Intelligent determination system for external safety protection distance of chemical enterprise

Abstract

The invention relates to the field of safety protection, and discloses a chemical enterprise external safety protection distance intelligent determination system, which comprises a multi-scale topological feature fusion module, a quantum inspired random semi-definite programming optimization module and a dynamic protection distance decision and execution module. Through topological feature extraction and fusion, the comprehensiveness of protection distance calculation is improved; quantum calculation optimization is utilized, so that the calculation efficiency and precision are improved; and in combination with dynamic adjustment, real-time response to external environment change is realized, and the accuracy of safety protection is ensured. The problems of static setting of the protection distance, low calculation precision and slow response in the prior art are solved, and the safety protection capability of chemical enterprises is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety protection, and particularly to an intelligent determination system for the external safety protection distance of chemical enterprises. Background Art

[0002] In the current field of safety protection for chemical enterprises, many existing technologies adopt static safety protection distance calculation methods. These methods usually set the protection distance based on fixed rules and conventional environmental data, such as basic conditions like temperature and air pressure. However, the environment changes rapidly and complexly, especially during the chemical production process. The frequency and suddenness of accidents make it difficult for static models to cope. This fixed protection distance often fails to consider the dynamic changes in accident diffusion, resulting in phenomena of insufficient protection or overprotection in actual applications, and unable to effectively ensure the safety of personnel and facilities.

[0003] In addition, many traditional protection distance calculation methods do not make full use of modern computing technologies. Especially when dealing with high-dimensional data and diverse risk factors, they usually rely on simple linear models or data speculation based on experience. This is not only limited to simple environmental variables but also ignores various interaction factors in complex environments. For example, the complex interactions of multiple factors such as wind speed and climate change are ignored, making the calculation results inaccurate and possibly having large errors, affecting the quality and effectiveness of protection decisions.

[0004] Furthermore, most of the existing technologies rely on traditional optimization methods for calculating the protection distance. These optimization methods have low efficiency. When faced with a large amount of data and complex models, they often encounter limitations such as excessive calculation time and inability to respond in a timely manner. Especially in situations where the protection strategy needs to be adjusted quickly, the response time of traditional algorithms cannot meet the requirements of real-time protection. The update of the protection plan is not fast enough to make the optimal decision immediately when an accident occurs, which will increase the risk of safety accidents and even lead to more serious consequences.

[0005] Most importantly, the existing technologies lack the full utilization of topological features. When dealing with the safety protection distance, many methods only focus on the local environment or single factors, ignoring the complex network structure inside the plant area and the potential impact of these structures on accident diffusion. Topological features can provide a deep understanding of accident diffusion paths, interactions between hazard sources, and protection distances, while existing technologies often cannot comprehensively integrate this information, resulting in limitations and misjudgments of protection measures.

[0006] Therefore, the present invention proposes an intelligent determination system for the external safety protection distance of chemical enterprises to solve the deficiencies of the existing technologies. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the present invention provides an intelligent determination system for the external safety protection distance of a chemical enterprise, which solves the shortcomings of the traditional protection distance calculation method that cannot cope with dynamic environmental changes, has low data processing efficiency, and ignores topological features. Specifically, the present invention combines quantum computing, topological feature fusion and dynamic adjustment technology, and can intelligently calculate and adjust the safety protection distance based on real-time environmental data, accident diffusion warning information and plant topology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A chemical enterprise external safety protection distance intelligent determination system, comprising:

[0009] The multi-source data acquisition and preprocessing module is used to obtain environmental parameters, equipment status parameters and chemical characteristic parameters, and after preprocessing through the data standardization processing unit, the obtained standardized data is transmitted to the algebraic topology dynamic manifold embedding module;

[0010] The algebraic topology dynamic manifold embedding module is used to receive the standardized data from the multi-source data acquisition and preprocessing module, and build a high-dimensional topological structure of accident diffusion based on these data. After extracting the topological features through the topological feature extraction unit, the calculation results are passed to the multi-scale topological feature fusion module;

[0011] The multi-scale topological feature fusion module is used to receive the topological features from the algebraic topological dynamic manifold embedding module, and weightedly fuse the micro-scale chemical diffusion topological features with the macro-scale plant environment topological features to generate fused topological feature data, which is then passed to the quantum-inspired stochastic semidefinite programming optimization module;

[0012] The quantum-inspired stochastic semidefinite programming optimization module is used to calculate the external safety protection distance based on the topological feature data transmitted by the multi-scale topological feature fusion module, and optimize the calculation results through the quantum-inspired random perturbation method. The optimized protection distance data is transmitted to the dynamic protection distance decision and execution module;

[0013] The dynamic protection distance decision and execution module is used to receive external safety protection distance data from the quantum-inspired stochastic semidefinite programming optimization module, make safety decisions based on the calculated protection distance, and link the emergency response system to execute corresponding protection measures, where the emergency response system includes the evacuation system and the fire protection system.

[0014] Preferably, the multi-source data acquisition and preprocessing module includes:

[0015] Environmental data acquisition unit, used to obtain wind speed, wind direction, temperature and humidity;

[0016] Environmental factors have an important impact on the diffusion rate and range of chemicals. Therefore, accurately obtaining environmental data is crucial for calculating the protection distance.

[0017] An equipment monitoring unit for obtaining the leakage rate, pressure, and valve status;

[0018] Equipment failures in chemical production are often the main cause of accidents. Therefore, the equipment status data provided in real time by the equipment monitoring unit is crucial for accident prediction and protection distance calculation.

[0019] A chemical database for storing the diffusion coefficient, toxicity threshold, and reactivity of chemicals;

[0020] Different chemicals have different diffusion characteristics. Therefore, understanding the physical and chemical properties of the chemicals involved is the basis for calculating the protection distance.

[0021] A data normalization processing unit for removing noise using the Kalman filtering method and normalizing the data using the Z-score normalization method;

[0022] The data normalization processing unit uses the Kalman filter to remove noise from the data and uses the Z-score normalization method to uniformly process all the data so that it is suitable for subsequent calculations and analyses.

[0023] Preferably, the algebraic topology dynamic manifold embedding module includes:

[0024] A phase space reconstruction unit for reconstructing the high-dimensional manifold of time series data using the delay embedding method. The high-dimensional embedding vector of the time series data is defined as:

[0025] X i =(x i ,x i+τ ,x i+2τ ,...,x i+(m-1)τ );

[0026] Where X i is the data vector at the i-th time point, τ is the time delay, m is the embedding dimension, and X i is the reconstructed data vector.

[0027] Through this method, the dynamic process of accident diffusion can be effectively captured and further topological analysis data can be provided.

[0028] Preferably, the multi-scale topological feature fusion module is used to calculate the weighted fusion topological feature of the microscopic scale topological feature and the macroscopic scale topological feature, and the following calculation formula is adopted:

[0029]

[0030] Among them, is the fused topological feature, α is the fusion weight coefficient, is the topological feature at the micro scale, is the topological feature at the macro scale. Through fusion, local and global factors can be comprehensively considered to improve the accuracy of the calculation of the protection distance.

[0031] Among them:

[0032] The topological feature at the micro scale refers to the topological feature based on the diffusion characteristics of chemicals and the local environment;

[0033] A small-scale hydrodynamic model and local meteorological conditions are used for modeling. These data help to analyze the diffusion characteristics near the accident source. Usually, the environmental meteorology and chemical concentrations are monitored in real time through a local sensor network.

[0034] The topological feature at the macro scale refers to the feature based on the environmental characteristics of the plant area and the overall topological structure, such as the buildings, obstacles, wind direction, etc. in the plant area.

[0035] This part of the data comes from the overall layout of the plant area and the environmental monitoring system, and can help to model how chemicals diffuse in a large area after an accident occurs.

[0036] Preferably, the quantum-inspired stochastic semidefinite programming optimization module is used to calculate the external safety protection distance based on the topological feature, and the following optimization objective function is used for optimization calculation:

[0037]

[0038] Among them, F topo (R) is the topological feature corresponding to the protection distance R, is the topological feature of the accident diffusion area, λ is the regularization coefficient, R min is the minimum feasible protection distance, and R is the required protection distance;

[0039] This optimization objective combines the topological structure and actual requirements to ensure that the calculated protection distance not only meets the safety requirements but also conforms to the accident diffusion characteristics.

[0040] The topological feature refers to the Betti number and persistence interval calculated based on the Vietoris-Rips complex, which can characterize the geometric features and topological properties of the accident diffusion area;

[0041] The minimum feasible protection distance R min is the minimum safety protection distance set according to the legal safety standards and equipment condition factors.

[0042] Preferably, the quantum-inspired stochastic perturbation adopts the following calculation formula:

[0043]

[0044] Among them, ΔR is the perturbation step size, and η is the step size constant. is the gradient of the optimization objective function J, and T is the temperature parameter;

[0045] is the gradient of the objective function with respect to the protection distance R, representing the change rate of the error;

[0046] T is the temperature parameter in the simulated annealing algorithm, which controls the amplitude of the perturbation.

[0047] Through this perturbation method, it is possible to effectively avoid the optimization falling into local extrema and ensure that the global optimal solution is finally obtained.

[0048] Preferably, the dynamic protection distance decision-making and execution module includes:

[0049] A protection distance calculation unit for calculating the external safety protection distance in real time. This unit dynamically adjusts the protection distance by receiving the protection distance calculated by the quantum-inspired optimization module and combining real-time environmental data and accident information to cope with different types of chemical accidents and environmental conditions.

[0050] An emergency response linkage unit for triggering a warning signal when a chemical leak or accident occurs and controlling evacuation systems, fire protection systems, or other safety protection devices to execute safety protection measures. The emergency response linkage unit can quickly adjust the emergency response plan according to the type of accident to minimize accident losses.

[0051] Preferably, the dynamic protection distance decision-making and execution module further includes an accident data feedback unit for collecting actual diffusion situation data after an accident and optimizing the topological feature calculation model.

[0052] Preferably, a method for determining the external safety protection distance of a chemical enterprise based on topological optimization includes the following steps:

[0053] S1: Multi-source data collection and preprocessing, obtaining environmental data, equipment status data, and chemical property data, and performing standardization processing;

[0054] S2: Constructing a high-dimensional topological structure of accident diffusion, and using the phase space reconstruction and persistent homology methods to extract the topological features of the accident diffusion process;

[0055] S3: Multi-scale topological feature fusion, using a weighted fusion method to fuse the diffusion topological features at the micro scale with the environmental topological features at the macro scale, and calculating the comprehensive topological features;

[0056] S4: Optimize the protection distance and calculate the external safety protection distance based on the quantum-inspired stochastic semidefinite programming method;

[0057] S5: Dynamic decision-making and execution, formulate safety decisions based on the calculated external safety protection distance, and control the emergency response system to execute protection measures;

[0058] S6: Feedback optimization, optimize the topology calculation model based on the actual diffusion data after the accident occurs, and improve the accuracy of the external safety protection distance calculation.

[0059] The present invention provides an intelligent determination system for the external safety protection distance of chemical enterprises. It has the following

[0060] Beneficial effects:

[0061] 1. The present invention adopts the quantum-inspired stochastic semidefinite programming optimization technology, achieving the effect of accurately calculating the external safety protection distance in a complex environment. Traditional optimization methods often rely on empirical models or rough calculation methods, and it is difficult to cope with the highly complex and dynamically changing chemical environment. However, the present invention accelerates the optimization process by introducing quantum computing, enabling it to converge to the optimal solution more quickly when dealing with large-scale data and high-dimensional parameters. This not only improves the calculation efficiency but also enhances the accuracy of the protection distance, effectively avoiding the safety hazards caused by inaccurate calculations in traditional technologies.

[0062] 2. The present invention introduces a multi-scale topological feature fusion module, enabling the calculation of the safety protection distance to consider not only local risk factors but also the overall characteristics of the macro environment. Existing traditional technologies often calculate based on a single factor (such as the local environment or a single model), ignoring the complex relationship between the local and the global. However, the present invention comprehensively analyzes the multi-dimensional factors of accident diffusion by fusing topological features at the micro and macro levels, providing richer and more detailed data support for the calculation of the protection distance. In this way, not only the calculation accuracy is improved, but also the limitations of the single-factor model are effectively compensated, making the safety protection distance more scientific and reasonable.

[0063] 3. The dynamic protection distance decision-making and execution module of the present invention can quickly adjust the protection distance when danger occurs based on real-time environmental data and accident diffusion warnings. Traditional protection systems often adopt a fixed protection distance. Once an emergency occurs, it often takes a long time to adjust, resulting in a lag in response and being unable to protect the safety of personnel and facilities in a timely manner. However, the present invention realizes dynamic response by real-time monitoring of environmental data and timely adjusting the protection distance based on the prediction model. Compared with traditional static protection schemes, the present invention can quickly adjust according to real-time changes, ensuring the timeliness and effectiveness of protection measures, avoiding the lag problem in traditional schemes, and thus improving the overall safety protection ability.

[0064] 4. The overall system of the present invention integrates quantum computing, topological analysis, and dynamic adjustment technologies to form an intelligent system for determining the external safety protection distance that is efficient and accurate. Compared with existing single technical solutions, the present invention organically combines the powerful optimization ability of quantum computing, the comprehensiveness of topological analysis, and the flexibility of dynamic adjustment, providing a full - range and multi - level safety protection solution for chemical enterprises. Through real - time calculation and adjustment, the present invention can not only maximize the scientific nature of the protection distance, but also ensure the real - time performance and efficiency of the system, effectively cope with various complex safety risks, reduce the losses of enterprises in the event of accidents, and optimize the implementation effect of protection strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic framework diagram of an intelligent system for determining the external safety protection distance of chemical enterprises;

[0066] Figure 2 It is a flowchart of the steps of a method for determining the external safety protection distance of chemical enterprises based on topological optimization. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0068] Please refer to the attached Figure 1 drawings. An embodiment of the present invention provides an intelligent system for determining the external safety protection distance of chemical enterprises. The following will detail each module of the system of the present invention.

[0069] Multi - source data acquisition and pre - processing module

[0070] The multi - source data acquisition and pre - processing module is used to obtain the environmental parameters, equipment status parameters, and chemical characteristics parameters required for calculating the external safety protection distance of chemical enterprises, and after standardizing the data, transmit it to the algebraic topology dynamic manifold embedding module. This module includes an environmental data acquisition unit, an equipment monitoring unit, a chemical database, and a data standardization processing unit. The functions and implementation methods of each unit are as follows:

[0071] The environmental data acquisition unit includes a wind speed measurement part, a wind direction measurement part, a temperature and humidity monitoring part, and a meteorological data interface part. The wind speed measurement part and the wind direction measurement part respectively obtain real - time meteorological data through sensors and are connected to the meteorological data interface part for synchronizing the data of the external meteorological database.

[0072] The temperature and humidity monitoring unit is used to obtain the temperature and humidity data of the surrounding air, and the data is transmitted to the data standardization processing unit through a wireless network.

[0073] Wind speed v w is calculated using the following formula:

[0074]

[0075] where ΔP is the wind pressure, ρ is the air density, and after obtaining the wind speed, it is used to calculate the pollutant diffusion speed.

[0076] The wind direction angle θ is measured by a sensor and needs to be converted to radians when calculating the diffusion direction:

[0077]

[0078] where θ r is the wind direction angle in radians.

[0079] The equipment monitoring unit includes a pipeline pressure detection section, a flow detection section, and a leakage monitoring section, and each component is connected through a data transmission line.

[0080] The pipeline pressure detection section obtains the internal pressure value ρ of the pipeline and calculates the leakage rate according to the following equation:

[0081]

[0082] where C d is the discharge coefficient, A is the leakage hole area, and Q is the leakage flow rate.

[0083] The flow detection section is used to monitor the fluid flow rate under normal working conditions and provide reference data. When the leakage flow rate Q is significantly higher than the reference value, a warning signal is triggered.

[0084] The chemical database stores the physical and chemical properties of various hazardous chemicals, including but not limited to diffusion coefficients, toxicity thresholds, explosion limits, etc.

[0085] The diffusion coefficient D is calculated using the following formula:

[0086]

[0087] where k B is the Boltzmann constant, T is the ambient temperature, η is the gas viscosity, and r is the molecular radius. This data is used to calculate the diffusion behavior of chemicals in the air.

[0088] The data standardization processing unit includes a noise filtering section, a normalization processing section, and a data storage section. The noise filtering section uses the Kalman filtering method to denoise the collected data, and the filtering equation is as follows:

[0089]

[0090] Among them, is the state estimate value, and K k is the Kalman gain, z k is the observed value, and H is the measurement matrix.

[0091] The normalization processing unit adopts the Z-score standardization method:

[0092]

[0093] Among them, X is the original data, μ is the mean value, and σ is the standard deviation.

[0094] Finally, the data storage unit formats and stores the processed data and transmits it to the algebraic topology dynamic manifold embedding module for subsequent topology calculation and safety protection distance optimization.

[0095] Algebraic topology dynamic manifold embedding module

[0096] The algebraic topology dynamic manifold embedding module is used to receive the standardized data from the multi-source data acquisition and preprocessing module and construct a high-dimensional topological structure of accident diffusion based on these data. This module includes a phase space reconstruction unit, a topological feature extraction unit, and a topological transformation calculation unit. The physical / logical connection relationships of each part are as follows:

[0097] The phase space reconstruction unit includes a time series conversion component and a delay embedding calculation component.

[0098] The time series conversion component is used to receive environmental parameter, device status parameter, and chemical property parameter and convert them into a time series data set.

[0099] The delay embedding calculation component performs phase space reconstruction on the time series data and represents the accident diffusion state using the delay embedding method. The mathematical model is as follows:

[0100] X i =(x i ,x i+τ ,x i+2τ ,...,x i+(m-1)τ );

[0101] Among them, X i is the state vector at the i-th time point, τ is the time delay, and m is the embedding dimension.

[0102] The embedding dimension m is determined by the singular value decomposition (SVD) method, and the calculation is as follows:

[0103]

[0104] Among them, λk is the eigenvalue of the covariance matrix of time series data, and σ k represents the standard deviation of the main components of the data.

[0105] The calculation result of the phase space reconstruction unit is transmitted to the topological feature extraction unit for further analysis of the topological characteristics of accident diffusion.

[0106] The topological feature extraction unit includes a complex network construction component and a Betti number calculation component. The complex network construction component generates a topological network based on time series data and constructs a network adjacency matrix using a similarity measurement method:

[0107]

[0108] where d(X i , X j ) is the Euclidean distance and ∈ is the threshold.

[0109] The Betti number calculation component calculates topological features based on the Vietoris-Rips complex, including the number of connected components β0 and the number of holes β1. The calculation method is as follows:

[0110] β k = rank(H k );

[0111] where H k represents the rank of the k-th dimensional homology group, and k = 0, 1 represents topological features of different dimensions. The calculation result of the topological feature extraction unit is transmitted to the topological transformation calculation unit for high-dimensional topological mapping.

[0112] The topological transformation calculation unit includes a topological embedding component and a topological feature conversion component.

[0113] The topological embedding component uses the extended manifold learning method to map high-dimensional topological features to the embedding space and calculates the embedding coordinates using the Laplacian Eigenmap method:

[0114]

[0115] where L is the Laplacian matrix, D is the degree matrix, A is the adjacency matrix, λ is the eigenvalue, and y is the embedding coordinate vector. The topological feature conversion component further performs a standardization transformation on the topological features to facilitate subsequent calculation of the safety protection distance. The conversion formula is as follows:

[0116]

[0117] where F topo is the normalized topological feature, and min(y) and max(y) are the minimum and maximum values of the eigenvalues.

[0118] The calculation result of the topological transformation calculation unit is finally transmitted to the multi-scale topological feature fusion module to support the intelligent calculation of the external safety protection distance for chemical enterprises.

[0119] Multi-scale topological feature fusion module

[0120] The multi-scale topological feature fusion module is used to receive the topological features calculated by the algebraic topology dynamic manifold embedding module and perform weighted fusion at different scales to generate topological feature data applicable to the calculation of the external safety protection distance. This module includes a micro-topological feature calculation unit, a macro-topological feature calculation unit, and a feature weighted fusion unit. The physical / logical connection relationships of each component are as follows:

[0121] The micro-topological feature calculation unit includes a local topological feature extraction component and a scale normalization component.

[0122] The local topological feature extraction component calculates the micro-scale topological features based on the manifold structure of accident diffusion, mainly including local Betti numbers and topological persistence vectors.

[0123] Local Betti number β k The calculation is as follows:

[0124] β k =rank(H k );

[0125] Where, H k is the rank of the k-th dimensional homology group, and k = 0, 1 represent the number of connected components and the number of holes respectively

[0126] Topological persistence vector P k The calculation is:

[0127]

[0128] Where, b i and d i are the generation and extinction times of the i-th topological feature respectively, and P k reflects the stability of the topological structure. The scale normalization component performs standardization processing on the calculated topological features, using min-max normalization:

[0129]

[0130] Where, F micro is the normalized micro-topological feature. The calculation result is transmitted to the feature weighted fusion unit for subsequent fusion calculation.

[0131] The macro anvil cylinder calculation unit includes a global topology feature extraction component and a scale adjustment component. The global topology feature extraction component calculates macro-scale features based on the overall topology of the factory area environment, mainly including Laplacian features and topological density.

[0132] The Laplacian feature L is calculated as follows:

[0133] L = D - A;

[0134] where D is the degree matrix, A is the adjacency matrix, and L reflects the overall structure of the topological graph.

[0135] The topological density ρ is calculated as:

[0136]

[0137] where |E| is the number of network edges, |V| is the number of network nodes, and ρ reflects the complexity scale of the factory area topological structure. The scale adjustment component performs a linear transformation on the macro-topological features to match them with the micro-features:

[0138] F macro = α·X + β;

[0139] where α and β are scaling coefficients.

[0140] The calculation result is transmitted to the feature weighted fusion unit for topological feature fusion.

[0141] The feature weighted fusion unit includes a feature weighted calculation component and a fusion optimization component. The feature weighted calculation component calculates the weights of the micro and macro topological features based on the entropy weight method:

[0142]

[0143] where p ik is the probability of the i-th feature in the k-th dimension, and H i is the feature entropy value. The fusion optimization component calculates the final topological feature using the weighted average method:

[0144] F final = w micro F micro + w macro F macro ;

[0145] where F final is the fused topological feature data.

[0146] The calculation result is finally transmitted to the quantum-inspired stochastic semi-definite programming optimization module for calculating the external security protection distance.

[0147] Quantum-inspired stochastic semi-definite programming optimization module

[0148] The quantum-inspired stochastic semidefinite programming optimization module is used to perform optimization calculations for the external security protection distance based on the topological data generated by the multi-scale topological feature fusion module. This module includes a quantum optimization algorithm unit, a semidefinite programming solver unit, and an optimization result output unit. The physical / logical connection relationships of each component are as follows:

[0149] The quantum optimization algorithm unit includes a quantum computing model construction component and a quantum random walk component. The quantum computing model construction component is used to construct the Hamiltonian of the quantum optimization problem according to the topological feature data. Let the optimization objective be to minimize the external security protection distance D safe , and its Hamiltonian is expressed as:

[0150]

[0151] where X i is the i-th topological feature, α i is the weight associated with the feature X i , γ ij is the coupling strength between the features X i and X j , and β is the global adjustment parameter of the system.

[0152] The quantum random walk component implements the random walk algorithm based on a quantum simulator, accelerates the search process through quantum superposition states and quantum entanglement, and searches for the optimal solution in the state space of qubits. The calculation process updates the Hamiltonian through the entangled state of qubits and solves the minimization problem. The output of the quantum optimization algorithm unit is the probability distribution of the quantum optimization result, which is used for subsequent semidefinite programming solving.

[0153] The semidefinite programming solver unit includes a problem transformation component and a semidefinite programming solver.

[0154] The problem transformation component maps the probability distribution of the quantum optimization result to the constraint conditions of the semidefinite programming problem. Assume that the security protection distance D safe is a variable in the constraint conditions. The transformed semidefinite programming problem is:

[0155] Minimize D safe subject to X≥0, AX = b;

[0156] where X is a semidefinite matrix variable, A is a constraint matrix, b is a constraint vector, and X≥0 represents the semidefinite matrix condition to ensure that the solution result satisfies the system stability.

[0157] The semi - definite programming solver uses modern optimization algorithms, such as the Interior Point Method, to solve this optimization problem. This algorithm updates the matrix X through an iterative process to ensure system stability and meet the safety protection distance constraint conditions.

[0158] The optimization result output section includes a result evaluation component and an optimization plan generation component. The result evaluation component evaluates the optimized safety protection distance to ensure it meets the specified safety standards and outputs the final calculation result.

[0159] The calculation formula is:

[0160]

[0161] Where, is the optimized safety protection distance.

[0162] The optimization plan generation component generates a specific safety protection recommendation plan based on the optimization result to guide chemical enterprises in formulating appropriate safety protection distance strategies. The final plan includes the recommended range of safety protection areas, emergency response measures, etc.

[0163] The calculation result and the optimization plan are transmitted to the terminal device through the data transmission system to ensure the real - time response and implementation of the system.

[0164] The technical implementation of this module combines quantum computing and semi - definite programming optimization, leveraging the advantages of quantum - inspired algorithms and traditional optimization methods to achieve efficient and secure calculation of the external safety protection distance.

[0165] Dynamic Protection Distance Decision - making and Execution Module

[0166] The Dynamic Protection Distance Decision - making and Execution Module is used to adjust the safety protection distance of chemical enterprises in real - time according to external environmental changes and accident diffusion early - warning information and execute corresponding protection measures. This module includes a decision - making engine section, a dynamic protection distance adjustment section, and an execution control section. The physical / logical connection relationships of each component are as follows:

[0167] The decision - making engine section includes a risk assessment calculation component and a decision - making model construction component.

[0168] The risk assessment calculation component evaluates potential risks based on external environmental data (such as wind speed, wind direction, temperature, humidity, etc.) and accident diffusion models (such as the Gaussian diffusion model). When setting the wind speed v w and the wind direction θ, the risk assessment formula is as follows:

[0169]

[0170] Where, R risk is the potential risk, C pollutantis the pollutant concentration, v w is the wind speed, and θ is the wind direction angle.

[0171] The decision model construction component constructs a decision model based on the risk assessment results and topological feature data, and the model is optimized and calculated through a multi-layer neural network algorithm

[0172] The optimal safety protection distance. The optimization objective is to minimize the potential damage of the accident to personnel and facilities, and the objective function is:

[0173]

[0174] Among them, is the optimized safety protection distance, R risk is the risk assessment result, f(D safe ) is the damage function related to the safety protection distance.

[0175] The calculation result of the decision engine department is transmitted to the dynamic protection distance adjustment department to provide a basis for real-time adjustment.

[0176] The dynamic protection distance adjustment department includes a distance adjustment algorithm component and a data feedback component.

[0177] The distance adjustment algorithm component dynamically adjusts the protection distance by receiving the calculation result of the decision engine department in real time and according to environmental and risk factors. An adaptive adjustment method based on the genetic algorithm is used for optimization:

[0178]

[0179] Among them, is the adjusted protection distance, ΔD is the adjustment amount based on the current risk assessment, and the calculation formula is dynamically generated according to the algorithm iteration result.

[0180] The data feedback component is used to monitor the adjustment effect of the protection distance and transmit the feedback information to the decision engine department for the next round of optimization. The feedback signal is:

[0181]

[0182] Among them, Feedback is the feedback signal, indicating the gap between the current protection distance and the optimization target.

[0183] The execution control department includes a protection measure execution component and a safety protection monitoring component.

[0184] The protection measure execution component executes specific safety protection measures according to the adjusted protection distance, such as personnel evacuation, hazard source isolation, etc. The execution process is completed through an automated control system to ensure the rapid response and execution of the protection measures.

[0185] The safety protection monitoring component monitors the implementation of measures in real time and returns the monitoring data to the safety decision-making engine department through the data transmission system to ensure the stable operation of the protection system. The monitoring signal calculation formula is:

[0186]

[0187] Among them, S current is the current protection status, S opt is the optimized target protection status, and Monitor is the monitoring signal, representing the deviation of the implementation of protection measures.

[0188] The calculation and management results of the execution control department are finally transmitted to the terminal device to ensure the real-time response and implementation of the system.

[0189] Through real-time risk assessment, decision model optimization, and dynamic protection distance adjustment, this module can timely adjust the safety protection distance according to environmental changes to ensure the safety of personnel and equipment in chemical enterprises, with high flexibility and reliability.

[0190] Please refer to the appendix Figure 2 This invention also provides a method for determining the external safety protection distance of chemical enterprises based on topology optimization. The following will explain the specific implementation methods of each step in combination with the working process of the method of this invention.

[0191] A method for determining the external safety protection distance of chemical enterprises based on topology optimization includes the following steps:

[0192] S1: Multi-source data collection and preprocessing, obtaining environmental data, equipment status data, and chemical property data, and performing standardization processing;

[0193] S2: Construct a high-dimensional topological structure of accident diffusion, and use the phase space reconstruction and persistent homology methods to extract the topological features of the accident diffusion process;

[0194] S3: Multi-scale topological feature fusion, using a weighted fusion method to fuse the diffusion topological features at the microscale with the environmental topological features at the macroscale, and calculating the comprehensive topological features;

[0195] S4: Optimize the protection distance, and calculate the external safety protection distance based on the quantum-inspired stochastic semidefinite programming method;

[0196] S5: Dynamic decision-making and execution, formulating safety decisions based on the calculated external safety protection distance, and controlling the emergency response system to execute protection measures;

[0197] S6: Feedback optimization, optimizing the topological calculation model based on the actual diffusion data after the accident to improve the accuracy of calculating the external safety protection distance

[0198] The method of this embodiment can be used to implement the above system embodiment, and its principle and technical effects are similar, which will not be elaborated here.

[0199] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent determination system for the external safety protection distance of chemical enterprises, characterized in that, include: The multi-source data acquisition and preprocessing module is used to obtain environmental parameters, equipment status parameters and chemical characteristic parameters, and after preprocessing through the data standardization processing unit, the obtained standardized data is transmitted to the algebraic topology dynamic manifold embedding module; The algebraic topology dynamic manifold embedding module is used to receive the standardized data from the multi-source data acquisition and preprocessing module, and build a high-dimensional topological structure of accident diffusion based on these data. After extracting the topological features through the topological feature extraction unit, the calculation results are passed to the multi-scale topological feature fusion module; The multi-scale topological feature fusion module is used to receive the topological features from the algebraic topological dynamic manifold embedding module, and weightedly fuse the micro-scale chemical diffusion topological features with the macro-scale plant environment topological features to generate fused topological feature data, which is then passed to the quantum-inspired stochastic semidefinite programming optimization module; The quantum-inspired stochastic semidefinite programming optimization module is used to calculate the external safety protection distance based on the topological feature data transmitted by the multi-scale topological feature fusion module, and optimize the calculation results through the quantum-inspired random perturbation method. The optimized protection distance data is transmitted to the dynamic protection distance decision and execution module; The dynamic protection distance decision and execution module is used to receive external safety protection distance data from the quantum-inspired stochastic semidefinite programming optimization module, make safety decisions based on the calculated protection distance, and link the emergency response system to execute corresponding protection measures, where the emergency response system includes the evacuation system and the fire protection system.

2. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, wherein The multi-source data acquisition and preprocessing module includes: Environmental data acquisition unit, used to obtain wind speed, wind direction, temperature and humidity; Equipment monitoring unit to obtain leakage rate, pressure and valve status; Chemical database, which is used to store diffusion coefficients, toxicity thresholds and reactivity of chemicals; The data standardization processing unit is used to remove noise using the Kalman filtering method and to standardize the data using the Z-score standardization method.

3. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, characterized in that, The algebraic topology dynamic manifold embedding module includes: The phase space reconstruction unit is used to reconstruct the high-dimensional manifold of time series data using the delayed embedding method. The high-dimensional embedding vector of the time series data is defined as: X i =(xi, xi +τ , x i+2τ ,..., x i+ ( m-1τ ); where X i is the data vector at the i-th time point, τ is the time delay, m is the embedding dimension, and X i is the reconstructed data vector.

4. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, wherein, The multi-scale topological feature fusion module is used to calculate the weighted fusion topological features of the micro-scale topological features and the macro-scale topological features, and adopts the following calculation formula: Among them, is the fused topological feature, and α is the fusion weight coefficient. is the topological feature at the microscale, is the topological feature at the macroscale; The micro-scale street characteristics refer to the topological characteristics based on the diffusion characteristics of chemicals and the local external environment; The macro-scale topological features refer to features based on the plant environment characteristics and the overall topological structure.

5. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, wherein The quantum-inspired stochastic semidefinite programming optimization module is used to calculate the external safety protection distance based on topological characteristics, and the following optimization objective function is used for optimization calculation: Among them, F topo R) is the topological feature corresponding to the protection distance R, is the topological feature of the accident diffusion area, λ is the regularization coefficient, R min is the minimum feasible protection distance, and R is the protection distance to be obtained; The topological features refer to the Betti numbers and persistence intervals calculated based on the Vietoris-Rips complex, which can characterize the geometric features and topological characteristics of the accident diffusion area; Minimum viable protection distance R min It is the minimum safety protection distance set according to legal safety standards and equipment condition factors.

6. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, wherein, The quantum inspired random perturbation is calculated using the following formula: where ΔR is the perturbation step size, η is the step size constant, is the gradient of the optimization objective function J, and T is the temperature parameter; is the gradient of the objective function with respect to the protection distance R, representing the rate of change of the error; T is the temperature parameter in the simulated annealing algorithm, which controls the amplitude of the disturbance.

7. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, characterized in that The dynamic protection distance decision-making and execution module includes: A protection distance calculation unit for calculating the external safety protection distance in real time; An emergency response linkage unit for triggering a warning signal and controlling the evacuation system, fire protection system or other safety protection devices to execute safety protection measures when a chemical leak or accident occurs.

8. The intelligent determination system for the external safety protection distance of a chemical enterprise according to claim 1, characterized in that, The dynamic protection distance decision-making and execution module further includes an accident data feedback unit for collecting actual diffusion situation data after an accident and optimizing the topological feature calculation model.

9. A method for determining the external safety protection distance of chemical enterprises based on topology optimization, which is applied to the system described in any one of claims 1-8, and is characterized in that, It includes the following steps: S1: Multi-source data collection and preprocessing, obtaining environmental data, equipment status data and chemical property data, and performing standardization processing; S2: Constructing a high-dimensional topological structure of accident diffusion, and extracting topological features of the accident diffusion process by using the phase space reconstruction and persistent homology methods; S3: Multi-scale topological feature fusion, using a weighted fusion method to fuse the diffusion topological features at the micro scale with the environmental topological features at the macro scale, and calculating the comprehensive topological features; S4: Optimizing the protection distance, calculating the external safety protection distance based on the quantum-inspired stochastic semidefinite programming method; S5: Dynamic decision-making and execution, making safety decisions based on the calculated external safety protection distance, and controlling the emergency response system to execute protection measures; S6: Feedback optimization, optimizing the topological calculation model based on the actual diffusion data after an accident to improve the accuracy of the external safety protection distance calculation.

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