Enterprise climate risk prevention and control system using big data and artificial intelligence

Abstract

The invention relates to the technical field of enterprise climate risk prevention and control, and provides an enterprise climate risk prevention and control system applying big data and artificial intelligence, and the system comprises a multi-source climate data collection module which integrates satellite, ground station and enterprise Internet of Things data through federal learning; the enterprise asset digital twin module integrates BIM / GIS to construct a dynamic asset model; the AI risk prediction engine analyzes climate-asset relevance by adopting a space-time convolutional neural network, and quantifies a physical / transformation risk in combination with physical simulation and a generative adversarial network; the dynamic risk assessment module is used for generating a risk thermodynamic diagram and calculating a supply chain interruption probability; and the adaptive response decision module outputs graded early warning and is linked with the insurance platform to trigger automatic claim settlement. The system verifies the credibility of data through a block chain, realizes a local disaster recovery decision when a network is interrupted by using an edge computing node, forms a'monitoring-prediction-evaluation-response-optimization 'closed-loop management and control mechanism, and solves the problems of data island, response delay and unknown risk prediction of a traditional scheme.

Description

Technical Field

[0001] This invention relates to the field of enterprise climate risk prevention and control technology, and more specifically, to an enterprise climate risk prevention and control system that utilizes big data and artificial intelligence. Background Technology

[0002] The intensification of global climate change has led to more frequent extreme weather events. According to the IPCC Sixth Assessment Report, the number of climate-related disasters worldwide increased more than threefold between 1980 and 2020, posing increasingly severe physical risks to corporate assets (such as flood damage to factories and hurricane-induced supply chain disruptions). Simultaneously, 135 countries worldwide have enacted carbon neutrality targets, and policies such as the EU's Carbon Border Adjustment Mechanism (CBAM) have triggered risks for corporate transformation. Against this backdrop, enterprises urgently need intelligent prevention and control systems that integrate multi-dimensional climate data, real-time asset status, and policy changes to achieve a fundamental shift from passive disaster mitigation to proactive adaptation. Current mainstream solutions suffer from three major shortcomings:

[0003] (1) Data silo problem: Meteorological satellite data, enterprise IoT sensor information and policy text belong to independent systems and lack a spatiotemporal alignment mechanism.

[0004] (2) Static Model Deficiencies: Traditional risk assessment models rely on historical statistical patterns and cannot dynamically simulate unknown climate scenarios (such as a "once-in-a-millennium" rainstorm combined with sea-level rise). Swiss Re research confirms that such models have a loss prediction bias of up to 55%-70% for compound disasters.

[0005] (3) Bottleneck in response: Existing ERP systems are disconnected from climate early warning systems, and disaster response relies on manual decision-making. McKinsey research shows that 83% of companies need 4-6 hours to activate their contingency plans after receiving a typhoon warning, resulting in avoidable losses accounting for 28% of total losses.

[0006] Therefore, a corporate climate risk prevention and control system utilizing big data and artificial intelligence is proposed to address the above issues. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an enterprise climate risk prevention and control system that utilizes big data and artificial intelligence to address the problems mentioned in the background art.

[0008] To achieve the above objectives, this invention provides the following technical solution: an enterprise climate risk prevention and control system utilizing big data and artificial intelligence, comprising: a multi-source climate data acquisition module that accesses global meteorological satellite remote sensing data, national ground monitoring station networks, government open climate databases, and enterprise-deployed IoT sensor arrays in real time via a distributed API gateway, continuously collecting dynamic environmental parameters including temperature gradient fields, spatiotemporal distribution of precipitation, wind vectors, soil moisture content, and surface deformation monitoring values, and integrating historical typhoon path databases, flood inundation models, and drought index maps; an enterprise asset digital twin module that constructs millimeter-level precision virtual models covering factory building complexes, supply chain logistics nodes, and energy infrastructure based on laser point cloud scanning and BIM modeling technologies, and associates them with physical attribute databases such as asset structural strength, flood control design standards, and equipment weather resistance levels; and an AI risk prediction engine that uses a multi-scale spatiotemporal convolutional neural network (MS-STCNN) to fuse climate data and asset topology. The system predicts the probability of extreme weather events and their three-dimensional impact domain within the next 72 hours using a hybrid architecture of gated recurrent units (GRUs) and long short-term memory networks (LSTMs). Simultaneously, it drives a computational fluid dynamics (CFD)-based physical simulation engine to quantify structural stress damage, equipment immersion failure rates, and transportation network disruption risks. The dynamic risk assessment module generates a dynamic climate risk heat map with a geographic information system (GIS) base map, marking the physical exposure index and functional vulnerability level of each asset unit. It also uses Monte Carlo simulation algorithms to calculate the distribution of supply chain disruption duration, capacity loss range, and financial impact matrix. The adaptive response decision module outputs four levels of early warning signals (Level I alert to Level IV emergency) based on the risk level matrix, automatically generating flood protection and reinforcement plans, supply chain alternative paths, and personnel evacuation routes for specific plant areas. It also triggers reserve team scheduling, rapid insurance claims, and standby capacity activation instructions through industrial IoT protocols linked to the enterprise ERP system.

[0009] Preferably, the multi-source climate data acquisition module deploys a blockchain-based cross-institutional data ownership confirmation mechanism, uses zero-knowledge proof technology to verify the authenticity of the source and the continuity of timestamps of third-party climate data, and achieves joint modeling of cross-border meteorological agencies, government regulatory departments and enterprise private data through homomorphic encrypted gradient exchange under the federated learning framework, ensuring that risk features of sensitive commercial geographic information can be extracted without decryption.

[0010] Preferably, the enterprise asset digital twin module integrates oblique photogrammetry point cloud and structural health monitoring (SHM) sensor network to dynamically update real-time operating data, including building crack propagation rate, underground pipeline corrosion coefficient, and floodgate opening and closing status. When asset performance degradation or sudden changes in environmental parameters are detected, the digital twin model is automatically triggered to reconstruct the geometry and recalibrate the mechanical parameters, and the correction factor is fed back to the risk prediction engine.

[0011] Preferably, the AI ​​risk prediction engine has a built-in transfer learning adapter that uses a global disaster knowledge graph (covering nearly 30 years of hurricane, wildfire, and extreme low temperature event cases) to perform adversarial domain adaptation training on the regional basic model, significantly improving the prediction accuracy of small sample areas; at the same time, it integrates a conditional generative adversarial network (CGAN) to simulate extreme scenarios such as sea level rise + once-in-a-century storm surge combined disaster and underground cavity collapse caused by continuous drought, and outputs asset damage probability distribution surface and supply chain cascading failure path.

[0012] Preferably, the dynamic risk assessment module includes a transformation risk quantification sub-unit, which uses a policy text analysis engine based on the Transformer architecture to capture the Global Carbon Border Adjustment Mechanism (CBAM), new ESG disclosure rules, and green subsidy policies in real time. It calculates the impact elasticity of policy changes on corporate carbon tariff costs, financing rates, and market share through a causal inference model, and constructs a net present value (NPV) simulation sandbox for a low-carbon technology portfolio, dynamically generating a technology substitution roadmap and an economic sensitivity report.

[0013] Preferably, the adaptive response decision module is connected to an insurance technology platform, which transforms climate risk prediction results into trigger threshold parameters for parameterized insurance contracts (such as wind speed ≥32m / s for 10 minutes, 24-hour rainfall >200mm). Before a disaster occurs, it automatically generates supplementary clauses for the insurance policy with a smart contract. When satellite remote sensing confirms that the disaster has reached the preset threshold, it immediately initiates instantaneous claims payment based on IoT-based loss assessment and activates a preset dynamic logistics routing algorithm to replan the transportation route.

[0014] Preferably, the system deploys hardened computing nodes with autonomous decision-making capabilities at the edge of the plant area. It has a built-in lightweight risk model with knowledge distillation and compression (including a combination architecture of random forest decision tree and 1D-CNN). When the backbone network is interrupted, it automatically takes over the local sensor data stream, continuously performs power failure protection for critical equipment, emergency sealing of hazardous materials and dynamic planning of personnel evacuation routes, and uploads disaster summary through LoRa wide area low power network.

[0015] Preferably, the system provides a multimodal interactive console that supports risk penetration analysis in a three-dimensional digital twin scenario, including: tracing back the timeline of the impact of historical climate events on specific production lines; simulating the ten-year impact of transition risks under different carbon neutrality pathways on financial statements; visualizing the gradual inundation process of coastal storage bases caused by sea level rise superimposed on storm surge, and linking it to the prediction of water damage to inventory.

[0016] Preferably, the system integrates a climate resilience optimization engine, which analyzes historical disaster response data based on deep reinforcement learning (DRL) algorithms, dynamically optimizes the distribution of material reserve points, backup supplier selection strategies, and capacity flexibility scheduling schemes in the emergency plan database, and forms a self-evolving risk prevention and control decision-making mechanism.

[0017] The technical effects and advantages of this invention are as follows:

[0018] Compared with existing technologies, this invention integrates meteorological satellite, ground monitoring station, and enterprise private IoT data through a multi-source heterogeneous data federation mechanism, under the protection of blockchain rights confirmation, to construct a digital twin of assets with millimeter-level precision, solving the early warning bias caused by data silos in traditional systems. It utilizes a coupled architecture of spatiotemporal convolutional neural networks and a physics simulation engine to dynamically quantify the probability of physical damage to specific building structures from extreme weather. Simultaneously, it uses generative adversarial networks to simulate unknown climate scenarios, overcoming the prediction bottleneck of static models for new complex disasters. It designs a linkage response chain between parameterized insurance smart contracts and edge computing disaster recovery, enabling instantaneous claims payment when disaster thresholds are triggered and localized emergency operations in network outage environments, compressing the delay of manual decision-making to the second level. It innovatively integrates a policy text analysis engine and a transformation cost sandbox, using natural language processing to analyze changes in carbon tariff policies in real time and simulate the economic sensitivity of low-carbon technology investments, providing enterprises with dual-path prevention and control support to simultaneously avoid physical damage and transformation risks. Ultimately, it forms a closed-loop management and control system from risk perception, prediction and early warning, automatic response to continuous optimization. Attached Figure Description

[0019] Figure 1 This is a system framework diagram of the present invention.

[0020] Figure 2 This is a flowchart of the process of the present invention.

[0021] Figure 3 This is a diagram of the risk prediction technology architecture of the present invention. Detailed Implementation

[0022] 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.

[0023] Example 1:

[0024] As attached Figure 1-3The enterprise climate risk prevention and control system (1) shown includes: a multi-source climate data acquisition module that accesses global meteorological satellite remote sensing data, national ground monitoring station network, government open climate database and enterprise-deployed IoT sensor array in real time through a distributed API gateway, continuously collecting dynamic environmental parameters including temperature gradient field, precipitation spatiotemporal distribution, wind vector, soil moisture content, and surface deformation monitoring values, and integrating historical typhoon path database, flood disaster inundation model and drought index map; an enterprise asset digital twin module that constructs a millimeter-level precision virtual model covering factory building complex, supply chain logistics nodes and energy infrastructure based on laser point cloud scanning and BIM modeling technology, and associates physical attribute databases such as asset structural strength, flood control design standards and equipment weather resistance level; and an AI risk prediction engine that uses a multi-scale spatiotemporal convolutional neural network (MS-STCNN) to fuse climate data and asset topology, predicts the probability of extreme weather events and three-dimensional impact domain in the next 72 hours through a hybrid architecture of gated recurrent unit (GRU) and long short-term memory network (LSTM), and simultaneously drives the physical twin based on computational fluid dynamics (CFD). The simulation engine quantifies the risks of structural stress damage, equipment immersion failure rate, and transportation network disruption. The dynamic risk assessment module generates a dynamic heat map of climate risk based on a Geographic Information System (GIS), marking the physical exposure index and functional vulnerability level of each asset unit. It also uses Monte Carlo simulation algorithms to calculate the distribution of supply chain disruption duration, capacity loss range, and financial impact matrix. The adaptive response decision-making module outputs four-level early warning signals (Level I alert to Level IV emergency) based on the risk level matrix, automatically generating flood protection and reinforcement plans, supply chain alternative paths, and personnel evacuation routes for specific plant areas. It also triggers reserve team scheduling, rapid insurance claims, and standby capacity activation instructions through industrial IoT protocols linked to the enterprise ERP system. The multi-source climate data acquisition module accesses real-time data from the microwave radiometer of the US NOAA meteorological satellite, minute-level precipitation observations from the China Meteorological Administration's ground monitoring stations, century-scale reanalysis data from the EU Copernicus Climate Database, and a LoRaWAN IoT sensor array deployed in the plant area (including the SHT35 temperature and humidity sensor and the Gill WindSonic ultrasonic anemometer). This module collects three-dimensional temperature distribution field (measurement accuracy ±0.5 degrees Celsius), radar precipitation intensity distribution map (spatial resolution 1 square kilometer), wind speed vector (range 0-60 m / s), and soil moisture content data every 5 minutes. It also integrates historical typhoon path lattice data from the international disaster database EM-DAT and the HEC-RAS flood inundation model (vertical accuracy 0.1 meters).The enterprise asset digital twin module uses a RIEGL VZ-400i laser scanner to acquire millimeter-level point cloud data of the factory building. A building information model including steel structure nodes is built on the Autodesk Revit platform, and linked to the asset's seismic resistance level (e.g., 8-degree fortification as specified in China's GB50011 standard) and flood wall design elevation (based on a 50-year flood level plus 0.5 meters for safety). The AI ​​risk prediction engine employs a four-layer spatiotemporal convolutional neural network with a temporal kernel size of 7×7 pixels and a spatial kernel size of 3×3 pixels, fusing climate raster and asset topology data. A GRU-LSTM hybrid model predicts typhoon paths over 72 hours in 15-minute increments, driving the ANSYS CFX module to calculate wind pressure distribution on building surfaces (calculation grid size 0.5 cubic meters), outputting a probability matrix of equipment immersion failure. The dynamic risk assessment module generates heat maps on the ArcGIS platform. The physical exposure index is calculated using a weighted sum formula of the intensity weights of each disaster and the asset vulnerability coefficient. After 100,000 Monte Carlo iterations, the output supply chain disruption duration has a 90% confidence interval of 12 to 72 hours. When a Level IV warning is triggered, the adaptive response decision module sends instructions to the SAP ERP system via the OPC UA protocol: activate the flood control hydraulic system (working pressure above 20 MPa), activate the Southeast Asian backup supplier order (response time less than 4 hours), and invoke the parameterized insurance smart contract (wind speed trigger threshold 32.6 m / s).

[0025] (2) The multi-source climate data acquisition module deploys a blockchain-based cross-institutional data ownership confirmation mechanism. It utilizes zero-knowledge proof technology to verify the authenticity of third-party climate data sources and the continuity of timestamps. Under a federated learning framework, it achieves joint modeling of data from multinational meteorological agencies, government regulatory departments, and enterprise private data through homomorphic encrypted gradient exchange. This ensures that sensitive commercial geographic information can be extracted for risk features without decryption. Specifically, a blockchain ownership confirmation network is built based on the Hyperledger Fabric framework, with six consensus nodes including meteorological bureaus and insurance companies. Zero-knowledge proof technology is used to verify the continuity of data timestamps (time error less than ±1 second). Federated learning employs the Paillier homomorphic encryption scheme. After the enterprise trains the ResNet-18 feature extraction model locally, it only uploads gradient data encrypted with a 2048-bit key to the coordination server. When multiple parties jointly update the global risk model, a weighted average algorithm is used (weights are allocated according to the proportion of data volume of each institution), ensuring that the enterprise's sensitive geographic information is always in a 256-bit AES encrypted state, achieving data usability without visibility.

[0026] (3) The enterprise asset digital twin module integrates oblique photogrammetry point cloud and structural health monitoring (SHM) sensor network to dynamically update real-time operating data, including building crack propagation rate, underground pipeline corrosion coefficient, and floodgate opening and closing status. When asset performance degradation or sudden changes in environmental parameters are detected, the digital twin model's geometric reconstruction and mechanical parameter recalibration are automatically triggered, and the correction factor is fed back to the risk prediction engine. Oblique photogrammetry (image resolution 0.5 cm) is performed using a DJI P4RTK drone, combined with an FBG fiber optic grating sensor network (sampling frequency 1 kHz) embedded in the building structure to monitor crack width in real time (automatic alarm when exceeding 0.3 mm). Electrochemical corrosion probes (range 0-200 micrometers / year) are deployed in the underground pipeline network, and the floodgate status is transmitted back in real time by magnetostrictive displacement sensors (accuracy up to 0.1% of full range). When the carbonation depth of concrete exceeds 30% of the protective layer thickness, the system automatically calls the finite element analysis module to recalculate the beam and column bearing capacity (mesh size no greater than 5 mm) and pushes the corrected material yield strength parameters (in MPa) to the risk prediction engine in real time.

[0027] (4) The AI ​​risk prediction engine incorporates a transfer learning adapter, which uses a global disaster knowledge graph (covering nearly 30 years of hurricane, wildfire, and extreme low-temperature event cases) to conduct adversarial domain adaptation training on the regional basic model, significantly improving the prediction accuracy for small sample areas. Simultaneously, it integrates a conditional generative adversarial network (CGAN) to simulate scenarios where extreme events do not occur, such as sea-level rise combined with a once-in-a-century storm surge and underground cavity collapse caused by prolonged drought. It outputs an asset damage probability distribution surface and supply chain cascading failure paths. The transfer learning adapter loads global disaster cases (containing 2 million nodes) stored in the Neo4j graph database and employs an adversarial training strategy: a feature extractor trained using US hurricane data is used to perform game optimization with the target region classifier, improving prediction accuracy for small sample areas such as Southeast Asia by minimizing the domain difference loss function. The conditional generative adversarial network uses the RCP8.5 climate scenario as input to simulate a combined disaster of 1.2-meter sea-level rise and 3.5-meter storm surge, outputting the probability of layer-by-layer flooding of warehouse shelves (shelf height 2 meters). It also calculates the rate of change of betweenness centrality of supply chain nodes based on complex network theory to assess cascading failure risk.

[0028] (5) The dynamic risk assessment module includes a transformation risk quantification sub-unit. It utilizes a policy text analysis engine based on the Transformer architecture to capture real-time data on the Global Carbon Border Adjustment Mechanism (CBAM), new ESG disclosure regulations, and green subsidy policies. Through a causal inference model, it calculates the impact elasticity of policy changes on corporate carbon tariff costs, financing rates, and market share. It also constructs a net present value (NPV) simulation sandbox for a low-carbon technology portfolio, dynamically generating technology substitution roadmaps and economic sensitivity reports. Specifically, a policy analysis engine (with a hidden dimension of 768) is built based on the BERT-large model to crawl real-time data from the EU's draft carbon border adjustment mechanism and the China carbon trading platform. Carbon tariff costs are quantified using a structural equation model: an industry adjustment factor α is set, and the tax payable is calculated by multiplying the value of the company's exports to the EU by the difference between the local carbon intensity and the benchmark value, and then by the carbon price. Elasticity analysis shows that a 10% increase in electricity prices will lead to a 2.3% increase in electrolytic aluminum costs. The low-carbon investment simulation sandbox sets a discount rate of 6.5% to compare the dynamic investment payback period of photovoltaic power plants (unit investment of RMB 4.2 / watt, internal rate of return of 8.7%) and carbon capture and transformation (operating cost of RMB 120 / ton of carbon dioxide).

[0029] (6) The adaptive response decision module connects to the insurance technology platform, transforming climate risk prediction results into trigger threshold parameters for parameterized insurance contracts (such as wind speed ≥ 32 m / s for 10 minutes, 24-hour rainfall > 200 mm). Before a disaster occurs, it automatically generates supplementary policy clauses with a smart contract. When satellite remote sensing confirms that the disaster reaches the preset threshold, it immediately initiates instantaneous claims payment based on IoT-based loss assessment and activates a preset dynamic logistics routing algorithm to replan the transportation route. The parameterized insurance contract obtains official meteorological data through the Chainlink oracle and sets the typhoon claims trigger conditions as: wind speed ≥ 32.6 m / s for 10 minutes and the enterprise is located within the radius of a typhoon's 7-level wind circle. The smart contract deployed on the Ethereum testnet (developed in Solidity) automatically pays the claim amount (amount = insured amount × actual wind speed / cube of trigger wind speed) to the insured account after receiving images from the WorldView-3 satellite showing a factory roof damage rate greater than 15%. Simultaneously, the dynamic logistics routing algorithm is activated: when replanning transportation routes, priority is given to highways with an altitude 10 meters higher than the predicted flood level, and the ETA (Estimated Time of Arrival) is corrected in real time through vehicle GPS.

[0030] (7) The system deploys hardened computing nodes with autonomous decision-making capabilities at the edge of the plant area. These nodes are equipped with a lightweight risk model that has undergone knowledge distillation and compression (including a combination architecture of random forest decision trees and 1D-CNN). When the backbone network is interrupted, the nodes automatically take over the local sensor data stream and continuously perform power outage protection for critical equipment, emergency sealing of hazardous materials, and dynamic planning of personnel evacuation routes. They also upload disaster summaries via the LoRa wide-area low-power network. Specifically, the hardened computing nodes (NVIDIA Jetson AGX Xavier platform) deployed at the edge of the plant area run the lightweight model that has undergone knowledge distillation and compression (including a hybrid architecture of random forest decision trees and one-dimensional convolutional neural networks). When the backbone network is interrupted for more than 5 seconds, the nodes automatically take over the local sensor data stream and continuously perform three core operations: cutting off the power supply to the hazardous area via the Modbus RTU protocol (response time <500 milliseconds), activating the emergency pressure relief valve in the tank area (pressure threshold 1.8 times the working pressure), and planning evacuation routes based on the real-time fire spread model (update frequency 1 time / second). The nodes also upload the key disaster summaries to the emergency command center via the LoRa wireless network.

[0031] (8) The system provides a multimodal interactive console that supports risk penetration analysis in a three-dimensional digital twin scenario, including: tracing back the timeline of the impact of historical climate events on specific production lines; simulating the ten-year impact of transition risks on financial statements under different carbon neutrality paths; visualizing the gradual inundation process of coastal storage bases caused by sea level rise and storm surge, and linking it to the prediction of water damage to inventory. The multimodal console integrates the Unity3D engine and supports three penetration analyses in the digital twin scenario: replaying the shutdown timeline of specific production lines during historical typhoons (accurate to the minute level); simulating the cumulative impact of carbon costs on the company's balance sheet over the next ten years under different carbon neutrality paths (calculated according to accounting standard IFRS-S2); and dynamically visualizing the gradual inundation process of coastal warehouses caused by sea level rise and storm surge (water level step value 0.1 meters), and linking it to the prediction model of water damage to inventory (calculated according to the waterproof level of the goods).

[0032] (9) The system integrates a climate resilience optimization engine, which analyzes historical disaster response data based on deep reinforcement learning (DRL) algorithms to dynamically optimize the distribution of material reserve locations, backup supplier selection strategies, and capacity flexibility scheduling schemes in the emergency plan database, forming a self-evolving risk prevention and control decision-making mechanism. The climate resilience optimization engine uses a deep reinforcement learning algorithm (PPO architecture) with historical disaster response data as the training set to dynamically adjust the parameters of the emergency plan database: optimizing the location of emergency material reserve points (minimizing the 90th percentile transportation time), establishing a dynamic scoring model for backup suppliers (40% weight for quality pass rate and 60% weight for extreme weather response speed), and generating capacity transfer schemes (prioritizing backup plant areas with climate risk levels below II). A strategy optimization report is automatically generated quarterly, recommending adjustments to key parameters.

[0033] Example 2: Enterprise Climate Risk Prevention and Control System Utilizing Big Data and Artificial Intelligence

[0034] Phase 1: Disaster Warning Activation (72 hours before typhoon landfall)

[0035] 1. Multi-source data fusion

[0036] Meteorological satellites detected the formation of a tropical depression in the western Pacific Ocean. Ground monitoring stations collected real-time sea surface temperature (28.5℃), vertical wind shear (12m / s), and water vapor flux (35kg / m·s) data, which were then transmitted to the system via a distributed API gateway.

[0037] The enterprise's IoT sensors synchronously transmit operating data such as wind pressure on the factory roof (-120Pa) and water level in drainage pipes (1.2m / 80% of pipe diameter).

[0038] Blockchain nodes verify the continuity of meteorological bureau data timestamps (error < 0.5 seconds), and the federated learning engine integrates data from multiple sources to generate an initial typhoon path probability map (62% probability of landfall within 48 hours).

[0039] 2. Activation of the digital twin model

[0040] The comparison between the laser scanning point cloud and the BIM model showed that the corrosion rate of the weld seams of the steel structure of Plant No. 3 exceeded the standard (measured 0.25mm vs. safety threshold 0.15mm). The system automatically marked this area as a highly vulnerable unit.

[0041] The floodgate sensor reported insufficient hydraulic pressure at gate #3 (measured at 18MPa vs. design value of 22MPa), and the digital twin updated the equipment status parameters in real time.

[0042] 3. AI Risk Prediction

[0043] MS-STCNN network analysis predicts that the maximum wind speed in the typhoon's wind circle will be 39 m / s within 200 km of the landfall point 72 hours later (confidence level 85%).

[0044] CFD physical simulation shows that the wind pressure on the windward side of Plant 3 will reach -2.8 kPa (15% higher than the design value), and the probability of roof damage will rise to 73%.

[0045] CGAN generates a composite disaster scenario of "typhoon superimposed on spring tide", simulating a 92% probability of flooding on the first floor of a warehouse (bottom shelf height 0.8m).

[0046] Phase 2: Risk Assessment and Decision-Making (24 hours before typhoon landfall)

[0047] 1. Dynamic risk quantification

[0048] GIS heat map generated: The physical exposure index of Plant No. 3 reached 0.87 (the highest level), and the supply chain disruption hotspots were concentrated in the G15 expressway section (expected water depth > 0.5m).

[0049] Monte Carlo simulation of 100,000 times: median duration of supply chain disruption is 58 hours, with capacity loss ranging from ¥23 million to ¥41 million.

[0050] Transformation Risk Unit Early Warning: Updates to the EU CBAM draft have resulted in an increase of ¥1.26 million per quarter in carbon tariff costs for exported products.

[0051] 2. Adaptive response triggering

[0052] The system issues a Level II warning (typhoon center <350km from the factory area), and executes automatically:

[0053] Engineering reinforcement: The temporary support plan for Plant No. 3 was initiated (supported by 32 Φ200mm steel pipes, increasing wind pressure resistance to 3.2kPa).

[0054] Supply chain adjustment: Switch G15 high-speed transport orders to railway lines (via the Shanghai-Kunming line at an altitude of 82m, flood risk level I).

[0055] Financial hedging: Purchase wind speed options through a parameterized insurance platform (trigger value 32.6 m / s, premium ¥380,000, coverage ¥12 million).

[0056] Phase 3: Disaster Emergency Response (During Typhoon Landfall)

[0057] 1. Edge computing disaster recovery

[0058] The typhoon caused a backbone network outage, and the edge node (Jetson AGX) immediately took over:

[0059] The system receives local anemometer data (peak value 34.1 m / s) via LoRa network, triggering the decision tree model to output instructions.

[0060] Power supply to the high-risk area of ​​Plant 3 will be cut off within 500 milliseconds, and the emergency pressure relief valve will be activated (pressure relief rate 120m). 3 / min).

[0061] Evacuation routes are planned based on a real-time flood inundation model (dynamically avoiding areas with water depth > 0.3m), and navigation information is pushed to employees' mobile apps.

[0062] 2. Automatic insurance claims processing

[0063] If the Chainlink oracle confirms that the wind speed in the factory area is ≥32.6m / s for 10 consecutive minutes, the smart contract will execute automatically.

[0064] The roof damage rate was analyzed using WorldView-3 satellite imagery (measured at 19.3%, exceeding the threshold of 15%).

[0065] Instant payment of claim amount: ¥7.63 million (Calculation formula: ¥12 million × (34.1 / 32.6)^3 × 19.3%).

[0066] The claims status is synchronized to the ERP system's finance module in real time.

[0067] Phase 4: Post-disaster optimization and upgrading (7 days after the typhoon passes)

[0068] 1. Resilience optimization closed loop

[0069] The deep reinforcement learning engine analyzes the response data:

[0070] The temporary support plan was found to be delayed by 2 hours (due to the excessive distance of material transportation).

[0071] New strategy: Add an emergency supplies reserve point on the east side of the factory area (making the response time of 90% of the area <45 minutes).

[0072] Supply chain scoring model update: Railway supplier response speed score rises from 82 to 95 points, and is included in the priority cooperation list.

[0073] 2. Three-dimensional review and verification

[0074] Replay the entire disaster in the Unity3D console:

[0075] Visualized wind pressure cloud map of Plant No. 3 (maximum negative pressure -2.9kPa vs. predicted value -2.8kPa).

[0076] The actual flood level (1.05m) was compared with the CGAN prediction (1.12m), with an error of 6.7%.

[0077] Generate a transformation risk report: Deploying solar rooftops two years in advance can reduce carbon tariff costs by ¥410,000 per quarter.

[0078] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change.

[0079] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0080] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An enterprise climate risk prevention and control system utilizing big data and artificial intelligence, characterized by... include: The multi-source climate data acquisition module accesses global meteorological satellite remote sensing data, national ground monitoring station networks, government open climate databases, and enterprise-deployed IoT sensor arrays in real time through a distributed API gateway. It continuously collects dynamic environmental parameters, including temperature gradient fields, spatiotemporal distribution of precipitation, wind vectors, soil moisture content, and surface deformation monitoring values. It also integrates historical typhoon path databases, flood disaster inundation models, and drought index maps. The enterprise asset digital twin module, based on laser point cloud scanning and BIM modeling technology, constructs millimeter-precision virtual models covering factory building complexes, supply chain logistics nodes, and energy infrastructure, linking them to a database of physical attributes such as asset structural strength, flood control design standards, and equipment weather resistance ratings. AI risk prediction is also implemented. The engine employs a multi-scale spatiotemporal convolutional neural network (MS-STCNN) to fuse climate data with asset topology relationships. Through a hybrid architecture of gated recurrent units (GRU) and long short-term memory networks (LSTM), it predicts the probability of extreme weather events and their three-dimensional impact domains within the next 72 hours. Simultaneously, it drives a computational fluid dynamics (CFD)-based physical simulation engine to quantify structural stress damage, equipment immersion failure rates, and transportation network disruption risks. The dynamic risk assessment module generates a dynamic heat map of climate risk with a geographic information system (GIS) base map, marking the physical exposure index and functional vulnerability level of each asset unit. Combined with Monte Carlo simulation algorithms, it calculates the distribution of supply chain disruption duration, capacity loss range, and financial impact matrix. The adaptive response decision module outputs four-level early warning signals (Level I alert to Level IV emergency) based on the risk level matrix, automatically generates flood protection and reinforcement plans, supply chain alternative paths and personnel evacuation routes for specific factory areas, and triggers reserve team scheduling, rapid insurance claims and standby capacity activation instructions through the industrial Internet of Things protocol linked with the enterprise ERP system.

2. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The multi-source climate data acquisition module deploys a blockchain-based cross-institutional data ownership confirmation mechanism, uses zero-knowledge proof technology to verify the authenticity of the source and the continuity of timestamps of third-party climate data, and achieves joint modeling of cross-border meteorological agencies, government regulatory departments and enterprise private data through homomorphic encrypted gradient exchange under the federated learning framework, ensuring that risk features of sensitive commercial geographic information can be extracted without decryption.

3. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The enterprise asset digital twin module integrates oblique photogrammetry point cloud and structural health monitoring (SHM) sensor network to dynamically update real-time operating data, including building crack propagation rate, underground pipeline corrosion coefficient, and floodgate opening and closing status. When asset performance degradation or sudden changes in environmental parameters are detected, the digital twin model is automatically triggered to reconstruct the geometry and recalibrate the mechanical parameters, and the correction factor is fed back to the risk prediction engine.

4. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The AI ​​risk prediction engine has a built-in transfer learning adapter that uses a global disaster knowledge graph (covering nearly 30 years of hurricane, wildfire, and extreme low temperature events) to perform adversarial domain adaptation training on the regional basic model, significantly improving the prediction accuracy for small sample areas. At the same time, it integrates a conditional generative adversarial network (CGAN) to simulate extreme scenarios such as sea level rise + once-in-a-century storm surge combined disasters and underground cavity collapse caused by continuous drought, and outputs asset damage probability distribution surface and supply chain cascading failure paths.

5. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The dynamic risk assessment module includes a transformation risk quantification sub-unit. It uses a policy text analysis engine based on the Transformer architecture to capture the Global Carbon Border Adjustment Mechanism (CBAM), new ESG disclosure rules, and green subsidy policies in real time. It calculates the impact elasticity of policy changes on corporate carbon tariff costs, financing rates, and market share through a causal inference model, and constructs a net present value (NPV) simulation sandbox for a low-carbon technology portfolio, dynamically generating a technology substitution roadmap and an economic sensitivity report.

6. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The adaptive response decision module connects to the insurance technology platform and transforms climate risk prediction results into trigger threshold parameters for parameterized insurance contracts (such as wind speed ≥32m / s for 10 minutes, 24-hour rainfall >200mm). Before a disaster occurs, it automatically generates supplementary clauses for the insurance policy with a smart contract. When satellite remote sensing confirms that the disaster has reached the preset threshold, it immediately initiates instant claims payment based on IoT-based loss assessment and activates a preset dynamic logistics routing algorithm to replan the transportation route.

7. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The system deploys ruggedized computing nodes with autonomous decision-making capabilities at the edge of the factory area. It has a built-in lightweight risk model with knowledge distillation and compression (including a combination architecture of random forest decision tree and 1D-CNN). When the backbone network is interrupted, it automatically takes over the local sensor data stream, continuously performs power failure protection for critical equipment, emergency sealing of hazardous materials and dynamic planning of personnel evacuation routes, and uploads disaster summary through LoRa wide area low power network.

8. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The system provides a multimodal interactive console that supports risk penetration analysis in three-dimensional digital twin scenarios, including: tracing the timeline of the impact of historical climate events on specific production lines; simulating the ten-year impact of transition risks under different carbon neutrality pathways on financial statements; visualizing the gradual inundation process of coastal storage bases caused by sea level rise superimposed on storm surge, and linking it to the prediction of water damage to inventory.

9. The enterprise climate risk prevention and control system utilizing big data and artificial intelligence as described in claim 1, characterized in that: The system integrates a climate resilience optimization engine, which analyzes historical disaster response data based on deep reinforcement learning (DRL) algorithms, dynamically optimizes the distribution of material reserve locations, backup supplier selection strategies, and capacity flexibility scheduling schemes in the emergency plan database, and forms a self-evolving risk prevention and control decision-making mechanism.

Images