9819results about "Database indexing" patented technology

The invention discloses a multi-path recall mixed retrieval method and system based on intelligent dynamic weight distribution, and aims to solve the problems that semantic comprehension and keyword matching are difficult to balance and the adaptability is poor due to the adoption of a fixed weight in the existing retrieval technology. The invention provides a multi-path recall mechanism fusing vector semantic retrieval, BM25 keyword retrieval and entity retrieval. A query feature vector containing 13-dimensional features such as semantic complexity, keyword density and entity coverage rate is constructed, a query type is recognized in combination with an SVM and a random forest integration model, a dynamic weight distribution algorithm is designed, and the final weight of each retrieval path is calculated in real time. And an adaptive multi-source enhanced reciprocal ranking fusion (AMSE-RRF) algorithm is further adopted to carry out optimization fusion on multiple paths of results, and a depth reordering model can be selected to improve the precision. According to the method, the accuracy and robustness of retrieval can be remarkably improved in multiple scenes of medical treatment, finance, government affairs and the like according to a millisecond-level self-adaptive adjustment strategy of query features.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention relates to the technical field of natural language processing, in particular to an enterprise number asking system and method based on combination of a large language model and an NL2SQL, and the method comprises the following steps: filling a prompt project template with a natural language query request and a structured metadata context, and generating a standard prompt; the standard prompt is input into the large language model, deep semantic analysis is carried out, and an SQL query draft is generated; self-evaluation is carried out on the questions, and when the questions are recognized, clarified questions are generated and returned to the user; receiving feedback of a user, and iteratively optimizing the SQL query draft based on the feedback; performing grammar verification and security verification on the final SQL query draft to generate an executable SQL statement; sending the executable SQL statement to a target enterprise database for execution; and the information is visually displayed to a user. The problem that the SQL generated in the prior art deviates from the real intention of a user and cannot meet the requirements of enterprise-level applications for accuracy and reliability can be solved.

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

The invention relates to the technical field of data processing and storage management, and discloses a multi-source index data intelligent storage management method and system, and the method comprises the steps: building a unified data integration and processing channel, and forming a standardized index data set; meanwhile, an enterprise-level index unified management system is established, and a credible index data set is generated; the credible index data set is stored through an optimized storage strategy and a data organization model, a data consanguinity association map is constructed based on data full-link processing, and visual and traceable index data assets are obtained; according to the method, a real-time quality evaluation engine is used for carrying out continuous monitoring and intelligent warning on all links of synchronization, processing, storage and service of an index data set so as to construct a data anomaly detection and repair mechanism, and quality controllable supervision is carried out on the whole processing process of the index data set through a data quality billboard and an anomaly processing workflow; therefore, the intelligent management level and the data quality guarantee capability of multi-source index data processing and storage can be improved.

The invention provides an online map tile fast loading and caching optimization method, medium and system, and belongs to the technical field of map processing.The method includes the steps that a tile index database and an exchange priority matrix are established, a viewport moving trend vector is calculated according to user sliding operation, a future viewport range is predicted, and a preloaded tile list is screened; the method comprises the following steps: performing cache validity check on tiles, establishing a small change detection vector to judge whether updating is needed, establishing a tile hit rate statistical matrix, fusing a time decay factor and a spatial correlation factor to calculate a popularity score, and gathering high-popularity tiles in a quick access area by adopting a thermodynamics-imitating entropy reduction algorithm to realize cache ordering. And the next tile demand of the user is predicted, so that the technical problem that the overall loading response speed is reduced due to an overlong high-value tile access path caused by disordered distribution of cache resources in the online map tile loading process is solved.

The invention discloses a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention relates to an enhanced LLM-RAG multi-hop question and answer method based on logic tree reasoning, and belongs to the technical field of new-generation information, and the method comprises the following steps: inputting a multi-hop question and answer question into a computer system; the computer system calls a pre-training large language model LLM, the multi-hop question-answer question is decomposed into a hierarchical logic tree in a recursive mode, and each node of the logic tree comprises a sub-question and a corresponding hypothesis answer; performing image retrieval from a structured knowledge source Wikidata and performing text retrieval from an unstructured knowledge source Wikipedia on the basis of each node sub-question and the hypothesis answer to obtain corresponding evidence; traversing the logic tree, verifying the consistency between the hypothetical answer of each node and the evidence through LLM, if the contradiction exists, reconstructing the corresponding sub-tree, and dynamically correcting the reasoning path; and integrating the verified logic tree node information, and outputting an accurate answer to the multi-hop question and answer question.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

The invention discloses a method for carrying out data cleaning decision making and automatic tuning by using AI workflow, and relates to the technical field of data processing, the method comprises the following steps: obtaining original data from various heterogeneous data sources, and generating a correlation table and a result through field extraction, structure analysis and language attribute recognition; based on this, storage and task generation are performed, and standardized data and task queue information are output; identifying a quality problem through feature analysis, defining a target and constructing a cleaning task atlas, and generating cleaning metadata through preliminary screening and marking; and an AI cleaning workflow containing multiple strategy paths is constructed and optimized, after execution, a downstream feedback evaluation effect is combined and adjustment is carried out, and finally a multi-version cleaning result is integrated and output. According to the method, the adaptation capability to different types of data sources is effectively improved, the dependence on a static preset strategy is reduced, the manual intervention requirement is reduced, the tuning efficiency is improved, and the systematicness and normalization of data cleaning are enhanced.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses an information retrieval system and method based on semantic normalization, and relates to the technical field of artificial intelligence information, and the method comprises the steps: collecting a semantic query record input by a user, carrying out the preliminary semantic analysis, and generating structured data; on the basis of the structured data, entity disambiguation is carried out by utilizing a knowledge graph, abstract classes are generated through a neural network, calibration and dynamic weight adjustment are carried out, and high-confidence entity abstract classes and confidence scores are generated; entity abstract classes and confidence scores are combined with user contexts, an action-value function is calculated through a value network, and an optimal action is selected by utilizing a-greedy algorithm; executing semantic normalization mapping according to the optimal action, and obtaining an intermediate expression by using a meta-symbol dynamic generator; and performing index retrieval and multi-dimensional sorting based on the intermediate expression to generate a sorted retrieval result list. According to the method, the semantic fragmentation problem of multi-modal query is solved, and deep semantic alignment and dynamic weight calibration of heterogeneous data are realized.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses an intelligent building equipment historical data storage method, and relates to the field of building equipment historical data storage, and the method comprises the following operation steps: S1, collecting and preprocessing global data; s2, intelligent storage strategy decision making; s3, designing a semantic storage structure; s4, constructing and managing a distributed storage cluster; s5, intelligently writing and indexing historical data; and S6, efficient query and deep analysis of an interface. According to the historical data storage method for the intelligent building equipment, provided by the invention, all-around improvement is realized in the aspects of data quality, storage efficiency, association capability, system reliability, operation convenience and the like through refined design of a data full link; and finally, the equipment data of the intelligent building really becomes a core asset which can be perceived, analyzed and utilized, and powerful support is provided for efficient operation, energy conservation, consumption reduction and safety guarantee of the building.

A Retrieval-Augmented Generation (“RAG”) framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.

Systems and methods disclosed herein are enabled to dynamically generate structured documents using one or more artificial intelligence models. A computing device receives an output generation request and uses a first AI model to retrieve data chunks from source documents and applicable templates. A second AI model ranks the retrieved chunks based on one or more metrics, such as vector similarity, keyword density, and temporal relevance. A third AI model subsequently generates a response using the ranked chunks, templates, and predefined operational boundaries for each chunk. The generated response is tagged with source identifiers to enable the traceability of the response back to corresponding chunks. The system transmits, via the computing device, the response, the retrieved chunks, and / or the source identifiers.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention discloses a product full-life-cycle traceability after-sales service system based on multi-source heterogeneous data, and relates to the technical field of product traceability after-sales management. Manufacturing resumes, work orders, spare part warehouse-in and warehouse-out and equipment logs are accessed, mapping of product instances, parts and software primary keys is established, and the product traceability after-sales service system is established. And normalizing the configuration change event into a configuration change event containing double time axes of effective time and warehousing time, and writing the configuration change event carrying differential operation, an evidence packet and an idempotent event identifier into an immutable event library. And constructing a conflict set for the same configuration target, executing uniqueness, causality and compatibility constraint verification, determining an effective event based on evidence packet arbitration, and outputting an arbitration record and confidence. And deriving a configuration version chain according to the effective event and generating a snapshot, performing incremental playback on the late or error correction event and generating an influence propagation list. And when any time point configuration is queried, playback restoration is performed, an evidence chain and confidence are output, an active verification backflow error correction event is triggered if the confidence is insufficient, and the accuracy of quality guarantee judgment and spare part matching is improved.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention provides a final consistency processing system and method for transaction messages. The method comprises the following steps: acquiring transaction state metadata and a service message body about a transaction message in a distributed system, storing the service message body into a distributed message queue, and storing the transaction state metadata into a memory database; constructing a transaction state index structure; determining a physical offset based on the business message body, creating a transaction state record in the memory database based on the physical offset, and generating a to-be-rechecked transaction task according to the transaction state record; based on the transaction creation time and the system load factor, determining a rechecking interval, and executing the to-be-rechecked transaction task; when it is detected that the transaction state is changed into a transaction confirmation state from a to-be-confirmed state, pulling a service message body according to the physical offset; and performing double confirmation on the pulled service message body. The problems of performance bottleneck and low recovery efficiency caused by strong coupling of the transaction state and the message body are solved.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The invention discloses a business English scene knowledge graph generation and intelligent retrieval method, and relates to the technical field of natural language processing, and the method comprises the steps: constructing a document time sequence index and anaphora alignment mapping table, and registering an evidence anchor point index; extracting event units under the constraint and merging the event units into event clusters in a cross-document manner; arranging an event chain according to unified time and generating a process graph, and binding evidence anchor points to relation edges; analyzing the query into a query intention expression and a path constraint, and generating an evidence presentation sub-plan and an image-text combined execution plan; and positioning answers in the atlas, integrally returning the answers, paths and evidences, and meanwhile, forming replayable audit tracks. The method solves the problems of cross-channel time dislocation and confusion of references, and supports interpretable and auditable retrieval. The method is suitable for scenes such as mails, conference summary, instant communication and the like, guarantees locatable sources, path replay and evidence verification, and is convenient for internal control review.