4188results about "Database models" patented technology

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention relates to the technical field of artificial intelligence and enterprise decision making, and discloses an intelligent decision making system and method based on an enterprise life index large model, and the system comprises a multi-source heterogeneous data collection module which is used for obtaining and associating various data in real time, and generating target enterprise multi-source heterogeneous data; the dynamic index system generation module is used for sequencing feature importance through a graph neural network and generating a dynamic evaluation index set containing survival, risk and growth indexes; the enterprise life index large model building module is used for fusing index sets, capturing enterprise state evolution, embedding risk factors and outputting multi-dimensional enterprise life indexes; the intelligent decision dynamic generation module is used for inputting the indexes into the decision strategy tree model and matching a strategy library to generate a decision instruction set; and the closed-loop feedback optimization module is used for realizing closed-loop optimization by acquiring execution data and combining Bayesian optimization dynamic correction parameters. According to the invention, the accuracy and timeliness of enterprise intelligent decision making can be improved.

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The present invention proposes a vector database retrieval method and system, which relates to the technical field of big data processing and intelligent information retrieval. The method includes preprocessing high-dimensional multimodal data to obtain a dimension reduction feature vector; constructing a cluster center based on the dimension reduction feature vector; establishing an inverted file and an intra-cluster plane index based on the cluster center, compressing the inverted file and the intra-cluster plane index to obtain a compressed inverted file and an intra-cluster plane index; obtaining a user's query vector, using the inverted file to screen candidate clusters for the query vector, introducing a hierarchical navigable small-world graph in the intra-cluster plane index, performing an intra-cluster search on the cluster cluster, and obtaining a vector database retrieval result. The present invention effectively takes into account the efficiency and accuracy of high-dimensional data retrieval, and improves the retrieval efficiency and accuracy.

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 a method for diagnosing a structural service behavior based on a high dam accident mode and a failure path, and the method comprises the steps: constructing a high dam accident case database, and extracting accident feature parameters; by applying multi-scale layered adaptive corresponding analysis, through feature weight calculation, risk-out mode layered weighting, physical similarity index establishment and structural difference compensation, the problem of unbalanced feature weight is solved, and a weighted similar project sorting list is generated; constructing a multi-failure mode coupled time sequence adaptive network model, solving the problem that a failure path time sequence evolution mechanism is imperfect, and outputting a time sequence failure path prediction model; and in combination with the similar project sorting list and the time sequence failure path prediction model, performing project analogy analysis, and generating a high dam safety state evaluation report. The technical problems that the operation time of the ultra-high dam is short, the monitoring information is insufficient, the numerical analysis result is difficult to completely reflect the engineering practice and the like are solved, and a new technical path and a new solution are provided for high dam safety assessment and risk management.

Techniques for generating AI-powered responses tailored to a specific entity's communication style. The techniques involve selecting a particular AI agent associated with an entity, receiving a user query, and generating an embedding from it. This embedding is used to retrieve relevant content from the entity's knowledge database. The entity's communication type is then determined. A large language model (LLM) prompt is created, combining the retrieved content and instructions to apply the entity's communication style. This prompt is submitted to an LLM service, which generates an output. A response based on this output is returned to the user. The techniques enable the creation of AI-generated responses that are both informative and aligned with the entity's preferred communication style, enhancing the consistency and effectiveness of AI-powered customer interactions or information dissemination for the entity.

The invention discloses an entity alignment method for a multi-modal crop knowledge graph, and belongs to the technical field of knowledge graphs and agricultural intelligent analysis, and the method comprises the steps: obtaining a data feature item set of an agricultural field, and carrying out the time sequence compensation, and generating a growth time sequence feature parameter; performing feature extraction on the data feature item set, performing fusion to generate a multi-modal fusion feature mapping graph, constructing a dynamic feature matching network based on growth time sequence feature parameters, and performing entity node traversal on a reference knowledge graph to generate a candidate alignment set and a corresponding difference unit set; filtering conflict nodes in the candidate alignment set according to a three-dimensional confidence evaluation model to generate an effective alignment chain; and generating a graph updating instruction based on the difference unit set, and reconstructing a knowledge graph topological structure in combination with the effective alignment chain. According to the method, multi-modal feature bridging, dynamic weight optimization of growth stage perception and an incremental conflict resolution mechanism are adopted, so that cross-domain agricultural entity accurate alignment and real-time adaptive updating of the knowledge graph can be realized.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a query generation method in a Text2SQL (Structured Query Language) task based on a large language model, which comprises the following steps of: collecting statement information in a plurality of user interaction rounds, carrying out user dialogue intention identification according to a learning model and context tracking, and fusing the obtained dialogue intention information into a prompt; constructing a problem analysis model to obtain an analysis target result, wherein the analysis target result comprises user SQL query intention recognition and entity extraction; and identifying entities, conditions and sorting requirements involved in the question by utilizing the question analysis model, analyzing a mode of a required SQL structure, realizing SQL mode identification, obtaining a mapping relationship between the question and the SQL query to generate an SQL query statement meeting user requirements, and performing SQL query structure supervised fine adjustment on the generated SQL query statement. According to the method, the query generation method in the Text2SQL task emphasizing situation learning, new prompt engineering and supervised fine tuning can be realized.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a live-line work safety monitoring system based on artificial intelligence, and relates to the technical field of intelligent electric power safety, and the system comprises a data collection module which is used for collecting and preprocessing work data in a live-line work area in real time, and extracting live-line body point cloud data based on the work data; the finite element grid unit is used for dividing all charged bodies into finite element grid units and judging high-temperature charged bodies; and the violation judgment module is used for calculating the electric field intensity value of the position where the operator is located in real time. By combining an electric field, a temperature field and three-dimensional point cloud multi-modal data, comprehensive dynamic perception of an operation area is realized, finite element modeling and dynamic non-uniform heat source distribution analysis are adopted, a high-temperature charged body is accurately identified, the equipment fault early warning capability is improved, action characteristics of an operator are captured through a long-short-term memory network, and the working efficiency is improved. And the illegal behaviors are accurately judged by dynamically combining environmental data, and an optimal operation path is generated in real time by utilizing a three-dimensional grid division and dynamic planning algorithm.

The invention discloses an equipment operation and maintenance decision support method and system, computer equipment and a storage medium, and relates to the technical field of equipment operation and maintenance intelligent management. Constructing a semantic association model of the structured data and the unstructured data; and a reasoning rule base is constructed to perform automatic reasoning on the equipment data, and fault early warning and operation optimization suggestions are provided. According to the method, through dynamic management and expansion of equipment entity information, the accuracy and relevance of equipment data are improved, and problems caused by data omission or redundancy are reduced; the problem that unstructured data is difficult to use is effectively solved, and the richness and accuracy of the knowledge graph are improved; through automatic verification and complementation, the integrity and accuracy of the data are improved, and the complexity of manual maintenance is reduced; and through dynamic generation and automatic reasoning of the reasoning rule base, the intelligent level of equipment operation and maintenance is improved, and accurate fault early warning and operation optimization are realized.

The invention provides a low-altitude three-dimensional traffic road network division method and system. The method comprises the following steps: S1, data acquisition and processing: performing real-time data acquisition and processing on low-altitude three-dimensional traffic road network division areas; s2, dynamically dividing the processed data to obtain divided logic data, and integrating low-altitude air traffic data and low-altitude road surface data to form three-dimensional traffic data; s3, performing structured processing on the dynamically divided logic data and the multi-modal resource integrated three-dimensional traffic data; obtaining a structured result of the low-altitude three-dimensional traffic road network; and S4, calculating a corresponding action specification based on a structured result. Efficient utilization and intelligent management of low-altitude traffic resources are realized through the steps of dynamic division of a low-altitude three-dimensional area, multi-mode resource integration, structured management, intelligent operation standardization and the like.

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 new-generation medical equipment integration engine, which comprises an equipment data standardization module used for automatically identifying and converting various equipment data formats; the equipment data standardization module can process various medical equipment data such as HL7, DICOM and MODBUS through a built-in multi-protocol analyzer and convert the medical equipment data into a uniform standard format, so that the consistency and interoperability of the data are ensured; the real-time data processing engine module is used for collecting, transmitting and processing the data of the medical equipment in real time; the intelligent analysis and decision support module is used for performing intelligent analysis on the data from the medical equipment through deep learning and time sequence analysis technologies; a micro-service architecture design module; and a multi-level security mechanism module. The device data can be deeply analyzed and predicted, the accuracy of diagnosis and treatment decisions can be improved, early warning can be achieved, and the safety of patients is ensured.

The invention discloses a heat supply system multi-working-condition scheduling control method based on a large model and multiple agents. The method comprises the steps that a heat supply system digital twinborn model is established; the method comprises the following steps: setting a scheduling control center management agent, predicting a thermal load demand by adopting a large language model and combining a thermal load prediction model, dividing a system into a plurality of operation conditions, and setting a plurality of condition sub-agents corresponding to the operation conditions; the dispatching control center management agent judges whether the working condition sub-agents under the corresponding operation working conditions are triggered to start working or not, and meanwhile heat load demand values, predicted through a large language model, of all time periods in the future and the heat production amount of the new energy heat supply unit are transmitted to the corresponding working condition sub-agents; and the working condition sub-agent adopts a large language model to establish a system optimization scheduling control knowledge graph based on the large language model, obtains a scheduling control strategy of each device on the source network load side of the heat supply system, and feeds back the scheduling control strategy to the digital twinborn model for guiding the actual operation management of the heat supply system.

The invention discloses a power distribution network load transfer strategy intelligent recommendation method, intelligent control method and device, and the method comprises the steps: enabling a large model to receive an input request of a user, enabling the large model to recognize a scene of the input request, and extracting an intention scene; performing entity identification on the input request; extracting parameter information of the transfer constraint and the transfer target, obtaining complete parameter information through multiple rounds of dialogues, converting the complete parameter information into mechanism model request parameters, sending a request to the mechanism model, and receiving multiple candidate transfer strategies and evaluation indexes returned by the mechanism model; and reading a historical wheeling strategy and a strategy constraint standard through the RAG to determine a group of evaluation rules, evaluating each candidate wheeling strategy by adopting a thinking chain mode, screening out the candidate wheeling strategies meeting conditions, sorting the candidate wheeling strategies, determining an optimal wheeling strategy, and outputting the optimal wheeling strategy. The method can be suitable for a complex large-scale power distribution system to quickly and accurately generate the power distribution network load transfer strategy.

The invention discloses a natural language query method and system for a relational database, and the method comprises the steps: extracting a list structure from a local connection database, and generating a sub-database based on a natural language problem; inputting the sub-database mode and the natural language question into a screening cue word template to obtain a complete cue word, inputting the complete cue word into a large language model, and generating a simplified database mode after obtaining a table name and a column name; putting a natural language question into a prediction cue word template, obtaining a predicted SQL keyword skeleton, then converting the predicted SQL keyword skeleton into a prediction vector, screening out an example database, extracting question and answer pairs in the example database, extracting a keyword skeleton from an SQL statement of the question and answer pairs, and after extracting the keyword skeleton, converting the extracted keyword skeleton into a question and answer pair vector; calculating the similarity to obtain a similarity result and a corresponding prediction vector; the content is input into a cue word template, cue words are input into a large language model, the large language model generates SQL statements through a thinking chain, and natural language query is completed.

The invention belongs to the field of customs declaration distribution, and particularly relates to a customs declaration intelligent dispatching and cooperative processing system, which carries out compliance auditing on declaration information through a business declaration module in combination with a user historical credit score and an initial auditing model, and marks processing difficulty and danger level on the information passing the auditing; the intelligent generation module generates a compliance declaration form based on the pre-dispatch label and a compliance rule base and performs encryption locking; and the collaborative dispatching module accurately matches and dispatches the customs declarations to auditing personnel by using a distributed enhanced dispatching model according to information such as customs declaration processing difficulty, comprehensive risk assessment scores and employee proficiency. The auditing feedback module monitors auditing progress and abnormity in real time and realizes dynamic closed-loop feedback through a declaration feedback adjustment chain; according to the invention, intelligent evaluation, precise dispatching and closed-loop collaborative management of customs declaration processing are realized.

The invention provides a report generation and optimization method and system based on an offline data warehouse. The system comprises a data extraction layer, a data loading layer, a data cleaning layer, a data integration layer, a model data layer, a report mart layer, a report generation layer and a report optimization layer. The method comprises the following steps: S1, extracting data; s2, data cleaning; s3, integrating the data; s4, ensuring data quality; s5, storing model data; s6, a report market is constructed; and step S7, report generation. A layered architecture is adopted, independent operation of each module of the system is ensured, and expansion and maintenance are facilitated. Data extraction, cleaning and integration processes are simplified by introducing an automatic tool, and redundant calculation is reduced by optimizing SQL and solidifying logic. Bottom logic is optimized for large-scale data volume and complex query, and report generation performance is improved. And monitoring and management functions of the whole life cycle of the data flow are provided, and data quality and report accuracy are ensured.

The invention discloses an information management method based on a big data processing technology, and the method comprises the steps: S1, data collection and pre-processing optimization: S1.1, multi-source data cleaning and pre-processing: automatically detecting and correcting errors, missing and abnormal values in data through an integrated intelligent data cleaning technology, such as a machine learning algorithm (such as abnormal detection and missing value filling); the data quality is ensured, data standardization and format unification tools are adopted, heterogeneous data from different sources are converted into a unified format, and follow-up processing is facilitated. The problems of data privacy, quality, real-time performance, complexity and compliance in a traditional big data method are solved by enhancing data quality management, improving data security, optimizing storage and processing architecture and introducing modes such as real-time processing and intelligent analysis. Meanwhile, the expandability and the fault-tolerant capability of the system ensure the high efficiency and the stability of the system in a large-scale data environment, and a more intelligent, safer and more efficient information management solution is provided for enterprises.

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 discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention relates to the technical field of intelligent railways, and provides a method for constructing a railway multi-mode knowledge base question-answering system of a hybrid RAG architecture, which comprises the following steps: 1, deploying and finely adjusting a local model of a server; 2, data processing and database establishment; establishing a railway specification knowledge base database management system based on a MongoDB content memory and a PostgreSQL vector retrieval library; for multiple groups of data, search results are optimized and merged based on hybrid retrieval and an RRF algorithm, and finally reordering and search filtering are carried out; 3, constructing a multi-modal knowledge base; the method comprises the following steps of: embedding pictures and texts into a vector database by using multi-modal embedding, storing corresponding original pictures and texts in a document storage, acquiring pictures in the document storage during hybrid similarity retrieval, and transmitting the original pictures and text blocks to a large model to generate answers; and 4, multi-platform expansion. According to the method, the railway multi-mode knowledge base question-answering system can be well constructed.

The invention relates to the technical field of data management, and discloses an iterative updating method for a data consanguinity atlas, which comprises the following steps of: capturing data increment and metadata change; constructing a directed acyclic graph of the data blood relationship; performing layered modeling on the data blood relationship graph; performing iterative updating on the blood relationship map based on a layered modeling result; processing and storage of incremental data are optimized through distributed calculation; real-time blood relationship map updating is realized by using a stream processing technology; a machine learning model is used for predicting blood relationship path change, abnormal nodes are detected and repaired, and the step of capturing data increment and metadata change comprises the substeps that data snapshots are obtained, and the data snapshots comprise new snapshots and old snapshots of data. Through a stream processing technology, a machine learning model and an automatic repair mechanism, efficient real-time updating of the data blood relationship map, intelligent path prediction and automatic repair of abnormal nodes are realized, and the system stability and the updating efficiency are improved.