A quantitative evaluation method for real-time consequences of accident scenarios in a smart factory platform

Abstract

The present invention discloses a method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform, and the steps are as follows: (S1) constructing an underlying model of a chemical plant accident scenario; (S2) obtaining the working parameters of all equipment that may cause accident scenarios in real-time operation, and the real-time meteorological condition parameters of the location of the chemical plant; (S3) performing accident consequence simulation calculations on the accident scenario model of the chemical plant after updating the real-time data, and outputting the results; (S4) three-dimensionally visualizing the impact range of typical results in the three-dimensional model of the chemical plant in the smart factory platform, and judging the accident scenario consequence level according to the range of the results; (S5) when a person enters a risk area with a higher or lower consequence risk level, or when the consequence risk level of the person's location changes, a corresponding signal reminder will be received. The method of the present invention provides data support for the risk early warning system of the smart factory platform, and provides certain guiding significance for the real-time safety risk management and control of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of quantitative evaluation of accident consequences of chemical plants, and in particular to a method for quantitative evaluation of real-time consequences of accident scenarios in a smart factory platform. Background Art

[0002] With the continuous development of science and technology, digital transformation has become an important trend in all walks of life. As an important pillar industry of the national economy, the digital transformation of the chemical industry is of great significance in improving production efficiency, reducing energy consumption, and ensuring safety. As one of the key technologies for digital transformation, the 3D visualization technology of the digital twin of the smart factory has also become a hot topic in the industry. The 3D visualization technology of the digital twin of the smart factory is an advanced technology that combines virtual reality technology with industrial Internet technology. Through this technology, a full range of 3D modeling of chemical plants can be carried out to realize real-time monitoring of production processes, data analysis, and early warning prediction. This can not only improve production efficiency, reduce energy consumption and emissions, but also improve the safety and reliability of the factory and protect the lives of workers.

[0003] At present, the risk warning systems of most smart factory platforms are often monitoring of operating scenarios, such as real-time detection of production, equipment, and environmental protection, or display of safety and environmental protection information, such as the four-color distribution map of safety risks. The former is real-time monitoring of possible accidents, that is, potential risk points, but it cannot quantify the consequences of accidents; the latter is the division of risk levels in various areas of the factory, that is, the division of the severity of accident risk scenarios, but it is often static and unchanging. At present, no smart factory platform has developed a module that can combine real-time monitoring data with actual consequences.

[0004] The research on quantitative evaluation of real-time consequences of chemical plants and its application in smart factories aims to provide a method for real-time quantitative evaluation of the consequences of typical accident scenarios in chemical plants, provide data support for the risk early warning system of the smart factory platform, and provide certain guidance for the real-time safety risk management of enterprises.

[0005] In order to solve the problem of lack of real-time quantitative assessment of consequences in the risk warning system of the smart factory platform, technical personnel in this field have improved the risk warning system of the traditional smart factory platform. For example, the invention patent disclosure of a method for analyzing the impact of explosions of canned materials in a smart chemical park, published by the China Patent Office on June 29, 2021, with the application publication number CN113051715A, conducted a quantitative assessment of the pressure storage tanks in the chemical park, but its application has certain limitations. The main reason is that: although there are various forms of explosions, vapor cloud explosions are the primary concern in petrochemicals. For pressure equipment explosions, liquid explosions, etc., the risk of accidents can be reduced by regularly conducting equipment inspections, maintenance, and strengthening safety management and control measures, which are generally not considered in engineering. For example, the invention patent disclosure of a chemical storage tank steam cloud explosion accident simulation analysis method and system published by the China Patent Office on May 14, 2020, with the application publication number CN111539126A, for chemical storage tank steam cloud explosion, the TNT equivalent method, which is not suitable for use in API752 and "Petrochemical Process Risk Quantitative Analysis Standard" SH / T3226-2024, was used to simulate the accident. Summary of the invention

[0006] The purpose of the present invention is to solve the problem of lack of real-time quantitative evaluation of consequences in the risk warning system of the smart factory platform, to provide data support for the risk warning system of the smart factory platform, and to provide certain guidance for the real-time safety risk management of the enterprise. The present invention provides a method for quantitative evaluation of real-time consequences of accident scenarios in a smart factory platform.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions.

[0008] A quantitative evaluation method for real-time consequences of accident scenarios in a smart factory platform.

[0009] The method comprises the following steps:

[0010] Step S1: Based on the actual situation of the chemical enterprise, construct the underlying model of the accident scenario of the chemical plant of the enterprise. The underlying model mainly includes the following four aspects: leakage source, leakage scenario, weather scenario, and blocked space. Step S11: Construct the leakage source in the underlying model of the accident scenario of the chemical plant. The leakage source should include the material type of all process equipment (facilities) where the accident scenario may occur, the content V of the material under standard working conditions, and the leakage source of the material under standard working conditions. 标 , the working temperature of the equipment under standard working conditions T 标 , Working pressure of equipment under standard working conditions P 标 , the actual size of the equipment, the installation height of the equipment, etc.;

[0011] Step S12, constructing a leakage scenario in the underlying model of the chemical plant accident scenario, wherein the leakage scenario should include representative leakage apertures d of all equipment that may cause accident scenarios. rep , effective leakage time t e , leakage aperture d rep The selection should follow the following principles:

[0012] Maximum pipe diameter range / mm Representative leakage diameter / mm <![CDATA[d max ≥50]]> 25 <![CDATA[25<d max <50]]> 15～25 <![CDATA[d max ≤25]]> 15 or maximum pipe diameter

[0013] Representative effective leakage time t e The selection should follow the following principles:

[0014] Take the minimum value of the following three items:

[0015] ①60min;

[0016] ②The ratio of the maximum possible leakage volume to the leakage rate;

[0017] ③ Leakage time based on the detection and interlocking and cutting system level, refer to Tables 1 and 2 for the determination of the detection and interlocking and cutting system, and Table 3 for the leakage time of the corresponding detection and interlocking and cutting system level.

[0018] Table 1 Classification table of detection systems

[0019]

[0020] Table 2 Classification table of interlocking cut-off system

[0021]

[0022] Table 3 Leakage time based on detection and interlock cut-off system level

[0023]

[0024] Step S13, constructing a weather scenario in the underlying model of the chemical plant accident scenario, wherein the weather scenario should include the main meteorological conditions of the location of the chemical plant, including the average atmospheric temperature T 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind direction over the years, etc.

[0025] Step S14, construct the obstruction space in the underlying model of the accident scene of the chemical plant, the obstruction space should include the spatial location, length, width, and height of the obstruction space in the chemical plant to determine the location of the obstruction space and the volume it contains. And according to the obstacles in the obstruction space in the chemical plant, determine the congestion ratio VBR and the explosion source intensity R0 of the obstruction space, the congestion ratio VBR of the obstruction space = the spatial volume occupied by the obstruction in the obstruction space / the total spatial volume of the obstruction space, the explosion source intensity is divided into 10 levels from low to high, 1 to 10, the higher the congestion level, the higher the explosion source intensity, the selection of the explosion source intensity should follow the following rules:

[0026]

[0027] The explosion source intensity R0 should also meet the following conditions:

[0028] a) The explosion source intensity of the oil tank area can be selected from 3 to 4;

[0029] b) When the tank area is surrounded by trees, at least 5 should be selected;

[0030] c) For liquefied hydrocarbon tank area, option 5 can be selected;

[0031] d) For Class A and B petrochemical plant areas, 6 to 10 can be selected, and 7 to 9 are commonly used;

[0032] Step S2, obtaining the working parameters of all equipment in real-time operation that may cause accident scenarios, and the real-time meteorological condition parameters of the location of the chemical plant, the acquisition time and validity time of the parameters shall not exceed 10s; the working parameters of the equipment in real-time operation obtained shall replace the working parameters of the leakage source under the standard working conditions in the underlying model; the real-time meteorological condition parameters of the location of the device obtained shall replace the weather scene parameters in the underlying model, the specific steps are as follows:

[0033] Step S21, according to the liquid level sensor device of the process equipment (facility), obtain all the equipment that may cause accident scenes, and the content V of the material under real-time operation 实 , replace the material content V under standard working conditions of the leakage source in the bottom model 标 ;

[0034] Step S22, based on the process equipment (facility) temperature sensor device, obtain all equipment that may cause accident scenarios, and the operating temperature T of the equipment under real-time operation 实 , replace the operating temperature T of the equipment under standard working conditions of the leakage source in the bottom model 标 ;

[0035] Step S23, according to the process equipment (facility) pressure sensor device, obtain all equipment that may cause accident scenarios, and the working pressure P of the equipment under real-time operation 实, replace the working pressure P of the equipment under the standard working condition of the leakage source in the bottom model 标 ;

[0036] Step S24, using the integrated meteorological monitoring sensor device, obtain the real-time meteorological condition parameters of the location of the chemical plant, including the real-time atmospheric temperature T 气实 , Real-time relative humidity H 气实 , Real-time wind speed v 气实 , Real-time atmospheric stability P 气实 , real-time wind direction, etc., to replace the historical average atmospheric temperature T of the weather scene in the underlying model 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind directions over the years, etc.; based on step S2, it is implemented by a device that can receive the operating data provided by the remote sensor in real time and replace the corresponding static data in the underlying model, the device comprising:

[0037] a receiving device capable of receiving a plurality of data signals from a plurality of sensor devices;

[0038] A memory, which can store data signals received by the receiving device;

[0039] A processing device, which can process the data signal received by the receiving device, integrate the received data signal according to a rule, and the processed data signal corresponds to the replacement item in the underlying model one by one;

[0040] A transmission device, which can transmit the signal processed by the processing device to the bottom model end;

[0041] Step S3, performing accident consequence simulation calculation on the accident scenario model of the chemical plant after updating the real-time data, the simulation calculation is performed using DNV Phast&Safeti software, and the output results include the impact range under typical thermal radiation intensity and the impact range under typical explosion overpressure value;

[0042] Step S4: In the three-dimensional model of the chemical plant in the smart factory platform, the influence range under the typical thermal radiation intensity and the influence range under the typical explosion overpressure value are three-dimensionally visualized, and the risk level of the accident scene consequences is judged according to the range of the results, and different colors are used to distinguish them. The classification of the levels can follow the following principles:

[0043]

[0044]

[0045]

[0046] Through S4, in the three-dimensional model of the chemical plant in the smart factory platform, at any time, any point in the space of the three-dimensional model must belong to one of the following accident scenario consequence risk levels: major risk, relatively large risk, general risk, low risk, and below low risk;

[0047] Step S5, combining the accident scenario consequence risk level classification range obtained in S4 with the personnel positioning system in the smart factory platform, when a person enters a risk area with a higher or lower consequence risk level, or when the consequence risk level of the person's location changes, a corresponding signal reminder will be received;

[0048] Based on the steps of S4 and S5, a device is provided which can receive the results provided by the simulation software in real time, visualize the results in three dimensions in the three-dimensional model of the chemical plant smart factory platform, and display them in colors representing corresponding consequence risk levels, and when personnel enter the risk area of ​​corresponding consequence risk level, they receive corresponding signal reminders in combination with the personnel positioning system, and the device includes:

[0049] A receiving device, the receiving device can receive multiple data signals from multiple devices, the signals including: result data signals provided by simulation software, data signals when personnel enter risk areas of corresponding consequence risk levels;

[0050] A memory, which can store data signals received by the receiving device;

[0051] A processing device, the processing device can process the data signal received by the receiving device, the signal including: a result data signal provided by the received simulation software is three-dimensionally visualized in a three-dimensional model in the chemical device smart factory platform, and is displayed in a color-coded manner representing the corresponding consequence risk level; when a data signal is received when a person enters a risk area of ​​a corresponding consequence risk level, the corresponding data signal is identified and converted into a corresponding reminder signal;

[0052] A transmission device, which can transmit a signal that the processing device has completed processing to a corresponding person;

[0053] The beneficial effects of the present invention are:

[0054] 1) The present invention constructs a method for quantitatively evaluating the real-time consequences of accident scenarios. The method establishes an underlying model of the accident scenario of a chemical plant and receives real-time operation data of the chemical plant and local real-time meteorological data to achieve real-time quantitative evaluation of the consequences, providing real-time data support for the risk warning system of the smart factory platform to ensure the safety of the production process.

[0055] 2) The accident scenarios selected for the real-time consequence quantitative assessment of the present invention include intelligent analysis of typical accident scenarios of various chemical enterprises, such as fire (including fireball, jet fire, pool fire) and explosion, which improves the analysis effect of real-time consequence quantitative assessment and is more comprehensive than a single type of accident scenario. For the consequence assessment of explosion, the steam cloud explosion, which is the explosion type that needs to be paid primary attention in the petrochemical industry, is adopted, and the TNO multi-energy method, which is the currently commonly used empirical model of steam cloud explosion, is used for simulation calculation.

[0056] 3) The present invention combines the typical results of real-time consequence quantitative evaluation with the three-dimensional model of the chemical plant in the smart factory platform to achieve three-dimensional visualization of real-time consequence quantitative evaluation.

[0057] 4) The present invention presents typical results of real-time consequence quantitative assessment (the impact range under typical thermal radiation intensity, the impact range under typical explosion overpressure value) in three dimensions, and determines the risk level of accident scene consequences according to the range of the results, thereby realizing real-time dynamic identification of the accident risk level of chemical enterprises.

[0058] 5) The present invention combines the accident risk level of chemical enterprises with the personnel positioning system in the smart factory platform, and informs the operators entering the corresponding level risk area of ​​the real-time dynamic risk of the chemical equipment, providing certain guidance for the real-time safety risk management of the enterprise.

[0059] 6) The "device that can receive the operation data provided by the remote sensor in real time and replace the corresponding static data in the underlying model" and "a device that can receive the results provided by the simulation software in real time, visualize the results in three dimensions in the three-dimensional model of the chemical plant smart factory platform, and display them in different colors representing the corresponding consequence risk levels, and combined with the personnel positioning system, when the personnel enter the risk area of ​​the corresponding consequence risk level, the device receives the corresponding signal reminder" described in the present invention has the function of storing historical data, which can trace back the monitored historical risk scenarios and provide strong data support for tracing the safety risk.

[0060] 7) The present invention constructs a quantitative evaluation method for real-time consequences of accident scenarios in a smart factory platform. The system provides the functions of uniformly aggregating data from multiple parties and centrally managing safety risks, which can improve the efficiency of safety risk management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the process of the method of the present invention;

[0062] Figure 2 This is a three-dimensional visualized hierarchical display of the impact range under typical thermal radiation intensity in the three-dimensional model of the chemical plant in the smart factory platform of Example 1 of the present invention. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with specific embodiments and the accompanying drawings. A person of ordinary skill in the art will be able to implement the present invention based on these descriptions. In addition, the embodiments of the present invention involved in the following description are generally only embodiments of a part of the present invention, rather than all embodiments. Therefore, based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without making creative work should fall within the scope of protection of the present invention.

[0064] Example 1

[0065] Taking a chemical enterprise as an example, a quantitative evaluation method for real-time consequences of accident scenarios in a smart factory platform is implemented. The flow chart of the method is as follows Figure 1 As shown, the specific steps include:

[0066] Step S1, constructing the underlying model of the accident scenario of the enterprise's chemical plant, wherein the underlying model includes the following four aspects: leakage source, leakage scenario, weather scenario, and blocked space.

[0067] Step S11, constructing the leakage source in the bottom layer model of the chemical plant accident scenario, wherein the leakage source includes the material type of all process equipment (facilities) where the accident scenario may occur, the content V of the material under standard working conditions, 标 , the working temperature of the equipment under standard working conditions T 标 , Working pressure of equipment under standard working conditions P 标 , the actual size of the equipment, the installation height of the equipment, etc.;

[0068] Step S12, constructing a leakage scenario in the underlying model of the chemical plant accident scenario, wherein the leakage scenario includes representative leakage apertures d of all equipment where accident scenarios may occur. rep , effective leakage time t e The maximum pipe diameter range of all equipment in the enterprise where accidents may occur is ≥50mm, and the representative leakage aperture d rep Take 25mm. The detection system of the enterprise is graded as A, the interlocking and cutting system is graded as A, and the effective leakage time t e Take 10min;

[0069] Step S13, constructing a weather scenario in the underlying model of the chemical plant accident scenario, wherein the weather scenario includes the main meteorological conditions of the location of the chemical plant, including the average atmospheric temperature T 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind direction over the years, etc.

[0070] Step S14, constructing a blocking space in the underlying model of the chemical plant accident scene, wherein the blocking space includes the spatial location, length, width, and height of the blocking space in the chemical plant. The congestion ratio VBR of the blocking space is 0.3, and the explosion source intensity R0 is 7;

[0071] Step S2, obtaining the working parameters of all equipment in the chemical plant that may cause an accident scenario in real time, and the real-time meteorological condition parameters of the location of the chemical plant, and the acquisition time and validity time of the parameters shall not exceed 10s;

[0072] Based on the step of S2, S21 to S24 are implemented by a device that can receive the operation data provided by the remote sensor in real time and replace the corresponding static data in the underlying model;

[0073] Step S21, the device obtains the content V of the material in real-time operation of all equipment that may cause accident scenes 实 , replace the material content V under standard working conditions of the leakage source in the bottom model 标 ;

[0074] Step S22, the device obtains the operating temperature T of all devices that may cause accident scenarios in real time. 实 , replace the operating temperature T of the equipment under standard working conditions of the leakage source in the bottom model 标 ;

[0075] Step S23, the device obtains the working pressure P of all equipment that may cause accident scenarios in real time. 实 , replace the working pressure P of the equipment under the standard working condition of the leakage source in the bottom model 标 ;

[0076] Step S24, the device obtains real-time meteorological condition parameters at the location of the chemical plant, including real-time atmospheric temperature T 气实 , Real-time relative humidity H 气实 , Real-time wind speed v 气实 , Real-time atmospheric stability P 气实 , real-time wind direction, etc., to replace the historical average atmospheric temperature T of the weather scene in the underlying model 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind direction over the years, etc.

[0077] Step S3, performing accident consequence simulation calculation on the accident scenario model of the chemical plant after updating the real-time data, the simulation calculation is performed using DNV Phast&Safeti software, and the output results include the influence range under typical thermal radiation intensity and the influence range under typical explosion overpressure value;

[0078] The following is the impact range under typical thermal radiation intensity at a certain moment:

[0079]

[0080]

[0081] Step S4, in the three-dimensional model of the chemical plant in the smart factory platform, the influence range under the typical thermal radiation intensity and the influence range under the typical explosion overpressure value are three-dimensionally visualized, and the risk level of the accident scene consequences is judged according to the range of the results, and different colors are used to distinguish them, such as Figure 2 As shown;

[0082] Step S5, combining the accident scenario consequence risk level classification range obtained in S4 with the personnel positioning system in the smart factory platform, when a person enters a risk area with a higher or lower consequence risk level, or when the consequence risk level of the person's location changes, a corresponding signal reminder will be received;

[0083] Based on the steps of S4 and S5, a device that can receive the results provided by the simulation software in real time, visualize the results in three dimensions in the three-dimensional model of the chemical plant smart factory platform, and display them in colors representing corresponding consequence risk levels, and in combination with a personnel positioning system, when a person enters a risk area with a corresponding consequence risk level, a device that receives a corresponding signal reminder implements S4 and S5;

[0084] The above embodiment is a specific implementation of the present invention, but the implementation of the present invention is not limited to the embodiment. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A quantitative evaluation method for real-time consequences of accident scenarios in a smart factory platform, characterized in that: The method comprises the following steps: 1) According to the actual situation of the chemical enterprise, the underlying model of the accident scenario of the chemical plant of the enterprise is constructed, and the underlying model includes the following four aspects: leakage source, leakage scenario, weather scenario, and blocked space; 2) Obtain the working parameters of all equipment in the chemical plant that may cause accident scenarios during real-time operation, and the real-time meteorological condition parameters of the location of the chemical plant, and the acquisition time and validity time of the parameters shall not exceed 10s; replace the working parameters of the leakage source under standard conditions in the underlying model with the obtained working parameters of the equipment in real-time operation; replace the weather scene parameters in the underlying model with the obtained real-time meteorological condition parameters of the location of the device; 3) Performing accident consequence simulation calculation on the accident scenario model of the chemical plant after updating the real-time data, the simulation calculation is performed using DNV Phast&Safeti software, and the output results include the impact range under typical thermal radiation intensity and the impact range under typical explosion overpressure value; 4) In the three-dimensional model of the chemical plant in the smart factory platform, the influence range under typical thermal radiation intensity and the influence range under typical explosion overpressure value are three-dimensionally visualized, and the risk level of accident scene consequences is judged according to the range of the results, and distinguished by different colors; 5) Combine the accident scenario consequence risk level classification range obtained in the previous step with the personnel positioning system in the smart factory platform. When a person enters a risk area with a higher or lower consequence risk level, or when the consequence risk level of the person's location changes, the person will receive a corresponding signal reminder.

2. According to the method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform in claim 1, it is characterized in that: Step 1) specifically includes the following process: Step S11, constructing the leakage source in the bottom model of the chemical plant accident scenario, the leakage source should include the material type of all process equipment where the accident scenario may occur, the content V of the material under standard working conditions 标 , the working temperature of the equipment under standard working conditions T 标 , Working pressure of equipment under standard working conditions P 标 , the actual size of the equipment, the installation height of the equipment; Step S12, constructing a leakage scenario in the underlying model of the chemical plant accident scenario, wherein the leakage scenario should include representative leakage apertures d of all equipment that may cause accident scenarios. rep , effective leakage time t e ; Step S13, constructing a weather scenario in the underlying model of the chemical plant accident scenario, wherein the weather scenario should include the main meteorological conditions of the location of the chemical plant, including the average atmospheric temperature T 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind direction over the years; Step S14, constructing a blocked space in the underlying model of the chemical plant accident scenario, wherein the blocked space should include the spatial positioning, length, width, and height of the blocked space in the chemical plant to determine the location of the blocked space and the volume it contains; and determining the congestion ratio VBR and explosion source intensity R0 of the blocked space based on the obstacle conditions in the blocked space in the chemical plant.

3. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 2 is characterized in that: In step S12, the representative leakage aperture d rep The selection should follow the following principles:

4. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 2 is characterized in that: In step S12, the representative effective leakage time t e The selection of should follow the following principle: take the minimum value of the following three items: ①60min; ②The ratio of the maximum possible leakage volume to the leakage rate; ③ Leakage time based on detection and interlocking cut-off system level.

5. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 2 is characterized in that: In step S14, the congestion ratio VBR of the blocked space = the space volume occupied by the congested objects in the blocked space / the total space volume of the blocked space. The explosion source intensity is divided into 10 levels from 1 to 10 from low to high. The higher the congestion level, the higher the explosion source intensity. The selection of the explosion source intensity should follow the following rules: The explosion source intensity R0 should also meet the following conditions: a) The explosion source intensity of the oil tank area can be selected from 3 to 4; b) When the tank area is surrounded by trees, at least 5 should be selected; c) For liquefied hydrocarbon tank area, option 5 can be selected; d) For Class A and B petrochemical plant areas, 6 to 10 can be selected, and 7 to 9 are commonly used.

6. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 1 is characterized in that: In the step 2), the working parameters of the equipment in real-time operation are used to replace the working parameters of the leakage source under standard working conditions in the underlying model; the real-time meteorological condition parameters of the equipment location are used to replace the weather scene parameters in the underlying model. The specific steps are as follows: Step S21, according to the process equipment liquid level sensor device, obtain all equipment that may cause accident scenes, and the content V of the material under real-time operation 实 , replace the material content V under standard working conditions of the leakage source in the bottom model 标 ; Step S22, based on the process equipment temperature sensor device, obtain all equipment that may have accident scenarios, and the operating temperature T of the equipment under real-time operation 实 , replace the operating temperature T of the equipment under standard working conditions of the leakage source in the bottom model 标 ; Step S23, according to the process equipment pressure sensor device, obtain all equipment that may cause accident scenarios, and the working pressure P of the equipment under real-time operation 实 , replace the working pressure P of the equipment under the standard working condition of the leakage source in the bottom model 标 ; Step S24, using the integrated meteorological monitoring sensor device, obtain the real-time meteorological condition parameters of the location of the chemical plant, including the real-time atmospheric temperature T 气实 , Real-time relative humidity H 气实 , Real-time wind speed v 气实 , Real-time atmospheric stability P 气实 , real-time wind direction, replace the average atmospheric temperature T of the weather scene in the underlying model 气历 、The average relative humidity over the years H 气历 、The average wind speed over the years v 气历 , the average atmospheric stability over the years P 气历 , wind direction over the years.

7. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 6 is characterized in that: The step 2) is implemented by a device that can receive the operating data provided by the remote sensor in real time and replace the corresponding static data in the underlying model. The device includes a receiving device, a memory, a processing device, and a transmission device.

8. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 1 is characterized in that: In the step 4), in the three-dimensional model of the chemical plant in the smart factory platform, at any time, any point in the space of the three-dimensional model must belong to one of the following accident scenario consequence risk levels: major risk, greater risk, general risk, low risk, and below low risk.

9. The method for quantitatively evaluating the real-time consequences of accident scenarios in a smart factory platform according to claim 1 is characterized in that: The steps 4) and 5) are implemented by a device that can receive the results provided by the simulation software in real time, visualize the results in three dimensions in a three-dimensional model in the smart factory platform of the chemical plant, and display them in colors representing corresponding consequence risk levels. When a person enters a risk area with a corresponding consequence risk level, the person receives a corresponding signal reminder in combination with a personnel positioning system. The device includes a receiving device, a memory, a processing device, and a transmission device.

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