Intelligent Early Warning System for State-Owned Asset Risks Based on Multi-Source Data Fusion

Abstract

This invention relates to the field of state-owned asset risk early warning technology and discloses a state-owned asset risk intelligent early warning system based on multi-source data fusion. The system includes a risk stage identification module that obtains enterprise operational parameters through a state-owned asset supervision data interface, divides the life cycle of state-owned enterprises into a stable operation period, an investment expansion period, and an asset restructuring period, and outputs corresponding risk stage identifiers; a multi-source data fusion module that assigns stage-specific weights to financial, public opinion, and compliance data according to the identifiers, obtains a differentiated fusion dataset, and constructs a dynamic risk twin model; a risk feature extraction module that calculates the deviation between the model and real-time monitoring data, generating a multi-dimensional risk feature vector containing quantifiable indicators of financial anomaly degree, public opinion heat, and compliance deviation; and an intelligent early warning output module that uses a staged risk propagation tree algorithm to evaluate the vector, generate risk level signals and associated risk source location results, thus assisting in state-owned asset risk early warning and supervision.

Description

Technical Field

[0001] This invention relates to the field of state-owned asset risk early warning technology, specifically to a state-owned asset risk intelligent early warning system based on multi-source data fusion. Background Technology

[0002] State-owned enterprises (SOEs) currently face a complex and ever-changing internal and external environment, and their risk supervision work faces many pressing issues. Traditional SOE risk supervision methods often rely on a single data source, focusing solely on financial data analysis while neglecting the value of other crucial information such as public opinion data and compliance data. This reliance on a single data source significantly reduces the comprehensiveness of risk identification, making it difficult to cover all potential risks that may arise during enterprise operations.

[0003] In managing the life cycle of state-owned enterprises, traditional methods lack effective stage-based classification and cannot formulate targeted risk supervision strategies according to the different development stages of the enterprises. For example, when an enterprise is in the investment expansion phase, it may face significant risks related to its cash flow and market expansion, while in the stable operation phase, the risks are more concentrated on operational efficiency and compliance. Because the risk stage of an enterprise cannot be accurately identified, supervision work is often reactive and it is difficult to predict risks in advance.

[0004] Traditional risk assessment methods often rely on static analysis, failing to dynamically adjust to the latest operational data. With the accelerating pace of business operations and rapid changes in the market environment, risk conclusions drawn from static assessments often lag behind reality, leading to untimely risk warnings and missed opportunities for optimal risk mitigation. Furthermore, in terms of risk feature extraction and risk source identification, traditional methods lack scientific quantitative indicators and effective algorithmic support, making it difficult to accurately capture multidimensional risk characteristics or quickly pinpoint the underlying causes of risks. This results in a lack of clear direction in risk management, reducing the efficiency and effectiveness of risk supervision. Summary of the Invention

[0005] The purpose of this invention is to provide a state-owned asset risk intelligent early warning system based on multi-source data fusion to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a state-owned asset risk intelligent early warning system based on multi-source data fusion, the system comprising:

[0007] The risk stage identification module is used to obtain enterprise operation parameters through the state-owned assets supervision data interface, and to divide the life cycle of state-owned enterprises into stages based on the enterprise operation parameters, resulting in three risk stage identifiers: stable operation period, investment expansion period, and asset restructuring period.

[0008] The multi-source data fusion module is used to perform phased weight allocation processing on financial data, public opinion data, and compliance data according to the risk stage identifier to obtain a differentiated fusion dataset, and to construct a dynamic risk twin model based on the differentiated fusion dataset.

[0009] The risk feature extraction module is used to perform deviation calculation processing on the dynamic risk twin model and real-time monitoring data to obtain a multi-dimensional risk feature vector. The multi-dimensional risk feature vector includes quantitative indicators of financial anomaly degree, public opinion heat and compliance deviation degree.

[0010] The intelligent early warning output module is used to perform risk assessment processing on the multidimensional risk feature vector through a phased risk propagation tree algorithm, and generate risk level signals and associated risk source location results.

[0011] Preferably, the risk stage identification module includes:

[0012] The parameter normalization unit is used to standardize the revenue growth rate, debt-to-asset ratio, proportion of major investments, and asset turnover rate to generate a corporate status feature matrix.

[0013] The stage discrimination unit is used to establish a stage threshold rule base based on the enterprise state feature matrix, perform rule matching processing on the enterprise state feature matrix, and output the stage discrimination result. The discrimination condition for the stable operation period is that the asset-liability ratio is lower than the threshold and the asset turnover rate is stable.

[0014] The identifier generation unit is used to input the stage discrimination result into the state transition engine for logical verification processing, generate the corresponding risk stage code, and assign the business stability period identifier, investment expansion period identifier, and asset restructuring period identifier according to the risk stage code.

[0015] Preferably, the multi-source data fusion module includes:

[0016] The weight configuration unit is used to query the preset fusion strategy library based on the risk stage identifier and extract the weight coefficients of financial data during the stable operation period, public opinion data during the investment expansion period, and compliance data during the asset restructuring period.

[0017] The data fusion unit is used to perform weighted aggregation calculations on heterogeneous data sources based on the weighting coefficients of financial data, public opinion data, and compliance data to generate the differentiated fusion dataset.

[0018] The twin building unit is used to configure the entity attribute set of the dynamic risk twin model based on the differentiated fusion dataset, wherein the cash flow health attribute is configured during the stable operation period, the project feasibility attribute is configured during the investment expansion period, and the legal compliance attribute is configured during the asset restructuring period.

[0019] Preferably, when the risk feature extraction module performs the deviation calculation process:

[0020] The time-series alignment unit is used to perform time-slicing processing on the real-time monitoring data according to the risk stage identifier, and extract the financial monitoring value, public opinion monitoring value, and compliance monitoring value of the corresponding stage.

[0021] The theoretical data matching unit is used to retrieve the theoretical cash flow value during the stable operation period, the theoretical public opinion threshold during the investment expansion period, and the theoretical compliance baseline during the asset restructuring period from the dynamic risk twin model.

[0022] The feature quantification unit is used to calculate the deviation between the financial monitoring value and the theoretical cash flow value to generate the financial anomaly degree, compare the public opinion monitoring value with the theoretical public opinion threshold to generate the public opinion heat, and perform difference analysis between the compliance monitoring value and the theoretical compliance baseline to generate the compliance deviation degree.

[0023] Preferably, the intelligent early warning output module includes:

[0024] The algorithm selection unit is used to activate the risk propagation tree for the stable operation period, the risk propagation tree for the investment expansion period, and the risk propagation tree for the asset restructuring period based on the risk stage identifier.

[0025] The risk assessment unit is used to input the multidimensional risk feature vector into the activated risk propagation tree for node traversal processing, and calculate the quantitative values ​​of the probability of capital chain risk, the probability of investment failure, and the probability of legal disputes.

[0026] The positioning output unit is used to perform maximum value filtering on the probability of capital chain risk, probability of investment failure, and probability of legal disputes, and output the highest risk level signal and the corresponding positioning information of supply chain risk sources, investment project risk sources, and counterparty risk sources.

[0027] Preferably, the system further includes:

[0028] The dynamic baseline calibration module is used to trigger the equilibrium state capture program when the enterprise's operating indicators are continuously at the steady-state threshold, and to collect the financial benchmark value, public opinion benchmark value, and compliance benchmark value corresponding to the steady-state threshold.

[0029] The model correction module is used to perform consistency verification between the financial benchmark value, public opinion benchmark value, compliance benchmark value and the theoretical value of the dynamic risk twin model. If the deviation exceeds the fault tolerance threshold, the model parameter update mechanism is activated.

[0030] Preferably, the dynamic baseline calibration module includes:

[0031] The steady-state detection unit is used to perform stability analysis on the volatility of operating revenue, the gradient of changes in debt ratio, and the frequency of disclosure of major events, and output steady-state duration data.

[0032] The benchmark acquisition unit is used to extract the median of financial data, the mean of public opinion data, and the mode of compliance data within the preset period as a calibration benchmark set when the steady-state duration data exceeds the preset period.

[0033] The verification triggering unit is used to mark the calibration benchmark set as data to be verified and push it to the model correction module.

[0034] Preferably, when the model correction module performs the consistency verification process:

[0035] The deviation calculation unit is used to calculate the absolute error between the calibration benchmark set and the theoretical cash flow benchmark value during the stable operation period, the theoretical public opinion benchmark value during the investment expansion period, and the theoretical compliance benchmark value during the asset restructuring period in the dynamic risk twin model;

[0036] The parameter update unit is used to recalculate the theoretical cash flow benchmark value, theoretical public opinion benchmark value, and theoretical compliance benchmark value using a sliding window algorithm when the absolute error exceeds a preset fault tolerance threshold, and update the attribute parameters of the dynamic risk twin model.

[0037] Preferably, the system further includes:

[0038] The emergency routing module is used to receive external risk warning instructions, perform routing and distribution processing on the risk level signals according to the preset emergency response strategy library, and generate distribution instructions for reporting signals to regulatory agencies, pushing warning signals to enterprises, and initiating internal audit signals.

[0039] Preferably, the emergency routing module includes:

[0040] The strategy matching unit is used to match the corresponding processing paths for major financial risk strategies, public opinion crisis strategies, and compliance and non-compliance strategies based on the risk category identifier of the risk level signal.

[0041] The instruction generation unit is used to generate an instruction to freeze fund flows based on the major financial risk strategy, an instruction to respond to media incidents based on the public opinion crisis strategy, and an instruction to conduct legal review based on the compliance and non-compliance strategy.

[0042] The signal distribution unit is used to send the frozen funds flow instruction to the bank interface, push the media response instruction to the public relations system, and transmit the legal review instruction to the legal platform.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By setting up a risk stage identification module, the system can obtain enterprise operational parameters through the state-owned assets supervision data interface and accurately divide the life cycle of state-owned enterprises into three risk stage identifiers: stable operation period, investment expansion period, and asset restructuring period. This design enables the system to clearly grasp the risk characteristics of enterprises at different development stages, breaking the ambiguity in the traditional supervision of enterprise life cycle stages. This makes risk supervision more targeted, allowing for the formulation of corresponding regulatory priorities based on the risk characteristics of different stages, avoiding the waste of regulatory resources and deviations in regulatory direction.

[0045] The multi-source data fusion module assigns weights to financial, public opinion, and compliance data in stages based on risk stage identifiers, resulting in a differentiated fused dataset and constructing a dynamic risk twin model. This overcomes the limitations of traditional regulatory reliance on single data sources. At different risk stages, the impact of various data types on risk assessment varies. Stage-based weighting allows data fusion to better align with actual risk assessment needs, while the dynamic risk twin model reflects the company's risk status in real time, continuously adjusting as operational data is updated. This ensures the model's representation of risk aligns with the company's actual operational status, providing a more comprehensive and accurate data foundation for subsequent risk analysis.

[0046] The risk feature extraction module calculates the deviation between the dynamic risk twin model and real-time monitoring data, resulting in a multi-dimensional risk feature vector that includes quantifiable indicators such as financial anomaly degree, public opinion intensity, and compliance deviation. This enables precise quantification of risk characteristics. Traditional regulation struggles to quantify risks, leading to a lack of intuitive basis for risk assessment. This module, through scientific deviation calculation methods, transforms abstract risks into concrete quantitative indicators, clearly presenting the magnitude and severity of risks. This allows regulators to more intuitively understand a company's risk situation and accurately grasp the key aspects of risk.

[0047] The intelligent early warning output module employs a phased risk propagation tree algorithm to assess multi-dimensional risk feature vectors, generating risk level signals and associated risk source location results. This phased risk propagation tree algorithm, combined with the enterprise's current risk stage, more scientifically analyzes the propagation path and scope of impact of risks, thereby accurately determining the risk level and enabling regulators to quickly assess the urgency of risks. Simultaneously, the associated risk source location results directly pinpoint the relevant sources leading to the risk, avoiding the blindness of traditional risk source searching. This allows regulators to take effective measures against specific risk sources, improving the efficiency of risk management, ensuring the sound operation of state-owned enterprises, and safeguarding the security of state-owned assets. Attached Figure Description

[0048] Figure 1This is a timeline diagram of the intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in this invention.

[0049] Figure 2 A flowchart for multi-source data fusion module processing;

[0050] Figure 3 Flowchart for intelligent early warning output module processing;

[0051] Figure 4 A flowchart for processing by the dynamic baseline calibration module;

[0052] Figure 5 A flowchart for the emergency routing module. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see Figure 1 This invention provides a state-owned asset risk intelligent early warning system based on multi-source data fusion, the system comprising:

[0055] The risk stage identification module obtains enterprise operational parameters from the state-owned assets supervision data interface, including key indicators such as operating revenue growth rate, asset-liability ratio, proportion of major investments, and asset turnover rate. These parameters are normalized to generate an enterprise status feature matrix, which is then matched using a stage threshold rule base to divide the enterprise's business lifecycle into a stable operating period, an investment expansion period, and an asset restructuring period, generating corresponding risk stage identifiers. The multi-source data fusion module assigns weight coefficients to financial data, public opinion data, and compliance data based on the risk stage identifiers, constructing a differentiated fusion dataset and establishing a dynamic risk twin model based on this dataset. The risk feature extraction module calculates the deviation between real-time monitoring data and the theoretical values ​​of the dynamic risk twin model, generating a multi-dimensional risk feature vector containing financial anomaly degree, public opinion intensity, and compliance deviation degree. The intelligent early warning output module uses a staged risk propagation tree algorithm to assess the risk of the multi-dimensional risk feature vector, outputting risk level signals and associated risk source location results, achieving accurate early warning of state-owned asset risks.

[0056] Example 1: See Figure 2The risk stage identification module collects enterprise operational parameters through the state-owned assets supervision data interface, including core indicators such as revenue growth rate, debt-to-asset ratio, proportion of major investments, and asset turnover rate. The parameter normalization unit performs standardized preprocessing on these raw data to eliminate dimensional differences. For example, the original debt-to-asset ratio of an energy group was 72%, which was normalized to a standardized value of 0.85 after normalization to the industry benchmark; its asset turnover rate of 1.2 times / year was mapped to a standardized value of 0.42 after logarithmic transformation. All parameters are arranged in a fixed-dimensional manner to form an enterprise status feature matrix, where the row vectors correspond to the time series, and the column vectors represent different parameter dimensions.

[0057] The rule base includes three categories of criteria: For the stable operation period, the standard asset-liability ratio must be ≤0.6 and the asset turnover fluctuation must be <15%; for the investment expansion period, significant investments must account for ≥30% or the revenue growth rate must increase by more than 50%; and for the asset restructuring period, the asset turnover must decrease by 20% for three consecutive periods and there must be a record of significant asset disposal. Taking an infrastructure group as an example, when its significant investments reach 42%, the investment expansion period is triggered; when a chemical company announces the divestiture of non-core assets and its asset turnover drops to 0.8, the asset restructuring period is activated. The identifier generation unit uses a state transition engine for logical verification to eliminate misjudgments caused by abnormal data collection. For example, when a sudden change in the asset-liability ratio is detected, it needs to be cross-validated with bank credit line change records, ultimately generating a three-digit binary code (001 represents the stable operation period, 010 represents the investment expansion period, and 100 represents the asset restructuring period) and binding it to the corresponding identifier.

[0058] The weight configuration unit queries the fusion strategy library to obtain the phased weight coefficients: during the stable operation period, financial data is assigned a weight coefficient of 0.6, public opinion data 0.2, and compliance data 0.2; during the investment expansion period, the weights are adjusted to 0.4 for financial data, 0.4 for public opinion data, and 0.2 for compliance data; and during the asset restructuring period, the weights are configured to 0.3 for financial data, 0.3 for public opinion data, and 0.4 for compliance data. Taking a transportation construction group in the investment expansion period as an example, the system automatically increases the weight of public opinion data, focusing on monitoring policy change reports and community protest information related to its overseas projects.

[0059] Financial data is obtained from structured data such as cash flow statements and profit and loss statements through the enterprise's ERP system; public opinion data is crawled from news media API interfaces and transformed into a public opinion index ranging from -1 to 1 through sentiment analysis; compliance data integrates discrete data such as industrial and commercial administrative penalties and environmental violation records. Weighted calculations employ a hierarchical aggregation method: first, the indicators within each data source are weighted and averaged, and then a secondary fusion is performed according to the stage weight coefficients. For example, in calculating financial anomalies for a certain commercial group during a stable operating period, cash flow health (weight 0.4) and solvency (weight 0.2) are aggregated first, and then fused with public opinion data (weight 0.2) and compliance data (weight 0.2) to generate a fusion index of 0.78.

[0060] The model for the stable operation period incorporates cash flow health attributes, including seven sub-attributes such as operating cash flow ratio and free cash flow adequacy ratio. The model for the investment expansion period activates project feasibility attributes, integrating 12 parameters such as return on investment and policy support. The model for the asset restructuring period strengthens legal compliance attributes, covering nine dimensions such as property rights clarity and litigation risk index. When a manufacturing company triggers an asset restructuring period, the model automatically loads a property rights transaction compliance analysis module, comparing the deviation between the asset transfer price and the valuation benchmark value in real time.

[0061] The entire implementation process utilizes a distributed computing framework for real-time processing, with the enterprise status feature matrix updated once per minute and the stage discrimination response time controlled within 200 milliseconds. Data fusion employs a streaming computing engine, processing over 500 heterogeneous data records per second. The dynamic risk twin model stores entity attributes in an in-memory database, ensuring millisecond-level response capabilities. The system establishes an independent computing instance for each state-owned enterprise to avoid cross-interference between data from different enterprises. When a regional investment platform simultaneously monitors 37 subsidiaries, the system achieves dynamic resource allocation through containerized deployment, with computing nodes automatically scaling up to 32 virtual CPU cores based on load.

[0062] When a company transitions from a stable operational phase to an investment expansion phase, the system gradually adjusts the weighting coefficients within 5 seconds to prevent abrupt changes in indicators. During this process, a dual-model parallel computation is employed: the original stable-phase model and the newly activated expansion-phase model run synchronously for 10 computation cycles. Once the difference rate of the merged data is below 2%, the system automatically switches to the primary model. Historical data is stored in a time-series database, supporting retrospective analysis of the company's lifecycle evolution patterns.

[0063] The error handling mechanism includes triple verification: the data acquisition layer sets outlier filtering rules (e.g., automatically discarding values ​​with a debt-to-asset ratio > 100%), the calculation layer deploys variance testing (triggers review if the fluctuation of three consecutive calculation results exceeds 20%), and the output layer implements logical rationality review (e.g., initiating manual review when the probability of a cash flow risk exceeding 80% occurs during a stable operating period). All processing logs record the operation trajectory, and auditors can view the complete data processing chain through a unique traceability code. The system deployment adopts a modular architecture, with risk stage identification and multi-source data fusion running as independent microservices. Services interact via RESTful APIs, and communication messages use the ASN.1 encoding standard to ensure data consistency. When a provincial state-owned asset supervision platform connects to 83 enterprises, the system achieves load balancing through a service mesh, processing 3000 API call requests per second during peak periods. The configuration management center dynamically adjusts operating parameters, such as automatically reducing the matching frequency of the stage discrimination rule base from 5 times per second to 3 times per second based on server load.

[0064] Example 2: See Figure 3 The risk feature extraction module uses a time-series alignment unit to perform phased slicing of real-time monitoring data. When an energy group is marked as being in a stable operating period, the system automatically extracts the financial data of the most recent 30 days as an analysis window, including 12 indicators such as daily cash flow records and accounts receivable turnover days. Public opinion monitoring values ​​are obtained from news aggregation platforms, collecting sentiment values ​​from media reports related to the group's name and forming a time series at an hourly granularity. Compliance monitoring values ​​integrate regulatory data such as tax filing on-time rate and environmental compliance frequency, updated daily. After timestamp alignment, these data form a multi-dimensional monitoring matrix under a unified time coordinate, ensuring the comparability of data across all dimensions.

[0065] For companies in a stable operating period, the theoretical cash flow value includes parameters such as a minimum safe balance and the expected range of revenue and expenditure fluctuations. For example, a provincial investment company sets its theoretical cash flow benchmark during the stable period as follows: operating cash inflows should not be less than 90% of the monthly budget, and operating expenditure fluctuations should be controlled within ±15%. For companies in the investment expansion phase, the theoretical public opinion threshold includes the industry average attention index and negative public opinion warning lines. For instance, the media attention threshold for a certain infrastructure project during its expansion phase is set at 1.8 times the industry benchmark. For companies in the asset restructuring phase, the theoretical compliance baseline involves the compliance rate of property rights transactions and the incidence of legal disputes. For example, a chemical group's compliance baseline for property rights transfer during its restructuring phase is set at a 98% completion rate.

[0066] Financial anomaly score is generated by monitoring deviations between cash flow and theoretical values. When a trading group's cash outflow exceeds the theoretical value by 20% for five consecutive days, the system calculates an anomaly index of 1.35 based on the cumulative deviation. Public opinion intensity is analyzed using a composite algorithm, combining the number of media reports, the level of reposting, and sentiment scores. For example, a transportation construction group's overseas project triggered a 2.4 intensity warning due to three consecutive days of negative media coverage in local media. Compliance deviation analysis uses a rule engine. When a manufacturing company experienced delays in property registration during asset restructuring, the system generated a deviation score of 0.87 after comparing it to the theoretical compliance baseline. These quantitative indicators constitute a risk feature vector with 12 dimensions, each dimension's value standardized to a range of 0-3 for unified assessment.

[0067] The intelligent early warning output module's algorithm selection unit activates the corresponding analysis model based on the risk stage. During the stable operation period, a capital chain risk propagation tree is loaded, containing 23 analysis nodes covering risk sources such as the supply chain and financing channels. When a port group experienced an abnormal extension of accounts payable during the stable period, the system traced along the supplier performance nodes and found signs of financial distress in three major suppliers. During the investment expansion period, a project risk assessment tree is used. In the overseas power plant construction of a new energy company, the system identified five high-risk factors, including local policy changes and exchange rate fluctuations, through 26 assessment nodes. During the asset restructuring period, a legal compliance analysis tree is applied. During the asset divestiture process of a steel group, the system discovered historical records of property rights defects through 19 review nodes.

[0068] The cash flow risk calculation incorporates Monte Carlo simulation. For example, a logistics group experienced delayed payments from a major customer; the system simulated 500 cash flow scenarios and determined a 35% probability of cash flow disruption. Investment project risk assessment utilizes Bayesian networks; for instance, an airport expansion project faced escalating environmental protests, leading the system to estimate a 28% probability of work stoppage. Legal dispute prediction employs case-based reasoning techniques; a pharmaceutical group's technology transfer agreement showed 72% similarity to historical disputed cases, prompting the system to warn of potential litigation risks. All probability values ​​are normalized to form a standardized 0-1 risk assessment matrix.

[0069] The initial screening identifies risk nodes with a probability value exceeding 0.4. For example, a real estate group during its investment expansion phase exhibited a land acquisition risk probability of 0.63. Secondary analysis linked upstream and downstream nodes, revealing that the risk stemmed from simultaneous financial difficulties experienced by two related parties. The final output module generates a three-tiered risk positioning: Level 1 identifies the direct risk source (the land deposit payer), Level 2 identifies the transmission path (the related enterprise guarantee chain), and Level 3 identifies the potential impact scope (the progress of five ongoing projects). The output results are in a structured data format, containing 12 fields including risk coordinates, impact radius, and urgency level, and are pushed to the decision-making terminal in real time via a message queue.

[0070] When a provincial investment platform simultaneously monitors 18 subsidiaries, the computing engine can complete a risk tree traversal for all companies within 800 milliseconds. The parallel processing framework supports simultaneous calculation of 200 risk nodes, with each node's analysis time controlled within 5 milliseconds. An in-memory database caches risk characteristic data from the past 72 hours, accelerating the response speed of correlation analysis. In one instance, after detecting an anomaly in the parent company's cash flow, the system completed a risk transmission analysis of 23 subsidiaries and 56 investment projects within 1.2 seconds, identifying three main risk diffusion paths. When data is missing in the calculation of a risk node, the system automatically uses nearest neighbor interpolation to supplement the data. For contradictory data (such as normal financial data but elevated public opinion warnings), a multi-source verification process is initiated, retrieving third-party data such as audit reports for arbitration. All calculation processes retain snapshots of intermediate results, supporting risk analysts in retrospectively analyzing paths. In one case of system misjudgment, retrieving a snapshot of the calculation process revealed that the issue stemmed from an outdated theoretical compliance baseline version, subsequently triggering an automatic baseline update mechanism.

[0071] The decision-making interface displays a 3D risk topology map, with node size representing risk probability and line thickness indicating transmission intensity. In one alert, the interface visually showed that the risk of an overseas project (a major node) of a certain infrastructure group was propagating to domestic projects through the material supply line (a thicker line). The timeline control supports risk evolution playback, tracing the change trajectory of risk values ​​over the past 72 hours. The decision support module provides historical similar case recommendations; when a compliance risk of a mining company is detected, it automatically displays three penalty cases of similar companies for reference. Risk calculation units are deployed at each regional regulatory node, handling 80% of routine analysis tasks locally. The central server only receives aggregated risk indicators, reducing network transmission pressure. After adopting this architecture, a provincial state-owned assets system reduced its daily data transmission volume by 62% and improved analysis response speed by 40%. The secure communication module uses national cryptographic algorithms to encrypt all transmitted data, with the key automatically rotating every 6 hours to prevent man-in-the-middle attacks.

[0072] Within 15 seconds, the feature extraction module detected a media sentiment value plummeting to -0.8. The theoretical matching unit immediately compared this value with the expansion period sentiment threshold of 0.5, generating an anomaly value of 2.1. Within 3 seconds, the intelligent early warning module activated the public opinion risk propagation tree, analyzing 42 related nodes to pinpoint the crisis as stemming from inappropriate remarks made by a senior executive. The system automatically generated a three-tiered response plan: immediately delete the controversial remarks (Level 1), prepare a media statement (Level 2), and initiate an investor communication meeting (Level 3).

[0073] Example 3: See Figure 4The dynamic baseline calibration module continuously monitors the operational status of an enterprise through a steady-state detection unit. A certain power group's operating revenue volatility remained within ±2.5% over the past 90 trading days, its debt ratio change gradient did not exceed 0.1% per week, and the frequency of disclosure of major events remained stable at 1-2 times per month. The system determined that it had entered a steady-state operation. The duration of the steady-state was calculated using a sliding window algorithm, with a window width of 30 calendar days and a sliding step of 1 day. When the volatility index was below the threshold for five consecutive window periods, the benchmark acquisition procedure was triggered. When the power group's steady-state duration reached 147 days, the system automatically initiated the benchmark value extraction process.

[0074] The calculation of median financial data excludes the impact of special time points such as the end of quarterly settlement dates, selecting 12 core indicators such as cash holdings and accounts receivable turnover on ordinary trading days. In one data collection instance, the median cash holdings of this power group over the past 30 working days was 1.27 billion yuan, and the median accounts receivable turnover was 6.8 times / year. The calculation of the mean of public opinion data covers three dimensions: media coverage volume, sentiment index, and repost volume, and a comprehensive public opinion benchmark value is obtained after weighted processing. The mode analysis of compliance data focuses on the distribution of regulatory penalty types. The group did not incur any administrative penalties during the steady-state period, and the compliance benchmark value is recorded as 100% compliance.

[0075] The verification trigger unit performs structured encapsulation of the collected benchmark dataset. The data packet includes metadata such as timestamps, enterprise codes, and benchmark value types, as well as a digitally signed verification value. The encapsulation format adopts the ASN.1 standard to ensure the integrity and traceability of data transmission. In a certain data transmission, the benchmark dataset contains descriptive information in 32 fields, and a digital fingerprint is generated using the SHA-256 algorithm. This fingerprint is then pushed to the waiting buffer of the model correction module via a message queue.

[0076] The deviation calculation unit of the model correction module performs a comparative analysis between theoretical and measured values. For companies in a stable operating period, the cash flow benchmark value is verified using the following formula to calculate the degree of difference:

[0077]

[0078] in: This represents the median amount of cash held. This represents the theoretical cash flow benchmark value in the model. This represents the company's maximum cash holdings over the past 12 months. This was discovered during a certain verification process for a water utilities group. The figure reached 7.3%, exceeding the preset tolerance threshold of 5%.

[0079] A three-dimensional coordinate system incorporating media coverage volume, sentiment polarity, and dissemination speed is constructed to calculate the spatial distance between measured and theoretical values. In one analysis, the measured vector of public opinion for a certain rail transit group was (85, 0.6, 12), while the theoretical value was (70, 0.5, 8), resulting in a calculated spatial distance deviation of 18.7 degrees. The compliance benchmark value is verified using a rule-matching method, comparing actual regulatory records with the model's pre-set compliance clauses one by one, and statistically analyzing the proportion of failed matches.

[0080] When the deviation of the cash flow benchmark exceeds a threshold, the model parameters are updated using a weighted moving average algorithm. The new benchmark = original benchmark × 0.7 + measured value × 0.3. In one update, the theoretical cash flow benchmark of an energy investment company was gradually adjusted from 1.5 billion yuan to 1.38 billion yuan, completing a smooth transition over six iteration cycles. For the correction of the public opinion benchmark, a time decay factor is introduced, with the weight coefficient of recent data higher than that of historical data. Updates to the compliance benchmark require confirmation from legal counsel to ensure that the adjustments comply with the latest regulatory requirements.

[0081] Each update generates a new model version number and records information such as the change time, operator, and adjustment magnitude. The version rollback function allows for quick restoration to a previous stable state when an anomaly is detected. During a system audit, version tracing revealed that an abnormal adjustment to a cash flow benchmark value stemmed from a sensor malfunction at the data acquisition end, triggering a data source quality check procedure. Level 1 alerts target deviations of a single parameter; when… A yellow alert is issued when the level is between 5% and 7%; a level two alert is triggered when multiple parameters deviate in tandem, such as when cash flow and public opinion benchmark values ​​are both abnormal, resulting in an orange alert; a level three alert is triggered when core parameters deviate collectively, initiating a red emergency response. In one instance of a level three alert, it was discovered that the cash flow, public opinion, and compliance benchmark values ​​of a port group deviated simultaneously. After investigation, it was confirmed that this was a normal fluctuation prior to a major asset restructuring, and the system automatically switched to a special monitoring mode.

[0082] The system scores the health of sensors at the data acquisition end, including indicators such as data missing rate, outlier frequency, and transmission latency. When the score of a data source falls below a threshold, it automatically switches to a backup data channel. In one monitoring instance, a data transmission interruption was detected in the environmental monitoring equipment of a chemical plant; the system immediately switched to the manual data entry channel and marked the data for that period as requiring additional verification.

[0083] The MapReduce model is used to process benchmark data from multiple enterprises in parallel, with each computing node handling verification tasks for 3-5 enterprises. In a single system-wide verification, benchmark data from 217 state-owned enterprises was processed simultaneously, with the processing time kept under 8 minutes. An in-memory database caches the benchmark value change trajectory for the past 7 days, accelerating trend analysis calculations.

[0084] The transport layer uses the TLS 1.3 protocol to encrypt data streams, the storage layer uses the AES-256 algorithm to encrypt static data, and the application layer implements field-level encryption to protect sensitive information. During a security audit, the system successfully intercepted 23 abnormal access attempts targeting the baseline data storage. In a practical operation, the system monitored the consistently stable operating indicators of a certain aviation group. After meeting the conditions for 45 consecutive days, the steady-state detection unit triggered baseline data collection, obtaining its median cash holdings of 980 million yuan, a comprehensive public opinion index of 0.72, and a compliance rate of 100%. Model correction module verification revealed a deviation in the theoretical cash flow value of 1.02 billion yuan, which was subsequently calculated... The deviation was 4.1%, below the threshold, but observation mode was initiated. The trend of this deviation was continuously monitored over the next 7 days, and it was ultimately confirmed to be within the normal fluctuation range, so the original model parameters remained unchanged. The entire process was executed automatically, generating a complete audit log recording all decision-making basis.

[0085] Example 4: See Figure 5 During consistency verification, the model correction module employs a multi-dimensional comparison mechanism to comprehensively check the theoretical values ​​of the calibration benchmark set and the dynamic risk twin model. For example, the calibration benchmark set of a provincial energy group during its stable operating period showed a median cash balance of 1.86 billion yuan and an accounts receivable turnover rate of 5.2 times per year, while the theoretical cash flow benchmark value in the model was set at 2.03 billion yuan. The deviation calculation unit found a difference of 170 million yuan through absolute value comparison, and the system automatically marked the difference and initiated a review process. For companies in the investment expansion phase, the verification of public opinion benchmark values ​​uses a composite index analysis method. For example, the measured public opinion index of a rail transit construction group during its expansion phase was 0.68 (range 0-1), while the theoretical value in the model was 0.55. The system confirmed whether this deviation was within a reasonable fluctuation range through a three-stage verification.

[0086] When the cash flow benchmark deviation is between 5% and 10%, the system initiates a gradual adjustment, recalculating the theoretical value using a sliding window algorithm. In one update, the cash holding benchmark of a port group was adjusted from 2.21 billion yuan to 2.03 billion yuan in three stages, with each adjustment controlled within 3%. For significant deviations exceeding 10%, the system triggers an expert review process, sending the abnormal data packet to the risk management committee for manual confirmation. In one instance, when a deviation of 15% in the public opinion benchmark of an aviation group was detected, the system automatically froze the automatic update function and generated a pending dataset containing all public opinion monitoring records from the past 30 days.

[0087] The emergency routing module's instruction generation logic adopts an event-driven architecture. When a received risk level signal contains a "significant financial risk" flag, the system immediately generates a set of financial control instructions. In one alert, an investment company triggered a financial risk signal, and the system generated seven specific instructions within 200 milliseconds, including large-amount payment interception, financing channel verification, and reserve fund activation. The instruction data structure uses a standardized template, including 12 required fields such as the executing entity, operation code, timestamp, and validity period. For public opinion crisis events, the media response instruction package has 21 built-in preset response plans, automatically matching the response intensity according to the crisis level. In one instance, a building materials group encountered a product quality public opinion crisis, and the system invoked a level 3 response plan, including a complete response process such as an official website statement template, media communication scripts, and third-party testing arrangements.

[0088] The bank interface communication uses a standard financial industry protocol to convert fund freeze instructions into SWIFT message format. In one instance, the system sent payment suspension instructions in MT202 format to three partner banks, containing key information such as a 16-digit company code and a 9-digit transaction number. The public relations system interface supports multimedia data transmission, automatically pushing press conference material packages to designated media ports. The legal platform interface uses blockchain notarization technology; all sent legal review instructions generate hash values ​​and are stored on the blockchain. In a significant contract risk warning, the instruction package transmitted by the system to the legal platform included a 37-page scanned contract and 12 risk markers, with end-to-end encryption implemented during transmission.

[0089] The bank's interface employs a dual-channel confirmation mechanism. If the primary channel times out without a response, it automatically switches to the backup channel to resend the instruction. In one instance, during the transmission of a fund freeze instruction, a delay in the primary channel caused by a bank system upgrade resulted in the system switching to the API backup channel within 15 seconds to complete the instruction delivery. The public relations system tracks the impact of media responses, automatically monitoring public opinion trends over the following 6 hours after instruction execution. The legal platform establishes an instruction processing progress dashboard, displaying the real-time status of each stage of legal review, and automatically triggering alerts for any unprocessed nodes that time out.

[0090] The emergency response strategy library is updated quarterly, adding handling solutions for typical industry risk cases. A recent update included strategies for responding to new regulations on cross-border investment, encompassing six sub-items such as foreign exchange control compliance review and international arbitration contingency plans. The historical version archive retains strategy versions from the past 24 months, allowing users to search for response solutions for specific periods via a timeline.

[0091] The entire process, from receiving risk signals and generating instructions to distributing and executing them, generates timestamped operation logs. During a security inspection, log tracing revealed that a transmission delay stemmed from network routing anomalies, leading to optimization of the transmission path selection algorithm. The access control system implements a "four-eyes" principle, requiring critical instructions to be reviewed by both a risk analyst and the department head. A master control node is deployed at the group headquarters, with edge computing nodes set up in each regional branch. In a full-system drill, 32 edge nodes simultaneously processed instruction distribution tasks, with an average response time controlled within 1.5 seconds. A load balancing mechanism dynamically allocates network bandwidth; when a surge in traffic is detected in a certain region, adjacent nodes are automatically reassigned to share the load.

[0092] Each version upgrade begins with a pilot run on 5% of enterprise nodes. After 24 hours of continuous observation without any anomalies, the scope is gradually expanded. During a policy library update, a compatibility issue between the new version and a local regulatory system was promptly identified through a canary rollout, preventing a large-scale failure. Intelligent scheduling is implemented during maintenance windows, automatically selecting off-peak business hours for system optimization, keeping each maintenance session under 15 minutes.

[0093] Example 5: The strategy matching unit of the emergency routing module activates the corresponding processing path through a risk category identifier. When the system receives an external risk warning instruction, it first parses the risk code field in the instruction header. In one instance, an energy group triggered the "Significant Financial Risk" identifier (coded FIN_CRITICAL). The system immediately retrieved the 32-bit risk type code registered in the emergency response strategy library and accurately matched it to the fund flow control strategy. This strategy includes a three-level response mechanism: a primary response freezes non-essential expenditure accounts, a secondary response initiates a financing channel verification, and a high-level response activates the risk reserve fund. The strategy matching process uses multi-level hash indexing technology to complete strategy positioning within 50 milliseconds and generate a strategy instance object containing 19 execution parameters.

[0094] In response to financial risk events, the system calls upon a pre-set instruction template library, fills in specific enterprise parameters, and generates operational instructions. For example, when a transportation construction group triggered a risk warning, the system automatically generated an instruction package containing the following elements: an instruction to freeze a single payment exceeding 5 million yuan (effective immediately), an instruction to verify debts maturing within the next 7 days (feedback timeframe of 2 hours), and an instruction to activate 30% of the risk reserve (triggered when a cash flow gap exceeds 20%). The instruction data structure employs a layered design. The instruction header contains basic information such as the enterprise's unified social credit code, instruction serial number, and timestamp; the instruction body carries specific operational parameters; and the instruction tail includes a digital signature and encrypted verification code.

[0095] When a chemical group triggers the "public opinion crisis" identifier (coded MEDIA_ALERT), the system extracts the most similar historical response plan from the case library as a base template. Based on the current quantitative value of public opinion intensity (2.7 / 3.0), the response level is automatically upgraded to Level 3, and requirements for media communication meetings and third-party expert endorsement are added to the base template. The generated instruction package contains 11 action items: issue an initial statement within 1 hour, establish a media response team within 3 hours, prepare a fact-checking report within 6 hours, etc., with each action item linked to a responsible person and completion standards.

[0096] In one instance, a pharmaceutical group triggered the "compliance violation" identifier (coded LEGAL_VIOLATION). The system automatically scanned 23 recently signed major contracts of the company and identified 5 agreements related to the risk event. The instruction generation unit extracted key clauses from the contracts, marking 12 areas requiring focused review, such as breach of contract clauses and dispute resolution mechanisms, creating an annotated review task list. Simultaneously, it linked to the regulatory database and automatically added relevant penalty clauses from the latest version of the "Drug Supervision and Administration Regulations" as the basis for review.

[0097] The bank interface communication adopts the financial data exchange standard, converting fund freeze instructions into XML messages that conform to the bank-enterprise direct connection specification. A single instruction transmission includes the following key fields: enterprise account number, frozen amount, effective time, and 18 other data elements. The messages are transmitted via a message queue (MQ) with the highest priority set to ensure priority processing when the bank system is busy.

[0098] When a media response instruction is generated, the system automatically packages relevant materials: vector graphics of the company logo, photos of senior executives, and video footage of the factory premises, among other asset files. The transmission uses a chunked upload mechanism; large files are automatically split into 5MB data packets, which are then reassembled at the receiving end to generate a download link. In one crisis management instance, the system completed the transmission of an instruction package containing 82 pages of background information and three interview videos within 45 seconds.

[0099] Instructions are automatically assigned to specialized teams based on the type of legal risk: contract dispute instructions are sent to the commercial law team, regulatory compliance instructions are routed to the administrative law team, and intellectual property instructions are directed to the IP team. The transmission protocol supports version negotiation; when a lower version of the receiving legal system is detected, it automatically downgrades to a compatibility mode to transmit a simplified version of the instructions.

[0100] The instruction transmission process implements six-stage status tracking: generation queuing, protocol conversion, network transmission, reception confirmation, execution feedback, and result return. Each stage has a timeout threshold. If, during a bank interface transmission, network latency causes a timeout for reception confirmation, the system initiates a retransmission mechanism within 8 seconds and delivers the result through a backup channel. The feedback information parsing module analyzes the execution results in real time. When it detects a failed execution of a fund freeze instruction, it automatically escalates the instruction strength to a full account freeze.

[0101] After each risk event is handled, the system automatically generates a case summary report and extracts valid action items to add to the template library. After successfully handling a supply chain risk, a new instruction template for "verifying the financial status of second-tier suppliers" was added. Version iterations adopt a blue-green deployment model. After the new version instruction library is verified in the sandbox environment, it replaces the online version through hot-swapping, keeping service interruption time within 200 milliseconds.

[0102] The transport layer uses the national standard SM4 algorithm to encrypt command content, and the key is dynamically updated every 2 hours. Access control implements three-factor authentication, requiring operators to provide a digital certificate, dynamic password, and biometric verification simultaneously. The audit trail module records the entire lifecycle of command operations. During a security audit, log tracing revealed an abnormal access originating from an overseas IP address, triggering a firewall rule update.

[0103] A command distribution cluster is deployed at the provincial regulatory node, using a consistent hashing algorithm to allocate enterprise nodes. During a regional risk event, the system simultaneously processed emergency commands from 18 related enterprises. The cluster automatically expanded to 32 computing nodes, achieving a peak processing capacity of 120 commands per second. The load balancer monitors node pressure in real time, automatically redirecting 30% of traffic to adjacent nodes when a node experiences a sudden surge in requests.

[0104] System maintenance is intelligently scheduled. Routine maintenance windows automatically select off-peak business hours, avoiding critical periods such as month-end settlements through traffic prediction models. Version updates employ a canary release strategy, initially pushing the new version to 5% of enterprise nodes, and releasing it to the entire system after 24 hours of monitoring for any anomalies. A failover mechanism establishes a dual-active data center; when the primary center detects a network anomaly, it switches traffic to the disaster recovery center within 15 seconds.

[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0106] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A state-owned asset risk intelligent early warning system based on multi-source data fusion, characterized in that, The system includes: The risk stage identification module is used to obtain enterprise operation parameters through the state-owned assets supervision data interface, and to divide the life cycle of state-owned enterprises into stages based on the enterprise operation parameters, resulting in three risk stage identifiers: stable operation period, investment expansion period, and asset restructuring period. The multi-source data fusion module is used to perform phased weight allocation processing on financial data, public opinion data, and compliance data according to the risk stage identifier to obtain a differentiated fusion dataset, and to construct a dynamic risk twin model based on the differentiated fusion dataset. The risk feature extraction module is used to perform deviation calculation processing on the dynamic risk twin model and real-time monitoring data to obtain a multi-dimensional risk feature vector. The multi-dimensional risk feature vector includes quantitative indicators of financial anomaly degree, public opinion heat and compliance deviation degree. The intelligent early warning output module is used to perform risk assessment processing on the multidimensional risk feature vector through a phased risk propagation tree algorithm, and generate risk level signals and associated risk source location results. The system also includes: The dynamic baseline calibration module is used to trigger the equilibrium state capture program when the enterprise's operating indicators are continuously at the steady-state threshold, and to collect the financial benchmark value, public opinion benchmark value, and compliance benchmark value corresponding to the steady-state threshold. The model correction module is used to perform consistency verification between the financial benchmark value, public opinion benchmark value, compliance benchmark value and the theoretical value of the dynamic risk twin model. If the deviation exceeds the fault tolerance threshold, the model parameter update mechanism is activated.

2. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 1, characterized in that, The risk stage identification module includes: The parameter normalization unit is used to standardize the revenue growth rate, debt-to-asset ratio, proportion of major investments, and asset turnover rate to generate a corporate state feature matrix. The stage discrimination unit is used to establish a stage threshold rule base based on the enterprise state feature matrix, perform rule matching processing on the enterprise state feature matrix, and output the stage discrimination result. The discrimination condition for the stable operation period is that the asset-liability ratio is lower than the threshold and the asset turnover rate is stable. The identifier generation unit is used to input the stage discrimination result into the state transition engine for logical verification processing, generate the corresponding risk stage code, and assign the business stability period identifier, investment expansion period identifier, and asset restructuring period identifier according to the risk stage code.

3. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 2, characterized in that, The multi-source data fusion module includes: The weight configuration unit is used to query the preset fusion strategy library based on the risk stage identifier and extract the weight coefficients of financial data during the stable operation period, public opinion data during the investment expansion period, and compliance data during the asset restructuring period. The data fusion unit is used to perform weighted aggregation calculations on heterogeneous data sources based on the weighting coefficients of financial data, public opinion data, and compliance data to generate the differentiated fusion dataset. The twin building unit is used to configure the entity attribute set of the dynamic risk twin model based on the differentiated fusion dataset, wherein the cash flow health attribute is configured during the stable operation period, the project feasibility attribute is configured during the investment expansion period, and the legal compliance attribute is configured during the asset restructuring period.

4. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 3, characterized in that, When the risk feature extraction module performs the deviation calculation process: The time-series alignment unit is used to perform time-slicing processing on the real-time monitoring data according to the risk stage identifier, and extract the financial monitoring value, public opinion monitoring value, and compliance monitoring value of the corresponding stage. The theoretical data matching unit is used to retrieve the theoretical cash flow value during the stable operation period, the theoretical public opinion threshold during the investment expansion period, and the theoretical compliance baseline during the asset restructuring period from the dynamic risk twin model. The feature quantification unit is used to calculate the deviation between the financial monitoring value and the theoretical cash flow value to generate the financial anomaly degree, compare the public opinion monitoring value with the theoretical public opinion threshold to generate the public opinion heat, and perform difference analysis between the compliance monitoring value and the theoretical compliance baseline to generate the compliance deviation degree.

5. The intelligent early warning system for state-owned asset risks based on multi-source data fusion according to claim 4, characterized in that, The intelligent early warning output module includes: The algorithm selection unit is used to activate the risk propagation tree for the stable operation period, the risk propagation tree for the investment expansion period, and the risk propagation tree for the asset restructuring period based on the risk stage identifier. The risk assessment unit is used to input the multidimensional risk feature vector into the activated risk propagation tree for node traversal processing, and calculate the quantitative values ​​of the probability of capital chain risk, the probability of investment failure, and the probability of legal disputes. The positioning output unit is used to perform maximum value filtering on the probability of capital chain risk, probability of investment failure, and probability of legal disputes, and output the highest risk level signal and the corresponding positioning information of supply chain risk sources, investment project risk sources, and counterparty risk sources.

6. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 1, characterized in that, The dynamic baseline calibration module includes: The steady-state detection unit is used to perform stability analysis on the volatility of operating revenue, the gradient of changes in debt ratio, and the frequency of disclosure of major events, and output steady-state duration data. The benchmark acquisition unit is used to extract the median of financial data, the mean of public opinion data, and the mode of compliance data within the preset period as a calibration benchmark set when the steady-state duration data exceeds the preset period. The verification triggering unit is used to mark the calibration benchmark set as data to be verified and push it to the model correction module.

7. The intelligent early warning system for state-owned asset risks based on multi-source data fusion according to claim 6, characterized in that, When the model correction module performs the consistency verification process: The deviation calculation unit is used to calculate the absolute error between the calibration benchmark set and the theoretical cash flow benchmark value during the stable operation period, the theoretical public opinion benchmark value during the investment expansion period, and the theoretical compliance benchmark value during the asset restructuring period in the dynamic risk twin model; The parameter update unit is used to recalculate the theoretical cash flow benchmark value, theoretical public opinion benchmark value, and theoretical compliance benchmark value using a sliding window algorithm when the absolute error exceeds a preset fault tolerance threshold, and update the attribute parameters of the dynamic risk twin model.

8. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 1, characterized in that, The system also includes: The emergency routing module is used to receive external risk warning instructions, perform routing and distribution processing on the risk level signals according to the preset emergency response strategy library, and generate distribution instructions for reporting signals to regulatory agencies, pushing warning signals to enterprises, and initiating internal audit signals.

9. The intelligent early warning system for state-owned asset risks based on multi-source data fusion as described in claim 8, characterized in that, The emergency routing module includes: The strategy matching unit is used to match the corresponding processing paths for major financial risk strategies, public opinion crisis strategies, and compliance and non-compliance strategies based on the risk category identifier of the risk level signal. The instruction generation unit is used to generate an instruction to freeze fund flows based on the major financial risk strategy, an instruction to respond to media incidents based on the public opinion crisis strategy, and an instruction to conduct legal review based on the compliance and non-compliance strategy. The signal distribution unit is used to send the frozen funds flow instruction to the bank interface, push the media response instruction to the public relations system, and transmit the legal review instruction to the legal platform.

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