Standard information automatic acquisition interface calling system

Abstract

The invention relates to a standard information automatic acquisition interface calling system, which comprises a natural language analysis module used for receiving a text, image or voice query request input by a user, extracting standard related semantic information through a semantic structuring mode, and sending the standard related semantic information to a server; the semantic information comprises standard subject terms, applicable objects and time dimension labels; the standard matching engine is used for retrieving matched standard entities in a constructed standard knowledge graph based on semantic information, and the standard knowledge graph is constructed by aggregating a plurality of standard databases and supports ontology reasoning and fuzzy semantic matching; wherein the natural language analysis module and the standard matching engine cooperate to complete an automatic processing flow from natural language input to standard entity return.

Description

Technical Field

[0001] The present invention relates to the technical field of information retrieval and data processing, and in particular to a standard information automatic acquisition interface calling system. Background Art

[0002] In the standard information management practices of large industry organizations, especially those of units such as the State Grid, the acquisition and invocation of standards are facing multiple technical challenges. On the one hand, the sources of standards are highly diverse, covering national standards, industry standards, local standards, and internal enterprise standards. There are often cross-references, inconsistent naming, and overlapping versions between different standards, which leads to frequent semantic conflicts and matching errors in the unified identification, indexing, and invocation process of the system, thus affecting the accuracy and consistency of standard references. On the other hand, the content of standards is updated frequently and covers a wide range of fields. However, most business units currently still rely on manual maintenance of standard libraries or use document-driven static processes for standard invocation. They lack the ability to automatically identify, prompt, and replace the life cycle status of standards (such as abolition, replacement, mandatory update, etc.), which can easily lead to the use of expired or invalid standards, posing compliance risks.

[0003] Furthermore, since most standard documents are published as unstructured text, lacking a unified semantic tagging system and structured element identification, this severely limits the effectiveness of natural language processing technology in understanding standard semantics, extracting information, and identifying call intent. This, in turn, hinders the efficient retrieval of standard information, the implementation of semantic reasoning, and the linkage of system automation interfaces. Furthermore, there is currently a lack of support for systematic tracking and dynamic evolution management of standard citation behavior, making it difficult to construct a standard citation path map or usage behavior profile covering the entire lifecycle. This results in the invisibility of standard usage status, the untraceability of citation history, and the uncontrollability of version differences, and the inability to meet the needs of enterprise-level auditing and version compliance analysis.

[0004] Although some solutions have attempted to introduce knowledge graphs and question-answering system technologies to enhance standard query capabilities, most are still limited to static text matching at the keyword level, and are unable to achieve cross-source fusion, upstream and downstream reference structure restoration, and intelligent response in complex semantic scenarios. They are unable to support the comprehensive requirements of large organizations such as the State Grid for "full-link structured management, dynamic semantic fusion, and platform-level calling interface stability" of standard information.

[0005] Therefore, the existing technology still has large technical gaps and system bottlenecks in key links such as standard information semantic analysis, multi-source fusion, version control, interface call stable scheduling and behavior traceability. It is urgent to propose a comprehensive technical solution that integrates natural language understanding, cross-source knowledge graph construction, reference path evolution management, user behavior portrait construction and multi-channel API fault-tolerant scheduling capabilities to meet the power industry's high reliability, high compliance and intelligent development needs in standard information management. Summary of the Invention

[0006] To overcome the shortcomings of existing standards information management technologies, such as weak semantic parsing capabilities, heterogeneous standards sources, low matching accuracy, and lack of citation consistency, this paper proposes a system for automatically acquiring standards information through an interface. This system aims to automate the entire process from natural language input to standard entity output. This system integrates two core components, a natural language parsing module and a standards matching engine, to create a structured semantic extraction and knowledge graph-driven intelligent matching mechanism for multi-source standards data. This system addresses technical challenges such as inconsistent standard expressions, frequent version conflicts, untraceable citation paths, and unstable interface calls.

[0007] To this end, the present invention provides a standard information automatic acquisition interface calling system, a standard information automatic acquisition interface calling system, including: a natural language parsing module and a standard matching engine, wherein the natural language parsing module is used to receive text, image or voice query requests input by the user, and extract standard-related semantic information in a semantically structured manner, and the semantic information includes standard subject terms, applicable objects and time dimension labels; the standard matching engine is used to retrieve matching standard entities in a constructed standard knowledge graph based on the semantic information, and the standard knowledge graph is constructed by aggregating multiple standard databases and supports ontology reasoning and fuzzy semantic matching; wherein the natural language parsing module and the standard matching engine collaborate to complete the automatic processing flow from natural language input to standard entity return.

[0008] In one example of the present invention, the natural language parsing module includes a named entity recognition unit based on the pre-trained language model BERT, an intent recognition unit based on a bidirectional gated recurrent neural network (BiGRU), and an embedding fusion module that uses a cross-modal attention mechanism to achieve semantic alignment.

[0009] In an example of the present invention, the natural language parsing module also includes an input cache unit based on an asynchronous event queue and a feature alignment unit based on a sliding window mechanism, which is used to perform feature fusion of text, image and voice input within a unified time window.

[0010] In an example of the present invention, the standard matching engine includes a semantic vector construction component, an inverted index retrieval system and a relevance ranking module, and the semantic vector construction component generates a dense vector representation based on a context encoding model.

[0011] In an example of the present invention, the ranking module uses a linear weighted scoring function to combine the standard reference category distribution, click frequency and recent access behavior in the user portrait to generate a ranking priority, and the user portrait is constructed by the user's historical operation log.

[0012] In one example of the present invention, an API aggregation scheduling module is also included, which includes an interface frequency limiting engine that implements frequency limiting based on a time window counter, a circuit breaker mechanism that is automatically triggered according to the number of interface failures and response delays, and a retry scheduling controller based on an exponential backoff time increment strategy.

[0013] In an example of the present invention, when the number of consecutive interface failures reaches a set threshold and the average response time exceeds a preset time T, the circuit breaker mechanism enters a blown state and switches to a backup interface channel.

[0014] In an example of the present invention, a version tracking and reference recording module is further included, which is used to build a reference graph structure based on the unique hash identifier and timestamp of the standard reference, and record the historical versions during the reference evolution process.

[0015] In an example of the present invention, the compliance detection unit includes an embedded rule matching engine for determining whether the current referenced standard is in a state of abolition, replacement or mandatory update based on a regulatory validity status mapping table, and outputting corresponding prompts.

[0016] In one example of the present invention, the standard matching engine further includes a federated graph collaborative update mechanism, which is used to realize ontology alignment and version conflict detection of standard entities based on standard vector summaries uploaded from different source databases, using sparse feature coding and Jaccard similarity calculation methods.

[0017] Additional aspects and advantages of the present invention will be described in part in the following description and will become apparent from the following description or through practice of the present invention. The present invention proposes a standard information automatic acquisition interface calling system. The core of the system is to integrate the natural language semantic parsing capability with the knowledge graph-driven standard matching mechanism, build an intelligent retrieval and calling framework for multi-source standard data, and realize the full-process automatic processing from user natural language input to standard entity output.

[0018] The system includes two key functional modules: the first is the natural language parsing module, which receives query requests submitted by users in the form of text, voice or image, and uses semantic structuring technology to extract core semantic elements related to the standard, including standard subject terms, applicable objects and time dimension labels, etc., to construct a structured query vector with semantic labels; the second is the standard matching engine, which, based on the above semantic vectors, performs semantic retrieval and entity matching in the standard knowledge graph constructed by the fusion of multiple standard databases, supports ontology reasoning and fuzzy semantic comparison, and can achieve accurate positioning of standard entities in multiple dimensions such as standard description, alias expression, and cross-reference chain.

[0019] With the synergistic effect of the above two modules, the system has built a closed-loop response mechanism covering input parsing, semantic construction, entity reasoning and result output, effectively realizing the structured perception and intelligent call of standard information. This technical solution can significantly improve the automation level and semantic understanding ability of standard information retrieval, and is particularly suitable for dealing with complex management scenarios such as inconsistent standard expressions, frequent cross-source cross-references, and the coexistence of multiple versions of standards. Relying on graph reasoning capabilities and semantic consistency matching strategies, the system effectively avoids problems such as semantic deviations, entity omissions, and version mismatches that are common in traditional keyword-based matching methods.

[0020] To sum up, the standard information automatic acquisition interface calling system provided by the present invention not only improves the semantic accuracy and execution efficiency in the standard calling process, but also breaks through the traditional standard management's reliance on manual identification and static processes. It is suitable for building a structured-driven, highly consistent standard service system, and is particularly suitable for large-scale industry units such as the State Grid that have high requirements for standard application consistency, standardization and compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic diagram of the overall architecture of the standard information automatic acquisition interface calling system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a federated graph collaborative update mechanism provided by an embodiment of the present invention; Figure 3 This is a standard reference graph structure and version tracking flowchart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, features defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0025] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0026] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0027] Figure 1 A schematic diagram of the overall architecture of the standard information automatic acquisition interface calling system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a federated graph collaborative update mechanism provided by an embodiment of the present invention; Figure 3 This is a standard reference graph structure and version tracking flowchart provided by an embodiment of the present invention.

[0028] See also Figure 1In one possible implementation, the proposed automatic standard information acquisition interface call system comprises a natural language parsing module and a standard matching engine, both of which form a clearly structured and well-defined system flow around three phases: input, processing, and output. Starting with user input, the system covers the entire process, from natural language semantic extraction and mapping of standard semantic structures to the precise retrieval and output of standard entities. This system enables automatic response and intelligent call-up of standard information under multimodal input, resolving issues such as semantic mismatches, version confusion, and delayed responses that plague traditional standards management methods.

[0029] As the first processing unit of the system, the natural language parsing module is capable of receiving user query requests in the form of text, voice, and images. The system design fully takes into account the characteristics of diverse input forms and non-standard language expressions in actual business operations. Therefore, an asynchronous event cache queue is used to perform preliminary buffering of received requests to avoid system congestion due to untimely interface responses. Next, the module calls the semantic structuring subsystem to perform content recognition and semantic extraction on the input data. By introducing a pre-trained language model, it identifies core semantic elements such as standard keywords, applicable objects, and time dimensions, and constructs a query-oriented structured semantic representation vector. The labels of each dimension in the semantic vector are not only traceable, but also meet the requirements of cross-modal semantic fusion, so that they can adapt to the computational recognition and semantic reasoning requirements of the subsequent standard matching engine for input information.

[0030] After receiving the semantic vector output by the natural language parsing module, the standard matching engine performs retrieval and comparison tasks based on the pre-built standard knowledge graph. The graph system aggregates multi-source heterogeneous databases such as national standards, industry standards, local standards and internal enterprise standards, and adopts a unified semantic model for ontology abstraction and attribute normalization. A multi-level connection based on semantic relevance and reference chain structure is established between the node entities in the graph. When dealing with standard name alias expressions, version iterations, and cross-reference scenarios between standards, the matching engine accurately locks the standard entity pointed to by the user's intention through the layer-by-layer propagation of the entity reasoning chain. The system also integrates fuzzy matching strategies to handle situations where the expression difference between user input and the standard database is large, such as non-standard expressions such as industry slang, abbreviations and historical names, thereby improving the overall matching robustness and fault tolerance.

[0031] During the information output phase, the system prioritizes candidate entities using a relevance scoring mechanism. This scoring logic is based on the similarity between the standard entity and the input semantic vector, and is weighted based on the standard entity's frequency of citation, upstream and downstream relationships, and user profile preferences. Once the ranking is complete, the system feeds the results back to the caller in the form of structured data. It also supports interface response output and graph path visualization, making it easy for users to track and verify the standard's source, citation path, and version status.

[0032] The system's structural design clearly demarcates the boundaries of functional modules, and all modules exchange data through a unified interface protocol, ensuring the stability and maintainability of the system under complex task loads. At the same time, a sliding window mechanism and fault-tolerant control strategy are added to the call path to achieve asynchronous collaboration and failure retry between modules, greatly enhancing the system's stable performance in large-scale concurrent access scenarios. Especially in industries with high standard data update frequency, long call chains, and large semantic interference, the system constructs a complete path of "input semantics-entity nodes-reference structure" through a binding strategy of semantic tags and ontology logic, improving the transparency, accuracy, and compliance of standard references.

[0033] Unlike traditional keyword-based standard queries, the system proposed in this paper utilizes a dual-driven mechanism of natural language understanding and knowledge graph reasoning, fundamentally addressing standard call deviations caused by language ambiguity, naming conflicts, and version inconsistencies. This structure not only deepens the understanding of query semantics but also enhances the expressive power of standard matching. It is particularly suitable for implementation in standard management scenarios by large organizations, such as the State Grid Corporation of China, which have extremely high requirements for consistent standard usage and legal compliance.

[0034] In other implementations, the natural language parsing module can further introduce a multi-head attention mechanism to improve semantic alignment accuracy, or build a graph structure relationship between semantic tags through a graph neural network to enhance context recognition capabilities. The standard matching engine can be configured with a vector index acceleration mechanism to replace the traditional inverted structure to achieve low-latency response in scenarios with massive standard data. The standard knowledge graph also supports distributed deployment and incremental training and update strategies to support large-scale cross-departmental standard synchronization management and intelligent evolution requirements. The system also retains a flexible semantic tag extension interface that can quickly define customized semantic fields for different industry application scenarios, meeting the dual needs of generalization and customization in the context of industry specialization and data diversification.

[0035] In summary, this implementation method realizes a high-precision, high-stability, and high-scalability standard information automatic acquisition mechanism by constructing a complete business closed-loop path covering natural language reception, semantic structured processing, graph reasoning retrieval, and standard entity output. It not only fills the technical gap in the current semantic parsing and standard matching collaborative mechanism, but also provides a practical engineering paradigm for building a structured-driven standard service platform.

[0036] See also Figure 1 In one possible implementation, the natural language parsing module includes a named entity recognition unit based on a pre-trained language model BERT, an intent recognition unit based on a bidirectional gated recurrent neural network (BiGRU), and an embedding fusion module that uses a cross-modal attention mechanism to achieve semantic alignment.

[0037] As the core carrier unit of the natural language understanding capability in the system, this module undertakes the task of deep semantic modeling of the input data. In this embodiment, the named entity recognition unit performs context-aware modeling on the text fragments input by the user through the pre-trained language model BERT, and can accurately identify entity phrases such as standard names, applicable industries, execution entities, time tags, etc. that appear therein. Due to its powerful context modeling capability, the BERT model significantly improves the accuracy of semantic parsing, especially when dealing with polysemous words, industry terms and abbreviation aliases, it shows higher fault tolerance and generalization. Next, the intent recognition unit completes the deep modeling of the input semantic structure based on the BiGRU structure, and combines the position encoding and semantic guidance mechanism to generate a representation vector of the query intent, so as to determine whether the user is performing standard query, version verification or reference path tracing and other operations. The unit introduces bidirectional temporal information, which improves the ability to capture semantics that are sensitive to the order of input expression, and effectively solves the problem of misjudgment of intent caused by input ambiguity.

[0038] Based on the outputs of the above two sub-units, the system further completes the semantic consistency alignment through the embedding fusion module. The embedding fusion module adopts a cross-modal attention mechanism to support the construction of multi-dimensional semantic linkage expressions between inputs such as text, images, and voice. This mechanism maps multimodal inputs to a unified vector space by constructing a self-attention weight matrix, and establishes a semantic channel between modalities, so that the system has the ability to maintain semantic continuity when the input modalities are not completely consistent. Through this mechanism, when the system accepts voice input (such as reading aloud standard numbers) or image input (such as taking photos of standard covers), it can also accurately extract core semantic elements and smoothly connect with subsequent semantic vector streams, thereby greatly enhancing the adaptability and expansion flexibility of multi-source input processing.

[0039] The natural language parsing module uses a modular design between the various units within it. Each functional node is independently trained and deployed, and the input and output interfaces are standardized to ensure the stability and maintainability of the system during operation. Especially when faced with high-concurrency input requests or sudden large-scale query loads, the execution rhythm can be adjusted between modules through asynchronous calls and sliding window caching mechanisms to avoid request blocking or semantic retention. In addition, the system also reserves interfaces for online model updates and dynamic parameter adjustment, supporting hot updates of the model based on historical recognition accuracy and domain offsets, thereby ensuring the stable evolution of the system's semantic recognition performance in long-term operation scenarios.

[0040] This module's design emphasizes the depth of semantic perception and the ability to integrate multimodal input, breaking through the limitations of traditional natural language processing models in standard domains, which have limited input types and poor adaptability. By introducing two types of structures, BERT and BiGRU, the system is not only capable of processing short text keywords, but can also extract implicit intent logic from long text and nested expressions, significantly improving the system's ability to understand complex query scenarios. In particular, in the mixed expression of "title + number + description + reference chain" commonly seen in standard domains, this module can accurately parse entity tags with query directivity, providing high-quality semantic input for subsequent matching.

[0041] Specifically, the named entity recognition unit can be replaced with optimized language models such as RoBERTa or MacBERT to improve recognition accuracy for specific semantic categories. The intent recognition module can also adopt a multi-layer Transformer structure instead of BiGRU to accommodate the needs of longer texts and complex intent modeling. In the embedding fusion module, a unified representation model (such as CLIP) can be introduced to achieve pre-trained transfer of cross-modal expression alignment, further improving processing performance and output consistency in mixed image and text input scenarios. The overall module architecture supports plug-in access, heterogeneous model deployment, and multi-language expansion, with strong adaptability and engineering feasibility.

[0042] See also Figure 1 In a possible implementation, the natural language parsing module further includes an input cache unit based on an asynchronous event queue and a feature alignment unit based on a sliding window mechanism, for performing feature fusion within a unified time window on text, image, and voice inputs.

[0043] This implementation further expands the scheduling and integration capabilities of the natural language parsing module for multimodal input. In actual business scenarios, user query behaviors often exhibit continuous, nonlinear, and multi-form concurrent characteristics. For example, when querying a standard document, one may first enter a voice number description, then upload an image attachment, and then supplement the keyword text. If processed according to the traditional static single-point input logic, it is very easy to cause information fragmentation, semantic loss, and timing deviation. To this end, this implementation introduces an asynchronous event queue mechanism as an organizational framework for input preprocessing, which uniformly incorporates input requests submitted in different modalities and at different time points into the buffer, and normalizes the encoding according to the arrival time and identification code to ensure that the system can effectively aggregate all input content in a user's query behavior.

[0044] After completing the input event cache, the system batches the cached content and synchronizes features through a sliding window mechanism. The sliding window mechanism constructs the input data within a certain time range into time series blocks, and performs segmented fusion processing on them according to the set time granularity and window length, ensuring that all inputs within the window participate in the feature alignment operation. The feature alignment unit is based on vector time series resampling technology and modal embedding normalization strategy, mapping content arriving asynchronously between different modalities to a unified semantic representation space, achieving time alignment and semantic unification at the feature level, thereby effectively avoiding the semantic drift problem caused by time series misalignment between cross-modal inputs.

[0045] The alignment mechanism of this embodiment is particularly outstanding in processing multi-step queries, dynamic input supplements or fuzzy expression distributed input scenarios. The system can identify the logical associations in the user's "speak first and then upload the picture" or "concurrent picture and text" behaviors, and restore them to complete semantic requests through a windowed synchronization strategy, thereby significantly improving the system's responsiveness to natural user behaviors and the depth of semantic capture. In particular, when processing standard queries containing multimodal cross-distributed expressions such as "standard number in image", "reference time in speech" and "applicable objects in text", this mechanism can automatically realize input fusion and semantic integration, providing a semantically unified and temporally consistent high-quality input foundation for subsequent semantic vector construction and entity matching.

[0046] Internally, the module utilizes event queue monitoring and timestamp index modeling to establish logical connections between input content. This is supplemented by a feature embedding calibration network to automatically complete inter-modal alignment and mapping operations. The system supports asynchronous enqueuing of concurrent input events, decoupling processing logic from the main semantic parsing process to ensure stable operation despite high input concurrency or uneven latency. Window processing strategies can adjust parameters based on business scenarios, such as shortening the window period to accommodate real-time requirements or extending the window length to enhance tolerance for fuzzy semantics, providing excellent flexibility.

[0047] It's worth noting that the input cache unit can be built using a message-based middleware architecture to enable cross-node input scheduling and event multicasting. The feature alignment unit can also integrate a modal attention mechanism to automatically assign weights based on the input modality type, enabling semantically driven control of the fusion strategy. The time window mechanism can also incorporate contextual slicing logic to semantically segment input content, improving feature compression and aggregation efficiency. This overall solution is suitable for complex interaction scenarios such as multimodal concurrency, multi-step delayed input, and multi-round interactive queries, establishing a semantically unified, multi-source synchronized, and time-stable input processing mechanism for the system.

[0048] See also Figure 1 In one possible implementation, the standard matching engine includes a semantic vector construction component, an inverted index retrieval system, and a relevance ranking module, wherein the semantic vector construction component generates a dense vector representation based on a context encoding model.

[0049] This implementation further refines the internal structure of the standard matching engine and clearly divides its functional links in the process of processing semantic retrieval and entity matching. After the system receives the structured semantic information transmitted by the natural language parsing module, the semantic vector construction component first completes the vectorization conversion. Based on the context-sensitive encoding model, this component models the input semantic structure as a whole, and maps elements such as standard subject terms, applicable objects and time dimensions to a unified high-dimensional dense vector space. The encoding process not only considers the static association between terms, but also introduces contextual semantics, so that the same vocabulary can generate semantically differentiated representations in different query contexts, thereby improving the accuracy of subsequent matching.

[0050] After the vector construction is completed, the system passes the generated semantic vector to the inverted index retrieval system, which serves as the retrieval entry to perform the standard entity matching task. The inverted index system is built on the index structure between the entity nodes of the knowledge graph and their attribute documents. All standard entities are indexed and grouped according to multiple dimensions such as subject, category, version, and reference relationship to build a fast mapping between semantic labels and entity paths. The retrieval system supports the coexistence strategy of Boolean queries and vector space models, and can flexibly respond to concurrent calls of structured strong queries and fuzzy queries. When executing a query, the inverted index system will preliminarily screen the input semantic vector and the graph node set, and output the candidate entity set as the input of the subsequent sorting module.

[0051] After receiving the candidate entity set, the relevance ranking module performs a preliminary score based on the cosine similarity between the input semantic vector and the entity node attribute vector, and integrates structural features such as cross-reference density, version stability, and citation frequency recorded in the graph for weighted correction. A soft semantic comparison mechanism is also introduced in the scoring function to identify related entities with partial semantic loss or word meaning drift, thereby improving the system's fault tolerance for non-standardized expressions. After the sorting is completed, the system returns the standard entity with the highest score as the final output result to the caller. At the same time, it can provide optional result display and auxiliary information for citation path backtracking to enhance the user's understanding and judgment of the standard source and citation logic.

[0052] The standard matching engine features independent interfaces between its components, allowing vector construction, index query, and ranking calculations to be independently deployed and run in parallel, ensuring excellent scalability and system stability. This is particularly true for large-scale standard knowledge graphs, multiple concurrent user requests, and complex citation relationships. The system can scale inverted index nodes and deploy sorting services in a distributed manner, ensuring overall search efficiency and response time.

[0053] It's worth noting that the semantic vector construction component can be replaced by pre-trained models with stronger contextual modeling capabilities, such as SBERT and ERNIE, to adapt to the needs of modeling standard content that is ambiguous and rich in industry terminology. The inverted index structure can also be combined with vector indexing mechanisms (such as HNSW and FAISS) to achieve fast approximate matching in dense semantic spaces. The relevance ranking module can define weight adjustment factors based on usage habits in different industries, for example, increasing the ranking priority of technical specifications in the power industry. The overall structure retains open interfaces and pluggable models, supporting dynamic adjustment of search strategies based on user profiles, providing personalized standard retrieval services tailored to industry, user behavior, and task type.

[0054] See also Figure 1 In one possible implementation, the ranking module uses a linear weighted scoring function to combine the standard reference category distribution, click frequency and recent access behavior in the user profile to generate a ranking priority, and the user profile is constructed by the user's historical operation log.

[0055] This implementation implements behavior-driven optimization of the relevance ranking module, introducing a user profiling mechanism to enhance the personalized ranking of search results. Building on the traditional logic of sorting based on semantic similarity and document frequency, the system further integrates historical user behavior to construct a dynamic profile, which is embedded in the ranking scoring function as an additional dimension influencing the ranking priority of candidate criteria. This scoring mechanism incorporates semantic matching scores and profile behavior indicators into a unified scoring system through a linear weighting strategy, ensuring that the ranking results are both globally relevant and user-specific.

[0056] The process of constructing a user profile is based on the user's historical operation log data in the system, including but not limited to standard click records, reference confirmation behavior, query term category distribution, and interface call frequency. Through behavioral clustering and label extraction algorithms, the system converts operation logs into structured behavior vectors, capturing the user's standard usage preferences and focus in different time periods and task scenarios. In the profile vector, standard category preferences (such as security and power dispatch) are used to characterize the user's long-term usage habits, click frequency is used to identify hot topics in the current stage, and recent access behavior reflects their short-term search intent. The dynamic combination of these three dimensions constitutes the profile input channel, which is used to participate in the weighted calculation of the ranking function.

[0057] In the linear scoring function, the system presets weight coefficients for each dimension and calculates a comprehensive score for each candidate criterion, combining the user profile and semantic similarity score. The function design incorporates an adaptive weight adjustment mechanism. When user behavior favors a particular criterion, the corresponding dimension weight is automatically increased, thereby increasing the ranking weight of the preferred criterion. Furthermore, during cold starts or when profiles are missing, the system reverts to a standard semantic ranking strategy, ensuring system robustness and basic service stability.

[0058] Through this behavioral fusion ranking mechanism, the system demonstrates excellent performance in multiple key application scenarios. For example, when processing retrieval requests for multiple similar standard names, similar execution objects, or ambiguous reference semantics, the ranking module prioritizes standard versions with high historical click-through rates, recent frequent use, or a close match to the user's profile, significantly reducing user secondary screening costs and improving standard call efficiency. In particular, in cross-departmental or cross-task scenarios, the profiling mechanism effectively adapts to the differences in semantic perception among different user groups, enhancing the system's task adaptability and service precision.

[0059] This module is designed to decouple profile data from ranking logic. User profile maintenance is performed by the behavior modeling subsystem, while ranking logic is independently executed by the scoring engine. Both utilize an interface protocol to communicate parameters and implement scheduling control. The collection and analysis of behavior logs adheres to data compliance and privacy protection standards. The system supports anonymous profile generation, profile clearing, and authorization control, ensuring that the profile-driven ranking mechanism provides personalized services without exposing user information risks.

[0060] In other variant implementations, user portraits can be integrated with time-weighted decay strategies to automatically reduce the weight of the impact of outdated behaviors on sorting; the scoring function weights can also be tuned online through reinforcement learning algorithms to optimize the sorting effect based on user feedback signals; in industry applications, the portrait labeling system can be customized and expanded based on the standard classification system. For example, in the power industry, professional labels such as "dispatching standards" and "equipment maintenance standards" can be introduced to enhance the discriminability and directionality of portrait labels, thereby further optimizing the system's sorting strategy in domain-based retrieval scenarios.

[0061] See also Figure 2 In one possible implementation, the system also includes an API aggregation scheduling module, which includes an interface frequency limiting engine that implements frequency limiting based on a time window counter, a circuit breaker mechanism that is automatically triggered according to the number of interface failures and response delays, and a retry scheduling controller based on an exponential backoff time increment strategy.

[0062] This implementation aims to improve the stability and resiliency of the standard information automatic acquisition system at the interface call layer, particularly in scenarios with high-frequency requests, unstable interfaces, or cross-system collaboration, thereby ensuring overall service quality and user experience. This module, located at the end of the service call after the standard matching engine, serves as the scheduling and control core for the system's requests to external standard service APIs. It is responsible for controlling the request rate, evaluating interface status, and dynamically deciding whether to continue the call or initiate a backup strategy.

[0063] The interface rate limiting engine monitors and constrains the frequency of interface requests in real time based on a time window counter strategy. Each target API is configured with a fixed time window and a maximum request threshold. If the number of requests within a unit time window reaches the upper limit, the rate limiting engine will temporarily suspend subsequent requests to prevent denial of service (DoS) due to sudden high load on the target interface. The rate limiting mechanism dynamically adjusts through a sliding window algorithm, ensuring the responsiveness of critical interfaces while preventing excessive consumption of system resources due to abnormal request frequencies.

[0064] During the interface call process, if the target API fails to call continuously or times out, the system will trigger the circuit breaker mechanism for protective fuse. The circuit breaker mechanism has fault thresholds and response time limit indicators. When a certain interface fails continuously for more than a set number of times in a short period of time, and the average response time is higher than the preset upper limit T, the system enters the fuse state, stops initiating new requests to the interface, and starts the backup interface channel or prompts to wait for recovery. The circuit breaker mechanism is divided into three states: closed, half-open, and open, and supports periodic detection of the interface health status, realizing a complete management path from fault blocking to call recovery.

[0065] If a fault does not reach the fuse threshold but a temporary anomaly exists, the system will activate an exponential backoff mechanism to regulate the retry strategy. The retry scheduling controller uses an exponential time increment algorithm to multiply the waiting time after each failure before retrying, thereby reducing the instantaneous pressure on network resources and allowing sufficient recovery space for the interface backend. This mechanism also supports a maximum retry limit and a dynamically adjustable backoff factor, adapting the recovery rhythm to different interface load characteristics and preventing ineffective retries from causing system congestion.

[0066] The entire aggregate scheduling module supports unified scheduling and management of multiple external standard interfaces, and features multiple control capabilities, including interface status monitoring, fault isolation, dynamic switching, and fallback buffering. Each submodule is decoupled and deployed through a service middleware architecture, and operational status can be visualized using interface call logs and response behavior data. The system also reserves a dynamic policy configuration interface, allowing operations personnel to adjust frequency limit window parameters, circuit breaker thresholds, or retry strategies based on business needs, enabling flexible linkage between scheduling behavior and business load.

[0067] In other alternative implementations, the frequency limiting strategy can be replaced with a token bucket algorithm or a leaky bucket algorithm to address different concurrency scenarios. The circuit breaker mechanism can also introduce a comprehensive scoring mechanism that integrates failure rate and average response time to determine the timing of circuit breaking. As for the retry control strategy, a probabilistic control strategy can be used to perform sampling retries under partial failure conditions to further conserve resources. The overall module structure is open, the strategy is configurable, and the data is monitorable, providing strong call support capabilities for standard information acquisition systems in complex interface service systems.

[0068] See also Figure 3 In one possible implementation, when the number of consecutive interface failures reaches a set threshold and the average response time exceeds a preset time T, the circuit breaker mechanism enters a blown state and switches to a backup interface channel.

[0069] This implementation focuses on ensuring high reliability during system interface scheduling, specifically addressing issues such as overall system response blockage, request accumulation, and error transmission that may arise when external standard data service interfaces are unstable. As a key fault-tolerance measure within the API aggregation and scheduling module, the circuit breaker mechanism performs circuit breaking control based on the results of interface abnormal behavior monitoring. It proactively cuts off interface access when predetermined failure counts and latency thresholds are met, preventing subsequent calls from further converging to the abnormal interface, thereby protecting primary system resources and controlling the spread of faults.

[0070] In the specific implementation, the system maintains a status tracking recorder for each monitored interface, which continuously collects interface response results and response time data, and updates the interface status cache after each request. When the number of consecutive failed calls reaches the threshold N set by the system, and the average response time of the interface in the recent period exceeds the set time T (such as 1000ms), the system determines that the interface has entered the "unavailable" state, automatically changes the circuit breaker state to "open", and then terminates all subsequent normal request scheduling for the interface. At this time, the system will direct the interface traffic to the configured backup channel, including asynchronous cache query, local degradation logic or other data source redundant interfaces, to ensure that the user side will not experience request failure due to the unavailability of the main interface.

[0071] The circuit breaker state model follows a standard three-stage design, including three states: "closed", "open", and "half-open". In the initial state, the interface is in the "closed" state, and system requests can pass freely; when the circuit breaker condition is triggered, it enters the "open" state, blocking the request flow; after the circuit breaker duration is reached, the system enters the "half-open" state, and uses a small number of probing requests to test whether the interface has recovered. Once the test passes, the circuit breaker automatically switches back to the "closed" state to resume normal service, otherwise it re-enters the "open" state to continue circuit breaker protection. This state switching logic is driven by a combination of historical response logs and dynamic feedback indicators, and has self-recovery and state rollback capabilities to ensure the high availability of the system in the event of external interface fluctuations.

[0072] To enhance control precision and service flexibility, the system supports differentiated circuit breaking policies for different interfaces. For core interfaces with high business dependency and frequent calls, the circuit breaking threshold can be appropriately increased to avoid false triggering. For non-critical auxiliary interfaces or those easily downgraded and replaced, more sensitive circuit breaking parameters can be set to achieve rapid failover. Furthermore, the triggering and recovery process of circuit breaking events is recorded synchronously in system logs and on the operations and maintenance monitoring platform, enabling full lifecycle management of the interface stability evolution process.

[0073] In other replacement or variant implementations, the circuit breaker status judgment can introduce the interface error rate (number of failures / total number of calls) indicator as the basis for judgment to improve the relative accuracy of abnormal judgment; the backup interface switching mechanism can also adopt a weighted priority polling method to achieve load distribution and reliability balance among multiple backup sources; the circuit breaker control logic can also be extended to the service discovery layer, and cooperate with the gateway and registration center to realize dynamic circuit breaking and service stripping at the interface routing level, thereby realizing the interface elastic protection capability under the microservice system.

[0074] See also Figure 3In a possible implementation, the system further includes a version tracking and reference recording module for constructing a reference graph structure based on the unique hash identifier and timestamp of the standard reference, and recording historical versions during the reference evolution process.

[0075] This implementation primarily addresses issues such as uncontrollable version changes during standard use, untraceable reference paths, and difficulties in compliance audits. As the core unit for standard lifecycle management in the system, the version tracking and reference recording module leverages the unique identification mechanism for standard entities and time series modeling capabilities to construct a graph model of standard reference behavior. This model records the reference relationships and version status of standards at different business nodes and time points, and provides a complete evolutionary chain for user query, analysis, and audit calls.

[0076] This module first calculates a unique hash identifier based on the content summary of the standard entity, which is used to distinguish different versions or variant forms of standard documents. The system has generated a hash fingerprint for each standard entity during the construction phase of the standard knowledge graph, and also records attribute information such as version release time, version number, and revision status. When the user confirms that a standard entity is referenced during the process of calling the interface or performing standard matching, the system automatically records the reference behavior, including the reference time, initiator identifier, referenced standard hash value, call scenario identifier and other data, and maps the behavior to an edge in the graph structure, connecting the current business node with the referenced standard node.

[0077] The reference graph structure is constructed around time, displaying the state differences of standard version entities at the node level and carrying the reference logic and evolution process in the edge structure. Through graph visualization and path query mechanisms, the system can quickly restore the standard version used by any business node at any point in time and determine whether it has been subsequently replaced, abolished, or revised. This structure is not only suitable for static analysis, but also supports trend analysis, anomaly detection, and version prediction of the dynamic evolution of standard references.

[0078] During the recording process, the system ensures that all citations are time-stamped with high precision and supports cross-version citation continuation and tracing back citation directions, safeguarding the integrity and continuity of the reference chain. The version tracking module also maintains a data interface linkage with the standard matching engine. When users query or confirm a standard entity, the system provides a prompt for the current version status (e.g., whether it is the latest version and whether there are any mandatory updates) and displays the complete reference graph node links to assist users in making appropriate version selections.

[0079] This module is particularly well-suited for industry applications with high compliance requirements, such as power grids, transportation, and healthcare, providing strong technical support for system audits, technical filings, fault tracing, and responsibility delineation. Its structural design is horizontally scalable, supporting unified modeling and querying of standard referenced version paths across departments and systems. At the data interface layer, the system supports exporting reference graph structures in JSON-LD or RDF formats, adapting to the multi-system integration requirements of knowledge management platforms, audit analysis systems, and data middleware.

[0080] It is worth noting that the standard hash identifier can adopt a more powerful anti-collision algorithm such as SHA-3 to enhance uniqueness protection; the timestamp recording mechanism can integrate a trusted time source or blockchain timestamp service to meet higher audit credibility requirements; the graph structure construction method can also introduce a directed acyclic graph (DAG) model to enhance the evolution trajectory analysis capability, or combine it with an event-driven engine to realize the automatic triggering of reference behavior and version status warning prompts, further enriching the system's intelligent processing capabilities in standard lifecycle management.

[0081] See also Figure 2 In one possible implementation, the compliance detection unit includes an embedded rule matching engine for determining whether the current referenced standard is in a state of abolition, replacement, or mandatory update based on a regulatory validity status mapping table, and outputting corresponding prompts.

[0082] This implementation expands the system's compliance review capabilities after standards are referenced. It establishes a rules-based compliance checking mechanism for common status changes throughout a standard's lifecycle, such as replacement by a new standard, obsolescence, or mandatory updates by regulators. This ensures that the referenced standard remains valid within the current business context. This module automatically triggers the checking process before users confirm or invoke a standard, providing proactive compliance notifications based on criteria such as "whether the reference is legitimate, whether the version is expired, and whether the use is compliant."

[0083] The compliance detection unit uses a mapping table of effective regulatory status as its core knowledge base. This mapping table pre-includes the lifecycle status of all standard entities, including metadata such as publication time, revocation time, alternative version reference, and mandatory enforcement notice. It is periodically updated by the standard knowledge graph maintenance system to ensure consistency with the content published by the national standards management department. When a standard entity is referenced by a user or pushed by the system, the detection unit quickly locates its status record in the mapping table based on the standard's hash identifier and matches it to see whether there is a "revocation identifier," "alternative identifier," or "mandatory update notice" tag.

[0084] The rule matching engine serves as the module's judgment core. It analyzes the matching relationship between the standard status and the reference context based on a preset rule set. For example, when standard A is replaced by standard B, if the user still references standard A, the system will output a prompt that "the current standard has been replaced by standard B, and it is recommended to reference the newer version"; if the standard is in the "abolished" state, the system will output "This standard has been abolished in X month of X year, and it is not recommended to be referenced at this time"; if the standard must use a new version due to regulatory requirements, the system will determine it as a "mandatory update" and block the standard from being referenced in some interface call paths. The above rules are all configured in a parameterized manner, supporting customized rule strength and warning levels based on business scenarios.

[0085] This module has dual trigger paths of active push and passive review. On the one hand, it provides prompt information when users perform standard confirmation operations. On the other hand, it calls the judgment interface in real time during the process of building task flows within the system to prevent non-compliant standards from being written into automated processes or data results, thereby enhancing the security of the entire system and the rigor of the standards.

[0086] To enhance review transparency and operational explainability, the system returns each rule-matching result to the caller in a structured format of "status type - matching path - prompt explanation." This information is combined with reference graph path location and reference time records to provide a state evolution view, allowing users to clearly understand the basis and recommendations for standard state changes. The system also supports data interface integration of compliance result output with external audit systems or business risk control platforms, enabling an automated closed-loop record of reference behavior and regulatory changes.

[0087] In other variant implementations, the status mapping table can introduce authoritative external data sources for dynamic docking, such as the national standard information public service platform API or industry regulatory platform; the rule engine can also introduce fuzzy logic and multi-dimensional condition combination judgment mechanism to provide multi-order reasoning and auxiliary judgment for scenarios such as complex reference chains and alternative multi-hop chains; in high-risk scenarios, the module can be configured with a hard blocking strategy, that is, once the reference standard status hits "forced abolition", the system immediately terminates the call process and requires the administrator to manually review and intervene, further enhancing the compliance assurance capability during use.

[0088] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" indicate that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, illustrative uses of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0089] In one possible embodiment, the standard matching engine further includes a federated graph collaborative update mechanism for realizing ontology alignment and version conflict detection of standard entities based on standard vector summaries uploaded from different source databases, using sparse feature coding and Jaccard similarity calculation.

[0090] The current standard graphs mostly adopt a single-source convergence construction model. Although the structure is simple, it is difficult to cope with the challenges of accessing and updating multi-source heterogeneous standard data in practical applications. Especially within large industry organizations such as electricity, transportation, medical care, and construction, the sources of standard information usually include national standard platforms, industry association systems, and self-built standard libraries within enterprises. The three types of sources differ significantly in data expression, update cycles, metadata specifications, etc., which brings three typical complexity problems: First, the same standard entity has inconsistent fields, alias usage, term abbreviations and other expression differences in different sources; second, the update frequency is different, some are published in batches on a monthly basis, and some are pushed in real time, resulting in the graph version timeline being out of sync; third, there is a lack of a unified version arbitration mechanism, and version misjudgment, standard conflicts, or upstream and downstream chain breaks are prone to occur in the reference path. In view of the above-mentioned industry-specific complexity, the present invention introduces a federated graph mechanism to perform cross-source collaborative alignment of the structured representation of the standard entity and the context reference path to achieve version consistency control and conflict management.

[0091] Different from the traditional centralized graph merging strategy, the present invention constructs a "federal collaborative mechanism" with autonomous asynchronous update capabilities. Each data source retains an independent update channel and submits standard entity summary information in the form of a standard vector summary. The system retains the source identifier, submission timestamp and version annotation as the basis for subsequent graph merging and audit tracking. In terms of data structure standardization, the system uses sparse feature coding to convert uploaded standards into high-dimensional discrete semantic vectors, covering core fields such as standard number, title keywords, executive agency, version label, etc., while supporting source-specific field mapping completion to improve the fault tolerance of information fusion.

[0092] Semantic matching between standard vectors is achieved through Jaccard similarity calculations. The system sets a matching threshold. When the degree of vector overlap between two standard abstracts exceeds the threshold, they are considered to be potentially merging and then enter the ontology alignment process. The alignment process preserves the independent index paths of each source and marks any inconsistent fields for conflict detection and manual verification.

[0093] To address version conflicts, the system has built a multi-dimensional adjudication mechanism based on a fusion of rule-driven and statistically driven strategies. It comprehensively considers the following factors: first, the authority of the source, for example, national standards platforms take precedence over internal industry standards; second, citation frequency, which measures the density of paths through which the reference standard is referenced by other entities in the graph; third, the publication time series, which combines historical evolution trends to determine actual effectiveness; and fourth, upstream and downstream dependencies, which identify potential broken links or redundant citations within the reference loop. This conflict scoring mechanism not only automatically sorts but also, when scores are close, pushes results into a "conflict candidate pool" for manual review and feedback, creating a composite conflict resolution loop of "system judgment + expert confirmation."

[0094] To further enhance the intelligence and foresight of the collaborative mechanism, the system also includes a built-in predictive conflict mitigation module. This module leverages the historical release time series and evolution patterns of standards to build a version evolution trend model, providing proactive warnings for potential naming overlaps or standard system mergers. For example, in the power grid industry, some "enterprise standards" coexist in multiple parallel versions during the transition to "industry standards" or "national standards." The system automatically identifies these issues and issues warnings of potential conflict evolution, allowing operations and maintenance personnel to prepare for structural integration.

[0095] The system as a whole supports asynchronous deployment and parallel updates of federated nodes. The graph merging process features transaction isolation and state rollback mechanisms, ensuring the stability and auditability of the standard data governance process. At the interface level, the system supports outputting merged standard graph snapshots, conflicting version comparison lists, and path dependency reports to external business systems. This makes it widely applicable to knowledge governance tasks in scenarios with high-frequency changes in multi-source standards, such as those within the State Grid Corporation of China, the China Railway Group, and industry standard-setting organizations.

[0096] In other variant implementations, feature encoding can be replaced with a TF-IDF weighted bag-of-words model, BERT embedding, or graph neural network expression; semantic similarity calculation can combine multiple indicators such as cosine similarity and edit distance; the manual review process can also be connected to an external standard expert system or audit mechanism to achieve intelligent adjudication and collaborative handling of larger-scale graph conflicts.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0098] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" indicate that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, illustrative uses of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0099] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A standard information automatic acquisition interface calling system, characterized in that: include: A natural language parsing module is used to receive text, image, or voice query requests input by users and extract standard-related semantic information in a semantically structured manner. The semantic information includes standard subject terms, applicable objects, and time dimension labels; A standard matching engine, configured to retrieve matching standard entities in a constructed standard knowledge graph based on the semantic information, wherein the standard knowledge graph is constructed by aggregating multiple standard databases and supports ontology reasoning and fuzzy semantic matching; The natural language parsing module and the standard matching engine collaborate to complete the automatic processing flow from natural language input to standard entity return.

2. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: The natural language parsing module includes a named entity recognition unit based on the pre-trained language model BERT, an intent recognition unit based on a bidirectional gated recurrent neural network (BiGRU), and an embedding fusion module that uses a cross-modal attention mechanism to achieve semantic alignment.

3. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: The natural language parsing module also includes an input cache unit based on an asynchronous event queue and a feature alignment unit based on a sliding window mechanism, which is used to perform feature fusion within a unified time window on text, image and voice inputs.

4. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: The standard matching engine includes a semantic vector construction component, an inverted index retrieval system and a relevance ranking module. The semantic vector construction component generates a dense vector representation based on a context encoding model.

5. The standard information automatic acquisition interface calling system according to claim 4, characterized in that: The ranking module uses a linear weighted scoring function to combine the standard reference category distribution, click frequency and recent access behavior in the user portrait to generate a ranking priority. The user portrait is constructed by the user's historical operation log.

6. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: It also includes an API aggregation scheduling module, which includes an interface frequency limiting engine that implements frequency limiting based on a time window counter, a circuit breaker mechanism that is automatically triggered according to the number of interface failures and response delays, and a retry scheduling controller based on an exponential backoff time increment strategy.

7. The standard information automatic acquisition interface calling system according to claim 6, characterized in that: When the number of consecutive interface failures reaches a set threshold and the average response time exceeds a preset time T, the circuit breaker mechanism enters a fuse state and switches to a backup interface channel.

8. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: It also includes a version tracking and citation recording module, which is used to build a citation graph structure based on the unique hash identifier and timestamp of the standard citation, and record the historical versions during the evolution of the citation.

9. The standard information automatic acquisition interface calling system according to claim 8, characterized in that: The compliance detection unit includes an embedded rule matching engine for determining whether the current referenced standard is in a state of abolition, replacement or mandatory update based on a regulation validity status mapping table, and outputting corresponding prompts.

10. The standard information automatic acquisition interface calling system according to claim 1, characterized in that: The standard matching engine further includes a federated graph collaborative update mechanism for realizing ontology alignment and version conflict detection of standard entities based on standard vector summaries uploaded from different source databases, using sparse feature coding and Jaccard similarity calculation.

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