger of the storage environment

[0159]

[0160] in:

[0161] Sum represents the total number of people near the hazardous chemicals storage environment, ∈ represents the preset personnel correction coefficient threshold;

[0162] num represents the total amount of hazardous chemicals stored, and NUM represents the critical amount of hazardous chemicals stored;

[0163] S13: Construct an emergency response evaluation index system P 4 The risk assessment indicator data corresponding to the 1st to 4th hazardous chemicals risk assessment indicators in in If the storage environment of hazardous chemicals has hazardous chemicals risk assessment indicators The indicator description is otherwise

[0164] S14: Based on the leak gas concentration evaluation index system P 5 , collect sequence data of different gas concentrations in the risk assessment indicators of hazardous chemicals as the risk assessment indicators of hazardous chemicals Corresponding risk assessment indicator data in in Indicates the risk assessment index of hazardous chemicals The sequence data of the associated gas, Indicates the gas concentration monitoring of the hazardous chemicals storage environment, and the hazardous chemicals risk assessment indicators monitored at N gas concentration monitoring moments The gas concentration value of the associated gas, Indicates the risk assessment index of hazardous chemicals monitored at the nth gas concentration monitoring moment The gas concentration value of the associated gas, n∈[1,N];

[0165] S15: Construct the risk assessment indicator data set x corresponding to the risk assessment indicators of hazardous chemicals:

[0166]

[0167] in:

[0168] Indicates the risk assessment index of hazardous chemicals Corresponding risk assessment indicator data.

[0169] S2: Preprocessing the collected risk assessment indicator data to obtain preprocessed risk assessment indicator data.

[0170] In the step S2, the collected risk assessment indicator data is preprocessed, including:

[0171] The collected risk assessment indicator data are preprocessed to obtain preprocessed risk assessment indicator data, wherein the preprocessing process of the risk assessment indicator data is as follows:

[0172] S21: Extract the risk assessment index data representing the storage risk level of hazardous chemicals from the risk assessment index data set x, and calculate the storage risk level of hazardous chemicals α 1 ;

[0173] S22: Extract the risk assessment indicator data representing the safety factor of the hazardous chemicals storage environment from the risk assessment indicator data set x, and calculate the safety factor α of the hazardous chemicals storage environment 2 ; Among them, the safety factor of hazardous chemicals storage environment is α 2 The calculation formula is:

[0174]

[0175] in:

[0176] Indicates the risk assessment index of hazardous chemicals The corresponding standard assessment data value; the standard assessment data value is the risk assessment index of most hazardous chemicals. The data value below which it falls;

[0177] exp(·) represents an exponential function with a natural constant as the base;

[0178] S23: Extract risk assessment indicator data And the risk assessment indicator data Perform data dimensionality reduction to obtain the risk assessment index data y after dimensionality reduction, where the risk assessment index data The data dimension reduction process is as follows:

[0179] S231: Risk assessment indicator data Convert to multi-dimensional time series indicator data x:

[0180] x=(x 1 ,x 2 ,...,x n ,...,x n );

[0181]

[0182] in:

[0183] x n Represents the multi-dimensional index data obtained from monitoring the nth gas concentration at the monitoring moment;

[0184] S232: Calculate the distance between different multidimensional indicator data in the multidimensional time series indicator data x, where the multidimensional indicator data x n With multidimensional indicator data x p The distance between them is dis(x n ,x p )=||x n -x p || 2 ,||·|| 2 is the L2 norm, p∈[1,N],p≠n;

[0185] S233: Traverse to obtain K nearest neighbor index data of any multidimensional index data in the multidimensional time series index data x, where the multidimensional index data x n The K nearest neighbor index data is (x n (1),x n (2),...,x n (K)), multidimensional indicator data x n The K nearest neighbor index data is the distance multidimensional index data x n The most recent K multi-dimensional indicator data, x n (K) represents the distance multidimensional index data x n The K-th nearest neighbor indicator data;

[0186] S234: Calculate the neighbor weight information of each neighbor index data in the K nearest neighbor index data of the multi-dimensional index data, where the neighbor index data x n The neighbor weight information of (k) is:

[0187]

[0188] I K =[1,1,...,1] T ;

[0189] in:

[0190] Represents the neighbor index data x n (k) neighbor weight information;

[0191] Represents the neighbor index data x n (k)’s nearest neighbor difference sequence;

[0192] I K Represents a vector with a length of K and all element values ​​are 1;

[0193] T stands for transpose;

[0194] S235: Constructing a target function for solving the dimension reduction index data corresponding to the multidimensional index data, where the multidimensional index data x n The corresponding dimension reduction index data y n The objective function to be solved is:

[0195]

[0196] in:

[0197] Tr(·) represents the calculation function of the matrix trace;

[0198] I K (k) represents a vector of length K, where I K The element value of the kth element in (l) is 1, and the element values ​​of other elements are 0;

[0199] To solve the objective function Solve it and get Tr(y n E n (y n ) T ) to achieve the minimum dimension reduction index data y n ;

[0200] S236: Construct the risk assessment index data y after dimensionality reduction = (y 1 ,y 2 ,...,y n ,...,y N );

[0201] S24: Constitutes the risk assessment index data after preprocessing: [y,α 1 ,α 2 ].

[0202] S3: Construct a hazardous chemical leakage warning model structure, and optimize and solve the model parameters of the hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and takes the hazardous chemical leakage warning result as output.

[0203] The structure of the hazardous chemicals leakage warning model is constructed in step S3, including:

[0204] Constructing a hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and takes the hazardous chemical leakage warning result as output, wherein a noise-robust support vector machine is an implementation method for constructing the hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model structure includes an input layer, a risk degree mapping layer, and a risk warning layer;

[0205] The input layer is used to receive the pre-processed risk indicator data;

[0206] The risk level mapping layer is used to map the storage risk level of hazardous chemicals themselves and the safety factor of the hazardous chemicals storage environment to the risk assessment index data after dimensionality reduction, and obtain the risk assessment index mapping data;

[0207] The risk warning layer is a support vector machine structure. It uses a noise-robust method to construct the training loss function of the support vector machine and solve the model parameters of the support vector machine. The risk warning layer is used to receive risk assessment indicator mapping data and output hazardous chemical leakage warning results.

[0208] Get the D group of training data to form the training data set data for the hazardous chemicals leakage warning model:

[0209] data={(Y d ,S d )|d∈[1,D]};

[0210] in:

[0211] (Y d ,S d ) represents the obtained d-th group of training data, Y d represents the risk assessment index mapping data of the dth group of hazardous chemicals obtained, S d Represents risk assessment indicator mapping data Y d The corresponding degree of leakage of hazardous chemicals, S d ∈[0,1];

[0212] Use the training data set data to construct the training loss function of the risk warning layer:

[0213]

[0214] in:

[0215] ||·|| represents the L1 norm;

[0216] F(w,b) represents the training loss function of the risk warning layer, w,b represents the model parameters in the risk warning layer;

[0217] L w,b (Y d ,S d ) represents the risk warning layer constructed with model parameters w, b, for the dth group of training data (Y d ,S d ), where the smaller the training loss, the closer the hazardous chemical leakage warning result output by the risk warning layer is to the hazardous chemical leakage degree in the training data, and the higher the accuracy of the hazardous chemical leakage warning result;

[0218] ρ represents the target quantile, ρ∈[-1,1];

[0219] The model parameters of the hazardous chemicals leakage warning model are optimized based on the training loss function.

[0220] The method of optimizing the model parameters of the hazardous chemicals leakage warning model based on the training loss function includes:

[0221] S31: Initialize and generate U groups of model parameters, where the uth group of model parameters generated by initialization is in They correspond to the model parameters w, b, u∈[1,U] in the risk warning layer respectively, and set the iteration upper limit θ of the model parameters up and the iterative lower limit θ down ;

[0222] S32: Set the current iteration number of the model parameters to z, the initial value of z is 0, and the maximum value is Max. Then the zth iteration result of the uth group of model parameters is

[0223] S33: Calculate the population disturbance value of each group of model parameters, where the model parameters The population perturbation value of is:

[0224]

[0225] in:

[0226] Represents model parameters The population disturbance value of

[0227] S34: Using the U groups of model parameters obtained in the z-th iteration as the input value of the training loss function, obtaining the training loss function value of each group of model parameters, and selecting the model parameter with the smallest training loss function value as the optimal model parameter obtained in the z-th iteration

[0228] S35: Iterate each set of model parameters, where the model parameters The iteration formula is:

[0229]

[0230] in:

[0231] rand(0,1) represents a random number between 0 and 1, and rand(1,2) represents a random number between 1 and 2;

[0232] represents the random model parameters in the U group of model parameters obtained in the z-th iteration;

[0233] Represents model parameters The population-optimized random value of ;

[0234] S36: Let z=z+1, return to step S33, until the maximum number of iterations is reached, and select the optimal model parameters obtained by the iteration at this time as the model parameter optimization solution result θ * =(w * ,b * ).

[0235] S4: Based on the hazardous chemicals leakage warning model structure and the model parameters obtained by optimization, a hazardous chemicals leakage warning model is constructed, and hazardous chemicals leakage risk warning is performed on the preprocessed risk assessment indicator data.

[0236] In the step S4, a hazardous chemical leakage warning model is constructed, and a hazardous chemical leakage risk warning is performed on the pre-processed risk assessment indicator data, including:

[0237] Based on the structure of the hazardous chemicals leakage warning model and the model parameters (w * ,b * ), build a hazardous chemical leakage warning model, and conduct hazardous chemical leakage risk warning for the pre-processed risk assessment index data. The hazardous chemical leakage risk warning process is as follows:

[0238] S41: The input layer receives the pre-processed risk indicator data and splits it into the storage risk level of hazardous chemicals α 1 , Hazardous chemicals storage environment safety factor α 2 And the risk assessment index data y after dimension reduction;

[0239] S42: The risk level mapping layer converts the storage risk level of hazardous chemicals itself into α 1 And the safety factor α of hazardous chemicals storage environment 2 Mapped to the risk assessment indicator data y after dimensionality reduction, the risk assessment indicator mapping data Y is obtained:

[0240]

[0241] in:

[0242] Q 1 The control parameter representing the storage risk level of hazardous chemicals, Q 2 Represents the control parameter of the safety factor of the hazardous chemicals storage environment;

[0243] Q represents the mapping processing matrix;

[0244] S43: The risk warning layer receives the risk assessment index mapping data Y and outputs the hazardous chemical leakage warning result:

[0245] S=w* Y+b * ;

[0246] in:

[0247] S represents the hazardous chemical leakage warning result. If S is higher than the preset leakage threshold, it means that there is a hazardous chemical leakage and it will cause serious harm to the storage environment, and a safety alarm will be issued immediately.

[0248] Embodiment 2:

[0249] like Figure 2 , is a functional module diagram of a hazardous chemicals safety prevention risk warning system provided by an embodiment of the present invention, which can implement the hazardous chemicals safety prevention risk warning method in Example 1.

[0250] The hazardous chemicals safety risk warning system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the hazardous chemicals safety risk warning system can include a data acquisition module 101, a data processing module 102 and a safety warning device 103. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0251] The data collection module 101 is used to construct a hazardous chemical risk assessment indicator system and collect risk assessment indicator data corresponding to the hazardous chemical risk assessment indicators;

[0252] The data processing module 102 is used to pre-process the collected risk assessment indicator data to obtain pre-processed risk assessment indicator data;

[0253] The safety warning device 103 is used to construct a hazardous chemical leakage warning model based on the hazardous chemical leakage warning model structure and the model parameters obtained by optimization, and to issue a hazardous chemical leakage risk warning for the pre-processed risk assessment indicator data.

[0254] In detail, each module in the hazardous chemicals safety risk warning system 100 in the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means are the same as the hazardous chemicals safety prevention risk warning method described in, and can produce the same technical effects, so they will not be repeated here.

[0255] Embodiment 3:

[0256] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a method for early warning of safety risks of hazardous chemicals provided by an embodiment of the present invention.

[0257] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0258] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may be used not only to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also to temporarily store data that has been output or is to be output.

[0259] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the program 12 for realizing the risk warning of hazardous chemicals safety prevention), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0260] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0261] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0262] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0263] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0264] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0265] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0266] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0267] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0268] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for early warning of safety risks of hazardous chemicals, characterized in that: The method comprises: S1: Construct a risk assessment indicator system for hazardous chemicals and collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals; S2: preprocessing the collected risk assessment indicator data to obtain preprocessed risk assessment indicator data; S3: constructing a hazardous chemical leakage warning model structure, and optimizing and solving the model parameters of the hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and takes the hazardous chemical leakage warning result as output; Constructing a hazardous chemical leakage warning model structure, wherein the hazardous chemical leakage warning model takes the preprocessed risk indicator data as input and takes the hazardous chemical leakage warning result as output, wherein the hazardous chemical leakage warning model structure includes an input layer, a risk degree mapping layer, and a risk warning layer; The input layer is used to receive the pre-processed risk indicator data; The risk level mapping layer is used to map the storage risk level of hazardous chemicals themselves and the safety factor of the hazardous chemicals storage environment to the risk assessment index data after dimensionality reduction, and obtain the risk assessment index mapping data; The risk warning layer is a support vector machine structure. It uses a noise-robust method to construct the training loss function of the support vector machine and solve the model parameters of the support vector machine. The risk warning layer is used to receive risk assessment indicator mapping data and output hazardous chemical leakage warning results. Get the D group of training data to form the training data set data for the hazardous chemicals leakage warning model: data={(Y d ,S d )|d∈[1,D]}; in: (Y d ,S d ) represents the obtained d-th group of training data, Y d represents the risk assessment index mapping data of the dth group of hazardous chemicals obtained, S d Represents risk assessment indicator mapping data Y d The corresponding degree of leakage of hazardous chemicals, S d ∈[0,1]; Use the training data set data to construct the training loss function of the risk warning layer: in: ||·|| represents the L1 norm; F(w,b) represents the training loss function of the risk warning layer, w,b represents the model parameters in the risk warning layer; L w,b (Y d ,S d ) represents the risk warning layer constructed with model parameters w, b, for the dth group of training data (Y d ,S d ), where the smaller the training loss, the closer the hazardous chemical leakage warning result output by the risk warning layer is to the hazardous chemical leakage degree in the training data, and the higher the accuracy of the hazardous chemical leakage warning result; ρ represents the target quantile, ρ∈[-1,1]; Optimize the model parameters of the hazardous chemicals leakage warning model based on the training loss function; S4: Based on the hazardous chemicals leakage warning model structure and the model parameters obtained by optimization, a hazardous chemicals leakage warning model is constructed, and hazardous chemicals leakage risk warning is performed on the preprocessed risk assessment indicator data.

2. A dangerous chemicals safety risk warning method as claimed in claim 1, characterized in that: The hazardous chemicals risk assessment indicator system is constructed in step S1, including: Construct a risk assessment index system for hazardous chemicals, where the risk assessment index system for hazardous chemicals is a risk assessment index system for hazardous chemicals at different levels, including the physical and chemical properties assessment index system P1, toxicity assessment index system P2, environmental impact assessment index system P3, emergency disposal assessment index system P4 and leaked gas concentration assessment index system P5. Based on the risk assessment index system for hazardous chemicals, a set of risk assessment indicators for hazardous chemicals is obtained: in: Represents the risk assessment index system P of hazardous chemicals at the i-th level i The risk assessment index of the jth hazardous chemical; the risk assessment indexes of the 1st to 4th hazardous chemicals in the physical and chemical property assessment index system P1 are the flash point of hazardous chemicals, the ignition point of hazardous chemicals, the auto-ignition point of hazardous chemicals and the boiling point of hazardous chemicals; The 1st to 4th hazardous chemicals risk assessment indicators in the toxicity assessment index system P2 are acute toxicity, reproductive toxicity, carcinogenicity, and mutagenicity; The risk assessment indicators for hazardous chemicals of types 1 to 4 in the environmental impact assessment indicator system P3 are environmental biodegradability, environmental bioaccumulation, environmental persistence, and storage environment hazard level; The 1st to 4th hazardous chemicals risk assessment indicators in the emergency response assessment indicator system P4 are, in order, whether there are hazardous chemicals safety operation regulations and procedures, whether there are hazardous chemicals leakage treatment measures, whether there are first aid measures, and whether there are firefighting measures; The 1st to 4th hazardous chemicals risk assessment indicators in the leakage gas concentration assessment index system P5 are ammonia concentration, chlorine concentration, hydrogen sulfide concentration and carbon monoxide concentration.

3. A dangerous chemicals safety risk warning method as claimed in claim 2, characterized in that: The risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals collected include: Collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals. The collection process of risk assessment indicator data is as follows: S11: Based on the risk assessment indicators of hazardous chemicals in the physical and chemical property assessment indicator system P1 and the toxicity assessment indicator system P2, the characteristics of hazardous chemicals are collected to form risk assessment indicator data; S12: Hazardous chemicals risk assessment indicators based on the environmental impact assessment indicator system P3 Calculate and test the storage environment of hazardous chemicals to obtain the oxygen consumption of microorganisms required for the complete degradation of hazardous chemicals in the storage environment After the leakage of hazardous chemicals, the ratio of the concentration of hazardous chemicals in the organism to the concentration of hazardous chemicals in the storage environment After a hazardous chemical leaks, the time required for the concentration of hazardous chemicals in the storage environment to be reduced by half And the degree of danger of the storage environment S13: Construct the risk assessment index data corresponding to the 1st to 4th hazardous chemicals risk assessment index in the emergency response assessment index system P4 in If the storage environment of hazardous chemicals has hazardous chemicals risk assessment indicators The indicator description is otherwise S14: Based on the leaked gas concentration assessment index system P5, collect sequence data of different gas concentrations in the hazardous chemicals risk assessment index as the hazardous chemicals risk assessment index Corresponding risk assessment indicator data S15: Construct the risk assessment indicator data set x corresponding to the risk assessment indicators of hazardous chemicals: in: Indicates the risk assessment index of hazardous chemicals Corresponding risk assessment indicator data.

4. A dangerous chemicals safety risk warning method as claimed in claim 3, characterized in that: In the step S2, the collected risk assessment indicator data is preprocessed, including: The collected risk assessment indicator data are preprocessed to obtain preprocessed risk assessment indicator data, wherein the preprocessing process of the risk assessment indicator data is as follows: S21: extracting risk assessment index data representing the storage risk level of hazardous chemicals from the risk assessment index data set x, and calculating the storage risk level α1 of hazardous chemicals; S22: extracting risk assessment indicator data representing the safety factor of the hazardous chemicals storage environment from the risk assessment indicator data set x, and calculating the safety factor α2 of the hazardous chemicals storage environment; S23: Extract risk assessment indicator data And the risk assessment indicator data Perform data dimensionality reduction to obtain the risk assessment indicator data y after dimensionality reduction; S24: Constructs the risk assessment index data after preprocessing: [y, α1, α2].

5. A dangerous chemicals safety risk warning method as claimed in claim 4, characterized in that: In the step S21, risk assessment index data representing the storage risk level of hazardous chemicals themselves are extracted from the risk assessment index data set x, and the storage risk level of hazardous chemicals themselves α1 is calculated, including: S211: Extract risk assessment indicator data that characterizes the storage risk level of hazardous chemicals: S212: Obtain temperature information tem of the hazardous chemicals storage environment; S213: Calculate the storage risk level of hazardous chemicals α1: in: exp(·) represents an exponential function with a natural constant as the base; Represents risk assessment indicator data The weight of the indicator.

6. A dangerous chemicals safety risk warning method as claimed in claim 1, characterized in that: The method of optimizing the model parameters of the hazardous chemicals leakage warning model based on the training loss function includes: S31: Initialize and generate U groups of model parameters, where the uth group of model parameters generated by initialization is in They correspond to the model parameters w, b, u∈[1,U] in the risk warning layer respectively, and set the iteration upper limit θ of the model parameters up and the iterative lower limit θ down ; S32: Set the current iteration number of the model parameters to z, the initial value of z is 0, and the maximum value is Max, then the zth iteration result of the uth group of model parameters is S33: Calculate the population disturbance value of each group of model parameters, where the model parameters The population disturbance value is S34: Using the U groups of model parameters obtained in the z-th iteration as the input value of the training loss function, obtaining the training loss function value of each group of model parameters, and selecting the model parameter with the smallest training loss function value as the optimal model parameter obtained in the z-th iteration S35: iterating each set of model parameters; S36: Let z=z+1, return to step S33, until the maximum number of iterations is reached, and select the optimal model parameters obtained by the iteration at this time as the model parameter optimization solution result θ * =(w * ,b * ).

7. A dangerous chemicals safety risk warning method as claimed in claim 6, characterized in that: In the step S4, a hazardous chemical leakage warning model is constructed, and a hazardous chemical leakage risk warning is performed on the pre-processed risk assessment indicator data, including: Based on the structure of the hazardous chemicals leakage warning model and the model parameters (w * ,b * ), build a hazardous chemical leakage warning model, and conduct hazardous chemical leakage risk warning for the pre-processed risk assessment index data. The hazardous chemical leakage risk warning process is as follows: S41: The input layer receives the preprocessed risk index data and splits it into the storage risk level α1 of hazardous chemicals, the safety factor α2 of the hazardous chemicals storage environment, and the risk assessment index data y after dimensionality reduction; S42: The risk level mapping layer maps the storage risk level α1 of the hazardous chemicals themselves and the safety factor α2 of the hazardous chemicals storage environment to the risk assessment index data y after dimensionality reduction, and obtains the risk assessment index mapping data Y; S43: The risk warning layer receives the risk assessment index mapping data Y and outputs the hazardous chemical leakage warning result: S=w * Y+b * ; in: S represents the hazardous chemical leakage warning result. If S is higher than the preset leakage threshold, it means that there is a hazardous chemical leakage and it will cause serious harm to the storage environment, and a safety alarm will be issued immediately.

8. A hazardous chemicals safety risk warning system, characterized in that: The system comprises: The data collection module is used to build a risk assessment indicator system for hazardous chemicals and collect risk assessment indicator data corresponding to the risk assessment indicators of hazardous chemicals; A data processing module is used to preprocess the collected risk assessment indicator data to obtain preprocessed risk assessment indicator data; A safety warning device is used to construct a hazardous chemical leakage warning model based on the hazardous chemical leakage warning model structure and the model parameters obtained by optimization and solution, and to issue a hazardous chemical leakage risk warning for the preprocessed risk assessment index data, so as to realize a hazardous chemical safety prevention risk warning method as described in any one of claims 1 to 7.

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