System and method for systematic risk real-time monitoring and artificial intelligence intervention based on multilayer dynamic game model

Abstract

The invention relates to a systematic risk real-time monitoring and artificial intelligence intervention method and system based on a multilayer dynamic game model, and belongs to the technical field of risk management and decision support. Microscopic, mesoscopic and macroscopic multi-level data are collected in real time through technologies such as Internet of Things equipment and a mobile terminal APP, and cross-level data fusion under privacy protection is realized based on federated learning; constructing a three-level dynamic game model comprising an individual layer, a group layer and a system layer, quantifying a decision trap index (DTI), strategic elasticity (delta S) and a resource regeneration rate (delta R), and calculating a systematic risk index (SRI) in combination with a reinforcement learning dynamic optimization weight coefficient; an intelligent intervention strategy is triggered based on an SRI threshold value, policy tools such as dynamic subsidy and supply chain recombination are automatically executed through a block chain intelligent contract, and a'monitoring-early warning-intervention-feedback 'closed loop is formed. The method can be widely applied to the fields of financial supervision, public policy regulation and control, supply chain risk management and the like, and has remarkable social and commercial values.

Description

1. Technical Field

[0001] The present invention belongs to the technical field of risk management and decision support, and specifically relates to a real-time monitoring and artificial intelligence intervention system and method for systemic risk based on a multi-layer dynamic game model, which is applicable to risk early warning and governance in fields such as finance, public policy, and supply chain management. 2. Background Art

[0002] Traditional risk management methods have the following deficiencies:

[0003] Limitations of static indicators: Existing risk indicators (such as asset-liability ratio, volatility) cannot dynamically reflect cross-level risk transmission under resource constraints.

[0004] Lack of real-time feedback: Policy interventions mostly rely on manual decision-making and are difficult to cope with the non-linear risk accumulation in complex systems.

[0005] Data fragmentation: The lack of integration between micro-individual behavior data and macro-system data leads to a lag in risk prediction.

[0006] The definitions of the Decision Trap Index (TDI) and Strategic Resilience (ΔS) involved in this application partly refer to the prior patent application with the application number CN2025103827940, and the complete implementation method shall be subject to the content disclosed in this application. The TDI (Decision Trap Index) is a quantitative indicator to measure the risk of the decision-making system falling into "strategic degradation", and its value range is [0,1]. Its calculation is based on parameters such as the resource constraint intensity (RC), the time discount rate (δ), and the information asymmetry index (I), reflecting the degree of erosion of long-term strategic goals by short-term decision-making behaviors. Functions of TDI: 1. Risk warning: By dynamically monitoring the TDI threshold, identify whether an enterprise or organization has entered the vicious cycle of "resource degradation - decision-making deterioration". 2. Policy design: Provide a basis for intervention for the government or management (such as adjusting resource taxes and optimizing information transparency) to reduce the TDI value. 3. Case application: In the case of the photovoltaic industry, enterprises with TDI>0.75 stagnated in technology due to over-reliance on subsidies and were finally eliminated by the market. ΔS (Strategic Space Resilience) represents the range of strategic choices and adjustment capabilities available to decision-makers under resource constraints, with the unit of strategic dimension / resource unit. The higher its value, the stronger the system's ability to maintain strategic flexibility in a dynamic environment. Functions: 1. Flexibility assessment: Quantify the ability of an enterprise to cope with resource fluctuations. For example, a new energy enterprise with ΔS = 2.5 can diversify risks through multiple technical routes (such as LFP / NCM batteries), while a traditional enterprise with ΔS = 1.2 is vulnerable to being restricted by a single technical path. 2. Resilience optimization: Enhance the system's risk resistance ability by increasing ΔS (such as increasing R & D investment and establishing a resource buffer pool). In the case, a manufacturing enterprise successfully survived the supply chain crisis by increasing ΔS from 1.5 to 3.0. The synergistic effect, dynamic balance mechanism of TDI and ΔS, high TDI + low ΔS: The risk of the system falling into a decision trap is extremely high (such as resource exhaustion and strategic rigidity); low TDI + high ΔS: The system has strong adaptability (such as sufficient resources and diverse strategies). Protect the TDI calculation tool. This application protects the intervention system based on TDI, dynamically couples TDI with the resource regeneration rate ΔR to construct the SRI index. This application further couples ΔS with the resource regeneration rate ΔR to construct a cross-level risk conduction model. III. Summary of the Invention

[0007] 1. Core innovation points

[0008] Quantification method of the Systemic Risk Index (SRI): Integrate the Decision Trap Index (DTI), Strategic Resilience (ΔS) and Resource Regeneration Rate (ΔR), and calculate the cross-level risk conduction intensity through a dynamic game model.

[0009] Real-time risk monitoring and artificial intelligence intervention system: Trigger automated policy tools (such as dynamic subsidies, debt restructuring funds) based on the SRI threshold to achieve a closed-loop of "risk warning → strategy generation → execution feedback".

[0010] 2. Technical Solutions

[0011] 1) Cross - level Coupling Equation

[0012] Introduce the joint dynamic equations for the individual, group, and system levels:

[0013] Individual level:

[0014] Group level:

[0015] System level:

[0016] 2) Risk Transmission Coefficient (RTC)

[0017] Define the systemic risk transmission coefficient:

[0018]

[0019] Explanation: RTC > 1 indicates that individual myopic behavior (δ↑) significantly reduces the group's regeneration ability (α↓) through resource consumption (β↑), ultimately driving up the system DTI.

[0020] 3) Systemic Risk Early Warning and Intervention

[0021] 1. Systemic Risk Index (SRI)

[0022] Integrate the individual DTI, group elasticity (ΔS), and system resource status (R):

[0023]

[0024] Threshold: SRI ≥ 1 triggers the systemic risk early warning.

[0025] Empirical calibration: Based on a certain enterprise database, the bankruptcy risk of enterprises with SRI > 1 increases by 42% (p < 0.01).

[0026] 2. Intervention Strategy Equation

[0027] Dynamic resource buffering and strategic diversity optimization:

[0028]

[0029] Parameters: γ is the intervention intensity (γ = 0.3 - 0.6), and ΔS benchmark = 3 (healthy strategic dimension).

[0030] Case: A new energy vehicle enterprise reduced the SRI from 1.2 to 0.8 by ΔR buffer = 5%, avoiding technology route lock - in.

[0031] Systemic Risk Index (SRI): Integrating individual DTI, group resilience (ΔS), and system resource status (R), a systemic risk warning is triggered when the threshold SRI ≥ 1.

[0032] 4) Three-level Conduction Mechanism

[0033] 1. Micro level: Short-sighted decision-making and resource consumption

[0034] Core equation: Individual utility function

[0035]

[0036] Among them, a high δ value (such as young people choosing to "lie flat", δ ≈ 0.8) leads to overconsumption of current resources (such as depletion of savings) and inhibits long-term investment (education, childbirth).

[0037] Case: Young people do not buy houses (s_i = 0) → Cash flow break of real estate enterprises (R_t↓) → Atrophy of land finance (government α drops from 0.9 to 0.7).

[0038] 2. Meso level: Group path dependence and information attenuation

[0039] Dynamic equation: Group strategic space resilience

[0040]

[0041] Information asymmetry (such as social media amplifying the concept of "not getting married and having children", I = 0.7) compresses the strategic diversity of the group (ΔS drops from 3.0 to 1.5), forming a path dependence on a "low-desire society".

[0042] 3. Macro level: System equilibrium lock-in and risk outbreak

[0043] Systemic Risk Index (SRI):

[0044]

[0045] When SRI ≥ 1 (such as China's fertility rate of 1.18 in 2022, R / R_{\min} = 0.6), it triggers a population structure collapse and a pension system crisis.

[0046] 5) Step 1: Data collection and integration

[0047] Multi-source data input:

[0048] Micro level: Individual decision-making data (such as housing purchase intention, consumption behavior) are collected in real time through Internet of Things devices and mobile apps.

[0049] Meso level: Enterprise operation data (debt ratio, supply chain status) are obtained from ERP systems and blockchain ledgers.

[0050] Macro level: Macroeconomic indicators (GDP, policy documents) are accessed through government open APIs.

[0051] Data standardization: Adopt federated learning technology to achieve cross-level data fusion while protecting privacy.

[0052] 6) Step 2: SRI dynamic calculation model

[0053] Algorithm formula:

[0054]

[0055] (where λ1, λ2, λ3 are weight coefficients, dynamically optimized through reinforcement learning)

[0056] Dynamic game modeling: Based on the three-level PDP framework, simulate the feedback mechanism of "individual myopia → group path dependence → system lock-in" to predict the SRI evolution path.

[0057] 7) Step 3: Generation of artificial intelligence intervention strategies

[0058] Threshold trigger mechanism:

[0059] Low risk (SRI < 0.5): Initiate long-term strategic investment (such as AI algorithm recommending R&D priorities).

[0060] Medium risk (0.5 ≤ SRI < 1.0): Activate the balance strategy (such as flexible supply chain restructuring).

[0061] High risk (SRI ≥ 1.0): Execute emergency intervention (such as automatically releasing 5% of the strategic reserve funds).

[0062] Policy optimization: Combine the quantum genetic algorithm to solve the optimal policy combination under multiple constraints.

[0063] 8) Step 4: System architecture

[0064] Hardware layer: Edge computing nodes (real-time data processing) + cloud servers (model training and policy storage).

[0065] Software layer:

[0066] Risk monitoring module: Dynamically update SRI and visualize it (such as a graphical dashboard).

[0067] Policy execution module: Automatically call policy tools through smart contracts (such as issuing digital consumer vouchers on the chain).

[0068] 9) Formula definition and parameter explanation

[0069] δ is the time discount rate, reflecting the preference difference of individuals between current benefits and future benefits (δ ∈ [0, 1], and the higher δ is, the more short-sighted).

[0070] Through game equilibrium analysis, the SRI formula transforms the dynamic coupling relationship of DTI, ΔS, and ΔR into a risk index.

[0071] The optimization of λ1, λ2, and λ3 is achieved through a reinforcement learning algorithm. The specific process is as follows: initialize weights → calculate the SRI prediction error → update weights through backpropagation.

[0072] The calculation of RTC is divided into two steps: Step 1: Calculate the partial derivative of the myopic decision (δ) at the individual level with respect to DTI at the system level; Step 2: Calculate the partial derivative of the resource consumption (β) at the group level with respect to the regeneration ability (α); the final RTC value is the product of the two partial derivatives.

[0073] ΔS is the strategic space elasticity, with a value range of 0, 3.0, reflecting the strategic flexibility of the organization under resource constraints (such as technological diversity, labor mobility). Low ΔS (<1.5): Limited strategic choices (such as a single technological path). High ΔS (≥2.0): Multi-path collaboration (such as parallel R & D, flexible supply chain).

[0074] ΔR is the resource regeneration efficiency, with a value range of 0, 1.2, measuring the sustainable recovery ability of the resource stock. Low ΔR (<0.8): Irreversible resource depletion (such as mineral exhaustion). High ΔR (≥1.0): Renewable recycling (such as ecological restoration).

[0075] Threshold lines: ΔS = 2.0 (critical value of strategic diversity), ΔR = 0.8 (resource regeneration safety line). IV. Description of the Drawings

[0076] 1. Drawings

[0077] Figure 1 : System architecture diagram;

[0078] Figure 2 : Data collection and fusion flow chart;

[0079] Figure 3 : SRI dynamic calculation model diagram;

[0080] Figure 4 : Intelligent intervention strategy generation flow chart. 2. Description of the Drawings

[0082] Figure 1 : System architecture diagram

[0083] Description

[0084] Show the overall architecture of the system, including the hardware layer, software layer, and blockchain network. Help to understand the various components of the system and their interrelationships. The risk monitoring module provides an SRI dynamic visualization dashboard and a simulation of the risk transmission path. The policy execution module automatically invokes policy tools through blockchain smart contracts.

[0085] Content: Show the overall architecture of the system, including the hardware layer, software layer, and blockchain network.

[0086] Function: Help to understand the various components of the system and their interrelationships. Description of the drawings:

[0088] Hardware layer:

[0089] Edge computing nodes: Deployed at the data source end for real-time data collection and preprocessing.

[0090] Cloud servers: Equipped with a dynamic game model and a quantum genetic algorithm module for SRI calculation and policy generation.

[0091] Software layer:

[0092] Risk monitoring module: Provide an SRI dynamic visualization dashboard and a simulation of the risk transmission path.

[0093] Policy execution module: Automatically invoke policy tools through blockchain smart contracts, including the issuance of dynamic subsidies, supply chain restructuring instructions, and the distribution of digital consumption vouchers.

[0094] Blockchain network:

[0095] Adopt a consortium chain architecture to ensure the transparency and immutability of the execution of intervention policies.

[0096] Integrate with digital RMB wallets through on-chain smart contracts to achieve targeted fund placement.

[0097] Figure 2 : Flowchart of data collection and fusion

[0098] Description

[0099] Micro layer: Individual decision-making data (such as housing purchase intention, consumption behavior) is collected in real time through IoT devices and mobile APPs. Meso layer: Enterprise operation data (debt ratio, supply chain status) is obtained from the ERP system and blockchain ledger.

[0100] Macro layer: Macroeconomic indicators (GDP, policy documents) are accessed through government open APIs. Data standardization: Adopt federated learning technology to achieve cross-level data fusion while protecting privacy.

[0101] Content: Show the process of multi-source data input, data standardization, and cross-level data fusion.

[0102] Function: Helps to understand how data is collected from different levels and integrated into the system. Description of the drawings:

[0104] Micro level:

[0105] Individual decision-making data (such as housing purchase intention, consumption behavior) is collected in real time through Internet of Things devices and mobile APPs.

[0106] Mesoscopic level:

[0107] Enterprise operation data (debt ratio, supply chain status) is obtained from the ERP system and the blockchain ledger.

[0108] Macro level:

[0109] Macroeconomic indicators (GDP, policy documents) are accessed through government open APIs.

[0110] Data standardization:

[0111] Adopt federated learning technology to achieve cross-level data fusion while protecting privacy.

[0112] Figure 3 : Diagram of the SRI dynamic calculation model

[0113] Description

[0114] Shows the calculation process of the Systemic Risk Index (SRI), including the dynamic game models at the individual, group, and system levels.

[0115] Helps to understand the calculation logic of SRI and the operating mechanism of the dynamic game model.

[0116] Content: Shows the calculation process of the Systemic Risk Index (SRI), including the dynamic game models at the individual, group, and system levels.

[0117] Function: Helps to understand the calculation logic of SRI and the operating mechanism of the dynamic game model. Description of the drawings:

[0119] Individual level:

[0120] Core equation: Individual utility function, quantifying the impact of myopic decision-making (δ) on resource consumption (β).

[0121] Group level:

[0122] Dynamic equation: Elasticity of the group's strategic space, evaluating the compression effect of information asymmetry (I) on strategic diversity (ΔS).

[0123] System level:

[0124] Systemic Risk Index (SRI): Integrate individual DTI, group resilience (ΔS), and system resource status (R).

[0125] Weight coefficient optimization:

[0126] The weight coefficients (λ1, λ2, λ3) are dynamically optimized through reinforcement learning.

[0127] Figure 4 : Flowchart for generating intelligent intervention strategies

[0128] Description

[0129] Show the process of triggering intelligent intervention strategies according to the SRI threshold, including strategy selection for low risk, medium risk, and high risk. Help understand how the system selects and executes intervention strategies based on the risk level.

[0130] Content: Show the process of triggering intelligent intervention strategies according to the SRI threshold, including strategy selection for low risk, medium risk, and high risk.

[0131] Function: Help understand how the system selects and executes intervention strategies based on the risk level. Description of the drawings:

[0133] Low risk (SRI < 0.5):

[0134] Start long-term strategic investment strategies, such as AI algorithm recommended R&D priorities.

[0135] Medium risk (0.5 ≤ SRI < 1.0):

[0136] Activate balance strategies, such as flexible supply chain restructuring.

[0137] High risk (SRI ≥ 1.0):

[0138] Execute emergency intervention strategies, such as automatically releasing strategic reserve funds or multi-source procurement emergency agreements.

[0139] Strategy optimization:

[0140] Combine quantum genetic algorithm to solve the optimal policy combination under multiple constraints.

[0141] V. Implementation cases

[0142] Application scenario 1: Systemic risk governance in the real estate industry

[0143] Data input: Youth home purchase intention (questionnaire survey APP), real estate enterprise debt data (blockchain ledger), land auction failure rate (government database).

[0144] SRI calculation: real-time monitoring of real estate companies' DTI and land resource regeneration rate, and prediction of SRI breakthrough threshold (1.2) within 3 months.

[0145] Intervention trigger: The system automatically starts the "Youth Housing Subsidy Fund" and distributes subsidies through the digital RMB wallet, reducing the SRI to 0.8.

[0146] Application scenario 2: Risk prevention and control of the global electric vehicle supply chain

[0147] Data input: battery failure rate (on-board sensors), raw material inventory (IoT logistics tags), policies and regulations (NLP parsing of government documents).

[0148] SRI calculation: Identify the decrease in ΔR caused by a fire at a battery supplier and predict the risk of supply chain disruption (SRI = 1.5).

[0149] Intervention trigger: The system initiates the "Multi-Source Procurement Emergency Agreement", places orders to backup suppliers through smart contracts, and simultaneously adjusts the production plans of automobile companies.

Claims

1. A real-time monitoring and intelligent intervention method for systemic risk based on a multi-layer dynamic game model, characterized in that It includes the following steps: (1) Through Internet of Things devices, mobile APPs, blockchain ledgers, and government open APIs, real-time collect individual decision-making data at the micro level, enterprise operation data at the meso level, and economic indicator data at the macro level, and achieve cross-level data fusion based on federated learning technology; (2) Construct a three-level Prisoner's Dilemma (PDP) dynamic game model including the individual layer, group layer, and system layer. Quantify the decision trap index (DTI), strategic resilience (ΔS), and resource regeneration rate (ΔR) through joint dynamic equations, and based on the formula Dynamically calculate the systemic risk index (SRI), where λ1, λ2, and λ3 are weight coefficients, and are dynamically optimized through reinforcement learning; (3) Trigger an intelligent intervention strategy according to the SRI threshold: When SRI < 0.5, activate the long-term strategic investment strategy; When 0.5 ≤ SRI < 1.0, activate the flexible supply chain restructuring or resource reallocation strategy; When SRI ≥ 1.0, execute the emergency intervention strategy, including automatically releasing strategic reserve funds or multi-source procurement emergency agreements; ΔS Strategic space resilience, with a value range of 0, 3.0, reflecting the strategic flexibility of the organization under resource constraints. Low ΔS (<1.5): Limited strategic choices. High ΔS (≥2.0): Multi-path collaboration. ΔR Resource regeneration efficiency, with a value range of 0, 1.2, measuring the sustainable recovery ability of resource stocks. Low ΔR (<0.8): Irreversible resource loss. High ΔR (≥1.0): Renewable cycle. Threshold line: ΔS = 2.0 (critical value of strategic diversity), ΔR = 0.8 (resource regeneration safety line). (4) Automatically execute the intervention strategy through blockchain smart contracts and provide real-time feedback to the dynamic game model to optimize subsequent strategies.

2. The method according to claim 1, wherein In the three-level Prisoner's Dilemma (PDP) dynamic game model:

1. Individual layer: Used to quantify the impact of short-sighted decisions (δ) on resource consumption (β); 2. Group layer: Used to evaluate the compression effect of information asymmetry (I) on strategic diversity (ΔS); 3. System layer:

4. System layer risk conduction coefficient (RTC): When RTC > 1, it is determined that micro-behaviors lead to the outbreak of macro risks through resource consumption. RTC > 1 indicates that individual short-sighted behaviors (δ↑) significantly reduce the group's regeneration ability (α↓) through resource consumption (β↑), ultimately driving up the system DTI.

5. Systemic risk early warning and intervention 1) Systemic risk index (SRI) Integrate individual DTI, group resilience (ΔS), and system resource status (R): Threshold: SRI ≥ 1 triggers a systemic risk early warning. 2) Intervention strategy equation Dynamic resource buffering and strategic diversity optimization: Parameters: γ is the intervention intensity (γ = 0.3 - 0.6), ΔS benchmark = 3 (healthy strategic dimension).

3. The method according to claim 1, wherein In the SRI threshold trigger mechanism: The intensity parameter γ of the emergency intervention strategy is 0.3 - 0.6, and the strategy combination is optimized through quantum genetic algorithms under multiple constraints; The strategic diversity benchmark value ΔS benchmark is set to 3, and the resource buffer threshold ΔR buffer is 5%.

4. The method according to claim 1, wherein In the data fusion step: Micro-level data includes individuals' housing purchase intentions, consumption behaviors, and fertility decisions, which are collected in real time through mobile APPs; Mesoscopic-level data includes corporate debt ratios, supply chain status, and inventory data, which are connected to enterprise ERP systems through blockchain ledgers; Macro-level data includes GDP growth rates, policies and regulations, and land finance indicators, which are obtained through government open APIs.

5. A system for implementing the method according to claims 1-4, characterized in that, It includes: (1) Hardware layer: Computing nodes are deployed at the data source end for real-time data collection and preprocessing; Cloud servers are equipped with dynamic game models and quantum genetic algorithm modules for SRI calculation and strategy generation; (2) Software layer: The risk monitoring module provides an SRI dynamic visualization dashboard and risk conduction path simulation; The strategy execution module automatically invokes policy tools through blockchain smart contracts, including dynamic subsidy distribution, supply chain restructuring instructions, and digital consumer coupon distribution; (3) Blockchain network: Adopts a consortium chain architecture to ensure the transparency and immutability of the execution of intervention strategies; Integrates with digital RMB wallets through on-chain smart contracts to achieve targeted fund investment.

6. The system according to claim 5, characterized in that, In the said strategy execution module: The dynamic subsidy distribution strategy is pushed to the target individual account through the digital RMB wallet; The multi-source procurement emergency protocol automatically triggers backup supplier orders through smart contracts and synchronously adjusts the production plan.

7. The system according to claims 1-6, characterized in that, In the said risk monitoring module: The real-time monitoring of the system resource status (R) is achieved through Internet of Things sensor data and supply chain logistics tags; The risk conduction path prediction is modeled based on the feedback mechanism of the three-level PDP framework; Monitor risks in real time to form an intelligent intervention plan Generate risk management reports, including basic reports, AI decision-making reports, and mixed decision-making reports.

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