A production-research full-link AI collaboration system and method

Abstract

This invention discloses a full-chain AI collaboration system and method for research and development, employing an in-process integrated deployment architecture that deploys the AI ​​processing engine and the host application in the same process and shares a data layer. The system comprises four main modules: pipeline management, role skill configuration, AI orchestration engine, and human-machine collaborative review. The pipeline management module defines the processing flow state machine; the role skill configuration module pre-sets multiple capability containers as an abstract framework for processing logic, supporting dynamic addition, removal, and memory mounting of pluggable execution units during runtime; the AI ​​orchestration engine dynamically schedules execution units and drives capability containers to complete tasks within the process based on the flow state machine; and the human-machine collaborative review module displays the comparison results between AI-generated content and historical assets, collecting manually corrected features to drive model self-evolution. This invention achieves deep integration of AI and the host, flexible capability expansion, eliminates cross-system communication overhead and data fragmentation, and ensures semantic consistency between generated content and historical assets.

Description

Technical Field

[0001] This invention relates to the fields of computer software engineering and artificial intelligence technology, specifically to an AI collaboration system and method for the entire R&D chain. Background Technology

[0002] Currently, in software development and content generation, the introduction of AI-assisted tools to improve processing efficiency has become a mainstream trend. However, existing AI-assisted tools are typically deployed as independent systems, isolated from the host business platform, with communication and data interaction occurring only through cross-system interfaces. This fragmented architecture prevents AI tools from directly accessing the historical assets and contextual data within the host platform, resulting in severe information attenuation and communication latency issues. Consequently, AI-generated content often becomes disconnected from actual business logic. Furthermore, the processing logic of existing AI tools is mostly fixed, single-point applications, making it difficult to flexibly expand and dynamically adapt to the specific requirements of different business domains, thus limiting system scalability. In addition, in content generation scenarios, AI models are prone to generating illusory content that does not conform to business logic, lacking effective alignment mechanisms based on historical assets and a closed-loop feedback mechanism for manual correction. Therefore, there is an urgent need for a collaborative system that can deeply integrate AI capabilities with business processes, eliminate data fragmentation and communication latency, and support flexible expansion and continuous optimization of processing capabilities. Summary of the Invention

[0003] The purpose of this invention is to provide an AI collaboration system and method for the entire R&D chain. This invention integrates the AI ​​processing engine and the host application in the same process space and shares a data layer. Simultaneously, it employs a two-layer structure of capability containers and pluggable execution units to achieve dynamic runtime expansion of processing capabilities. This achieves deep integration and flexible expansion, eliminates cross-system communication overhead and data fragmentation, and ensures semantic consistency and continuous optimization between generated content and historical assets.

[0004] The technical solution provided by this invention is as follows: A full-chain AI collaboration system for research and development, wherein the system adopts an in-process fusion deployment architecture, fusion deployment of the AI ​​processing engine and the host application in the same process space, and sharing a data layer; the system includes: The pipeline management module is used to define the state machine of the processing flow; The character skill configuration module is used to preset multiple capability containers that define processing logic frameworks, and supports the dynamic addition and removal of pluggable execution units at runtime and dynamic mounting at the memory level. The capability container is the abstract layer that defines the processing logic framework, and the pluggable execution unit is the implementation layer that implements specific processing functions. The two form a two-layer structure. The AI ​​orchestration engine is used to dynamically schedule pluggable execution units within a process and drive the corresponding capability containers to execute tasks based on the flow of the processing flow state machine. The human-machine collaborative review module is used to display the comparison between AI-generated content and historical assets, and to capture human correction features to drive the model's self-evolution.

[0005] In the aforementioned AI collaboration system across the entire R&D chain, the capability container and the pluggable execution unit are associated through a mapping relationship in the role skill configuration module; one capability container can be associated with multiple pluggable execution units, and one pluggable execution unit can be reused by multiple capability containers.

[0006] The aforementioned AI collaboration system across the entire R&D chain dynamically loads or unloads the pluggable execution unit at runtime through a configuration center, without restarting the host application; when loading, the pluggable execution unit is instantiated as a memory object and a mapping is established with the capability container; when unloading, memory resources are released and the mapping is removed.

[0007] In the aforementioned AI collaboration system covering the entire R&D chain, the pluggable execution units adopt an atomic design. Each pluggable execution unit includes: metadata description, input / output interface specifications, execution logic unit, and dependency configuration; the dependency configuration declares the prerequisite dependencies or mutual exclusion relationships with other pluggable execution units.

[0008] The aforementioned AI collaboration system across the entire R&D chain handles logical conflicts between multiple pluggable execution units through a scheduling arbiter. Based on the priority and dependency relationships determined by the task metadata profile, the system executes sequential calls or mutual exclusion filtering of pluggable execution units on the same processing node.

[0009] The aforementioned AI collaboration system across the entire R&D chain includes a human-machine collaborative review module that includes an embedded editor for displaying a comparison view of AI-generated content and historical assets, and capturing features for manual correction; the comparison view marks the differences in additions, modifications, and deletions.

[0010] In the aforementioned AI collaboration system across the entire R&D chain, the human-machine collaborative review module extracts the text difference vector before and after manual correction, calculates the correlation between the text difference vector and the confidence level generated by AI, and incorporates positively correlated samples into the fine-tuning dataset to adjust the generation model parameters and prompt word templates.

[0011] Furthermore, this invention also provides a method for incremental content generation and continuous optimization based on semantic alignment of historical assets. The method is executed by the AI ​​collaboration system across the entire R&D chain and includes: acquiring the original text of the current processing task and decomposing it into atomic functional feature vectors using a natural language processing algorithm; retrieving historical assets with a correlation greater than a preset threshold using a vector index within the shared data layer to identify the inheritance relationship of business logic; establishing a logic state matrix to automatically mark the inheritance, modification, and obsolescence logic points between new and old versions; performing incremental content merging on the historical asset base using a generation model to generate the evolutionary content of the current version; capturing manual correction behavior through an embedded editor, calculating the deviation features between the AI-generated vector and the manually corrected vector, and feeding them back into the model weights.

[0012] Preferably, the historical assets retrieved with a relevance greater than a preset threshold are matched using a combination of semantic similarity and path matching features; when the semantic similarity exceeds the preset threshold and the path matching is consistent, it is determined to be logical inheritance; when the semantic similarity exceeds the preset threshold but the terminology changes, it is determined to be terminology evolution.

[0013] Furthermore, this invention also provides an incremental content generation and continuous optimization device based on historical asset semantic alignment. The device is used to execute the method and includes: a feature extraction module for acquiring the original text of the current processing task and decomposing it into atomic functional feature vectors using a natural language processing algorithm; a semantic retrieval module for retrieving historical assets with a correlation greater than a preset threshold within a shared data layer using a vector index, and identifying the inheritance relationship of business logic; a conflict detection module for establishing a logical state matrix and automatically marking the inheritance, modification, and obsolescence logic points between new and old versions; an incremental merging module for performing incremental content merging on the historical asset base using a generation model to generate the evolutionary content of the current version; and a feedback loop module for capturing manual correction behavior through an embedded editor, calculating the deviation features between the AI-generated vector and the manually corrected vector, and feeding it back to the model weights.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses an in-process fusion deployment architecture to integrate and deploy the AI ​​processing engine and the host application in the same process space and share the data layer. This allows the AI ​​processing engine to directly access the host application's data through memory objects, completely eliminating network latency and data serialization overhead caused by cross-system communication. At the same time, it avoids information attenuation caused by data fragmentation, and achieves deep integration of AI capabilities and business data.

[0015] 2. This invention uses a two-layer structure design of capability container and pluggable execution unit to decouple and layer the processing logic framework from the specific processing functions, and supports dynamic addition and removal of pluggable execution units at runtime and dynamic mounting at the memory level. While maintaining the low latency and high data consistency brought by in-process fusion deployment, it gives the system processing capabilities flexible scalability, thus resolving the contradiction between deep fusion and flexible expansion.

[0016] 3. This invention uses an incremental content generation mechanism based on the semantic alignment of historical assets to retrieve similar historical assets and identify inheritance relationships within the shared data layer, establish a logical state matrix to classify and identify change types, and perform incremental merging on the historical asset base. This enables the generated content to maintain semantic consistency with historical assets, effectively overcoming the illusion risk of AI models and adapting to the incremental change needs of business.

[0017] 4. This invention captures manually corrected features and calculates deviation vectors to feed back to the model weights through a human-machine collaborative review module and feedback closed-loop mechanism, thereby achieving continuous self-evolution of the generated model and continuously improving the accuracy and business adaptability of AI-generated content. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the fully converged technology architecture of the system; Figure 3 The full-link AI collaboration flowchart of the system of this invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0020] Example 1: This example provides an AI collaboration system covering the entire R&D and production chain. The overall system architecture of this invention is as follows: Figure 1 As shown, the system comprises upstream input data sources, the core system itself, downstream output systems, and internal standard workflow modules. Upstream input data sources connect to the core system through standardized data interfaces, providing basic business data such as requirements, use cases, code, and defect tickets. The core system acts as a central node, handling data processing and task collaboration. The downstream output system executes automated testing, generates project reports, and stores collaborative data. The internal standard workflow modules sequentially include a pipeline management module, a role skill configuration module, an AI orchestration engine, and a human-machine collaborative review module. These modules work together to automate process scheduling, configure role skills, enable multi-AI collaborative processing, and conduct closed-loop human-machine review, supporting hot-swappable skills and continuous model optimization.

[0021] The fully integrated technical architecture of the system of this invention is as follows: Figure 2As shown, the overall architecture is divided into four layers: the interface layer, the core application layer, the unified database platform, and the infrastructure layer. The interface layer provides multiple types of interfaces such as RESTful API and WebSocket to adapt to external interactions; the core application layer is the integration point, integrating the R&D management platform and the AI ​​execution kernel, and achieving network-boundary-free communication through memory sharing; the unified database platform integrates storage components such as MySQL and Redis; and the infrastructure layer provides basic resource support such as servers and networks.

[0022] This embodiment adopts an in-process fusion deployment architecture, merging the AI ​​processing engine and the host application within the same process space and sharing a data layer. In this architecture, the AI ​​processing engine and the host business logic share the same memory address space and computing resources. Data interaction between them directly accesses the shared data layer through memory objects, eliminating reliance on cross-process network communication. This completely eliminates the network latency and data serialization overhead caused by cross-system communication in traditional fragmented architectures, while also avoiding information attenuation and context loss problems caused by data being scattered across different database platforms. It should be understood that although this embodiment demonstrates a preferred mode of fusion deployment of the AI ​​processing engine and the host application within the same process space, other fusion methods capable of achieving direct memory-level data access should also be considered reasonable extensions of the scope of protection of this invention, provided that the core principle of eliminating cross-system communication latency is not violated.

[0023] The pipeline management module is used to define the processing flow state machine. Based on the state machine pattern, the module defines the lifecycle nodes of business processing, including state transition stages such as REQ_ANALYSIS (requirements analysis), TECH_DESIGN (technical solution design), DEV_IMPLEMENT (development implementation), TEST_EXECUTION (test execution), and VERSION_ACCEPT (version acceptance). Each state node is associated with specific processing logic triggering conditions, and the state transition drives the scheduling and execution of subsequent modules.

[0024] The role skill configuration module is used to pre-define multiple capability containers that define processing logic frameworks, and supports dynamic addition and removal of pluggable execution units at runtime and dynamic mounting at the memory level. The capability container is an abstract layer defining the processing logic framework, while the pluggable execution unit is the implementation layer that implements specific processing functions, forming a two-layer structure. The capability container specifies the input / output specifications, processing boundaries, and collaboration rules that the processing task should possess, but does not contain the specific algorithm execution logic; the pluggable execution unit is the specific algorithm or function implementation entity that follows the container specification. Preferably, the role skill configuration module pre-defines the following capability containers and their associated pluggable execution units: a requirement analyst capability container, associated with pluggable execution units such as requirement text parsing, function point decomposition, and test case generation; a test engineer capability container, associated with pluggable execution units such as test case design, script generation, and execution verification; a code auditor capability container, associated with pluggable execution units such as static code analysis, vulnerability scanning, and specification checking; and a technical reviewer capability container, associated with pluggable execution units such as architecture design evaluation, technology selection advice, and risk identification. It should be understood that the above roles are merely illustrative examples and not restrictive. In actual business operations, other capability containers can be configured according to the needs of different domains.

[0025] Preferably, in the role skill configuration module, the capability container and the pluggable execution unit are associated through a mapping relationship. One capability container can be associated with multiple pluggable execution units, and one pluggable execution unit can be reused by multiple capability containers. For example, the requirements analyst capability container can be associated with multiple pluggable execution units such as requirements text parsing, function point decomposition, and test case generation; while the function point decomposition pluggable execution unit can be called by the requirements analyst capability container and reused by the technical reviewer capability container to assist in architecture evaluation. This one-to-many and many-to-many mapping reuse relationship eliminates the need for the system to repeatedly develop the same functional logic for each capability container, significantly reducing the redundancy and maintenance costs of execution units.

[0026] The system dynamically loads or unloads pluggable execution units at runtime through a configuration center, without restarting the host application. During loading, the pluggable execution unit is instantiated as a memory object and a mapping is established with the capability container. During unloading, memory resources are released and the mapping is removed. Specifically, the configuration center maintains a registration list of pluggable execution units, recording each unit's identifier, its associated capability container, current status (loaded / unloaded), and version information. When business requirements change or a new execution unit version is released, the configuration center uses a hot-loading mechanism to read the new pluggable execution unit from the pluggable execution unit library and instantiate it as a memory object. Then, it establishes a mapping relationship between the new unit and its corresponding capability container in the role skill configuration module. The entire process is completed within the same process space of the host application, without interrupting the running business process. Conversely, when an execution unit needs to exit due to version iteration or business shrinkage, the configuration center first removes it from the active mapping and then releases the memory resources it occupies, ensuring on-demand elastic scaling of system resources. It should be understood that the above-mentioned dynamic loading and unloading mechanism is one of the core advantages of the in-process integrated deployment architecture. It is precisely because the AI ​​processing engine and the host application share the same process space that the memory-level mounting and unloading of the execution unit becomes possible. In contrast, the cross-system plugin management under the traditional fragmented architecture must rely on inter-process communication and restart deployment.

[0027] Preferably, the pluggable execution unit adopts an atomic design. Each pluggable execution unit includes: metadata description, input / output interface specifications, execution logic unit, and dependency configuration. The metadata description records summary information such as the function identifier, version number, and applicable scenario tags of the execution unit, which is used by the AI ​​orchestration engine for quick matching and filtering during scheduling. The input / output interface specifications strictly define the parameter types, format constraints, and data structures of the returned results received by the unit, ensuring consistency with the specification contract of the capability container. The execution logic unit is the core entity that carries the implementation of specific algorithms or functions, such as the BERT-based semantic encoding logic in the requirement text parsing unit, or the rule engine-based test case generation logic in the test case design unit. The dependency configuration declares the pre-dependencies or mutual exclusion relationships with other pluggable execution units. For example, the test script generation unit declares a pre-dependency on the test case design unit (test cases must be generated before scripts can be generated), and the code audit unit and the manual review unit declare a mutual exclusion relationship (automatic auditing and manual review cannot be triggered simultaneously on the same node). This atomized design makes each execution unit a self-contained, independently replaceable smallest functional particle. Upgrading, replacing, or removing any unit does not affect the normal operation of other units, thus achieving smooth online evolution of the system's processing capacity.

[0028] The AI ​​orchestration engine is used to dynamically schedule pluggable execution units within a process and drive the corresponding capability containers to execute tasks based on the flow of the processing flow state machine.

[0029] As the scheduling hub within the system, the orchestration engine operates based on the aforementioned in-process integrated deployment and two-layer structure. When the state machine of the pipeline management module transitions, the AI ​​orchestration engine, within the same process space of the host application, matches the corresponding capability container from the role skill configuration module according to the logical framework required by the current state node. It then further searches for or dynamically mounts associated pluggable execution units within the process, directly passing task parameters to the execution units via memory for driving. The AI ​​orchestration engine's in-process scheduling mechanism further solidifies the system's advantage of eliminating cross-system communication overhead.

[0030] The orchestration engine internally includes a task dispatcher, execution monitor, result aggregator, and exception handler. The task dispatcher is responsible for initially distributing tasks to the corresponding capability containers based on the state machine flow of the pipeline management module; the execution monitor tracks the running status and resource usage of each execution unit in real time; the result aggregator summarizes and logically integrates the output results after multiple non-conflicting and parallel execution units complete; and the exception handler captures exceptions during execution and triggers retry or rollback mechanisms. Preferably, the exception handler adopts a tiered strategy: for recoverable exceptions (such as network interruptions or temporary resource unavailability), an automatic retry mechanism is triggered, with a maximum of 3 retries, and the retry interval increasing exponentially with a backoff strategy; for unrecoverable exceptions (such as logical errors or incompatible data formats), a rollback mechanism is triggered, reverting the task status to the nearest safe node and notifying the human-machine collaborative review module to intervene manually. It should be understood that the above exception handling strategy is only an illustrative example; in actual engineering, the retry limit and backoff parameters can be flexibly adjusted according to business tolerance and recovery costs.

[0031] The system handles logical conflicts between multiple pluggable execution units through a scheduling arbiter. Based on the priorities and dependencies determined by the task metadata profile, it executes sequential calls or mutual exclusion filtering of pluggable execution units on the same processing node. The scheduling arbiter intervenes to handle potential unit conflicts after the task dispatcher distributes the task and before the execution monitor monitors its execution. The task metadata profile is an abstract characterization of the full-dimensional attributes of the task to be processed, including at least four key dimensions: business priority, execution timeliness, resource constraints, and compliance requirements. When there are execution order dependencies between multiple pluggable execution units, the scheduling arbiter constructs a directed acyclic graph based on the dependencies declared by each unit and executes sequential calls according to the topology. When there are logical mutual exclusion relationships between multiple pluggable execution units, the scheduling arbiter selects the unit that best matches the current task profile and has the highest priority based on the weights of each dimension in the task metadata profile, while filtering out mutually exclusive low-priority units. It should be understood that mutual exclusion filtering does not permanently delete the filtered units; it merely temporarily deactivates the profile of the current specific task on the current node.

[0032] The human-machine collaborative review module is used to display the comparison between AI-generated content and historical assets, and to capture manually corrected features to drive the model's self-evolution.

[0033] The human-machine collaborative review module provides a closed-loop interface for human-machine interaction and continuous optimization. It uses an embedded editor to compare AI-generated content (specifically, evolving content in this embodiment) with historical assets in the shared data layer, and captures user correction behavior characteristics in real time during the review process. Preferably, the embedded editor uses a code editing component based on the Monaco Editor kernel, directly integrated into the UI interface within the host application process, supporting syntax highlighting, difference annotation, and real-time preview. The comparison view automatically annotates added, modified, and deleted differences, and transforms user GUI interactions into structured data processing instructions. More importantly, the human-machine collaborative review module extracts the text difference vector before and after manual correction, calculates the correlation between the text difference vector and the AI-generated confidence level, and includes positively correlated samples in the fine-tuning dataset to adjust the generation model parameters and prompt word templates. The system only includes positively correlated samples in the fine-tuning dataset; this filtering mechanism effectively filters out noisy data, ensuring that the model's fine-tuning direction is to compensate for its own cognitive deficiencies rather than catering to accidental preferences. Preferably, the fine-tuning employs a hierarchical optimization strategy: for high-frequency correction modes (where the same type of error repeatedly occurs in multiple tasks), the system triggers a structural reconstruction of the prompt word template, adding targeted constraint instructions to the template; for low-frequency correction modes (occasional individual corrections), the system only adjusts the local fine-tuning of the model weight parameters to avoid overfitting. Furthermore, the system supports A / B testing of the prompt word template, running the reconstructed template and the original template in parallel on the same task set, comparing the accuracy of the generated results with the manual correction rate. The new template is only formally replaced when its metrics are significantly better than the original template, ensuring the reliability of the optimization direction.

[0034] The above modules form a complete closed loop in the end-to-end collaborative process: the pipeline management module drives the state machine to transition to the current processing node; the role skill configuration module matches the corresponding capability container and pluggable execution unit; the AI ​​orchestration engine schedules the execution unit within the process and drives the capability container to generate evolving content; the human-machine collaborative review module displays a comparison view and captures manually corrected features; these corrected features are fed back to the generation model to achieve self-evolution; and the optimized model generates more accurate content in the next task, thus forming a continuous optimization closed loop of "scheduling → generation → review → feedback → regeneration". In a specific case, such as Figure 3 As shown, the process begins with three main entry points: requirement input, technical solution, and code submission. These are matched with AI requirement analysts, AI technical reviewers, and AI code auditors to complete the initial processing. After meeting the status transition conditions such as complete functional point breakdown, satisfactory test case coverage, and manual review approval, the process enters the test execution, version acceptance, and quality report stages, corresponding to AI test engineers, AI acceptance specialists, and automatically generated quality reports. Finally, through cyclical feedback, knowledge updates and continuous process iteration are achieved. It should be understood that... Figure 3 The process shown is only a preferred business flow model. In actual applications, the status nodes and module collaboration order can be adjusted according to the processing needs of different fields.

[0035] Through the aforementioned in-process integrated deployment architecture, two-layer structure, internal collaboration of the AI ​​orchestration engine, scheduling arbitrator, and feedback loop mechanism of the human-machine collaborative review module, this embodiment constructs a complete system closed loop from task scheduling to content generation and continuous optimization. This fundamentally eliminates cross-system communication latency, achieves on-demand elastic scaling and smooth online evolution of processing capabilities, and ensures continuous improvement in system output quality through manual correction and feedback. The above description of Embodiment 1 is only used to explain the specific implementation of the present invention under this system-side subject matter, and is not intended to limit the scope of protection of the present invention.

[0036] Example 2: This example provides an incremental content generation and continuous optimization method based on historical asset semantic alignment. The method is executed by the end-to-end AI collaboration system described in Example 1, specifically operating on an in-process integrated deployment architecture and a shared data layer. This achieves semantic consistency between generated content and historical assets, as well as continuous self-evolution, within a system environment that eliminates data fragmentation and communication latency. It should be understood that the descriptions of the specific implementation methods for each step in this example are illustrative only, not restrictive.

[0037] Step S100: Obtain the original text of the current processing task and decompose it into atomic functional feature vectors using a natural language processing (NLP) algorithm. Specifically, the system parses the original text using a NLP algorithm, breaking it down into the smallest indivisible logical particles. Preferably, the NLP algorithm employs a BERT-based semantic encoding model, encoding each functional description segment in the original text into a 768-dimensional semantic vector representation. For example, the composite description "add mobile phone number verification function" is decomposed into multiple independent feature vector representations such as "user object," "mobile phone number field," "verification action," and "security constraints." Through this atomic decomposition, the system transforms ambiguous human language into precise mathematical expressions that are machine-computable and comparable, providing indispensable anchor input for subsequent semantic retrieval and alignment in the historical asset database.

[0038] Step S200 involves retrieving historical assets with a correlation greater than a preset threshold using a vector index within the shared data layer to identify the inheritance relationship of business logic. This application emphasizes retrieval "within the shared data layer," and its defense logic is that only by relying on the shared data layer provided by the in-process fusion deployment architecture can the AI ​​processing engine directly access the entire set of historical assets at memory-level speed, avoiding information attenuation and delays caused by cross-system interface calls. During the retrieval process, the system uses a pre-established vector index to calculate the correlation between the atomic functional feature vectors extracted in step S100 and the feature vectors in the historical asset database. Preferably, the vector index adopts the HNSW index structure built using the FAISS framework, supporting millisecond-level nearest neighbor retrieval for a database of hundreds of millions of vectors. The correlation calculation uses cosine similarity as a metric, and its calculation formula is as follows: ; Formula explanation: Where This represents the atomic function feature vector for the current task. These are feature vectors from the historical asset database. The cosine similarity between the two in the semantic space ranges from -1 to 1, with larger values ​​indicating higher semantic relevance. The system only retains... Historical assets that exceed a preset threshold θ (preferably θ=0.75) are selected as the candidate set.

[0039] More importantly, the core purpose of this step is not merely "finding similarities," but "identifying the inheritance relationship of business logic." The search for historical assets with a relevance greater than a preset threshold is performed using a combination of semantic similarity and path matching. When semantic similarity exceeds the preset threshold and path matching is consistent, it is determined to be logical inheritance; when semantic similarity exceeds the preset threshold but terminology changes, it is determined to be terminology evolution. The system must introduce dual features rather than a single semantic similarity because its defense logic is that a single semantic similarity can only determine "whether two functional points are doing similar things," but cannot determine "whether they are doing similar things in the same business location." For example, when the current requirement is "member mobile phone number verification," the semantic similarity of the "user email verification" function in the historical database may reach 0.82, but the two belong to different business object path nodes. Direct reuse based solely on a single similarity would lead to serious business conflicts and AI illusions. By introducing path matching, the system can block such cross-path mismatches and reclassify them as logical innovations or modification conflicts.

[0040] Step S300: Establish a logical state matrix to automatically mark the inheritance, modification, and obsolescence logic points between the old and new versions. After identifying the inheritance relationship, the system establishes a logical state matrix to accurately label the state of each atomic function point between the old and new versions. The logical state matrix is ​​a structured data model, where rows represent the functional logic points of historical versions, lists represent the requirement logic points of the current version, and matrix elements record the evolutionary relationship between the two. The system automatically marks three types of core logic points: inheritance logic points (functionality is completely consistent between the old and new versions and can be directly reused), modification logic points (functional framework exists but specific parameters or rules have changed, requiring incremental overriding), and obsolescence logic points (historical functions are no longer needed in the current version and need to be obsolescence). Table 1 below provides an example structure of the logical state matrix: Table 1 Function ID Historical status Current status Conflict Types Processing strategy F001 exist exist No conflict inherit F002 exist Revise Modify conflict Incremental Coverage F003 exist Does not exist Abandoned Conflict Mark as obsolete F004 Does not exist exist New Conflicts New generation It should be understood that although this embodiment lists three states—inheritance, modification, and obsolescence—more granular state markers such as "merge" and "split" can be added in more complex business evolutions, as long as the change type can be clearly described. By establishing a logical state matrix, the system transforms vague "change requirements" into a clear "list of operation instructions," providing a precise roadmap for subsequent incremental merging.

[0041] Step S400 involves using a generative model to perform incremental content merging on a historical asset base, generating the current version's evolved content. The core defense of this application lies in "performing incremental content merging on a historical asset base," rather than "full generation" or "generation without a historical base" detached from history. Incremental merging, guided by the logical state matrix marked in step S300, uses retrieved historical assets as a stable "base." Only for logical points marked as "modified" or "added," the generative model is invoked to generate and replace local content. For "inherited" logical points, the historical base content is directly retained; for "obsolete" logical points, deletion is performed. Preferably, the generative model adopts a large language model based on the GPT-4 architecture. A carefully designed prompt word template guides the model to perform local generation only for incremental logical points, rather than a full rewrite. For example, in a test case evolution scenario, the system uses the historical "user registration" test case as a base, modifying only the "phone number verification" logical point, generating the following incremental code snippet: #Logic snippet for verifying phone numbers generated by incremental merging: phone = generate_phone_number() result = register(username, password, phone=phone) assert result.phone_verified == False # Newly registered users' phone numbers are not verified by default This "base + incremental" generation model leverages the creativity of the generation model to adapt to business changes while utilizing the stability of historical assets to ensure system consistency, achieving a balance between creativity and stability. The generated "evolutionary content" is not an isolated new file, but a continuum with a clear connection to historical versions, greatly reducing the cognitive cost of manual review and acceptance.

[0042] Step S500: The system captures human correction behavior through an embedded editor, calculates the deviation features between the AI-generated vector and the human-corrected vector, and feeds this information back into the model weights. The delivery of generated content is not the end point, but the starting point for continuous optimization. The system captures user correction behavior on evolving content during collaborative review using the embedded editor described in Example 1. The system vectorizes the text before and after human correction and calculates the deviation features between them in the semantic space. This deviation feature accurately characterizes the gap between AI model cognition and human expert cognition. Subsequently, the system calculates the correlation between this deviation feature and the confidence distribution when the AI ​​generated the content: if the deviation direction highly overlaps with the low-confidence region, it is determined to be a positively correlated sample, indicating that the human correction accurately hit the model's cognitive blind spot; if the correction occurs in the high-confidence region, it may be an adjustment caused by special business preferences, unrelated to model defects. The system only includes positively correlated samples in the fine-tuning dataset, correcting the probability distribution within the model through backpropagation or cue word constraint adjustments, enabling the model to automatically avoid corrected errors when facing similar scenarios in the future. By combining the hierarchical optimization strategy and A / B testing verification mechanism described in Example 1, this step ensures the reliability and stability of the model's self-evolution direction.

[0043] To more clearly illustrate the operational mechanism of the method provided by this invention in actual business, the following explanation uses a test case evolution scenario in the field of software development as an example. It should be understood that this scenario is merely illustrative and not restrictive; this method is also applicable to any scenario requiring incremental content generation and continuous optimization based on historical assets, such as requirement document evolution and code logic completion.

[0044] In this scenario, the host application is set as a research and development management platform, and the current task is to generate test cases for the newly added "phone number verification" function. In step S100, the system obtains a product requirement document containing a description of the "new phone number verification function" and uses the BERT semantic encoding model to decompose atomic functional feature vectors such as "user object," "phone number field," "verification action," and "security constraints." In step S200, historical assets are retrieved using the FAISS-HNSW vector index within the shared data layer. Combining the dual features of semantic similarity and path matching, it is found that the semantic similarity of the test cases for the "user registration" function exceeds the threshold and the path matching is consistent, which is determined to be logical inheritance; the semantic similarity of the test cases for the "email verification" function exceeds the threshold but the terminology has changed, which is determined to be terminology evolution. The system prioritizes the historical test cases of "user registration" with the strongest logical inheritance relationship as the merging base. In step S300, a logical state matrix is ​​established, marking the general registration logic as the inheritance logic point, the new phone number verification logic as the modification logic point, and the old version of the verification-free logic as the obsolete logic point. In step S400, using the historical "user registration" test case as a base, incremental code snippets are generated by using the GPT-4 generation model to modify the logic points only for mobile phone number verification. These snippets are then merged into the base, and obsolete logic points are removed to generate evolutionary test cases. In step S500, the test engineer discovers in the embedded editor of the human-machine collaborative review module that the AI-generated assertion "assertresult.phone_verified==True" does not conform to the business rule (newly registered users' mobile phone numbers are not verified by default), and corrects it to "assert result.phone_verified==False". The system extracts the text difference vector before and after correction, calculates the correlation with the confidence level generated by the AI, determines it to be a positively correlated sample, and includes it in the fine-tuning dataset to adjust the generation model parameters and prompt word template. A constraint instruction of "the initial default state of specific business rules must be verified first" is added to the prompt word template.

[0045] As background information, in a multi-tenant business environment, the shared data layer in the above method employs a tenant isolation mechanism to ensure that historical assets of different tenants are not visible to each other. Specifically, the shared data layer maintains an independent vector index space and asset storage partition for each tenant, and automatically injects tenant identifiers as filtering conditions during retrieval, ensuring that the historical assets retrieved in step S200 are limited to the business scope of the current tenant. It should be understood that the multi-tenant isolation mechanism is an engineering implementation strategy at the data access level and does not constitute a substantial limitation on the core steps of the method of this invention.

[0046] Through the sequential execution of steps S100 to S500, this embodiment establishes a complete defense framework: semantic alignment ensures consistency, incremental merging adapts to changes, and feedback loops ensure continuous optimization. Semantic alignment ensures that the generated content does not deviate from the actual business, incremental merging ensures the efficiency and stability of the generation method, and feedback loops ensure the long-term evolution of the generation model. All three are indispensable and together solve the technical pain points of existing technologies, such as the tendency for full-scale generation to produce illusions, the tendency for baseless generation to become disjointed, and the difficulty in optimizing one-time generation. The above description of Embodiment 2 is only used to explain the specific implementation of this invention under the topic of this method, and is not intended to limit the scope of protection of this invention.

[0047] Example 3: Corresponding to the aforementioned method flow examples, this example provides an incremental content generation and continuous optimization device based on historical asset semantic alignment. It should be understood that the device in this example, as a compliant accessory to the method, focuses on describing the structural connections and data flow topology between functional modules, rather than the temporal execution flow. This device does not introduce features such as in-process fusion deployment or a two-tier structure on the system side, maintaining a strict correspondence with the method's exclusivity.

[0048] The device includes a feature extraction module, a semantic retrieval module, a conflict detection module, an incremental merging module, and a feedback loop module. These five modules do not exist in isolation, but rather form a complete closed-loop link through a rigorous data flow pipeline, from input parsing to output optimization, and then from optimization back to input parsing.

[0049] The feature extraction module is used to acquire the original text of the current processing task and decompose it into atomic functional feature vectors using natural language processing algorithms. As the entry point for the entire device, the feature extraction module is responsible for transforming unstructured business requirements into machine-computable structured expressions. Its output is directly connected to the input of the semantic retrieval module via a data pipeline, passing the extracted atomic functional feature vectors as anchor inputs for retrieval. It should be understood that although this embodiment describes a unidirectional output from the feature extraction module to the semantic retrieval module, after the feedback loop mechanism is activated, the algorithm parameters within the feature extraction module (such as word segmentation strategies and entity recognition weights) will also receive feedback adjustment signals from the feedback loop module, thereby achieving dynamic evolution of the extraction strategy.

[0050] The semantic retrieval module is used within the shared data layer to retrieve historical assets with a relevance greater than a preset threshold using vector indexes, identifying the inheritance relationship of business logic. The semantic retrieval module receives feature vectors output by the feature extraction module and performs matching calculations in the historical asset database. Its core output is not a simple similarity score, but rather an identifier of an "inheritance relationship" with clear business semantics. The output of this module connects to the input of the conflict detection module, passing the identified logical inheritance relationships, terminology evolution relationships, and other structured data to subsequent modules, providing factual evidence for conflict determination. Similarly, parameters such as retrieval thresholds and matching weights within the semantic retrieval module also receive optimization instructions from the feedback closed-loop module through a reverse data channel, ensuring that the retrieval strategy can adaptively adjust with business evolution.

[0051] The conflict detection module is used to establish a logical state matrix, automatically marking inheritance, modification, and obsolescence logic points between new and old versions of functionality. The conflict detection module receives inheritance relationship data output by the semantic retrieval module, compares the differences between current requirement characteristics and historical asset characteristics, and constructs the logical state matrix. This matrix clearly depicts the evolution of each functional point between the old and new versions. The output of the conflict detection module is connected to the input of the incremental merging module, passing down the marked logic points (especially modified and obsolescence logic points) as an incremental merging operation instruction list, guiding the generated model to perform local updates or removals at specific locations.

[0052] The incremental merging module is used to perform incremental content merging on the historical asset base using a generative model, generating the evolved content of the current version. The incremental merging module is the core generation hub of the device. Its input not only receives logical point instructions from the conflict detection module but also directly accesses the shared data layer through another data channel to obtain the original base text of the historical assets. Guided by the logical point instructions, the incremental merging module only calls the generative model for the parts that need to be changed, rather than regenerating the entire dataset, thus outputting evolved content with a clear connection to the historical versions. The output of this module connects to the input of the feedback loop module, submitting the generated evolved content to the manual review and correction stage.

[0053] The feedback loop module captures human correction actions through an embedded editor, calculates the deviation features between the AI-generated vector and the human-corrected vector, and feeds this information back to the model weights. This feedback loop module is the engine of the device's continuous evolution and the closing point of the closed-loop chain. Its input receives the evolving content output by the incremental merging module and captures the text differences before and after human correction through the embedded editor. After calculating the deviation features, the feedback loop module not only directly feeds the optimized parameters back to the generated model weights and prompt word templates within the incremental merging module, but more importantly, its output also constructs three reverse data channels, connecting to the parameter adjustment interfaces of the feature extraction module, semantic retrieval module, and conflict detection module, respectively. Through this feedback connection, the feedback loop module transforms the business rule deviation signals contained in the human correction into adjustments to the feature extraction strategy, corrections to the retrieval threshold, and optimizations to the conflict marking strategy, enabling all four front-end modules of the entire device to continuously fine-tune parameters and strategies based on real human feedback.

[0054] Through the five modules described above and the close data flow and feedback connections between them, the device in this embodiment constructs a complete closed-loop topology from feature extraction to content generation and then to feedback optimization. This structural connection ensures the semantic consistency between the generated content and historical assets, adapts to the incremental changes in business needs, and endows the device with the ability to continuously self-evolve through the feedback loop feedback channel. It should be understood that the division of each module in this embodiment is only a logical functional division. In actual software implementation, the functions of multiple modules can be combined and executed by a single software entity, or the functions of a single module can be split and executed collaboratively by multiple software entities. As long as the functional topology of the above data flow and feedback connections is satisfied, it should fall within the protection scope of this invention. The above description of Embodiment 3 is only used to explain the specific implementation of this invention under the subject matter of this device, and is not intended to limit the protection scope of this invention.

[0055] The foregoing embodiments provide a detailed explanation and implementation of the present invention through specific technical features such as in-process fusion deployment architecture, a two-layer structure of capability containers and pluggable execution units, dual feature retrieval based on semantic similarity and path matching, incremental merging of logical state matrices, and feedback loop of collaborative auditing. The purpose is to illustrate the specific implementation methods of each technical feature and why this implementation is adopted instead of other alternatives, rather than to exhaustively limit the scope of protection of the present invention.

[0056] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A full-chain AI collaboration system for research and development, characterized in that, The system adopts an in-process fusion deployment architecture, fusion-deploying the AI ​​processing engine and the host application in the same process space and sharing a data layer; the system includes: The pipeline management module is used to define the state machine of the processing flow; The character skill configuration module is used to preset multiple capability containers that define processing logic frameworks, and supports the dynamic addition and removal of pluggable execution units at runtime and dynamic mounting at the memory level. The capability container is the abstract layer that defines the processing logic framework, and the pluggable execution unit is the implementation layer that implements specific processing functions. The two form a two-layer structure. The AI ​​orchestration engine is used to dynamically schedule pluggable execution units within a process and drive the corresponding capability containers to execute tasks based on the flow of the processing flow state machine. The human-machine collaborative review module is used to display the comparison between AI-generated content and historical assets, and to capture human correction features to drive the model's self-evolution.

2. The AI ​​collaboration system for the entire R&D chain according to claim 1, characterized in that, In the character skill configuration module, the capability container and the pluggable execution unit are associated through a mapping relationship; one capability container can be associated with multiple pluggable execution units, and one pluggable execution unit can be reused by multiple capability containers.

3. The AI ​​collaboration system for the entire R&D chain according to claim 1, characterized in that, The system dynamically loads or unloads the pluggable execution unit at runtime through a configuration center without restarting the host application; when loading, the pluggable execution unit is instantiated as a memory object and a mapping is established with the capability container; when unloading, memory resources are released and the mapping is removed.

4. The AI ​​collaboration system for the entire R&D chain according to claim 2, characterized in that, The pluggable execution unit adopts an atomic design. Each pluggable execution unit includes: metadata description, input / output interface specification, execution logic unit, and dependency configuration; the dependency configuration declares the prerequisite dependencies or mutual exclusion relationships with other pluggable execution units.

5. The AI ​​collaboration system for the entire R&D chain according to claim 2, characterized in that, The system handles logical conflicts between multiple pluggable execution units through a scheduling arbiter. Based on the priority and dependency relationships determined by the task metadata profile, it executes sequential calls or mutual exclusion filtering of pluggable execution units on the same processing node.

6. The AI ​​collaboration system for the entire R&D chain according to claim 1, characterized in that, The human-machine collaborative review module includes an embedded editor for displaying a comparison view of AI-generated content and historical assets, and capturing features for manual correction. The comparison view highlights the differences between the added, modified, and deleted parts.

7. The AI ​​collaboration system for the entire R&D chain according to claim 6, characterized in that, The human-machine collaborative review module extracts the text difference vector before and after manual correction, calculates the correlation between the text difference vector and the confidence level generated by AI, and includes positively correlated samples in the fine-tuning dataset to adjust the generation model parameters and prompt word templates.

8. A method for incremental content generation and continuous optimization based on historical asset semantic alignment, wherein the method is executed by the AI ​​collaboration system across the entire R&D chain as described in claim 1, characterized in that, include: Obtain the original text of the current processing task and decompose it into atomic functional feature vectors using natural language processing algorithms; Within the shared data layer, vector indexes are used to retrieve historical assets with a correlation greater than a preset threshold, thus identifying the inheritance relationship of business logic. Establish a logical state matrix to automatically mark the inheritance, modification, and obsolescence logic points between new and old versions of features; Using a generative model, incremental content merging is performed on a historical asset base to generate the evolutionary content for the current version; The embedded editor captures human correction behavior, calculates the deviation features between AI-generated vectors and human-corrected vectors, and feeds them back into the model weights.

9. The incremental content generation and continuous optimization method based on historical asset semantic alignment according to claim 8, characterized in that, The search for historical assets with a relevance greater than a preset threshold is performed by combining semantic similarity and path matching. When the semantic similarity exceeds the preset threshold and the path matching is consistent, it is determined to be logical inheritance. When the semantic similarity exceeds the preset threshold but the terminology changes, it is determined to be terminology evolution.

10. An apparatus for incremental content generation and continuous optimization based on historical asset semantic alignment, the apparatus being used to perform the method of claim 8, characterized in that, include: The feature extraction module is used to obtain the original text of the current processing task and decompose it into atomic functional feature vectors using natural language processing algorithms. The semantic retrieval module is used to retrieve historical assets with a correlation greater than a preset threshold within the shared data layer using vector indexes, and to identify the inheritance relationship of business logic. The conflict detection module is used to establish a logical state matrix and automatically mark the inheritance, modification, and obsolescence logic points between the old and new versions of functions; The incremental merging module is used to perform incremental content merging on the historical asset base using a generative model to generate the evolutionary content of the current version. The feedback loop module is used to capture human correction behavior through the embedded editor, calculate the deviation features between the AI-generated vector and the human-corrected vector, and feed them back to the model weights.

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