1746 results about "Result set" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention discloses a freight logistics information intelligent tracking management method and system, and belongs to the technical field of freight management, and the method comprises the steps: obtaining unique identification information of a freight unit, building the association with order content, carrier information and a preset transportation path, constructing a freight entity relation graph, and carrying out the management of the freight logistics information; performing preprocessing and modal fusion on multi-source real-time data in a transportation process, and extracting a structured cargo state feature set; based on a dynamic space-time modeling mechanism, the current freight state is intelligently judged, and a risk prediction result set is generated; predictive reconstruction is carried out on the transportation path in combination with a risk result; automatically executing path reconstruction, vehicle scheduling or user notification after the abnormal event is identified; synchronizing the evolution state and the risk data to a cloud platform for subsequent task optimization; according to the invention, full-flow intelligent sensing, real-time scheduling and abnormal closed-loop response in the logistics process are realized, and the transparency, robustness and aging control capability of a transportation system are remarkably improved.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention provides a road disaster detection method and system based on air-ground cooperative unmanned inspection, and the method comprises the steps: obtaining a multi-source road monitoring data set, carrying out the time-space alignment processing of the multi-source road monitoring data set, generating a road monitoring data set integrated with a time-space label, and carrying out the recognition of a road disaster based on a preset multi-mode disaster recognition model. Disaster feature extraction processing is carried out on the road monitoring data set fused with the space-time labels to obtain a road disaster feature set, a disaster detection result set is generated according to the road disaster feature set, the disaster detection result set comprises a plurality of disaster detection units, and an inspection optimization strategy set is generated based on the disaster detection result set. According to the method, the automation degree and the response efficiency of road disaster detection are comprehensively improved under the condition of not depending on artificial experience intervention, and the problems of high misjudgment rate, large monitoring blind area and rigid resource allocation caused by data splitting and time-space asynchronization in a traditional method are solved.

The invention provides a network security protection method and system based on information fusion, and the method comprises the steps: firstly obtaining a multi-source heterogeneous data set, which comprises a traffic interaction data unit, an equipment log data unit and a protocol analysis data unit, of a target network, then carrying out the time sequence correlation analysis of the traffic interaction data unit, and generating a traffic behavior feature set; and executing state mode analysis on the equipment log data unit to generate an equipment operation feature set, and executing semantic recognition on the protocol analysis data unit to generate a protocol analysis feature set. Then, on the basis of a multi-dimensional feature fusion rule, cross-dimensional feature fusion processing is carried out on the feature set, and a network situation feature set is generated; and calling a threat identification model to carry out threat identification on the set, generating a threat identification result set containing threat type identifiers and influence range parameters, and finally generating a security response strategy set according to the threat identification result and issuing the security response strategy set to a security control node to execute protection operation, thereby effectively improving the network security protection capability.

The invention provides a task scheduling method and system based on multi-agent collaboration, and the method comprises the steps: firstly receiving an externally input target task demand text, carrying out the structural analysis processing, obtaining a task element set, calling a task planning module of a main agent to carry out the planning disassembly of the task element set, generating a sub-task sequence set, and carrying out the calculation of the sub-task sequence set; the sub-task units are in one-to-one correspondence with preset professional agent types, the sub-task units are allocated to matched professional agents for execution according to professional attribute identifiers of the sub-task units, a sub-task execution instruction is generated, and each professional agent executes sub-task operation based on the instruction, generates a sub-task execution result set and feeds back the sub-task execution result set to the main agent; and finally, the main agent performs multi-source data integration processing on the sub-task execution result set to generate a final task result file meeting task output requirements, so that task scheduling efficiency, quality and flexibility can be improved, and complex task requirements are met.

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

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.

Techniques are provided for personal data and AI system integration. It is determined to respond to a user prompt. An AI system is provided a user prompt description, a database description describing a user database comprising user data associated with the user, and instructions to provide a query for relevant data in the user database that the AI system can use to answer to the user prompt. A query is received from the AI system and executed against the user database to generate a result set. The result set is submitted to the AI system. The answer is received from the AI system based on the user prompt description and the result set. A response is generated for the user based on the answer from the AI system.

Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.

The invention provides an intelligent supervision management method and platform based on multi-terminal interaction and data fusion, and the method comprises the following steps: obtaining a supervision standardized file and a to-be-used supervision resource of an electric power engineering project, extracting a key entity through a natural language processing technology, and recognizing an entity relationship; based on the entities and the incidence relation thereof, a multi-level project knowledge graph is constructed, and a heterogeneous graph neural network inference engine is formed in combination with a graph neural network model. Multi-dimensional supervision data are collected through an intelligent supervision terminal, the data are processed by using a self-adaptive cross-modal attention mechanism, and a project event cause representation is generated. Based on the representation, multi-level reasoning is carried out in a reasoning engine through an improved graph traversal algorithm, and an abnormal event result set caused by project event causes in a future time period is predicted. And in combination with the to-be-used supervision resources and the abnormal event result set, adopting a reinforcement learning optimization algorithm to generate an optimal supervision dispatching strategy. According to the invention, efficient supervision and resource optimization configuration can be realized.

The invention discloses a bird flight path intelligent tracking and monitoring method and system based on big data analysis. The method comprises the following steps: S1, generating an original bird observation data set; s2, outputting a standardized bird observation data set; s3, calling a multi-target tracking algorithm based on the standardized bird observation data set to obtain a bird trajectory fragment data set; s4, constructing a lion group optimization search model, and inputting the bird trajectory fragment data set into the lion group optimization search model to form a candidate trajectory data set; s5, inputting the candidate trajectory data set into a space-time Transform model configured with rotation position embedding, and generating a continuous flight path data set; and S6, outputting a cross-region bird migration monitoring result set. According to the method, bird migration continuous tracking requirements under different time periods, multiple geographical zones and heterogeneous equipment acquisition conditions can be covered, application scenes of ecological protection, migration research and airspace management and control are effectively supported, and the method has remarkable engineering popularization and ecological protection values.

The invention relates to the technical field of video detection, in particular to a monitoring video detection and identification method and system based on deep learning, and the method comprises the steps: obtaining a target monitoring video of a monitoring region based on monitoring camera equipment, obtaining a video frame sequence of the target monitoring video, and carrying out the target screening of the video frame sequence, so as to obtain a video frame set; and performing target detection on each video frame in the video frame set based on a trained target detection model to obtain a target detection result set of each video frame in the video frame set. According to the method, through combination of keyword extraction and a large language model, complex monitoring data can be converted into structured event description, the textualized information not only facilitates management personnel to rapidly know the field condition, but also can provide reference for subsequent event processing, and the event description generated in real time is combined, so that the management personnel can conveniently and rapidly know the field condition. And a manager can make a coping strategy more efficiently.

The invention relates to the technical field of artificial intelligence, and discloses an education data report content interaction method and system generated based on retrieval enhancement, and the method comprises the steps: judging whether a natural language problem is an education field problem or not through a large language model, and if yes, carrying out semantic analysis to generate a structured query instruction; when the problem relates to cross-document association analysis, retrieving the structured semantic index database to generate a retrieval result set; if policy association analysis is involved, matching a policy knowledge graph by combining semantic similarity calculation and an entity linking technology, and then performing cross-modal fusion processing to obtain a retrieval result set; and inputting the retrieval result set into the retrieval enhancement generation model, and calling an education field language model to generate an analysis report. According to the method, the industrial pain points of inaccurate intention recognition, low cross-document analysis efficiency, incapability of dynamically combining with latest policies and the like in a traditional interaction mode can be solved, the efficiency and quality of data report interaction in the education field are remarkably improved, and the user interaction experience is optimized.

Implementations of the present disclosure provide receiving, by a conversational commerce optimizer system, a user input from a user, the user input being provided to a chatbot of an e-commerce platform, providing a refined query based on the user input, selecting, by the conversational commerce optimizer system, a foundation model that is to be queried using the refined query, determining a set of search results from a database that stores data representative of products available through the e-commerce platform, prompting the foundation model using a prompt that is generated based on the refined query and the set of search results, the foundation model providing an output responsive to the prompt, and providing a response to the user through the user interface of the chatbot, the response being generated based on the output.

The invention relates to the technical field of parameter optimization, in particular to a plastic mold forming parameter optimization method and system based on big data, and the method comprises the following steps: collecting pressure data, calculating an acceleration sequence, recognizing a peak window, matching a pressure-temperature ratio, and building a combined cluster to obtain an optimization result set. According to the method, a speed increasing sequence is constructed based on the die cavity pressure change rate, a high-dynamic section is screened through a peak value ratio, automatic identification of an effective forming section is achieved in combination with continuous ratio stability judgment, forming pressure, die temperature and a cooling time sequence are collected to construct a parameter combination sequence, trend consistency is compared, and available combinations are screened; the parameter acquisition granularity, the process identification precision and the parameter group suitability are obviously improved, the problems of subjective interference and error accumulation in the parameter configuration process are effectively avoided, the objective quantitative analysis capability of the forming process state is enhanced, and the accuracy of the forming process state is improved. And the plastic mold forming process stability and the parameter configuration efficiency are improved.

Method, data processing system, and computer-readable storage media for responding to a user query. Receiving query from user, query pertaining to request for information. Based on query, generate prompts by masking sensitive information in query. Receive responses from foundation models in response to inputting prompts. Based on responses, generate common result set. By validating common result set with sensitive information, generate response. By supplementing response with sensitive information, generate user response. Providing user response in response to query to the user.

The embodiment of the invention provides a hydropower station knowledge graph construction method and system, and belongs to the technical field of knowledge graphs. The method comprises the following steps: collecting multi-source heterogeneous data related to a hydropower station, and executing format standardization processing and semantic normalization processing to obtain basic data; performing data classification on the basic data to obtain a plurality of data sets; matching a corresponding extraction model based on each data set to respectively execute entity extraction, relation extraction and attribute extraction to obtain an extraction result set; screening out repeated extraction results to obtain an atlas construction data set; and executing knowledge graph construction of the ternary array based on the graph construction data set, and obtaining and outputting a hydropower station knowledge graph. According to the scheme, the problem of heterogeneous data integration is solved, the practicability and expandability of the atlas in application scenes such as intelligent question answering and operation monitoring are remarkably improved, and reliable data support and technical guarantee are provided for intelligent application in the field of hydropower stations.

A recommendation system identifies resale goods corresponding to retail goods being viewed by a user on a retail goods website. Retail goods metadata and images are extracted from the retail goods website using heuristics and LLM-based methods. The extracted retail goods data undergoes ML model-based product category classification, intelligent image cropping, and color detection. Vector embeddings are generated for images and text using ML models and compared to resale goods vector embeddings stored in a vector database. Multiple result sets are retrieved, fused, and re-ranked using LLM-based and preference-aware re-ranking. A data pipeline continuously loads, cleans, and processes resale goods inventory from resale goods websites.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention relates to the technical field of data, and discloses a logging data intelligent processing method. The method comprises the following steps: acquiring data through a multi-source logging device and preprocessing the data to form a result set; identifying and classifying stratum features according to the result set; analyzing oil and gas parameters based on the stratigraphic map and the result set; identifying risks by using the oil-gas evaluation table and the result set and establishing an early warning mechanism; the reservoir physical property is evaluated according to the oil and gas evaluation table and the early warning mechanism; and integrating the stratigraphic map, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure prediction enrichment area. According to the method, full-process intelligent processing from acquisition and preprocessing of multi-source heterogeneous logging data to stratum identification, oil and gas evaluation, drilling risk early warning, reservoir assessment and oil and gas reservoir description is realized, and the problems of data space-time mismatching, parameter isolated analysis, high evaluation subjectivity, early warning lag and the like in a traditional method are solved; and the efficiency, accuracy and systematicness of logging data processing are improved.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

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 a large-model knowledge retrieval method based on knowledge graph enhancement, which comprises the following steps: S1, collecting multi-source data, constructing an original knowledge triple set, and generating a knowledge graph; s2, inputting natural language query data, executing semantic analysis and intention recognition, and forming a query semantic vector and a structured query template; s3, performing combined convolution operation on the target entity node and the relation vector through an improved combined graph convolution network CompGCN to generate a candidate knowledge list and distribute confidence; s4, calculating the similarity between the query vector and the knowledge vector by utilizing a semantic embedding mechanism and DistilBERT acceleration to obtain a first retrieval result set and a credibility score; s5, fusing the two types of retrieval results to obtain a knowledge result set; and S6, generating an answer text according to the fused knowledge result set, and outputting a reasoning path, entity reference and a relation link. According to the method, the accuracy, the response speed and the knowledge reasoning transparency of complex query processing are improved.

Techniques are described herein for generating a semantically related search query that differs by at least one lexical token from document data provided as input. A machine-learning model may be trained using supervised and / or deep learning algorithms and a training data set including historical queries and the document data identified as being semantically related to those queries. The semantically-related search query may be associated with the document and utilized in subsequent searches to match the document to a subsequent query based on identifying a lexical match between the subsequent search query and the semantically-related search query associated with the document. These techniques enable semantically related search queries to be assigned offline but utilized to identify semantically related documents using lexical matching techniques. Identifying these types of matches create more diverse search results while maintaining a high degree of relevance between the query and the documents of the result set.

The invention discloses a large model retrieval enhancement generation method and device, equipment, a storage medium and a product, and relates to the technical field of question and answer large models.The method comprises the steps that questions input by a user are vectorized, retrieval is conducted in a preset vector database according to the vectorized questions, and a result set meeting preset conditions is obtained; and combining the vectorized question and result set into context information according to the prompt project template, and inputting the context information into a large language model for processing to generate a question answer. According to the method, retrieval is carried out according to the index tree of the vectorized question in the vector database, then the large model generates the answer according to the result set and the question, information in the long document is effectively integrated on different levels, different abstract levels are covered, the question can be understood and answered on different levels, and the user experience is improved. And the capability of processing complex queries is effectively improved.

Techniques are disclosed herein for improving model robustness on operators and triggering keywords in natural language to a meaning representation language system. The techniques include augmenting an original set of training data for a target robustness bucket by leveraging a combination of two training data generation techniques: (1) modification of existing training examples and (2) synthetic template-based example generation. The resulting set of augmented data examples from the two training data generation techniques are appended to the original set of training data to generate an augmented training data set and the augmented training data set is used to train a machine learning model to generate logical forms for utterances.