A chemical storage tank pressure abnormality intrinsic safety risk assessment system

Abstract

The application discloses a kind of chemical storage tank pressure abnormality intrinsically safe risk assessment systems, it is related to chemical safety monitoring and risk assessment technical field, the system includes following component parts: data acquisition module, topological construction module, risk quantification module, atlas visualization module and traceability early warning module;The application breaks through traditional limitations by constructing topological correlation model, realizes risk real-time accurate measurement, so that risk grade division is more scientific;Dynamic risk atlas is constructed, equipment risk grade and transmission path are directly presented, dangerous unit is accurately positioned, risk rapid traceability and targeted early warning are realized;Closed-loop management is formed, the timeliness and effectiveness of risk control are improved, the probability of safety accident is reduced, and comprehensive technical support is provided for chemical plant safety management.

Description

Technical Field

[0001] This invention relates to the field of chemical safety monitoring and risk assessment technology, specifically to an intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks. Background Technology

[0002] Chemical storage tanks, as core and critical equipment in chemical production and storage, are widely used to hold flammable, explosive, toxic, and hazardous media such as crude oil and various chemicals. The stability of their operation directly affects the production safety of the entire plant. Abnormal pressure is a major cause of serious safety accidents such as tank leaks and explosions, which can lead to severe casualties, property damage, and environmental pollution. As the chemical industry develops towards large-scale and refined production, and production processes become increasingly complex, the coupling between storage tanks and related equipment is constantly increasing. Traditional risk assessment methods are struggling to meet the demands of complex scenarios, making the industry's need for a real-time, accurate, and visualized intrinsic safety risk assessment system for tank pressure anomalies increasingly urgent.

[0003] Current risk assessment technologies for chemical storage tanks have many shortcomings. The data acquisition process lacks a unified unique equipment identification system, resulting in inconsistent data formats for different types and regions. Noise interference during acquisition is not effectively eliminated, and the precise association between timestamps and equipment / parameters is not achieved, leading to low data usability. Topology construction often relies on simple pipeline connections, failing to construct a topology association model including a basic layer, association layer, and attribute layer. The lack of static association parameters results in inaccurate mapping between equipment and pipelines, making it unsuitable for risk transmission analysis in complex scenarios. Risk quantification often employs single-dimensional or static calculation methods, failing to perform three-dimensional coupled dynamic calculations of equipment operating status, topology association strength, and process parameters, making it difficult to accurately reflect the real-time risk level of the storage tank. Visualization is limited to single risk level labeling, lacking dynamic coupled visualization effects of risk transmission strength, and failing to intuitively display risk propagation paths. The source tracing and early warning process struggles to accurately locate hazardous units, lacks scientific prioritization of risk transmission paths, and lacks a complete risk management closed loop, leading to delayed early warning responses and insufficiently targeted control measures.

[0004] The shortcomings of existing technologies have resulted in persistent problems in the risk assessment of abnormal pressure in chemical storage tanks, including low accuracy, poor real-time performance, insufficient visualization, and inefficient traceability and control. These limitations prevent the system from meeting the core requirements of inherently safe management and hinder the early prediction and effective mitigation of potential safety risks. Therefore, developing an integrated risk assessment system that combines data acquisition, hierarchical topology construction, multi-dimensional dynamic risk quantification, dynamic visualization, and efficient traceability and early warning has become crucial for addressing the deficiencies of existing technologies and filling technological gaps. This system is of great significance for improving the ability to predict and control abnormal pressure risks in chemical storage tanks and ensuring the safety of chemical production. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intrinsically safe risk assessment system for abnormal pressure in chemical storage tanks. This system can assign unique identifiers to physical equipment, collect and preprocess real-time operating parameters, construct a topological association model to clearly present the equipment association logic, use a three-dimensional coupling algorithm to achieve real-time risk quantification, construct a dynamic risk map to intuitively display the risk level and transmission path, accurately locate hazardous units, achieve rapid risk tracing and early warning, and form a complete closed loop for risk control.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks, the system comprising the following components: a data acquisition module, a topology construction module, a risk quantification module, a graph visualization module, and a source tracing and early warning module;

[0007] The data acquisition module collects real-time operating parameters of each storage tank and associated physical equipment within the plant area; assigns a unique identifier to each physical equipment; preprocesses the real-time operating parameters and adds a timestamp; the real-time operating parameters include pressure parameters and associated process parameters.

[0008] The topology construction module: receives physical device identification information, analyzes the connection relationship between physical devices and pipeline elements according to the plant pipeline and instrumentation flow diagram, establishes the mapping relationship between pipeline elements and physical devices, and constructs a topology association model by supplementing static association parameters;

[0009] The risk quantification module receives real-time operating parameters and a topological association model, uses a three-dimensional coupled dynamic risk quantification algorithm to calculate the real-time risk quantification value of each storage tank, and classifies the risk level according to the real-time risk quantification value to generate corresponding risk identification data.

[0010] The map visualization module: receives risk identification data and topological association model, uses a risk transmission strength visualization coupling algorithm to calculate the risk transmission strength between physical equipment nodes; generates a dynamic risk map based on the factory electronic map; the dynamic risk map includes a base layer, a data layer and a visualization layer.

[0011] The source tracing and early warning module receives a dynamic risk map, locates dangerous units based on the dynamic risk map, prioritizes risk transmission paths, and provides a risk source tracing query interface to form a risk control closed loop.

[0012] Furthermore, the process of collecting real-time operating parameters of each storage tank and associated physical equipment within the plant area in the data acquisition module is as follows: A unique identifier is assigned to each storage tank and associated physical equipment according to the plant equipment classification, installation area, and deployment order, establishing a unique mapping relationship for physical equipment identity; a communication link is established between the physical equipment and the data acquisition module through interface with the deployed acquisition terminal, collecting and caching the real-time operating parameters of each storage tank and associated physical equipment; the cached real-time operating parameters are standardized in a unified format to eliminate noise during data transmission and acquisition; a timestamp is added to each set of preprocessed real-time operating parameters, and the timestamp is associated and bound with the unique identifier of the corresponding physical equipment and the real-time operating parameters to form structured data.

[0013] Furthermore, the process of establishing the mapping relationship between pipeline elements and physical equipment in the topology construction module is as follows: receiving the plant's pipeline and instrumentation flow diagram and physical equipment identification information; parsing the diagram to extract physical equipment, pipeline elements, and their connection node information; matching the correspondence between physical equipment and pipelines based on the connection node characteristics; identifying the connection ports of physical equipment inlets and outlets and pipelines; establishing a bidirectional mapping relationship between pipeline elements and physical equipment; labeling the connection port attributes and associating them with the corresponding physical equipment's unique identifier.

[0014] Furthermore, the topology association model in the topology construction module has a hierarchical structure, specifically including a base layer, an association layer, and an attribute layer. The base layer stores the unique identifier of physical devices, the pipe element number, and the connection node information between them, forming a basic identity association library. The association layer, based on the connection nodes, constructs a bidirectional mapping relationship between physical devices and pipes, and labels the connection port attributes and corresponding association strengths. The attribute layer embeds static association parameters and associates and binds them with the data in the base layer and the association layer. The three layers of data interact with each other to form a traceable and updatable topology association model structure.

[0015] Furthermore, the calculation formula for the three-dimensional coupled dynamic risk quantification algorithm in the risk quantification module is as follows: ,in, For the storage tank in Real-time risk quantification value at any given moment; Weighting coefficients for the equipment operating status dimension; These are the weighting coefficients for the topological association strength dimension; These are the weighting coefficients for the coupling dimensions of process parameters; For the storage tank in Quantitative value of the device's operating status at any given moment; For the storage tank in The quantized value of the topological correlation strength dimension at any given time; For the storage tank in The process parameters at any given time are coupled with the quantified values ​​of the three dimensions. By performing weighted coupling operations on the three dimensions, a comprehensive quantitative calculation of the real-time risk of the storage tank is achieved, taking into account the impact of each dimension on the risk of abnormal pressure in the storage tank, so that the calculated risk quantification value can comprehensively and dynamically reflect the actual safety risk status of the storage tank.

[0016] Method for determining weighting coefficients ω1, ω2, and ω3:

[0017] The initial weights are determined using the entropy weight method, and then dynamically iteratively optimized based on the plant's historical accident data, equipment failure records, and process optimization feedback over the past three years. The initial weight allocation reference range is: ω1∈[0.3,0.4], ω2∈[0.25,0.35], ω3∈[0.3,0.4], satisfying ω1+ω2+ω3=1. The weights are recalibrated quarterly based on newly added operating data to ensure adaptation to process changes and equipment aging patterns.

[0018] Quantification value calculation model for each dimension:

[0019] The equipment operating status dimension S(t): Normalization is used to convert indicators such as equipment runtime, failure frequency, maintenance cycle, and real-time operating parameter deviation rate into quantified values ​​in the 0-1 range. The formula is: ,in, , The sub-indicator weights are α+β=1. Equipment health is calculated based on the equipment's operating years and maintenance records. The parameter deviation rate is calculated using the following formula: ;

[0020] Topological association strength dimension T(t): Based on the association layer data of the topological association model, it integrates pipe diameter, connection port sealing level, and static association parameters, and performs weighted summation followed by normalization. The formula is as follows: ,in, , For sub-dimension weights, and The pipeline correlation strength is determined by a combination of pipeline diameter and pressure resistance rating, while the equipment port attribute weight is determined by sealing rating and interface type.

[0021] Process parameter coupling dimension P(t): Three core process parameters—pressure, temperature, and flow rate—are selected. After calculating the proportion of each parameter deviating from the safety threshold, a multiple linear regression model is used to achieve coupling quantification. The formula is as follows: ,in, , , For process parameter weights, and The safety threshold references the equipment design parameters, and the formulas for calculating each deviation rate are as follows: , , ;

[0022] Dynamic adaptation mechanism:

[0023] Dynamic quantization is achieved using a sliding time window with a window duration of 10 minutes, and real-time parameters are updated every 5 minutes. During quantization calculation, the current data accounts for 60% of the weight, and the historical window data accounts for 40%, balancing real-time performance and stability.

[0024] Furthermore, the specific steps in the risk quantification module to classify risk levels based on the real-time risk quantification value and generate corresponding risk identification data are as follows: Preset and store the real-time risk quantification value ranges corresponding to four risk levels, namely: Level 1 low risk 0-0.3, Level 2 medium risk 0.3-0.6, Level 3 high risk 0.6-0.8, and Level 4 extremely high risk 0.8-1; retrieve the preset range data, compare and match the real-time risk quantification value of each storage tank with the range range to determine the risk level of the corresponding storage tank; generate exclusive risk identification data according to the determined risk level, the identification data containing the risk level code and corresponding feature information; and store the risk identification data after associating and binding it with the unique identifier and real-time risk quantification value of the corresponding storage tank.

[0025] Furthermore, the calculation formula for the risk transmission intensity visualization coupling algorithm in the spectrum visualization module is as follows: ,in, for physical device nodes at all times To physical device node The risk transmission intensity value; for physical device nodes at all times Real-time risk quantification value; For physical device nodes and The strength of the topological association between them; For physical device nodes and The risk attenuation coefficient between pipelines; by coupling the risk status of physical equipment nodes with the topological correlation characteristics between nodes, the risk transmission intensity between physical equipment nodes is calculated, which can objectively characterize the risk transmission pattern and strength difference between equipment in the plant area, and provide quantitative support for the visualization and intensity characterization of risk transmission paths in dynamic risk maps.

[0026] Furthermore, the dynamic risk map in the visualization module has a three-layer structure, including a base layer, a data layer, and a visualization layer. The base layer is an electronic map of the factory area; the data layer contains risk identification data of physical equipment nodes, risk transmission intensity data between equipment, and topological relationship data; and the visualization layer contains risk level icons and visualization elements of risk transmission paths. The specific steps for generating a dynamic risk map based on the electronic map of the factory area are as follows: using the electronic map of the factory area as the map base, calibrating the geographical coordinates of each physical equipment node and matching and binding them with the unique identifier of the physical equipment; based on the calculation results of the risk transmission intensity visualization coupling algorithm, associating the physical equipment connection relationships in the topological relationship model, and extracting the risk transmission intensity data between all physical equipment nodes; generating corresponding risk level icons for each physical equipment node based on the risk identification data, and anchoring the icons to the matched geographical coordinate positions; constructing risk transmission paths between corresponding nodes based on the risk transmission intensity between physical equipment, and setting the path visual attributes according to the risk transmission intensity; and integrating all risk level icons, risk transmission paths, and the electronic map base of the factory area to generate a dynamic risk map.

[0027] Furthermore, the specific steps in the source tracing and early warning module for locating hazardous units based on the dynamic risk map and prioritizing risk transmission paths are as follows: extract physical device nodes with a risk level of level three or above from the dynamic risk map; combine this with risk transmission intensity data to filter out risk source nodes and high transmission intensity starting nodes, and locate them as hazardous units; collect risk transmission paths associated with hazardous units, extract the three indicators corresponding to the path—risk level, average transmission intensity, and number of devices involved—and perform a comprehensive score; preset the correspondence between the score range 0-1 and the priority and store it, with 0.8-1 being high priority, 0.6-0.8 being medium priority, and 0-0.6 being low priority; match the comprehensive score of each path with the preset score range to determine the corresponding priority; mark the hazardous units and the priorities corresponding to each path, and synchronize them to the risk source tracing query interface.

[0028] Compared with existing technologies, this inherent safety risk assessment system for abnormal pressure in chemical storage tanks has the following advantages:

[0029] I. This invention ensures data integrity and consistency by assigning unique identifiers to each physical device, standardizing and preprocessing real-time operating parameters, and associating them with timestamps. It analyzes the connection relationships between equipment and pipelines based on the plant's piping and instrumentation flow diagrams, supplements static correlation parameters to construct a topological correlation model, clearly presenting the correlation logic and strength between devices, breaking the limitations of traditional topology construction. Furthermore, it conducts dynamic risk quantification calculations by combining multiple key factors, determining weights through entropy weighting, normalizing the quantified values ​​of each dimension through multi-indicator normalization, and achieving dynamic adaptation through a sliding time window. This comprehensively considers the core dimensions of equipment operating status, topological correlation strength, and process parameter coupling, achieving real-time and accurate risk level measurement. This solves the problem that traditional assessment methods cannot reflect the true risk status, making risk level classification more scientific.

[0030] Second, this invention integrates a plant electronic map with various risk-related data through a risk transmission intensity visualization coupling algorithm to construct a dynamic risk map with a multi-level structure. This map intuitively presents the risk level of equipment and the risk transmission path, helping staff to quickly and clearly grasp the overall risk distribution of the plant. Based on the dynamic risk map, it accurately locates hazardous units, extracts key indicators to prioritize risk transmission paths, and, combined with a risk tracing query interface, enables rapid risk tracing and targeted early warning responses. This forms a complete closed loop from risk monitoring, quantitative analysis, visualization, to tracing and control, improving the timeliness and effectiveness of risk management, reducing the probability of safety accidents caused by abnormal pressure, effectively ensuring the inherent safety of chemical production processes, and providing comprehensive and efficient technical support for plant safety management.

[0031] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0033] Figure 1 A block diagram showing the modular components of a chemical storage tank pressure anomaly intrinsic safety risk assessment system.

[0034] Figure 2 A flowchart of an intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks;

[0035] Figure 3 This is a data transmission diagram of the entire process of tank topology construction and risk quantification for a chemical storage tank pressure anomaly intrinsic safety risk assessment system. Detailed Implementation

[0036] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0037] Example 1

[0038] The data acquisition module assigns a unique identifier to each tank and associated physical device according to the classification standards of equipment such as storage tanks, pump sets, heat exchangers, pipelines, and valves, combined with the installation area division and actual deployment sequence of each device. This unique identifier establishes a unique mapping relationship for physical device identity, ensuring that each device can be accurately identified in the entire evaluation system and avoiding data association errors caused by device confusion. Subsequently, a reliable communication link is established between the physical devices and the data acquisition module by stably connecting with acquisition terminals such as pressure sensors, flow sensors, and temperature sensors deployed at key locations on each device. This allows for real-time acquisition of pressure parameters of each storage tank, as well as supporting process parameters such as pump set operating power, heat exchanger inlet and outlet temperatures, pipeline medium flow rate, and valve on / off status. The acquired real-time data is cached in real time to effectively prevent data loss or interruption during transmission. The cached real-time operating parameters undergo standardized processing to convert heterogeneous data output from different acquisition terminals into a unified format recognizable by the system. At the same time, noise data caused by equipment interference and signal fluctuations during data transmission and acquisition is filtered out to ensure data accuracy and consistency. A precise timestamp is added to each set of preprocessed real-time operating parameters. The timestamp is then deeply associated and bound with the unique identifier of the corresponding physical device and the real-time operating parameters to form structured data. This structured storage method not only facilitates the rapid retrieval and processing of data by subsequent modules, but also enables full traceability of data, clearly identifying the source device and generation time of each set of data.

[0039] The topology construction module receives the complete piping and instrumentation diagrams and identification information of all physical equipment in the plant area. It performs detailed analysis of the drawings, comprehensively extracting key information such as the model, location, and function of physical equipment; the material, specifications, and direction of pipe elements; and the location and structural type of connection nodes between them. This lays the foundation for establishing subsequent relationships. Based on key elements such as the structural characteristics and dimensional matching of connection nodes, it accurately matches the correspondence between physical equipment and pipes, accurately identifies the connection ports between the inlets and outlets of physical equipment and pipes, and clarifies the pipe affiliation of each port, avoiding confusion in connection relationships. On this basis, a two-way mapping relationship between pipe elements and physical equipment is established. This allows for quick lookup of all pipes connected to a physical device and for tracing the connected physical equipment through a pipe. It also labels the type, specifications, and sealing level of the connection ports and associates these attributes with the unique identifier of the corresponding physical equipment, ensuring accurate correspondence between port information and equipment identity. Subsequently, static correlation parameters such as equipment material, pipe diameter, design pressure, equipment service life, and pipe corrosion protection level are added to construct the topology association model. The model's base layer stores unique identifiers for physical devices, pipe element numbers, and information about their connection nodes, forming a unified basic identity association library that provides core data support for the entire model. The association layer, based on connection nodes, further strengthens the bidirectional mapping relationship between physical devices and pipes, clearly marking connection port attributes and corresponding association strengths, making the degree of association between devices and pipes clearly verifiable. The attribute layer embeds supplementary static association parameters and deeply associates and binds them with the data in the base and association layers, enabling static parameters to correspond with dynamically collected data. This allows the topology association model to contain both fixed device and pipe association information and inherent device attributes, providing comprehensive association data support for subsequent risk quantification calculations.

[0040] The risk quantification module receives preprocessed real-time operating parameters from the data acquisition module and a complete topology model generated by the topology construction module. It then uses a three-dimensional coupled dynamic risk quantification algorithm to accurately calculate the real-time risk quantification value for each storage tank. The formula is as follows: ,in, For the storage tank in Real-time risk quantification value at any given moment; Weighting coefficients for the equipment operating status dimension; These are the weighting coefficients for the topological association strength dimension; These are the weighting coefficients for the coupling dimensions of process parameters; For the storage tank in Quantitative value of the device's operating status at any given moment; For the storage tank in The quantized value of the topological correlation strength dimension at any given time; For the storage tank in The process parameters at any given time are coupled with quantified dimensions. The specific implementation process of the algorithm is as follows: Determining the weight coefficients: Analyzing 500 sets of historical equipment operation data and 200 sets of process parameter data using the entropy weight method, the information entropy of each dimension is calculated and the initial weights are determined. =0.35、 =0.3、 =0.35; the initial weights were finally determined; new equipment failure records, process adjustment data, and safety accident cases were collected quarterly to recalculate the entropy weights and iteratively optimize the weight coefficients. Quantitative value calculation for each dimension: Equipment operating status dimension S(t): , S(t) = 0.6 × (1 - equipment health) + 0.4 × parameter deviation rate, normalized to the 0-1 interval; topological association strength dimension T(t): Equipment port attribute weight = sealing level coefficient (Level 1 sealing = 1, Level 2 sealing = 0.8, Level 3 sealing = 0.6) × 0.7 + interface type coefficient (flange interface = 1, threaded interface = 0.8) × 0.3, T(t) = 0.5 × pipeline association strength + 0.5 × equipment port attribute weight, normalized to the 0-1 interval; process parameter coupling dimension P(t): , , P(t) = 0.4 × pressure deviation rate + 0.3 × temperature deviation rate + 0.3 × flow deviation rate, normalized to the 0-1 range; dynamic calculation: using a 10-minute sliding time window, real-time parameters are updated every 5 minutes, with the current window data weighted at 0.6 and historical window data weighted at 0.4, to calculate R(t). This algorithm comprehensively considers three key dimensions: equipment operating status, topological correlation strength, and process parameter coupling, avoiding risk misjudgment caused by single-dimensional assessment, making the risk quantification results more consistent with the actual situation. After the calculation is completed, the real-time risk quantification value range data corresponding to the four preset risk levels in the system is retrieved, and the real-time risk quantification value of each tank is compared and matched with the range one by one to accurately determine the risk level of the corresponding tank and clarify whether the tank is currently in a low, medium, high, or extremely high risk state. According to the determined risk level, exclusive risk identification data is generated. The identification data contains a unique risk level code and characteristic information such as the impact range, potential hazard type, and possible accident consequences corresponding to the risk level, allowing staff to quickly grasp the core situation of the risk. Finally, the risk identification data is associated with and stored in conjunction with the unique identification code of the corresponding storage tank and the real-time risk quantification value.

[0041] The graph visualization module receives risk identification data from the risk quantification module and the topology association model from the topology construction module. It then uses a risk transmission strength visualization coupling algorithm to calculate the risk transmission strength between physical device nodes. The formula is as follows: ,in, for physical device nodes at all times To physical device node The risk transmission intensity value; for physical device nodes at all times Real-time risk quantification value; For physical device nodes and The strength of the topological association between them; For physical device nodes and The algorithm calculates the pipeline risk attenuation coefficient, accurately reflecting the strength and probability of risk propagation between different equipment nodes, providing a data foundation for the subsequent presentation of risk transmission paths. A high-precision electronic map of the plant area serves as the base layer for the dynamic risk map. This electronic map fully recreates the geographical information of equipment, pipelines, roads, etc., within the plant area, providing an accurate carrier for the spatial presentation of risk data. The system calibration function determines the precise geographical coordinates of each physical equipment node on the electronic map and matches and binds these coordinates with the unique identifier of the physical equipment, ensuring that each equipment node can be accurately located on the electronic map, avoiding misjudgments of risk due to location deviations. Based on the calculation results of the risk transmission intensity visualization coupling algorithm, the algorithm correlates the physical equipment connection relationships in the topology association model, comprehensively extracting the risk transmission intensity data between all physical equipment nodes, and clarifying the specific values ​​of risk propagation between different nodes. Based on risk identification data, corresponding risk level icons are generated for each physical device node. For example, extremely high risk is represented by a solid red icon, high risk by a solid orange icon, medium risk by a hollow yellow icon, and low risk by a hollow green icon. These icons are precisely anchored to matching geographical coordinates, allowing staff to intuitively see the risk level of each device. Risk transmission paths are constructed between corresponding nodes based on the intensity of risk transmission between physical devices. The visual attributes of these paths are set according to the intensity of risk transmission: the higher the transmission intensity, the thicker the path lines and the darker the color; the lower the transmission intensity, the thinner the path lines and the lighter the color. This visual difference allows staff to quickly distinguish the risk transmission strength of different paths. Finally, all risk level icons, risk transmission paths, and the plant's electronic map base are organically integrated to generate a dynamic risk map containing a base layer, a data layer, and a visualization layer. The data layer fully carries the risk identification data of physical equipment nodes, the risk transmission intensity data between equipment, and the topological relationship data. The visualization layer presents risk information through intuitive icons and paths, making the risk distribution, risk level, and risk transmission path of the entire tank area clear at a glance, providing clear and intuitive visualization support for real-time monitoring by staff.

[0042] The source tracing and early warning module receives a dynamic risk map generated by the map visualization module. Through system algorithms, it automatically extracts physical equipment nodes with a risk level of three or higher from the map. Combined with risk transmission intensity data, it filters out risk source nodes and high-intensity transmission initiation nodes, accurately locating these nodes as hazardous units. This allows staff to quickly identify core risk points requiring focused attention. The module comprehensively collects all risk transmission paths associated with hazardous units, extracting three core indicators for each path: risk level, average transmission intensity, and number of involved devices. These are then comprehensively scored according to the system's preset weighting rules. The module retrieves pre-stored data on the correspondence between scoring intervals and priorities, matching the comprehensive score of each path with the preset scoring intervals to determine the priority of each path, categorizing them as extremely high priority, high priority, medium priority, and low priority, ensuring staff clearly understand the processing order. Hazardous units and their corresponding priorities are clearly marked, and the marked information is synchronized to the risk tracing query interface. This interface allows for rapid querying of the source equipment of the risk, the risk propagation path, all equipment involved in the path, and the urgency level of each path. This enables timely development of targeted control measures, prioritizing the handling of high-priority paths and hazardous units, effectively blocking risk propagation, reducing the probability of accidents, and forming a complete risk management closed loop from risk identification, assessment, visualization to tracing and control. Figure 1 As shown.

[0043] Example 2

[0044] The data acquisition module is divided into categories such as storage tanks, feed pumps, transfer pumps, temperature control devices, pipelines, and valves according to equipment type. It is also divided into three major areas: raw material area, storage tank area, and finished product area, based on the installation area. Then, according to the actual deployment order of each type of equipment, a unique identification code is assigned to each storage tank and associated physical equipment. This classification and hierarchical allocation method makes equipment identification management more organized and can quickly locate the type and location of equipment based on the identification code, establishing a clear and accurate unique mapping relationship of physical equipment identity. By fully integrating with the data acquisition terminals deployed on various devices—including storage tanks equipped with pressure transmitters and level sensors, feed pumps and delivery pumps equipped with flow sensors and power sensors, temperature control devices equipped with temperature sensors, and pressure and flow sensors at key locations in pipelines—a stable and efficient communication link is established between the physical equipment and the data acquisition module. This allows for real-time acquisition of pressure parameters from each storage tank, as well as feed pump flow rate, delivery pump discharge rate, temperature control device setpoint and actual operating temperature, pipeline medium flow rate and pressure, valve on / off status, and other relevant process parameters. All real-time operating parameters are cached to prevent data loss due to network fluctuations or temporary equipment failures, ensuring the integrity of the data acquisition. The cached real-time operating parameters undergo standardized format processing, converting data from different brands and models of acquisition terminals into a unified, system-recognized standard format. Simultaneously, noise interference caused by electromagnetic interference and sensor accuracy errors during data transmission and acquisition is filtered out, improving data accuracy and reliability and providing a high-quality data foundation for subsequent risk assessments. Each set of preprocessed real-time operating parameters is given a timestamp accurate to the second. The timestamp is then deeply associated and bound with the unique identifier of the corresponding physical device and the real-time operating parameters to form structured data, which is then categorized and stored in the system database. This structured storage not only facilitates the rapid retrieval and retrieval of data by various modules of the system, but also enables full traceability of the data. When anomalies are subsequently discovered, the time of generation and the source device of the abnormal data can be quickly located, providing strong support for risk tracing.

[0045] The topology construction module receives detailed piping and instrumentation diagrams and identification information for all physical equipment within the plant area. It performs a refined analysis of the drawings, region by region and equipment by equipment, comprehensively extracting information such as the model, specifications, installation location, and functional parameters of physical equipment; the material, diameter, wall thickness, direction, and length of pipe elements; and detailed information on the location, structural type, and sealing method of connection nodes, ensuring no critical connection details are overlooked. Based on the structural characteristics, dimensional parameters, and interface types of connection nodes, it accurately matches the correspondence between physical equipment and pipes, identifying the connection ports between the physical equipment inlets / outlets and pipes, and clarifying the specific location and connection method of each port. A two-way mapping relationship is established between pipe elements and physical equipment. The unique identifier of a physical equipment allows for quick lookup of the number and direction of all connected pipes, and the pipe element number allows for reverse tracing back to the connected physical equipment. It also marks key attributes such as the type, specifications, sealing level, and pressure resistance of connection ports, closely associating these attributes with the unique identifier of the corresponding physical equipment to ensure a one-to-one correspondence between port information and equipment identity. This model supplements static correlation parameters such as equipment operating years, pipeline corrosion levels, interface sealing performance, equipment maintenance records, and pipeline insulation measures. These parameters directly affect the operational safety and risk transmission efficiency of equipment and pipelines, thus constructing a topological correlation model. The model's base layer specifically stores the unique identifiers of physical equipment, pipeline element numbers, and their connection node information, forming a unified and standardized basic identity correlation library that provides core data support for the entire model. The correlation layer, centered on connection nodes, further strengthens the bidirectional mapping relationship between physical equipment and pipelines, clearly marking connection port attributes and corresponding correlation strengths. Correlation strength is determined based on factors such as connection tightness and pressure resistance, making the degree of correlation between equipment and pipelines readily verifiable. The attribute layer embeds the supplemented static correlation parameters and deeply correlates and binds them with the data in the base and correlation layers, enabling static parameters to be combined with dynamically acquired operational parameters. This allows the topological correlation model to include both fixed equipment and pipeline connection relationships and incorporate static factors affecting risk, making the model more comprehensive and accurate, and providing all-round correlation data support for subsequent risk quantification and risk transmission analysis.

[0046] The risk quantification module receives high-quality real-time operating parameters transmitted from the data acquisition module and a complete topology association model generated by the topology construction module. It then uses a three-dimensional coupled dynamic risk quantification algorithm to comprehensively and accurately calculate the real-time risk quantification values ​​of each fine chemical intermediate storage tank. This algorithm comprehensively considers three key aspects: equipment operating status, topology association strength, and process parameter coupling, avoiding the one-sidedness of single-dimensional assessments. It can comprehensively reflect the inherent safety risks related to abnormal tank pressure, making the risk quantification results more consistent with actual production scenarios. The module retrieves the real-time risk quantification value ranges corresponding to the four pre-stored risk levels in the system, comparing and matching the real-time risk quantification value of each tank with the range to determine the risk level of each tank, clarifying whether the tank is currently in a low-risk, medium-risk, high-risk, or extremely high-risk state. Based on the determined risk level, it generates unique risk identification data, such as... Figure 3 As shown, this identification data contains a unique risk level code, along with characteristic information such as the risk impact range, potential hazard type, possible accident consequences, and initial prevention and control measures required for that risk level. This allows staff to quickly and comprehensively understand the specific details of the risk. After associating the risk identification data with the unique identification code and real-time risk quantification value of the corresponding storage tank, it is stored in a categorized manner according to risk level and equipment type.

[0047] The map visualization module receives risk identification data from the risk quantification module and topology association models from the topology construction module. It employs a risk transmission intensity visualization coupling algorithm to calculate the risk transmission intensity between physical equipment nodes. This algorithm accurately quantifies the strength, speed, and probability of risk propagation between different equipment nodes, providing a scientific and accurate data foundation for the subsequent visualization of risk transmission paths. A high-precision electronic map of the plant area serves as the base layer for the dynamic risk map. This electronic map meticulously recreates the geographical information of equipment distribution, pipeline routes, road layouts, and safety passage locations within the plant area, providing a precise carrier for the spatial presentation of risk data. Through the system's coordinate calibration function, the precise geographical coordinates of each physical equipment node on the electronic map are determined. These coordinates are then precisely matched and bound to the unique identifier of the physical equipment, ensuring that each equipment node can be uniquely located on the electronic map, avoiding misjudgments and monitoring errors due to location discrepancies. Based on the calculation results of the risk transmission intensity visualization coupling algorithm, and by associating the physical equipment connections in the topology association model, the risk transmission intensity data between all physical equipment nodes is comprehensively and completely extracted, clarifying the specific values ​​and trends of risk propagation between different nodes. Based on risk identification data, corresponding risk level icons are generated for each physical device node, using color to distinguish risk levels. For example, extremely high risk is represented by a dark red circle, high risk by an orange circle, medium risk by a yellow triangle, and low risk by a green square. The icon size is adjusted appropriately according to the importance of the device. These icons are precisely anchored to matching geographical coordinates, allowing staff to quickly identify the risk level and importance of each device. Based on the intensity of risk transmission between physical devices, risk transmission paths are constructed between corresponding nodes. The visual attributes of these paths are set according to the intensity of risk transmission: extremely high transmission paths are presented with thick lines in dark red, medium-thickness paths in orange, medium-thickness paths in yellow, and low-thickness paths in light green. This intuitive visual difference allows staff to quickly distinguish the risk transmission intensity and urgency of different paths. Finally, all risk level icons and risk transmission paths are organically integrated with the plant's electronic map base to generate a dynamic risk map containing a base layer, a data layer, and a visualization layer. The data layer fully carries the risk identification data of physical equipment nodes, the risk transmission intensity data between equipment, and the topological relationship data. The visualization layer presents the risk information intuitively through clear icons and paths, making the risk distribution of the entire fine chemical intermediate storage tank area, the risk level of each equipment, and the specific paths and intensity of risk transmission clear at a glance. This provides clear, intuitive, and comprehensive visualization support for staff to monitor, assess risks, and make decisions in real time.

[0048] The source tracing and early warning module receives a dynamic risk map generated by the map visualization module. Through the system's built-in intelligent recognition algorithm, it automatically extracts physical equipment nodes with a risk level of three or higher from the map. These nodes are the core risk points of the tank area. Combined with risk transmission intensity data, it further filters out risk source nodes and high-intensity transmission initiation nodes, accurately locating these nodes as hazardous units. This allows staff to quickly pinpoint key risk points requiring immediate attention and handling among numerous devices. The module comprehensively collects all risk transmission paths associated with hazardous units, extracting three core indicators for each path: risk level, average transmission intensity, and number of devices involved. These are then comprehensively scored according to the system's preset scientific weighting rules. The module retrieves pre-stored data on the correspondence between scoring intervals and priorities, matching the comprehensive score of each path with the preset scoring intervals to determine the priority of each path. This ensures staff clearly understand the order of risk handling, avoiding resource waste. Hazardous units and their corresponding priorities are clearly and conspicuously marked. The marked hazardous unit information, path information, and priority data are synchronized to a risk tracing query interface. This interface allows for rapid querying of specific information about the source equipment of the risk, the detailed path of risk transmission, a list of all equipment involved in the path, the risk status of each piece of equipment, and the urgency level of each path. Based on this, staff can quickly formulate targeted control measures, prioritizing the handling of extremely high-priority and high-priority risk paths and hazardous units, promptly cutting off risk transmission channels, and implementing measures such as depressurization, shutdown, and isolation of hazardous units. This effectively prevents further spread of risk, reduces the probability and severity of accidents, and forms a complete risk management closed loop from risk identification, quantification, visualization to tracing, early warning, and control. Figure 2 As shown, this ensures the production safety of the fine chemical intermediates storage tank area.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A system for assessing the inherent safety risks of abnormal pressure in chemical storage tanks, characterized in that, The system comprises the following components: a data acquisition module, a topology construction module, a risk quantification module, a graph visualization module, and a source tracing and early warning module. The data acquisition module collects real-time operating parameters of each storage tank and associated physical equipment within the plant area; assigns a unique identifier to each physical equipment; preprocesses the real-time operating parameters; and adds a timestamp. The real-time operating parameters include pressure parameters and supporting process parameters; The topology construction module: receives physical device identification information, analyzes the connection relationship between physical devices and pipeline elements according to the plant pipeline and instrumentation flow diagram, and establishes the mapping relationship between pipeline elements and physical devices; And a topological association model is constructed by supplementing static association parameters; The risk quantification module receives real-time operating parameters and topological correlation models, and uses a three-dimensional coupled dynamic risk quantification algorithm to calculate the real-time risk quantification value of each storage tank. Based on the real-time risk quantification value, risk levels are classified, and corresponding risk identification data is generated; The three-dimensional coupled dynamic risk quantification algorithm takes the three dimensions of equipment operating status, topological correlation strength and process parameter coupling that affect the safety risk of the storage tank as independent quantification inputs, and performs dynamic coupling calculation on the quantification values ​​of the three dimensions through weighted fusion to comprehensively evaluate the real-time risk level of the storage tank under the dynamic interaction of multiple factors. The graph visualization module receives risk identification data and topology association model, and uses a risk transmission strength visualization coupling algorithm to calculate the risk transmission strength between physical device nodes. A dynamic risk map is generated based on the factory area electronic map; the dynamic risk map includes a base layer, a data layer, and a visualization layer. The risk transmission strength visualization coupling algorithm is based on the real-time risk quantification value of the risk source node, coupled with the topological association strength between adjacent nodes, and introduces a pipeline to comprehensively calculate the risk attenuation effect, so as to quantify the actual strength of risk transmission from one device node to another along the physical connection path. The source tracing and early warning module: receives a dynamic risk map, locates dangerous units based on the dynamic risk map, and prioritizes the risk transmission paths; It also provides a risk tracing and query interface to form a closed loop for risk management.

2. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The process of collecting real-time operating parameters of each storage tank and associated physical equipment in the plant area in the data acquisition module is as follows: according to the equipment classification, installation area and deployment order in the plant area, a unique identifier is assigned to each storage tank and associated physical equipment to establish a unique mapping relationship of physical equipment identity; by connecting with the deployed acquisition terminal, a communication link between the physical equipment and the data acquisition module is established to collect and cache the real-time operating parameters of each storage tank and associated physical equipment. The real-time operating parameters of the cache are standardized and processed in a unified format to eliminate noise during data transmission and acquisition. A timestamp is added to each set of preprocessed real-time operating parameters. The timestamp is then associated with and bound to the unique identifier of the corresponding physical device and the real-time operating parameters to form structured data.

3. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The process of establishing the mapping relationship between pipeline elements and physical equipment in the topology construction module is as follows: receiving the plant's pipeline and instrumentation flow diagram and physical equipment identification information, parsing the drawings to extract physical equipment, pipeline elements, and their connection node information; matching the correspondence between physical equipment and pipelines based on the connection node characteristics, identifying the connection ports of physical equipment inlets and outlets and pipelines; establishing a bidirectional mapping relationship between pipeline elements and physical equipment, marking the connection port attributes, and associating them with the corresponding physical equipment's unique identifier.

4. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The topology construction module has a hierarchical topology association model, specifically including a base layer, an association layer, and an attribute layer. The base layer stores the unique identifier of physical devices, pipe element numbers, and information on the connection nodes between them, forming a basic identity association library. The association layer, based on the connection nodes, constructs a bidirectional mapping relationship between physical devices and pipes, and labels the connection port attributes and corresponding association strengths. The attribute layer embeds static association parameters and associates and binds them with the data in the base layer and the association layer.

5. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The calculation formula for the three-dimensional coupled dynamic risk quantification algorithm in the risk quantification module is as follows: ,in, For the storage tank in Real-time risk quantification value at any given moment; Weighting coefficients for the equipment operating status dimension; These are the weighting coefficients for the topological association strength dimension; These are the weighting coefficients for the coupling dimensions of process parameters; For the storage tank in Quantitative value of the device's operating status at any given moment; For the storage tank in The quantized value of the topological correlation strength dimension at any given time; For the storage tank in The quantization value of the coupling dimension of the process parameters at any given time.

6. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The specific steps in the risk quantification module to divide risk levels according to the real-time risk quantification value and generate corresponding risk identification data are as follows: preset and store the real-time risk quantification value range corresponding to the four risk levels. Data from a preset range is retrieved, and the real-time risk quantification value of each storage tank is compared and matched with the range range to determine the risk level of the corresponding storage tank. Generate unique risk identification data according to the determined risk level. The identification data includes the risk level code and corresponding characteristic information. The risk identification data is stored after being associated and bound with the unique identification code of the corresponding storage tank and the real-time risk quantification value.

7. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The calculation formula for the risk transmission intensity visualization coupling algorithm in the map visualization module is as follows: ,in, for physical device nodes at all times To physical device node The risk transmission intensity value; for physical device nodes at all times Real-time risk quantification value; For physical device nodes and The strength of the topological association between them; For physical device nodes and The risk attenuation coefficient of pipelines between them.

8. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The dynamic risk map in the visualization module has a three-layer structure, including a base layer, a data layer, and a visualization layer. The base layer is an electronic map of the plant area, the data layer contains risk identification data of physical equipment nodes, risk transmission intensity data between equipment, and topological relationship data, and the visualization layer contains risk level icons and visualization elements of risk transmission paths. The specific steps for generating a dynamic risk map based on the electronic map of the plant area are as follows: using the electronic map of the plant area as the map base, calibrating the geographical coordinates of each physical equipment node and matching and binding them with the unique identifier of the physical equipment. Based on the calculation results of the risk transmission intensity visualization coupling algorithm, and the connection relationship of physical devices in the associated topology model, risk transmission intensity data between all physical device nodes is extracted; based on the risk identification data, a corresponding risk level icon is generated for each physical device node, and the icon is anchored to the matching geographic coordinates; based on the risk transmission intensity between physical devices, a risk transmission path is constructed between the corresponding nodes, and the path visual attributes are set according to the risk transmission intensity; all risk level icons, risk transmission paths, and the electronic map base of the plant area are integrated to generate a dynamic risk map.

9. The intrinsic safety risk assessment system for abnormal pressure in chemical storage tanks according to claim 1, characterized in that, The specific steps of locating hazardous units and prioritizing risk transmission paths in the source tracing and early warning module based on the dynamic risk map are as follows: extract physical equipment nodes with a risk level of level three or above in the dynamic risk map, combine them with risk transmission intensity data, screen out risk source nodes and high transmission intensity starting nodes, and locate them as hazardous units; collect the risk transmission paths associated with hazardous units, extract the three indicators corresponding to the path, namely risk level, average transmission intensity, and number of devices involved, and perform a comprehensive score. The system stores the correspondence between preset scoring intervals and priorities, matches the comprehensive score of each path with the preset scoring intervals to determine the corresponding priority, marks the hazardous units and the priorities corresponding to each path, and synchronizes them to the risk tracing query interface.

Images