Self-adaptive intelligent symbiotic data ecosystem and cooperative implementation method thereof

Abstract

The invention discloses a self-adaptive intelligent symbiotic data ecosystem and a cooperative implementation method thereof. The system comprises an intelligent agent ecosystem module, a self-adaptive domain knowledge graph module, a self-adaptive workflow engine module, a CLID configuration system module, a man-machine cooperation interface module, a data processing pipeline module and an innovation trigger module. Through cooperation of diversified intelligent agents, the dynamic knowledge graph, the self-adaptive workflow and CLID multi-dimensional configuration, high-flexibility and self-adaptive data processing and analysis capacity is achieved. The system can automatically construct a complex workflow, perform multi-modal data fusion, and integrate human judgment and AI capabilities at key points, thereby providing an innovative solution. The method is suitable for complex and dynamic big data processing and analysis scenes, and the data processing efficiency and innovation ability are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence, and specifically relates to an adaptive intelligent symbiotic data ecosystem and a collaborative implementation method thereof. Background Art

[0002] With the explosive growth of data volume and the increasing complexity of data processing requirements, traditional data processing systems can no longer meet current needs. Existing technologies mainly include traditional big data processing platforms (such as Hadoop and Spark), machine learning-driven data analysis systems (such as systems based on TensorFlow and PyTorch), industrial Internet of Things platforms (such as GE's Predix and Siemens' MindSphere), enterprise resource planning systems (such as SAP and Oracle), and manufacturing execution systems (such as Siemens' SIMATIC IT and Rockwell's FactoryTalk).

[0003] However, these existing technologies generally have some common defects. First, they lack true adaptive capabilities and have difficulty coping with rapidly changing data environments. Second, there is insufficient human-machine collaboration and the insights of human experts cannot be fully utilized. In addition, there is insufficient collaboration between system components, and there are data and functional islands. They also lack an overall ecosystem perspective and have difficulty achieving long-term sustainable development. Finally, these systems have limited scalability and flexibility and are difficult to adapt to the needs of different fields and scales. Therefore, in the current complex and changing data environment, there is an urgent need for a new type of intelligent data processing system that can achieve high adaptability, deep collaboration, continuous evolution, and effectively integrate the advantages of artificial intelligence and human intelligence. Summary of the invention

[0004] The purpose of the present invention is to provide an Adaptive Intelligent Symbiotic Data Ecosystem (hereinafter referred to as "AISDE") and a collaborative implementation method thereof, so as to solve the problems existing in the prior art, such as lack of true adaptive capability, insufficient human-machine collaboration, insufficient collaboration between system components, lack of overall ecosystem perspective, and limited scalability and flexibility.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, an adaptive intelligent symbiotic data ecosystem and a collaborative implementation method thereof are provided, including:

[0007] An intelligent agent ecosystem, wherein the intelligent agent ecosystem comprises a diverse and evolvable group of intelligent agents that can spontaneously form an efficient collaborative network according to task requirements and environmental changes;

[0008] An adaptive domain knowledge graph module, wherein the adaptive domain knowledge graph module stores a domain knowledge graph, wherein the adaptive domain knowledge graph module is used to provide rich semantic information and reasoning capabilities for the entire ecosystem;

[0009] An adaptive workflow engine for understanding high-level task descriptions and automatically building complex workflow graphs, including directed cyclic graphs, to handle iterations and feedback loops;

[0010] CLID (Case-Layer-Interface-Dot) configuration system, which is used to provide a highly flexible, sophisticated and standardized configuration mechanism for the entire system;

[0011] Human-machine collaborative interface, used to achieve the deep integration of artificial intelligence and human wisdom, integrating human judgment and AI capabilities at key points;

[0012] Data processing pipelines, which are used to efficiently process various types of data and dynamically adjust their structure and behavior based on data characteristics and processing goals;

[0013] Innovation triggers are used to promote data-driven innovation, combining multimodal data fusion technology, intelligent analysis, and human-machine collaboration methods to provide users with a powerful and flexible innovation support platform.

[0014] In one possible design, the intelligent agent ecosystem includes:

[0015] Specialized agents that focus on specific types of task processing, such as data cleaning, feature extraction, or model training;

[0016] General-purpose agents, with cross-domain understanding and problem-solving capabilities, can handle a variety of unstructured tasks and quickly adapt to new roles;

[0017] Meta-agents, which are responsible for supervising and coordinating the activities of other agents to optimize the performance of the entire system;

[0018] The agent evolution mechanism enables the agent to improve its capabilities and adaptability through continuous learning and feedback optimization.

[0019] In a possible design, the intelligent agent ecosystem further includes:

[0020] Dynamic task orchestration, which automatically decomposes complex tasks into subtasks and dynamically assigns them to the most appropriate agent or agent group;

[0021] Self-organizing collaboration mechanism, which enables intelligent agents to autonomously decide whether to participate in specific collaborations based on their own capabilities and task requirements;

[0022] Adaptive resource allocation system, used to ensure that computing resources, data resources, etc. are used most efficiently.

[0023] In a possible design, the adaptive domain knowledge graph module includes:

[0024] Multimodal knowledge encoding capabilities for uniformly encoding and representing complex knowledge from different sources, including semantic information in structured data, unstructured text, images, audio, and video;

[0025] Distributed storage system for efficient storage and fast retrieval of large-scale knowledge;

[0026] Semantic embedding function, which is used to map entities and relations into low-dimensional vector space, supporting efficient similarity calculation, relation reasoning, and knowledge completion;

[0027] The knowledge update mechanism is used to integrate new domain knowledge in real time and maintain the timeliness and accuracy of the knowledge graph.

[0028] In one possible design, the adaptive workflow engine includes:

[0029] Semantic task understanding, which uses a large language model to deeply parse natural language task descriptions, extract explicit key goals and constraints, and infer implicit requirements and dependencies;

[0030] Dynamic graph generation function, which is used to automatically generate comprehensive workflow graphs based on task understanding results, including identifying and creating necessary task nodes, determining logical relationships between nodes, designing control flow structures, arranging parallel execution paths, and building feedback mechanisms;

[0031] Context-aware scheduling function is used to realize intelligent task execution scheduling, considering multi-dimensional factors such as current system load, resource availability, task priority, historical performance data, etc.

[0032] In one possible design, the CLID configuration system includes:

[0033] Case configuration unit, used to define and manage specific usage scenarios or task types, which can automatically identify the current operating environment and match the corresponding case configuration;

[0034] Layer configuration unit, used to organize multi-dimensional structures for data analysis and display, supporting dynamic hierarchical adjustment of data processing and visualization;

[0035] Index configuration unit, which is used to define, manage and dynamically adjust key performance or analysis indicators, including pre-defined common indicators and support for user-defined new indicators;

[0036] Dot configuration unit, used to achieve refined control and real-time optimization of the smallest unit of the system, supporting detailed attribute customization and automatic parameter adjustment;

[0037] Configuration collaborative optimization unit is used to achieve dynamic balance and overall optimization of the four dimensions of CLID to ensure the consistency and effectiveness of configuration;

[0038] Real-time feedback and adaptation unit, used to collect real-time feedback during system operation and quickly adjust CLID configuration according to the feedback to achieve continuous optimization and adaptability improvement of the system;

[0039] The configuration version management unit is used to track and manage different versions of configurations, supporting fast rollback and A / B testing.

[0040] In one possible design, the human-machine collaborative interface includes:

[0041] Key decision point identification function, which is used to autonomously identify key decision points in the workflow that require human intervention;

[0042] Interaction interface generation function, used to dynamically generate the most suitable interaction interface according to the characteristics of the decision point;

[0043] Contextual information provision capabilities to provide rich, relevant background information to human decision makers;

[0044] AI-assisted decision-making function is used to provide AI-generated decision suggestions while explaining its reasoning process.

[0045] In one possible design, the data processing pipeline includes:

[0046] Automatic optimal processing flow construction function, used to automatically build the optimal processing flow according to data characteristics and processing objectives;

[0047] Real-time adaptive adjustment mechanism, used to dynamically adjust its structure and parameters according to data flow characteristics, system resource status and processing effect;

[0048] The data fitness evaluation system is used to comprehensively evaluate the adaptability and potential value of data under different processing environments and objectives.

[0049] In one possible design, the innovation trigger includes:

[0050] Intelligent fusion of multimodal data to achieve deep cross-modal understanding and innovative associations;

[0051] Idea-stimulating interfaces to promote data-driven innovative thinking and human-machine collaboration;

[0052] AI-assisted idea realization capabilities for converting data-driven innovation insights into actionable business improvements.

[0053] In a second aspect, an application method based on the above-mentioned adaptive intelligent symbiotic data ecosystem and its collaborative implementation method is provided, including:

[0054] Receive data processing task requests, and the adaptive workflow engine performs task analysis and requests initial configuration from the CLID configuration system;

[0055] The CLID configuration system generates an initial configuration including four dimensions: Case, Layer, Indicator, and Dot, and returns it to the adaptive workflow engine;

[0056] The adaptive workflow engine dynamically selects and assembles an agent team suitable for the current task from the agent pool based on CLID configuration;

[0057] The agent team queries the knowledge graph for relevant domain knowledge and begins to collaboratively process data, continuously interacting with the knowledge graph to update and query knowledge;

[0058] The agent reports performance indicators and status to the adaptive workflow engine, and the workflow engine requests configuration adjustments from the CLID system based on this to achieve dynamic optimization;

[0059] The system performs multimodal data fusion, integrating structured and unstructured data from different sources to discover potential data associations and patterns;

[0060] Based on the integrated data and analysis results, the system's innovation triggers generate innovative solutions or optimization suggestions;

[0061] Key decision points and system recommendations are presented to human experts through a human-computer collaborative interface, and the final results are output after integrating the feedback. The system knowledge is updated, the configuration strategy is optimized, and the capabilities of the intelligent agent are adjusted based on the overall execution situation.

[0062] Beneficial effects:

[0063] The adaptive intelligent symbiotic data ecosystem and the collaborative implementation method thereof provided by the present invention have the following beneficial effects:

[0064] 1. Significantly improve the efficiency and accuracy of data processing and analysis. Through intelligent collaboration and adaptive workflows, the system can quickly process large-scale complex data, while using advanced machine learning algorithms to improve analysis accuracy and provide more reliable support for decision-making.

[0065] 2. Enhance the system's adaptability to complex and dynamic data environments. With its co-evolution mechanism, the system can autonomously learn and adjust, quickly adapting to new data types, format changes, and processing requirements, allowing it to maintain efficient operation in a rapidly changing data environment.

[0066] 3. Realize the deep integration of human-machine collaboration and give full play to the advantages of both parties. Through the intelligent human-machine interaction interface and knowledge sharing mechanism, the system can effectively integrate human creativity, domain expertise and AI computing power and pattern recognition capabilities to achieve a synergistic effect of 1+1>2.

[0067] 4. Promote the discovery and innovation of cross-domain data value. Its multimodal data fusion capabilities and innovation trigger functions enable the system to discover new connections and insights in data from different fields, providing strong support for interdisciplinary research and innovation.

[0068] 5. Provide a sustainable smart data ecosystem. Through continuous learning and self-optimization, the system can continuously improve its performance and functionality, adapt to future technological developments and changes in demand, and ensure long-term availability and value.

[0069] 6. Greatly improve the scalability and universal applicability of the system. The modular design and CLID configuration system of the system enable it to be easily expanded to new application areas and adapt to data processing needs of different scales and types, thus being widely used in various industries.

[0070] 7. Enhance the interpretability and credibility of data processing and analysis. Through knowledge graphs and transparent decision-making processes, the system can provide clear explanations for its analysis results and decisions, enhancing users' trust and understanding of the system.

[0071] 8. Improve the system's fault tolerance and stability. Through distributed architecture and intelligent fault detection mechanism, the system can maintain operation when some components fail, ensuring the continuity of critical tasks.

[0072] 9. Reduce the overall cost of data processing and analysis. Although the initial investment may be high, through automation, optimized resource utilization and reduced manual intervention, long-term operating costs will be significantly reduced, creating a higher return on investment for the enterprise.

[0073] In general, the present invention has brought revolutionary progress to the field of modern data processing and analysis through its innovative design and comprehensive functions, and has provided strong technical support for the digital transformation and intelligent upgrading of various industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A system architecture diagram of an adaptive intelligent symbiotic data ecosystem provided by an embodiment of the present invention;

[0075] Figure 2 A structural diagram of an intelligent agent ecosystem provided by an embodiment of the present invention;

[0076] Figure 3 A structural diagram of an adaptive domain knowledge graph module provided in an embodiment of the present invention;

[0077] Figure 4 An architecture diagram of an adaptive workflow engine provided by an embodiment of the present invention;

[0078] Figure 5 A schematic diagram of a CLI ID configuration system provided by an embodiment of the present invention and its interaction with other modules;

[0079] Figure 6 A design diagram of a human-machine collaboration interface provided by an embodiment of the present invention;

[0080] Figure 7 An architectural diagram of a data processing pipeline provided by an embodiment of the present invention;

[0081] Figure 8 A system architecture diagram of an innovative trigger provided by an embodiment of the present invention;

[0082] Fig. 9 A sequence diagram of agent collaboration and data flow provided for an embodiment of the present invention;

[0083] Fig.10 A flowchart of the steps of a method for implementing collaboration in an adaptive intelligent symbiotic data ecosystem provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0085] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0086] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0087] Example:

[0088] See also Figure 1 As shown, the adaptive intelligent symbiotic data ecosystem AISDE provided by this embodiment is an innovative data processing and value discovery platform. The system integrates intelligent agent technology, adaptive workflow, knowledge graph and human-computer collaboration to create a dynamic, intelligent and efficient data processing environment. By deeply integrating core AI technologies such as artificial intelligence, machine learning, deep learning and reinforcement learning, as well as cutting-edge technologies such as large language models (LLM), retrieval enhanced generation (RAG), graph computing and graph neural networks, the system realizes the symbiotic collaboration of data, algorithms and human experts. This innovative design not only significantly improves the processing efficiency and accuracy of complex data tasks, but also enhances the interpretability and adaptability of the system. Therefore, this system can provide powerful data-driven decision support for all walks of life, especially in fields facing complex data challenges, such as manufacturing, finance, and medical care.

[0089] In specific applications, the aforementioned adaptive intelligent symbiotic data ecosystem can include, but is not limited to: intelligent agent ecosystem, adaptive domain knowledge graph module, adaptive workflow engine, CLID configuration system, human-computer collaboration interface, data processing pipeline and innovation trigger. These components can be further divided into two categories: core components and supporting components.

[0090] The core components are the backbone of the system, including the intelligent agent ecosystem, the adaptive domain knowledge graph module, and the adaptive workflow engine. The intelligent agent ecosystem is a dynamic and evolvable intelligent agent network that can spontaneously form an efficient collaborative network based on task requirements and environmental changes; the adaptive domain knowledge graph module provides a dynamic and adaptive knowledge representation and management system, providing rich semantic information and reasoning capabilities for the entire ecosystem; the adaptive workflow engine can understand high-level task descriptions and automatically build complex workflow graphs, including directed cyclic graphs, to handle iterations and feedback loops.

[0091] Support components provide the necessary configuration, interface, data processing and innovation capabilities for core functions, including CLID configuration system, human-computer collaboration interface, data processing pipeline and innovation trigger. CLID configuration system provides a highly flexible, sophisticated and standardized configuration mechanism for the entire system; human-computer collaboration interface realizes the deep integration of artificial intelligence and human wisdom, and can integrate human judgment and AI capabilities at key points; data processing pipeline can efficiently process various types of data and dynamically adjust its structure and behavior according to data characteristics and processing goals; innovation triggers are designed to promote innovative thinking and creativity generation, combining multimodal data fusion technology, creative thinking simulation, and human-computer collaboration innovation methods.

[0092] These modules work together through a carefully designed interaction mechanism to form a highly flexible and adaptive system. The intelligent agent ecosystem and the adaptive domain knowledge graph module perform knowledge query and update. The intelligent agent queries the necessary information from the knowledge graph and updates the newly acquired knowledge to the graph. The intelligent agent ecosystem and the adaptive workflow engine perform task execution and feedback. The workflow engine assigns tasks to the intelligent agent, and the intelligent agent provides feedback after execution. The adaptive domain knowledge graph module provides knowledge support for the adaptive workflow engine, and the workflow engine builds and optimizes workflows based on the information in the knowledge graph. The adaptive workflow engine and the CLID configuration system perform configuration applications. The CLID system provides configuration for the workflow, and the workflow provides feedback on the configuration effect based on the execution situation.

[0093] The core components and the human-computer collaborative interface achieve two-way interaction, enabling the interaction between human experts and the core components of the system. The core components input data to the data processing pipeline and receive processing results. Patterns in the data processing pipeline are identified by innovation triggers, and the discovery of innovation triggers is input into the intelligent agent system to promote innovation. The CLID configuration system provides configuration for intelligent agents, workflows, and data processing pipelines.

[0094] This architectural design makes the adaptive intelligent symbiotic data ecosystem highly flexible and adaptable. Through the close collaboration of intelligent agents, knowledge graphs, and adaptive workflows, the system can effectively handle complex data tasks. At the same time, the CLID configuration system and human-computer collaborative interface ensure the configurability and controllability of the system, while the data processing pipeline and innovation triggers enhance the system's data processing capabilities and innovation potential.

[0095] Optionally, the following provides the detailed structure and working process of each of the aforementioned modules.

[0096] First, one of the specific structures of the aforementioned intelligent agent ecosystem module is disclosed:

[0097] In this embodiment, see Figure 2As shown, the example intelligent agent ecosystem module can include but is not limited to four main parts: multi-type intelligent agent composition, dynamic symbiotic relationship and adaptive collaboration, continuous learning and role optimization, and intelligent agent-driven adaptive niche market.

[0098] The multi-type agent structure includes professional agents, general agents and meta-agents. Professional agents focus on specific types of task processing, such as data cleaning, feature extraction or model training, and can efficiently handle problems in specific fields and perform well on specialized tasks. General agents have cross-domain understanding and problem-solving capabilities, can handle a variety of unstructured tasks, and quickly adapt to new roles, showing flexibility in diverse and unknown tasks. Meta-agents are responsible for supervising and coordinating the activities of other agents, optimizing the performance of the entire system, and can balance multiple performance indicators such as efficiency, accuracy and resource utilization.

[0099] Dynamic symbiotic relationships and adaptive collaboration include dynamic task scheduling, self-organizing collaboration mechanisms, and adaptive resource allocation systems. The dynamic task scheduling function automatically decomposes complex tasks into subtasks and dynamically allocates them to the most suitable agents or agent groups, which can efficiently handle complex task structures. The self-organizing collaboration mechanism enables agents to independently decide whether to participate in specific collaborations based on their own capabilities and task requirements, thus achieving flexible and efficient team organization. The adaptive resource allocation system ensures that computing resources, data resources, etc. are used most efficiently, and can optimize resource allocation in real time based on task requirements and system status.

[0100] Continuous learning and role optimization include online learning modules, role evolution mechanisms, and collective learning. The online learning module enables the agent to learn and update its knowledge and strategies in real time from the task execution process, while avoiding catastrophic forgetting and ensuring continuous adaptation and improvement of the system. The role evolution mechanism allows the agent to dynamically adjust its role in the ecosystem based on its performance and system requirements, achieving specialization or generalization of the agent. Collective learning achieves collaborative learning of the entire agent network through experience sharing and knowledge distillation, thereby improving the intelligence level of the overall system.

[0101] The agent-driven adaptive niche market includes a value assessment system, resource allocation, and innovation incentive mechanism. The value assessment system continuously evaluates the value of each agent, based on its performance in completing tasks, scarcity, and contribution to the overall system, to accurately quantify the contribution of the agent. Resource allocation is automatically adjusted through market mechanisms to ensure that the most valuable agents obtain sufficient resources, achieve efficient use of resources and fair distribution. The innovation incentive mechanism encourages agents to continuously innovate and optimize to obtain more favorable niches, and promote the continuous evolution and improvement of the entire system.

[0102] The realization of the intelligent agent ecosystem can be achieved by adopting a modular design approach and building a technical framework that integrates deep learning and symbolic reasoning. Professional agents can use neural network architectures and rule systems suitable for specific fields. General agents can be based on advanced natural language processing models combined with few-sample learning technology. Meta-agents can use appropriate reinforcement learning algorithms to optimize system performance.

[0103] The realization of dynamic symbiotic relationships and adaptive collaboration can use distributed ledger technology to record the interactions of intelligent agents, adopt graph structure algorithms to achieve dynamic routing, use advanced planning algorithms for task scheduling, and use multi-agent learning technology to optimize collaboration strategies and realize dynamic resource allocation mechanisms.

[0104] The implementation of continuous learning and role optimization may include meta-learning algorithms, incremental learning techniques, knowledge transfer methods, and distributed learning frameworks to achieve the continuous evolution of intelligent agents and the collaborative learning of the overall system.

[0105] The realization of the agent-driven adaptive niche market can achieve dynamic pricing through multi-factor analysis and machine learning technology, use smart contract systems to execute automated transactions, combine time series analysis and probability models for predictive analysis, and use graph structure algorithms to capture the complex relationships and contributions of agents in the ecosystem.

[0106] Through the above detailed description of the intelligent ecosystem module, the present invention creates a dynamic, adaptive and continuously evolving intelligent environment, breaking through the limitations of traditional AI systems. This highly flexible and scalable intelligent network can spontaneously form an efficient collaborative network according to task requirements and environmental changes, providing powerful intelligent processing capabilities and adaptability for the entire system.

[0107] Optionally, in one implementation, the intelligent agent ecosystem interacts with the adaptive domain knowledge graph module to achieve dynamic query and update of knowledge. Specifically, a possible implementation of the adaptive domain knowledge graph module is as follows.

[0108] In this embodiment, see Figure 3 As shown, the example adaptive domain knowledge graph module can include but is not limited to three main parts: knowledge representation and storage, dynamic knowledge update mechanism, and knowledge-enhanced contextual understanding and reasoning.

[0109] The knowledge representation and storage function adopts a multimodal knowledge representation method to achieve unified encoding and storage of cross-domain knowledge. This function can uniformly encode and represent complex knowledge from different sources, including structured data, unstructured text, images, audio and video semantic information. The distributed storage system realizes efficient storage and fast retrieval of large-scale knowledge, can handle massive data, ensure high availability and consistency, and provide fast query response. The semantic embedding function maps entities and relationships to low-dimensional vector space, supporting efficient similarity calculation, relationship reasoning and knowledge completion.

[0110] The dynamic knowledge update mechanism is equipped with self-update capability, which can continuously learn and integrate new knowledge during the operation of the system. The real-time knowledge capture function can capture new knowledge points in real time from the interaction of intelligent agents, data processing and external input. The consistency maintenance function can automatically detect and resolve potential knowledge conflicts during the update process to ensure the consistency and reliability of the knowledge base. The knowledge verification function can effectively filter out noise information and only integrate high-quality knowledge.

[0111] The contextual understanding and reasoning function of knowledge enhancement is not only a static information storage, but also a dynamic cognitive reasoning engine. The context-aware query function can fully understand the semantics, intent, and background of the query and provide the most relevant knowledge response. The multi-hop reasoning function can perform multi-step reasoning in the knowledge graph to discover explicit and implicit relationship chains. The uncertainty reasoning function can perform uncertainty reasoning when faced with incomplete or ambiguous information to handle complex and ambiguous situations in the real world.

[0112] The implementation of the adaptive domain knowledge graph module can integrate a variety of advanced technologies. Multimodal knowledge encoding can use a multimodal encoder based on deep learning, combined with a graph structure network to achieve a unified representation across modalities. The distributed storage system can use an optimized distributed graph database to support large-scale concurrent operations. Semantic embedding can use advanced graph embedding technology to map entities and relationships to low-dimensional vector space.

[0113] The dynamic update mechanism can adopt incremental learning and distributed learning technologies to ensure the continuous updating of knowledge and privacy protection. The system can develop a multi-source knowledge fusion engine that can identify and extract key information from unstructured data. Consistency maintenance can implement a graph-based consistency check algorithm, including logical contradiction detection, temporal consistency verification, and conceptual hierarchy coordination. Knowledge verification can effectively filter out noise information through multi-source cross-validation and confidence-based assessment models.

[0114] Context-aware query can implement a context encoder based on the attention mechanism to fully understand the semantics, intent, and context of the query. Multi-hop reasoning can implement a learning-based path search algorithm to support complex multi-step reasoning. Uncertainty reasoning can use a probabilistic model to handle incomplete or ambiguous information. The reasoning engine can integrate advanced neural symbolic reasoning methods to achieve a combination of deep learning and symbolic reasoning.

[0115] Through the above detailed description of the adaptive domain knowledge graph module, the present invention has created a dynamic and adaptive knowledge representation and management system, providing rich semantic information and reasoning capabilities for the entire ecosystem. This system can not only efficiently store and manage large-scale knowledge, but also continuously learn and update, providing powerful knowledge support for intelligent agents and workflows, thereby significantly improving the intelligence level and adaptability of the entire system.

[0116] Furthermore, in a preferred embodiment of the present invention, the adaptive domain knowledge graph module can perform data interaction and collaborative operation with the adaptive workflow engine module. A possible specific implementation of the adaptive workflow engine module will be described in detail below, which fully utilizes the semantic information and reasoning capabilities provided by the adaptive domain knowledge graph module.

[0117] See also Figure 4 As shown, the adaptive workflow engine module may include, but is not limited to, a dynamic workflow construction and adjustment unit, an intelligent dynamic data processing pipeline unit, and a task execution feedback and self-optimization unit. This module represents a paradigm shift in the field of workflow management, realizing an intelligent workflow system with cognitive capabilities.

[0118] The dynamic workflow construction and adjustment unit can include semantic task understanding function, dynamic graph generation function and context-aware scheduling function. The semantic task understanding function can use natural language processing technology to deeply analyze the task description, not only extract explicit key goals and constraints, but also infer implicit requirements and dependencies. The system can understand the context of the task, identify key parameters and expected outputs, and even handle vague or incomplete task descriptions. The dynamic graph generation function can automatically generate a comprehensive workflow graph based on the task understanding results. This includes identifying and creating necessary task nodes, determining the logical relationship between nodes, designing control flow structures (such as conditional branches, loops), arranging parallel execution paths, and building feedback mechanisms. The system can dynamically adjust the graph structure to adapt to new situations during task execution. The context-aware scheduling function can realize intelligent task execution scheduling, considering multi-dimensional factors such as current system load, resource availability, task priority, historical performance data, etc. The scheduler can predict potential resource conflicts and performance bottlenecks and adjust the execution plan in advance. It also has dynamic load balancing capabilities, which can reallocate tasks at runtime to optimize overall performance.

[0119] The intelligent dynamic data processing pipeline unit can include a self-organizing processing unit, a multimodal data fusion function, and a real-time adaptive adjustment function. The self-organizing processing unit is an intelligent independent entity that can autonomously analyze the characteristics of the input data, understand the current processing goals, and dynamically adjust its own functions and parameters accordingly. This adaptive capability enables the processing unit to flexibly switch roles in different data processing scenarios, greatly improving the adaptability of the system. The multimodal data fusion function has the ability to process and fuse multiple data types, including structured data, unstructured text, images, audio, video, etc. It can intelligently identify the associations between different data types, automatically select the most suitable fusion strategy, and realize in-depth multimodal data analysis. The real-time adaptive adjustment function can monitor the data flow characteristics, processing effects, and system feedback in real time, and dynamically adjust the structure and parameters of the processing network accordingly. This includes adding and removing processing nodes, reconfiguring processing paths, adjusting algorithm parameters, etc., to ensure that the processing pipeline always maintains the best performance state.

[0120] The task execution feedback and self-optimization unit can include execution trajectory analysis function, creative optimization function and cross-domain knowledge transfer function. The execution trajectory analysis function deeply analyzes the entire process of workflow execution, not only recording the surface execution steps, but also digging deep into the reasons behind each decision point, the efficiency of resource utilization, the quality of intermediate results, etc. This all-round analysis provides a rich data foundation for subsequent optimization. The creative optimization function can creatively generate new optimization strategies based on historical data, current context and predetermined goals. This includes not only fine-tuning at the parameter level, but also macro-level innovations such as the reorganization of workflow structure and the design of new processing units. The system can simulate and evaluate multiple possible optimization schemes and select the best strategy for implementation. The cross-domain knowledge transfer function has the ability to transfer the optimization knowledge learned in one field to new and unseen task fields. This cross-domain migration is not limited to similar tasks, but can also find common optimization principles between seemingly unrelated fields, greatly accelerating the system's adaptation and optimization process in new fields.

[0121] The implementation of the adaptive workflow engine module can integrate multiple technologies. In terms of dynamic workflow construction, the system can adopt a framework that supports complex directed and cyclic workflow structures, and use a natural language processing model based on deep learning for task understanding and workflow generation. Context-aware scheduling can optimize task allocation by implementing a scheduler based on a graph structure. The intelligent dynamic data processing pipeline can use neural architecture search technology to develop adaptive processing units that can dynamically adjust their internal structure. The system can also use multimodal deep learning models to achieve unified representation and processing of different types of data, and use online meta-learning algorithms to enable the processing network to quickly adapt to new data distributions and task requirements. The task execution feedback and self-optimization module can implement an execution trajectory encoder based on an attention mechanism to capture the key features of workflow execution. The system can use generative artificial intelligence models to create new optimization strategies and use meta-reinforcement learning techniques to achieve rapid cross-domain adaptation.

[0122] In addition, the module can also integrate a distributed computing framework to ensure efficient performance when processing large-scale data. The synergy of these technologies enables the adaptive workflow engine module to cope with complex and changing data processing scenarios and continuously improve its efficiency and innovation capabilities.

[0123] Through the above design, the adaptive workflow engine module achieves a high degree of intelligence and adaptability, can understand complex task requirements, dynamically build and adjust workflows, and continuously evolve through continuous learning, providing a powerful workflow management capability for the entire system. Further, in a preferred embodiment of the present invention, the adaptive workflow engine module can interact bidirectionally with the CLID configuration system module. Specifically, the CLID configuration system module can provide configuration for the adaptive workflow engine module, and the adaptive workflow engine module can feedback the configuration effect to the CLID configuration system module according to the execution situation. This interactive mechanism enables the configuration of the workflow to be dynamically adjusted to adapt to different task requirements and system states.

[0124] In one possible implementation, the CLID configuration system module may provide the following configuration to the adaptive workflow engine module:

[0125] 1. Workflow structure configuration: define the overall architecture of the workflow, including node types, connection relationships between nodes, control flow, etc.

[0126] 2. Task execution parameters: Set specific execution parameters for each task node in the workflow, such as resource allocation strategy, priority, timeout settings, etc.

[0127] 3. Data flow configuration: specifies the data transmission method and format conversion rules between nodes in the workflow.

[0128] 4. Exception handling strategy: define the exceptions that may occur during the workflow execution and their corresponding handling methods.

[0129] At the same time, the adaptive workflow engine module can feedback the following information to the CLID configuration system module:

[0130] 1. Performance indicators: including but not limited to the overall execution time, resource utilization, throughput, etc. of the workflow.

[0131] 2. Exception report: record the exceptions encountered during the workflow execution, including exception type, occurrence time, impact scope, etc.

[0132] 3. Optimization suggestions: Based on observations and analysis during the execution process, possible optimization directions are proposed, such as task reordering, resource reallocation, etc.

[0133] 4. Configuration effectiveness assessment: Evaluate the applicability of the current configuration and point out configuration items that may need to be adjusted.

[0134] Through this two-way interaction mechanism, the CLID configuration system module can dynamically adjust the configuration strategy according to the actual operation of the adaptive workflow engine module, thereby continuously optimizing the execution efficiency and reliability of the workflow. This adaptive configuration method significantly improves the system's ability to adapt to different task types and changing environments.

[0135] A possible specific implementation of the CLID configuration system module will be described in detail below. This implementation makes full use of the interaction mechanism with the adaptive workflow engine module to achieve highly flexible and refined configuration management.

[0136] See also Figure 5 As shown, the CLID configuration system module may include but is not limited to three main parts: Case-Layer-Interface-Dot configuration framework, dynamic configuration adjustment mechanism and configuration and system performance optimization.

[0137] The Case-Layer-Interceptor-Dot configuration framework provides four core dimensions of configuration, each targeting a different aspect of the system:

[0138] 1. Case configuration: defines a specific usage scenario or task type. The system can automatically generate an initial case configuration while allowing users to customize and select. This flexibility enables the system to handle a wide range of application scenarios, from simple data processing to complex multi-step analysis tasks. Through case configuration, the system is context-aware and can understand and adapt to specific usage environments, thereby providing more accurate data processing and analysis services.

[0139] 2. Layer configuration: represents different levels or perspectives of data processing and visualization. It organizes the structure of data analysis and display, supports multi-dimensional data display, and provides matching visualization templates for each layer. Layer configuration enables the system to process all levels from raw data to advanced analysis results, ensuring the consistency and visualization of data at different processing stages. Each layer can selectively display a specific Inter icator, thereby providing analysis views of different granularity and angles.

[0140] 3.Indicator configuration: define and manage quantitative indicators for Dot. The system predefines a series of commonly used indicators, while allowing users to customize new indicators and adjust the calculation logic. Indicator converts the characteristics of Dot into quantifiable and comparable values, which are then presented in different layers for analysis and decision-making. Through Indicator configuration, the system can continuously monitor and optimize its performance to ensure that the system always runs in the best state.

[0141] 4. Dot configuration: represents the smallest unit or object of data analysis. It supports detailed attribute customization, allowing users to precisely control the behavior of data processing objects. Dot is the object quantified by the Intericator, and its attributes and behaviors directly affect the calculation results of the Intericator. Through Dot configuration, the system realizes multi-level configuration capabilities from macro to micro, enabling the system to fine-tune specific data processing links while maintaining overall consistency.

[0142] The dynamic configuration adjustment mechanism includes real-time monitoring and intelligent triggering, incremental update and conflict resolution, and adaptive learning functions:

[0143] 1. Real-time monitoring and intelligent triggering: The system continuously monitors various performance indicators and environmental variables, and automatically triggers the configuration adjustment process when a situation that requires optimization is detected. The monitoring scope covers all levels from the system level to the individual data processing unit, ensuring a comprehensive and accurate performance evaluation.

[0144] 2. Incremental update and conflict resolution: The system adopts an incremental update strategy, adjusting only necessary configuration items each time to minimize interference with system operation. At the same time, the system can automatically detect and resolve potential conflicts between configuration items to ensure that the system remains consistent and stable after configuration changes.

[0145] 3. Adaptive learning: The system is able to learn from past configuration history and usage patterns to continuously optimize its configuration strategy. The system analyzes historical configuration data, identifies successful patterns and potential areas for improvement, and provides intelligent suggestions for future configuration adjustments.

[0146] Modular design and collaborative working capabilities are another important feature of the dynamic configuration adjustment mechanism. Each CLID configuration unit (such as a function ball) can be configured and operated independently, while working with other units to share data and results. This design ensures the flexibility of each part of the system and achieves overall coordination, enabling the system to efficiently handle complex multi-step analysis tasks.

[0147] Configuration and system performance optimization includes multi-objective optimization, configuration effect evaluation, scalability support and user-friendly interface:

[0148] 1. Multi-objective optimization: The system can simultaneously consider multiple performance indicators, such as processing speed, resource utilization, output quality, etc., and use advanced multi-objective optimization algorithms to find the best configuration combination. The system can dynamically adjust the weight of each indicator according to different application scenarios and user preferences to achieve personalized performance optimization.

[0149] 2. Configuration effect evaluation: The system evaluates the impact of configuration changes on performance in real time and maintains a configuration effect history database. This enables the system to conduct long-term performance trend analysis, identify long-term change patterns in system performance, and provide valuable guidance for future optimization decisions.

[0150] 3. Scalability support: The system supports adding new indicators, levels, and point types, so that the configuration framework can adapt to new technological developments and application requirements. This scalability is not only reflected in the increase of configuration items, but also includes the ability to integrate new optimization algorithms and evaluation methods.

[0151] 4. User-friendly interface: The system provides intuitive visual configuration tools, supports drag-and-drop configuration process, and also implements natural language processing interface, so that users can describe their needs in natural language. In addition, the system also supports no-code or low-code configuration mode, which greatly simplifies the configuration process and improves user efficiency.

[0152] The implementation of the CLID configuration system can integrate multiple advanced technologies. In terms of natural language processing, deep learning models can be used to achieve dynamic prompt generation and user intent understanding, enhancing the interactive intelligence of the system. Knowledge graph technology can be combined with graph structure networks to achieve modeling and reasoning of complex relationships between configuration items and support intelligent configuration decisions. Reinforcement learning algorithms can be used to automatically adjust configuration parameters and long-term optimization strategies to improve the system's adaptability.

[0153] Distributed stream processing technology can be used to achieve large-scale real-time data analysis and monitoring to ensure real-time evaluation of system performance. A configuration management system based on distributed version control can be used to support rapid rollback of configurations and A / B testing. A graph structure network model can be used to analyze the dependencies between configuration items and intelligently resolve configuration conflicts. A causal reasoning model can be used to analyze the causal relationship between configuration changes and performance changes to improve the accuracy of optimization decisions.

[0154] Federated learning technology can be used to securely aggregate the configuration optimization experience of multiple system instances, and realize cross-instance knowledge sharing while protecting privacy. Multi-objective optimization algorithms can be used to achieve multi-indicator trade-offs and optimization. Meta-learning methods can be used to quickly adapt to new optimization scenarios and enhance the versatility of the system. Bayesian optimization technology can be applied to automate configuration search and hyperparameter tuning to improve optimization efficiency.

[0155] Through the coordinated application of these technologies, the CLID configuration system achieves high flexibility, fine control, standardized management, continuous optimization and interpretability. It works closely with other core components of the system to build a powerful, flexible and intelligent data processing and analysis platform, providing comprehensive and powerful support for processing complex and changing data tasks.

[0156] In another embodiment of the present invention, the CLID configuration system module can interact with the human-machine collaboration interface module to achieve refined configuration adjustment and real-time integration of user feedback. This interaction not only improves the flexibility of system configuration, but also enhances the system's responsiveness to user needs.

[0157] Specifically, the CLID configuration system can display the current configuration status to the user through the human-computer collaborative interface and receive the user's configuration modification suggestions. At the same time, the human-computer collaborative interface can feed back the user's behavior patterns and preferences during the interaction process to the CLID configuration system for further optimization of the configuration strategy. This two-way interactive mechanism enables the system configuration to better adapt to the user's actual needs and work habits.

[0158] Next, we will describe in detail a possible specific implementation of the human-machine collaboration interface module, which fully utilizes the interactive characteristics with the CLID configuration system to achieve efficient and intuitive human-machine collaboration.

[0159] See also Figure 6 As shown, the human-computer collaboration interface module may include but is not limited to four main parts: human-computer interaction design of key decision points, intelligent task allocation system, collaborative crowdsourcing task management, and human-computer collaboration evolution mechanism.

[0160] The human-computer interaction design of key decision points includes decision point identification, interactive interface generation, contextual information provision and AI-assisted decision-making functions. The decision point identification function uses advanced algorithms, such as key node identification based on graph analysis and time series anomaly detection, to automatically identify key points in the workflow that require human intervention. This function not only takes into account the complexity and importance of the task, but also dynamically adjusts the identification criteria based on historical data and current system status. The interactive interface generation function dynamically generates the most suitable interactive interface based on the characteristics of the decision point, taking into account factors such as the type, complexity, and time urgency of the decision. The contextual information provision function provides rich and relevant background information for human decision makers, not only displaying data directly related to the current decision, but also intelligently integrating multi-dimensional information such as historical data, similar cases, and potential impacts. The AI-assisted decision-making function provides AI-generated decision suggestions and explains its reasoning process, showing how AI came to this conclusion, including the factors considered, the data used, and the reasoning logic.

[0161] The intelligent task allocation system includes task complexity assessment, resource capability matching, dynamic load balancing, and learning and adaptation functions. The task complexity assessment function analyzes the difficulty of the task, required expertise, and other characteristics in multiple dimensions, and uses natural language processing and machine learning technology to build a comprehensive task feature model. The resource capability matching function evaluates the capabilities of the AI ​​system and available human resources, makes the best match, and maintains a dynamically updated resource capability model. The dynamic load balancing function monitors and adjusts task allocation in real time to ensure efficient use of resources and continuously tracks the working status of each resource. The learning and adaptation function learns from past task allocation results, continuously optimizes the allocation strategy, and records the results of each task allocation.

[0162] Collaborative crowdsourcing task management includes intelligent task decomposition, dynamic task matching, quality control, and incentive mechanism design functions. The intelligent task decomposition function automatically decomposes complex tasks into subtasks suitable for crowdsourcing, and uses task analysis algorithms to identify the structure and dependencies of complex tasks. The dynamic task matching function performs intelligent matching based on participant capabilities and task characteristics, and maintains a dynamically updated participant capability model. The quality control function uses AI technology for real-time quality monitoring and correction, continuously evaluates output quality, and identifies potential errors or quality issues through machine learning models. The incentive mechanism design function implements a dynamic incentive system based on contribution and quality, and establishes a multi-dimensional evaluation system.

[0163] The human-machine collaborative evolution mechanism includes collaborative mode optimization, knowledge integration, capability complementarity, and collaborative effect evaluation functions. The collaborative mode optimization function analyzes and optimizes the way and timing of human-machine interaction, and identifies the most effective interaction mode by analyzing historical collaborative data. The knowledge integration function intelligently integrates the input of human experts into the system's knowledge base, and transforms it into knowledge that the system can understand and use through deep learning and knowledge graph technology. The capability complementarity function identifies and strengthens the respective areas of strength of AI systems and human experts, and clarifies the performance advantages of AI and humans in different types of tasks through long-term collaborative data analysis. The collaborative effect evaluation function establishes comprehensive evaluation indicators, continuously monitors and improves collaborative effects, and evaluates multiple dimensions of the collaborative process.

[0164] The implementation of the human-machine collaboration interface module can integrate multiple advanced technologies, including reinforcement learning, large language models, multi-agent reinforcement learning, graph structure networks, meta-learning, neural symbolic integration, and causal reasoning. In addition, the module can also integrate technologies such as knowledge graphs, federated learning, and natural language processing to further enhance the effectiveness and security of human-machine collaboration.

[0165] Through the above design, the human-machine collaborative interface module has achieved a high degree of intelligence and adaptability, and can effectively integrate the judgment of human experts at key decision points, which not only improves the performance and reliability of the system, but also enhances its ethics and fairness. This design makes the system a truly intelligent collaborative platform, providing an unprecedented collaborative intelligent solution for solving complex problems.

[0166] In another preferred embodiment of the present invention, the data processing pipeline module, as a key supporting component of the system, further enhances the capabilities of data processing, analysis and value mining on the basis of intelligent dynamic data processing. A possible specific implementation of the data processing pipeline module will be described in detail below to show how it enhances the overall performance and intelligence level of the system.

[0167] See also Figure 7 As shown, the data processing pipeline module can include, but is not limited to, three main parts: automated optimal processing flow construction, real-time adaptive adjustment mechanism, and data fitness evaluation system. This module can not only efficiently process various types of data, but also dynamically adjust its structure and behavior according to data characteristics and processing goals.

[0168] The automated optimal processing flow construction function can automatically build the optimal processing flow according to data characteristics and processing objectives. The system can autonomously analyze the requirements and constraints of data processing tasks, intelligently select and combine the most suitable components from a large-scale processing unit pool, and continuously optimize the processing flow based on performance models and historical data. This function includes core capabilities such as deep task analysis, intelligent component selection and combination, adaptive process optimization, intelligent parallel processing design, dynamic resource allocation, and explainability design.

[0169] The deep task analysis capability enables the system to deeply understand the requirements and constraints of data processing tasks, not only analyzing explicit task descriptions but also inferring implicit processing requirements. The intelligent component selection and combination function selects the most suitable components from a massive pool of processing units, and intelligently selects and combines these components based on task requirements, data characteristics, and historical performance. The adaptive process optimization capability continuously optimizes the processing process based on performance models and historical data. The system continuously learns from the experience gained from historical tasks and uses this knowledge to improve future process designs.

[0170] The intelligent parallel processing design function automatically identifies and designs parallel processing steps, significantly improving efficiency. The system analyzes task dependencies, identifies subtasks that can be executed in parallel, and designs the optimal parallel strategy. The dynamic resource allocation capability dynamically allocates computing resources based on task requirements and system load. The system can predict the resource requirements of each processing step and adjust resource allocation in real time during the processing process. The explainable design function generates explainable process design documents. The system not only builds an efficient processing flow, but also generates detailed flowcharts and explanation documents to explain the role of each processing step and the reasons for selection.

[0171] The real-time adaptive adjustment mechanism has a strong real-time adaptive capability, and can dynamically adjust its structure and parameters according to the characteristics of data flow, system resource status and processing effect. The mechanism includes core functions such as all-round real-time performance monitoring, intelligent dynamic reconstruction, resource adaptive regulation, intelligent exception handling, predictive adjustment and self-learning optimization.

[0172] The comprehensive real-time performance monitoring function continuously monitors various performance indicators of the pipeline, including processing delay, throughput, resource utilization, data quality, etc. The intelligent dynamic reconstruction capability adjusts the pipeline structure and parameters in real time according to the monitoring data. The system can dynamically add, delete or reconfigure processing components without interrupting the data flow. The resource adaptive control function dynamically adjusts the processing strategy according to the available computing resources. The system can evaluate the usage of various resources in real time and adjust the data processing strategy accordingly.

[0173] Intelligent exception handling capability automatically detects and handles data anomalies and processing failures. The system uses advanced anomaly detection algorithms to identify various types of anomalies. The predictive adjustment function predicts possible performance problems and makes adjustments in advance based on historical data and trend analysis. The self-learning optimization capability enables the system to learn from past adjustment experiences and continuously improve its adaptive strategy.

[0174] The data fitness evaluation system introduces the innovative concept of "data fitness" to comprehensively evaluate the adaptability and potential value of data under different processing environments and goals. The system includes core functions such as multi-dimensional feature extraction, context-aware evaluation, predictive analysis, fitness optimization suggestions, dynamic fitness tracking, and cross-dataset collaborative evaluation.

[0175] The multidimensional feature extraction function extracts a wide range of key features from the data, including but not limited to the structural characteristics, quality indicators, statistical characteristics, and semantic characteristics of the data. The context-aware evaluation capability considers the processing objectives and environmental factors for fitness evaluation. The system not only analyzes the characteristics of the data itself, but also evaluates it in a specific analysis context. The predictive analysis function predicts the potential value and cost of data under different processing paths. The system uses machine learning models to predict the value that data can generate under various possible processing schemes based on historical experience and current context.

[0176] The fitness optimization suggestion function provides specific suggestions for improving data fitness. Based on comprehensive evaluation results, the system can generate detailed optimization suggestions. The dynamic fitness tracking capability continuously monitors and updates the fitness score of the data. As the data environment and analysis requirements change, the system adjusts the fitness evaluation in real time. The cross-dataset collaborative evaluation function evaluates the fitness of a single dataset while considering its synergy with other related datasets.

[0177] The implementation of the data processing pipeline module can integrate multiple advanced technologies, including natural language processing, graph neural networks, reinforcement learning, distributed stream processing, deep reinforcement learning, adaptive sampling and compression, anomaly detection, deep learning models, graph embedding technology, Bayesian optimization and meta-learning, etc. The coordinated application of these technologies enables the data processing pipeline module to achieve significant improvement in processing efficiency, enhanced adaptability, optimized resource utilization, maximized data value, and improved processing quality.

[0178] Through the above design, the data processing pipeline module is not only an efficient data conversion tool, but also an intelligent system that can autonomously discover the potential patterns and value of data, providing the entire system with a significant competitive advantage in a complex big data environment.

[0179] On this basis, the present invention also includes a subsystem specially designed to promote data-driven innovation - the innovation trigger module. This module works closely with the data processing pipeline module to convert data analysis results into innovative ideas and business value. A possible specific implementation of the innovation trigger module will be described in detail below.

[0180] See also Figure 8 As shown, the innovation trigger module can include, but is not limited to, three main parts: multimodal data intelligent fusion, creative stimulation interface design, and AI-assisted creative realization. This module is tightly integrated with other core components of the system to form an ecosystem that synergistically promotes data value discovery and business innovation.

[0181] The multimodal data intelligent fusion engine enables deep cross-modal understanding and innovative associations. The engine is built on the basis of the data processing pipeline and further expands its multimodal processing capabilities. It can not only process and integrate different types of data, such as structured database records, unstructured text, sensor data and images, but also discover potential and non-obvious connections between these data.

[0182] The core functions of the multimodal data intelligent fusion engine include cross-modal feature extraction, semantic alignment, fusion model and innovative association discovery. The cross-modal feature extraction function can extract key features from different types of data, including patterns of time series data, semantic information of text data, visual features of image data, etc. The semantic alignment function realizes semantic level alignment between different modal data, such as associating equipment status data with text descriptions of maintenance logs. The fusion model builds a unified data representation space to support comprehensive analysis of cross-type data, such as analyzing production data, quality data and financial data under the same framework. The innovative association discovery function uses advanced pattern recognition algorithms to discover hidden and potentially valuable associations in the data, providing new perspectives for business innovation.

[0183] The Creative Stimulation Interface is a key interactive platform for innovation triggers, designed to promote data-driven innovative thinking and human-machine collaboration. This interface not only displays AI-generated data insights and innovative suggestions, but also captures and integrates users' expertise and creativity to form a co-creative ecosystem.

[0184] The core functions of the Creative Stimulation Interface include interactive data exploration, concept visualization, feedback learning, and collaborative innovation platform. The interactive data exploration function provides an intuitive interface that allows users to deeply explore complex data relationships and patterns. The concept visualization function transforms complex data insights and innovative concepts into intuitive visual representations. The feedback learning function enables the system to continuously learn from user interactions and feedback, optimizing its data analysis and innovative suggestion generation strategies. The collaborative innovation platform supports multi-user collaboration, integrates insights from different departments and professional backgrounds, and promotes cross-domain data-driven innovation.

[0185] The AI-assisted creative realization module is dedicated to transforming data-driven innovation insights into feasible business improvement plans. It evaluates the feasibility of innovation suggestions, provides implementation paths, and generates preliminary implementation plans to accelerate the transformation process from data insights to actual business value.

[0186] The core functions of the AI-assisted creative realization module include creative evaluation, realization path planning, prototype generation, and iterative optimization. The creative evaluation function evaluates data-driven innovation suggestions in multiple dimensions, taking into account factors such as technical feasibility, business impact, and resource requirements. The realization path planning function provides detailed implementation steps for data-driven innovation, including the required data infrastructure adjustments, process changes, and resource allocation. The prototype generation function generates conceptual prototypes or simulation models of innovative solutions based on data analysis results. The iterative optimization function continuously integrates new data and implementation feedback to continuously optimize innovative solutions.

[0187] The implementation of the innovation trigger module can integrate multiple advanced technologies, including deep learning technology, knowledge graph technology, natural language processing technology, reinforcement learning algorithm, advanced visualization technology and human-computer interaction design. The coordinated application of these technologies enables the innovation trigger module to effectively connect data analysis and actual business innovation, improve the application value of data analysis, and accelerate the process from data insight to actual business improvement, providing organizations with powerful data-driven innovation capabilities.

[0188] Based on the above description, the overall workflow of the system provided in this embodiment is provided as follows:

[0189] See also Fig. 9 As shown, the operation process of this system can be illustrated by a detailed sequence diagram, which shows how the various components of the system work together to process data tasks.

[0190] The specific workflow is as follows:

[0191] 1. Data task request: The data source sends a new data processing task request to the adaptive workflow engine to start the entire processing process.

[0192] 2. Request initial task configuration: After the adaptive workflow engine recognizes a new task, it immediately requests the initial configuration from the CLID configuration system.

[0193] 3. Return to the initial CLID configuration: The CLID system returns to the initial configuration, including the configuration of four dimensions: Case (use case scenario), Layer (data layer), Indexer (performance indicator) and Dot (specific attributes).

[0194] 4. Dynamically assemble agents: The adaptive workflow engine dynamically selects and assembles an agent team suitable for the current task from the agent pool based on the received CLID configuration.

[0195] 5-6. Agents request and receive detailed configurations: The assembled agent team requests a more detailed configuration from the CLID system, and the CLID system provides refined configurations based on the characteristics and task requirements of each agent.

[0196] 7-8. Knowledge query: Based on the received configuration, the intelligent agent queries the adaptive domain knowledge graph module for relevant knowledge, and the knowledge graph returns the required information.

[0197] 9-10. Raw data request and acquisition: The agent requests raw data from the data source, and the data source provides the required data.

[0198] 11-18. Data processing loop:

[0199] 11. Intelligent agents collaborate to process data.

[0200] 12-13. During the processing, the agent continuously interacts with the adaptive domain knowledge graph module, updates and queries knowledge and returns results.

[0201] 14. The agent reports performance metrics and status to the adaptive workflow engine.

[0202] 15. Based on the full-dimensional analysis, the adaptive workflow engine requests configuration adjustments from the CLID system.

[0203] 16-17. The CLID system evaluates the request, makes necessary configuration adjustments, and returns the updated configuration.

[0204] 18. The adaptive workflow engine applies the new configuration to the agent.

[0205] 19. Output processing results: After the data processing is completed, the agent outputs the results to the human-computer collaboration interface for users to view and evaluate.

[0206] 20. User feedback: The human-computer collaborative interface collects user feedback on the processing results and passes it to the adaptive workflow engine.

[0207] 21. Update system knowledge: The adaptive workflow engine updates the adaptive domain knowledge graph based on user feedback to enhance the system’s knowledge base.

[0208] 22. Update CLID configuration strategy: The adaptive workflow engine feeds back the overall situation of task execution to the CLID system for updating and optimizing the configuration strategy.

[0209] 23. Optimize CLID configuration: Based on the received feedback, the CLID system comprehensively optimizes the configuration of four dimensions: Case, Layer, Interconverter, and Dot, in preparation for subsequent tasks.

[0210] This sequence diagram fully demonstrates the core features and key interactions of this system:

[0211] 1. Flexibility and dynamic adaptation: Through the dynamic assembly of intelligent agents by the adaptive workflow engine (step 4) and the real-time configuration adjustment provided by the CLID system (steps 15-18), the system demonstrates a high degree of flexibility and dynamic adaptability.

[0212] 2. Multi-level configuration mechanism: The CLID system provides initial configuration (step 3) and detailed configuration (step 6), and continuously optimizes during operation (steps 16-17 and 23), reflecting the system's multi-level and refined configuration capabilities.

[0213] 3. Knowledge-driven: The adaptive domain knowledge graph runs through the entire process (steps 7-8, 12-13, 21), supporting the decision-making and operation of the intelligent agent, demonstrating the knowledge-driven nature of the system.

[0214] 4. Continuous optimization and learning: The system achieves continuous optimization and learning through performance reporting and configuration adjustments in the data processing cycle (steps 14-18), as well as feedback and optimization after the task is completed (steps 20-23).

[0215] 5. Central coordination and distributed execution: The adaptive workflow engine acts as the central coordinator (steps 4, 15, 18, 21, and 22), while the agents perform distributed task execution (step 11), which reflects the efficient coordination and execution capabilities of the system.

[0216] 6. Human-computer collaboration: By collecting user feedback through the human-computer collaboration interface (step 20), the system realizes human-computer collaboration, allowing human experts to participate and guide system behavior.

[0217] 7. Full-dimensional system optimization: After the task is completed, the CLID system optimizes the full-dimensional configuration (step 23) to ensure that the system can continue to evolve and improve performance.

[0218] This design fully reflects the characteristics of this system as an intelligent, adaptive, and continuously evolving data processing ecosystem. It can effectively handle complex and ever-changing data tasks, continuously improve its performance and adaptability through continuous learning and optimization, while maintaining a high degree of flexibility and configurability.

[0219] After describing the overall workflow of the system, this embodiment further provides a co-evolution mechanism of the system. This mechanism is a key innovation of this embodiment, which enables the system to continuously optimize, adapt to changes, and continuously improve its performance and capabilities. The system in this embodiment realizes the trinity of co-evolution of data sets, algorithms, and intelligent agents. This evolution is not isolated, but a process of mutual promotion and common improvement.

[0220] The system's autonomous optimization mechanism is implemented by the autonomous optimization engine. The engine continuously monitors the operating status and performance indicators of each component through comprehensive performance monitoring, including but not limited to processing speed, resource utilization, and result accuracy. By analyzing performance data, the system can automatically identify performance bottlenecks and potential improvement points. Based on real-time needs, the system dynamically adjusts the allocation strategy of computing resources, storage resources, etc. to ensure efficient use of resources. In addition, the system can automatically adjust configuration parameters to adapt to different workloads and task types.

[0221] In terms of data set evolution, the system can automatically identify and correct noise and anomalies in the data, and dynamically adjust the organizational structure of the data according to the needs of the algorithm and the agent. For example, when the agent finds that certain data features are particularly useful during processing, the system automatically enhances the representation of these features in the data set. Algorithm evolution is achieved through automated tuning and reconstruction, and the system continuously improves algorithm performance. Based on existing algorithms and task requirements, the system can automatically generate new and more efficient algorithms. For example, the system may combine the advantages of multiple existing algorithms to create a hybrid algorithm that is more suitable for a specific task.

[0222] Agent evolution is reflected in the fact that agents continuously expand their functional scope through learning and adaptation. At the same time, the system optimizes the interaction and collaboration strategies between agents. For example, when dealing with complex tasks, the system may develop new types of agents or optimize the collaboration mode of existing agents. These three elements influence each other: better data sets help develop more efficient algorithms, more efficient algorithms promote the evolution of agents, and evolved agents can better process and organize data sets.

[0223] The evolutionary strategy of this system not only responds to current needs, but also develops forward-lookingly based on long-term strategic goals. The system is able to decompose abstract long-term goals into quantifiable and executable sub-goals. For example, "improving data processing efficiency" may be decomposed into specific sub-goals such as improving algorithm performance, optimizing resource allocation, and improving data structure. Based on the decomposed goals, the system formulates a detailed evolutionary path, including the development direction and priority of each component. The system continuously evaluates the progress of evolution and adjusts the strategy based on new situations and feedback to ensure that the development direction is consistent with the strategic goals. By simulating the system performance under different evolutionary paths, the system can predict long-term development trends and provide support for decision-making.

[0224] The co-evolution mechanism runs through all the core and supporting components of the system. The intelligent agent ecosystem evolves to handle more complex tasks and optimizes the collaboration mode between intelligent agents through continuous learning and adaptation. The adaptive domain knowledge graph is continuously expanded and optimized as the system runs, reflecting the latest domain knowledge and data relationships. The adaptive workflow engine learns from past execution experience to optimize task allocation and process design. The CLID configuration system continuously optimizes its configuration strategy based on system performance and task requirements. The data processing pipeline dynamically adjusts its structure and processing algorithm according to the processing effect and emerging data characteristics. The innovation trigger continuously improves its innovation generation mechanism by analyzing the system operation mode.

[0225] To further illustrate the adaptability and evolutionary capabilities of the system, this embodiment provides two specific examples. When the system needs to switch from processing structured data to processing unstructured text data, the data processing pipeline will adjust its processing flow and introduce new text analysis components. The adaptive domain knowledge graph will be expanded to include knowledge related to text analysis. The CLID configuration system will optimize the configuration and provide appropriate parameter settings for text processing tasks. The intelligent agent ecosystem may evolve new intelligent agents that specialize in processing text.

[0226] When dealing with complex tasks involving multiple domains, such as combining financial data and social media data for market analysis, the adaptive workflow engine designs cross-domain workflows to integrate processing steps from different domains. The adaptive domain knowledge graph integrates knowledge from different domains to support cross-domain reasoning. Innovation triggers may propose new analysis methods based on unique patterns of cross-domain data. The agent ecosystem forms a team of agents that collaborate across domains, with each agent contributing its expertise.

[0227] In a possible design, the second aspect of this embodiment provides an application example of the first aspect of the embodiment:

[0228] Adaptive process automation for smart factories

[0229] This case demonstrates how the Adaptive Intelligent Symbiotic Data Ecosystem (AISDE) can achieve highly intelligent and adaptive process automation in large manufacturing companies.

[0230] After the AISDE system was deployed, it quickly integrated all aspects of the factory's production. The core of the system, the intelligent agent ecosystem, including agents such as production planning, quality control, equipment maintenance, supply chain management, and energy optimization, began to work together. These agents achieve efficient information exchange through a communication mechanism based on a graph attention network, forming a dynamic collaborative network. For example, when the quality control agent detects a fluctuation in product quality, it immediately sends an alert to other related agents through this network, triggering a series of coordinated actions.

[0231] During the production process, AISDE's adaptive workflow engine demonstrated its strong flexibility. Using reinforcement learning algorithms, the engine can autonomously design and adjust workflows based on real-time conditions. When sudden changes in market demand are detected, the system automatically redesigns the production process without human intervention. For example, when a large number of urgent orders are received, the production planning agent immediately consults with the supply chain management agent to evaluate the supply of raw materials. At the same time, the equipment maintenance agent proactively adjusts the maintenance plan to ensure that key equipment remains in optimal condition during high-load periods.

[0232] Another core innovation of AISDE, the CLID configuration system, plays a key role in this process. The CLID system uses a multi-objective optimization algorithm to continuously adjust the configuration of four dimensions: Case, Layer, Indexer, and Dot. Specifically:

[0233] 1.Case configuration includes scenarios such as regular production, high demand, supply chain emergency, equipment maintenance and energy optimization.

[0234] 2. Layer configuration covers data collection, preprocessing, analysis, decision support and execution.

[0235] 3.Ind icator configuration includes indicators such as production efficiency, quality control, supply chain, energy efficiency and innovation.

[0236] 4. Dot configuration involves specific parameters such as production line speed, quality control threshold, inventory safety level, equipment maintenance trigger conditions and energy allocation priority.

[0237] The CLID system is no longer limited to preset scenarios, but can dynamically generate new configuration plans based on real-time data. In the face of a sudden supply chain disruption, the CLID system quickly created a new "supply chain emergency" scenario and adjusted related indicators and parameters. For example, it increased the weight of the "supplier diversity" indicator while lowering the "minimum inventory" threshold, enhanced the data collection layer's monitoring of supplier status, and adjusted procurement decision parameters to allow higher procurement costs to ensure stable supply. These dynamic adjustments enable factories to quickly adapt to new production environments.

[0238] The system's knowledge graph is also constantly evolving. It uses graph neural network technology to not only store static production knowledge, but also learn new patterns from daily operations. During a production process, the system independently discovered a new process optimization method by analyzing the correlation between quality data and production parameters. This discovery was immediately integrated into the knowledge graph and applied to actual production through an adaptive workflow engine, significantly improving product quality.

[0239] AISDE's multimodal data fusion capabilities play an important role in product innovation. The system uses deep learning models, such as transformer networks, to process and integrate different types of data. It not only analyzes internal production data, but also integrates market feedback, social media trends, and competitor information. Through this all-round data analysis, AISDE's innovation trigger proposed a brand new product concept, which was quickly adopted and implemented by the development team.

[0240] Throughout the operation, AISDE's human-machine collaborative interface ensures that the wisdom of human experts can intervene at critical moments. The interface uses natural language processing technology to understand complex human instructions and provide intelligent suggestions. For example, when making a major capacity expansion decision, the system automatically generates multiple feasible plans and presents them to the management team through an intuitive visual interface. The system not only shows the expected results of each plan, but also explains the reasons for recommending a specific plan, which greatly improves the quality and speed of decision-making.

[0241] After several months of operation, the AISDE system has shown remarkable results: production efficiency has increased by 40%, product quality defect rate has decreased by 60%, new product development cycle has been shortened by 50%, and energy efficiency has increased by 30%. More importantly, the factory has demonstrated unprecedented flexibility and innovation capabilities, able to quickly adapt to market changes and lead industry trends.

[0242] This case fully demonstrates how AISDE achieves true intelligent manufacturing through innovative features such as intelligent agent collaboration, adaptive workflow, dynamic knowledge update and multimodal data fusion. It not only optimizes daily operations, but more importantly, cultivates an intelligent ecosystem that continuously learns and evolves, pointing the way for the future of manufacturing.

[0243] See also Fig.10 As shown, in a possible design, the third aspect of this embodiment provides a method for implementing collaboration in an adaptive intelligent symbiotic data ecosystem based on the first aspect of the embodiment, wherein the operating steps of the method may be but are not limited to the following steps S1 to S8.

[0244] S1. Receive data processing task requests, and the adaptive workflow engine performs task analysis and requests initial configuration from the CLID configuration system.

[0245] S2. The CLID configuration system generates an initial configuration consisting of four dimensions: Case, Layer, Indicator, and Dot, and returns it to the adaptive workflow engine.

[0246] S3. The adaptive workflow engine dynamically selects and assembles an agent team suitable for the current task from the agent pool based on CLID configuration.

[0247] S4. The agent team queries the knowledge graph for relevant domain knowledge and begins to collaboratively process data, while continuously interacting with the knowledge graph to update and query knowledge.

[0248] S5. The agent reports performance indicators and status to the adaptive workflow engine, and the workflow engine requests configuration adjustments from the CLID system based on this to achieve dynamic optimization.

[0249] S6. The system performs multimodal data fusion, integrating structured and unstructured data from different sources to discover potential data associations and patterns.

[0250] S7. Based on the integrated data and analysis results, the system’s innovation triggers generate innovative solutions or optimization suggestions.

[0251] S8. Present key decision points and system recommendations to human experts through a human-computer collaborative interface, integrate feedback and output the final results, and update system knowledge, optimize configuration strategies and adjust agent capabilities based on the overall execution status.

[0252] The working process, working details and technical effects of the method provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0253] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive intelligent symbiotic data ecosystem, characterized in that: include: An intelligent agent ecosystem module, wherein the intelligent agent ecosystem module includes a diverse and evolvable group of intelligent agents that can spontaneously form an efficient collaborative network according to task requirements and environmental changes; An adaptive domain knowledge graph module, wherein the adaptive domain knowledge graph module stores a domain knowledge graph, wherein the adaptive domain knowledge graph module is used to provide rich semantic information and reasoning capabilities for the entire ecosystem; An adaptive workflow engine module, the adaptive workflow engine module is used to understand high-level task descriptions and automatically build complex workflow graphs, including directed cyclic graphs, to handle iterations and feedback loops; CLID configuration system module, used to provide a highly flexible, sophisticated and standardized configuration mechanism for the entire system; The human-machine collaborative interface module is used to achieve the deep integration of artificial intelligence and human wisdom, integrating human judgment and AI capabilities at key points; Data processing pipeline module, which is used to efficiently process various types of data and dynamically adjust its structure and behavior according to data characteristics and processing goals; The innovation trigger module is used to promote data-driven innovation. It combines multimodal data fusion technology, intelligent analysis, and human-machine collaboration methods to provide users with a powerful and flexible innovation support platform.

2. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The intelligent agent ecosystem module includes: Specialized agents that focus on specific types of task processing, such as data cleaning, feature extraction, or model training; General-purpose agents, with cross-domain understanding and problem-solving capabilities, can handle a variety of unstructured tasks and quickly adapt to new roles; Meta-agent, responsible for supervising and coordinating the activities of other agents to optimize the performance of the entire system.

3. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The intelligent agent ecosystem module also includes: Dynamic task scheduling unit, which is used to automatically decompose complex tasks into subtasks and dynamically assign them to the most suitable agent or agent group; Self-organizing collaborative units, which enable agents to autonomously decide whether to participate in specific collaborations based on their own capabilities and task requirements; The adaptive resource allocation unit is used to ensure that computing resources, data resources, etc. are used most efficiently.

4. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The adaptive domain knowledge graph module includes: Multimodal knowledge encoding units for uniformly encoding and representing complex knowledge from different sources, including semantic information in structured data, unstructured text, images, audio, and video; Distributed storage units for efficient storage and fast retrieval of large-scale knowledge; Semantic embedding unit, used to map entities and relations into low-dimensional vector space, supporting efficient similarity calculation, relation reasoning, and knowledge completion.

5. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The adaptive workflow engine module includes: The semantic task understanding unit is used to deeply parse natural language task descriptions using a large language model, extract explicit key goals and constraints, and infer implicit requirements and dependencies; A dynamic graph generation unit, which is used to automatically generate a comprehensive workflow graph based on the task understanding results, including identifying and creating necessary task nodes, determining the logical relationship between nodes, designing control flow structures, arranging parallel execution paths, and building feedback mechanisms; The context-aware scheduling unit is used to implement intelligent task execution scheduling, taking into account multi-dimensional factors such as current system load, resource availability, task priority, historical performance data, etc.

6. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The CLID configuration system module includes: Case configuration unit, used to define and manage specific usage scenarios or task types; Layer configuration unit, used to organize the structure of data analysis and display, supporting multi-dimensional data display; Indicator configuration unit, used to define and manage key performance or analytical indicators; Dot configuration unit, used to achieve refined control and configuration of the smallest unit of the system; Configure collaborative optimization units to achieve dynamic balance and overall optimization of the four dimensions of CLID; The real-time feedback and adaptation unit is used to collect real-time feedback during system operation and quickly adjust the CLID configuration based on the feedback.

7. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The human-machine collaboration interface module comprises: Key decision point identification unit, used to autonomously identify key decision points in the workflow that require human intervention; An interactive interface generation unit, used to dynamically generate the most suitable interactive interface according to the characteristics of the decision point; A context information provision unit, which is used to provide rich and relevant background information to human decision makers; AI-assisted decision-making unit, which is used to provide AI-generated decision suggestions while explaining its reasoning process.

8. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The data processing pipeline module includes: An automated optimal processing flow construction unit, which is used to automatically construct the optimal processing flow according to data characteristics and processing objectives; A real-time adaptive adjustment unit is used to dynamically adjust its structure and parameters according to data flow characteristics, system resource status and processing effect; The data fitness evaluation unit is used to comprehensively evaluate the adaptability and potential value of data under different processing environments and objectives.

9. The adaptive intelligent symbiotic data ecosystem according to claim 1, characterized in that: The innovation trigger module includes: Multimodal data intelligent fusion unit, used to achieve deep cross-modal understanding and innovative association; Idea-stimulating interface unit to promote data-driven innovative thinking and human-machine collaboration; AI-assisted idea realization unit for converting data-driven innovation insights into actionable business improvements.

10. A method for realizing collaboration of an adaptive intelligent symbiotic data ecosystem based on any one of claims 1 to 9, characterized in that: include: Receive data processing task requests, and the adaptive workflow engine performs task analysis and requests initial configuration from the CLID configuration system; The CLID configuration system generates an initial configuration including four dimensions: Case, Layer, Indicator, and Dot, and returns it to the adaptive workflow engine; The adaptive workflow engine dynamically selects and assembles an agent team suitable for the current task from the agent pool based on CLID configuration; The agent team queries the knowledge graph for relevant domain knowledge and begins to collaboratively process data, continuously interacting with the knowledge graph to update and query knowledge; The agent reports performance indicators and status to the adaptive workflow engine, and the workflow engine requests configuration adjustments from the CLID system based on this to achieve dynamic optimization; The system performs multimodal data fusion, integrating structured and unstructured data from different sources to discover potential data associations and patterns; Based on the integrated data and analysis results, the system's innovation triggers generate innovative solutions or optimization suggestions; Key decision points and system recommendations are presented to human experts through a human-computer collaborative interface, and the final results are output after integrating the feedback. The system knowledge is updated, the configuration strategy is optimized, and the capabilities of the intelligent agent are adjusted based on the overall execution situation.

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