A Method for Safety Risk Assessment and Emergency Response in a Chemical Industrial Park

Abstract

The present invention provides a method for safety risk assessment and emergency response in a chemical industrial park, which relates to the field of safety engineering and includes: obtaining the corresponding first real-time state based on the real-time monitoring data of each first area in the chemical industrial park; determining a risk label based on the first real-time state and the corresponding first risk; obtaining the corresponding risk assessment level based on the risk label corresponding to each first area and an early warning model; giving an early warning to the chemical industrial park based on the risk assessment level and an emergency response database, and optimizing the early warning result, so as to perform an assessment based on the optimized data to obtain an optimization degree index; and updating the emergency response database based on the optimization degree index. By performing status monitoring and combining risk labels, the risk assessment of the chemical industrial park is realized, and major safety risks can be more timely and effectively prevented and resolved, ensuring the safety of the chemical industrial park.

Description

Technical Field

[0001] The present invention relates to the field of safety engineering, and particularly to a method for safety risk assessment and emergency response in a chemical industrial park. Background Art

[0002] At present, with the development of information technology, chemical industrial parks increasingly adopt intelligent risk management platforms to improve safety management levels. In a chemical industrial park, an accident occurring at a hazard source of an enterprise may trigger a chain reaction and major accidents. The chemical industrial park needs to formulate detailed emergency plans to ensure a rapid and effective response in case of an accident.

[0003] However, the existing safety risk assessment and emergency response methods have a slow response speed and cannot be evaluated and updated in a timely manner, thus easily causing losses to the chemical industrial park.

[0004] Therefore, the present invention provides a method for safety risk assessment and emergency response in a chemical industrial park. Summary of the Invention

[0005] The present invention provides a method for safety risk assessment and emergency response in a chemical industrial park, which realizes the risk assessment of the chemical industrial park by performing status monitoring and combining risk tags, can more timely and effectively prevent and defuse major safety risks, ensure the safety of the chemical industrial park, and contribute to the high-quality development of the safety of the chemical industrial park.

[0006] The present invention provides a method for safety risk assessment and emergency response in a chemical industrial park, including:

[0007] Step 1: Obtain the corresponding first real-time status based on the real-time monitoring data of each first area in the chemical industrial park;

[0008] Step 2: Determine risk tags based on the first real-time status and the corresponding first risks;

[0009] Step 3: Obtain the corresponding risk assessment level based on the risk tags corresponding to each first area and the early warning model;

[0010] Step 4: Give an early warning to the chemical industrial park based on the risk assessment level and the emergency response database, and optimize the early warning result, so as to perform an assessment based on the optimized data and obtain an optimization degree index;

[0011] Step 5: Update the emergency response database based on the optimization degree index.

[0012] According to the present invention, obtaining the corresponding first real-time status based on the real-time monitoring data of each first area in the chemical industrial park includes:

[0013] Based on the real-time monitoring data of each first area in the chemical industrial park, obtain the corresponding first data set;

[0014] Based on the first area corresponding to the first data set, match the first standard data of the corresponding area;

[0015] Based on each first data set and the corresponding first standard data, obtain the first difference set between the real-time monitoring data and the corresponding first standard data;

[0016] Based on the first average value and the standard average value in each first difference set, obtain the first real-time state of the chemical industrial park.

[0017] According to the present invention, based on the first real-time state and the corresponding first risk, determine the risk label, including:

[0018] If the first real-time state does not conform to the standard state, obtain the first risk corresponding to the corresponding first area;

[0019] Based on the first risk, obtain the first risk data;

[0020] Based on the real-time monitoring data and the first risk data of the same first area, determine the risk label.

[0021] According to the present invention, based on the risk label corresponding to each first area and the early warning model, obtain the corresponding risk assessment level, including:

[0022] Based on all the first risks corresponding to each first area, obtain the relevant first association combination;

[0023] Based on the first association combination and the risk label of the same first area, obtain the corresponding second association combination;

[0024] Obtain the first cycle of the first area corresponding to the post-associated risk in the second association combination;

[0025] Obtain the first monitoring data of the pre-associated risk corresponding to the post-associated risk in the first cycle, and construct the corresponding first data point graph;

[0026] Based on the second monitoring data after the first cycle of the post-associated risk, construct the corresponding second data point graph;

[0027] Based on the first data point graph and the second data point graph, perform analysis respectively to obtain the corresponding first data curve graph and second data curve graph;

[0028] Based on the first data curve graph and the corresponding first standard data curve, obtain the corresponding first data difference graph;

[0029] Based on the second data curve graph and the corresponding second standard data curve, the corresponding second data deviation graph is obtained;

[0030] Based on the first data deviation graph and the second data deviation graph, the corresponding first data function and second data function are respectively analyzed and obtained;

[0031] Based on the first data function and the second data function, the first coefficient and the second coefficient are respectively obtained;

[0032] Compare the first coefficient and the second coefficient, so as to obtain the first difference between the first coefficient and the second coefficient;

[0033] If the first difference is within the corresponding standard error, it is associated with the risk in the second associated combination to obtain the third associated combination;

[0034] Based on the first difference set corresponding to each risk label, the corresponding first risk matrix is constructed;

[0035] Extract the second difference set corresponding to the pre-associated risk and the third difference set corresponding to the post-associated risk in each pair of third associated combinations, so as to construct the second risk matrix;

[0036] Input all the risk labels corresponding to each first region, the corresponding first risk matrix, all the third associated combinations and the corresponding second risk matrix into the early warning model to obtain the corresponding risk assessment level.

[0037] According to the present invention, based on the risk assessment level and the emergency response database, the chemical industrial park is warned, and the warning result is optimized, so as to evaluate based on the optimized data to obtain the optimization degree index, including:

[0038] Based on each warning risk in the risk assessment level and the emergency response database, the corresponding emergency response information and the corresponding identification code are obtained;

[0039] Based on the identification code and the identification resolution system, the corresponding real-time park information is obtained;

[0040] Based on the emergency response information and the corresponding real-time park information, the chemical industrial park is warned.

[0041] According to the present invention, based on the risk assessment level and the emergency response database, the chemical industrial park is warned, and the warning result is optimized, so as to evaluate based on the optimized data to obtain the optimization degree index, and further includes:

[0042] Obtain the third monitoring data of an optimized optimization period corresponding to each warning risk;

[0043] Calculate the corresponding optimization degree index based on the third monitoring data and the third standard data corresponding to all early warning risks.

[0044] Calculating the corresponding optimization degree index according to the third monitoring data and the third standard data corresponding to all early warning risks provided by the present invention includes:

[0045] ;

[0046] ;

[0047] where D represents the optimization degree index of the optimization data; n represents the number of early warning risks;

[0048] represents the preset optimization coefficient of the early warning risk; represents the th calculation coefficient of the th early warning risk among all early warning risks; represents the optimization weight of the th early warning risk among all early warning risks; represents the optimization weight of the (i - 1)th early warning risk among all early warning risks; represents the optimization index of the th early warning risk among all early warning risks; represents the number of moments corresponding to the early warning risk; represents the th third monitoring data of the th early warning risk among all early warning risks at the th moment; represents the

[0049] th third standard data of the

[0050] th early warning risk among all early warning risks at the

[0051] Update the emergency response database based on the updated emergency response information.

[0052] The chemical industrial park provided by the present invention analyzes the safety risk assessment and emergency response methods of the chemical industrial park. By conducting status monitoring and combining risk tags, the risk assessment of the chemical industrial park is realized, which can prevent and resolve major safety risks more timely and effectively, ensure the safety of the chemical industrial park, and contribute to the high-quality development of the safety of the chemical industrial park. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a flowchart of a method for safety risk assessment and emergency response of a chemical industrial park provided by an embodiment of the present invention. Detailed Embodiments

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0056] Embodiment 1:

[0057] The embodiment of the present invention provides a method for safety risk assessment and emergency response of a chemical industrial park, as Figure 1 shown, including:

[0058] Step 1: Based on the real-time monitoring data of each first area of the chemical industrial park, obtain the corresponding first real-time state;

[0059] Step 2: Based on the first real-time state and the corresponding first risk, determine the risk tag;

[0060] Step 3: Based on the risk tag corresponding to each first area and the early warning model, obtain the corresponding risk assessment level;

[0061] Step 4: Based on the risk assessment level and the emergency response database, give an early warning to the chemical industrial park, and evaluate the optimized data after the early warning to obtain the optimization degree index;

[0062] Step 5: Update the emergency response database based on the optimization degree index.

[0063] In this embodiment, the first area refers to the area of each type of chemical produced in the chemical industrial park obtained by classifying the area of the chemical industrial park according to the types of chemicals produced in the park.

[0064] In this embodiment, the real-time monitoring data refers to the data obtained by sensors in real time for each first area of the chemical industrial park.

[0065] In this embodiment, the first real-time status refers to the safety status of the production corresponding to the first area.

[0066] In this embodiment, the first risk refers to all possible risks that may occur in each first area.

[0067] In this embodiment, the risk label refers to the name of the possible risk corresponding to the first real-time status.

[0068] In this embodiment, the early warning model refers to a model trained by the data of each risk in each first area and the corresponding hazards.

[0069] In this embodiment, the risk assessment level refers to the probability of occurrence of all risks in the chemical industrial park obtained by analyzing the risk labels corresponding to each first area and the early warning model.

[0070] In this embodiment, the emergency response database refers to a database that contains emergency response information corresponding to each risk and identification codes. Among them, the emergency response information refers to the actions required to respond to risks, such as remotely controlling the closing of valves and evacuating staff. The identification code refers to a coded character obtained by registering specific persons or machines, such as the person in charge of the park, the person in charge of enterprise safety, and relevant valve controllers, who receive emergency response information into the identification resolution system. The identification resolution system is a system that can register, query, modify, and configure the objects to which emergency response information is sent.

[0071] In this embodiment, the optimized data refers to the production rate of the corresponding first area obtained during an optimization cycle after early warning. The optimization cycle refers to the time required for each risk to be optimized. Among them, the production rate refers to the production efficiency of the corresponding first area in producing chemical products after an optimization cycle.

[0072] In this embodiment, the optimization degree index refers to an index obtained by evaluating and calculating the optimized data after early warning, which represents the degree of risk optimization and resolution.

[0073] The beneficial effects of the above technical solution are as follows: By conducting status monitoring and combining risk labels, the risk assessment of the chemical industrial park is realized, which can more timely and effectively prevent and resolve major safety risks, ensure the safety of the chemical industrial park, and contribute to the high-quality development of the safe development of the chemical industrial park.

[0074] Example 2:

[0075] Based on the real-time monitoring data of each first area in the chemical industrial park on the basis of Example 1, the corresponding first real-time status is obtained, including:

[0076] Based on the real-time monitoring data of each first area in the chemical industrial park, the corresponding first data set is obtained;

[0077] Based on the first area corresponding to the first data set, the first standard data of the corresponding area is matched;

[0078] Based on each first data set and the corresponding first standard data, the first difference set between the real-time monitoring data and the corresponding first standard data is obtained;

[0079] Based on the first average value and the standard average value in each first difference set, the first real-time status of the chemical industrial park is obtained.

[0080] In this embodiment, the first data set refers to the set of real-time monitoring data of all sensors in the first area.

[0081] In this embodiment, the first default data refers to the data obtained by each type of sensor under normal production.

[0082] In this embodiment, the first difference set refers to the difference between the data obtained by the same sensor at each moment.

[0083] In this embodiment, the first average value refers to the average value of all differences in the first difference set.

[0084] In this embodiment, the standard average value refers to the average value of all differences in the first difference set under normal production.

[0085] The beneficial effects of the above technical solutions are: By analyzing the real-time monitoring data of each first area in the chemical industrial park, the corresponding first real-time status is obtained, which is beneficial to accurately analyze the real-time monitoring data and ensure the production safety of the chemical industrial park.

[0086] Example 3:

[0087] Based on the basis of Example 1, it is characterized in that, based on the first real-time status and the corresponding first risk, a risk label is determined, including:

[0088] If the first real-time status does not meet the standard status, the first risk corresponding to the corresponding first area is obtained;

[0089] Based on the first risk, the first risk data is obtained;

[0090] Determine a risk label based on the real-time monitoring data and the first risk data of the same first region.

[0091] In this embodiment, the standard state refers to the safe state of each first region under normal production.

[0092] In this embodiment, the first risk data refers to the data of the impact of the first risk on production.

[0093] The beneficial effect of the above technical solution is that by analyzing the first real-time state and the corresponding first risk, matching and determining the risk label is beneficial to accurately determine the possible risks in the follow-up.

[0094] Embodiment 4:

[0095] Based on Embodiment 2, based on the risk label corresponding to each first region and the early warning model, obtain the corresponding risk assessment level, including:

[0096] Based on all the first risks corresponding to each first region, obtain the relevant first association combination therein;

[0097] Based on the first association combination and the risk label of the same first region, obtain the corresponding second association combination;

[0098] Obtain the first period of the first region corresponding to the post-associated risk in the second association combination;

[0099] Obtain the first monitoring data of the pre-associated risk corresponding to the post-associated risk in the first period, and construct the corresponding first data point graph;

[0100] Based on the second monitoring data after the first period of the post-associated risk, construct the corresponding second data point graph;

[0101] Based on the first data point graph and the second data point graph, perform analysis respectively to obtain the corresponding first data curve graph and second data curve graph;

[0102] Based on the first data curve graph and the corresponding first standard data curve, obtain the corresponding first data difference graph;

[0103] Based on the second data curve graph and the corresponding second standard data curve, obtain the corresponding second data difference graph;

[0104] Based on the first data difference graph and the second data difference graph, perform analysis respectively to obtain the corresponding first data function and second data function;

[0105] Based on the first data function and the second data function, obtain the first coefficient and the second coefficient respectively;

[0106] Compare the first coefficient and the second coefficient to obtain a first difference between the first coefficient and the second coefficient;

[0107] If the first difference is within the corresponding standard error, associate it with the risk in the second associated combination to obtain a third associated combination;

[0108] Construct a corresponding first risk matrix based on the first difference set corresponding to each risk label;

[0109] Extract the second difference set corresponding to the pre-associated risk and the third difference set corresponding to the post-associated risk in each pair of third associated combinations, so as to construct a second risk matrix;

[0110] Input all the risk labels corresponding to each first region, the corresponding first risk matrix, all the third associated combinations, and the corresponding second risk matrix into the early warning model to obtain the corresponding risk assessment level.

[0111] In this embodiment, the first associated combination refers to the combination of risks with an associated relationship among all the first risks corresponding to the first region, and is arranged in the order of influence and being influenced.

[0112] In this embodiment, the second associated combination refers to the first associated combination in which at least one of the risk labels in the first region is the same as all the risks in all the first associated combinations.

[0113] In this embodiment, the post-associated risk refers to the risk at the back in the second associated combination.

[0114] In this embodiment, the first cycle refers to the production cycle in the first region.

[0115] In this embodiment, the pre-associated risk refers to the risk at the front in the second associated combination.

[0116] In this embodiment, the first monitoring data refers to the production rate of the pre-associated risk at each moment in the first cycle.

[0117] In this embodiment, the first data point graph refers to the point graph in which the first monitoring data is arranged in the order of time.

[0118] In this embodiment, the second monitoring data refers to the production rate of the post-associated risk at each moment in the first cycle.

[0119] In this embodiment, the second data point graph refers to the point graph in which the second monitoring data is arranged in the order of time.

[0120] In this embodiment, the first data curve refers to the curve graph obtained by performing fitting analysis on the first data point graph.

[0121] In this embodiment, the second data curve refers to the curve graph obtained by performing fitting analysis on the second data point graph.

[0122] In this embodiment, the first standard data curve refers to the first data curve graph of the corresponding first area under normal production.

[0123] In this embodiment, the second standard data curve refers to the second data curve graph of the corresponding first area under normal production.

[0124] In this embodiment, the first data difference point graph refers to the point graph constructed from the differences between the values corresponding at the same time of the first data curve graph and the corresponding first standard data curve.

[0125] In this embodiment, the second data difference point graph refers to the point graph constructed from the differences between the values corresponding at the same time of the second data curve graph and the corresponding first standard data curve.

[0126] In this embodiment, the first data function refers to the function obtained by performing fitting analysis on the first data difference point graph and representing the change trend of the point graph.

[0127] In this embodiment, the second data function refers to the function obtained by performing fitting analysis on the second data difference point graph and representing the change trend of the point graph.

[0128] In this embodiment, the first coefficient refers to the coefficient of the independent variable of the first data function.

[0129] In this embodiment, the second coefficient refers to the coefficient of the independent variable of the second data function.

[0130] In this embodiment, the first difference refers to the difference between the first coefficient and the second coefficient.

[0131] In this embodiment, the standard error refers to the normal error of the difference between the first coefficient and the second coefficient when the two risks are related.

[0132] In this embodiment, the third associated combination refers to the combination of two risks corresponding to the first difference within the standard error.

[0133] In this embodiment, the first risk matrix refers to the matrix representing the likelihood of risk occurrence constructed from all the data in the first difference set corresponding to the risk labels and the risk levels in the risk level comparison table. Among them, the risk level comparison table refers to the table in which the differences corresponding to each moment in the first difference set and the corresponding risk levels are in one-to-one correspondence.

[0134] In this embodiment, the second difference set refers to the first difference set of the pre-associated risks of the third associated combination.

[0135] In this embodiment, the third difference set refers to the first difference set of the subsequent associated risks of the third associated combination.

[0136] In this embodiment, the second risk matrix refers to a matrix representing the likelihood of risk occurrence constructed from all the data of the second difference set and the third difference set and the risk levels in the risk level comparison table. Among them, the risk level comparison table refers to a table in which the differences corresponding to each moment in the first difference set and the corresponding risk levels are in one-to-one correspondence.

[0137] The beneficial effects of the above technical solution are as follows: By analyzing the risk labels corresponding to each first region, analyzing the relevance of all risks, inputting them into the early warning model, obtaining the corresponding risk assessment levels, effectively preventing and resolving major safety risks, ensuring production safety, realizing data sharing within the park, and contributing to the high-quality development of the chemical industrial park.

[0138] Embodiment 5:

[0139] Based on Embodiment 1, based on the risk assessment level and the emergency response database, the chemical industrial park is warned, and the warning result is optimized. Then, based on the optimized data, an optimization degree index is obtained, including:

[0140] Based on each warning risk in the risk assessment level and the emergency response database, the corresponding emergency response information and the corresponding identification code are obtained;

[0141] Based on the identification code and the identification resolution system, the corresponding real-time park information is obtained;

[0142] Based on the emergency response information and the corresponding real-time park information, the chemical industrial park is warned.

[0143] In this embodiment, the emergency response information refers to the actions required to respond to risks, such as remotely controlling the closing of valves and evacuating staff.

[0144] In this embodiment, the identification code refers to a coded character obtained by registering specific persons or machines such as the park person in charge, enterprise safety person in charge, and relevant valve controllers that receive emergency response information into the identification resolution system.

[0145] In this embodiment, the identification resolution system refers to a system that can register, query, modify, and configure the objects to which emergency response information is sent.

[0146] In this embodiment, the real-time park information refers to the service information of the park person in charge, enterprise safety person in charge, relevant valve controllers, etc. corresponding to the identification code in the identification resolution system that receive emergency response information.

[0147] The beneficial effects of the above technical solution are as follows: By analyzing the risk assessment level and the emergency response database, early warnings are issued for chemical industrial parks to ensure the production safety of chemical industrial parks.

[0148] Example 6:

[0149] Based on Example 5, based on the risk assessment level and the emergency response database, early warnings are issued for chemical industrial parks, and the early warning results are optimized. Then, evaluations are carried out based on the optimized data to obtain the optimization degree index, and it further includes:

[0150] Obtain the third monitoring data of an optimized cycle corresponding to each early warning risk;

[0151] Based on the third monitoring data corresponding to all early warning risks and the third standard data, calculate the corresponding optimization degree index.

[0152] In this example, the optimization cycle refers to the time required for optimizing each risk.

[0153] In this example, the third monitoring data refers to the production rate of the corresponding first area obtained in an optimized cycle after early warning.

[0154] In this example, the third standard data refers to the production rate of the corresponding first area obtained in an optimized cycle after early warning under normal production conditions.

[0155] The beneficial effects of the above technical solution are as follows: By evaluating the optimized data after early warning to obtain the optimization degree index and conducting precise evaluations, it is beneficial to optimize the emergency response information and improve the risk resolution efficiency.

[0156] Example 7:

[0157] Based on Example 6, based on the third monitoring data corresponding to all early warning risks and the third standard data, calculate the corresponding optimization degree index, including:

[0158] ;

[0159] ;

[0160] Among them, D represents the optimization degree index of the optimized data; n represents the number of early warning risks;

[0161] represents the preset optimization coefficient of the early warning risk; represents the th calculation coefficient of the th early warning risk among all early warning risks; The optimized weight of a warning risk; It represents the optimized weight of the (i - 1)-th warning risk among all warning risks; It represents the optimized index of the j-th warning risk among all warning risks; It represents the number of moments corresponding to the warning risk; It represents the k-th moment's third monitoring data of the j-th warning risk among all warning risks; It represents the k-th moment's third standard data of the j-th warning risk among all warning risks.

[0162] In this embodiment, the preset optimization coefficient refers to the coefficient corresponding to the preset optimization rate.

[0163] In this embodiment, the calculation coefficient refers to the coefficient corresponding to each risk for calculation.

[0164] In this embodiment, the optimized weight refers to the value representing the importance degree corresponding to each risk.

[0165] In this embodiment, the optimized index refers to the index representing the rate of the optimization process of each risk.

[0166] The beneficial effect of the above technical solution is that by evaluating and calculating the optimized data after warning, the optimization degree index is obtained, and accurate evaluation is carried out, which is beneficial to optimizing the emergency response information.

[0167] Embodiment 8:

[0168] Based on Embodiment 1, based on the optimization degree index, the emergency response database is updated, including:

[0169] If the optimization degree index is greater than the preset optimization degree index, the optimization degree index and the corresponding emergency response information are input into the emergency response optimization model to obtain the corresponding updated emergency response information;

[0170] Based on the updated emergency response information, the emergency response database is updated.

[0171] In this embodiment, the preset optimization degree index refers to the preset index representing the degree of risk optimization and resolution.

[0172] In this embodiment, the emergency response optimization model refers to the model trained by risks and the corresponding optimal emergency response information.

[0173] The beneficial effects of the above technical solution are as follows: By analyzing the optimization degree index and updating the emergency response database, it is beneficial to optimize the emergency response information and improve the efficiency of risk resolution.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for safety risk assessment and emergency response in a chemical industrial park, characterized in that, Including: Step 1: Obtain the corresponding first real-time status based on the real-time monitoring data of each first area in the chemical industrial park; Step 2: Determine the risk label based on the first real-time status and the corresponding first risk; Step 3: Obtain the corresponding risk assessment level based on the risk label corresponding to each first area and the early warning model; Step 4: Based on the risk assessment level and the emergency response database, give an early warning to the chemical industrial park and optimize the early warning result, so as to evaluate based on the optimized data and obtain the optimization degree index; Step 5: Update the emergency response database based on the optimization degree index; Among them, Step 3 includes: Based on all the first risks corresponding to each first area, obtain the relevant first associated combination among them; Based on the first associated combination and the risk label of the same first area, obtain the corresponding second associated combination; Obtain the first period of the first area corresponding to the post-associated risk in the second associated combination; Obtain the first monitoring data of the pre-associated risk corresponding to the post-associated risk in the first period, and construct the corresponding first data point graph; Based on the second monitoring data after the first period of the post-associated risk, construct the corresponding second data point graph; Based on the first data point graph and the second data point graph, perform analysis respectively to obtain the corresponding first data curve graph and second data curve graph; Based on the first data curve graph and the corresponding first standard data curve, obtain the corresponding first data difference graph; Based on the second data curve graph and the corresponding second standard data curve, obtain the corresponding second data difference graph; Based on the first data difference graph and the second data difference graph, analyze respectively to obtain the corresponding first data function and second data function; Based on the first data function and the second data function, obtain the first coefficient and the second coefficient respectively; Compare the first coefficient and the second coefficient, so as to obtain the first difference between the first coefficient and the second coefficient; If the first difference is within the corresponding standard error, it is associated with the risk in the second associated combination to obtain the third associated combination; Based on the first difference set corresponding to each risk label, construct the corresponding first risk matrix; Extract the second difference set corresponding to the pre-associated risk and the third difference set corresponding to the post-associated risk in each pair of third associated combinations, so as to construct the second risk matrix; Input all the risk labels corresponding to each first area, the corresponding first risk matrix, all the third associated combinations and the corresponding second risk matrix into the early warning model to obtain the corresponding risk assessment level.

2. The chemical industrial park safety risk assessment and emergency response method according to claim 1, wherein, Obtain the corresponding first real-time status based on the real-time monitoring data of each first area in the chemical industrial park, including: Based on the real-time monitoring data of each first area in the chemical industrial park, obtain the corresponding first data set; Based on the first area corresponding to the first data set, match the first standard data of the corresponding area; Based on each first data set and the corresponding first standard data, obtain the first difference set between the real-time monitoring data and the corresponding first standard data; Based on the first average value in each first difference set and the standard average value, obtain the first real-time state of the chemical industrial park.

3. A method for safety risk assessment and emergency response in a chemical industrial park according to claim 1, characterized in that, Based on the first real-time state and the corresponding first risk, determine risk labels, including: If the first real-time state does not conform to the standard state, obtain the first risk of the corresponding first area; Based on the first risk, obtain first risk data; Based on the real-time monitoring data and the first risk data of the same first area, determine risk labels.

4. A safety risk assessment and emergency response method for a chemical industrial park according to claim 1, characterized in that, Based on the risk assessment level and the emergency response database, give an early warning to the chemical industrial park and optimize the early warning result, so as to evaluate based on the optimized data and obtain the optimization degree index, including: Based on each warning risk in the risk assessment level and the emergency response database, obtain the corresponding emergency response information and the corresponding identification code; Based on the identification code and the identification resolution system, obtain the corresponding real-time park information; Based on the emergency response information and the corresponding real-time park information, give an early warning to the chemical industrial park.

5. A method for safety risk assessment and emergency response in a chemical industrial park according to claim 4, characterized in that Based on the risk assessment level and the emergency response database, give an early warning to the chemical industrial park and optimize the early warning result, so as to evaluate based on the optimized data and obtain the optimization degree index, and also include: Obtain the third monitoring data of an optimized cycle corresponding to each warning risk; Based on the third monitoring data corresponding to all warning risks and the third standard data, calculate the corresponding optimization degree index.

6. The chemical industrial park safety risk assessment and emergency response method according to claim 5, characterized in that, Based on the third monitoring data corresponding to all warning risks and the third standard data, calculate the corresponding optimization degree index, including: ； ； Among them, D represents the optimization degree index of the optimized data; n represents the number of warning risks; A preset optimization coefficient representing a warning risk; Represents the calculation coefficient of the th warning risk among all warning risks; Represents the optimization weight of the th warning risk among all warning risks; Represents the optimization weight of the (i - 1)th warning risk among all warning risks; Represents the optimization index of the th warning risk among all warning risks; Represents the number of moments corresponding to the warning risk; The third monitoring data of the th moment of the th warning risk among all warning risks; The third standard data of the th moment of the th warning risk among all warning risks.

7. A method for safety risk assessment and emergency response in a chemical industrial park according to claim 1, characterized in that, Based on the optimization degree index, update the emergency response database, including: If the optimization degree index is greater than the preset optimization degree index, input the optimization degree index and the corresponding emergency response information into the emergency response optimization model to obtain the corresponding updated emergency response information; Based on the updated emergency response information, update the emergency response database.

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