Risk assessment method based on digital economy

Abstract

The invention belongs to the technical field of risk assessment, and discloses a risk assessment method based on digital economy. The method comprises the following specific steps: S1, cross-modal data federal acquisition; s2, risk factor dynamic deconstruction; s3, dynamic modeling of game theory enabling is carried out; s4, generating a real-time risk thermodynamic diagram; s5, an edge intelligent decision center; s6, dynamic knowledge graph evolution; and S7, sandbox verification and anti-fact deduction are carried out. According to the method, all-around innovation is brought to risk management and control, first, data processing is performed, block chains, 5G and NLP technologies are utilized, multi-source data are collected, public opinions are insighted, privacy compliance is guaranteed based on federal learning, and a safety footstone is built, and second, market strategies are provided, and by means of reinforcement learning, Monte Carlo deduction and a quantum annealing algorithm, subject behaviors are simulated, policy impact is pre-estimated, and a model is optimized, so that risk management and control are realized. And thirdly, knowledge application, leading-edge technology labeling new risk database establishment, federal learning optimization atlas and enhancement of practical operation to cope with complex risks are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment, and specifically relates to a risk assessment method based on the digital economy. Background Technique

[0002] With the rapid development of information technology, the digital economy has emerged vigorously, profoundly changing people's production and living styles. From the convenient shopping on e-commerce platforms to the efficient collaboration of remote work, the digital economy covers a wide range of fields. However, the risks hidden behind it cannot be underestimated. On the one hand, network security vulnerabilities and algorithmic biases at the technical level may lead to privacy leaks and decision-making mistakes; in the economic field, the instability of the token economy threatens the financial order; at the social level, the digital divide intensifies and the lack of labor rights protection leads to fairness issues. These multi-dimensional risks are intertwined, and there is an urgent need for an accurate and comprehensive risk assessment system to escort the steady progress of the digital economy.

[0003] Existing data has grown explosively, with a deluge of massive information; business models are changing as rapidly as lightning, with high-frequency changes making it impossible to keep up; cross-domain risks are even more rampant, intersecting and spreading with each other. Traditional risk assessment methods seem powerless in this wave and encounter numerous difficulties. First, the disadvantages of staticity are fully exposed. It clings tightly to historical data and can only sigh in the face of real-time risks such as the instant market rise and fall of cryptocurrencies and the quietly emerging biases in AI algorithms, unable to accurately capture them. Second, the data dimension is too narrow, only focusing on a single type of data and turning a blind eye to multi-source heterogeneous information such as the public opinion heat of social media, the real-time logs of IoT devices, and the details of blockchain transactions, lacking the ability to integrate effectively. Third, the reliance on manual labor is serious, and automated early warning is almost absent, and it is even more helpless when making dynamic decisions and difficult to continue. Summary of the Invention

[0004] The purpose of the present invention is to provide a risk assessment method based on the digital economy to solve the problems raised in the above background technique.

[0005] To achieve the above purpose, the present invention provides the following technical solution: The specific steps of a risk assessment method based on the digital economy are as follows:

[0006] S1: Cross-modal data federated collection: Deploy blockchain nodes to mine the value of on-chain transactions, access IoT device logs through 5G edge computing, use NLP sentiment analysis to capture social media graphs, and at the same time rely on the federated learning framework to achieve circulation on the basis of ensuring data privacy and ensuring that the data is available but invisible, effectively avoiding privacy compliance risks;

[0007] S2: Dynamic Deconstruction of Risk Factors: At the technical level, focus on the potential risks of AI and API, and accurately locate the vulnerabilities of cutting-edge technologies; at the economic level, closely monitor token and NFT metrics to measure the fluctuations of the virtual economy; at the social level, pay attention to public opinion and labor risks to control the key social points. The cooperation of all levels makes joint efforts to lay a solid foundation for accurate risk prevention and control;

[0008] S3: Dynamic Modeling Empowered by Game Theory: On the one hand, use reinforcement learning agents to deeply simulate the behaviors of market players, helping them find the optimal decisions in complex situations; on the other hand, use Monte Carlo simulations to rigorously estimate the impacts of regulatory policies and plan countermeasures in advance. The addition of the quantum annealing algorithm breaks through traditional bottlenecks, efficiently optimizes the equilibrium solution, and significantly improves the accuracy and adaptability of the model;

[0009] S4: Generation of Real-time Risk Heat Maps: With the cutting-edge ST-GCN technology, transform it into an intuitive three-dimensional risk heat map. The time dimension is refreshed at the millisecond level to instantly grasp the risk dynamics; in the space dimension, with the help of the metaverse geographical mapping, intuitively present the risk distribution in the virtual space; in the domain dimension, focus on the cross-infection index in the DeFi field to clarify the risk transmission path, and comprehensively assist decision-makers in making accurate decisions;

[0010] S5: Edge Intelligence Decision-making Center: Innovatively deploy lightweight AI models to edge devices and design three response modes according to scenarios. When the risk exceeds the threshold, the automatic fusing mode quickly cuts off the source through smart contracts; in the human-machine collaboration mode, rely on the AR visual decision-making cabin to jointly handle complex situations with humans and machines; the cross-chain early warning mode uses multi-blockchain broadcasts to achieve coordinated linkage of risk prevention and control;

[0011] S6: Evolution of Dynamic Knowledge Graphs: Focus on the continuous accumulation and update of knowledge, and strive to build a self-evolving risk knowledge base. Use cutting-edge technologies to automatically label new risks, such as the copyright risks of AI-generated content, keeping up with the times. At the same time, promote model iteration with federated learning, optimize the knowledge graph, integrate multi-source knowledge, and use a neuro-symbolic system to integrate rule reasoning and deep learning to improve the efficiency of knowledge application;

[0012] S7: Sandbox Verification and Counterfactual Deduction: Put the proposed strategies and models into the simulation of real scenarios for repeated verification to confirm their effectiveness and stability. At the same time, conduct counterfactual deductions, assume different conditions, and explore the possible results of diverse decision-making paths, laying a solid foundation for optimizing decisions and improving models, and helping to forge ahead steadily in complex markets.

[0013] Preferably, the specific steps of cross-modal data federated collection in S1 are as follows:

[0014] Step 1: Start multi-source data collection: Deploy blockchain nodes first, and use their decentralized and tamper-proof characteristics to deeply explore the potential commercial value and key information of capital flow behind the on-chain transaction data. At the same time, with the powerful connection capabilities of 5G edge computing with ultra-high speed and low latency, seamlessly access the real-time status log of IoT devices, accurately capture the real-time dynamics of the physical world, and reserve first-hand information for subsequent analysis;

[0015] Step 2: Capture social media insights: Use sentiment analysis technology in natural language processing (NLP) to capture semantic graphs on social media platforms. By analyzing massive amounts of text, comments, and topic content, we can gain insights into public sentiment, hot topics, and brand reputation and social public opinion information, further broaden the breadth and depth of data, and make the collected information more comprehensive.

[0016] Step 3: Implementation of privacy compliance assurance: After completing multi-source data collection, it is particularly critical to build a data circulation channel based on the federated learning framework. This framework strictly adheres to the principle of data availability but invisibility, and encrypts data from all parties to enable data to flow safely and compliantly between different entities without exposing the original privacy, cleverly avoiding privacy compliance risks and ensuring that the entire process is legal and robust.

[0017] Preferably, the dynamic deconstruction of risk factors in S2 refers to the fact that it is crucial to build a comprehensive risk prevention and control system under the current complex and changeable risk landscape. First, at the technical level, the professional team focuses on the black box degree of AI algorithms and the vulnerability of API interfaces. Through rigorous code review and simulated attack testing methods, the loopholes that may appear in the actual application of cutting-edge technologies are accurately located to prevent them before they happen. At the economic level, professional analysts keep a close eye on the token liquidity entropy and the NFT valuation bubble index, use advanced quantitative models, combine real-time market dynamics, accurately measure the potential unstable factors in the virtual economy, and predict the outbreak point of economic crises in advance. At the social level, the public opinion monitoring team always pays attention to the fission coefficient of public opinion dissemination and captures the direction of public opinion in a timely manner. At the same time, legal experts control digital labor compliance risks and protect the rights and interests of workers. All levels work closely together to create a solid barrier and a solid foundation for accurate risk prevention and control.

[0018] Preferably, the specific steps of the game theory-enabled dynamic modeling in S3 are as follows:

[0019] Step 1: Simulate the behavior of market players: Use reinforcement learning agent technology to delve into the complex texture of the market environment, collect massive historical data, and cover the trading habits and strategy preference information of market players. Based on this, build a sophisticated model to accurately restore the behavior patterns of market players in different situations, helping them to gain insight into opportunities in turbulent situations and approach the optimal decision-making path;

[0020] Step 2: Estimation of regulatory policy impacts: Employ the Monte Carlo simulation method, closely track regulatory dynamics, parameterize various possible regulatory policies, input them into the model for multiple simulation operations, and rigorously estimate the impact of the shocks from multiple dimensions including policy intensity, implementation rhythm, and market feedback. Tailor response strategies for market entities in advance so that they can plan ahead and calmly face policy changes;

[0021] Step 3: Breakthrough in model optimization and upgrade: Introduce the quantum annealing algorithm, a cutting-edge technological tool, to directly address the efficiency bottleneck of traditional algorithms in solving multi-objective game equilibrium solutions. Relying on its unique quantum tunneling characteristics, quickly traverse the complex solution space, efficiently screen out the optimal equilibrium solution, greatly improve the accuracy of the model, broaden its adaptability to different market scenarios, and reshape the competitive advantage.

[0022] Preferably, the generation of the real-time risk heat map in S4 refers to the fact that in the field of risk management and control, the advanced ST-GCN technology shows its prowess. It carefully plots the intricate risk data into an intuitive three-dimensional risk heat map, which can be refreshed at the millisecond level on the time axis, enabling decision-makers to instantly capture the ever-changing risks. At the spatial level, it cleverly uses the metaverse geographical mapping to clearly present the hidden distribution of risks in the virtual space. Focusing on the domain dimension of the cross-infection index in the DeFi field, it accurately disassembles the risk conduction link and fully empowers decision-makers for accurate decision-making.

[0023] Preferably, the edge intelligent decision-making center in S5 refers to the bold and ingenious deployment of a lightweight AI model to edge devices, enabling it to quickly respond to local risk situations. Three response modes are carefully designed according to different scenario characteristics: Once the risk value exceeds the set threshold, the automatic fusing mode is immediately activated, and the efficient execution power of the smart contract is used to quickly cut off the risk source; In the human-machine collaboration mode, with the help of the AR visualization decision-making cabin, operators and intelligent machines closely cooperate to jointly solve complex problems; The cross-chain early warning mode uses multi-blockchain broadcasting to break information silos and achieve collaborative linkage for cross-regional risk prevention and control.

[0024] Preferably, the specific steps of the dynamic knowledge graph evolution in S6 are as follows:

[0025] Step 1: Initial construction of the risk knowledge base and risk annotation: Focus on the requirement of continuous knowledge update, start the construction project of the self-evolving risk knowledge base, use natural language processing and image recognition cutting-edge technologies to screen and mine massive information, automatically and accurately annotate new risks, especially the current hot AI-generated content copyright risks, inject fresh and timely blood into the knowledge base, and initially build a knowledge reserve framework;

[0026] Step 2: Model Iteration and Knowledge Graph Optimization: Introduce federated learning technology to break data silos, enabling scattered data to be aggregated and utilized. Based on this, promote the iterative upgrade of the model. During the iteration process, continuously optimize the knowledge graph structure to accurately reflect the relationships between risk factors. At the same time, integrate multi-source heterogeneous knowledge, including industry reports, academic research, and case analyses, to broaden the breadth and depth of knowledge.

[0027] Step 3: Enhancement of Knowledge Application Efficiency: Leverage the unique advantages of the neuro-symbolic system to organically integrate the rigorous logicality of rule-based reasoning and the powerful learning and induction capabilities of deep learning. On the one hand, strengthen the understanding and derivation of knowledge, and on the other hand, facilitate the rapid and flexible application of knowledge, enabling the knowledge base to no longer be static storage but to efficiently output in complex and changing risk scenarios, effectively enhancing the efficiency of knowledge application.

[0028] Preferably, the sandbox verification and counterfactual inference in S7 refer to: First, place the carefully formulated strategies and cutting-edge models into a specially constructed simulation environment. Referring to the real market scenario, conduct multiple rounds of comprehensive verification to strictly confirm their effectiveness and stability, ensuring absolute safety. At the same time, innovatively carry out counterfactual inference, boldly assume various different conditions, deeply explore the possible results under diverse decision-making paths, accurately analyze the pros and cons, and lay a solid foundation for subsequent decision-making optimization and model improvement, assisting in advancing steadily in the complex market.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1. The present invention starts with multi-source data collection, utilizes blockchain and 5G edge computing to mine the value of on-chain transactions, capture the dynamics of the physical world, provide rich first-hand materials for analysis, ensure the authenticity and real-time nature of data, capture social media insights, use NLP sentiment analysis technology to analyze massive content, understand social public opinion, broaden the breadth and depth of data, enhance the comprehensiveness of information, provide a multi-dimensional perspective for subsequent risk assessment, implement privacy compliance protection, rely on the federated learning framework, encrypt data, achieve secure and compliant circulation, avoid privacy risks, ensure the legality and stability of the process, protect the rights and interests of data subjects, and lay a data security foundation for risk assessment in the digital economy.

[0031] 2. Through the market entity behavior simulation link, the present invention uses reinforcement learning agents and a large amount of historical data to accurately restore the behavior pattern, enabling market entities to anticipate the situation in advance, seize the decision-making opportunity in the complex business ocean, and effectively enhance their competitiveness. Secondly, in the regulatory policy impact estimation step, Monte Carlo simulation is used to closely track regulations, simulate the policy impact from multiple dimensions, customize response strategies for entities, help them plan ahead, calmly cope with policy fluctuations, and reduce business risks. Finally, in the model optimization and upgrade breakthrough stage, the quantum annealing algorithm is introduced to break through the limitations of traditional algorithms, improve accuracy and adaptability, enabling the model to handle different scenarios with ease, helping enterprises shape a leading edge and consolidate their market position.

[0032] 3. Through the initial construction of the risk knowledge base and risk annotation, the present invention uses cutting-edge technologies to mine a large amount of information and accurately annotate new risks such as the copyright risk of AI-generated content, injecting vitality of the times into the knowledge base and building a knowledge framework that keeps pace with the times. Second, in model iteration and knowledge graph optimization, federated learning breaks data islands, aggregates data to promote model upgrade and optimize the knowledge graph, integrates diverse knowledge to broaden its boundaries, and makes risk awareness more comprehensive and in-depth. Third, in improving the efficiency of knowledge application, the neuro-symbolic system combines two capabilities, enabling the knowledge base to output knowledge flexibly and efficiently, helping to quickly respond in complex risk scenarios, and effectively enhancing the practicality of risk control. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flowchart of the risk assessment method based on the digital economy of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] As Figure 1 shown, the embodiments of the present invention provide the following specific steps for a risk assessment method based on the digital economy:

[0036] S1: Cross-modal data federated collection: Deploy blockchain nodes to mine the value of on-chain transactions, access IoT device logs through 5G edge computing, capture social media graphs using NLP sentiment analysis, and at the same time rely on the federated learning framework to achieve circulation on the basis of ensuring data privacy and ensuring that the data is available but invisible, effectively avoiding privacy compliance risks;

[0037] S2: Dynamic Deconstruction of Risk Factors: At the technical level, focus on the potential risks of AI and APIs to accurately locate vulnerabilities in cutting-edge technologies; at the economic level, closely monitor token and NFT metrics to measure the fluctuations of the virtual economy; at the social level, pay attention to public opinion and labor risks to grasp the key social points. The collaboration of all levels makes joint efforts to lay a solid foundation for accurate risk prevention and control;

[0038] S3: Dynamic Modeling Empowered by Game Theory: On the one hand, use reinforcement learning agents to deeply simulate the behaviors of market players to help them find optimal decisions in complex situations; on the other hand, use Monte Carlo simulations to rigorously estimate the impacts of regulatory policies and plan countermeasures in advance. The addition of the quantum annealing algorithm breaks through traditional bottlenecks, efficiently optimizes the equilibrium solution, and boosts the accuracy and adaptability of the model;

[0039] S4: Generation of Real-time Risk Heat Maps: With the cutting-edge ST-GCN technology, transform it into an intuitive three-dimensional risk heat map, with the time dimension refreshed in milliseconds to instantly grasp risk dynamics; in the space dimension, with the help of metaverse geographical mapping, visually present the risk distribution in the virtual space; in the domain dimension, focus on the cross-infection index in the DeFi domain to clarify the risk transmission path, and comprehensively assist decision-makers in making accurate decisions;

[0040] S5: Edge Intelligence Decision-making Center: Innovatively deploy lightweight AI models to edge devices and design three response modes according to scenarios. When the risk exceeds the threshold, the automatic fusing mode quickly cuts off the source through smart contracts; the human-machine collaboration mode relies on the AR visual decision-making cabin to enable humans and machines to jointly handle complex situations; the cross-chain early warning mode uses multi-blockchain broadcasts to achieve coordinated linkage of risk prevention and control;

[0041] S6: Evolution of Dynamic Knowledge Graphs: Focus on the continuous accumulation and update of knowledge, and strive to build a self-evolving risk knowledge base. Use cutting-edge technologies to automatically label new risks, such as the copyright risk of AI-generated content, keeping up with the times. At the same time, promote model iteration with federated learning, optimize the knowledge graph, integrate multi-source knowledge, and use a neuro-symbolic system to integrate rule reasoning and deep learning to improve the efficiency of knowledge application;

[0042] S7: Sandbox Verification and Counterfactual Deduction: Put the proposed strategies and models into it, simulate real scenarios and repeatedly verify to confirm their effectiveness and stability. At the same time, carry out counterfactual deductions, assume different conditions, and explore the possible results of various decision-making paths, laying a solid foundation for optimizing decisions and improving models, and helping to forge ahead steadily in the complex market.

[0043] Among them, the specific steps of cross-modal data federated collection in S1 are as follows:

[0044] Step 1: Start multi-source data collection: Deploy blockchain nodes first, and use their decentralized and tamper-proof characteristics to deeply explore the potential commercial value and key information of capital flow behind the on-chain transaction data. At the same time, with the powerful connection capabilities of 5G edge computing with ultra-high speed and low latency, seamlessly access the real-time status log of IoT devices, accurately capture the real-time dynamics of the physical world, and reserve first-hand information for subsequent analysis;

[0045] Step 2: Capture social media insights: Use sentiment analysis technology in natural language processing (NLP) to capture semantic graphs on social media platforms. By analyzing massive amounts of text, comments, and topic content, we can gain insights into public sentiment, hot topics, and brand reputation and social public opinion information, further broaden the breadth and depth of data, and make the collected information more comprehensive.

[0046] Step 3: Implementation of privacy compliance assurance: After completing multi-source data collection, it is particularly critical to build a data circulation channel based on the federated learning framework. This framework strictly adheres to the principle of data availability but invisibility, and encrypts data from all parties to enable data to flow safely and compliantly between different entities without exposing the original privacy, cleverly avoiding privacy compliance risks and ensuring that the entire process is legal and robust.

[0047] This process is divided into three steps. The first step is to use blockchain and 5G edge computing to collect on-chain transactions and IoT device data and reserve first-hand information; the second step is to use NLP sentiment analysis to capture social media semantic graphs, gain insight into public opinion, and broaden the breadth and depth of data; the third step is to rely on the federated learning framework to encrypt data, achieve safe and compliant circulation, avoid privacy risks, and ensure the legality and stability of the process.

[0048] Among them, the dynamic deconstruction of risk factors in S2 refers to the fact that in today's complex and changeable risk landscape, it is crucial to build a comprehensive risk prevention and control system. First, at the technical level, the professional team focuses on the black box degree of AI algorithms and the vulnerability of API interfaces. Through rigorous code review and simulated attack testing methods, it accurately locates the loopholes that may appear in the actual application of cutting-edge technologies to prevent them before they happen. At the economic level, professional analysts keep a close eye on the token liquidity entropy and the NFT valuation bubble index, and use advanced quantitative models combined with real-time market dynamics to accurately measure the potential unstable factors in the virtual economy and predict the outbreak point of the economic crisis in advance. At the social level, the public opinion monitoring team always pays attention to the fission coefficient of public opinion dissemination and captures the direction of public opinion in a timely manner. At the same time, legal experts control digital labor compliance risks and protect the rights and interests of workers. All levels work closely together to create a solid barrier and a solid foundation for accurate risk prevention and control.

[0049] In the dynamic deconstruction of risk factors in S2, it is of great significance to build a risk prevention and control system. The professional team at the technical level uses code review and simulated attacks to deeply explore the vulnerabilities of AI and APIs; analysts at the economic level use quantitative models to closely monitor the unstable factors measured by token and NFT indicators; the public opinion team at the social level collaborates with legal experts. The former captures the trend of public opinion, and the latter controls the labor compliance risks. All levels cooperate to firmly build the foundation for precise prevention and control.

[0050] Among them, the specific steps of the dynamic modeling empowered by game theory in S3 are as follows:

[0051] Step 1: Simulation of market entity behavior: Using reinforcement learning agent technology, deeply explore the complex texture of the market environment, collect a large amount of historical data, covering the trading habits and strategy preference information of market entities, and build a fine model based on this to accurately restore the behavior patterns of market entities in different situations, helping them gain insight into the opportunities in the unpredictable situation and approach the optimal decision-making path;

[0052] Step 2: Estimation of the impact of regulatory policies: Enable the Monte Carlo simulation method, closely track regulatory dynamics, parameterize various possible regulatory policies, and input them into the model for multiple simulation operations. Rigorously estimate the impact of the shock from multiple dimensions, including policy intensity, implementation rhythm, and market feedback, and customize coping strategies for market entities in advance, enabling them to plan ahead and calmly face policy changes;

[0053] Step 3: Breakthrough in model optimization and upgrade: Introduce the quantum annealing algorithm, a cutting-edge scientific and technological tool, to directly address the efficiency bottleneck of traditional algorithms in solving multi-objective game equilibrium solutions. Relying on its unique quantum tunneling characteristics, quickly traverse the complex solution space, efficiently screen out the optimal equilibrium solution, greatly improve the accuracy of the model, broaden its adaptability to different market scenarios, and reshape the competitive advantage.

[0054] These three steps empower market entities to cope with complex situations. First, use reinforcement learning agents to simulate the behavior of market entities, collect a large amount of data to build a fine model, helping them gain insight into opportunities and approach the optimal decision. Second, use Monte Carlo simulation to track regulatory dynamics, parameterize policy simulation operations, and customize coping strategies in advance, enabling the entity to respond calmly. Finally, introduce the quantum annealing algorithm to break through traditional limitations, improve the accuracy and adaptability of the model, and reshape the competitive advantage.

[0055] Among them, the real-time risk heat map generation in S4 refers to that in the field of risk control, the cutting-edge ST-GCN technology shows its prowess. It meticulously draws intricate risk data into an intuitive three-dimensional risk heat map, achieving a millisecond-level rapid refresh on the time axis, enabling decision-makers to instantly capture the ever-changing risks. At the spatial level, it cleverly uses the metaverse geographical mapping to clearly present the hidden distribution of risks in the virtual space. Focusing on the domain dimension of the cross-infection index in the DeFi field, it precisely disassembles the risk transmission link and empowers decision-makers to make accurate decisions in all aspects.

[0056] With the cutting-edge ST-GCN technology, complex risk data is transformed into a three-dimensional heat map, refreshed at the millisecond level in terms of time, helping decision-makers grasp the risk dynamics in real time. At the spatial level, the metaverse mapping is used to show the virtual risk distribution. The domain dimension focuses on the DeFi index to disassemble the transmission link, comprehensively assisting in accurate decision-making.

[0057] Among them, the edge intelligent decision-making center in S5 refers to boldly and skillfully deploying a lightweight AI model to edge devices, enabling them to quickly respond to local risk situations. Three response modes are carefully designed according to different scenario characteristics: Once the risk value exceeds the set threshold, the automatic fuse mode is immediately activated, and the efficient execution power of the smart contract is used to quickly cut off the risk source. In the human-machine collaboration mode, with the help of the AR visualization decision-making cabin, operators and intelligent machines closely cooperate to jointly solve complex problems. The cross-chain early warning mode uses multi-blockchain broadcasting to break information islands and achieve coordinated linkage for cross-regional risk prevention and control.

[0058] In the edge intelligent decision-making center of S5, deploying a lightweight AI model to edge devices can quickly respond to local risks. Three modes are set according to scenarios: When the risk exceeds the threshold, the automatic fuse mode uses the smart contract to cut off the source; the human-machine collaboration mode relies on the AR visualization decision-making cabin for human-machine cooperation to solve problems; the cross-chain early warning mode uses multi-blockchain broadcasting to break the islands and achieve coordinated linkage for cross-regional risk prevention and control.

[0059] Among them, the specific steps of the dynamic knowledge graph evolution in S6 are as follows:

[0060] Step 1: Initial construction of the risk knowledge base and risk annotation: Focusing on the requirement of continuous knowledge update, start the construction project of the self-evolving risk knowledge base. Using cutting-edge technologies such as natural language processing and image recognition, screen and mine massive information, and automatically and accurately annotate new risks, especially the current hot AI-generated content copyright risks, injecting fresh and timely blood into the knowledge base and initially building a knowledge reserve framework.

[0061] Step 2: Model Iteration and Knowledge Graph Optimization: Introduce federated learning technology to break data silos, enabling the convergence and utilization of scattered data. Based on this, drive the iterative upgrade of the model, continuously optimize the knowledge graph structure during the iteration process to accurately reflect the relationships between risk factors, and at the same time integrate multi-source heterogeneous knowledge, including industry reports, academic research, and case studies, to broaden the breadth and depth of knowledge;

[0062] Step 3: Enhancement of Knowledge Application Efficiency: Leverage the unique advantages of the neuro-symbolic system to organically integrate the rigorous logic of rule-based reasoning and the powerful learning and induction capabilities of deep learning. On the one hand, strengthen the understanding and derivation of knowledge, and on the other hand, facilitate the rapid and flexible application of knowledge, enabling the knowledge base to no longer be static storage but to efficiently output in complex and changing risk scenarios, effectively enhancing the knowledge application efficiency.

[0063] Building a risk control system is divided into three steps: First, use cutting-edge technologies to initiate the construction of a risk knowledge base, accurately label new risks, such as AI copyright risks, and build a knowledge framework; Second, introduce federated learning to converge data, iterate the model, optimize the knowledge graph, and integrate diverse knowledge to broaden the boundaries; Finally, leverage the neuro-symbolic system to integrate reasoning and learning capabilities to enhance the application efficiency of the knowledge base in complex scenarios.

[0064] Among them, the sandbox verification and counterfactual reasoning in S7 refer to first, placing the carefully formulated strategies and cutting-edge models into a specially built simulation environment, and referring to the real market scenario, conducting multiple rounds of comprehensive verification to strictly confirm their effectiveness and stability to ensure absolute safety. At the same time, innovatively carry out counterfactual reasoning, boldly assume various different conditions, deeply explore the possible results under diverse decision-making paths, accurately analyze the advantages and disadvantages, and lay a solid foundation for subsequent decision-making optimization and model improvement, helping to forge ahead steadily in the complex market.

[0065] In the sandbox verification and counterfactual reasoning of S7, first place the formulated strategies and cutting-edge models in the simulation environment, conduct multiple rounds of verification with reference to the real scenario to ensure their effectiveness and stability. At the same time, pioneeringly carry out counterfactual reasoning, boldly assume different conditions, deeply explore the results of diverse decision-making paths, carefully analyze the advantages and disadvantages, and lay a solid foundation for optimizing decisions and improving models, helping to move forward calmly in the complex market.

[0066] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0067] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A risk assessment method based on the digital economy, characterized in that: The specific steps of the digital economy-based risk assessment method are as follows: S1: Cross-modal data federation collection: Deploy blockchain nodes to mine the transaction value on the chain, use 5G edge computing to access IoT device logs, use NLP sentiment analysis to capture social media graphs, and rely on the federated learning framework to achieve data circulation on the basis of protecting data privacy and ensuring that data is available but invisible, effectively avoiding privacy compliance risks; S2: Dynamic deconstruction of risk factors: The technical layer focuses on AI and API hidden dangers, accurately locates cutting-edge technology vulnerabilities; the economic layer keeps a close eye on tokens and NFT indicators, measures virtual economic fluctuations, and the social layer pays attention to public opinion and labor risks, controls key social points, and all layers work together to lay a solid foundation for accurate risk prevention and control; S3: Dynamic modeling empowered by game theory: On the one hand, the behavior of market players is deeply simulated by reinforcement learning agents to help them find the best decision in complex situations; on the other hand, Monte Carlo simulation is used to rigorously estimate the impact of regulatory policies and plan countermeasures in advance. The addition of quantum annealing algorithm breaks through traditional bottlenecks and efficiently optimizes equilibrium solutions, making the model accuracy and adaptability soar; S4: Real-time risk heat map generation: With the cutting-edge ST-GCN technology, it is transformed into an intuitive three-dimensional risk heat map. The time dimension is refreshed at the millisecond level, and the risk dynamics are instantly grasped; the spatial dimension uses the metaverse geographical mapping to intuitively present the risk distribution of virtual space, and the field dimension focuses on the cross-infection index in the DeFi field, clarifies the risk transmission path, and assists decision makers in making accurate decisions in all aspects; S5: Edge Intelligent Decision Center: Innovatively deploys lightweight AI models to edge devices and designs three response modes based on scenarios. When the risk exceeds the threshold, the automatic fuse mode uses smart contracts to quickly cut off the source, and the human-machine collaboration mode relies on AR visualization decision cabins to allow humans and machines to work together to deal with complex situations. The cross-chain early warning mode uses multi-blockchain broadcasting to achieve coordinated risk prevention and control; S6: Dynamic knowledge graph evolution: Focus on the continuous accumulation and updating of knowledge, make every effort to build a self-evolving risk knowledge base, use cutting-edge technology to automatically label new risks, and AI to generate content copyright risks, keep up with the pace of the times. At the same time, use federated learning to promote model iteration, optimize knowledge graphs, integrate multi-source knowledge, and use neural symbolic systems to integrate rule reasoning and deep learning to improve the efficiency of knowledge application; S7: Sandbox verification and counterfactual reasoning: Put the proposed strategies and models into the sandbox, simulate real scenarios for repeated verification, and confirm their effectiveness and stability. At the same time, conduct counterfactual reasoning, assume different conditions, and explore the possible results of various decision-making paths, so as to lay a solid foundation for optimizing decisions and improving models, and help to make steady progress in complex markets.

2. The risk assessment method based on the digital economy according to claim 1, characterized in that: The specific steps of cross-modal data federation collection in S1 are as follows: Step 1: Start multi-source data collection: First deploy blockchain nodes, and use their decentralized and tamper-proof characteristics to deeply explore the potential commercial value and key information of capital flow behind the on-chain transaction data. At the same time, with the powerful connection capabilities of 5G edge computing with ultra-high speed and low latency, seamlessly access the real-time status log of IoT devices, accurately capture the real-time dynamics of the physical world, and reserve first-hand information for subsequent analysis; Step 2: Capture social media insights: Use sentiment analysis technology in natural language processing (NLP) to capture semantic graphs on social media platforms. By analyzing massive amounts of text, comments, and topic content, we can gain insights into public sentiment, hot topics, and brand reputation and social public opinion information, further broaden the breadth and depth of data, and make the collected information more comprehensive. Step 3: Implementation of privacy compliance assurance: After completing multi-source data collection, it is particularly critical to build a data circulation channel based on the federated learning framework. This framework strictly adheres to the principle of data availability but invisibility, and encrypts data from all parties to enable data to flow safely and compliantly between different entities without exposing the original privacy, cleverly avoiding privacy compliance risks and ensuring that the entire process is legal and robust.

3. A risk assessment method based on the digital economy according to claim 1, characterized in that: The dynamic deconstruction of risk factors in S2 refers to the fact that in today's complex and ever-changing risk landscape, it is crucial to build a comprehensive risk prevention and control system. First, at the technical level, the professional team focuses on the black box degree of AI algorithms and the vulnerability of API interfaces. Through rigorous code review and simulated attack testing methods, they accurately locate the loopholes that may appear in the actual application of cutting-edge technologies to prevent them before they happen. At the economic level, professional analysts keep a close eye on the token liquidity entropy and the NFT valuation bubble index, use advanced quantitative models, combine real-time market dynamics, accurately measure the potential unstable factors in the virtual economy, and predict the outbreak point of economic crises in advance. At the social level, the public opinion monitoring team always pays attention to the fission coefficient of public opinion dissemination and captures the direction of public opinion in a timely manner. At the same time, legal experts control digital labor compliance risks and protect the rights and interests of workers. All levels work closely together to create a solid barrier and a solid foundation for accurate risk prevention and control.

4. A risk assessment method based on the digital economy according to claim 1, characterized in that: The specific steps of the game theory-enabled dynamic modeling in S3 are as follows: Step 1: Simulate the behavior of market players: Use reinforcement learning agent technology to delve into the complex texture of the market environment, collect massive historical data, and cover the trading habits and strategy preference information of market players. Based on this, build a sophisticated model to accurately restore the behavior patterns of market players in different situations, helping them to gain insight into opportunities in turbulent situations and approach the optimal decision-making path; Step 2: Prediction of regulatory policy impact: Use the Monte Carlo simulation method to closely track regulatory dynamics, parameterize various possible regulatory policies, input them into the model for multiple simulations, and rigorously predict the impact of the impact from multiple dimensions, including policy intensity, implementation rhythm, and market feedback. Tailor-made response strategies for market players in advance so that they can prepare for a rainy day and calmly face policy changes; Step 3: Model Optimization and Upgrade Breakthrough: Introduce the cutting-edge technology of quantum annealing algorithm, which directly addresses the efficiency bottleneck of traditional algorithms in solving multi-objective game equilibrium solutions. Relying on its unique quantum tunneling characteristics, it can quickly traverse complex solution spaces, efficiently screen out the optimal equilibrium solutions, significantly improve the accuracy of the model, broaden its adaptability to different market scenarios, and reshape the competitive advantage.

5. A risk assessment method based on the digital economy according to claim 1, characterized in that: The generation of the real-time risk heat map in S4 refers to that in the field of risk control, the advanced ST-GCN technology shows its prowess, meticulously mapping intricate risk data into an intuitive three-dimensional risk heat map. On the time axis, it can be refreshed at the millisecond level, enabling decision-makers to instantly capture the rapid changes of risks. At the spatial level, it cleverly uses the metaverse geographical mapping to clearly present the hidden distribution of risks in the virtual space. Focusing on the domain dimension of the cross-infection index in the DeFi field, it precisely disassembles the risk conduction link, comprehensively empowering decision-makers to make accurate decisions.

6. The risk assessment method based on the digital economy according to claim 1, characterized in that: The edge intelligent decision-making center in S5 means boldly deploying lightweight AI models to edge devices, enabling them to quickly respond to local risk situations. Three response modes are carefully designed according to different scenario characteristics: Once the risk value exceeds the set threshold, the automatic fusing mode is immediately activated, and the efficient execution power of smart contracts is used to quickly cut off the risk source. In the human-machine collaboration mode, with the help of the AR visualization decision-making cabin, operators and intelligent machines closely cooperate to jointly overcome complex problems. The cross-chain warning mode uses multi-blockchain broadcasting to break information silos and achieve coordinated linkage for cross-regional risk prevention and control.

7. A risk assessment method based on the digital economy according to claim 1, characterized in that: The specific steps of the dynamic knowledge graph evolution in S6 are as follows: Step 1: Initial construction of the risk knowledge base and risk annotation: Focusing on the requirement of continuous knowledge update, start the construction project of the self-evolving risk knowledge base. Use natural language processing and image recognition cutting-edge technologies to screen and mine massive information, automatically and accurately annotate new risks, especially the current hot AI-generated content copyright risks, inject fresh and timely blood into the knowledge base, and initially build a knowledge reserve framework. Step 2: Model iteration and knowledge graph optimization: Introduce federated learning technology to break data silos, enabling scattered data to be aggregated and utilized. Based on this, promote model iteration and upgrade. During the iteration process, continuously optimize the knowledge graph structure to accurately reflect the relationships between risk factors, and at the same time integrate multi-source heterogeneous knowledge, including industry reports, academic research, and case analysis, to broaden the breadth and depth of knowledge. Step 3: Improvement of knowledge application efficiency: Relying on the unique advantages of the neuro-symbolic system, organically integrate the rigorous logic of rule reasoning and the powerful learning and induction ability of deep learning. On the one hand, strengthen the understanding and derivation of knowledge, and on the other hand, facilitate the rapid and flexible application of knowledge, so that the knowledge base is no longer a static storage, but can efficiently output in complex and changing risk scenarios, effectively improving the knowledge application efficiency.

8. A risk assessment method based on the digital economy according to claim 1, characterized in that: The sandbox verification and counterfactual deduction in S7 refer to: First, carefully formulated strategies and cutting-edge models are placed in a specially built simulation environment. Referring to the real market scenario, multiple rounds of comprehensive verification are carried out to strictly confirm their effectiveness and stability to ensure absolute safety. At the same time, counterfactual deduction is innovatively carried out, boldly assuming various different conditions, deeply exploring the possible results under diverse decision-making paths, accurately analyzing the pros and cons, laying a solid foundation for subsequent decision-making optimization and model improvement, and helping to forge ahead steadily in the complex market.

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