Chemical Park Safety Risk Management System

Abstract

The present invention discloses a chemical park safety risk management and control system, which specifically relates to the technical field of risk diffusion prediction. It is used to solve the problem in the existing technology that emergency instructions and risk transmission paths are dynamically mismatched and cross-regional chain reactions cannot be blocked in a coordinated manner; environmental parameters and equipment status data are obtained through a data acquisition module, and a fluid correction module extracts the continuous coherent barcode of turbulent vortices to generate path correction coefficients; a coupling modeling module combines multi-physics field coupling equations to generate dynamic spatiotemporal propagation parameters, and a path prediction module predicts the diffusion direction and affected area based on the vector superposition of parameters and three-dimensional topological maps; an instruction optimization module adjusts the control instruction priority and trigger range, and an execution control module organizes emergency equipment to block the diffusion path according to preset logic; through dynamic modeling and path matching optimization, the spatiotemporal coordination of emergency response and control execution is achieved, and the accident diffusion risk of chemical parks is significantly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of risk diffusion prediction, and more specifically, to a chemical park safety risk management and control system. Background Art

[0002] Existing technologies for safety risk management in chemical parks typically implement risk monitoring and emergency response through the deployment of sensor networks, alarm systems, and automated control equipment. Alarm systems identify abnormal conditions (such as gas leaks or temperature excursions), while control systems execute actions (such as closing valves and activating sprinklers) based on pre-set rules. However, these systems often operate independently, relying primarily on local data (such as individual device status or fixed regional thresholds) to generate alarm information and control commands. These systems fail to fully consider the dynamic propagation characteristics of risk events across time and space. For example, the spread of fires or leaks is influenced by real-time environmental parameters (such as wind direction and equipment layout), yet existing solutions lack the ability to collaboratively analyze spatial topological relationships and temporal evolution.

[0003] Existing technologies independently process alarm, control, and spatial data, resulting in insufficient coordinated response capabilities to the cross-regional and spatiotemporal spread of risk events. This manifests itself in a mismatch between emergency instructions (such as isolation and fire extinguishing) and the dynamic changes in risk diffusion paths, making it impossible to effectively block chain reactions and thus increasing the severity of accident consequences. This problem is particularly prominent in scenarios involving large-scale industrial parks, complex equipment layouts, or changing weather conditions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a chemical park safety risk management and control system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The chemical park safety risk management and control system includes the following modules:

[0007] Data acquisition module, used to obtain real-time environmental parameters and equipment status data at multiple monitoring points within the chemical park;

[0008] The fluid correction module is used to extract the persistent coherent barcode of turbulent vortices through computational fluid dynamics based on real-time environmental parameters and equipment status data, and to generate path correction coefficients by combining the resonant frequency matching between equipment layout and microclimate vortices;

[0009] A coupled modeling module is used to generate dynamic spatiotemporal propagation parameters through multi-physics coupling equations based on the path correction coefficient and the risk diffusion rate in the time dimension;

[0010] The path prediction module is used to predict the diffusion direction and impact area of risk events based on the vector superposition results of dynamic spatiotemporal propagation parameters and the three-dimensional topological map of the chemical park;

[0011] The instruction optimization module is used to adjust the priority and trigger range of automated control instructions based on the diffusion direction and impact area, and generate a dynamic control instruction set that matches the risk diffusion path;

[0012] The execution control module is used to execute the dynamic control instruction set to control the emergency equipment in the target area to block the risk diffusion path according to the preset logic.

[0013] In a preferred embodiment, obtaining real-time environmental parameters and equipment status data at multiple monitoring points within a chemical park includes:

[0014] The temperature data, gas concentration data and wind speed data of the real-time environmental parameters are collected through temperature sensors, gas concentration sensors and wind speed sensors;

[0015] Collect pipeline pressure data and valve opening data in equipment status data through pressure sensors and valve opening sensors;

[0016] The current risk level is calculated in real time based on temperature data, gas concentration data, and wind speed data, and the data collection frequency is dynamically adjusted based on the current risk level.

[0017] In a preferred embodiment, based on real-time environmental parameters and equipment status data, computational fluid dynamics is used to extract the persistent coherent barcode of the turbulent vortex, and the path correction coefficient is generated by combining the resonant frequency matching degree of the equipment layout and the microclimate vortex, including:

[0018] Dynamic filtering is performed on the wind speed and direction data in real-time environmental parameters to separate the continuous wind speed component and the instantaneous pulsation component that characterize the macroscopic flow field trend;

[0019] Based on the sustained wind speed component, a three-dimensional transient flow field model is constructed through computational fluid dynamics to extract the sustained coherent barcode of turbulent vortices in the flow field;

[0020] Synchronously perform spatial Fourier transform on the equipment layout coordinates in the equipment status data to identify the energy concentration area of the equipment layout under the dominant frequency of the microclimate vortex;

[0021] According to the topological evolution characteristics of the continuous coherent barcode and the spectral distribution of the energy concentration area, the resonance coupling strength of the vortex energy diffusion path and the device layout is calculated to generate the path correction coefficient.

[0022] In a preferred embodiment, the persistent coherent barcode is generated by quantifying the topological evolution characteristics of the topological connectivity of the vortex boundary within a preset time window.

[0023] In a preferred embodiment, based on the path correction coefficient and the risk diffusion rate in the time dimension, dynamic spatiotemporal propagation parameters are generated through multi-physics field coupling equations, including:

[0024] Normalize the path correction coefficient to generate a correction weight factor;

[0025] Perform time series weighting on the risk diffusion rate in the time dimension to generate a time attenuation coefficient. The weighting process dynamically adjusts the weight based on the attenuation trend of the historical accident diffusion rate.

[0026] Establish a multi-physics coupling equation, which uses the correction weight factor as the spatial correction term and the time attenuation coefficient as the time correction term, and combines the fluid mechanics diffusion equation with the equipment layout resistance equation for simultaneous solution;

[0027] By iteratively solving the multi-physics field coupling equations, dynamic space-time propagation parameters are output. The dynamic space-time propagation parameters include the risk diffusion direction vector, propagation rate gradient and spatial energy density distribution.

[0028] In a preferred embodiment, the diffusion direction and impact area of a risk event are predicted based on the vector superposition result of the dynamic spatiotemporal propagation parameter and the three-dimensional topological map of the chemical park, including:

[0029] The risk diffusion direction vector in the dynamic spatiotemporal propagation parameter is vector-superimposed with the geographic coordinates in the three-dimensional topological map of the chemical park to generate a superimposed risk propagation path vector field.

[0030] Based on the superimposed risk propagation path vector field, the chemical park is dynamically meshed;

[0031] The spatial variation rate of the risk diffusion rate within the grid is calculated based on the transmission rate gradient, and the risk diffusion priority coefficient of each grid is generated by combining the fluid viscosity and equipment layout resistance equations.

[0032] Based on the risk diffusion priority coefficient and the preset diffusion threshold, the continuous grid area where the risk diffusion priority coefficient exceeds the preset diffusion threshold is marked as the affected area, and the diffusion direction and the spatial coordinate set of the affected area are output.

[0033] In a preferred embodiment, the grid resolution in the dynamic grid division of the chemical park is adjusted according to the spatial energy density distribution, and the grid side length in the high energy density area is smaller than that in the low energy density area.

[0034] In a preferred embodiment, based on the diffusion direction and impact area, the priority and trigger range of the automated control instructions are adjusted to generate a dynamic control instruction set that matches the risk diffusion path, including:

[0035] A control instruction priority mapping table is established based on the spatial coordinate set of the impact area and the risk diffusion direction vector. The level of the control instruction in the priority mapping table is dynamically adjusted according to the spatial energy density distribution and the risk diffusion priority coefficient;

[0036] Based on the topological relationship between the dynamic control instruction set and the equipment layout of the chemical park, the trigger range of the automation control instruction is adjusted to cover the coordinates of the equipment in the affected area and its adjacent grids whose risk diffusion priority coefficient exceeds the preset diffusion threshold;

[0037] Based on the real-time diffusion rate and equipment operating status data, the emergency control instructions that need to be executed immediately are marked in the priority mapping table to generate a dynamic control instruction set;

[0038] The logical consistency of the dynamic control instruction set is verified by the spatial matching degree between the risk diffusion path vector field and the device topology relationship, and the instructions that conflict with the diffusion path are eliminated.

[0039] In a preferred embodiment, executing a dynamic control instruction set to control the emergency equipment in the target area to block the risk diffusion path according to a preset logic includes:

[0040] Sort the control instructions in the dynamic control instruction set from high to low priority, and send control instructions to the emergency equipment in the target area step by step. The control instructions include the equipment operation type and execution parameters;

[0041] Based on the spatial matching degree between the topological relationship of chemical park equipment and the risk diffusion path vector field, control instructions with the same priority level and consistent diffusion path direction are executed in parallel;

[0042] Collect the execution status data of emergency equipment and adjust the priority level of unexecuted instructions according to the deviation value between the execution status data and the preset response logic;

[0043] The effectiveness of the executed control instructions is verified through the feedback data of the real-time risk diffusion rate and the equipment execution status. If the diffusion rate does not drop to the safety threshold, the redundant control instructions are triggered.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. By integrating environmental parameters, equipment layout, and continuous coherent analysis of fluid mechanics, the topological evolution characteristics of fluid vortices are extracted and path correction coefficients are generated. This overcomes the defect of traditional static scene modeling that ignores the dynamic coupling of microclimates. The risk diffusion path prediction can accurately reflect the resonance effect of equipment layout and gas diffusion, improving the physical authenticity and reliability of the prediction results. At the same time, multi-physics field coupling equations are introduced in the process of generating dynamic spatiotemporal propagation parameters. The path correction coefficient is used as a spatial inhomogeneity correction term, and the time weight is dynamically adjusted in combination with the historical attenuation trend to form a composite transmission model covering spatiotemporal dimensions, avoiding the problem of deviation from the actual propagation path caused by traditional single-dimensional prediction.

[0046] 2. Through real-time vector superposition of risk diffusion paths and equipment topology maps and dynamic control instruction generation, multi-factor matching of instructions and diffusion directions is achieved; based on the two-way feedback of the priority mapping table and the equipment execution status, the spatial coverage and execution order of instruction triggering are adjusted to ensure that emergency response actions and risk propagation processes evolve synchronously, effectively blocking chain reactions between regions; at the same time, a redundant instruction triggering mechanism is designed to dynamically supplement the emergency control strategy through real-time diffusion rate feedback, thereby improving the system's fault tolerance to sudden diffusion surges in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural diagram of the chemical park safety risk management and control system of the present invention. DETAILED DESCRIPTION

[0048] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] Example: Figure 1 The structural diagram of the chemical park safety risk management and control system of the present invention is given. The chemical park safety risk management and control system includes the following modules:

[0050] Data acquisition module, used to obtain real-time environmental parameters and equipment status data at multiple monitoring points within the chemical park;

[0051] The fluid correction module is used to extract the persistent coherent barcode of turbulent vortices through computational fluid dynamics based on real-time environmental parameters and equipment status data, and to generate path correction coefficients by combining the resonant frequency matching between equipment layout and microclimate vortices;

[0052] A coupled modeling module is used to generate dynamic spatiotemporal propagation parameters through multi-physics coupling equations based on the path correction coefficient and the risk diffusion rate in the time dimension;

[0053] The path prediction module is used to predict the diffusion direction and impact area of risk events based on the vector superposition results of dynamic spatiotemporal propagation parameters and the three-dimensional topological map of the chemical park;

[0054] The instruction optimization module is used to adjust the priority and trigger range of automated control instructions based on the diffusion direction and impact area, and generate a dynamic control instruction set that matches the risk diffusion path;

[0055] The execution control module is used to execute the dynamic control instruction set to control the emergency equipment in the target area to block the risk diffusion path according to the preset logic.

[0056] Obtain real-time environmental parameters and equipment status data at multiple monitoring points within the chemical park. The specific implementation is as follows:

[0057] Based on the equipment distribution density, process risk level and historical accident data, monitoring point deployment areas are divided within the chemical park, and temperature sensors, gas concentration sensors and wind speed sensors are deployed.

[0058] The temperature sensor should be a corrosion-resistant probe with a response speed that meets the process requirements. The installation position should be determined based on the heat conduction characteristics of the equipment surface and the surrounding ventilation conditions.

[0059] The gas concentration sensor selects the corresponding sensitive element according to the type of target gas and is installed at the connection of equipment with a higher probability of leakage or at the bottom of a confined space.

[0060] The wind speed sensor should be installed away from areas blocked by buildings or equipment, and preferably in high points or open areas in the park.

[0061] All sensors are connected to the data collector via an industrial bus protocol. The data collector configures filtering parameters based on the sensor type, uses a sliding average filtering algorithm to eliminate noise in temperature data, and dynamically adjusts the filter window length based on the frequency of ambient temperature fluctuations. Gas concentration data automatically switches to high-precision acquisition mode when the detection value approaches the upper limit of the range, and cross-checks the data validity through spare sensing channels. Wind speed data uses a dynamic filtering algorithm to separate instantaneous gusts from continuous wind speed components.

[0062] The installation location of the pressure sensor is selected based on the fluid mechanics characteristics. For example, at the pipe elbow, pump outlet, and straight pipe section upstream of the valve, the length of the straight pipe section before and after is retained to meet the requirements of stable fluid flow. The valve opening sensor is mechanically coaxially connected to the actuator and adopts non-contact measurement technology. The pressure sensor and valve opening sensor communicate with the distributed system through the industrial control protocol, and the data refresh cycle is set synchronously with the equipment control cycle. The setting logic of the preset threshold is as follows: the pipeline pressure threshold is based on a comprehensive calculation of the equipment design pressure, material fatigue factor and safety margin. For example, the first-level warning threshold is dynamically adjusted according to the ratio of the design pressure to the safety factor; the valve opening preset value is set according to the standard operating parameters in the process flow chart, and the tolerance range is calculated based on the valve repeatability accuracy and the actuator error tolerance.

[0063] When calculating the current risk level in real time, the temperature data, gas concentration data, and wind speed data are normalized. For example, the temperature data is based on the temperature tolerance of the equipment material and the allowable fluctuation range of the process. The normalized parameters are linearly weighted according to the preset weights, and the weight distribution principle is determined based on the causal contribution of each parameter in the historical accident data. The weighted calculation results are mapped to risk level values, and the risk level intervals are divided into four intervals: low risk, medium risk, high risk, and extremely high risk. The data collection frequency is dynamically adjusted based on the real-time risk level value. For example, the basic sampling frequency is maintained in the low-risk interval, and exponential incremental sampling is enabled and redundant sensing channels are activated in the high-risk interval.

[0064] In data verification and exception handling, temperature data anomalies are determined through difference analysis of adjacent sensor data, and the difference threshold is calculated based on sensor accuracy and environmental thermal inertia characteristics. When gas concentration data is abnormal, the system automatically switches to the backup sensor and initiates the manual calibration process. The validity of wind speed data is determined through consistency verification of adjacent sensors and trend correlation analysis. If there is an anomaly, the wind speed field is reconstructed based on a spatial interpolation algorithm.

[0065] Based on real-time environmental parameters and equipment status data, computational fluid dynamics is used to extract the persistent coherent barcode of turbulent vortices. Path correction coefficients are generated by combining the matching degree between equipment layout and the resonant frequency of microclimate vortices. The specific implementation is as follows:

[0066] Dynamic filtering of wind speed and direction data from real-time environmental parameters uses a sliding window filtering algorithm based on time series analysis to decompose the raw wind speed and direction data into a continuous wind speed component and an instantaneous pulsating component, representing macroscopic flow field trends. The window length of the sliding window filtering algorithm is dynamically adjusted based on the statistical characteristics of the wind speed data. Specifically, the standard deviation of the wind speed data over a preset time period is calculated. When the standard deviation exceeds a set threshold, the window length is shortened to capture high-frequency pulsating components; otherwise, the window length is extended to enhance low-frequency trend extraction. The thresholds are determined based on the statistical distribution of historical wind speed data: the mean plus twice the standard deviation is used as the trigger threshold for the high-frequency component, and the mean minus one standard deviation is used as the switching threshold for the low-frequency component. For example, if the historical wind speed mean in a region is 3 meters per second and the standard deviation is 1 meter per second, the high-frequency trigger threshold is 5 meters per second, and the low-frequency switching threshold is 2 meters per second. The separated continuous wind speed component is used to construct the flow field model, while the instantaneous pulsating component is analyzed using a short-time Fourier transform to detect abnormal fluctuations and assign timestamps.

[0067] When constructing a three-dimensional transient flow model based on sustained wind speed components, the finite volume method from computational fluid dynamics (CFD) was used to grid the chemical park. The grid resolution was graded based on the equipment density and terrain complexity. The specific rules were as follows: high-resolution grids were used around high-risk equipment such as storage tanks and reactors, with grid edges no longer than 1 / 10 of the equipment diameter; low-resolution grids were used in open areas, with grid edges no longer than 1 / 5 of the spacing between equipment. The inlet boundary conditions of the flow model were set based on the direction and magnitude of the real-time wind speed components, while the outlet boundary conditions were dynamically adjusted based on the pressure gradient and terrain drag coefficient. The flow field distribution was simulated by solving the transient Navier-Stokes equations, extracting the persistent coherent barcodes of turbulent vortices in the flow field. The persistent coherent barcode generation method involves performing eigenvalue decomposition on the velocity gradient tensor of the simulated flow field to identify the vortex core region. The vortex core is determined when the imaginary part of the eigenvalue is greater than zero and the vorticity amplitude exceeds three times the average vorticity of the flow field. Based on the principle of topological connectivity, the evolution of the vortex boundary within a preset time window is analyzed, including vortex mergers, splitting, and dissipation events. The spatiotemporal evolution trajectory of the vortex is encoded into a barcode sequence that includes its life cycle, spatial scale, and energy intensity. For example, in a simulation with a time window of 60 seconds, a vortex lasts 45 seconds from generation to death, and the corresponding barcode segment length is 45 seconds, with the segment color depth representing the vortex energy intensity.

[0068] When performing a spatial Fourier transform on the equipment layout coordinates in the equipment status data, the coordinates are first converted into two-dimensional spatial grid data. The grid size matches the resolution of the flow field model to ensure data alignment. The specific conversion method involves discretizing the equipment coordinates according to the grid size and calculating the device density (number of devices per unit area) within each grid to form an equipment density distribution matrix. A discrete Fourier transform is then performed on the device density distribution matrix to obtain the spatial spectrum distribution of the equipment layout. Energy concentration areas at the dominant frequency of microclimate vortices are identified by extracting frequency components in the spectrum whose energy amplitude exceeds a set multiple of the average energy level. This set multiple is determined based on the statistical distribution of equipment resonance events in historical accident data. For example, if the energy amplitude at a frequency of 0.1 Hz in the equipment layout spectrum of a certain area is 2.5 times the average level and this frequency accounts for more than 70% of historical resonance accidents, the spatial region corresponding to this frequency is determined to be an energy concentration area.

[0069] To calculate the resonant coupling strength based on the topological evolution characteristics of the persistent coherent barcode and the spectral distribution of the energy concentration region, the vortex energy diffusion paths in the barcode sequence are first mapped to the spatial grid of the device layout, and the overlap area between each vortex path and the energy concentration region is calculated. The overlapping area is calculated by counting the number of grids covered by the vortex path within the energy concentration region. The weight of each grid is set according to its spectral energy amplitude. The weight distribution rule is: for every doubling of the spectral energy amplitude above the average level, the grid weight increases by 0.2. For example, if the spectral energy amplitude of a grid is 3 times the average level, its weight is 1.0 + 0.2 × 2 = 1.4. The resonant coupling strength is quantified by the ratio of the weighted overlap area to the total vortex energy, which is the sum of the product of the barcode segment length and the color depth. The resulting path correction factor is the normalized result of the resonant coupling strength and a preset baseline value. The baseline value is set based on the average diffusion rate in historical data without resonant coupling. Specifically, the baseline value is the average diffusion rate over the past year without device resonance events. If the real-time diffusion rate deviates from the baseline by more than 20%, the path correction factor is adjusted linearly in proportion to the deviation. For example, if the baseline value is 2 meters per second and the real-time diffusion rate is 2.4 meters per second, the path correction factor is adjusted to 1.2.

[0070] Dynamic filtering is used to separate the continuous and pulsating components of wind speed to ensure the stability of flow field modeling. Based on computational fluid dynamics, a transient flow field model is constructed and continuous coherent barcodes are extracted to quantify vortex evolution characteristics, breaking through the limitations of traditional steady-state flow field analysis. A spatial Fourier transform of the equipment layout is simultaneously performed to identify energy concentration areas, revealing the resonant coupling mechanism between equipment and microclimate. Compared with the defects of existing technologies that process fluid, equipment, and meteorological data in isolation, the diffusion path is dynamically corrected through multi-physics field coupling (fluid topology + equipment spectrum), significantly improving prediction accuracy. Topological data analysis is associated with the equipment layout spectrum to address the situation where traditional models ignore spatiotemporal coordination. For example, the interactive effect of vortex energy diffusion path and equipment resonance can provide early warning of high-risk areas, allowing control instructions to accurately match real-time risk situations, reducing emergency response delays and resource waste, and thus achieving more reliable safety management and control in complex meteorological and equipment layout scenarios.

[0071] Based on the path correction coefficient and the risk diffusion rate in the time dimension, dynamic spatiotemporal propagation parameters are generated through multi-physics field coupling equations. The specific implementation is as follows:

[0072] The path correction factor Perform normalization to generate a modified weight factor , specifically using the extreme value normalization method: ;in, It represents the maximum value of the path correction coefficient under typical working conditions without equipment resonance coupling in historical data. It is determined by counting the peak values of different seasonal scenarios within at least one complete annual cycle. For example, in the summer high temperature and high humidity scenario, the viscosity of the fluid decreases. Increased, low temperature and dry scene in winter due to increased fluid resistance reduce; Indicates the minimum value in the data of the same period. Extreme value screening needs to exclude sensor failure or extremely abnormal data (such as typhoon weather); is the normalized correction weight factor, which ranges from [0,1] and is used to quantify the impact of equipment layout on the spatial heterogeneity of fluid diffusion. For example, a park determined by analyzing three years of historical data 2.5 (summer), is 0.5 (winter), when real time When 1.8, , indicating that the current spatial correction strength is 65% of the maximum value.

[0073] Risk diffusion rate in the time dimension Perform time series weighting processing to generate time decay coefficient , the specific logic is:

[0074] Fitting an exponential decay model based on historical accident data: ,in, is the initial diffusion rate, and the historical accident triggering time is extracted ( ) is determined by the mean diffusion rate; is the decay rate constant, which is obtained by fitting the historical decay curve using the least squares method; is the steady-state offset, which is taken from the baseline rate of the risk-free diffusion scenario. For example, in a chlorine gas leak accident, the fitting result is 2.2 m / s, 0.05, is 0.3 m / s, indicating that the diffusion rate decays exponentially with time to a stable value of 0.3 m / s;

[0075] Real-time calculation of diffusion rate and the fitted value If the relative error exceeds the preset threshold (e.g. 20%), the For example, when is 2.6 m / s and When the speed is 1.5 m / s, the deviation is (2.6−1.5) / 1.5=73%, triggering Adjusted down by 30% to prevent overcorrection of equipment resistance due to diffusion acceleration.

[0076] When establishing the multi-physics coupling equation, the modified fluid mechanics diffusion equation and the equipment layout resistance equation are combined:

[0077] Construct the fluid diffusion equation based on Fick's law: ;in The concentration of risk substances is monitored in real time through gas sensors deployed in the chemical park (e.g. electrochemical sensors to detect chlorine concentration); is the effective diffusion coefficient, which is determined by querying the fluid property table. For example, the effective diffusion coefficient of chlorine in air is 1.2×10 -5 square meters / second; As a spatial correction term, it is used to characterize the local blocking or enhancing effect of equipment layout on the diffusion path; represents the gradient operator, which is used to calculate spatial derivatives;

[0078] Construct the equipment resistance equation based on Darcy's law: ,in The resistance of the equipment layout is expressed in Newtons per square meter and is output by a resistance sensor or a fluid dynamics simulation model. Fluid viscosity is obtained by matching the physical parameter library with the real-time temperature (for example, the viscosity of air decreases as the temperature increases); As a time correction term, it is used to characterize the dynamic weakening or strengthening effect of the historical decay trend on the current resistance; Indicates the flow field velocity in meters per second, which comes from the flow field model output; It represents spatial coordinates in meters, which is consistent with the grid division of the chemical park geographic information system (GIS).

[0079] By iteratively solving the multi-physics coupling equations, the dynamic space-time propagation parameters are output. The specific steps include:

[0080] The separation variable method is used to decouple the spatial correction term from the temporal correction term, and the concentration distribution is updated separately. and equipment resistance ,in is the time step, which is set to 0.1 s according to the CFL condition to avoid numerical divergence; Indicates the number of iterations, the initial value is set to 0, and the maximum number of iterations is set to 50 according to the computing resources; Indicates the The spatial concentration distribution of the iteration; Indicates the The device resistance value for this iteration.

[0081] The convergence condition is the residual: ;in, Represents the L2 norm of the residual, which is used to quantify the difference between the results of two adjacent iterations; represents the total number of spatial grids, Indicates the index number of the spatial grid, ranging from 1 to , used to traverse all grid points; Represents the preset residual threshold, which is set according to historical simulation error statistics. .

[0082] The final output dynamic space-time propagation parameters include:

[0083] Risk diffusion direction vector for: ;in is the flow field velocity direction vector (output by the flow field model), is the device resistance direction vector (generated by resistance gradient calculation), weight and Dynamic allocation based on real-time risk level, such as high risk 、 .

[0084] The propagation rate gradient is calculated by Sure, Represents the partial derivative operator, and the grid gradient step is set according to the equipment layout density (the step in high-risk areas is ≤1 meter, and the step in open areas is ≤5 meters); the spatial energy density distribution is calculated by integration Obtained, its expression is: ;in, Represents the spatial energy density distribution (unit: J / m3); Indicates the left boundary coordinate of the integral (unit: meter), which is determined by the minimum position of the current area; Indicates the coordinates of the right boundary of the integral (meters), which is determined by the maximum position of the current area; represents the correction weight factor (dimensionless), which is calibrated by experiments and has a value range of [0,2]; represents the time attenuation coefficient; Represents the spatial integral differential element (unit: meter).

[0085] Integration priorities are divided according to risk levels (e.g., Gaussian integration is used in high-risk areas and rectangular integration is used in low-risk areas).

[0086] By integrating the dynamic coupling mechanism of the path correction coefficient and the time attenuation coefficient, the problem of prediction deviation caused by the isolated treatment of space or time dimensions in traditional risk diffusion models is solved. Compared with existing technologies, the extreme value normalization method driven by historical data is used to quantify the impact of the non-uniformity of equipment layout on the diffusion path, rather than relying on static empirical coefficients; the time attenuation coefficient is adjusted based on the dynamic deviation of the real-time diffusion rate and the historical attenuation model, replacing the fixed time weight; the fluid diffusion equation and the equipment resistance equation are combined to achieve accurate modeling of multi-physical field interactions through iterative decoupling solution. The model can simultaneously characterize the synergistic effects of equipment layout resistance, microclimate flow field and historical accident attenuation trends, reduce diffusion prediction errors in complex environments, shorten emergency response time, and solve the problems of warning lag and misjudgment caused by ignoring multi-field coupling in existing technologies, providing a reusable solution for dynamic risk management and control in chemical parks.

[0087] Based on the vector superposition results of dynamic spatiotemporal propagation parameters and the three-dimensional topological map of the chemical park, the diffusion direction and impact area of the risk event are predicted. The specific implementation is as follows:

[0088] The risk diffusion direction vector in the dynamic spatiotemporal propagation parameters is vector-superimposed with the geographic coordinates in the three-dimensional topological map of the chemical park to generate a superimposed risk diffusion path vector field. The direction of the risk diffusion direction vector is a composite of the flow field velocity and the equipment resistance direction, measured in meters per second. The geographic coordinates of the three-dimensional topological map of the chemical park are a three-dimensional Cartesian coordinate system, containing the spatial coordinates of all equipment, buildings, and terrain, measured in meters. During the superposition operation, the risk diffusion direction vector is decomposed into components along the X, Y, and Z axes of the three-dimensional coordinate system. The velocity values of each component are matched with the coordinates of the corresponding geographic grid center point and then superimposed to generate a vector field containing direction and velocity. For example, if the center coordinates of a grid are (100 meters, 50 meters, 10 meters), and the corresponding risk diffusion direction vector is (1.2 meters per second, -0.3 meters per second, 0.5 meters per second), then the propagation direction of the grid in the superimposed vector field is from the coordinate point (100, 50, 10) along the direction (1.2, -0.3, 0.5), with a velocity of 1.36 meters per second (calculated by vector modulus).

[0089] Based on the superimposed risk propagation path vector field, the chemical park is dynamically meshed, with the mesh resolution adjusted based on the spatial energy density distribution. The spatial energy density distribution represents the energy intensity of risk diffusion per unit volume, measured in joules per cubic meter. The adjustment rule is that the mesh edge length in high-energy-density areas is smaller than that in low-energy-density areas. This is achieved by setting energy density segmentation thresholds. For example, when the energy density in a region is greater than or equal to 1000 joules per cubic meter, the mesh edge length is set to 1 meter; when the energy density is between 500 and 1000 joules per cubic meter, the mesh edge length is set to 2 meters; and when the energy density is less than 500 joules per cubic meter, the mesh edge length is set to 5 meters. The thresholds are set based on the statistical relationship between energy density and accident consequences in historical accidents. For example, statistical analysis shows that areas with energy density greater than or equal to 1000 joules per cubic meter will trigger equipment chain reactions in 80% of accidents.

[0090] The spatial variation of the risk diffusion rate within a grid is calculated based on the propagation rate gradient. This is combined with the fluid viscosity and equipment layout resistance equations to generate a risk diffusion priority coefficient for each grid. The propagation rate gradient is calculated by calculating the difference in diffusion rates between adjacent grids. For example, if the diffusion rate of a grid is 1.36 meters per second and the diffusion rate of the adjacent grid to the east is 1.28 meters per second, with a grid spacing of 1 meter, the eastward gradient decreases by 0.08 meters per second. Fluid viscosity is obtained by matching the physical property parameter library with real-time temperature. For example, at a temperature of 25 degrees Celsius, the air viscosity is 0.000018 Pascals per second. The equipment layout resistance equation is derived from the resistance calculation model. The risk diffusion priority coefficient is calculated by dividing the absolute value of the rate gradient by the fluid viscosity and multiplying it by the equipment resistance value. For example, if the absolute value of the rate gradient for a grid is 0.08 per second, the fluid viscosity is 0.000018 Pascals per second, and the equipment resistance is 120 Newtons per square meter, the priority coefficient is (0.08 / 0.000018) × 120 = 53333.

[0091] Based on the risk diffusion priority coefficient and a preset diffusion threshold, continuous grid areas with priority coefficients exceeding the threshold are marked as impacted areas, and the diffusion direction and spatial coordinates of the impacted areas are output. The preset diffusion threshold is set based on historical accident data statistics. For example, the lowest priority coefficient of 90% of accident cases in the past three years is used as the threshold (e.g., 50,000). When the real-time calculated priority coefficient exceeds this value, the marking is triggered. The rule for determining continuous grid areas is that the priority coefficients of at least three adjacent grids (including diagonally adjacent ones) exceed the threshold. In the output results, the spatial coordinates of the impacted areas are recorded as a list of grid center point coordinates. The diffusion direction is determined by the local principal direction of the vector field. For example, if the principal direction of an impacted area is northeast, the coordinate list includes adjacent grid points such as (100 meters, 50 meters, 10 meters) and (101 meters, 51 meters, 10 meters).

[0092] Through dynamic grid division and vector superposition mechanisms, the defects of fixed spatial resolution and neglect of multi-field coupling effects in traditional risk prediction models are solved. Compared with existing technologies, the grid resolution is dynamically adjusted based on energy density (1 meter in high-density areas and 5 meters in low-density areas), replacing static grid division to improve computational efficiency and accuracy; the risk diffusion direction vector is superimposed on the three-dimensional topological map (for example, the coordinate (100, 50, 10) matches the vector (1.2, -0.3, 0.5)) to achieve a precise fusion of geographic space and flow field parameters; the synergistic effects of gradient, viscosity, and resistance are quantified through priority coefficients, replacing the single rate threshold judgment. This solves the problem of early warning lag caused by traditional methods that ignore the interaction between equipment layout and climate, and provides a reusable technical path for dynamic risk management.

[0093] Based on the diffusion direction and impact area, the priority and trigger range of the automated control instructions are adjusted to generate a dynamic control instruction set that matches the risk diffusion path. The specific implementation is as follows:

[0094] A control instruction priority mapping table is established based on the spatial coordinates of the affected areas and the risk diffusion direction vector. The spatial coordinates of the affected areas include a list of grid center coordinates marked as high-risk areas. The risk diffusion direction vector is derived from the dynamic spatiotemporal propagation parameter, whose direction is a composite of the flow field velocity and the equipment resistance, and is expressed in meters per second. The priority mapping table is constructed by associating the spatial coordinates of each affected area with a corresponding risk diffusion priority coefficient. The level of control instructions is dynamically adjusted based on the spatial energy density distribution of that area. For example, if the priority coefficient of an affected area is 53333 and the spatial energy density is 1200 joules per cubic meter, the control instruction level for that area in the mapping table is set to the highest level (Level 1). If the priority coefficient is 45000 and the energy density is 800 joules per cubic meter, the level is set to intermediate (Level 2). Level thresholds are set based on statistically analyzed control response effectiveness in historical incidents. For example, Level 1 requires immediate power disconnection of equipment, while Level 2 reduces operating power.

[0095] The trigger range of automated control instructions is adjusted based on the dynamic control instruction set and the topological relationship between the chemical park's equipment layout. The equipment layout topology is derived from a three-dimensional topological map and includes equipment coordinates and connectivity relationships. The trigger range covers the coordinates of equipment within the impact area and its adjacent grids whose risk diffusion priority coefficient exceeds a preset diffusion threshold. Adjacent grids are considered to be grids that share at least one edge or corner point with the impact area grid. For example, if the coordinates of an impact area grid are (100 meters, 50 meters, 10 meters), its adjacent grid to the east is (101 meters, 50 meters, 10 meters), and its adjacent grid diagonally to the northeast is (101 meters, 51 meters, 10 meters). When adjusting the trigger range, if the priority coefficient of an adjacent grid exceeds a preset diffusion threshold (e.g., 50,000), the coordinates of the equipment within that grid are included in the control instruction trigger range. For example, if an adjacent grid with a priority coefficient of 52,000 contains the coordinates of a reactor equipment (101 meters, 51 meters, 10 meters), a shutdown instruction for that reactor is generated.

[0096] Based on the real-time diffusion rate and equipment operating status data, emergency control instructions that need to be executed immediately are marked in the priority mapping table to generate a dynamic control instruction set. The real-time diffusion rate is derived from the propagation rate gradient calculation result and is measured in meters per second. The equipment operating status data is collected in real time through equipment sensors, including equipment switch status, operating power, and temperature parameters. The marking rule is: if the real-time diffusion rate exceeds the preset safety threshold (for example, 2 meters per second) and the equipment is in operation, the corresponding control instruction is marked as an emergency instruction. For example, if the real-time diffusion rate in a certain area is 2.5 meters per second and the pump equipment in the area is in operation, then "turn off the pump" is marked as an emergency instruction. The generation logic of the dynamic control instruction set is: sort the instructions from high to low priority level, and within the same level, sort them according to the topological connection order of the equipment, for example, shutting down the upstream equipment before shutting down the downstream equipment.

[0097] The logical consistency of the dynamic control instruction set is verified by the spatial matching degree between the risk diffusion path vector field and the equipment topology relationship, and instructions that conflict with the diffusion path are eliminated. The risk diffusion path vector field is derived from the vector superposition result and contains the diffusion direction and rate of each grid; the equipment topology relationship is the defined device connection relationship. The verification method is: traverse each instruction in the dynamic control instruction set and check whether the corresponding device coordinates are located in the propagation direction of the diffusion path vector field. If the device coordinates are in the opposite direction or perpendicular to the diffusion direction, it is determined to be a logical conflict. For example, an instruction requires closing the tank valve located in the opposite direction of the diffusion direction, and the vector field shows that the diffusion positive area is from device A to device B, then the instruction is eliminated to avoid obstructing the diffusion path. The final output dynamic control instruction set only contains device control instructions that are consistent with the direction of the diffusion path.

[0098] Execute the dynamic control instruction set to control the emergency equipment in the target area to block the risk diffusion path according to the preset logic. The specific implementation is as follows:

[0099] The control instructions in the dynamic control instruction set are sorted from high to low priority, and control instructions are sent to the emergency equipment in the target area step by step. The dynamic control instruction set contains a list of equipment operation instructions sorted by priority level; the priority level of the control instructions is determined according to the priority mapping table, with level 1 being the highest priority and level 3 being the lowest priority. Each control instruction contains an equipment operation type and execution parameters. For example, the operation type of the valve closing instruction is "close", and the execution parameter is the valve closing speed (such as 0.5 meters per second); the operation type of starting the sprinkler system is "start", and the execution parameter is the sprinkler flow rate (such as 10 cubic meters per minute). When sending instructions, instructions are sent first to level 1 emergency equipment. After confirmation that the execution has completed, level 2 instructions are sent, and so on. For example, a level 1 instruction in a certain area is to close the reactor feed valve, a level 2 instruction is to start the surrounding sprinkler system, and a level 3 instruction is to reduce the pump power.

[0100] Based on the spatial matching degree between the topological relationship of chemical park equipment and the risk diffusion path vector field, control instructions in the same priority level that are consistent with the diffusion path direction are executed in parallel. The equipment topological relationship is derived from the three-dimensional topological map, which describes the spatial connection relationship between equipment; the risk diffusion path vector field is derived from the vector superposition result and contains the diffusion direction and rate of each grid. The parallel execution rule is as follows: within the same priority level, if multiple equipment are located in the same direction of the diffusion path vector field and there is no topological dependency conflict, then control instructions are sent simultaneously. For example, if the diffusion path direction is northeast, and a priority 1 instruction requires the closure of tank A and reactor B in the northeast direction, and there is no upstream or downstream dependency between the two, then the closure instructions are sent simultaneously. If there is a topological dependency between the equipment (such as tank A being the source of raw materials for reactor B), then execution is carried out in the order of dependency.

[0101] The system collects execution status data from emergency equipment and adjusts the priority level of unexecuted commands based on the deviation between this data and the preset response logic. Execution status data is acquired in real time from equipment sensors, including parameters such as valve opening, sprinkler flow rate, and pump power. The preset response logic represents the expected state after command execution. For example, the expected opening for closing a valve is 0%, while the expected flow rate for activating sprinklers is 10 cubic meters per minute. The deviation is calculated as the absolute percentage difference between the actual state and the expected state. For example, if the actual valve opening is 10% and the expected value is 0%, the deviation is 10%. The adjustment rule is: if the deviation exceeds a preset threshold (e.g., 5%), the priority of the associated unexecuted command is raised by one level. If the equipment status is normal (deviation ≤ 5%), the original priority is maintained. For example, if a Level 2 command to activate the sprinkler system has a real-time flow rate of only 8 cubic meters per minute (a deviation of 20%), the associated Level 3 command (e.g., shutting down the ventilation system) is raised to Level 2.

[0102] The effectiveness of executed control instructions is verified using feedback from real-time risk diffusion rates and equipment execution status. If the diffusion rate does not drop below a safety threshold, a redundant control instruction is triggered. The real-time risk diffusion rate is derived from the calculated diffusion rate gradient and is measured in meters per second. The safety threshold is set based on the average diffusion rate under historical accident-free operating conditions. For example, the safety threshold for a specific area is 0.5 meters per second. The verification logic is as follows: If the diffusion rate in the area corresponding to the executed instruction does not drop below the safety threshold within a preset time (e.g., 3 minutes), a redundant control instruction is triggered. The redundant instruction generation rule is as follows: Based on the device topology, a backup device with no dependencies on the executed device is selected to perform the same operation. For example, if the diffusion rate remains at 1.2 meters per second after closing the valve of tank A, a redundant instruction to close the valve of backup tank C is triggered. The priority of the redundant instruction is the same as the original instruction, and the device topology dependencies must be verified again before execution.

[0103] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0104] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0105] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0106] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0108] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0110] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0111] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0112] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Chemical Park Safety Risk Management System, characterized by: Includes the following modules: Data acquisition module, used to obtain real-time environmental parameters and equipment status data from multiple monitoring points within the chemical park; The fluid correction module is used to extract the continuous coherent barcode of turbulent vortices through computational fluid dynamics based on real-time environmental parameters and equipment status data. It also generates path correction coefficients by combining the resonant frequency matching between equipment layout and microclimate vortices. These include: Dynamic filtering is performed on the wind speed and direction data in real-time environmental parameters to separate the continuous wind speed component and the instantaneous pulsation component that characterize the macroscopic flow field trend; Based on the sustained wind speed component, a three-dimensional transient flow field model is constructed through computational fluid dynamics to extract the sustained coherent barcode of turbulent vortices in the flow field; Synchronously perform spatial Fourier transform on the equipment layout coordinates in the equipment status data to identify the energy concentration area of the equipment layout under the dominant frequency of the microclimate vortex; Based on the topological evolution characteristics of the persistent coherent barcode and the spectrum distribution of the energy concentration area, the resonance coupling strength between the vortex energy diffusion path and the device layout is calculated to generate a path correction coefficient. The persistent coherence barcode is generated by quantifying the topological evolution characteristics of the topological connectivity of the vortex boundary within a preset time window; A coupled modeling module is used to generate dynamic spatiotemporal propagation parameters through multi-physics coupling equations based on the path correction coefficient and the risk diffusion rate in the time dimension; The path prediction module is used to predict the diffusion direction and impact area of risk events based on the vector superposition results of dynamic spatiotemporal propagation parameters and the three-dimensional topological map of the chemical park; The instruction optimization module is used to adjust the priority and trigger range of automated control instructions based on the diffusion direction and impact area, and generate a dynamic control instruction set that matches the risk diffusion path; The execution control module is used to execute the dynamic control instruction set to control the emergency equipment in the target area to block the risk diffusion path according to the preset logic.

2. The chemical park safety risk management and control system according to claim 1, characterized in that: Obtain real-time environmental parameters and equipment status data from multiple monitoring points within the chemical park, including: The temperature data, gas concentration data and wind speed data of the real-time environmental parameters are collected through temperature sensors, gas concentration sensors and wind speed sensors; Collect pipeline pressure data and valve opening data in equipment status data through pressure sensors and valve opening sensors; The current risk level is calculated in real time based on temperature data, gas concentration data, and wind speed data, and the data collection frequency is dynamically adjusted based on the current risk level.

3. The chemical park safety risk management and control system according to claim 1, characterized in that: Based on the path correction coefficient and the risk diffusion rate in the time dimension, dynamic spatiotemporal propagation parameters are generated through multi-physics field coupling equations, including: Normalize the path correction coefficient to generate a correction weight factor; Perform time series weighting on the risk diffusion rate in the time dimension to generate a time attenuation coefficient. The weighting process dynamically adjusts the weight based on the attenuation trend of the historical accident diffusion rate. Establish a multi-physics coupling equation, which uses the correction weight factor as the spatial correction term and the time attenuation coefficient as the time correction term, and combines the fluid mechanics diffusion equation with the equipment layout resistance equation for simultaneous solution; By iteratively solving the multi-physics field coupling equations, dynamic space-time propagation parameters are output. The dynamic space-time propagation parameters include the risk diffusion direction vector, propagation rate gradient and spatial energy density distribution.

4. The chemical park safety risk management and control system according to claim 1, characterized in that: Based on the vector superposition results of dynamic spatiotemporal propagation parameters and the three-dimensional topological map of the chemical park, the diffusion direction and impact area of risk events are predicted, including: The risk diffusion direction vector in the dynamic spatiotemporal propagation parameter is vector-superimposed with the geographic coordinates in the three-dimensional topological map of the chemical park to generate a superimposed risk propagation path vector field. Based on the superimposed risk propagation path vector field, the chemical park is dynamically meshed; The spatial variation rate of the risk diffusion rate within the grid is calculated based on the transmission rate gradient, and the risk diffusion priority coefficient of each grid is generated by combining the fluid viscosity and equipment layout resistance equations. Based on the risk diffusion priority coefficient and the preset diffusion threshold, the continuous grid area where the risk diffusion priority coefficient exceeds the preset diffusion threshold is marked as the affected area, and the diffusion direction and the spatial coordinate set of the affected area are output.

5. The chemical park safety risk management and control system according to claim 4, characterized in that: The grid resolution in the dynamic grid division of the chemical park is adjusted according to the spatial energy density distribution, and the grid side length in the high energy density area is smaller than that in the low energy density area.

6. The chemical park safety risk management and control system according to claim 1, characterized in that: Based on the diffusion direction and impact area, the priority and trigger range of automated control instructions are adjusted to generate a dynamic control instruction set that matches the risk diffusion path, including: A control instruction priority mapping table is established based on the spatial coordinate set of the impact area and the risk diffusion direction vector. The level of the control instruction in the priority mapping table is dynamically adjusted according to the spatial energy density distribution and the risk diffusion priority coefficient; Based on the topological relationship between the dynamic control instruction set and the equipment layout of the chemical park, the trigger range of the automation control instruction is adjusted to cover the coordinates of the equipment in the affected area and its adjacent grids whose risk diffusion priority coefficient exceeds the preset diffusion threshold; Based on the real-time diffusion rate and equipment operating status data, the emergency control instructions that need to be executed immediately are marked in the priority mapping table to generate a dynamic control instruction set; The logical consistency of the dynamic control instruction set is verified by the spatial matching degree between the risk diffusion path vector field and the device topology relationship, and the instructions that conflict with the diffusion path are eliminated.

7. The chemical park safety risk management and control system according to claim 1, characterized in that: Execute dynamic control instruction sets to control emergency equipment in the target area to block the risk diffusion path according to preset logic, including: Sort the control instructions in the dynamic control instruction set from high to low priority, and send control instructions to the emergency equipment in the target area step by step. The control instructions include the equipment operation type and execution parameters; Based on the spatial matching degree between the topological relationship of chemical park equipment and the risk diffusion path vector field, control instructions with the same priority level and consistent diffusion path direction are executed in parallel; Collect the execution status data of emergency equipment and adjust the priority level of unexecuted instructions according to the deviation value between the execution status data and the preset response logic; The effectiveness of the executed control instructions is verified through the feedback data of the real-time risk diffusion rate and the equipment execution status. If the diffusion rate does not drop to the safety threshold, the redundant control instructions are triggered.

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