Operation system and method for digital transformation project

Abstract

The invention relates to the technical field of digitization, in particular to a digitization transformation project operation system and method.The digitization transformation project operation system comprises a data intelligent core layer, an elastic infrastructure layer, a man-machine cooperation layer, a safety compliance layer and a value operation layer, and the data intelligent core layer is used for achieving cross-business-domain data management and AI model full-life-cycle management; the data credibility and the model practicability are ensured; according to the method, the defects of cross-service domain data rights and responsibilities disorder and insufficient sensitive information protection due to adoption of a centralized architecture in traditional data governance, and resource waste caused by AI model development and operation and maintenance splitting are overcome; according to the scheme, the service domain sovereignty is divided through the distributed data governance framework, the field-level authority control is realized in combination with the dynamic desensitization technology, and the AI model full-life-cycle management platform is established, so that efficient collaboration of cross-domain data and automatic recovery of model resources are realized, the data credibility and the model practicability are remarkably improved, and the risk of data abuse is reduced.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to an operation system and method for a digital transformation project. Background Art

[0002] In the digital transformation practices of traditional enterprises, data governance mostly adopts a centralized architecture, which makes it difficult to effectively divide data responsibilities across business domains. The protection of sensitive fields relies on static desensitization rules, which easily leads to the risk of data abuse; AI model development is separated from the production environment, resulting in a large amount of idle computing power that cannot be recovered.

[0003] The infrastructure layer generally relies on centralized cloud-based computing, while real-time control at the edge is constrained by network latency, making millisecond-level responses difficult to achieve in scenarios like industrial quality inspection. Microservice governance lacks proactive fault-tolerance mechanisms, making fault recovery time prohibitively long. Business process automation primarily focuses on structured data processing, lacking the ability to parse unstructured information such as documents and images, and lacking the ability to dynamically predict the physical environment. Complex operations still require manual intervention. Cross-border data flows face compliance requirements across multiple jurisdictions, and traditional permission policies cannot dynamically adapt to regional regulations. Audit traceability relies on manual verification, posing the risk of tampering.

[0004] Furthermore, the long-standing inactivity of massive enterprise data assets and the lack of quantitative standards for the hidden costs of technical debt have led to an imbalance in the input-output of digital transformation, making it difficult to achieve a sustainable closed-loop value chain. These systemic flaws have severely hampered the overall effectiveness of enterprises in building digital capabilities and transforming them into commercial value. Summary of the Invention

[0005] In order to overcome the problems raised in the above background technology, the present invention proposes an operation system and method for a digital transformation project.

[0006] The technical solution of the present invention is: an operation system for a digital transformation project, comprising:

[0007] The data intelligence core layer is used to implement cross-business domain data governance and AI model lifecycle management, ensuring data credibility and model practicality;

[0008] The elastic infrastructure layer is used to build a low-latency, highly available distributed technology infrastructure through edge computing and microservices governance;

[0009] The human-machine collaboration layer integrates multimodal AI and digital twins to automate complex processes and predict the dynamics of the physical world;

[0010] A security and compliance layer, based on a zero-trust architecture and blockchain evidence storage, enables dynamic permission control and audit traceability for cross-border data flows;

[0011] The value operation layer is used to transform data resources into quantifiable business value through digital asset securitization and technical debt management.

[0012] Preferably, the data intelligence core layer specifically includes:

[0013] A11: Distributed Data Governance Center, used to build an enterprise-level data governance framework based on the Data Mesh concept, to achieve ownership division, quality monitoring, and security management of data across business domains;

[0014] A12: AI model full lifecycle platform, covering the entire process of model development, deployment, monitoring, and retirement;

[0015] A13: Graph Neural Network Optimizer, used to solve large-scale combinatorial optimization problems based on graph neural networks at a manageable cost.

[0016] Preferably, the data intelligence core layer, when working, specifically includes:

[0017] A21: For a distributed data governance center, first, raw data is accessed from multiple source systems, and business domain ownership is divided according to the Data Mesh architecture. Then, a real-time dynamic desensitization engine is used to obfuscate sensitive fields based on role permissions. A field-level lineage tracking system is then used to automatically generate a data flow map, monitor data quality SLAs, and push abnormality alerts. Finally, standardized data is output to the downstream end. The dynamic desensitization engine uses the following formula to obfuscate sensitive fields based on role permissions:

[0018]

[0019] Where x is the original sensitive data string, r is the retention ratio, len(x) is the total length of the original data, and r·len(x) is the number of characters to be retained. is the floor function, To intercept the first r·len(x) characters starting from index 0, The mask symbol representing the remaining portion of the generated part;

[0020] A22: For the AI ​​model full lifecycle platform, model effectiveness indicators are first defined in the joint laboratory, and the pre-trained model library is called for transfer learning. Then, it is deployed to the production environment and connected to real-time data streams. The monitoring system continuously tracks prediction errors and call frequency. When the model performance falls below the threshold or is idle for more than 30 days, resource recycling is automatically triggered, and the recycled computing power is preferentially allocated to high-value new models.

[0021] A23: For the graph neural network optimizer, the business problem is first converted into a graph structure, the pre-trained general optimization GNN model is loaded, and the current business parameters are injected for fine-tuning. The approximate optimal solution is dynamically generated, and the result is pushed to the ERP system for execution. The actual execution effect data is collected to reversely optimize the model.

[0022] Preferably, the elastic infrastructure layer specifically includes:

[0023] A31: Edge intelligent node, including hardware layer, network layer and application layer. The hardware layer includes industrial equipment terminals, edge computing nodes and base stations. The hardware layer is used to provide industrial equipment terminals, edge computing hardware and 5G access equipment, and build real-time control and computing capabilities of the physical layer. The network layer adopts TSN time-sensitive network. The application layer includes real-time control algorithm containers, which are used to run industrial control algorithms and AI quality inspection models, complete local real-time decision-making and selectively transmit key data back to the cloud.

[0024] A32: Microservice hub, including control plane, data plane and management plane. The control plane includes Istio service mesh and intelligent scheduling engine. The control plane is used to unify the management of traffic rules and dynamically optimize service routing strategies through Istio service mesh and intelligent scheduling algorithm. The data plane adopts Envoy Sidecar agent and elastic policy library to ensure service stability. The management plane is used to integrate chaos engineering to actively simulate failures and combine with the monitoring and alarm system to achieve full-link observability and rapid recovery.

[0025] Preferably, the human-machine collaboration layer includes:

[0026] A41: A multimodal automation engine, which integrates document understanding, RPA process automation, and digital workforce collaboration to achieve intelligent processing of unstructured data and closed-loop business processes.

[0027] A42: Predictive digital twins are used to build dynamic digital mappings of the physical world based on physical information neural networks and cross-system data fusion technology, enabling real-time simulation and forward-looking decision-making.

[0028] Preferably, the human-machine collaboration layer, when working, specifically includes:

[0029] S11: Task triggering, which starts the process through user requests and system warnings. Events are connected to the task routing engine via APIs.

[0030] S12: Multimodal perception, parsing documents and images through a multimodal automation engine and building physical constraint models using predictive digital twins and real-time data;

[0031] S13: Intelligent decision-making, using a multimodal automation engine to execute RPA based on confidence thresholds, while a predictive digital twin deduces risks and generates optimization solutions;

[0032] S14: Human-machine collaborative execution, with RPA automated operations and manual review running in parallel, enabling real-time monitoring of execution status;

[0033] S15: Feedback and continuous optimization: using actual results to reflux the training model and dynamically adjust thresholds and physical parameters. The update principle formula of the multimodal automation engine is:

[0034]

[0035] Among them, θ new is the updated model parameter, θ is the current parameter of the multimodal automation engine, η is the learning rate, L human is the loss function calculated based on manual feedback data, is the loss function L human The vector of partial derivatives with respect to the parameters θ.

[0036] Preferably, the security compliance layer includes:

[0037] A51: Zero Trust Dynamic Firewall, used to build a continuous verification system based on a software-defined perimeter architecture. It dynamically generates minimized access rights through multi-dimensional identity authentication and combines AI-driven behavioral baseline analysis to detect and block abnormal operations in real time.

[0038] A52: The blockchain compliance evidence storage network is used to integrate the compliance requirements of multiple jurisdictions through cross-chain protocols, automatically adapt the audit rules of different regions using smart contracts, and simultaneously write key evidence of business operations into multiple blockchain nodes that meet regulatory requirements, forming an unalterable cross-domain audit evidence pool.

[0039] Preferably, the security compliance layer operates as follows:

[0040] S21: Dynamic identity verification, multi-factor authentication combined with device fingerprint and access scenario assessment to establish a temporary trusted access channel;

[0041] S22: Fine-grained authorization, dynamically granting minimum data operation permissions based on role context and revoking unauthorized behavior in real time;

[0042] S23: Operation behavior monitoring: The AI ​​model continuously compares the user behavior baseline and formulates the abnormal risk index. The principle formula for formulating the abnormal risk index is:

[0043] D=∑ i ω i ·|x i -μi | / σ i ;

[0044] Among them, D is the abnormal risk index, x i is the current behavior indicator, μ i is the historical mean, σ i is the historical standard deviation, ω i is the weight;

[0045] S24: Real-time blocking response, triggering immediate interruption of high-risk operations and alerting the security team;

[0046] S25: Data fingerprint is uploaded to the blockchain, and the operation log hash value is written into the blockchain to form an initial evidence node that cannot be tampered with;

[0047] S26: Cross-chain compliance adaptation, automatically segmenting sensitive data through smart contracts and distributing and storing it on a compliant chain according to jurisdictional requirements;

[0048] S27: Multi-standard audit output, extracting a verifiable audit report that complies with the standards from the blockchain;

[0049] S28: Full-link traceability, using the evidence ID to reversely track the illegal operation path and locate the source of the leak and the responsible party.

[0050] As a preference, the value operation layer should specifically include:

[0051] S31: Screen high-value digital assets through the data governance center and use NFT to bind ownership and usage rules;

[0052] S32: Evaluate the value of asset securitization based on the STO framework and generate tradable assets that meet regulatory requirements;

[0053] S33: Complete bidding and matching on the digital exchange, and the smart contract automatically executes asset delivery and profit distribution;

[0054] S34: Quantify system technical debt using code entropy models to identify high-cost modules.

[0055] S35: Convert technical debt into financial indicators, prioritize the reconstruction of high-entropy modules, and verify the performance improvement effect;

[0056] S36: Invest the proceeds from digital asset transactions into technology optimization, improve system robustness, and contribute to the quality of data assets;

[0057] S37: Continuously track asset trading activity and technical debt repair ROI, and dynamically adjust securitization strategies and optimize priorities.

[0058] An operational approach to digital transformation projects, including:

[0059] S41: The data intelligence core layer is launched, implementing cross-domain data governance based on the Data Mesh architecture, combining AI model full lifecycle management with graph neural network optimization to build trusted data assets and intelligent decision-making capabilities;

[0060] S42: Deployment of elastic infrastructure layer, deploying edge intelligent nodes and microservice resilience hubs, and building distributed infrastructure through 5G+TSN network and Istio service mesh;

[0061] S43: Human-machine collaboration layer execution, through a multimodal automation engine and predictive digital twins, builds a full-process automated closed loop and dynamic decision-making from task triggering to feedback optimization;

[0062] S44: Security and compliance layer protection, based on zero-trust dynamic firewall and blockchain cross-chain evidence storage, realizes dynamic permission control of data access and compliance audit traceability in multiple jurisdictions;

[0063] S45: Increase efficiency at the value operation layer by leveraging digital asset securitization and quantitative management of technical debt to transform data resources into tradable assets and drive technical cost optimization;

[0064] S46: Collaborative monitoring of the entire system, through cross-layer linkage control and dynamic policy adjustment, continuously monitors data asset value, system health and compliance risks.

[0065] Beneficial effects of the present invention:

[0066] 1. Compared with traditional data governance, which uses a centralized architecture and suffers from confusion over data rights and responsibilities across business domains, insufficient protection of sensitive information, and the separation of AI model development and operation and maintenance, which leads to resource waste, this solution divides business domain sovereignty through a distributed data governance framework, combines dynamic desensitization technology to achieve field-level permission control, and establishes an AI model full lifecycle management platform to achieve efficient cross-domain data collaboration and automatic recycling of model resources, significantly improving data credibility and model practicality, and reducing the risk of data abuse.

[0067] 2. Compared to existing infrastructure that relies on centralized cloud computing, it struggles to meet real-time industrial control needs, and microservice fault recovery relies on manual intervention, resulting in insufficient system stability. This solution uses edge intelligent nodes to achieve local real-time decision-making. Combined with a microservice resilience hub, it integrates intelligent scheduling and fault self-healing capabilities to build a low-latency, highly available distributed architecture. This significantly improves industrial control response speed and system fault tolerance, supporting high-precision production scenarios.

[0068] 3. Compared to existing infrastructure that relies on centralized cloud computing, which is difficult to meet industrial real-time control needs, and microservice fault recovery relies on manual intervention, resulting in insufficient system stability, this solution uses edge intelligent nodes to achieve local real-time decision-making. Combined with the microservice resilience hub, it integrates intelligent scheduling and fault self-healing capabilities to build a low-latency, highly available distributed architecture, significantly improving industrial control response speed and system fault tolerance, and supporting high-precision production scenarios.

[0069] 4. Compared with the existing digital system, in which enterprise data assets are deposited for a long time and cannot be monetized, there is a lack of quantitative means to manage technical debt, resulting in an imbalance between digital resources and technology investment. This solution releases the value of data through the digital asset securitization mechanism, combines the code entropy model to quantify the hidden costs of technical debt, and builds a closed-loop system of "data transaction revenue feeding back into technology optimization", promoting the transformation of data resources into quantifiable commercial value and achieving sustainable value growth in digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Shown is a schematic diagram of the structure of the operation system of the digital transformation project of the present invention;

[0071] Figure 2 Shown is a workflow diagram of the operation system method of the digital transformation project of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described below with reference to the accompanying drawings and examples.

[0073] See also Figure 1-Figure 2 The present invention provides an embodiment: an operation system for a digital transformation project, comprising:

[0074] The data intelligence core layer is used to implement cross-business domain data governance and AI model lifecycle management, ensuring data credibility and model practicality;

[0075] The elastic infrastructure layer is used to build a low-latency, highly available distributed technology infrastructure through edge computing and microservices governance;

[0076] The human-machine collaboration layer integrates multimodal AI and digital twins to automate complex processes and predict the dynamics of the physical world;

[0077] A security and compliance layer, based on a zero-trust architecture and blockchain evidence storage, enables dynamic permission control and audit traceability for cross-border data flows;

[0078] The value operation layer is used to transform data resources into quantifiable business value through digital asset securitization and technical debt management.

[0079] As described above, the present invention realizes cross-business domain data trust governance and AI model full life cycle management through the data intelligence core layer, thereby improving data quality and model practicality; the elastic infrastructure layer combines edge computing and microservice governance to build a low-latency distributed architecture to enhance system fault tolerance; the human-machine collaboration layer integrates multimodal AI and digital twin technology to realize complex process automation and dynamic prediction of the physical environment; the security and compliance layer relies on zero-trust architecture and blockchain evidence storage to ensure the compliance of cross-border data flow and prevent security risks; the value operation layer activates data value and optimizes the technology input-output ratio through digital asset securitization and quantitative management of technical debt, forming a complete digital transformation empowerment system covering data governance, infrastructure, process automation, security compliance to commercial realization, significantly improving enterprise operational efficiency, reducing compliance risks and creating sustainable growth value.

[0080] Preferably, the data intelligence core layer specifically includes:

[0081] A11: Distributed Data Governance Center, used to build an enterprise-level data governance framework based on the Data Mesh concept, to achieve ownership division, quality monitoring, and security management of data across business domains;

[0082] A12: AI model full lifecycle platform, covering the entire process of model development, deployment, monitoring, and retirement;

[0083] A13: Graph Neural Network Optimizer, used to solve large-scale combinatorial optimization problems based on graph neural networks at a manageable cost.

[0084] As described above, the present invention constructs an enterprise-level data governance framework based on the Data Mesh concept through a distributed data governance center, realizing clear division of data ownership across business domains, real-time quality monitoring, and precise security management, effectively solving the problems of confused data rights and responsibilities and quality out of control under the traditional centralized architecture; the AI ​​model full life cycle platform covers the entire process from model development to retirement, breaking through the barriers between model development and production environment, and realizing dynamic allocation of model resources and continuous performance optimization; the graph neural network optimizer uses graph computing technology to efficiently solve large-scale combinatorial optimization problems while controlling computing costs, significantly improving the decision-making efficiency and accuracy of complex scenarios such as logistics scheduling and supply chain optimization, forming an integrated capability covering data governance, model management and intelligent decision-making, and comprehensively enhancing the value mining of enterprise data assets and business intelligence.

[0085] Preferably, the data intelligence core layer, when working, specifically includes:

[0086] A21: For a distributed data governance center, first, raw data is accessed from multiple source systems, and business domain ownership is divided according to the Data Mesh architecture. Then, a real-time dynamic desensitization engine is used to obfuscate sensitive fields based on role permissions. A field-level lineage tracking system is then used to automatically generate a data flow map, monitor data quality SLAs, and push abnormality alerts. Finally, standardized data is output to the downstream end. The dynamic desensitization engine uses the following formula to obfuscate sensitive fields based on role permissions:

[0087]

[0088] Where x is the original sensitive data string, r is the retention ratio, len(x) is the total length of the original data, and r·len(x) is the number of characters to be retained. is the floor function, To intercept the first r·len(x) characters starting from index 0, The mask symbol representing the remaining portion of the generated part;

[0089] A22: For the AI ​​model full lifecycle platform, model effectiveness indicators are first defined in the joint laboratory, and the pre-trained model library is called for transfer learning. Then, it is deployed to the production environment and connected to real-time data streams. The monitoring system continuously tracks prediction errors and call frequency. When the model performance falls below the threshold or is idle for more than 30 days, resource recycling is automatically triggered, and the recycled computing power is preferentially allocated to high-value new models.

[0090] A23: For the graph neural network optimizer, the business problem is first converted into a graph structure, the pre-trained general optimization GNN model is loaded, and the current business parameters are injected for fine-tuning. The approximate optimal solution is dynamically generated, and the result is pushed to the ERP system for execution. The actual execution effect data is collected to reversely optimize the model.

[0091] As described above, the present invention realizes the division of multi-source data according to business domain ownership through a distributed data governance center, uses a real-time dynamic desensitization engine to perform precise fuzzy processing on sensitive fields according to role permissions (the retention ratio is dynamically calculated by a formula), automatically generates field-level lineage tracking maps and monitors data quality in real time to ensure the security and credibility of data in cross-domain flow; the AI ​​model full life cycle platform quickly builds models through transfer learning, and continuously monitors performance indicators in the production environment, automatically recovers inefficient model computing resources to prioritize high-value new models, and significantly improves computing power utilization efficiency; the graph neural network optimizer converts complex business problems into graph structures and fine-tunes parameters, quickly generates combinatorial optimization approximate optimal solutions while controlling computing costs, and continuously optimizes the model through execution effect feedback, forming an intelligent closed loop of data governance, model optimization and business decision-making, and comprehensively improving the company's data asset value mining capabilities and intelligent decision-making level.

[0092] Preferably, the elastic infrastructure layer specifically includes:

[0093] A31: Edge intelligent node, including hardware layer, network layer and application layer. The hardware layer includes industrial equipment terminals, edge computing nodes and base stations. The hardware layer is used to provide industrial equipment terminals, edge computing hardware and 5G access equipment, and build real-time control and computing capabilities of the physical layer. The network layer adopts TSN time-sensitive network. The application layer includes real-time control algorithm containers, which are used to run industrial control algorithms and AI quality inspection models, complete local real-time decision-making and selectively transmit key data back to the cloud.

[0094] A32: Microservice hub, including control plane, data plane and management plane. The control plane includes Istio service mesh and intelligent scheduling engine. The control plane is used to unify the management of traffic rules and dynamically optimize service routing strategies through Istio service mesh and intelligent scheduling algorithm. The data plane adopts Envoy Sidecar agent and elastic policy library to ensure service stability. The management plane is used to integrate chaos engineering to actively simulate failures and combine with the monitoring and alarm system to achieve full-link observability and rapid recovery.

[0095] As described above, the present invention constructs physical layer real-time control capabilities covering industrial equipment terminals, edge computing hardware and 5G access equipment through edge intelligent nodes, adopts TSN time-sensitive network to ensure data transmission determinism, and realizes local industrial control and AI quality inspection decisions through application layer real-time control algorithm containers, significantly reducing cloud dependence and improving response speed; the microservice hub integrates Istio service grid and intelligent scheduling engine to dynamically optimize traffic management, combines EnvoySidecar agent to ensure service stability, integrates chaos engineering to actively simulate faults and realize full-link observability based on monitoring and alarm system, forming a flexible architecture with self-healing capabilities, effectively responding to traffic shocks and service anomalies in high concurrency scenarios, significantly improving system availability and fault tolerance, and providing low-latency, highly reliable distributed technology support for industrial automation and cloud-native applications.

[0096] Preferably, the human-machine collaboration layer includes:

[0097] A41: A multimodal automation engine, which integrates document understanding, RPA process automation, and digital workforce collaboration to achieve intelligent processing of unstructured data and closed-loop business processes.

[0098] A42: Predictive digital twins are used to build dynamic digital mappings of the physical world based on physical information neural networks and cross-system data fusion technology, enabling real-time simulation and forward-looking decision-making.

[0099] As described above, the human-machine collaboration layer in the present invention integrates document understanding, RPA process automation and digital employee collaboration capabilities through a multimodal automation engine to achieve intelligent analysis of unstructured data such as contracts and bills and automatic connection of cross-system processes, forming a full-link automated closed loop from data collection to business decision-making, significantly improving the processing efficiency of complex business processes; the predictive digital twin builds a dynamic digital mapping of the physical world based on the physical information neural network, and integrates multi-source heterogeneous information such as equipment operation data and environmental parameters in real time. Through simulation and deduction, it predicts risks such as equipment failures and production capacity bottlenecks in advance and generates optimization decision-making suggestions, effectively reducing operation and maintenance costs and improving the predictive management capabilities of the production system, forming a new intelligent operation model that combines virtual and real.

[0100] Preferably, the human-machine collaboration layer, when working, specifically includes:

[0101] S11: Task triggering, which starts the process through user requests and system warnings. Events are connected to the task routing engine via APIs.

[0102] S12: Multimodal perception, parsing documents and images through a multimodal automation engine and building physical constraint models using predictive digital twins and real-time data;

[0103] S13: Intelligent decision-making, using a multimodal automation engine to execute RPA based on confidence thresholds, while a predictive digital twin deduces risks and generates optimization solutions;

[0104] S14: Human-machine collaborative execution, with RPA automated operations and manual review running in parallel, enabling real-time monitoring of execution status;

[0105] S15: Feedback and continuous optimization: using actual results to reflux the training model and dynamically adjust thresholds and physical parameters. The update principle formula of the multimodal automation engine is:

[0106]

[0107] Among them, θ new is the updated model parameter, θ is the current parameter of the multimodal automation engine, η is the learning rate, L human is the loss function calculated based on manual feedback data, is the loss function L human The vector of partial derivatives with respect to the parameters θ.

[0108] As described above, the present invention integrates user requests and system warnings through a task triggering mechanism, uses a multimodal automation engine to parse document images and fuses real-time data to build a physical constraint model, thereby realizing intelligent perception and processing of unstructured data; dynamically executes RPA operations by setting confidence thresholds, and combines predictive digital twins to deduce risk generation optimization solutions, thereby realizing parallel processing and real-time status monitoring of automated operations and manual reviews during the human-machine collaborative execution stage; continuously optimizes model parameters through actual result feedback (accurately updated based on dynamic adjustment formulas), continuously improves the system's adaptability to complex scenarios and decision-making accuracy, and forms a full-process closed loop covering task triggering, perception analysis, intelligent decision-making, and execution feedback, significantly improving the level of business process automation and risk management capabilities, while reducing the cost of manual intervention and enhancing the system's self-iterative optimization capabilities.

[0109] Preferably, the security compliance layer includes:

[0110] A51: Zero Trust Dynamic Firewall, used to build a continuous verification system based on a software-defined perimeter architecture. It dynamically generates minimized access rights through multi-dimensional identity authentication and combines AI-driven behavioral baseline analysis to detect and block abnormal operations in real time.

[0111] A52: The blockchain compliance evidence storage network is used to integrate the compliance requirements of multiple jurisdictions through cross-chain protocols, automatically adapt the audit rules of different regions using smart contracts, and simultaneously write key evidence of business operations into multiple blockchain nodes that meet regulatory requirements, forming an unalterable cross-domain audit evidence pool.

[0112] As described above, the present invention builds a continuous verification system based on a software-defined boundary architecture through a zero-trust dynamic firewall, combines multi-dimensional identity authentication with a dynamic permission allocation mechanism, and uses AI behavioral baseline analysis to monitor abnormal operations in real time and automatically block them while finely controlling access rights, effectively preventing data leakage risks; relying on the blockchain compliance evidence network to integrate the differentiated compliance requirements of global jurisdictions through cross-chain protocols, using smart contracts to automatically adapt to local audit rules and simultaneously write business operation evidence into multiple regulatory-approved blockchain nodes, forming a cross-domain audit evidence chain with judicial effect, ensuring that the entire process of cross-border data flow is traceable and tamper-proof, significantly improving the company's compliance capabilities and data security management level in response to complex regulatory environments, and building a comprehensive security protection system covering dynamic access control and trusted evidence.

[0113] Preferably, the security compliance layer operates as follows:

[0114] S21: Dynamic identity verification, multi-factor authentication combined with device fingerprint and access scenario assessment to establish a temporary trusted access channel;

[0115] S22: Fine-grained authorization, dynamically granting minimum data operation permissions based on role context and revoking unauthorized behavior in real time;

[0116] S23: Operation behavior monitoring: The AI ​​model continuously compares the user behavior baseline and formulates the abnormal risk index. The principle formula for formulating the abnormal risk index is:

[0117] D=∑ i ω i ·|x i -μ i | / σ i ;

[0118] Among them, D is the abnormal risk index, x i is the current behavior indicator, μ i is the historical mean, σ i is the historical standard deviation, ω i is the weight;

[0119] S24: Real-time blocking response, triggering immediate interruption of high-risk operations and alerting the security team;

[0120] S25: Data fingerprint is uploaded to the blockchain, and the operation log hash value is written into the blockchain to form an initial evidence node that cannot be tampered with;

[0121] S26: Cross-chain compliance adaptation, automatically segmenting sensitive data through smart contracts and distributing and storing it on a compliant chain according to jurisdictional requirements;

[0122] S27: Multi-standard audit output, extracting a verifiable audit report that complies with the standards from the blockchain;

[0123] S28: Full-link traceability, using the evidence ID to reversely track the illegal operation path and locate the source of the leak and the responsible party.

[0124] As described above, the present invention establishes a temporary trusted access channel through dynamic identity verification combined with multi-factor authentication, device fingerprint and scenario assessment, implements fine-grained authorization based on role context and recovers unauthorized permissions in real time, uses AI models to continuously monitor user behavior baselines and accurately identifies high-risk operations by dynamically calculating abnormal risk indexes (taking into account the deviation and weight of behavioral indicators), triggers real-time blocking responses, and stores the operation log hash values ​​on the chain to form tamper-proof evidence, relies on cross-chain compliance adaptation technology to segment and store sensitive data according to jurisdictional requirements, and finally realizes automatic generation of multi-standard audit reports and full-link traceability tracking through smart contracts, forming a full-process security protection system covering identity authentication, authority control, behavior monitoring, risk blocking, compliance evidence storage and traceability, significantly improving the compliance capabilities and data security management levels of enterprises in complex regulatory environments, effectively preventing data leakage risks and ensuring the judicial compliance of cross-border business operations.

[0125] As a preference, the value operation layer should specifically include:

[0126] S31: Screen high-value digital assets through the data governance center and use NFT to bind ownership and usage rules;

[0127] S32: Evaluate the value of asset securitization based on the STO framework and generate tradable assets that meet regulatory requirements;

[0128] S33: Complete bidding and matching on the digital exchange, and the smart contract automatically executes asset delivery and profit distribution;

[0129] S34: Quantify system technical debt using code entropy models to identify high-cost modules.

[0130] S35: Convert technical debt into financial indicators, prioritize the reconstruction of high-entropy modules, and verify the performance improvement effect;

[0131] S36: Invest the proceeds from digital asset transactions into technology optimization, improve system robustness, and contribute to the quality of data assets;

[0132] S37: Continuously track asset trading activity and technical debt repair ROI, and dynamically adjust securitization strategies and optimize priorities.

[0133] As described above, the present invention realizes the assetization of data resources by screening high-value digital assets through the data governance center and using NFT to bind ownership and usage rules; evaluates the value of asset securitization based on the STO framework and generates compliant transaction targets, and automatically executes delivery and profit distribution by the smart contract after the bidding and matching is completed on the digital exchange, thereby building a closed loop of data asset circulation; quantifies system technical debt and identifies high-cost modules through the code entropy model, converts them into financial indicators to prioritize the reconstruction of high-entropy modules and verify the performance improvement effect, thereby realizing the explicit management of technical debt; feeds back the digital asset transaction income to technical optimization to improve the robustness of the system, forming a positive cycle of "data asset realization → technology upgrade → value re-creation", while continuously tracking the asset transaction activity and the dynamic adjustment strategy of technical debt repair ROI, significantly improving the enterprise data asset operation efficiency and technology input-output ratio, and building a sustainable growth system covering asset securitization, technical debt governance and value feedback.

[0134] An operational approach to digital transformation projects, including:

[0135] S41: The data intelligence core layer is launched, implementing cross-domain data governance based on the Data Mesh architecture, combining AI model full lifecycle management with graph neural network optimization to build trusted data assets and intelligent decision-making capabilities;

[0136] S42: Deployment of elastic infrastructure layer, deploying edge intelligent nodes and microservice resilience hubs, and building distributed infrastructure through 5G+TSN network and Istio service mesh;

[0137] S43: Human-machine collaboration layer execution, through a multimodal automation engine and predictive digital twins, builds a full-process automated closed loop and dynamic decision-making from task triggering to feedback optimization;

[0138] S44: Security and compliance layer protection, based on zero-trust dynamic firewall and blockchain cross-chain evidence storage, realizes dynamic permission control of data access and compliance audit traceability in multiple jurisdictions;

[0139] S45: Increase efficiency at the value operation layer by leveraging digital asset securitization and quantitative management of technical debt to transform data resources into tradable assets and drive technical cost optimization;

[0140] S46: Collaborative monitoring of the entire system, through cross-layer linkage control and dynamic policy adjustment, continuously monitors data asset value, system health and compliance risks.

[0141] As described above, the present invention implements cross-domain data governance based on the Data Mesh architecture through the data intelligence core layer, combines the full life cycle management of AI models with graph neural network optimization, and builds trusted data assets and intelligent decision-making capabilities; deploys edge intelligent nodes and microservice resilience centers, and builds a low-latency, highly available distributed infrastructure through 5G+TSN network and Istio service grid; realizes full-process automated closed-loop and dynamic decision-making from task triggering to feedback optimization through multimodal automation engine and predictive digital twin; based on zero-trust dynamic firewall and blockchain cross-chain evidence storage, dynamic permission control and multi-jurisdictional compliance audit traceability for data access; uses digital asset securitization and technical debt quantitative management to transform data resources into tradable assets and drive technology cost optimization; continuously monitors data asset value, system health and compliance risks through cross-layer linkage control and dynamic strategy adjustment, forming a full-stack closed-loop system covering data governance, infrastructure, process automation, security compliance to value operation, significantly improving the efficiency of enterprise digital transformation, reducing operational risks and creating sustainable growth value.

[0142] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge of those skilled in the art without departing from the spirit of the present invention.

Claims

1. An operating system for a digital transformation project; characterized by: include: The data intelligence core layer is used to implement cross-business domain data governance and AI model lifecycle management, ensuring data credibility and model practicality; The elastic infrastructure layer is used to build a low-latency, highly available distributed technology infrastructure through edge computing and microservices governance; The human-machine collaboration layer integrates multimodal AI and digital twins to automate complex processes and predict the dynamics of the physical world; A security and compliance layer, based on a zero-trust architecture and blockchain evidence storage, enables dynamic permission control and audit traceability for cross-border data flows; The value operation layer is used to transform data resources into quantifiable business value through digital asset securitization and technical debt management.

2. The operation system of a digital transformation project according to claim 1, characterized in that: The data intelligence core layer specifically includes: A11: Distributed Data Governance Center, used to build an enterprise-level data governance framework based on the Data Mesh concept, to achieve ownership division, quality monitoring, and security management of data across business domains; A12: AI model full lifecycle platform, covering the entire process of model development, deployment, monitoring, and retirement; A13: Graph Neural Network Optimizer, used to solve large-scale combinatorial optimization problems based on graph neural networks at a manageable cost.

3. The operation system of a digital transformation project according to claim 2, characterized in that: When the data intelligence core layer is working, it specifically includes: A21: For a distributed data governance center, first, raw data is accessed from multiple source systems, and business domain ownership is divided according to the Data Mesh architecture. Then, a real-time dynamic desensitization engine is used to obfuscate sensitive fields based on role permissions. A field-level lineage tracking system is then used to automatically generate a data flow map, monitor data quality SLAs, and push abnormality alerts. Finally, standardized data is output to the downstream end. The dynamic desensitization engine uses the following formula to obfuscate sensitive fields based on role permissions: Where x is the original sensitive data string, r is the retention ratio, len(x) is the total length of the original data, and r·len(x) is the number of characters to be retained. is the floor function, To intercept the first r·len(x) characters starting from index 0, The mask symbol representing the remaining portion of the generated part; A22: For the AI ​​model full lifecycle platform, model effectiveness indicators are first defined in the joint laboratory, and the pre-trained model library is called for transfer learning. Then, it is deployed to the production environment and connected to real-time data streams. The monitoring system continuously tracks prediction errors and call frequency. When the model performance falls below the threshold or is idle for more than 30 days, resource recycling is automatically triggered, and the recycled computing power is preferentially allocated to high-value new models. A23: For the graph neural network optimizer, the business problem is first converted into a graph structure, the pre-trained general optimization GNN model is loaded, and the current business parameters are injected for fine-tuning. The approximate optimal solution is dynamically generated, and the result is pushed to the ERP system for execution. The actual execution effect data is collected to reversely optimize the model.

4. The operation system of a digital transformation project according to claim 3, characterized in that: The elastic infrastructure layer specifically includes: A31: Edge intelligent node, including hardware layer, network layer and application layer. The hardware layer includes industrial equipment terminals, edge computing nodes and base stations. The hardware layer is used to provide industrial equipment terminals, edge computing hardware and 5G access equipment, and build real-time control and computing capabilities of the physical layer. The network layer adopts TSN time-sensitive network. The application layer includes real-time control algorithm containers, which are used to run industrial control algorithms and AI quality inspection models, complete local real-time decision-making and selectively transmit key data back to the cloud. A32: Microservice hub, including control plane, data plane and management plane. The control plane includes Istio service mesh and intelligent scheduling engine. The control plane is used to unify the management of traffic rules and dynamically optimize service routing strategies through Istio service mesh and intelligent scheduling algorithm. The data plane adopts Envoy Sidecar agent and elastic policy library to ensure service stability. The management plane is used to integrate chaos engineering to actively simulate failures and combine with the monitoring and alarm system to achieve full-link observability and rapid recovery.

5. The operation system of a digital transformation project according to claim 4, characterized in that: The human-machine collaboration layer includes: A41: A multimodal automation engine, which integrates document understanding, RPA process automation, and digital workforce collaboration to achieve intelligent processing of unstructured data and closed-loop business processes. A42: Predictive digital twins are used to build dynamic digital mappings of the physical world based on physical information neural networks and cross-system data fusion technology, enabling real-time simulation and forward-looking decision-making.

6. The operation system of a digital transformation project according to claim 5, characterized in that: When the human-machine collaboration layer is working, it specifically includes: S11: Task triggering, which starts the process through user requests and system warnings. Events are connected to the task routing engine via APIs. S12: Multimodal perception, parsing documents and images through a multimodal automation engine and building physical constraint models using predictive digital twins and real-time data; S13: Intelligent decision-making, using a multimodal automation engine to execute RPA based on confidence thresholds, while a predictive digital twin deduces risks and generates optimization solutions; S14: Human-machine collaborative execution, with RPA automated operations and manual review running in parallel, enabling real-time monitoring of execution status; S15: Feedback and continuous optimization: using actual results to reflux the training model and dynamically adjust thresholds and physical parameters. The update principle formula of the multimodal automation engine is: Among them, θ new is the updated model parameter, θ is the current parameter of the multimodal automation engine, η is the learning rate, L human is the loss function calculated based on manual feedback data, is the loss function L human The vector of partial derivatives with respect to the parameters θ.

7. The operation system of a digital transformation project according to claim 6, characterized in that: The security compliance layer includes: A51: Zero Trust Dynamic Firewall, used to build a continuous verification system based on a software-defined perimeter architecture. It dynamically generates minimized access rights through multi-dimensional identity authentication and combines AI-driven behavioral baseline analysis to detect and block abnormal operations in real time. A52: The blockchain compliance evidence storage network is used to integrate the compliance requirements of multiple jurisdictions through cross-chain protocols, automatically adapt the audit rules of different regions using smart contracts, and simultaneously write key evidence of business operations into multiple blockchain nodes that meet regulatory requirements, forming an unalterable cross-domain audit evidence pool.

8. The operation system of a digital transformation project according to claim 7, characterized in that: The security compliance layer works by: S21: Dynamic identity verification, multi-factor authentication combined with device fingerprint and access scenario assessment to establish a temporary trusted access channel; S22: Fine-grained authorization, dynamically granting minimum data operation permissions based on role context and revoking unauthorized behavior in real time; S23: Operation behavior monitoring: The AI ​​model continuously compares the user behavior baseline and formulates the abnormal risk index. The principle formula for formulating the abnormal risk index is: D=∑ i oh i ·|x i -m i | / s i ; Among them, D is the abnormal risk index, x i is the current behavior indicator, μ i is the historical mean, σ i is the historical standard deviation, ω i is the weight; S24: Real-time blocking response, triggering immediate interruption of high-risk operations and alerting the security team; S25: Data fingerprint is uploaded to the blockchain, and the operation log hash value is written into the blockchain to form an initial evidence node that cannot be tampered with; S26: Cross-chain compliance adaptation, automatically segmenting sensitive data through smart contracts and distributing and storing it on a compliant chain according to jurisdictional requirements; S27: Multi-standard audit output, extracting a verifiable audit report that complies with the standards from the blockchain; S28: Full-link traceability, using the evidence ID to reversely track the illegal operation path and locate the source of the leak and the responsible party.

9. The operation system of a digital transformation project according to claim 8, characterized in that: When the value operation layer performs its work, it specifically includes: S31: Screen high-value digital assets through the data governance center and use NFT to bind ownership and usage rules; S32: Evaluate the value of asset securitization based on the STO framework and generate tradable assets that meet regulatory requirements; S33: Complete bidding and matching on the digital exchange, and the smart contract automatically executes asset delivery and profit distribution; S34: Quantify system technical debt using code entropy models to identify high-cost modules. S35: Convert technical debt into financial indicators, prioritize the reconstruction of high-entropy modules, and verify the performance improvement effect; S36: Invest the proceeds from digital asset transactions into technology optimization, improve system robustness, and contribute to the quality of data assets; S37: Continuously track asset trading activity and technical debt repair ROI, and dynamically adjust securitization strategies and optimize priorities.

10. A method for operating a digital transformation project, characterized by: include: S41: The data intelligence core layer is launched, implementing cross-domain data governance based on the Data Mesh architecture, combining AI model full lifecycle management with graph neural network optimization to build trusted data assets and intelligent decision-making capabilities; S42: Deployment of elastic infrastructure layer, deploying edge intelligent nodes and microservice resilience hubs, and building distributed infrastructure through 5G+TSN network and Istio service mesh; S43: Human-machine collaboration layer execution, through a multimodal automation engine and predictive digital twins, builds a full-process automated closed loop and dynamic decision-making from task triggering to feedback optimization; S44: Security and compliance layer protection, based on zero-trust dynamic firewall and blockchain cross-chain evidence storage, realizes dynamic permission control of data access and compliance audit traceability in multiple jurisdictions; S45: Increase efficiency at the value operation layer by leveraging digital asset securitization and quantitative management of technical debt to transform data resources into tradable assets and drive technical cost optimization; S46: Collaborative monitoring of the entire system, through cross-layer linkage control and dynamic policy adjustment, continuously monitors data asset value, system health and compliance risks.

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