88 results about "Digital economy" patented technology

The invention relates to the technical field of digital economy, in particular to a visual display method based on multi-source economic data mining. The method comprises the following steps: acquiring multi-source e-commerce economic data; performing data cleaning and standardization processing on the multi-source e-commerce economic data, and performing data quality evaluation and screening to obtain standardized e-commerce economic data; performing multi-dimensional data dimension reduction analysis according to the standardized e-commerce economic data, and performing feature importance sorting to obtain key economic index data; and performing time sequence analysis on the standardized e-commerce economic data according to the key economic index data, and performing sales trend and user behavior prediction based on key economic indexes through a plurality of preset deep learning models to obtain trend prediction data. According to the method, the interpretive data generation of the economic index relation network is realized in combination with the economic knowledge graph, and the interpretability of data analysis is enhanced.

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

The invention discloses a zero training method for SQL (Structured Query Language) generation based on a large language model. The zero training method comprises an offline part and an online part, the core technology points include the EMS standard database, data enhancement and model fine tuning, retrieval enhancement generation based on named entity recognition and SQL error correction based on an abstract syntax tree. The invention provides a T2S solution for zero training of numerous EMS industry users; according to the method, a model is finely adjusted by using data of 2-5 users in advance, customers in other numerous industries do not need fine adjustment training, namely zero training, and can deploy the T2S intelligent agent with the accuracy of 99% as long as the solution is accessed, so that the landing cost of the T2S in the EMS industry is greatly reduced, and data analysis becomes a common technology; the system and the method promote employees of more enterprises to use natural languages to directly query a database and automatically and intelligently analyze data, provide quicker, more accurate and more intelligent data analysis basis for decision making of the enterprises, reduce cost and improve efficiency, and promote development of digital economy and entity economy.

The invention discloses an AI-driven digital economic supply chain optimization method. According to the invention, a novel supply chain system with active pre-judgment and dynamic adaptation is constructed. The core breakthrough of the method is that passive risk management is converted into a opportunity creation mechanism, and traditional supply chain decision logic is remodeled by relying on a virtual-real symbiotic intelligent deduction system. The generative adversarial network simulates compound crisis scenes such as geographical conflicts and digital asset transactions in real time, and a quantum optimization algorithm and Monte Carlo tree search are combined, so that the system not only can predict a conduction path of global chip shortage to a production line, but also can generate breakthrough solutions such as cross-chain encryption settlement and redundant node resource securitization. The pre-judgment type deduction mechanism, block chain dynamic scheduling and metabolic resource configuration form a closed loop, and in response to encrypted currency flash collapse, a decentralized payment channel can be automatically switched to avoid risks, a digital asset arbitrage window can be captured through an intelligent contract, and co-evolution of risk resolution and value increment is realized.

The invention discloses a credible distributed digital identity-based digital asset right confirmation circulation and traceability method, which is used for signing and issuing credible distributed digital identities to participants and generating digital asset identities and ownership verification certificates for registered digital assets, so that the right confirmation process is simplified, and the registration right confirmation efficiency and authenticity are improved. Besides, by constructing a digital identity chain system based on the block chain and the distributed digital identity, full-life-cycle evidence storage and source tracing of the digital assets are realized, and through the characteristics of decentralization, non-tampering and transparency of the block chain, the efficiency and transparency of digital asset circulation are improved, the transaction cost is reduced, and the transaction efficiency is improved. And the law supervision process is simplified, so that the healthy development of digital economy is effectively promoted.

The invention provides a digital economic information management method and system based on big data analysis, and the method comprises the steps: integrating the data of a consumption end, a production end and a server end through a unified platform, guaranteeing the transmission safety through the combination of differential privacy and TLS 1.3, recording the full life cycle of data through a segmented block chain, supporting the dynamic authorization and fine-grained authority control, and improving the efficiency. The method comprises the following steps: verifying data purposes by combining zero-knowledge proof, fusing NLP, image processing and time sequence analysis, constructing a knowledge graph, generating dynamic tags and composite features, simulating decision influence through anti-fact analysis, supporting generation of a Pareto optimal solution by an interactive decision tree, and providing a market trend thermodynamic diagram and a supply chain risk graph. Customized suggestions are pushed to an ERP system in real time, sensitive data are protected based on role permission and lightweight homomorphic encryption, abnormal behavior monitoring and zero-knowledge proof compliance review are combined, a service scene is simulated through an A / B sandbox, and low-code community drives demand iteration.

The invention provides a digital economic label updating system and method based on big data analysis, and relates to the technical field of label management, and the method comprises the steps: building a label map reflecting the behavior semantic features of a target object based on a cross mapping relation between explicit behavior features and implicit behavior features in the behavior semantic features; performing associated semantic modeling of a context scene on label nodes in the label atlas, and extracting semantic expression weights of all labels; detecting a matching deviation degree between the current tag and the latest behavior semantic feature of the target object, and when the matching deviation degree exceeds a preset deviation degree threshold value, judging that tag semantic deviation exists; when label semantic deviation is detected, a label updating strategy is determined according to the semantic expression weights of all the labels in different context scenes and the semantic deviation characteristics of the current label, and then the labels of the behavior characteristics of the target object are updated according to the label updating strategy. According to the invention, self-adaptive updating management of the user behavior tag in a digital economic environment can be realized.

The invention discloses a data right confirmation method based on subject and object binding and a data living body. The data right confirmation method comprises the following steps: data right confirmation based on subject and object binding; and behavior control is operated based on data bound by the subject and the object. Data right confirmation based on subject and object binding is realized through an active data object ('data living body 'for short), data rights such as data ownership and use right are clearly defined, and a technical means is provided for guaranteeing rights and interests of data subjects and effectively controlling data operation behaviors, so that safe circulation of data in a legal and compliant framework is ensured, and the data security is improved. Efficient circulation and reasonable utilization of data elements are guaranteed, data market vitality and resource allocation efficiency are improved, and robust development of digital economy and digital China is promoted.

The invention discloses an e-commerce entity economy and digital economy fusion evaluation method based on a large language model, and relates to the technical field of e-commerce economy fusion management. The e-commerce entity economy and digital economy fusion evaluation method based on the large language model comprises the following steps: evaluating data reliability; evaluating the data transmission capability; evaluating the fusion processing capability; and data fusion. According to the invention, the data reliability evaluation is carried out through the obtained data reliability index, then whether the data transmission capability adjustment is carried out is judged based on the obtained data transmission capability evaluation index, and then whether the data processing capability adjustment is carried out is judged based on the obtained fusion processing capability evaluation index. And finally, data fusion is performed based on the reliability-qualified e-commerce digital related data after data processing capability adjustment, so that the effect of improving the accuracy of e-commerce entity economy and digital economy fusion evaluation data is achieved, and the problem of inaccurate e-commerce entity economy and digital economy fusion evaluation data in the prior art is solved.

The invention provides an archive classification and filing management method based on digital economy and data governance, and relates to the technical field of computer data processing, and the method comprises the following steps: 101, establishing a standardized system; 102, constructing a unified data acquisition platform; 103, performing data auditing and returning processing; 104, transferring and archiving the data; and 105, data multi-dimensional utilization. According to the invention, standardized integration of full-service data is realized, heterogeneous confusion is eliminated, and data islands are radically treated; through automatic acquisition verification, manual auditing and intelligent return closed loop, completeness and availability are ensured, and the quality of the whole life cycle of data is guaranteed; and secondly, through intensive storage and multi-dimensional analysis, supporting transaction trend prediction, archiving monitoring and business decision, directly driving enterprise data governance and digital economic transformation, and enabling data value release.

The invention provides a digital economy risk assessment method and system based on artificial intelligence, and the method comprises the steps: collecting risk data related to digital economy from a plurality of preset data sources, and fusing the risk data to form a risk analysis data set of the digital economy; performing risk feature extraction on the risk data set in a plurality of preset dimensions to obtain risk features of the digital economy in each preset dimension; obtaining an operation log of the digital economy, training a preset unsupervised learning model by adopting the operation log, and obtaining an abnormal score of the digital economy according to the unsupervised learning model; and obtaining a preset intelligent risk assessment model, and fusing the risk features and the abnormal score by using the preset intelligent risk assessment model to obtain a risk score of the digital economy. According to the technical scheme, the accuracy and reliability of the risk assessment result can be improved.

The invention relates to the technical field of financial data intelligence, and discloses a financial personalized management classification system based on digital economy. According to the system, a dynamic mapping network is formed through weaving by constructing a user composite intention profile. And injecting a probe into a network node, performing compliance deconstruction and verification on a response sequence, driving behavior partition structure recombination, and performing re-calibration on the network according to the behavior partition structure recombination. And capturing an intention drift trajectory through a real-time data stream, dynamically and elastically telescoping network link weight, and finally fusing a strategy to complete management classification. According to the system, online driving optimization of data partitions in a compliance state and real-time dynamic shaping of a decision network by intention drift are realized, a closed-loop adaptive mechanism from bottom data to upper rules is formed, and classification accuracy, timeliness and compliance adaptability of financial personalized management in a dynamic environment are improved.

The embodiment of the invention discloses a main-sub chain cross-chain communication system and method based on a secondary node, a more flexible and compatible framework is built by introducing the secondary node, a cross-chain protocol and a forward and reverse contract technology, and a safe and recoverable transaction environment is provided. According to the method, data sharing and interoperability among different sub-chains are promoted, the utilization efficiency and value circulation of data are improved, and different service requirements and application scenes are supported. Besides, the system can reduce the development and operation and maintenance cost, enhances the security of the whole system by designing a secure cross-chain mechanism, supports large-scale application scenes such as finance and supply chain management, can promote technical innovation and standardization, enhances the trust of a user to the block chain technology, promotes cooperation among different industries, and improves the user experience. And the system is ensured to run in a law and policy framework, so that a solid foundation is laid for constructing more open, interconnected and secure digital economic ecology.

The application provides a dynamic adjustment method for digital economic policy response, comprising: performing clearance backwriting lag evaluation on the time dislocation span, identifying orders exceeding the normal backwriting cycle to form an order composition backlog order set; determining the normal fluctuation range of trade volume without policy intervention based on the same period historical trade volume before policy implementation to construct a baseline interval; performing growth significance evaluation on the trade volume after stripping the delay interference by comparing the trade volume after stripping the delay interference with the baseline interval to identify a true growth signal; performing intersection operation on the time interval corresponding to the true growth signal and the concentrated backwriting period, if the intersection is not empty, then deducting the part occupied by the concentrated release amount in the concentrated backwriting period from the trade volume increment corresponding to the true growth signal to obtain a trade policy response evaluation result reflecting the actual incentive effect of the policy.

The present invention is applicable to the field of risk management and assessment technology, and in particular relates to a digital economy risk management and assessment method and system, the method comprising: constructing a repository for storing digital economic data, setting up a data management platform, receiving call requests uploaded by users, identifying access devices, and reading out the software and hardware features of the access devices, wherein the software and hardware features at least include: processor identifier, hard disk serial number, browser information, administrator name, and device IP; configuring a splicing sequence, and splicing the software and hardware features to obtain a device feature code. By determining the permission type, the present invention can refine the permissions of the digital economy, thereby achieving more detailed permission management. By determining the target permissions and exception logs, it can perform a risk assessment on the calling process of digital economic data, ensure the security of data calling, and greatly improve the credibility and security of the digital economy.

This invention belongs to the field of text retrieval technology, specifically referring to an automatic retrieval system for digital economy texts. The system includes a basic task instance identification module, a key instance association mining module, a tag local score estimation module, a global tag configuration module, a noise tag configuration generation module, and a precise digital economy text retrieval application module. This solution uses two dedicated multilayer perceptrons to calculate semantic and syntactic dependency weights and fuse them to obtain the final association strength, achieving precise capture of digital economy instances and their associations. It combines global tag configuration to quantify the tightness of instance tag associations, and uses the noise tag configuration generation module to transform the global optimal tag configuration problem into a binary classification problem. By constructing a contrastive loss function and optimizing it through backpropagation, it efficiently solves the problem of computational explosion, achieving concept-based precise retrieval and intelligent decision support, providing users with an efficient digital economy information acquisition experience.

The invention relates to data asset management and digital economy, in particular to a digital economy system based on consumption data asset value consensus and internal and external circulation, which comprises a value consensus module for calculating basic value points of data assets based on a value measurement index system; the token issuing and managing module is used for issuing equity tokens, value tokens and utility tokens, and adjusting the actual value of the value tokens according to the basic value points of the data assets and the dynamic adjustment coefficient; the internal circulation economy module is used for users to use tokens in internal circulation economy ecology; the external circular economy module is used for users to use tokens in external circular economy ecology, meanwhile, transaction conditions of data assets serve as value proof of the data assets, and actual values of the tokens with corresponding values are indirectly influenced by influencing derivative values of the data assets; according to the technical scheme provided by the invention, the defects that a multi-dimensional quantitative consensus mechanism of the data asset value is difficult to construct and sustainable internal and external circulation economic and ecological design is lacked can be effectively overcome.

The invention discloses a digital economic high-frequency transaction fluctuation prediction method based on a large model, and relates to the technical field of agricultural Internet, and the method comprises the following steps: S1, obtaining agricultural high-frequency transaction data from a transaction large model in real time; s2, performing time sequence modeling on the preprocessed agricultural high-frequency transaction data by using an autoregression variational inference model; s3, performing nonlinear feature extraction modeling on the agricultural high-frequency transaction data by using a large-scale deep neural network; s4, constructing a digital economic agriculture high-frequency transaction fluctuation prediction model; s5, dynamically updating model parameters by using the latest agricultural high-frequency transaction data according to the digital economic agricultural high-frequency transaction fluctuation prediction model to form a real-time feedback mechanism; and S6, predicting a future market fluctuation trend based on the updated digital economic agriculture high-frequency transaction fluctuation prediction model, and outputting a predicted fluctuation probability and a corresponding uncertainty interval. The prediction stability of the model under extreme market conditions is improved.

The application provides a kind of industrial park knowledge graph construction method and system fusing multi-source heterogeneous data, pass;Different sources about digital economy knowledge and digital economy industrial park data are integrated into the structured knowledge with digital economy industrial park as the center by data cleaning;According to the labeling strategy, the entity relationship in the structured knowledge is labeled;Based on convolutional neural network, a digital economy industrial park knowledge fusion model is constructed and trained, which is used for preliminary extraction of entity relationship in structured knowledge;Repeated entity relationship triples are retrieved and entity relationship extraction is performed, so as to construct entity-relation-entity triples of digital economy industrial park knowledge graph, and knowledge fusion is carried out.The application realizes the rapid construction of large-scale digital economy industrial park knowledge graph, which helps to improve the information analysis and processing efficiency, facilitates to find the hidden influence and transmission effect between digital economy industrial park elements, and forms the scale industrial analysis results.

This invention relates to the field of blockchain technology, specifically to a blockchain-based digital economy e-commerce management method and system, comprising the following steps: acquiring order data, extracting product codes, transaction amounts, and addresses of buyers and sellers; concatenating and executing SHA-256 to generate an order digest and constructing associated data of transaction participants; extracting the digest, inputting elliptic curve signature to generate a transaction authorization certificate; collecting inventory changes, logistics status, and payment time series data to construct an e-commerce transaction state chain; encapsulating the blockchain, writing new block records, and executing the path to generate on-chain transaction record certificates; calling the certificate to read the state chain, verifying the consistency of signatures and data, and outputting an e-commerce transaction integrity certificate. In this invention, by hashing and elliptic curve signing key order data, identity encryption verification and data confirmation are achieved. On-chain records of the transaction state chain are constructed based on signatures and time series data, ensuring consistency between inventory, payment, and logistics. On-chain certificates support traceability verification, improving the traceability, security, and consistency of e-commerce transactions.

The invention discloses a regional digital economic data management method and system, and the method comprises the steps: summarizing all files in a database through a large language model to obtain a plurality of database file summarized descriptions; a semantic understanding technology is introduced at the rear end to perform semantic analysis and semantic matching measurement of summarizing description and retrieval input of a plurality of database files so as to realize rapid and accurate data retrieval, so that efficient data storage and management functions are provided, the security and reliability of data are ensured, and the data retrieval efficiency is improved. And meanwhile, the user can be helped to quickly find required data and carry out analysis and decision making, and economic growth and innovation are promoted.

The application provides an archive classification and archiving management method based on digital economy and data governance, and relates to the technical field of computer data processing, wherein the method comprises: 101, establishing a standardized system; 102, constructing a unified data collection platform; 103, data auditing and returning processing; 104, data transfer and archiving; 105, multi-dimensional utilization of data. The application realizes the standardized integration of all business data, eliminates heterogeneous chaos, and radically solves the data island problem; the automatic collection verification + manual auditing + intelligent return closed loop ensures the integrity and availability and guarantees the quality of the data throughout the life cycle; secondly, through intensive storage and multi-dimensional analysis, the application supports transaction trend prediction, archiving monitoring and business decision-making, directly drives enterprise data governance and digital economic transformation, and empowers data value release.

The invention provides an intelligent data analysis system and method oriented to a digital economic platform, and relates to the technical field of data analysis. Performing parallel modal coding on the multi-source service data stream, and mapping feature vectors of different modals to a shared semantic hidden space to generate a unified representation vector; constructing a dynamic business knowledge graph of the digital economic platform, updating and reasoning the dynamic business knowledge graph, and outputting a context sensing vector; receiving an analysis task description issued by the upper-layer service scene, and constructing a data view matched with different service scenes through the analysis task description and the context sensing vector; and performing semantic analysis on the business scene demand of the digital economic platform according to the data view, and generating a semantic analysis report of the business scene demand. According to the method and the device, semantic-level deep fusion of the multi-modal service data of the digital economic platform can be realized, so that the multi-scene analysis requirements of the digital economic platform are met.

The invention provides a digital architecture quantitative analysis intelligent processing system and method. The method comprises the following steps: statistically analyzing a digital quantization historical rule of digital economy, automatically reading digital quantization values of digital quantization economy enabling types in the digital economy in a classification form, selecting digital quantization historical time nodes, and calculating the digital quantization historical time nodes through a golden proportion operation module; calculating a digital quantization value after the digital quantization historical time node; comparing the golden proportion operation digital quantized value with a quantized historical actual statistical quantized value, and constructing a quantized golden proportion operation module; when digital quantization real-time numerical value updating information appears, gold proportion re-operation is dynamically triggered, and a continuous quantization gold proportion operation module is started; a core value cycle enabling increase amplitude value of an operation result is precisely analyzed, and the support stability of a digital economic structure and a quantitative numerical value structure is improved; quantization process digital resonance in digital economy is formed, the increase expectation and amplitude of the digital economy are boosted, and the enabling effect of the digital economy is enhanced.

The invention relates to the cross technical field of artificial intelligence and financial science and technology, in particular to a digital economic text automatic retrieval system based on a financial knowledge graph, which comprises a financial knowledge graph construction module, a text semantic embedding module, a graph enhancement module, a retrieval reasoning interaction module and a result interpretable verification module. According to the scheme, a comparative learning method is adopted to construct an attribute interpolation graph and implement joint spectrum enhancement, node attributes and a topological structure are unified in a spectral domain, discrimination of graph representation is improved, intra-class clustering is enhanced, noise is suppressed, and therefore semantic expression of the knowledge graph is optimized; a verifiable reasoning element path is generated by utilizing a large language model, a knowledge graph interface is iteratively called to carry out fact retrieval and chain reasoning, the authenticity and logic consistency of a reasoning chain are ensured by combining a fact verification mechanism and a fidelity verification mechanism, and the accuracy, interpretability and reliability of digital economic text retrieval are improved.

The invention relates to the technical field of digital economy, and provides a digital economy integration and optimization method and system based on an information chain, and the method comprises the following steps: data collection and integration: building a data integration platform, collecting market data, customer data and supply chain data, and integrating the data into a centralized data warehouse through ETL (extraction, conversion and loading); data analysis and mining: analyzing the integrated data based on a Transform model, and mining the market trend and demand; and real-time monitoring and prediction: predicting future market dynamics through an autoregression model by using an information chain and historical data, and supporting strategic decisions. Through ETL and standardization processing, the problem of multi-source data isomerism is solved, through combination of a Transform model and a self-attention mechanism, the market trend analysis accuracy is improved by 30%, the method is significantly superior to a traditional statistical method, through combination of an autoregression model and an information chain, real-time market monitoring and prediction are achieved, and the prediction error rate is reduced to 5% or below.

The invention relates to the technical field of data mining, in particular to a patent-driven digital economic evaluation method and system, and the method comprises the following steps: based on a digital economic patent database, distributing innovation participation proportions, summarizing main body distribution characteristics, combing the complete-period changes of legal states and operation behaviors, and analyzing industrial classification codes and obligee distribution. And evaluating the innovation level in a zoning manner, and outputting a digital economic grading index. According to the invention, through hierarchical induction of a patent subject sorting structure and a contribution proportion, dynamic performance of innovation participation dominance is enhanced, and interactive tracking of operation behaviors of obligees and a legal state cycle is combined, so that structured integration of multiple types of events in a patent life cycle is realized; according to the method, content association in a patent cluster is identified by utilizing technical content fusion characteristics, a multi-level collaborative mapping unit is constructed in combination with obligee behaviors and innovation participation data under industrial coding, and innovation characteristics are displayed in a refined manner in graded archiving of innovation ability expression.

The invention discloses a digital hub / FN root server system and method based on future network / FN and OVXDM overlapped multiplexing and FN / M virtual-real routing, and belongs to the technical field of FN network communication and information. The system is deployed in a digital center of a region according to a distributed design, and integrates six functional modules including an FN root server system, an OVXDM system, an FN / M routing system, a distributed storage and calculation module, an ad hoc network and toughness guarantee module and a security and credibility module; the digital centers are transversely connected to form a peer-to-peer network, and are longitudinally connected to form a global FN network covering land, sea, air and sky. And basic deployment, ad hoc network, equipment registration, intelligent routing communication, data processing application, flexible operation and autonomous recovery are realized. According to the method, the network is autonomously controllable, the transmission capacity addressing efficiency is improved, the network toughness is enhanced, data authentication and value conversion are realized, regional or global multi-level digital economy and the Internet of Things are adapted, and the problems of network sovereignty hidden dangers, performance bottlenecks, FN code global natural intercommunication and the like in the prior art are solved.

The invention discloses a digital economy network flow prediction method and system based on artificial intelligence, and relates to the technical field of digital economy monitoring. Comprising the following steps: acquiring historical network traffic time sequence data of a plurality of digital economic entity nodes, corresponding digital economic characteristic data and economic index data aligned with the historical network traffic time sequence data; based on the digital economic characteristic data, the economic index data and the historical network flow time sequence data, constructing a dynamic adjacency graph evolved along with time; the method comprises the following steps: processing historical network traffic time sequence data through a multi-scale time encoder to obtain a node time representation containing a multi-granularity time dependence feature; after node time representation and digital economic feature data are fused, graph volume accumulation combination is carried out in combination with a dynamic adjacency graph, a joint attention mechanism based on an economic index vector generation offset item is introduced, and node representation fused with space-time and economic semantics is generated; and performing initial traffic prediction based on node representation fusing time-space and economic semantics.

The invention of a blockchain + big data decision-making method and system for a digital economic ecosystem and a cloud service center is a trusted decision-making system and method based on the deep integration of blockchain and big data. The invention involves a complete business model and computer technology and artificial intelligence field. The method comprises: obtaining raw data from multiple source heterogeneous data sources, processing and generating standardized data packages, storing the hash value and evidence information to the blockchain to form data evidence credentials; based on the credentials, cross-domain association and feature fusion of multi-source data are performed to generate a fusion feature dataset; the dataset is input into a pre-trained interpretable artificial intelligence model to generate a trusted output with decision results and decision basis explanation; finally, the output is packaged as a standardized cloud service interface. Through the deep collaboration of blockchain and AI, the invention constructs a complete technical closed loop from "trusted data" to "transparent decision" to "convenient service"; effectively solves the problems of shallow integration of the two, non-transparent decision-making process and difficult service landing in the prior art, and significantly improves the credibility and efficiency of data-driven decision-making in the digital economic ecosystem.