Industrial park three-level environmental risk evaluation linkage method and system

Abstract

The invention provides a three-level environmental risk assessment linkage method and system for an industrial park, relates to the technical field of environmental risk management, and aims to realize a unified semantic framework of enterprise-level, special-level and park-level data by constructing an environmental risk assessment ontology. According to the method, an event-driven dynamic updating mechanism is adopted, data recalculation and risk map adjustment are automatically triggered, and the timeliness and accuracy of environmental risk response are improved. The whole system effectively solves the problems of data islands, updating lagging, verification missing and the like, and provides real-time and accurate decision support for environment management of industrial parks.

Description

Technical Field

[0001] This invention relates to the field of environmental risk management technology, and in particular to a three-level environmental risk assessment linkage method and system for industrial parks. Background Technology

[0002] The environmental risk management of industrial parks adopts a three-tiered evaluation system: enterprise-level, project-level, and park-level. Enterprise-level evaluation, based on the "Trial Measures for the Filing and Management of Emergency Response Plans for Sudden Environmental Incidents by Enterprises and Institutions," completes risk assessments through localized standalone software or paper documents, covering four modules: data collection, risk identification, risk analysis, and assessment of prevention and control measures. However, the data formats are diverse (e.g., Excel, paper documents), and updates do not automatically trigger synchronization with other levels of models. Project-level evaluation targets parks with significant water environment risks, using independent software (e.g., CAD) to analyze emergency space capacity and pollution pathways. However, the data is heterogeneous with enterprise-level and park-level data (e.g., CAD is incompatible with GIS), leading to difficulties in cross-system interaction. Park-level evaluation, based on the "Recommended Methods for Risk Assessment of Sudden Environmental Incidents in Administrative Regions," utilizes a GIS platform to construct risk maps and quantify the overall risk intensity of the region. However, it relies on manual input of enterprise and project data, has a long update cycle, and cannot reverse-verify the rationality of enterprise data (e.g., enterprises have a low probability of leakage, but similar incidents occur frequently in the park).

[0003] The main problems with existing technologies are concentrated in three aspects: data silos, delayed updates, and lack of verification. Data silos stem from the heterogeneous data formats (Excel, CAD, GIS) across the three-tiered systems, lacking a unified interface standard, making information exchange difficult. Delayed updates manifest as the need for manual re-import of data for specific projects and park models after changes in enterprise parameters, leading to a disconnect between emergency plans and actual risks. Lack of verification is reflected in the inability to cross-verify park-level macro-risk indices with enterprise-level micro-data, resulting in data inconsistencies (such as a significant discrepancy between the leakage probability reported by enterprises and the park's accident rate). The root cause lies in the lack of standardized linkage protocols and automated update mechanisms in the three-tiered evaluation system, relying on manual coordination and data transfer, which is inefficient and prone to errors.

[0004] Current technologies urgently require a dynamic linkage method to address the challenges of heterogeneous data integration, real-time updates, and data cross-verification. Existing attempts (such as environmental information management platforms) are mostly static data warehouses, unable to achieve dynamic analysis through three-level linkage. Alternative solutions such as blockchain or federated learning are difficult to promote due to performance limitations or high implementation complexity. Therefore, an innovative solution based on semantic mapping, event-triggered rules, and contradiction correction algorithms is urgently needed to achieve automatic synchronization of three-level evaluation data, model cross-verification, and real-time early warning, filling the gap in existing technologies. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a three-level environmental risk assessment linkage method and system for industrial parks, which realizes the deep integration of the three-level assessment system, supports real-time adaptive management of scenarios such as equipment changes and policy adjustments, and provides accurate and dynamic decision support for environmental risk prevention and control in industrial parks.

[0006] To achieve the above objectives, the present invention provides the following solution: A three-tiered environmental risk assessment linkage method for industrial parks includes: Based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are transformed into a unified semantic framework to obtain standardized data. Design an event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; Based on the standardized data and the update rules, the system automatically triggers special-level data recalculation and park-level risk map hotspot area adjustment to generate a real-time updated risk map. The system compares park-level data with enterprise-level data using a conflict detection algorithm. If the data exceeds a threshold, a reverse feedback mechanism is activated, pushing a data verification request to the enterprise-level system.

[0007] Preferably, the enterprise-level data includes production process parameters, hazardous substance lists, and equipment status records in Excel spreadsheets; the special-purpose data includes water environment emergency spatial layout and pipeline route information in CAD drawings; and the park-level data includes risk index models and gridded geographic information in a GIS platform.

[0008] Preferably, based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are converted into a unified semantic framework to obtain standardized data, including: Raw data is obtained from a preset data source, and the data types and formats of the raw data are classified and organized to obtain the enterprise-level data, the special-purpose data, and the park-level data. An environmental risk assessment ontology is constructed based on the OWL semantic framework, and core elements are defined according to the environmental risk assessment ontology. The core elements include entity classes, attribute classes, and relationship classes. The entity classes include hazardous substances, production facilities, and environmentally sensitive areas. The attribute classes include substance storage capacity, equipment failure probability, and emergency space capacity. The relationship classes include substance leakage paths, spatial association between facilities and the environment, and risk transmission links. For each type of data source in the enterprise-level data, the special-purpose data, and the park-level data, formulate mapping rules from the original format to the ontology model; The mapping rules are executed by automated tools to complete the data transformation and obtain the standardized data.

[0009] Preferably, the mapping rules include: Map the “hazardous substance name” in the enterprise-level data to the hazardous substance class in the ontology and associate it with the storage quantity attribute; Map the "emergency pool location" in the special-purpose data to the environmentally sensitive area class in the ontology, and associate it with spatial coordinate attributes; The "risk hotspots" in the park-level data are mapped to the risk transmission link relationship in the ontology, and the risk intensity value attribute is associated.

[0010] Preferably, the mapping rules are executed using automated tools to complete the data transformation and obtain the standardized data, including: The enterprise-level data is parsed into RDF triples and populated into the ontology model; The geometric data in the specialized data is converted into GIS-compatible GeoJSON format and bound to ontology attributes; The park-level data is integrated into the risk transmission link network in the ontology.

[0011] Preferably, the update rule includes: When an enterprise adds hazardous substances or equipment, it triggers a recalculation of the special-level water environment emergency space capacity and an update of the wastewater transfer route. When the capacity of the park's public emergency facilities is adjusted, an effectiveness assessment of enterprise-level prevention and control measures is triggered.

[0012] Preferably, such as Figure 3 As shown, this embodiment, based on the standardized data and the update rules, automatically triggers special-level data recalculation and park-level risk map hotspot area adjustment to generate a real-time updated risk map, including: Monitor change events in enterprise-level, project-level, and park-level data sources in real time, and parse event types and related parameters; The specific-level data analysis model is invoked according to the update rules to perform recalculation and obtain the specific-level recalculation result. The risk index was reassessed based on the recalculation results at the project level and the geographic information at the park level. Based on the updated risk index, the hotspot area labels on the park-level risk map are adjusted to obtain the updated hotspot areas; The updated hotspot areas, emergency facility locations, and risk transmission paths are visualized using a GIS platform to obtain the risk map.

[0013] Preferably, the formula for calculating the risk index is: in, As a risk index, For the first Storage quantity of Class II hazardous substances For the first Probability of leakage of such substances For the first Emergency space capacity, The overall sensitivity of environmentally sensitive areas The preset maximum environmental sensitivity threshold, For enterprise-level risk weighting coefficients, This is the environmental sensitivity amplification factor. The time decay factor, This represents the number of days the data has not been updated.

[0014] Preferably, a conflict detection algorithm is used to compare park-level data with enterprise-level data. If the conflict exceeds a threshold, reverse feedback is initiated, pushing a data verification request to the enterprise-level system, including: The enterprise-level data and the park-level data are converted into a unified comparison benchmark: the enterprise-level data and the park-level data after the unified comparison benchmark are as follows: and ;in, For the probability of leakage, For the amount of hazardous materials stored, For equipment failure rate, To unify the enterprise-level data after the comparison benchmark; Compared to the historical accident rate of similar companies, This is a regional risk index. To unify the comparison benchmark of the aforementioned park-level data; A correlation model between the enterprise-level data and the park-level data, established using statistical methods with a unified comparison benchmark, is used to obtain the predicted accident rate. and residual The formula for the association model is: The formula for calculating the residual is: ;in, The slope for regression analysis. The intercept for regression analysis; like It is marked as "Level 1 Anomaly"; like and It is marked as "Level 2 Anomaly"; Feedback instructions are generated based on the anomaly level. When a level 1 anomaly occurs, a data verification request is automatically pushed to the enterprise system, requiring re-verification. And equipment status data; when a level 2 anomaly occurs, a warning is simultaneously pushed to the park management terminal, and the display of the enterprise's risk value on the park map is frozen until the verification is completed; After receiving the request, the company uploads the revised data and supporting materials through the visualization platform; If the revised data passes verification, the enterprise database will be updated and the park map will be unfrozen.

[0015] A three-tiered environmental risk assessment linkage system for industrial parks includes: The cross-level data mapping unit is used to convert enterprise-level data, special-level data and park-level data into a unified semantic framework based on the environmental risk assessment ontology, so as to obtain standardized data. The event-driven dynamic update unit is used to design the event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; Based on the standardized data and the update rules, the system automatically triggers special-level data recalculation and park-level risk map hotspot area adjustment to generate a real-time updated risk map. The contradiction detection and reverse feedback unit is used to compare the park-level risk index with the enterprise-level reported data through a contradiction detection algorithm. If the data exceeds the threshold, reverse feedback is initiated, and a data verification request is pushed to the enterprise-level system.

[0016] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention provides a three-tiered environmental risk assessment linkage method and system for industrial parks, effectively solving the three core problems of data silos, delayed updates, and lack of verification in the background technology. By constructing an environmental risk assessment ontology, semantic alignment of heterogeneous data (enterprise-level Excel, project-level CAD, and park-level GIS) is achieved, breaking down data barriers. An event-triggered rule base drives the dynamic synchronization of the three-tiered data, shortening the traditional manual iteration cycle of several months to minutes, significantly improving the timeliness of risk response. A contradiction detection algorithm compares the enterprise-level leakage probability with the park's historical accident rate; when an anomaly is detected, reverse feedback is automatically triggered to calibrate the data and generate early warnings, improving the accuracy of risk warnings by more than 30%. Finally, this method achieves deep integration of the three-tiered assessment system, supporting real-time adaptive management of scenarios such as equipment changes and policy adjustments, providing precise and dynamic decision support for environmental risk prevention and control in industrial parks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of the steps provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of data conversion and integration provided in an embodiment of the present invention; Figure 3 This invention provides a risk map update route map for embodiments of the invention. Figure 4 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The purpose of this invention is to provide a three-level environmental risk assessment linkage method and system for industrial parks, which realizes the deep integration of the three-level assessment system, supports real-time adaptive management of scenarios such as equipment changes and policy adjustments, and provides accurate and dynamic decision support for environmental risk prevention and control in industrial parks.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Figure 1 The flowchart provided for the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a three-level environmental risk assessment linkage method for industrial parks, including: Step 100: Based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are converted into a unified semantic framework to obtain standardized data; Step 200: Design an event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; Step 300: Based on the standardized data and the update rules, automatically trigger the recalculation of special-level data and the adjustment of hot spots in the park-level risk map to generate a real-time updated risk map; Step 400: Compare park-level data with enterprise-level data using a contradiction detection algorithm. If the data exceeds the threshold, initiate reverse feedback and push a data verification request to the enterprise-level system.

[0023] Preferably, the enterprise-level data includes production process parameters, hazardous substance lists, and equipment status records in Excel spreadsheets; the special-purpose data includes water environment emergency spatial layout and pipeline route information in CAD drawings; and the park-level data includes risk index models and gridded geographic information in a GIS platform.

[0024] Preferably, based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are converted into a unified semantic framework to obtain standardized data, including: Raw data is obtained from a preset data source, and the data types and formats of the raw data are classified and organized to obtain the enterprise-level data, the special-purpose data, and the park-level data. An environmental risk assessment ontology is constructed based on the OWL semantic framework, and core elements are defined according to the environmental risk assessment ontology. The core elements include entity classes, attribute classes, and relationship classes. The entity classes include hazardous substances, production facilities, and environmentally sensitive areas. The attribute classes include substance storage capacity, equipment failure probability, and emergency space capacity. The relationship classes include substance leakage paths, spatial association between facilities and the environment, and risk transmission links. For each type of data source in the enterprise-level data, the special-purpose data, and the park-level data, mapping rules are formulated from the original format to the ontology model. The mapping rules include: mapping the "hazardous substance name" in the enterprise-level data to the hazardous substance class in the ontology and associating it with the storage quantity attribute; mapping the "emergency pool location" in the special-purpose data to the environmentally sensitive area class in the ontology and associating it with the spatial coordinate attribute; and mapping the "risk hot zone" in the park-level data to the risk transmission link relationship in the ontology and associating it with the risk intensity value attribute.

[0025] The mapping rules are executed by automated tools to complete the data transformation and obtain the standardized data. Specifically, this includes: parsing the enterprise-level data into RDF triples and populating them into the ontology model; converting the geometric data in the special-purpose data into GIS-compatible GeoJSON format and binding it to ontology attributes; and integrating the park-level data into the risk transmission link relationship network in the ontology.

[0026] Specifically, in the data standardization phase of this embodiment, raw data is first extracted from pre-defined heterogeneous data sources (enterprise-level Excel spreadsheets, project-level CAD drawings, and park-level GIS platforms), and then categorized and organized based on data type and format. Enterprise-level data includes production process parameters, hazardous substance lists, and equipment status records, stored in structured table format; project-level data covers water environment emergency spatial layout and pipeline route information, represented by CAD vector graphics; park-level data includes risk index models and gridded geographic information, stored in GIS layer format. Automated scripts (such as the Python pandas library) are used to perform preliminary data cleaning and labeling to ensure the executability of subsequent mapping rules.

[0027] Furthermore, based on the OWL semantic framework, an environmental risk assessment ontology model is constructed, clearly defining three core elements: (1) Entities: including “Hazardous Substances” (defining substance type and toxicity level), “Production Facilities” (defining equipment ID and process type) and “Environmentally Sensitive Areas” (defining water body boundaries and residential area coordinates); (2) Attributes: including “Material storage capacity” (numerical attribute, unit: tons), “Equipment failure probability” (floating-point type, range: [0, 1]) and “Emergency space capacity” (numerical type, unit: cubic meters); (3) Relationships: These include “Substance Leakage Path” (associating hazardous substances with pipeline paths), “Spatial Association between Facilities and Environment” (binding equipment coordinates with sensitive area locations), and “Risk Transmission Link” (describing the probability of a leak event transmitting to a sensitive area). The ontology is modeled using the Protégé tool and exported as an OWL / XMI format file for subsequent data mapping.

[0028] Furthermore, this embodiment defines specific mapping rules for each type of data source: (1) Enterprise-level data mapping: Map the “Hazardous substance name” column in the Excel spreadsheet to the hazardous substance class in the ontology, and bind the “storage quantity” column to the substance storage quantity attribute; (2) Specialized data mapping: parse the “emergency pool location” layer in the CAD drawing, map its geometric coordinates to the environmental sensitive area class of the ontology, and associate spatial coordinate attributes (such as WGS-84 latitude and longitude). (3) Park-level data mapping: Map the "risk hot zone" layer (GeoJSON format) in the GIS platform to the risk transmission link relationship of the ontology, and bind the risk intensity value attribute (such as the 0-100% range value). The mapping rules are defined through the JSON configuration file and support dynamic loading and expansion.

[0029] Furthermore, this embodiment executes mapping rules through a customized toolchain to complete data transformation, such as... Figure 2 As shown, the data transformation includes the following: (1) Enterprise-level data transformation: Use the RDFLib library to parse Excel data into RDF triples (such as <Material A, Storage quantity, 50 tons>) and populate them into the ontology model; (2) Specialized data conversion: Call the GDAL library to convert the geometric data (such as polygonal emergency pools) in the CAD drawings into GeoJSON format and bind it with the coordinate attributes of the environment sensitive area of ​​the ontology; (3) Park-level data integration: A risk transmission link network is constructed using the Neo4j graph database, and GIS risk hotspot data is imported as node attributes (such as risk intensity: 85%). The transformed data is uniformly stored in the SPARQL endpoint, supporting semantic query and linkage analysis.

[0030] Furthermore, this embodiment utilizes the Pellet inference engine to check the consistency of the ontology model. For example, it verifies whether a "hazardous substance" is associated with at least one leakage path; if a path is missing, an alarm is triggered. This embodiment also simulates a scenario where a company adds a hazardous substance, verifying whether the capacity of the special-purpose emergency space is automatically recalculated and checking whether the hotspot areas on the park's risk map are updated synchronously. For uncovered abnormal data (such as pipelines not marked in CAD drawings), mapping rules are manually added and configuration files are updated to ensure full coverage in subsequent conversions. Finally, standardized data is made available to the dynamic update and contradiction detection modules via an API interface, forming a closed-loop data flow.

[0031] Preferably, the update rule includes: When an enterprise adds hazardous substances or equipment, it triggers a recalculation of the special-level water environment emergency space capacity and an update of the wastewater transfer route. When the capacity of the park's public emergency facilities is adjusted, an effectiveness assessment of enterprise-level prevention and control measures is triggered.

[0032] Specifically, this embodiment deploys data change listeners (such as Kafka consumers based on database triggers or message queues) in enterprise-level databases, specialized models, and park-level GIS platforms to capture changes in key parameters in real time. For example, when an enterprise adds a hazardous substance, the listener identifies change events in the "hazardous substance name" and "storage quantity" fields by parsing database logs or API calls; it uses the Drools rule engine to define event response logic and encodes the rules into DRL files. Cross-level data updates are triggered based on the rules.

[0033] Furthermore, when a rule is triggered, this embodiment calls a specialized water environment analysis microservice, inputs enterprise-level hazardous substance data (such as storage capacity and leakage probability), recalculates the emergency space capacity and wastewater transfer path using a hydraulic model (such as EPANET), and writes the results into a specialized database. After the specialized data is updated, a park-level risk index model (such as the GIS-based spatial overlay analysis tool ArcPy) is automatically called, and an updated risk heat map is generated by combining the latest emergency space capacity and sensitive area distribution, and pushed to the management terminal in real time via WebSocket. When the capacity of the park's public emergency facilities is adjusted, this embodiment triggers an enterprise-level prevention and control assessment service in reverse, calls a pre-trained machine learning model (such as a random forest classifier), inputs enterprise equipment status and emergency facility data, outputs a score for the effectiveness of prevention and control measures, and generates an optimization suggestion report.

[0034] As an example, this embodiment achieves automated linkage updates of three levels of data by combining event listening, rule engine and microservice architecture, shortening the traditional manual coordination cycle of several days to the second level response, while ensuring data consistency and computational traceability.

[0035] Preferably, based on the standardized data and the update rules, the system automatically triggers special-level data recalculation and park-level risk map hotspot area adjustments to generate a real-time updated risk map, including: Monitor change events in enterprise-level, project-level, and park-level data sources in real time, and parse event types and related parameters; The specific-level data analysis model is invoked according to the update rules to perform recalculation and obtain the specific-level recalculation result. The risk index was reassessed based on the recalculation results at the project level and the geographic information at the park level. Based on the updated risk index, the hotspot area labels on the park-level risk map are adjusted to obtain the updated hotspot areas; The updated hotspot areas, emergency facility locations, and risk transmission paths are visualized using a GIS platform to obtain the risk map.

[0036] Optionally, this embodiment deploys a lightweight monitoring agent (using Change Data Capture technology or the message middleware Apache Kafka) in an enterprise-level database (MySQL in this embodiment), a specialized CAD system, and a park-level GIS platform to capture data change events in real time. For example, when the hazardous material storage quantity field in an enterprise-level Excel file is modified, the monitoring agent extracts the field name (e.g., "Storage_Volume"), old value (e.g., "50 tons"), and new value (e.g., "80 tons") from the change record and encapsulates it into a standardized event message (JSON format), containing metadata such as the event type ("enterprise-level parameter update"), timestamp, and associated enterprise ID. The event message is pushed to the rule engine via a message queue (RabbitMQ in this embodiment) to ensure low latency and high reliability.

[0037] Specifically, the rule engine (such as Drools) loads a predefined update rule library (DRL file), matches the event type, and triggers the corresponding action. For example, when the event type is "change in hazardous substance storage," the rule engine calls a specialized water environment analysis microservice (REST API), passing in the substance type, the new storage volume, and the enterprise's coordinate parameters. This microservice recalculates the emergency pool capacity requirement based on a hydraulic model (such as EPANET). If the current capacity is insufficient, it automatically generates a new wastewater transfer path (such as the shortest path avoiding sensitive areas) and writes the result to the specialized database in GeoJSON format. The recalculation process is logged for post-event auditing.

[0038] Furthermore, after the specific-level data is updated, a park-level risk assessment service is triggered. This service first overlays the specific-level recalculation results (e.g., the location of emergency pools) with the park's GIS layer (e.g., the distribution of sensitive areas) through spatial join, and combines this with enterprise-level standardized data (e.g., leakage probability), using a risk index formula to calculate a gridded risk value. For example, for a newly added high-storage substance within a grid, combined with its leakage probability (0.05) and the sensitivity of adjacent sensitive areas (0.8), the calculated risk index increases to 65 (from the original value of 40). The assessment results are stored in a spatial database (e.g., PostGIS) in spatial grid units.

[0039] Furthermore, this embodiment utilizes the rendering engine of a GIS platform (such as ArcGIS Online or the open-source Leaflet) to map updated risk indices into heatmap gradient color bands according to threshold ranges (e.g., 0-30 for low risk, 31-70 for medium risk, and 71-100 for high risk). Emergency facility icons (e.g., blue emergency pool symbols) and risk transmission paths (red arrow lines) are overlaid. The visualization results are pushed to the park management terminal in real time via WebSocket and support interactive queries (e.g., clicking on a hotspot displays specific risk values ​​and a list of associated companies). The map update cycle is configurable (default 10 seconds) to ensure administrators always have access to the latest risk situation.

[0040] Preferably, the formula for calculating the risk index is: in, As a risk index, For the first Storage quantity of Class II hazardous substances For the first Probability of leakage of such substances For the first Emergency space capacity, The overall sensitivity of environmentally sensitive areas The preset maximum environmental sensitivity threshold, For enterprise-level risk weighting coefficients, This is the environmental sensitivity amplification factor. The time decay factor, This represents the number of days the data has not been updated.

[0041] Exemplary, preset maximum environmental sensitivity threshold The value is 10. Environmental sensitivity amplification factor. The value is 2. The value of the time decay factor is 0.95. The enterprise-level risk weighting coefficient... It is adjusted in real time based on the company's compliance score.

[0042] Preferably, a conflict detection algorithm is used to compare park-level data with enterprise-level data. If the conflict exceeds a threshold, reverse feedback is initiated, pushing a data verification request to the enterprise-level system, including: The enterprise-level data and the park-level data are converted into a unified comparison benchmark: the enterprise-level data and the park-level data after the unified comparison benchmark are as follows: and ;in, For the probability of leakage, For the amount of hazardous materials stored, For equipment failure rate, To unify the enterprise-level data after the comparison benchmark; Compared to the historical accident rate of similar companies, This is a regional risk index. To unify the comparison benchmark of the aforementioned park-level data; A correlation model between the enterprise-level data and the park-level data, established using statistical methods with a unified comparison benchmark, is used to obtain the predicted accident rate. and residual The formula for the association model is: The formula for calculating the residual is: ;in, The slope for regression analysis. The intercept for regression analysis; like It is marked as "Level 1 Anomaly"; like and It is marked as "Level 2 Anomaly"; Feedback instructions are generated based on the anomaly level. When a level 1 anomaly occurs, a data verification request is automatically pushed to the enterprise system, requiring re-verification. And equipment status data; when a level 2 anomaly occurs, a warning is simultaneously pushed to the park management terminal, and the display of the enterprise's risk value on the park map is frozen until the verification is completed; After receiving the request, the company uploads the revised data and supporting materials through the visualization platform; If the revised data passes verification, the enterprise database will be updated and the park map will be unfrozen.

[0043] Specifically, the static threshold (fixed at 20%) in this embodiment ensures basic anomaly detection; the dynamic threshold (combined with the risk index) enhances the sensitivity of high-risk scenarios and avoids missed detections.

[0044] Corresponding to the above methods, such as Figure 4 As shown, this embodiment also provides a three-level environmental risk assessment linkage system for industrial parks, including: The cross-level data mapping unit is used to convert enterprise-level data, special-level data and park-level data into a unified semantic framework based on the environmental risk assessment ontology, so as to obtain standardized data. The event-driven dynamic update unit is used to design the event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; The risk map update unit is used to automatically trigger special-level data recalculation and park-level risk map hotspot area adjustment based on the standardized data and the update rules, and generate a real-time updated risk map. The contradiction detection and reverse feedback unit is used to compare the park-level risk index with the enterprise-level reported data through a contradiction detection algorithm. If the data exceeds the threshold, reverse feedback is initiated, and a data verification request is pushed to the enterprise-level system.

[0045] The beneficial effects of this invention are as follows: (1) By constructing an environmental risk assessment ontology, this invention achieves semantic alignment of enterprise-level, special-level and park-level data, breaks down data barriers, enables data of different formats (such as Excel, CAD, GIS) to be interconnected, and improves the efficiency of data integration.

[0046] (2) This invention utilizes an event-driven dynamic update mechanism to shorten the traditionally months-long manual data update process to just a few minutes, achieving real-time synchronization. This significantly improves the timeliness of risk response, providing strong support for timely assessment and early warning of environmental risks.

[0047] (3) This invention uses a contradiction detection algorithm to compare enterprise-level and park-level data, which can automatically trigger reverse feedback when anomalies are detected, thereby calibrating the data and generating early warnings. In practice, the accuracy of risk warnings has been improved by more than 30%, adding a guarantee for environmental risk management.

[0048] (4) The linkage method of the present invention can effectively cope with equipment changes and policy adjustments, ensure that the environmental risk management of industrial parks has real-time adaptability, provide managers with accurate and dynamic decision support, and help ensure the environmental safety of industrial parks.

[0049] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0050] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for linking three levels of environmental risk assessment in industrial parks, characterized in that, include: Based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are transformed into a unified semantic framework to obtain standardized data. Design an event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; Based on the standardized data and the update rules, the system automatically triggers special-level data recalculation and park-level risk map hotspot area adjustment to generate a real-time updated risk map. The system compares park-level data with enterprise-level data using a conflict detection algorithm. If the data exceeds a threshold, a reverse feedback mechanism is activated, pushing a data verification request to the enterprise-level system.

2. The three-level environmental risk assessment linkage method for industrial parks according to claim 1, characterized in that, The enterprise-level data includes production process parameters, hazardous substance lists, and equipment status records in Excel spreadsheets; the special-purpose data includes water environment emergency spatial layout and pipeline route information in CAD drawings; and the park-level data includes risk index models and gridded geographic information in a GIS platform.

3. The three-level environmental risk assessment linkage method for industrial parks according to claim 1, characterized in that, Based on the environmental risk assessment ontology, enterprise-level data, project-level data, and park-level data are transformed into a unified semantic framework to obtain standardized data, including: Raw data is obtained from a preset data source, and the data types and formats of the raw data are classified and organized to obtain the enterprise-level data, the special-purpose data, and the park-level data. An environmental risk assessment ontology is constructed based on the OWL semantic framework, and core elements are defined according to the environmental risk assessment ontology. The core elements include entity classes, attribute classes, and relationship classes. The entity classes include hazardous substances, production facilities, and environmentally sensitive areas. The attribute classes include substance storage capacity, equipment failure probability, and emergency space capacity. The relationship classes include substance leakage paths, spatial association between facilities and the environment, and risk transmission links. For each type of data source in the enterprise-level data, the special-purpose data, and the park-level data, formulate mapping rules from the original format to the ontology model; The mapping rules are executed by automated tools to complete the data transformation and obtain the standardized data.

4. The three-level environmental risk assessment linkage method for industrial parks according to claim 3, characterized in that, The mapping rules include: Map the "hazardous substance name" in the enterprise-level data to the hazardous substance class in the ontology, and associate the storage quantity attribute; Map the "emergency pool location" in the special-purpose data to the environmentally sensitive area class in the ontology, and associate it with spatial coordinate attributes; Map the "risk hotspots" in the park-level data to the risk transmission link relationship in the ontology, and associate them with the risk intensity value attribute.

5. The three-level environmental risk assessment linkage method for industrial parks according to claim 3, characterized in that, The mapping rules are executed using automated tools to complete the data transformation and obtain the standardized data, including: The enterprise-level data is parsed into RDF triples and populated into the ontology model; The geometric data in the specialized data is converted into GIS-compatible GeoJSON format and bound to ontology attributes; The park-level data is integrated into the risk transmission link network in the ontology.

6. The three-level environmental risk assessment linkage method for industrial parks according to claim 1, characterized in that, The update rules include: When an enterprise adds hazardous substances or equipment, it triggers a recalculation of the special-level water environment emergency space capacity and an update of the wastewater transfer route. When the capacity of the park's public emergency facilities is adjusted, an effectiveness assessment of enterprise-level prevention and control measures is triggered.

7. The three-level environmental risk assessment linkage method for industrial parks according to claim 1, characterized in that, Based on the standardized data and the update rules, the system automatically triggers special-level data recalculation and park-level risk map hotspot area adjustments to generate a real-time updated risk map, including: Monitor change events in enterprise-level, project-level, and park-level data sources in real time, and parse event types and related parameters; The specific-level data analysis model is invoked according to the update rules to perform recalculation and obtain the specific-level recalculation result. The risk index was reassessed based on the recalculation results at the project level and the geographic information at the park level. Based on the updated risk index, the hotspot area labels on the park-level risk map are adjusted to obtain the updated hotspot areas; The updated hotspot areas, emergency facility locations, and risk transmission paths are visualized using a GIS platform to obtain the risk map.

8. The three-level environmental risk assessment linkage method for industrial parks according to claim 7, characterized in that, The formula for calculating the risk index is as follows: in, As a risk index, For the first Storage quantity of Class II hazardous substances For the first Probability of leakage of such substances For the first Emergency space capacity, The overall sensitivity of environmentally sensitive areas The preset maximum environmental sensitivity threshold, For enterprise-level risk weighting coefficients, This is the environmental sensitivity amplification factor. The time decay factor, This represents the number of days the data has not been updated.

9. The three-level environmental risk assessment linkage method for industrial parks according to claim 1, characterized in that, A conflict detection algorithm compares park-level data with enterprise-level data. If the conflict exceeds a threshold, a reverse feedback mechanism is activated, pushing a data verification request to the enterprise-level system, including: The enterprise-level data and the park-level data are converted into a unified comparison benchmark: the enterprise-level data and the park-level data after the unified comparison benchmark are as follows: and ;in, For the probability of leakage, For the amount of hazardous materials stored, For equipment failure rate, To unify the enterprise-level data after the comparison benchmark; Compared to the historical accident rate of similar companies, This is a regional risk index. To unify the comparison benchmark of the aforementioned park-level data; A correlation model between the enterprise-level data and the park-level data, established using statistical methods with a unified comparison benchmark, is used to obtain the predicted accident rate. and residual The formula for the association model is: The formula for calculating the residual is: ;in, The slope for regression analysis. The intercept for the regression analysis; like It is marked as "Level 1 Anomaly"; like and It is marked as "Level 2 anomaly"; Feedback instructions are generated based on the anomaly level. When a level 1 anomaly occurs, a data verification request is automatically pushed to the enterprise system, requiring re-verification. And equipment status data; when a level 2 anomaly occurs, a warning is simultaneously pushed to the park management terminal, and the display of the enterprise's risk value on the park map is frozen until the verification is completed; After receiving the request, the company uploads the revised data and supporting materials through the visualization platform; If the revised data passes verification, the enterprise database will be updated and the park map will be unfrozen.

10. A three-level environmental risk assessment linkage system for industrial parks, characterized in that, include: The cross-level data mapping unit is used to convert enterprise-level data, special-level data and park-level data into a unified semantic framework based on the environmental risk assessment ontology, so as to obtain standardized data. The event-driven dynamic update unit is used to design the event-triggered update chain rule base, and define the logical association between enterprise-level parameter change events and special-level and park-level data updates based on the event-triggered update chain rule base to obtain update rules; The risk map update unit is used to automatically trigger special-level data recalculation and park-level risk map hotspot area adjustment based on the standardized data and the update rules, and generate a real-time updated risk map. The contradiction detection and reverse feedback unit is used to compare the park-level risk index with the enterprise-level reported data through a contradiction detection algorithm. If the data exceeds the threshold, reverse feedback is initiated, and a data verification request is pushed to the enterprise-level system.

Images