2695results about "Database models" 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.

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 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 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 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 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 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.

The invention discloses an electric energy metering risk monitoring method, system, equipment and medium based on time sequence model retrieval, and relates to the technical field of risk monitoring, and the method comprises the steps: collecting multi-dimensional time sequence operation data of a target electric energy metering terminal, carrying out the preprocessing, and obtaining a multi-modal time sequence data sample library; a pre-trained time sequence basic model is used for coding and prediction, a matched historical normal sample is dynamically searched from a sample library through a similarity-based retrieval mechanism, retrieved context information is integrated through a self-adaptive fusion mechanism, a fused comprehensive feature representation is generated, a comprehensive risk score is calculated, and the comprehensive risk score is obtained. And processing and judging the risk score by using a dynamic judgment mechanism, and outputting a monitoring result. According to the method, the pre-training time sequence basic model is combined with a lightweight self-adaptive mechanism based on multi-scale retrieval and gating fusion, a risk monitoring framework is constructed, and accurate risk monitoring with low false alarm and rapid cross-domain deployment on the heterogeneous electric energy metering terminal is realized.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

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 embodiment of the invention provides an urban rail signal system fault diagnosis method based on a knowledge graph and a large model, and the method comprises the steps: collecting system operation logs, state parameters and fault information in real time, and carrying out the data preprocessing and standardization; based on historical fault data, a fault classification and prediction model is constructed through feature extraction and mode recognition, and automatic fault recognition and risk early warning are achieved; constructing a fault diagnosis knowledge graph, and establishing an association relationship among entities such as equipment, faults, reasons, maintenance schemes and the like; a pre-training large language model and a LoRA technology are adopted for efficient fine tuning, a fault diagnosis reasoning model is trained, and end-to-end generation from fault description to diagnosis and maintenance suggestions is achieved; the knowledge graph and large model output are fused, real-time and historical data are combined, multi-path fault analysis and comprehensive diagnosis are carried out, and a diagnosis report is generated and displayed. The intelligent level, accuracy and efficiency of fault diagnosis can be improved, and technical support is provided for intelligent operation and maintenance management.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention discloses an online question and answer illusion detection method based on a knowledge graph and multi-stage fusion, and the method comprises the following steps: receiving a natural language question, recognizing a key entity set through a multi-temperature sampling entity extraction method, and carrying out the matching verification of the key entity set and a pre-constructed knowledge graph, and obtaining a successfully matched entity set; performing structured evidence mining from the knowledge graph according to the successfully matched entity set, calculating the final weight of the evidence by adopting a multi-dimensional weight evaluation mechanism, grading the evidence, and screening and retaining the evidence with different confidence levels; adopting a three-stage progressive reasoning strategy: conservative answer generation, extended answer enhancement and complete answer annotation, and generating a final answer; and performing declaration-level consistency judgment according to the evidence, and calling context information to perform error correction when a contradictory declaration is detected, thereby completing illusion detection. The problems that'illusion 'is easily generated in an existing online question-answering system and an effective illusion detection mechanism is lacked are solved.

The invention discloses an emergency plan generation method and device based on artificial intelligence, and a medium, belongs to the technical field of artificial intelligence, and is used for solving the technical problems of sensing one-sided lag, poor static plan adaptability, insufficient situation deduction and decision support and low whole process recovery efficiency caused by information islands in an existing emergency processing system. The method comprises the following steps: acquiring multi-source event situation data, identifying an emergency event type, and constructing a dynamic event situation map; matching a basic plan based on the dynamic event situation map, and generating a plurality of preliminary emergency plans based on a preset constraint condition; simulating and deducing each preliminary emergency plan in the digital twin scene model, and determining a comprehensive evaluation value of each preliminary emergency plan; determining an optimal emergency plan based on the comprehensive evaluation value, and sending the optimal emergency plan to an execution end; and capturing key data in the execution process of the optimal emergency plan, generating an event handling process report, and optimizing the optimal emergency plan based on the event handling process report.

The invention discloses a system and a method for automatically collecting metadata for a heterogeneous data source, the system and the method for efficiently collecting the metadata for the heterogeneous data source are constructed through a full-automation, unification and intelligentization technical path, and the system and the method have the advantages of comprehensiveness, automation, intelligentization and real-time performance, that is, the system and the method are not limited to a certain type of data sources any more. Comprehensive support for multiple types of data sources is realized through a unified framework; an end-to-end automatic process is constructed, and manual intervention is not needed from source discovery to metadata extraction and storage; natural language processing and machine learning technologies are fused, so that an acquisition result has business semantic comprehension and classification capabilities; through an event-driven dynamic monitoring and incremental updating mechanism, high consistency of metadata and source data is realized.

The invention particularly relates to a policy redemption method based on a large model technology. According to the policy redemption method based on the large model technology, policy original texts of all levels are gathered, key structured information is analyzed and extracted, and the key structured information is stored in a policy knowledge base; enterprise data are automatically collected, and a multi-dimensional enterprise dynamic portrait is constructed; developing an intelligent matching engine, and matching a declaration condition in the policy knowledge base with the enterprise dynamic portrait; automatically pushing policy information to the enterprise meeting the condition based on the bidirectional matching result of the policy and the enterprise; and after an online confirmation instruction of the enterprise is received, the government affair data is automatically called to complete qualification verification, an audit report is generated, and a cashing process is triggered. According to the policy redemption method based on the large model technology, the target enterprise group can be accurately positioned, the phenomena of policy report omission and error report are obviously reduced, the resource allocation efficiency is optimized, the manpower and time cost investment is greatly reduced, and the modernization of the governance capability is improved.

The invention discloses a system, a method, equipment and a medium for automatically identifying and integrating geological reservoir features based on multi-modal data. The system comprises a multi-modal file analysis engine, a geological feature extractor, a dynamic relation disambiguation unit, a multi-modal alignment engine, a knowledge graph builder and an incremental graph building engine. Therefore, automatic, precise and dynamic integration of geological features is realized through a multi-modal collaborative architecture, and the term is corrected in combination with the geological dictionary, so that the problem of professional symbol recognition of the scanned document is solved, and the data processing efficiency is effectively improved.

The invention discloses a production line data integration method based on a digital twin model, and belongs to the technical field of electronic data processing. The production line data integration method comprises the following steps: step S0, pre-preparation and standard definition; the method comprises the following steps: S1, data acquisition and preprocessing; step S2, data cleaning and integration; s3, data analysis and mining; 4, constructing a digital twinborn model; and 5, managing system-level data. The method has the following advantages: data islands are cracked, data quality and processing efficiency are improved, internal business logic of data is mined, high-precision feature extraction and pattern recognition are realized, and the method is suitable for popularization and application by means of unifying data standards and cleaning rules, optimizing a merge sorting strategy, building a double-layer association analysis model, adopting a CNN-LSTM hybrid model, perfecting data security and authority management and the like. The method supports the cross-line collaborative decision and precise production optimization, finally solves the problem that the prior art cannot support the system-level digital twinning application, and improves the application efficiency and effect of the digital twinning system.

The invention discloses a Text-to-SQL (Structured Query Language) agent collaboration system and method, and relates to the field of database technology, natural language processing and large language models.The system comprises a generator used for generating a plurality of candidate SQL based on a parallel strategy by adopting a predefined mixed different thinking chain prompt template, scoring each candidate SQL and screening out an optimal SQL; the refiner is used for correcting the optimal SQL when the optimal SQL is abnormal in the database execution stage; the reflex device is used for generating a reflex text when a user is not satisfied with a result or execution of the modified SQL database is abnormal, splicing the reflex text and the mixed thinking chain prompt template to obtain a new mixed thinking chain prompt template, sending the new mixed thinking chain prompt template to the generator and carrying out next SQL optimization, and the generated SQL statement has the characteristics of complete structure and high efficiency. And the consistency and robustness are high.

The invention discloses a data label generation and quality control method and system based on a pre-training large model, and relates to the technical field of data management. Aiming at the problems of low efficiency, poor label consistency and difficulty in continuously guaranteeing quality of an existing label generation mode, the adopted scheme comprises the following steps: collecting multi-source heterogeneous data based on a data governance demand; performing preprocessing and vectorization operation on the collected data, then outputting a pre-training large model, and realizing automatic generation of data labels by the pre-training large model in combination with rule constraints; performing quality detection of accuracy, consistency and coverage on the generated labels; performing optimization adjustment on the pre-trained large model or rule according to a quality detection result to realize feedback optimization of a label generation strategy; and outputting a label set passing the quality detection, and binding the label set with the original data to form a traceable label management record. The method is used for subsequent data management and large model training. The method is used for data governance and large model training.

The invention relates to the field of data storage, in particular to a storage and calculation separation multi-dimensional data block indexing method based on adaptive learning. Comprising the steps of receiving original multi-dimensional data and a query load, and outputting a low-dimensional key set through a first model; the low-dimensional key set and the historical query load generate a partitioning scheme through a second model, and the partitioning scheme performs physical recombination on the original multi-dimensional data to output a plurality of physical data blocks; constructing a learning type index through the physical data blocks and the corresponding partitioning schemes; identifying hot spot and cold spot areas through the query frequency of each node of the learning type index and the real-time query load continuous monitoring index; when the access mode changes, adjusting the index structure, and outputting a self-adaptive index; and quickly positioning and reading the target data block through the self-adaptive index to complete access. According to the method, the index of adaptive learning is provided, and the index structure and the physical data layout are adjusted, so that the query accuracy and efficiency in a complex scene are improved, and the data storage overhead is reduced.

The invention discloses an intelligent evaluation method and device for an RAG system based on a large model, and computer equipment. The method comprises the following steps: acquiring a knowledge base source file required by a to-be-evaluated RAG system; analyzing and converting the knowledge base source file, creating a structured document block set, and executing preprocessing operation to obtain an intermediate knowledge base; automatically generating an evaluation data set including the question, the answer and the document block of the corresponding evidence according to the intermediate knowledge base based on LLM; performing performance evaluation on the to-be-evaluated RAG system by using an NLP evaluation index and an LLM-based multi-dimensional scoring mechanism based on the evaluation data set to obtain an evaluation result; and generating a report according to the evaluation result, and outputting the report. By implementing the method provided by the invention, the performance evaluation and iterative optimization of the RAG system can be comprehensively and accurately improved at low cost.

The invention provides an intelligent recommendation method, system and device for MySQL dynamic parameters and a medium, and belongs to the technical field of databases. The method comprises the following steps: collecting structured performance indexes in real time, extracting unstructured text knowledge, and storing the unstructured text knowledge into a data lake; performing cleaning, fusion and feature extraction processing on the acquired data, constructing a time sequence data set, and generating a multi-modal feature vector; constructing a parameter optimization model fusing the text encoder, the parameter dependency graph encoder and the reinforcement learning decision maker, and training the parameter optimization model; according to the real-time system state vector and the user optimization target, using the parameter optimization model to output parameter adjustment suggestions, generating a parameter adjustment suggestion list, and predicting potential risks; according to the parameter adjustment suggestion, a progressive adjustment strategy is adopted to execute parameter adjustment operation, and the adjusted performance index is monitored in real time; and feeding back a parameter adjustment implementation result to a parameter optimization model training process, updating a model weight, generating a parameter adjustment case and storing the parameter adjustment case in a knowledge base.

The embodiment of the invention discloses a power transaction collaborative optimization method and device based on concentrated power prediction, and a medium. The method comprises the following steps: constructing a multi-dimensional correlation structure, and correcting a power evolution sequence to obtain a centralized prediction sequence of a whole province; inputting the centralized prediction sequence into a preset network power transmission mapping model to calculate a line load rate, and generating a congestion index of each power transmission channel; executing clustering merging processing to obtain a risk partitioning result; establishing a hierarchical-driven multi-objective optimization model, generating a scheduling instruction of each buffer device, and calculating marginal contribution degrees of the buffer devices; and calculating a comprehensive contribution degree of each variable power resource unit based on the marginal contribution degree, inputting the comprehensive contribution degree into a pre-configured calculation function, generating a distribution coefficient of each variable power resource unit in the cross-regional income, and constructing a benefit distribution scheme according to the distribution coefficient. According to the scheme, the reasonability of benefit distribution can be improved while the safety is ensured.

The invention discloses a system and method for ensuring data consistency of a low-voltage power distribution network, and the method comprises the following steps: obtaining multi-source operation data uploaded by all collection devices in the low-voltage power distribution network, and converging the multi-source operation data to a unified processing channel; a timestamp dynamic alignment and mapping mechanism is adopted, sampling frequency and time offset are detected and corrected, and a unified time axis is generated; executing unit conversion and field unification operation according to the data type label, and constructing a standard field structure; carrying out prediction compensation on missing data by adopting a time sequence sliding window interpolation model; a sliding window anomaly rate detection strategy is applied to identify numerical value abrupt change and execute anomaly replacement; and performing structured output on the data set subjected to the consistency processing. According to the method, fine alignment, standardized processing and high-reliability correction of multi-source heterogeneous data can be realized, and the data consistency level and decision support capability of the low-voltage power distribution network in operation monitoring, analysis and control scenes are improved.

The invention discloses a method for generating statements from natural languages to SQL (Structured Query Language) based on a large language model agent. The method comprises the following steps of: constructing an LLM (Logical Language Model) guide table mode index for a target database; obtaining a user natural language question and extracting a keyword; searching in an LLM guidance table mode index according to the keyword, and screening related data tables; splicing the table mode information and the keywords of the related data table into a user natural language problem, and constructing an enhanced cue word; based on the large language model intelligent agent, generating SQL statements according to the enhanced cue words and verifying the SQL statements, if the SQL statements succeed in verification, obtaining the required SQL statements, and if the SQL statements fail in verification, performing iterative closed-loop correction on the generated SQL statements through an SQL statement closed-loop correction workflow; if the corrected SQL statement is successfully verified, the required SQL statement is obtained, and if the corrected SQL statement fails and the number of times of correction reaches a threshold value, the suggestions that the natural language problem of the user has defects and is modified are output. According to the method, the accuracy and success rate of SQL statement generation can be improved.

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.