Digital governance analysis system and method for digital economy

Abstract

The invention belongs to the technical field of digital economic governance, and provides a digital governance analysis system and method for digital economy, and the system comprises a data collection module which collects and preprocesses original data from a plurality of data sources; the data fusion and storage module performs multi-modal fusion and classified storage; the real-time analysis and perception module performs real-time analysis, anomaly detection and trend prediction in a streaming data mode to obtain a data analysis result; the intelligent decision engine module performs intelligent decision and adaptive optimization on the data analysis result; the platform collaborative governance module is used for data collaboration and business integration among different systems; the security and privacy protection module is used for carrying out encryption protection on system data and carrying out identity authentication and access control on a visitor; the system management and operation and maintenance module carries out monitoring, fault recovery and performance optimization processing; and the interface management module performs data interaction and function integration. The beneficial effect of the invention is that the digital economic management capability and management efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of digital economy governance technology, and in particular to a digital governance analysis system and method for the digital economy. Background Technology

[0002] With the rapid development of the digital economy, global economic activities are increasingly reliant on data-driven and intelligent management. Digital governance, as a crucial support for the digital economy, has become an important means of promoting the digital transformation of the social economy. Against this backdrop, digital governance analysis methods and systems are gradually becoming core components of digital management for governments and enterprises worldwide. The digital economy refers to the digitization and intelligentization of traditional economic activities through technologies such as big data, cloud computing, and artificial intelligence, thereby achieving more efficient resource allocation and economic benefits. Currently, the digital economy is demonstrating tremendous vitality and potential in areas such as e-commerce, fintech, smart cities, and the industrial internet. However, due to the widespread existence of data silos across industries and low efficiency in data integration and sharing, governance capabilities are lagging, making it difficult to respond quickly to dynamic changes.

[0003] Digital governance refers to the use of modern information technology and big data analytics to monitor, dynamically manage, and make scientific decisions about digital economic activities in real time. Its core lies in establishing a digital governance system to achieve comprehensive supervision and optimization of economic activities. Currently, digital governance is mainly applied in the following areas:

[0004] Intelligent decision support provides a scientific basis for policy making and business strategies through data mining and predictive analysis; risk monitoring and early warning detects potential risks in economic activities in real time through multi-source data fusion; and resource optimization improves resource utilization and economic efficiency through digital management tools.

[0005] Existing digital governance methods still have the following shortcomings: lack of intelligent governance models, traditional governance methods rely mainly on human decision-making, and lack intelligent analysis and automated intervention capabilities; lack of cross-platform integration, digital governance systems are usually built independently, making it difficult to be compatible and coordinated with other digital economic systems. Summary of the Invention

[0006] Aimed at at least in addressing one of the technical problems existing in the prior art, this invention provides a digital governance analysis system and method for the digital economy.

[0007] One aspect of the present invention provides a digital governance analysis system for the digital economy, comprising:

[0008] The data acquisition module is used to collect raw data from multiple data sources and preprocess the raw data to obtain preprocessed raw data.

[0009] The data fusion and storage module is used to perform multimodal fusion and classification storage of preprocessed raw data, and also to retrieve and query the multimodal fused and classified data.

[0010] The real-time analysis and perception module is used to perform real-time analysis, anomaly detection, and trend prediction on multimodal fusion and classified data in a streaming data manner to obtain data analysis results.

[0011] The intelligent decision engine module is used to make intelligent decisions and adaptive optimizations based on data analysis results to obtain decision results.

[0012] The security and privacy protection module is used to encrypt and protect system data using layered encryption, data anonymization, key management, and access control, and to authenticate and control visitors.

[0013] The system management and operation module is used for monitoring, fault recovery, and performance optimization using log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods.

[0014] The interface management module is used to enable data interaction and function integration between external systems and the digital governance analysis system of the digital economy through open API interfaces.

[0015] According to the aforementioned digital governance analysis system for the digital economy, the data acquisition module includes:

[0016] The system is configured to collect the raw data from IoT devices, enterprise information systems, internet data, and mobile terminals; to perform preprocessing on the raw data using at least one of data cleaning, format conversion, anomaly detection, and tagging; and to perform local collection and response processing on the data to be collected via edge computing.

[0017] According to the aforementioned digital governance analysis system for the digital economy, the data acquisition module is also used for:

[0018] The compression algorithm is switched based on real-time network status, and integrity is verified by hash digest verification before and after data transmission. The load status is shared among multiple edge acquisition nodes, and the acquisition tasks of the edge acquisition nodes are dynamically allocated through a collaborative scheduling mechanism. Data from multiple data sources is acquired in real time using REST API, MQTT, and WebSocket protocols. The raw data is processed and pre-filtered locally by edge computing nodes. The raw data is categorized by label according to data source, time, and type.

[0019] According to the aforementioned digital governance analysis system for the digital economy, the data fusion and storage module includes:

[0020] This is used to perform multimodal fusion on the raw data, wherein multimodal fusion includes associating and mapping the raw data based on timestamps, geographic locations, and tag information, and wherein the raw data includes structured data, unstructured data, and semi-structured data;

[0021] It is used to process and store multimodal fusion data by employing a hot and cold data separation strategy, as well as compression, deduplication, and consistency verification.

[0022] A tag hash tree is used to establish a mapping tree structure between tags and data, and data retrieval is performed through the mapping.

[0023] Spatiotemporal semantic mapping is used to align and integrate cross-domain data based on the triplet of timestamp, geographic location, and business tag.

[0024] According to the aforementioned digital governance analysis system for the digital economy, the real-time analysis and sensing module includes:

[0025] This is used to perform real-time analysis and perception of multimodal fusion and classified storage data using a stream computing framework. The stream computing framework includes anomaly detection and trend prediction using complex event processing, as well as buffering of real-time data. It is also used to automatically trigger alarms based on the results of real-time analysis and perception.

[0026] According to the aforementioned digital governance analysis system for the digital economy, the intelligent decision-making engine module includes:

[0027] It is used to classify, regress, and make reinforcement learning judgments on the governance time of data sources using classification decision-making, regression prediction, reinforcement learning algorithms and multi-model collaboration mechanisms, and to perform model self-learning and self-optimization based on historical feedback;

[0028] The classification decision-making process uses a dynamic weight model based on the feature set of governance indicators to perform soft discrimination on multi-label targets, and employs an adaptive feature ablation mechanism to dynamically adjust weights based on feature contribution during the classification process. The classification decision-making model... for:

[0029]

[0030] Indicates the first One governance characteristic, Indicates its presence in the label The system dynamically updates the weights based on governance feedback data to optimize classification accuracy.

[0031] The regression prediction uses a weighted regression model with a time decay factor. The evolution of governance indicators' trends in short-term sensitivity and long-term stability was studied using different time decay factors, with a weighted regression model. for:

[0032]

[0033] For the intercept term, For regression coefficients, For the first The independent variables are Time observations, The total number of independent variables. The attenuation rate, For reference only;

[0034] The reinforcement learning algorithm employs a reward function that mitigates feedback latency. The ability to accumulate results in non-real-time scenarios is accumulated, including the reward function. A delayed feedback hierarchical reward mechanism is adopted, which records both immediate and cumulative rewards during the reinforcement learning process. The reward function... for:

[0035]

[0036] The efficiency coefficient representing delayed feedback. As a discount factor, For the first The actual reward at any moment.

[0037] According to the aforementioned digital governance analysis system for the digital economy, the security and privacy protection module includes:

[0038] It is used to encrypt and protect system data using a layered encryption method; it is also used to perform data backtracking and security auditing on accessed system data using audit logs.

[0039] According to the aforementioned digital governance analysis system for the digital economy, the platform collaborative governance module is used for:

[0040] It enables data sharing, task scheduling, and role-based access control among multiple systems, and facilitates multi-dimensional business collaboration and governance process linkage based on the enterprise-level data bus and multi-tenant architecture.

[0041] According to the aforementioned digital governance analysis system for the digital economy, the system management and operation module includes:

[0042] This system is used for status monitoring, anomaly detection, log tracking, and automatic recovery of the digital governance analysis system for the digital economy, which employs log management, anomaly detection, vulnerability scanning, and automated operation and maintenance. It is also used for deploying the digital governance analysis system for the digital economy using a containerized approach.

[0043] Another aspect of the present invention provides a digital governance analysis method for the digital economy, comprising:

[0044] Raw data is collected from multiple data sources and preprocessed to obtain preprocessed raw data;

[0045] The preprocessed raw data is fused in multiple modes and classified for storage. The data that has been fused and classified is then retrieved and queried.

[0046] The multimodal fusion and classified storage data are analyzed in real time, anomaly detection and trend prediction are performed using streaming data to obtain data analysis results;

[0047] Intelligent decision-making and adaptive optimization are performed on the data analysis results to obtain the final decision.

[0048] The system employs layered encryption, data anonymization, key management, and access control to encrypt and protect system data, and performs identity authentication and access control for visitors.

[0049] The system employs log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods for monitoring, fault recovery, and performance optimization.

[0050] It enables data interaction and functional integration between external systems and the digital governance analysis system of the digital economy through open API interfaces.

[0051] The beneficial effects of this invention are as follows:

[0052] (1) Data is collected from multiple sources, including IoT devices, enterprise information systems, the Internet, and mobile terminals, supporting unified processing of structured, unstructured, and semi-structured data. Preprocessing steps such as data cleaning, format conversion, and anomaly detection improve data accuracy and consistency. Simultaneously, the deployment of edge computing nodes reduces data transmission latency and enhances real-time response capabilities. The system is compatible with multiple data transmission protocols, such as REST API and MQTT, ensuring flexibility in data collection. Furthermore, a distributed storage architecture is adopted, supporting separate storage of hot and cold data to optimize storage costs and access efficiency. Data consistency and integrity are ensured through hash verification and timestamp verification.

[0053] (2) Based on classification decision-making, regression prediction, and reinforcement learning algorithms, the system achieves accurate decision-making for complex economic activities; it utilizes intelligent algorithms such as genetic algorithms and particle swarm optimization to support multi-objective optimization and dynamically adjust model parameters to improve decision accuracy; the system has long-cycle optimization capabilities to meet different levels of business needs. Comprehensive data protection measures ensure data security during transmission and storage through layered encryption and identity authentication; at the same time, it has anomaly detection and data backtracking functions to enhance system security.

[0054] (3) In the intelligent decision-making stage, the system introduces an adaptive feature ablation mechanism and a delayed feedback hierarchical reward mechanism to optimize feature weights and balance short-term and long-term goals, thereby enhancing the robustness and interpretability of the decision. Overall, the system meets the needs of complex business scenarios through efficient data processing and intelligent decision support. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of a digital governance analysis system for the digital economy, according to an embodiment of the present invention.

[0056] Figure 2 This is a schematic diagram of the digital governance analysis method for the digital economy according to an embodiment of the present invention. Detailed Implementation

[0057] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. In the following description, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" can be used interchangeably. Terms such as "first," "second," etc., are used only to distinguish technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the sequential relationship of the indicated technical features. In the following description, the consecutive reference numerals for method steps are for ease of review and understanding. Adjusting the implementation order of steps, in conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0058] refer to Figure 1 , Figure 1This is a schematic diagram of a digital governance analysis system for the digital economy, according to an embodiment of the present invention. It includes a data acquisition module 110, a data fusion and storage module 120, a real-time analysis and perception module 130, an intelligent decision-making engine module 140, a platform collaborative governance module 150, a security and privacy protection module 160, a system management and maintenance module 170, and an interface management module 180, wherein:

[0059] The data acquisition module 110 is used to collect raw data from multiple data sources and preprocess the raw data to obtain preprocessed raw data.

[0060] In some embodiments, the data acquisition module 110 is used to acquire raw data from IoT devices, enterprise information systems, Internet data, and mobile terminals; to perform preprocessing on the raw data using at least one of data cleaning, format conversion, anomaly detection, and tagging; and to perform local acquisition and response processing on the data to be acquired through edge computing.

[0061] In some embodiments, the data acquisition module 110 is further configured to switch compression algorithms according to real-time network status and perform integrity verification through hash digest verification before and after data transmission; share load status among multiple edge acquisition nodes and dynamically allocate acquisition tasks to edge acquisition nodes through a collaborative scheduling mechanism; acquire data from multiple data sources in real time using REST API, MQTT, and WebSocket protocols; perform local processing and pre-filtering of the acquired raw data through edge computing nodes; and classify the acquired raw data by labeling according to data source, time, and type.

[0062] In some embodiments, data sources include IoT devices (such as smart sensors, RFID), enterprise information systems (such as ERP, CRM), internet data (such as social media, news data), and mobile terminals (such as smartphones, wearable devices); the data acquisition protocol supports multiple data transmission protocols such as REST API, MQTT, WebSocket, and CoAP to ensure the real-time performance and reliability of data acquisition; data preprocessing: implements data cleaning, format conversion, anomaly detection, and tagging to improve data accuracy and consistency; edge computing reduces data transmission latency and improves real-time response capabilities by deploying edge nodes near the data source.

[0063] For example, in a smart city scenario, environmental sensors transmit environmental data such as temperature, humidity, and PM2.5 in real time via the MQTT protocol, and upload the data to the cloud for storage after preliminary processing at the edge node.

[0064] Building upon this foundation, this invention further introduces a dynamic bandwidth adaptive compression mechanism, automatically switching compression algorithms based on real-time network bandwidth: in low-bandwidth environments, a lightweight LZ4 algorithm is enabled to ensure transmission rate, while in high-bandwidth environments, a high-compression-ratio Zstd algorithm is employed to improve throughput efficiency; before data transmission, the acquisition end calculates a hash digest and compares it with the cloud to ensure end-to-end data consistency; for multi-source acquisition scenarios, the system automatically adds tags (protocol type, device ID, timestamp, confidence score, etc.) to the data and achieves load balancing among edge nodes through a collaborative scheduling mechanism to avoid single-point bottlenecks.

[0065] Through the above embodiments, the data acquisition module 110 improves data integrity and system adaptability while ensuring real-time performance, and can meet the continuous acquisition needs in complex network environments.

[0066] The data fusion and storage module 120 is used to perform multimodal fusion and classification storage on the preprocessed raw data, and also to retrieve and query the multimodal fused and classified data.

[0067] In some embodiments, the data fusion and storage module 120 is used to perform multimodal fusion on the original data, wherein the multimodal fusion includes associating and mapping the original data according to timestamps, geographic locations and tag information, wherein the original data includes structured data, unstructured data and semi-structured data; and is used to process and store the multimodal fused data by adopting a hot and cold data separation strategy, and by adopting compression, deduplication and consistency verification processing.

[0068] In some embodiments, the data fusion and storage module 120 is also used to establish a mapping tree structure between tags and data using a tag hash tree, and to perform data retrieval through the mapping; and to perform cross-domain data alignment and fusion based on timestamps, geographic locations and business tag triples through spatiotemporal semantic mapping.

[0069] In some embodiments, the data fusion and storage module 120 includes:

[0070] Multimodal data processing supports structured data (such as SQL databases), unstructured data (such as text, images, and videos), and semi-structured data (such as JSON and XML); distributed storage utilizes distributed storage systems such as HDFS, Cassandra, and MongoDB to achieve efficient storage of massive amounts of data; hot and cold data separation stores frequently accessed data on high-speed storage media and infrequently accessed data on low-cost devices, improving storage efficiency; data compression and deduplication employ efficient compression algorithms such as LZ4 and Snappy to reduce storage space and improve data transmission efficiency; data consistency verification ensures data integrity and consistency through hash verification and timestamp verification.

[0071] In some embodiments, the data fusion and storage module 120 introduces a fast indexing mechanism based on a tag hash tree, establishing a tag-data mapping tree between distributed nodes to support millisecond-level data retrieval and location. Simultaneously, multiple modules employ a spatiotemporal semantic mapping model, utilizing timestamp, geographic location, and business tag triples to achieve precise alignment of cross-domain data. For example, traffic data and environmental monitoring data can be semantically fused through geographic location and time stamps, which not only improves data retrieval speed but also achieves semantic-level unified management of cross-domain data, providing high-quality data support for real-time analysis and intelligent decision-making.

[0072] For example, in a supply chain management scenario, logistics data (location, temperature, humidity) and order information (customer, product, delivery time) are fused in a multimodal manner to achieve full-chain visual management.

[0073] The real-time analysis and perception module 130 is used to perform real-time analysis, anomaly detection, and trend prediction on multimodal fusion and classified data in a streaming data manner to obtain data analysis results.

[0074] In some embodiments, the data is used to perform real-time analysis and perception of multimodal fusion and classified storage data using a stream computing framework, wherein the stream computing framework includes anomaly detection and trend prediction using complex event processing, and buffering of real-time data; and to issue alarms using an automatic triggering method based on the results of real-time analysis and perception.

[0075] In some embodiments, the real-time analysis and perception module 130 further includes: streaming data processing, based on Apache Flink or Kafka Streams, to achieve millisecond-level data processing and dynamic response; an event-driven model, using CEP (Complex Event Processing) to achieve real-time detection and correlation analysis of complex events; time series analysis, using time series models such as sliding windows and skip windows to achieve real-time data aggregation and trend prediction; intelligent early warning, using machine learning models and rule engines to achieve anomaly detection and real-time early warning; and data caching and buffering, which can cache data to ensure data integrity in the event of network fluctuations or system anomalies.

[0076] For example, in financial risk control scenarios, abnormal transaction behavior can be detected by monitoring transaction data in real time, and risk warnings can be triggered within milliseconds.

[0077] The intelligent decision engine module 140 is used to make intelligent decisions and adaptive optimizations based on the data analysis results to obtain the decision results.

[0078] In some embodiments, the intelligent decision engine module 140 includes: classifying, regressing, and judging the governance time of the data source using classification decision, regression prediction, reinforcement learning algorithms, and multi-model collaboration mechanisms, and performing model self-learning and self-optimization based on historical feedback;

[0079] The classification decision-making process uses a dynamic weight model based on the feature set of governance indicators to perform soft discrimination on multi-label targets. It also employs an adaptive feature ablation mechanism, dynamically adjusting weights based on feature contribution during the classification process to address label shift issues under different governance tasks. The classification decision-making model... for:

[0080]

[0081] Indicates the first One governance characteristic, Indicates its presence in the label The system dynamically updates the weights based on governance feedback data to optimize classification accuracy.

[0082] The regression prediction uses a weighted regression model with a time decay factor. This model uses different time decay factors to study the trend evolution of governance indicators in terms of short-term sensitivity and long-term stability. It enhances the predictive contribution of recent data by using decay weights, thus improving its responsiveness to changing nodes. The weighted regression model is one such example. for:

[0083]

[0084] For the intercept term, For regression coefficients, For the first The independent variables are Time observations, The total number of independent variables. The attenuation rate, For reference only;

[0085] The reinforcement learning algorithm employs a reward function that mitigates feedback latency. The ability to accumulate results in non-real-time scenarios is accumulated, including the reward function. A delayed feedback hierarchical reward mechanism is adopted, which records both immediate and cumulative rewards during the reinforcement learning process. The reward function... for:

[0086]

[0087] The efficiency coefficient representing delayed feedback. As a discount factor, For the first The actual reward at any moment.

[0088] In some embodiments, the intelligent decision engine module 140 further includes:

[0089] The classification decision-making process can employ algorithms such as decision trees, support vector machines (SVM), and random forests to achieve classification decisions for multi-class data; regression prediction can be based on algorithms such as linear regression, logistic regression, and XGBoost to achieve prediction and trend analysis of continuous data; reinforcement learning can utilize algorithms such as Q-Learning and DQN to achieve adaptive optimization and long-term decision planning; multi-objective optimization is based on methods such as genetic algorithms and particle swarm optimization to achieve multi-objective optimization in complex business scenarios; and model self-learning improves the accuracy and efficiency of decision-making by supporting online model updates and offline training.

[0090] For example, in smart manufacturing scenarios, real-time data from the production line can be combined to optimize production scheduling and improve equipment utilization and capacity.

[0091] The platform collaboration governance module 150 is used to facilitate data collaboration and business integration between different systems using a modular architecture, cross-platform data fusion, enterprise data bus, and multi-tenant support.

[0092] In some embodiments, the platform collaborative governance module 150 is used to perform data sharing, task scheduling and role and permission coordination among multiple systems, and to perform multi-dimensional business collaboration and governance process linkage based on the enterprise-level data bus and multi-tenant structure.

[0093] The security and privacy protection module 160 is used to encrypt and protect system data using layered encryption, data anonymization, key management and access control, and to authenticate and control access to visitors.

[0094] In some embodiments, the security and privacy protection module 160 is used to encrypt and protect system data using a layered encryption method; it is also used to perform data backtracking and security review on the accessed system data using audit logs.

[0095] In some embodiments, the security and privacy protection module 160 is also used to employ encryption algorithms such as SSL / TLS, AES, and RSA to achieve data transmission security; data desensitization to desensitize sensitive data, protect user privacy, and avoid the risk of data leakage; key management to employ key management systems such as HSM and KMS to achieve secure key storage and access control; and access control to achieve fine-grained control of data access based on RBAC, ABAC, and PBAC models.

[0096] For example, in a financial transaction system, transaction data is encrypted using AES-256 during transmission and storage, while multi-factor authentication (MFA) is used to ensure account security.

[0097] The system management and operation module 170 is used for monitoring, fault recovery and performance optimization using log management, anomaly detection, vulnerability scanning and automated operation and maintenance methods.

[0098] In some embodiments, the system management and maintenance module 170 is used to encrypt and protect system data using a layered encryption method; it is also used to perform data backtracking and security review on accessed system data using audit logs.

[0099] In some embodiments, the system management and maintenance module 170 further includes:

[0100] Log management utilizes ELK (Elasticsearch, Logstash, Kibana) for real-time log collection and analysis, improving troubleshooting efficiency; anomaly detection and recovery leverages machine learning models to detect abnormal behavior in real time, enabling automatic fault recovery; vulnerability scanning and security testing are performed regularly to ensure overall system security; automated operations and maintenance support automated deployment, elastic scaling, and intelligent monitoring reduces operational costs and improves system reliability.

[0101] For example, in large e-commerce platforms, the operation and maintenance system can monitor the order processing status in real time and automatically switch traffic and recover from faults when anomalies occur.

[0102] The interface management module 180 is used to perform data interaction and function integration with external systems and the digital governance analysis system of the digital economy through an open API interface.

[0103] In some embodiments, the interface management module 180 is used to monitor the status, detect anomalies, track logs, and automatically recover the digital governance analysis system of the digital economy using log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods; and to deploy the digital governance analysis system of the digital economy using a containerized approach.

[0104] In some embodiments, reference Figure 2 The flowchart illustrating the digital governance analysis methodology for the digital economy shown includes:

[0105] S100 collects raw data from multiple data sources and preprocesses the raw data to obtain preprocessed raw data.

[0106] S200 performs multimodal fusion and classification storage on the preprocessed raw data, and also retrieves and queries the multimodal fusion and classification stored data.

[0107] The S300 uses streaming data to perform real-time analysis, anomaly detection, and trend prediction on multimodal fusion and classified storage data, and obtains data analysis results.

[0108] The S400 performs intelligent decision-making and adaptive optimization based on the data analysis results to obtain the final decision.

[0109] S500 adopts a modular architecture, cross-platform data fusion, enterprise data bus, and multi-tenant support to enable data collaboration and business integration between different systems.

[0110] The S600 employs layered encryption, data anonymization, key management, and access control to encrypt and protect system data, and performs identity authentication and access control for visitors.

[0111] The S700 employs log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods for monitoring, fault recovery, and performance optimization.

[0112] S800 enables data interaction and functional integration between external systems and the digital governance analysis system of the digital economy through an open API interface.

[0113] It is understood that the embodiments of the present invention achieve the following technical effects:

[0114] (1) Multi-source data fusion:

[0115] It can collect data from various sources, including IoT devices, enterprise information systems, internet data, and mobile terminals, supporting unified processing of structured, unstructured, and semi-structured data. Efficient data preprocessing, through data cleaning, format conversion, anomaly detection, and tagging, effectively improves data accuracy and consistency, reducing the complexity of subsequent analysis. Edge computing capabilities are provided by deploying edge nodes near the data source, reducing data transmission latency, improving real-time response capabilities, and reducing the pressure on central computing. Strong protocol compatibility is ensured, supporting REST. Multiple data transmission protocols such as API, MQTT, and WebSocket ensure the flexibility and compatibility of data acquisition; multimodal data processing supports the unified integration of structured, unstructured, and semi-structured data, enhancing the comprehensiveness and depth of analysis; a distributed storage architecture utilizes distributed storage technology to achieve efficient storage and management of massive amounts of data, possessing high scalability and high reliability; hot and cold data separation is implemented based on data access frequency to separate hot and cold data storage, reducing storage costs and improving access efficiency; efficient data compression and deduplication reduce storage space usage, optimize transmission efficiency, and improve overall system performance; data consistency and integrity are guaranteed based on hash verification and timestamp verification to ensure the integrity and consistency of data during storage and transmission; intelligent data management, through metadata management, enables efficient data retrieval, rapid location, and categorized storage; millisecond-level... The system boasts several key features: responsiveness, millisecond-level data processing and real-time response based on a stream computing framework, meeting the needs of high-frequency trading, financial risk control, and other scenarios; complex event processing (CEP) supporting complex event detection and correlation analysis, enabling real-time identification of abnormal behavior and potential risks; intelligent early warning and buffering mechanisms caching data in real-time during network fluctuations or system anomalies to ensure data integrity and business continuity; time series analysis supporting multi-dimensional, multi-time-window dynamic data analysis to meet the needs of different business scenarios; dynamic perception and trend prediction combining historical and real-time data to accurately predict future trends, providing support for decision optimization; and further, the system introduces dynamic bandwidth adaptive compression and edge node collaborative scheduling mechanisms during the acquisition phase, automatically adjusting the compression algorithm based on the network environment and balancing the load across multiple nodes to ensure the continuity of acquisition and data integrity.

[0116] (2) Precise decision support,

[0117] Based on classification decision-making, regression prediction, and reinforcement learning algorithms, it achieves accurate decision-making for complex economic activities; multi-objective optimization, through intelligent algorithms such as genetic algorithms and particle swarm optimization, enables multi-objective optimization in complex business scenarios; adaptive learning and feedback mechanisms can dynamically adjust model parameters based on real-time data feedback, improving the accuracy and efficiency of decision-making; efficient resource scheduling, through model self-learning mechanisms, enables intelligent allocation and dynamic optimization of resources, improving overall system efficiency; long-cycle optimization capabilities support long-term strategic decision-making and short-term dynamic optimization, meeting different levels of business needs; comprehensive data protection, through layered encryption, data anonymization, and key management, ensures data security during transmission and storage; access control and authentication, based on R... The BAC, ABAC, and PBAC models enable fine-grained access control for data, ensuring data compliance. Data integrity verification employs hash algorithms and digital signature technology to ensure data integrity during transmission and storage. Anomaly detection and protection features automatic anomaly detection, intrusion prevention, and data leakage protection, enhancing the overall system's security and stability. Data backtracking and auditing support comprehensive data auditing and backtracking functions, meeting the data management needs of high-security scenarios. In the intelligent decision-making process, this invention adds an adaptive feature ablation mechanism, a multi-scale time decay function, and a delayed feedback hierarchical reward mechanism, enabling optimization of feature weights and balancing short-term and long-term objectives in dynamic scenarios, thereby improving the robustness and interpretability of decisions.

[0118] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, considering the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed in the embodiments of the invention, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0119] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can include, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0121] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0122] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0123] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0124] Although embodiments of the invention have been shown and described, those skilled in the art will understand 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 claims and their equivalents.

[0125] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A digital governance analysis system for the digital economy, characterized in that, include: The data acquisition module is used to collect raw data from multiple data sources and preprocess the raw data to obtain preprocessed raw data. The data fusion and storage module is used to perform multimodal fusion and classification storage of preprocessed raw data, and also to retrieve and query the multimodal fused and classified data. The real-time analysis and perception module is used to perform real-time analysis, anomaly detection, and trend prediction on multimodal fusion and classified data in a streaming data manner to obtain data analysis results. The intelligent decision engine module is used to make intelligent decisions and adaptive optimizations based on data analysis results to obtain decision results. The platform collaboration and governance module is used to facilitate data collaboration and business integration between different systems using a modular architecture, cross-platform data fusion, enterprise data bus, and multi-tenant support. The security and privacy protection module is used to encrypt and protect system data using layered encryption, data anonymization, key management, and access control, and to authenticate and control visitors. The system management and operation module is used for monitoring, fault recovery, and performance optimization using log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods. The interface management module is used to enable data interaction and function integration between external systems and the digital governance analysis system of the digital economy through open API interfaces.

2. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The data acquisition module includes: The system is configured to collect the raw data from IoT devices, enterprise information systems, internet data, and mobile terminals; to perform preprocessing on the raw data using at least one of data cleaning, format conversion, anomaly detection, and tagging; and to perform local collection and response processing on the data to be collected via edge computing.

3. The digital governance analysis system for the digital economy according to claim 2, characterized in that, The data acquisition module is also used for: The compression algorithm is switched based on real-time network status, and integrity is verified by hash digest verification before and after data transmission. The load status is shared among multiple edge acquisition nodes, and the acquisition tasks of the edge acquisition nodes are dynamically allocated through a collaborative scheduling mechanism. Data from multiple data sources is acquired in real time using REST API, MQTT, and WebSocket protocols. The raw data is processed and pre-filtered locally by edge computing nodes. The raw data is categorized by label according to data source, time, and type.

4. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The data fusion and storage module includes: This is used to perform multimodal fusion on the raw data, wherein multimodal fusion includes associating and mapping the raw data based on timestamps, geographic locations, and tag information, and wherein the raw data includes structured data, unstructured data, and semi-structured data; It is used to process and store multimodal fusion data by employing a hot and cold data separation strategy, as well as compression, deduplication, and consistency verification. A tag hash tree is used to establish a mapping tree structure between tags and data, and data retrieval is performed through the mapping. Spatiotemporal semantic mapping is used to align and integrate cross-domain data based on the triplet of timestamp, geographic location, and business tag.

5. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The real-time analysis and sensing module includes: This is used to perform real-time analysis and perception of multimodal fusion and classified storage data using a stream computing framework. The stream computing framework includes anomaly detection and trend prediction using complex event processing, as well as buffering of real-time data. It is also used to automatically trigger alarms based on the results of real-time analysis and perception.

6. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The intelligent decision engine module includes: It is used to classify, regress, and make reinforcement learning judgments on the governance time of data sources using classification decision-making, regression prediction, reinforcement learning algorithms and multi-model collaboration mechanisms, and to perform model self-learning and self-optimization based on historical feedback; The classification decision-making process uses a dynamic weight model based on the feature set of governance indicators to perform soft discrimination on multi-label targets, and employs an adaptive feature ablation mechanism to dynamically adjust weights based on feature contribution during the classification process. The classification decision-making model... for: ； Indicates the first One governance characteristic, Indicates its presence in the label The system dynamically updates the weights based on governance feedback data to optimize classification accuracy. The regression prediction uses a weighted regression model with a time decay factor. The evolution of governance indicators' trends in short-term sensitivity and long-term stability was studied using different time decay factors, with a weighted regression model. for: ； For the intercept term, For regression coefficients, For the first The independent variables are Time observations, The total number of independent variables. The attenuation rate, For reference only; The reinforcement learning algorithm employs a reward function that mitigates feedback latency. The ability to accumulate results in non-real-time scenarios is accumulated, including the reward function. A delayed feedback hierarchical reward mechanism is adopted, which records both immediate and cumulative rewards during the reinforcement learning process. The reward function... for: ； The efficiency coefficient representing delayed feedback. As a discount factor, For the first The actual reward at any moment.

7. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The platform collaborative governance module is used for: It enables data sharing, task scheduling, and role-based access control among multiple systems, and facilitates multi-dimensional business collaboration and governance process linkage based on the enterprise-level data bus and multi-tenant architecture.

8. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The security and privacy protection module includes: It is used to encrypt and protect system data using a layered encryption method; it is also used to perform data backtracking and security auditing on accessed system data using audit logs.

9. The digital governance analysis system for the digital economy according to claim 1, characterized in that, The system management and maintenance module includes: This system is used for status monitoring, anomaly detection, log tracking, and automatic recovery of the digital governance analysis system for the digital economy, which employs log management, anomaly detection, vulnerability scanning, and automated operation and maintenance. It is also used for deploying the digital governance analysis system for the digital economy using a containerized approach.

10. A digital governance analysis method for the digital economy according to any one of claims 1-9, characterized in that, include: Raw data is collected from multiple data sources and preprocessed to obtain preprocessed raw data; The preprocessed raw data is fused in multiple modes and classified for storage. The data that has been fused and classified is then retrieved and queried. The multimodal fusion and classified storage data are analyzed in real time, anomaly detection and trend prediction are performed using streaming data to obtain data analysis results; Intelligent decision-making and adaptive optimization are performed on the data analysis results to obtain the final decision. It adopts a modular architecture, cross-platform data fusion, enterprise data bus, and multi-tenant support to achieve data collaboration and business integration between different systems; The system employs layered encryption, data anonymization, key management, and access control to encrypt and protect system data, and performs identity authentication and access control for visitors. The system employs log management, anomaly detection, vulnerability scanning, and automated operation and maintenance methods for monitoring, fault recovery, and performance optimization. It enables data interaction and functional integration between external systems and the digital governance analysis system of the digital economy through open API interfaces.

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