A Method and System for Real-Time Macroeconomic Monitoring and Control Based on Value Codes and Privacy Computation

Abstract

This invention belongs to the fields of macroeconomic management and artificial intelligence big data technology, specifically involving a method and system for real-time macroeconomic monitoring and regulation based on value codes and privacy computing. This system encodes micro-level individual economic behaviors using value codes, and utilizes privacy computing and data monetization mechanisms to achieve full integration of national income, consumption, and investment data. It constructs a national economic big data accounting system based on micro-level foundations, and uses this to train a large-scale macroeconomic model, applicable to national or regional economic operation monitoring, policy simulation and deduction, and precise regulation.

Description

Technical Field

[0001] This invention belongs to the fields of macroeconomic management and artificial intelligence big data technology, specifically involving a method and system for real-time monitoring and regulation of macroeconomics based on value codes and privacy computing. Background Technology

[0002] Against the grand historical backdrop of the accelerated evolution of human socio-economic forms from industrial civilization to digital civilization, macroeconomic regulation, as the "brain" and "central nervous system" of the national governance system, directly relates to the stable operation of the national economy and the optimal allocation of social resources through its scientific nature, foresight, and accuracy. In the traditional economic governance framework, the macroeconomic regulation system primarily relies on various macroeconomic indicators released by national statistical departments and the central bank as the basis for decision-making. These indicators cover key dimensions such as GDP, the Consumer Price Index (CPI), the Purchasing Managers' Index (PMI), and broad money supply, forming a "dashboard" for policymakers to perceive the economic climate. However, the acquisition and generation of these indicators have long followed the statistical model established in the industrial era: "bottom-up reporting" and "based on stratified sampling surveys." With the booming development of the global digital economy, the frequency of economic activity has evolved from traditional monthly and weekly cycles to second-level or even millisecond-level cycles. The complexity of economic structures exhibits characteristics of networking, decentralization, and virtualization, and the interconnections and transmission mechanisms between industries are becoming increasingly intricate. In this new economic landscape, existing macroeconomic control models based on traditional statistical sampling theory and lagged data aggregation are gradually encountering a series of deep-seated epistemological and methodological bottlenecks, such as inaccurate measurement, perception lag, transmission obstruction, and feedback failure. Faced with a massive surge of heterogeneous micro-level transactions, traditional methods struggle to penetrate the fog of aggregate data to understand structural changes, leading to macroeconomic policies often facing the risks of time lag effects and the fallacy of composition. Specifically, the limitations and technical defects of existing technological solutions in addressing the challenges of macroeconomic governance in the digital age are mainly reflected in the following three aspects.

[0003] The first category is the macroeconomic accounting system based on traditional statistical sampling surveys and administrative report aggregation. This type of system is the institutional cornerstone for economic management and policy-making by governments worldwide. Its technical approach mainly relies on stratified sampling questionnaires of key large-scale enterprises and typical household representatives, using statistical inference algorithms to estimate the overall economic situation. Although this system has played an important role in long-term historical practice, its inherent endogeneity defects have become increasingly prominent under the impact of the digital wave. First, the timeliness of the data suffers from a serious lag effect, which is known as "statistical time lag."

[0004] Core indicators such as GDP and fixed asset investment are typically released quarterly or annually. Data collection involves lengthy administrative processes, including grassroots reporting, local aggregation, data cleaning and accounting, and smoothing adjustments. This results in the final data often lagging behind actual economic performance by weeks or even months. When faced with sudden financial risks (such as liquidity crises) or external shocks (such as supply chain disruptions), policymakers can only rely on historical images in a "hindsight mirror" for intervention, often missing the optimal window for policy intervention. Secondly, the problem of "averages masking structural contradictions" caused by overly coarse data granularity is becoming increasingly prominent. Traditional aggregate statistical models tend to focus on macroeconomic averages, masking key structural characteristics such as uneven regional development, differentiated industry prosperity, and differences in marginal propensity to consume among different income groups. For example, a robust aggregate growth figure may mask the survival difficulties of small and medium-sized enterprises (SMEs). This lack of granularity makes it difficult for fiscal and monetary policies to be precisely targeted, often resulting in a "one-size-fits-all" approach. Thirdly, statistical errors and data black holes are difficult to avoid. In emerging fields such as the platform economy, gig economy, and data element trading, a large amount of economic activity exists in the form of fragmented and informal employment. These new economic forms exist outside of traditional directories and statistical standards, leading to systemic biases in national economic accounting due to insufficient coverage. Finally, the lack of a microeconomic foundation is a fatal flaw in the traditional system. There is a huge data gap between macroeconomic aggregate data and microeconomic enterprise and household bank account transactions and tax records. There is a lack of a fundamental logical mapping model that can directly penetrate from the behavior of micro-entities to macroeconomic performance, making macroeconomic regulation lack the support of microeconomic mechanisms.

[0005] The second category is economic monitoring platforms based on internet big data and single-domain data. To compensate for the shortcomings of traditional statistical data, in recent years, some commercial technology giants, financial institutions, and research think tanks have begun to utilize their high-frequency big data to assist in assessing the economic situation. This data includes industrial electricity consumption from the power sector, freight indices from logistics platforms, keyword popularity from search engines, and fund flows from third-party payment platforms. These monitoring methods based on "digital footprints" have improved the timeliness of economic perception to some extent, achieving a leap from lagging statistics to real-time monitoring. However, when used as the main channel for macroeconomic control, they still have significant technological limitations and logical loopholes. First, the data sources are highly biased and suffer from a severe "data silo" effect. While e-commerce platforms possess detailed consumer data, they lack income and savings data; commercial banks possess credit and savings data, but struggle to track the specific destinations of funds; tax authorities possess corporate tax data, but find it difficult to cover non-tax-related cash flows and hidden transactions. Due to the lack of a unified data view that connects the entire chain of income, consumption, and investment, existing monitoring platforms cannot verify that "total income = total consumption + total savings." This fundamental identity in national economic accounting leads to a lack of logical consistency among data points, making it difficult to form a complete economic closed-loop view. Secondly, it lacks rigorous economic theoretical support and causal inference capabilities. Current big data economics is more based on trend extrapolation and current prediction of correlations than on structured model analysis based on causal relationships. While machines can identify the correlation between declining electricity consumption and economic slowdown, they cannot explain whether the underlying driving force is insufficient demand or supply shocks. Therefore, it is difficult to conduct counterfactual inferences and policy simulations, and cannot answer core decision-making questions such as "how will the economy react if interest rates are cut by 25 basis points?" Thirdly, the paradox of privacy protection and data aggregation remains unresolved. Personal and corporate financial data are highly sensitive privacy information and are strictly protected by laws and regulations. In the absence of effective privacy protection technologies based on cryptography (such as multi-party secure computation and homomorphic encryption) and a reasonable data element benefit distribution mechanism, data subjects lack the willingness to share data, resulting in difficulties in data ownership confirmation, circulation, and aggregation across society. This makes it impossible to form a full-sample, full-dimensional national economic big data database, and can only rely on fragmented samples for "blind men and the elephant" style analysis.

[0006] The third category is policy analysis systems based on traditional econometric models (such as DSGE models). Dynamic stochastic general equilibrium (DSGE) models dominate the policy analysis toolboxes of academia and central banks. These models attempt to construct a macroeconomic framework based on microeconomic optimization. While they possess rigorous logical consistency in theory, they fall short in addressing the high-dimensional heterogeneity of the digital age. First, the parameter calibration and estimation of traditional models often rely on low-frequency historical macroeconomic time-series data, making it difficult to capture the nonlinear dynamic changes in economic structure under technological shocks or institutional reforms. This is the real-world manifestation of the famous "Lucas critique" in the digital context. Second, for the sake of mathematical feasibility, traditional models generally employ strong assumptions of "rational expectations" and "representative agents," assuming that all consumers and firms in the economy have the same preferences and behavioral patterns and can make perfectly rational predictions about the future. This assumption greatly simplifies reality but ignores the widespread heterogeneity, bounded rationality, and complex interactions in the real economy. For example, different income groups have drastically different sensitivities to inflation, and firms with different technological levels react very differently to credit policies. Ignoring these micro-level heterogeneities prevents models from accurately simulating the distributive effects of policies across different groups. Finally, the integration of existing macroeconomic models with the emerging Large Scale Models (LLMs) technology is still in its early stages. Currently, there is a lack of an integrated regulatory system capable of directly mapping the massive transactional behavior of hundreds of millions of micro-level individuals to macroeconomic conditions and achieving "emergent" predictions of the economic system through large-scale parameter training. Traditional models struggle to handle high-dimensional, non-uniform... Structured micro-behavioral data cannot leverage AI's powerful pattern recognition and generation capabilities to simulate the evolutionary path of economic systems.

[0007] In summary, the existing macroeconomic regulation technology system is at an awkward juncture of transition: on the one hand, the traditional statistical survey system, hampered by data lag, crudeness, and errors, is increasingly unable to adapt to the rapid changes of the digital economy; on the other hand, emerging big data monitoring methods are limited by data silos, theoretical deficiencies, and privacy barriers, making it difficult to form a systematic alternative; and mainstream econometric models are trapped in a dilemma of theoretical assumptions being disconnected from real-world data. The times urgently call for a new technological paradigm—a systematic solution that can start from the "value atoms" (income, consumption, investment) of micro-entities, utilize advanced privacy computing and data element technology to achieve secure hosting of all data, and build a digital accounting foundation based on a strict national economic identity, thereby training a macroeconomic model with deductive and decision-making capabilities. This is not only a technological innovation but also a crucial leap in the transformation of macroeconomic governance concepts from "experience-based decision-making" to "data intelligence," which is precisely the core technical problem and historical mission that this invention aims to solve. Existing technologies lack precise metrics for the value contribution of micro-entities. This invention proposes an invention value code as a measure of human value. Its technical essence is to construct a multidimensional tensor space. It maps discrete, unstructured economic behavior into standardized scalars with statistical completeness, providing an 'atomic' level of measurement standard for macroeconomic regulation and completely solving the problem of 'observation bias' in econometrics. Summary of the Invention

[0008] This invention aims to overcome the problems in the existing macroeconomic regulation system, such as poor data timeliness, lack of microeconomic foundation, prominent contradiction between data privacy and integration, and lack of precise simulation of regulation methods.

[0009] The specific technical problems that need to be solved include: To address the heterogeneity and semantic ambiguity of personal income, consumption, and investment data across different scenarios, and to achieve a digital mapping of the microeconomic foundation of the national economy; To address the challenges of data subjects lacking incentives for sharing and the difficulty in securely aggregating sensitive and private data, and to achieve full integration of national income statistics; To solve the problems of error and omission in traditional statistical accounting and realize a real-time dynamic mirror of the macroeconomic operation status; It addresses the problem that traditional econometric models cannot handle high-dimensional heterogeneous data, enabling scenario-based simulations, dynamic forecasting, and precise regulation of economic policies.

[0010] To address the aforementioned technical problems, this invention provides a macroeconomic control system based on value codes and its implementation method. This system is not merely a passive data statistics tool, but an active, self-evolving digital economy operating system. Its core technical logic lies in using "value codes," a micro-data carrier, to break down the "black box" between individual economic behavior and macroeconomic aggregates. This invention addresses the issue of data supply motivation by leveraging a data assetization incentive mechanism, solves the problem of statistical lag by utilizing a real-time big data accounting engine, and tackles the simulation challenge of policy transmission mechanisms by employing large-scale artificial intelligence models. The following section will elaborate and describe in detail the technical solutions employed in this invention, strictly adhering to the chronological logic of data flow and system construction.

[0011] (1) Constructing a standardization and coding model for national economic microeconomic data based on value codes. The first step in implementing this invention is to establish a standard system of "macroeconomic control value codes" that can accurately describe the smallest economic behavior unit of economic entities. This system is not a simple digital record, but a digital ontology model constructed based on the United Nations System of National Accounts (SNA2008) and the principles of modern microeconomic econometrics. The system first defines the general data structure of the value code, which includes, but is not limited to, a globally unique identifier (GUID), a distributed digital identity (DID), a spatiotemporal stamp, a value attribute domain, and a smart contract hook. On top of this general structure, the system further subdivides into three core business domain codes: income value codes, consumption value codes, and investment value codes. Specifically, the generation of value codes follows the logic of a log-normal distribution. Step 1: Constructing the probability density function: Step 2, calculate the expected value center and variance: Step 3: Perform normalization mapping to convert the original behavior... Convert to value code scale value This process ensures the stability of the value code as a 'measure'. For income-related value codes, the system designs multi-dimensional attribute fields to record source information such as labor compensation, net property income, and transfer payments, and mandates the association of the payer's unified social credit code or government fiscal payment account. This constructs a complete link mapping between "primary distribution" and "redistribution," providing atomic data for the income approach to Gross National Income (GNI) accounting. For consumption-related value codes, the system incorporates eight categories of goods and services corresponding to the National Bureau of Statistics' Consumer Price Index (CPI) basket, recording transaction amounts, product SKUs, consumption scenarios (online / offline), and subjective satisfaction evaluations based on utility functions, thereby mapping "final consumption expenditure." For investment-related value codes, the system tracks asset allocation behaviors such as changes in savings deposits, purchases of wealth management products, equity and debt investments, and real estate purchases, recording the flow and term structure of funds, thereby mapping gross capital formation and savings. To ensure data security and interoperability, all value codes are encrypted using national cryptographic algorithms and embedded with programmable smart contract hooks, supporting cross-system data verification and automatic execution. This establishes a standard "atom" for summarizing micro-level behaviors into macro-level totals, ensuring the homogeneity, additivity, and logical consistency of all data in the subsequent accounting system.

[0012] (2) Establish a data custody platform based on personal data monetization (Brazilian model) and privacy computing. Addressing the pain points of difficulty in collecting massive amounts of micro-data and high privacy protection requirements, this invention innovatively constructs a national or regional data element custody and value distribution platform. This platform introduces the operating model of a "personal data bank" or "data trust," establishing the data subject at both legal and technical levels. Individuals and businesses retain ultimate ownership of the data, while the platform only exercises custody and computation rights. To address the incentive compatibility issue of data supply, the system, based on the "Brazilian model" of data dividends, designs a data element revenue distribution algorithm based on blockchain token economics. This algorithm automatically calculates the user's data contribution index based on the completeness, continuity, authenticity, and depth of the data authorized for custody, and accordingly pays users "data tokens" or cash subsidies (such as direct tax deductions or basic income payments), thereby greatly stimulating public participation in national economic statistics. The system adopts Milgrom's information value equation. Let the prior benefits for macroeconomic decision-makers be: Access value code sequence Then, the posterior expected return is Monetization pricing of each value code satisfy: This algorithm enables precise allocation of data elements according to value metrics. At the underlying technical level, the hosting platform adopts a "zero-trust" architecture, fully deploying hardware isolation technology based on a Trusted Execution Environment (TEE) and a Multi-Party Secure Computation (MPC) protocol. After the user's raw transaction data is cleaned and anonymized on the local terminal, only the encrypted high-dimensional feature vectors are uploaded to the cloud-hosted repository. When the macro-control system performs statistical accounting and model training, it can only call the encrypted data for ciphertext calculations in a "usable but invisible" manner. The calculation results only output statistical totals or model parameters, and cannot be reversed to reconstruct any individual's specific privacy information. This architecture not only makes the platform a trust connector between micro-entities and the macro-control system, but also completely solves the persistent problems of low sample cooperation, data falsification, and privacy leaks in traditional statistical surveys.

[0013] (3) Develop a dynamic big data accounting engine based on the national economic identity. This system has a built-in dynamic big data accounting engine based on the basic principles of macroeconomics, which is the logical hub of the system. The core algorithm logic strictly follows the Keynesian theory of national income determination and the identity relationship in input-output analysis, that is, under the two-sector, three-sector and four-sector economic models, it verifies the dynamic balance of "total output = total income = total expenditure" and "investment (I) = savings (S)". The accounting engine accesses the data stream of hundreds of billions of value codes gathered by the hosting platform in real time through streaming computing frameworks (such as Apache Flink or Spark Streaming) and performs high-frequency calculations at the millisecond level. On the one hand, the engine performs the total amount accounting function based on micro-aggregation, and generates the daily or even hourly gross national income (GNI) indicator by aggregating the income value codes of all residents and enterprises in real time; and generates a real-time curve of total retail sales of consumer goods by aggregating the consumption value codes. On the other hand, the engine performs a more critical dynamic balance verification function, namely, constructing a cash flow table for the entire society and checking in real time whether the sources of funds (savings + taxes + imports) dynamically equal the uses of funds (investment + government spending + exports). When a significant statistical gap is detected on both sides of the identity, such as savings abnormally exceeding investment, it means that there is a serious shortage of effective demand and a deflationary gap in the economy. The system will automatically trigger a macroeconomic early warning mechanism and use big data drill-down technology to accurately locate the specific industry sectors, regional nodes, or individuals causing the imbalance. Group structure. This real-time accounting and logical verification based on full-volume micro data evolves macroeconomics from a "post-mortem report" relying on lagging data to a "dynamic physical examination" that senses the temperature of the economy in real time, greatly improving the timeliness and accuracy of macroeconomic monitoring.

[0014] (4) Training a macroeconomic model based on micro-heterogeneous agents (Macro-LLM). Utilizing the massive, high-dimensional, long-term micro-value code dataset accumulated in the preceding steps, this invention constructs and trains a macroeconomic AI model for a vertical domain. This model surpasses traditional text-based language models (LLM) in its architecture, integrating the time-series attention mechanism of Transformer with the correlation transmission characteristics of Graph Neural Networks (GNNs) to construct a virtual economic system containing billions of "heterogeneous agents." The model's input layer consists of encoded sequences of economic behaviors of billions of individuals, including their historical income changes, consumption preference shifts, and asset allocation paths. During the pre-training phase, the model deeply learns the behavioral response patterns of micro-entities under different income levels, price expectations, and policy environments through self-supervised learning tasks. For example, it learns "how the savings rate and home-buying intentions of people aged 30-40 in first-tier cities change when mortgage rates drop by 25 basis points." During the fine-tuning phase, the system inputs historical macroeconomic fluctuation data and policy intervention cases, using reinforcement learning (RLHF) algorithms to enable the model to grasp the evolutionary patterns of economic cycles and the lag effects of policy transmission. This large-scale model possesses powerful "emergence" capabilities, able to simulate the micro-decision interactions of countless individuals to generate macroeconomic states such as inflation, recession, and recovery from the bottom up. This marks a shift in macroeconomic research methods from DSGE models based on the "representative agent" assumption to a big data simulation paradigm based on "heterogeneous agents across the entire sample," capable of capturing the characteristics of nonlinear, irrational, and complex adaptive systems that traditional models cannot describe. The model uses the Nash algorithm to solve for response equilibrium. Each agent is defined. The utility function is: The system iteratively searches for a Nash equilibrium solution that satisfies the gradient zero-point condition. : At the same time, it can be done through Solving for the optimal policy This two-tiered optimization architecture ensures that policy projections can penetrate the complex psychology of individual decision-making.

[0015] (5) Construct a scientific, dynamic, and precise macroeconomic control decision support system. Based on a well-trained macroeconomic model, the system provides policymakers with a visualized policy simulation and automatic execution platform. Policymakers can input proposed policy parameters into the system, such as "reducing the reserve requirement ratio by 0.5 percentage points," "issuing consumption vouchers totaling 100 billion yuan to specific low-income groups," or "adjusting the special deduction standard for personal income tax." The macroeconomic model will quickly conduct multi-scenario Monte Carlo simulations in a virtual environment to simulate the transmission path of the policy at the micro level—that is, which groups will increase consumption, which enterprises will expand reproduction, whether funds will be idle—and the dynamic impact curve on macroeconomic aggregate indicators (such as GDP growth rate, CPI, and urban surveyed unemployment rate). Based on the simulation results, the system generates optimal policy combination recommendations. In the policy implementation stage, the system utilizes... The smart contract attributes embedded in the value code support precise, targeted regulation. For example, fiscal subsidies or monetary policy tools can directly penetrate to specific micro-entity accounts, and smart contracts can set restrictions on the use, timing, and flow path of funds. The system monitors the flow of policy funds and their marginal utility in real time. Once it detects that the policy effect deviates from expectations or arbitrage behavior occurs, the system will automatically provide feedback and trigger circuit breakers or adjustment mechanisms. This closed-loop regulation system ensures the precise achievement of macroeconomic control objectives and minimizes policy distortions and resource waste.

[0016] Beneficial effects This invention, by constructing a value code system and a data hosting mechanism, completely breaks down the barriers between micro-level behavioral data and macro-level statistical data, achieving real-time and full-sample national economic accounting, and greatly improving the timeliness and accuracy of macro-level data in reflecting economic reality. This invention introduces data monetization incentives and privacy computing technology, effectively solving the data silo and privacy protection problems of the big data era, and providing a sustainable data supply model for digital economic governance. Based on the national economic identity and artificial intelligence large-scale model technology, this invention innovates a digital macroeconomic system, enabling economic regulation to no longer rely on empirical judgments or lagging data, but rather on verifiable, deductive, and quantifiable scientific models, achieving a significant transformation in the regulation model, and possessing significant strategic importance for improving the modernization level of national governance capabilities. Detailed Implementation

[0017] This invention proposes a revolutionary digital macroeconomic control architecture. This architecture aims to fundamentally reshape the microeconomic foundations of macroeconomics by utilizing cutting-edge digital technologies such as blockchain, privacy computing, big data streaming, and large-scale artificial intelligence models. To facilitate a more detailed and clear understanding of the technical solution and its implementation, this paper provides a comprehensive and in-depth explanation of the specific implementation methods, combining the underlying theoretical model, complex network topology, and mathematical derivation logic of the core algorithms. This implementation method will strictly follow the data lifecycle flow logic, focusing on five core dimensions: the microeconomic data definition and collection network of value codes, the data custody and monetization incentive mechanism based on the "Brazilian model," the dynamic accounting engine based on the national economic identity, the training architecture of a large-scale macroeconomic model for heterogeneous entities, and the precise control decision-making and execution system.

[0018] The first step in implementing this invention is to construct a macroeconomic microeconomic foundational data layer that is fully covered, semantically unified, and possesses high fidelity, namely a distributed "value code" collection network. The essence of macroeconomics is... The emergence and summation of countless micro-individual transactions within a complex network necessitate the precise digital mapping and structured encapsulation of microeconomic behavior as the cornerstone of this system. This invention creatively defines the concept of a "value code," treating it as a standardized data container that carries rich economic meaning, is programmable, and tamper-proof. To comprehensively cover the accounting needs of the SNA (System of National Accounts), the system designs three core value code structures: income value code, consumption value code, and investment value code.

[0019] The income value code records in detail an individual's labor output and value return per unit of time. Its internal data structure encapsulates labor timestamps accurate to the second, labor nature codes based on the International Standard Industry Classification (ISIC), encrypted compensation amounts, the payer's distributed digital identity (DID), and real-time tax withholding information. This directly corresponds to the core data source of the income approach in GDP accounting and can accurately reflect workers' compensation and operating surplus.

[0020] Consumer value codes record an individual's spending behavior to meet their needs for survival and development. They include specific product SKU codes, service types, transaction amounts, geographical coordinates of the transaction, and subjective satisfaction evaluations based on utility functions. This forms the micro-foundation of expenditure-based GDP accounting and can reflect residents' consumption tendencies and price changes in real time.

[0021] The investment value code tracks the dynamic process by which individuals convert unconsumed income into assets, covering bank savings records, wealth management product purchase records, stock and bond transaction details, and real estate purchase information. This corresponds to the total savings and total capital formation in the macroeconomy.

[0022] To support the accurate mapping of the three core value codes between the physical and digital worlds, the system has built a hardware trust root architecture based on Physically Unclonable Functions (PUF) and multimodal biometric recognition at the underlying layer. In the specific data acquisition terminal design, the system requires all IoT devices connected to the macroeconomic monitoring network—including smart industrial production line controllers, retail terminals (POS), and personal mobile payment devices—to have a built-in Security Element (SE) that meets or exceeds the national cryptographic level 2 standard. This security element generates a unique, unreverse-engineerable device private key using PUF technology during the chip manufacturing stage. This private key remains within the security domain for life and is used for hardware-level digital signature of each generated "value code" data packet. Simultaneously, to prevent "human-generated fraud" or "fake transactions" from interfering with the authenticity of macroeconomic data, a multimodal behavior verification protocol is introduced at the acquisition layer. For example, when generating a "consumer value code," the system not only records the transaction amount but also collects environmental characteristics (such as GPS positioning accuracy, Wi-Fi fingerprint, and ambient light intensity) and the operator's biomechanical characteristics (such as pressure distribution when pressing the screen and fingerprint bioelectrical signals) through device sensors. These non-sensitive features are used to generate a spatiotemporal hash fingerprint, which is then encapsulated in the metadata header of the value code. The data transmission layer employs an encrypted tunnel based on the QUIC protocol. For high-frequency micro-transaction data, the system designs a deduplication mechanism based on Bloom filters and a transmission optimization algorithm based on differential compression to ensure low latency and high throughput in data reporting under massive concurrency. Furthermore, addressing the issue of heterogeneous data semantics across institutions, the system defines a set of "Value Code Semantic Ontology Standards" based on JSON-LD extensions. This standard mandates field mapping rules for economic behaviors across different industries and scenarios. For example, it standardizes and aligns the "GMV" of e-commerce platforms with the "taxable sales" of the tax system at the underlying data structure, ensuring semantic consistency of the source data and laying a solid standardized foundation for subsequent macro-accounting.

[0023] To ensure the authenticity and immutability of all source data, all value codes are digitally signed by trusted IoT devices (such as smart POS machines with built-in security chips and digital wallet apps) the moment they are generated. They are also cross-checked in real-time with the transaction hashes of the national legal digital currency (CBDC) system, forming a binary cross-verification mechanism for "fund flow" and "information flow," eliminating the possibility of data forgery at its source. The system seamlessly connects to core banking systems, tax systems, large e-commerce platform transaction engines, and third-party payment institutions by establishing a unified data exchange protocol and open API standards. This process cleans, denoises, and encapsulates the heterogeneous data scattered across different systems into a standard value code format in real time, thus constructing the cellular structure of a "digital twin" capable of reflecting the real-time operation of the national economy.

[0024] The second step in implementing this invention is to establish a national data custody and rights distribution system based on a modified version of the "Brazilian model" and cutting-edge privacy computing technology. In acquiring massive amounts of micro-data, the biggest obstacle lies in the legal red lines of data privacy protection and the subjective willingness of data subjects to share. This invention innovatively draws upon and improves the concept of "personal data monetization" (i.e., the Brazilian model) to establish a national or regional data trust platform. Under this system, every natural person and legal entity possesses a legally protected "data asset account." Users authorize the platform to entrust their income, consumption, investment, and other value-added data to the platform by signing a blockchain-based smart contract. The platform, as the data trustee, does not directly own the data but only possesses the right to calculate and manage it under specific conditions. To effectively solve the "data free-riding" problem and incentivize users to truthfully and comprehensively collect data, the platform has designed a sophisticated algorithm model that automatically distributes "data element dividends" to users daily based on the completeness, continuity, quality, and timeliness of the data they entrust. This dividend originates from fiscal surpluses generated by precise decision-making within the macroeconomic control system, or from fees paid by commercial institutions for renting anonymized statistical data, thus creating a positive feedback loop of "data contribution - economic benefit" across society. At the technical implementation level, the hosting platform employs a combination of the most advanced privacy-enhancing technologies (PETs).

[0025] The system is fully deployed with a Trusted Execution Environment (TEE) based on Intel SGX or ARM TrustZone. All decryption, association, cleaning, and statistical operations on personal data are performed within a hardware-encrypted "black box" memory (Enclave) inside the CPU. Even operating system administrators or cloud service providers cannot access the plaintext data. Simultaneously, the system employs a federated learning architecture to train local economic models in various locations without moving the raw data off the user's local device or data center. Only the encrypted model gradient parameters or intermediate statistical results are uploaded to the central server for aggregation.

[0026] At the algorithmic implementation level of data element value allocation, this system abandons the simple per-item billing model and introduces a cooperative game contribution measurement algorithm based on Shapley values. This algorithm runs within a Trusted Execution Environment (TEE) and is used to accurately calculate the marginal contribution rate of each micro-entity's private data to the accuracy of the macroeconomic model. Specifically, the system considers the improvement in the prediction accuracy of the large macroeconomic model as the "total benefit," and regards the hundreds of millions of users participating in data hosting as "game participants." Through Monte Carlo sampling, it approximates the Shapley value of each user's data in improving GDP prediction accuracy and the timeliness of inflation warnings, thereby achieving a fair distribution where "the greater the contribution, the higher the benefit." To further alleviate data subjects' privacy concerns, the system also adds a compliance verification layer based on zero-knowledge proofs (ZKP) in addition to the federated learning framework. When the system needs to verify whether a user's income data meets a specific tax bracket or whether they are eligible for credit, the user only needs to generate a zk-SNARK proof locally, proving that their data meets specific conditions (such as "annual income greater than X and no bad records"), without disclosing the specific amount to the platform. This "verification is computation" paradigm, This allows the platform to perform comprehensive economic stratification statistics and credit risk modeling without access to users' plaintext data. Furthermore, to address potential "data poisoning" attacks (i.e., malicious users uploading fake data to interfere with the model), the platform has deployed an adversarial defense mechanism based on anomaly detection. This mechanism uses an autoencoder to perform reconstruction error analysis on the uploaded encrypted gradients, identifying and removing anomalous nodes whose statistical characteristics significantly deviate from the population distribution in real time, ensuring the purity and robustness of the macroeconomic model training data. This composite data hosting architecture, integrating Shapley value pricing, zero-knowledge proof verification, and adversarial defense, constitutes a cornerstone of national economic statistics in the digital age. Trust in "new infrastructure".

[0027] This mechanism ensures the full, real-time, and high-fidelity integration of national economic data while fully protecting citizens' privacy and strictly complying with laws and regulations such as the Personal Information Protection Law and GDPR, providing unprecedented breadth and depth of data for macroeconomic regulation.

[0028] The third step in implementing this invention is to develop a dynamic big data real-time accounting engine based on the national economic identity. This is the "logic center" and "computing heart" of the system, responsible for converting massive amounts of micro-value data streams into macroeconomic indicators in real time. The engine internally encapsulates the core identities of macroeconomics, namely I=S (investment = savings) and Y=C+I+G+(XM) (total output = consumption + investment + government purchases + net exports).

[0029] In traditional statistical work, these identities are often roughly balanced afterward by adjusting the "statistical error" term. However, in this system, the accounting engine performs real-time classification and double-entry bookkeeping for every incoming value code. The system maintains a dynamically updated "National Balance Sheet" and "Flow of Funds Statement".

[0030] Whenever a consumption value code is generated, the system adds to the total consumption amount and subtracts from the corresponding total savings amount; whenever an investment occurs, the system records the increase in capital formation and the transformation of monetary savings. The engine uses streaming computing frameworks such as Apache Flink or Spark Streaming to calculate real-time key indicators such as GNI, GDP, and CPI for the whole country, provinces, cities, and industries with millisecond-level latency. More importantly, the engine has an "automatic diagnosis of structural imbalances" function.

[0031] It monitors the investment-saving gap across society in real time: if it detects that residents' willingness to save (S) continues to rise while corporate willingness to invest (I) remains low, resulting in S>I, the system will immediately issue a red warning signal of "insufficient effective demand" and "deflation risk", and automatically drill down to analyze which income group has increased precautionary savings and which industry has reduced capital expenditure.

[0032] To achieve millisecond-level dynamic accounting of the national economic operation, the accounting engine employs a "National Economic Panoramic Flow Map" based on a Directed Acyclic Graph (DAG) at its underlying data structure. In this map, each node represents an economic entity (individual, enterprise, or government department), and each directed edge represents a flow of funds or value based on a value code. The engine incorporates a dynamic input-output (IO) analysis algorithm based on graph computing, capable of tracking in real time how investment changes in a particular industry spread upstream and downstream through the industrial chain. Unlike traditional static input-output tables, this map possesses adaptive evolution capabilities, enabling it to capture breaks or reorganizations in the industrial chain. The engine incorporates dynamic characteristics. In terms of accounting logic, it introduces an automated verification mechanism based on the "three-stage accounting method," adding a layer of "timestamp and causal chain" verification beyond traditional debit and credit accounting. Whenever a macro-level transaction occurs, the engine not only verifies the balance of cash flow but also uses causal inference algorithms to trace the source and destination of funds in that transaction. If a large number of closed loops are detected in the cash flow graph of a certain region, the graph computing algorithm immediately calculates the "idle coefficient" of the closed loop and triggers a financial risk warning. Furthermore, to address potential out-of-order or delayed issues in streaming data processing, the engine adopts a windowed calculation strategy based on the Watermark mechanism. This allows for correction and recalculation of delayed value code data within a certain time tolerance, ensuring a dynamic balance between timeliness and accuracy for macro-level indicators, truly achieving a generational leap from "statistical reports" to "digital twins."

[0033] This real-time logical verification based on full data makes macroeconomic data no longer cold, lagging numbers, but a dynamic system with rigorous logical consistency. It provides policymakers with an economic perspective like a CT scan, enabling them to accurately locate the blood clots and lesions in economic operations.

[0034] The fourth step of this invention is to construct and train a large-scale macroeconomic model based on the behavior of micro-level heterogeneous agents. This is the core of the system's artificial intelligence, aiming to utilize big data to simulate, extrapolate, and predict economic systems. Traditional macroeconomic forecasting often relies on time series models (such as VAR) or simplified DSGE models, which struggle to capture the complex nonlinear relationships and abrupt changes in economic systems. This invention utilizes petabytes of historical micro-value code data accumulated on a hosting platform to train a deep neural network model with hundreds of billions of parameters. This model employs an improved Transformer architecture, introducing a "time attention mechanism" and an "agent relationship graph."

[0035] The model's input is no longer a single text sequence, but rather a vector of behavioral sequences from hundreds of millions of microeconomic agents (e.g., an agent's income change curve over 12 consecutive months, consumption structure adjustment paths, and financial management records). Through large-scale pre-training, the model learns the behavioral response functions of real economic agents when faced with income shocks, price fluctuations, and policy changes. For example, the model can "learn" the probability distribution of increased consumption by middle-class families in first-tier cities when mortgage rates decrease by 0.5%, as well as the induced investment effect of residents in third- and fourth-tier cities. In the fine-tuning phase, the system uses historical economic cycle data (boom, recession, recovery) to perform reinforcement learning (RLHF) on the model, enabling it to grasp the dynamic evolution patterns and bullwhip effects among macroeconomic variables.

[0036] The final trained model is actually a "digital virtual economy" containing hundreds of millions of virtual agents whose behavioral characteristics closely resemble those of real-world Chinese residents and businesses. High degree of fit. By running these agents in a virtual environment, the model is able to emerge from the bottom up to predict future macroeconomic trends, with predictive accuracy and explanatory power far exceeding traditional econometric models.

[0037] The fifth step in implementing this invention is to construct a scientific, quantitative, and precise macroeconomic control decision-making and execution system. Based on the powerful simulation capabilities of the aforementioned large-scale model, the system provides government departments with a robust "policy wind tunnel" or "economic sandbox." Before introducing major economic policies (such as adjusting the reserve requirement ratio, implementing large-scale tax and fee reductions, or issuing special treasury bonds), policymakers first input the proposed policy parameters into the system. The macroeconomic model will immediately conduct multiple rounds of Monte Carlo simulations, simulating the dynamic impact path of the policy over the next month, quarter, and year. The system will output a detailed assessment report, including not only forecasts of aggregate quantities such as GDP growth rate, employment rate, and inflation rate, but also in-depth structural impact analysis. Based on the assessment results, policymakers can repeatedly optimize policy combinations until the optimal solution is found.

[0038] Once a policy is implemented, the system leverages the smart contract attributes embedded in the value code to support automated execution in a "precision drip" style. For example, when the system determines that consumption needs to be boosted, the finance department can generate "digital consumption voucher" value codes with specific restrictions, directly airdropping them into the digital wallets of low-income groups with a high marginal propensity to consume. These vouchers are restricted to use only during specific periods and in specific sectors (such as green home appliances and catering services), and are strictly prohibited from being transferred or cashed out. The system tracks the redemption status and multiplier effect of each consumption voucher in real time, forming a closed-loop feedback loop. If the actual effect deviates from expectations, the system will automatically provide suggestions for fine-tuning. This mechanism transforms macroeconomic control from extensive "flood irrigation" to data-driven "precision surgery," greatly enhancing the modernization level and risk resistance of macroeconomic governance, and pioneering a new paradigm of macroeconomics in the digital age.

[0039] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A real-time macroeconomic monitoring and control system based on value codes and privacy computing, characterized in that, include: The value code encoding module is used to construct and standardize the value code data structure that describes the smallest economic behavior unit of economic agents based on the United Nations System of National Accounts (SNA2008) and the principles of microeconometrics. The value codes include income value codes, consumption value codes and investment value codes. The data custody and incentive module, based on the personal data monetization mechanism and privacy computing technology, constructs a data element custody and value distribution platform. It adopts at least one of the following technologies: Trusted Execution Environment (TEE), Multi-Party Secure Computation (MPC), Federated Learning, and Zero-Knowledge Proof (ZKP) to achieve secure data custody and usable but invisible computation, and distributes benefits to data subjects based on the data contribution index. The dynamic big data accounting engine, based on the national economic identity, accesses and processes value code data streams in real time, realizes real-time calculation and dynamic balance verification of macroeconomic indicators, and supports automatic diagnosis and early warning of structural imbalances. A large-scale macroeconomic model is trained on behavioral sequence data of micro-level heterogeneous agents, and integrates Transformer time series attention mechanism and graph neural network to realize the simulation and deduction of economic policies and the emergence prediction of macroeconomic conditions. The regulatory decision support module, based on the deduction results of the macroeconomic model, provides policy sandbox simulation and precise regulatory solutions, and supports the automatic execution and dynamic adjustment of regulatory measures through smart contracts embedded in the value code; The value code encoding module includes an energy level calibration unit, whose execution logic is as follows: solving for the microscopic behavior distribution parameters through maximum likelihood estimation. And based on the log-normal cumulative distribution function Generate standardized value codes; The data hosting and incentive module includes a marginal value allocation unit, which is based on Milgrom's information value equation: Calculate the contribution weight of each value code ; The macroeconomic model includes a game-theoretic evolution engine for use in the strategy space. Internal iterative solution to Nash equilibrium state To determine the steady-state response under policy disturbances.

2. The system according to claim 1, characterized in that, The value code data structure includes a globally unique identifier (GUID), a distributed digital identity (DID), a spatiotemporal stamp, a value attribute field, and a smart contract hook, and is encrypted using national cryptographic algorithms.

3. The system according to claim 1, characterized in that, The data hosting and incentive module further includes a cooperative game contribution measurement algorithm based on Shapley values, used to calculate the marginal contribution rate of each data subject to the accuracy of the macroeconomic model, and to distribute the benefits accordingly.

4. The system according to claim 1, characterized in that, The dynamic big data accounting engine adopts a streaming computing framework to calculate Gross National Income (GNI), total retail sales of consumer goods, and investment-savings gap indicators in real time. It also constructs a panoramic flow map of the national economy based on a directed acyclic graph (DAG) to achieve real-time tracking of the transmission effect of the industrial chain and the location of structural imbalances.

5. The system according to claim 1, characterized in that, The macroeconomic model is trained using self-supervised learning and reinforcement learning (RLHF) to learn the behavioral response patterns of micro-agents under changes in income, prices, and policies, and can simulate the nonlinear dynamics and complex adaptive characteristics of the macroeconomy.

6. The system according to claim 1, characterized in that, The regulatory decision support module supports policy parameter input, multi-scenario Monte Carlo simulation, and structural impact analysis. It also enables the precise allocation and usage restrictions of fiscal subsidies, consumption vouchers, and tax adjustments through smart contracts.

7. A method for real-time monitoring and regulation of the macroeconomy based on value codes and privacy computing. Its characteristic is that it includes the following steps: Construct and standardize the value code data structure to encode microeconomic behavior; The platform aggregates encrypted value code data and performs secure computation and incentive allocation based on privacy computing technology. Based on the national economic identity, macroeconomic indicators are calculated in real time and dynamic balance verification is performed. Using micro-value code data to train a large-scale macroeconomic model to achieve economic status prediction and policy simulation; Based on the model's simulation results, precise control policies are formulated and implemented, and closed-loop feedback and dynamic adjustments are achieved through smart contracts.

8. The method according to claim 7, characterized in that, During the data collection process of value codes, Physically Unclonable Functions (PUF) and multimodal biometrics are used to verify the authenticity and anti-counterfeiting of data, and cross-institutional data semantic alignment is achieved through the semantic ontology standard extended by JSON-LD.

9. The method according to claim 7, characterized in that, During the data hosting process, a federated learning architecture and zero-knowledge proof technology are used to complete model training and compliance verification while protecting data privacy.

10. The method according to claim 7, characterized in that, During policy implementation, the smart contracts embedded in the value code enable programmable control over the use, timeliness, and flow path of funds, and allow for real-time monitoring of policy effectiveness and dynamic optimization.

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