1161 results about "Shard" patented technology

The invention relates to the field of artificial intelligence, and discloses an intelligent AI semantic annotation method and system based on multi-modal analysis, images, audios, texts and multi-modal data of a sensor are synchronously collected through AI, deep semantic features of all modals are extracted by using a deep learning model after preprocessing, vectors are generated, and the multi-modal data are subjected to semantic annotation; fusing into a semantic scene vector by means of a cross-modal attention mechanism, constructing a cross-modal association graph, and learning a causal logic path by using a graph neural network; constructing a spatiotemporal causal diagram by combining spatiotemporal information, and integrating an event time sequence and spatial dependence by using a time sequence diagram neural network to form a venation path; according to the method, the two knowledge maps are fused into a comprehensive knowledge map, preliminary annotations are generated through rule base reasoning, a high-precision result is output after optimization, the problems of semantic loss and annotation fragmentation caused by modal splitting and context missing in a traditional method can be effectively solved, meanwhile, the manual verification cost is reduced, and the integrity and efficiency of semantic annotation in a complex scene are improved.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention discloses an MQTT message transmission optimization method and system. The method comprises the steps of analyzing a theme, extracting a device type, a data feature and a geographic position triple, calculating a hash value, and mapping a device to a specified Broker fragment cluster node to generate a fragment mapping table; processing the equipment data in the edge domain in the fragment mapping table through an edge calculation layer, removing invalid data according to a preset rule, merging the equipment data in the same fragment node, and embedding a fragment node ID for an aggregation message generated after merging; identifying a fragment node ID and routing to a target fragment node, positioning a corresponding shared memory pool, and writing the message into the shared memory pool; and responding to a direct access request of the client, so that the client directly accesses the data from the shared memory pool through the user mode network stack. According to the method, the problem of uneven load is effectively solved, multiple times of state switching in the data transmission process is avoided, and the MQTT message transmission efficiency is improved while the transmission cost is reduced.

The invention discloses a business-adaptive RAG knowledge base rapid question and answer implementation method, relates to the technical field of natural language processing and knowledge base retrieval, and realizes deep fusion and accurate context extraction of fragmented and structured business knowledge by constructing a business enhanced knowledge base and executing multi-stage dynamic retrieval. When an original business document is subjected to intelligent structured analysis and segmentation, a link business entity is identified, rich business attribute metadata is marked, in the process, an unstructured text is converted into an enhanced knowledge fragment carrying clear business semantics, a semantic vector is generated through an embedded model, and on the basis, the semantic vector is subjected to semantic segmentation; according to the method, rapid preliminary screening based on keywords and metadata, fine arrangement based on semantic vectors and association expansion based on a business knowledge graph are sequentially executed, the progressive retrieval strategy ensures that retrieval results are highly related to business contexts, and the accuracy of generated answers is improved.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention discloses a DCS operation and maintenance method based on fusion dynamic weight distribution and fault chain deduction, and relates to the technical field of operation and maintenance of a distributed control system, and the method comprises the steps: obtaining a plurality of groups of source data of a monitoring region; corresponding monitoring periods are determined according to the multiple groups of source data, and differential monitoring period information is obtained after combination; according to the technical key points, a multi-source data fusion module is utilized to associate data of a DCS system, an associated production system and a cross-plant cooperative system, and fragmented data such as equipment operation data, production load fluctuation and cross-plant interaction information are woven into a complete data network through adjustment of space-time alignment and multi-dimensional verification; the global sensing capability not only solves the one-sidedness of traditional single data judgment, for example, environmental interference is easily misjudged by only depending on equipment temperature data, but also can capture cross-system implicit associated faults; and secondly, when the multi-source data are mutually verified, an evidence chain is formed through logic consistency verification, so that the misjudgment rate of suspected faults is greatly reduced.

The invention discloses a data container storage and management method based on a multi-scale space subdivision grid, and the method comprises the steps: constructing a multi-scale space subdivision grid coding system covering the earth surface and the height dimension based on an earth space subdivision theory, and endowing each grid with a unique code to form a unified space-time framework; multi-source data of images, vectors, non-space and the like are collected and transmitted to a processing center, and after consistency detection and normalization processing are carried out, unified grid codes are generated for various kinds of data based on the coding system, and mapping is established; and finally, storing the coded data into a database, updating an index, realizing dynamic scheduling, logic aggregation and visual display of multi-source data by means of unified coding, realizing efficient management and control and cross-domain collaboration of massive heterogeneous data, and improving data storage consistency and retrieval and display efficiency. According to the method, the problems of fragmentation of spatial big data, slow retrieval response, difficulty in cross-domain fusion and the like are effectively solved, and the management efficiency and the sharing depth of the spatial data are remarkably improved.

The invention discloses an electric power production management system safety operation and maintenance management method based on a national secret algorithm. According to the method, the key and the real-time running state of the equipment are deeply bound through a dynamic key derivation mechanism, so that the key is dynamically adjusted along with parameters such as the load rate, the temperature and the bit error rate, state change, namely key updating, is realized, and the anti-cracking capability of the key is greatly improved; the SM2, SM3, SM4 and other national cryptographic standards are adopted from root key generation, identity authentication, data transmission encryption and log tampering prevention of the full-link application of the national cryptographic algorithm system, it is ensured that the encryption strength meets the national password management requirement, and the overall encryption protection level of the system is improved. Closed-loop protection is formed through deep linkage among multiple steps, fragmentation of a safety mechanism is avoided, meanwhile, the traceability and the fault recovery efficiency of system operation are further improved through the whole-process encryption archiving and emergency response rapid recovery process of audit logs, and transmission and storage safety of power production core data is guaranteed.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

The invention relates to an intelligent map making system and method based on a large language model, and the method comprises the steps: enabling a client to input data analysis information and a map making demand to a task planning Agent of a server, and carrying out the reasoning and disassembly of the input information into preliminary planning information through a double-LLM mechanism, the method comprises the following steps that Agent is used as a tool library, a tool library document, a knowledge library and a memory library are further inquired, a tool is selected through RAG and sent to a client side, if an existing tool cannot meet requirements, the Agent will generate a special code to complete a task, the client side calls the tool to carry out specific drawing according to received information, and a user can re-plan and adjust the drawing process through dialogue. The memory bank records session information and charting interaction operation information of this time and history. The problem of system fragmentation is solved through a unified architecture, stability and flexibility are balanced by combining large language model driving, tool agent and code generation, domain knowledge is introduced to enhance decision and improve specialty, and a user preference memory mechanism is constructed to realize personalized services.

The invention relates to the field of artificial intelligence, and discloses a multi-modal memory system and method for intelligent interaction. The system comprises a multi-modal information acquisition module, a confidence evaluation module, a scenario aggregation module, a knowledge graph construction module, a hierarchical storage module, an intelligent retrieval module, an active verification module and a memory management module. The core problems that in the long-term user interaction process of an existing artificial intelligence system, multi-modal information management is fragmented, information credibility is not quantitatively evaluated, memory organization lacks semantic association, a retrieval mode is single and passive, and memory life cycle is not adaptively managed are solved. Finally, unified collection, quantitative confidence evaluation, scenario semantic organization, associative intelligent retrieval and adaptive memory optimization of multi-modal information are realized, high-quality and high-efficiency long-term memory support is provided for scenes such as intelligent assistants, smart home, medical health and educational training, and the user experience and practical value of an artificial intelligence system are remarkably improved.

The embodiment of the invention provides an elastic fragment routing and cold and hot data optimization method based on multi-dimensional feature prediction and related equipment, and belongs to the technical field of distributed database storage optimization. The method comprises the following steps: receiving a data stream from Internet of Things equipment; performing analysis and feature extraction on the data stream to obtain multi-dimensional feature data; according to the multi-dimensional feature data and the time sequence prediction model, performing prediction analysis on a fragmentation strategy to obtain an optimal fragmentation strategy; analyzing the routing decision according to the data priority of the data stream and a dual-mode routing engine to obtain routing decision result data; generating a target index name and creating a target index according to the optimal fragmentation strategy and the routing decision result data; according to the target index name, the data stream is routed to the target index according to the fragmentation key for batch writing; the fragmentation keys are from multi-dimensional feature data. According to the embodiment of the invention, intelligent prediction of the fragment number and automatic hierarchical storage of data can be realized, and the problems of resource elastic scaling and cost efficiency optimization are fundamentally solved.

The invention discloses an archive data security backup method and system based on cloud computing, and the method comprises the steps: obtaining archive data and a cloud backup pool, obtaining an edge verification package based on to-be-backed-up data, carrying out the grouping encryption of the edge verification package through a national password, and enabling the cloud backup pool to carry out the splitting and storage of the backup data according to a regeneration code strategy, the method comprises the following steps: acquiring data fragments and fragment roots, writing Hash of the fragment roots into an alliance chain channel, hosting a decryption key of a confidential envelope according to a storage period, storing fragments of the decryption key into a cloud HSM, and if a cloud backup pool monitors a disaster trigger event, pushing decryption key sub-fragments to a disaster recovery undertaking pool according to a disaster level, the subset of the readable file data is recovered to the edge node, and a recovery log is written back to the alliance chain. According to the method, data backup, safety and reliability are improved through desensitization grading, differential safety protection, regeneration code strategy splitting and multi-node disaster recovery.

The invention discloses a document information input method and device and a storage medium, and relates to the technical field of electronic digital data processing.The document information input method comprises the steps that a document type is determined according to characters obtained by recognizing a to-be-processed document; calling a large language model to extract an entity in the character, and associating an entity relationship, a service intention, a context and a context relationship corresponding to the entity as reference information of the entity; constructing an extraction problem based on a field in a business data structure file corresponding to the document type; and in combination with the reference information, extracting a target field from the entity through the extraction question. According to the method and the device, the technical effect of efficiently and accurately converting the fragmented characters into the structural data available to the business system can be realized.

The invention relates to a service data distributed storage method and device, computer equipment, a medium and a product. Comprising the steps of generating a corresponding target data fragment identifier according to data fragment information of business data under the condition that a write-in request of the business data is detected; first mapping information and second mapping information are extracted from a preset storage routing mapping table, the first mapping information represents the mapping relation between the data fragment identifier and the storage group of the distributed database, and the second mapping information represents the mapping relation between the storage group and the database node of the distributed database; querying a target storage group identified by the target data fragment from a distributed database according to the first mapping information; querying a target database node corresponding to the target storage group according to the second mapping information; and performing distributed storage on the business data through the target database node. By adopting the method, the limitation of distributed storage of the service data is reduced.

The invention relates to a rail transit large model retrieval enhancement generation method and system, an application and a medium, and the method comprises the steps: receiving a user query text, converting the user query text into a triad sequence through a large language model, and sorting triads based on a semantic relationship; the method comprises the following steps: obtaining embedding vectors of entities and relationships in a sequence through an embedding model, and retrieving candidate entities and relationship sets from a knowledge graph according to the embedding vectors; and calculating a triple sequence semantic distance between the sequenced sequence and the candidate sequences by adopting a triple sequence retrieval algorithm, and selecting the first K candidate sequences meeting the structural constraint. And finally, inputting the user query and the first K candidate sequences into the large language model to generate an answer, thereby avoiding knowledge fragmentation and structure mismatch, and realizing reliable and accurate response to complex multi-hop query in the urban rail transit field.

The invention discloses a distributed block storage method and device, electronic equipment and a storage medium. The load index is calculated by collecting the multi-dimensional performance indexes of the storage nodes in real time, and the data fragment storage position is dynamically adjusted according to the load. The self-adaptive multi-dimensional load sensing model can accurately adapt to various service scenes, real-time balance of loads between nodes is achieved, hot nodes are effectively avoided, and the overall performance of the system is improved. And when the load index exceeds the preset threshold value, hot data identification, node affinity evaluation and migration income calculation modes are started to perform data fragmentation migration, so that the utilization rate of mixed medium resources is improved, a high-performance storage medium is reasonably utilized, and the data access efficiency is improved while the storage cost is reduced. Through cooperative work of load monitoring, fragmentation decision making, migration execution, metadata management and the like, a fragmentation strategy is automatically optimized according to input and output characteristics of different service types, and the overall performance under different service scenes is improved.

The invention relates to a project ledger intelligent generation method and system based on AI, and the method comprises the steps: responding to a ledger generation instruction, and extracting a target project identifier and ledger range information; obtaining a project data set, extracting a project entity and a planning index set, and constructing an initial knowledge graph; planning adjustment event information is extracted, incremental updating is carried out on the initial knowledge graph, and a compliance knowledge graph is generated; performing time sequence reasoning on the compliance knowledge graph to generate a compliance state time sequence chain; obtaining a structured standing book template and filling the structured standing book template to generate a project standing book report; according to the method, the knowledge graph is constructed by automatically integrating the multi-source project data, incremental updating and time sequence reasoning are performed based on the planning adjustment event, dynamic compliance state deduction and intelligent ledger generation are realized, the problems of information fragmentation and difficulty in dynamic compliance state deduction in traditional manual ledger compiling are effectively solved, and the user experience is improved. The method has the effect of remarkably improving the automation level of machine account generation and the accuracy of compliance verification.

The invention relates to a knowledge base construction method and system for multi-source heterogeneous files, and the method comprises the steps: receiving Word, PDF, Excel and other multi-source heterogeneous files, and converting the multi-source heterogeneous files into standardized representation through a file analysis function; an event boundary recognition technology fusing rule matching, entity recognition, semantic similarity and large language model verification is adopted, and a cross-paragraph complete business event is accurately extracted; generating structured information through an information extraction function; the information is converted into vectors, full-text indexes and a graph database to be stored by means of a multi-dimensional storage conversion function; and verifying and correcting an extraction result through a quality auditing function, and finally outputting a knowledge base containing an event set, an entity relationship and vector representation. According to the method, the problem of knowledge fragmentation in traditional document processing is effectively solved, and the consistency, the searchability and the reasonability of knowledge are remarkably improved.

The invention discloses a knowledge graph construction method, device and equipment and a readable storage medium, and is applied to the technical field of natural language processing and knowledge engineering.The method comprises the steps that document content is divided to obtain initial document fragments, and all the initial document fragments are merged and divided based on semantic similarity to obtain target division blocks; based on the target division block, subject-predicate-object formatting processing is carried out to obtain a subject-predicate-object formatting result; entity and relation extraction is carried out according to the subject-predicate-object formatting result to obtain a display triple, implicit relation reasoning is carried out to obtain an implicit relation triple, entity and relation type normalization is carried out to obtain a normalized triple, and the knowledge graph is constructed based on the normalized triple. The block segmentation driven by semantic similarity is adopted to avoid sentence breakage, cascade errors are reduced based on subject-object formatting processing, and the problems of fragmentation, link missing and the like are solved based on implicit relations, so that the integrity and accuracy of knowledge graph construction are improved.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The invention provides a retrieval recall method and system fusing document fragmentation and authority control, and relates to the technical field of artificial intelligence and information retrieve.The method comprises the steps that a role of a user is obtained according to the identity of the user, and a corresponding authority strategy is determined in combination with a preset multi-level authority mapping relation; the permission strategy comprises a label allowed to be accessed, a data set and a document set; the query request is converted into a semantic vector, and initial candidate document fragments are retrieved from a vector database in combination with an associated data set or tag identifier; according to the method, the candidate results are subjected to access control filtering on the basis of the authority strategy, and the final retrieval recall result is generated, so that role-based fine-grained access control is realized while high retrieval precision is ensured, the problems of rough authority management and weak context processing capability in traditional retrieval are solved, and the security and efficiency of retrieval are remarkably improved.

The invention provides an enhanced generation method based on topology perception graph coding and adaptive sub-graph retrieval, which comprises the steps of multi-granularity semantic perception segmentation and knowledge graph construction, structural feature matrix construction and explicit topology position code generation, and constrained sub-graph diffusion and dynamic pruning based on semantic-topology joint scoring. Generating a graph-text consistency loss constraint based on attention matrix structured alignment; explicit topological position coding is adopted in a coding layer to avoid overhead and excessive smoothness caused by online GNN aggregation; in a retrieval layer, a connected sub-graph instead of a fragment node is used as an enhanced context; in an alignment layer, an internal attention matrix of a large language model is directly constrained to be consistent with a sub-graph adjacent matrix, and structured guidance is realized from a reasoning mechanism level, so that logic illusion is inhibited, and the multi-hop reasoning accuracy is improved.

The invention relates to the technical field of enterprise service digitalization and intelligent management, in particular to an enterprise management method and management platform based on dynamic portraits. The method comprises the following steps: S1, accessing and standardizing multi-source heterogeneous data, calculating a source-level quality score and generating an evidence chain; s2, constructing a portrait map and a time axis taking an enterprise as a main node; s3, the rule engine and the LLM-RAG output numbers in parallel; s4, consistent arbitration and belief propagation; s5, event-driven dependency subgraph increment recalculation is carried out; s6, carrying out portrait-strategy matching and task linkage; s7, carrying out closed-loop write-back and auditable playback; and S8, multi-tenant fine-grained permission. According to the scheme, the problems of data fragmentation and insufficient timeliness and traceability are solved, and the linkage of portrait credibility, interpretability and real-time decision is realized.

The invention discloses a knowledge graph and RAG-based multi-modal engineering design intelligent generation method and system. The method comprises the following steps of S100, multi-modal data analysis and structured processing; s200, constructing a domain knowledge graph; s210, constructing a standardized module library; s300, performing intelligent retrieval and scheme generation; and S400, compliance verification and feedback learning are carried out. Through deep collaboration of the domain knowledge graph and the standardized module library, spanning of engineering design knowledge from'retrieval 'to'understanding and driving' is achieved, the problems of knowledge fragmentation and low reuse rate are solved, and the design efficiency, quality and automation level are remarkably improved.

The invention relates to the field of distributed storage, and discloses a distributed memory system based on erasure codes and a dynamic repair mechanism. Comprising a data node cluster, a metadata management cluster, a bandwidth token management module, a dynamic repair composer and a repair agent node. And the data node cluster divides the object into strip generation data fragments and verification fragments and dispersedly stores the data fragments and the verification fragments according to a placement rule. And the metadata management cluster maintains object stripe mapping, a fragment position set, erasure code protection configuration and a version epoch, and records a repair intention log. And the bandwidth token management module grants repair execution permission according to the fault domain budget. The dynamic repair composer generates availability repair and durability repair tasks, the temporary check fragments are written in when the number of the available check fragments is lower than a threshold value in the availability stage, and the configuration fragments are recovered and the temporary fragments are recovered in the durability stage. And the consistency of version epochs is verified before the agent node is repaired and written back, and if not, recalculation is carried out.

The invention discloses a software supply chain security detection method and system based on a large model agent, and relates to the technical field of network security. According to the method, a large-scale software supply chain knowledge graph is constructed based on a preprocessed large-scale software supply chain data set, and meanwhile, a software supply chain field large model is obtained; and after receiving a software supply chain question, directly generating a corresponding answer in the software supply chain security detection field by the software supply chain field large model in real time, or retrieving the large-scale software supply chain knowledge graph based on scene classification, and generating a corresponding answer in the software supply chain security detection field in combination with a retrieval result. According to the method, the problems of information fragmentation, knowledge updating lag, high manual analysis cost, low automation degree, high false alarm rate and the like in software supply chain security management of a traditional method are solved, and intelligent support is provided for constructing a credible, safe and transparent software supply chain system.