231results about How to "Easy retrieval" patented technology

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

The invention discloses a method for constructing a knowledge graph based on a large language model and a vector library, and relates to the technical field of knowledge graphs, and the method comprises the following steps: carrying out word segmentation, part-of-speech tagging and semantic analysis processing on a collected natural language text by using a pre-trained RoBERTa model to obtain a semantic representation vector of the natural language text; through a Seq2Seq framework and an attention mechanism, obtaining association between the semantic representation vector and the corresponding named entity, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset field to generate a fused knowledge graph; named entities and corresponding attributes are obtained through dependency syntax analysis, the entity relation of the named entities is obtained, and a structured triple fusing the named entities, the attributes and the entity relation of the knowledge graph is constructed. In order to solve the problem of high entity linking difficulty in the prior art, the knowledge graph is converted into low-dimensional dense vector representation, and an inverted index database and the like are constructed, so that the difficulty is reduced.

The invention relates to the field of natural language processing, and discloses an SQL statement generation method, system and device based on a large model. Respectively carrying out vectorization processing and document processing on the metadata and the metadata description text information from the database, and storing the metadata and the metadata description text information into a vector database and a full-text retrieval library; in response to the query instruction, respectively performing query matching on the vector database and the full-text retrieval library, screening matching results which are ranked in the front and are preset ranks according to the similarity from high to low, and determining an optimal SQL statement list and optimal metadata; on the basis of the current language of the query instruction, generating a cue word of the current language in combination with the optimal SQL statement list and the optimal metadata; the large model of the preset type is determined according to the language type of the cue word, the preset large model is called to conduct reasoning on the cue word, and the SQL statement is generated. Based on the large model and the generative AI technology, the accuracy of the generated SQL statement is improved, the use threshold of non-technical personnel is lowered, and the query efficiency of the SQL statement is also improved.

A text-to-structured query language conversion method based on a large language model comprises the steps that database outline information is cut, and tables and columns needed by query of a non-target structured query language SQL are eliminated through correlation scoring and Steiner tree problem solving; screening context samples, predicting a similarity score from a text to a structured query language Text-to-SQL by using a cross encoder language model, and selecting a sample most related to a query problem; and injecting the cut database table and column information and the screened context sample into a cue word template, and generating an SQL query statement by using a large language model. The method can be used for optimizing the automatic conversion process from the natural language to the SQL and improving the conversion accuracy, and is used for providing a quick and convenient database retrieval mechanism for a non-SQL user and providing applications such as a data access mode in a wider form for mechanisms such as big language model retrieval enhancement generation RAG and the like.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention discloses a multi-source information fusion query method based on RAG Fusion, which comprises the following steps: receiving original query data input by a user, and carrying out preprocessing and expansion processing on the original query data to obtain an expanded query set; retrieving each extended query in the extended query set in a preset vector library by using a plurality of retrieval strategies to obtain a document set corresponding to each retrieval strategy, and reordering all documents in the document set to obtain a document list; dynamic context construction is carried out on documents in the document list, and a context document set with weights is generated; performing sentence-level segmentation on each document in the context document set, extracting fragments related to the original query data from segmented sentences by utilizing a pre-trained question and answer model, and performing duplicate removal and combination on the extracted fragments to generate an information unit set; and inputting the original query data, the context document set and the information unit set into a pre-trained large-scale language model to generate a target answer.

The invention discloses a knowledge archiving method, device and equipment, a storage medium and a computer program product, and relates to the technical field of data processing, the disclosed knowledge archiving method comprises the following steps: performing service type classification on an unstructured target demand document by adopting a vector machine algorithm to obtain a first classification result; a basic large model is adopted to generate a document abstract of the target demand document, service type classification is conducted on the target demand document according to the document abstract, a second classification result is obtained, and the basic large model is a large language model obtained through training of historical demand documents and historical document abstracts; determining a target classification result of the target demand document based on the first classification result and the second classification result; and filing the target demand document, the document abstract and the target classification result into a preset knowledge base in a structured form. According to the method and the device, the unstructured business requirement document can be efficiently archived into the structured document knowledge.

The invention provides an agricultural park energy supply and consumption system regulation and control strategy generation method driven by a knowledge graph, and the method comprises the steps: extracting time series data feature indexes of structured data, completing the interval tagging processing, and forming a structured data feature tag library according to the complex and diversified scheduling operation demands of an agricultural park energy supply and consumption system; and combining knowledge extraction, fusion and storage of other semi-structured data and non-structured data to construct an agricultural park energy supply and consumption system knowledge graph. According to the method, the input feature information matrix is formed on the basis of the characterization processing of the to-be-optimized operation scene of the agricultural park energy supply and consumption system and in combination with the basic information, so that the corresponding historical strategy stored in the knowledge graph is retrieved layer by layer from the to-be-optimized operation scene, the system information visualization and the interactive query are realized, and meanwhile, the user experience is improved. Intelligent generation of the regulation and control strategy of the energy supply and consumption system in the agricultural park is completed, and support is provided for intelligent decision making of the energy supply and consumption system in the agricultural park.

The invention discloses a large-scale embedded index data desensitization method suitable for a forestry database, and belongs to the technical field of data desensitization, and the method comprises the steps: obtaining a forestry data description document D1, splitting the document D1 into sentences, and processing the sentences into embedded vectors; a user customizes a query sentence containing the desensitization word, and a query vector is generated; performing hash coding on the embedded vector and the query vector to obtain a corresponding hash function cluster; searching data to be desensitized through cosine similarity calculation; and inputting to-be-desensitized data and the selected desensitization method into the large language model to generate desensitized data, and replacing the corresponding sentences in the D1 to obtain a desensitized forestry data description document D2. According to the method, the input data is converted into the high-dimensional vector through the embedding layer, so that the semantic features of the data can be effectively expressed in the vector space, the retrieval speed and accuracy of large-scale data are improved by means of the locality sensitive hashing technology, the calculation overhead is reduced, and a technical guarantee is provided for real-time desensitization processing of a large-scale data set.

The invention discloses an intelligent question answering method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining an original input question of a user, and performing matching retrieval on the original input question in a pre-constructed professional word explanation library to obtain a target professional word matched with the original input question and an explanation of the target professional word; according to the target professional word and the explanation of the target professional word, rewriting the original input question to obtain a target input question; and performing knowledge retrieval in a pre-constructed knowledge base according to the target input question, and generating reply content for the user according to a retrieval result. According to the method, the original input question of the user is rewritten based on the professional words and explanations thereof, so that the rewritten input question and the text in the knowledge base have co-occurrence words more easily, the knowledge retrieval effect can be improved, and the accuracy of the reply content generated by the intelligent question and answer is ensured.

The invention discloses a highway maintenance knowledge question and answer method and system based on multi-modal information, and belongs to the technical field of knowledge question and answer. Comprising the steps of obtaining user question data and converting the user question data into a plurality of modal feature vectors; processing the plurality of modal feature vectors through a preset multi-modal decoupling network to obtain complementarity features and consistency semantic features; on the basis of the complementarity features and the consistency semantic features, independent retrieval of all modes is carried out in a preset multi-mode knowledge base with the consistency semantic features as guidance, cross-mode association retrieval is carried out through the association degree between different modes of knowledge, and candidate knowledge of all modes is obtained; and according to the consistent semantic features, processing each modal candidate knowledge through an answer generation network to obtain a multi-modal answer. The accuracy and flexibility of answering the expressway maintenance knowledge can be improved, and the problem that the processing accuracy of the existing question and answer task containing the multi-modal expressway maintenance knowledge is not high is solved.

The invention belongs to the technical field of natural language processing, and discloses a natural language processing platform and method based on deep learning, and the platform comprises a data collection module which is used for collecting font data, text data, image data and environment data; the data processing module is used for preprocessing the collected font data, text data, image data and environment data and obtaining a comprehensive feature data set; the character recognition module is used for predicting character recognition results through a character recognition model and judging whether the character recognition results are consistent or not; the semantic recognition module is used for generating corresponding character paraphrases through a semantic recognition model; collecting character recognition results and corresponding character paraphrases through the software terminal; the voice output module is used for converting the character recognition result and the paraphrases of the corresponding characters into natural voices and outputting the natural voices by adopting a TTS technology; through the implementation of the technology, students can be helped to better learn and understand.

The invention relates to an insurance industry-oriented customer full-life-cycle event data modeling method and system. The method comprises the steps of collecting customer full-life-cycle event data through a customer contact channel and a business processing link of an insurance industry; preprocessing the collected data, and converting the data into structured data; key elements are extracted from the preprocessed structured detail data; performing hierarchical and classified storage on the structured data based on a hierarchical and classified event data model, and respectively establishing a trunk index model and an expansion detail model in combination with the key elements; generating a unique event ID (Identity) for each event from the key elements; and performing data query optimization and providing data service based on the trunk index model, the expansion detail model and the event ID. Compared with the prior art, the method has the advantages that massive customer event data can be efficiently integrated and analyzed, the data processing and query capability is improved, more efficient and flexible data services are provided, and decision support is provided for the insurance industry.

The invention discloses a wood structure building information classification coding and data automatic identification mapping system and method, and the method comprises the steps: 1, creating a traditional wood structure building component-level model, and building a traditional wood structure building database; 2, performing overall view projection on the traditional wood structure building component-level model, matching the model with a traditional wood structure building database, and outputting a regulation code so as to screen corresponding data; 3, picking up the split components, performing isolated view projection, performing feature fuzzy matching with the simplified data pool, screening and defining the components, and outputting identification codes; 4, the traditional wood structure building is subjected to space division and tagging, and positioning codes of the components are generated; 5, the code segments are called according to purposes to be combined, and component codes are formed; and 6, integrating component parameters to carry out attribute addition and mapping, and endowing construction codes. The invention relates to the technical field of historic building information management, and can solve the technical problems in traditional wooden structure building information management in the prior art.

The invention discloses a virtual machine access control data acquisition method and a terminal, and the method comprises the steps: building a virtual machine cluster, and enabling virtual machines in the virtual machine cluster to store corresponding configuration information through employing a binary tree in advance; acquiring bridging end information by using a tuner, and transmitting the bridging end information to an external network controller, so that the external network controller transmits the bridging end information to a local area controller; the local area controller is used for matching the received bridging end information with the configuration information in the corresponding virtual machine cluster, if matching succeeds, the configuration information is returned to the external network controller, and the external network controller opens and uses the corresponding virtual machine according to the received configuration information and snapshots the use operation. According to the method, the virtual machine with the configuration information consistent with the bridging end information is provided, the virtual attack environment is provided, the requirements of the attacker are met, the operation of the attacker is snapshotted, the data of the attacker is captured, and the system security is protected.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention provides a multi-source software-defined science and education resource recommendation method and system based on a knowledge graph, and the method comprises the steps: constructing an artificial intelligence knowledge graph based on the relation between artificial intelligence knowledge entities; obtaining science and education resource data of a science and education institution, extracting knowledge entities in the science and education resource data, and connecting the knowledge entities to the artificial intelligence knowledge graph to obtain a science and education resource knowledge graph; extracting local resource features / user features based on a graph neural network to obtain local resource features and user features of each client; constructing a local knowledge graph based on the science and education resource knowledge graph, and performing model training by taking the local resource features of each client as initial features until the model converges; and inputting the users and the resources into the graph neural network for prediction, and recommending the resources with high scores to each user. According to the scheme, the relationship between knowledge entities in the field of artificial intelligence is effectively extracted, a structured knowledge graph is formed, and the recommendation precision is improved.

The invention discloses a uniform resource identifier-based stepless labeling method, which comprises the following steps of: obtaining a to-be-labeled data resource, and determining a resource identifier of the data resource; according to a preset labeling mode, labeling target labeling objects corresponding to the labeling mode in the data resource, and according to the labeling mode, generating a labeling identifier of each target labeling object in the data resource; and generating index data of the data resource according to the resource identifier of the data resource and the labeling identifiers of all the target labeling objects in the data resource. According to the method, the index between the resource identifier and the labeling identifier is established in the labeling process, so that the defects that in the prior art, a labeling tool is single in function and poor in flexibility, needs to be manually adjusted and configured and the like are overcome, the efficiency and accuracy of data labeling are improved, and the manpower and time cost is remarkably reduced.

The invention belongs to the technical field of Lustre file management, and particularly relates to a method and system for improving Lustre small file management and performance, and the system comprises a nested file system, a three-level index module, a cache index module and a self-adaptive cache module; the nested file system realizes management and storage of small files; the three-level index module comprises a small file global metadata index, a container file internal index and a cache index and is used for optimizing retrieval and storage management of small files; the cache index module is used for managing small files accessed at high frequency and reducing repeated I / O requests; and according to the self-adaptive caching module, the client dynamically adjusts a caching strategy of a small file according to the file access frequency and mode, so that the reading speed of a high-frequency access file is improved, and network I / O (Input / Output) operation is reduced. The performance bottleneck of a Lustre file system during small file processing is solved through a nested file system, and the access burden of a metadata server and an object storage server is reduced through a three-level index architecture.

The present application discloses a method for constructing a knowledge graph based on a large language model and a vector library, which relates to the field of knowledge graph technology, including: using a pre-trained RoBERTa model to perform word segmentation, part-of-speech tagging and semantic analysis on the collected natural language text to obtain a semantic representation vector of the natural language text; obtaining the association between the semantic representation vector and the corresponding named entity through the Seq2Seq framework and the attention mechanism, and obtaining the named entity through the obtained association; constructing a knowledge graph; fusing the constructed knowledge graph with a relational database of a preset domain to generate a fused knowledge graph; using dependency syntactic analysis to obtain the named entity and the corresponding attribute, obtaining the entity relationship of the named entity, and constructing a structured triple of the named entity, attribute and entity relationship of the fused knowledge graph. In view of the difficulty of entity linking in the prior art, the present application converts the knowledge graph into a low-dimensional dense vector representation, and constructs an inverted index library, etc., to reduce the difficulty.

The invention discloses a data storage method and system for a storage chip, and the method comprises the steps: selecting reference data and sample data from target storage data, and carrying out the analysis, and obtaining a data similarity value; the data similarity value is compared with a data similarity threshold value, and if the data similarity value is larger than or equal to the data similarity threshold value, the reference data and the sample data are classified into the same classification data set; obtaining a residual storage space, judging whether the residual storage space is smaller than a residual storage space threshold value or not, and if yes, analyzing data in the classified data set to obtain a data set key value; identifying the non-key data group to generate a compression optimization signal; based on the compression optimization signal, judging whether the residual storage space of the system is smaller than a residual storage space threshold after compression processing, and if yes, analyzing the non-high access frequency data to obtain a compression priority value; and performing optimization compression on the non-high access frequency data according to the compression priority value.

The invention provides an intelligent security archive management method and system based on big data analysis in the technical field of security archive management, and the method comprises the steps: S1, creating a plurality of data templates, and setting collection parameters; s2, acquiring security data based on the acquisition parameters and preprocessing the security data; s3, analyzing each piece of security data to obtain a classification label, and classifying each piece of security data based on the classification label; s4, matching corresponding data templates for the security data of each category based on the classification labels, and mapping and filling the corresponding data templates with the security data to obtain corresponding security archives; s5, encrypting each security file into a corresponding encrypted file, and storing each encrypted file in a cloud server; and S6, the cloud server decrypts the encrypted archive based on the archive management instruction to obtain the security archive, and adds, deletes, repairs or checks the security archive. The method has the advantages that the reliability, the efficiency and the quality of security file management are greatly improved.

The invention relates to the technical field of information and data processing, and discloses an intelligent recommendation method and system for a bone fracture rehabilitation training plan based on traditional Chinese medicine, and the method comprises the steps: dividing image information according to labels, and obtaining skeleton data sets with different functions; taking the first position information and the second position information as a special information set; deleting the special data set from the skeleton data set to obtain a target data set; obtaining position information of the bones to be subjected to bone fracture rehabilitation training from the target data set, and selecting matched contents such as shadowboxing, eight-section brocade or five-poultry play from a pre-stored traditional Chinese medicine rehabilitation training database; deleting related actions of the second position information from an action data set corresponding to the content; guiding the trainee to perform bone fracture rehabilitation training through the visual terminal; the system comprises a first data set construction module, a second data set construction module and a target action output module. According to the bone fracture rehabilitation training system and method, biological feature recognition and personalized, precise and systematic bone fracture rehabilitation training are achieved.

The invention relates to the technical field of digital archive resources, and discloses a knowledge graph construction method and system based on digital archive resources, and the method comprises the following steps: 1, converting a paper archive into an electronic document, obtaining the digital archive resources, and extracting the content of the digital archive resources; 2, based on the extracted content, performing multiple times of identification on entities in the same electronic archive text to obtain identification result data, processing the identification result data, and calculating an identification result evaluation value; 3, based on the obtained identification result evaluation value, the identification result of the same electronic file is evaluated, and the evaluation result comprises generation of an accurate identification signal; and step 4, based on the generated accurate identification signal. According to the method, when the entities are recognized, repeated recognition is carried out for multiple times, and the result is comprehensively analyzed, so that the accuracy and reliability of entity recognition are effectively improved, and powerful support is provided for knowledge graph construction based on digital archive resources.

The invention provides an unstructured data retrieval method, device and equipment and a readable medium, and is applied to an unstructured data retrieval system, the unstructured data retrieval system at least comprises a gateway layer, a business service layer and a data processing layer, and the data processing layer is utilized to obtain unstructured data; converting the unstructured data into vectors by using a data processing layer, and storing the vectors into a vector storage library; acquiring a retrieval request of the unstructured data by utilizing the gateway layer, and sending the retrieval request to the business service layer; sending the retrieval request to a data processing layer by using a business service layer; based on the vector storage library, retrieving the to-be-retrieved data in the retrieval request by using the data processing layer to obtain a retrieval result; the retrieval results are screened through the business service layer by using the similarity script to obtain a target retrieval result, and the target retrieval result is sent to a gateway layer; and the target retrieval result is output by utilizing the gateway layer, so that the problems of relatively high cost and relatively poor effect of manual weight adjustment are avoided.

The invention discloses an exception identification and calibration processing method and system based on a digestive tract endoscope image, and belongs to the technical field of exception identification and calibration processing. Extracting a focus area in the endoscopic image; endoscopic images and gastric acid value data are synchronously acquired in the same data acquisition period and are respectively recorded as periodic focus area and periodic gastric acid value data; calculating the change rate of the periodic focus area; calculating a fluctuation value of the periodic gastric acid value data; calculating comprehensive abnormal values in all data acquisition periods; calculating an abnormal difference value between different patients; presetting a threshold value, analyzing and performing alimentary canal abnormity identification calibration; according to the method, the change rate of the focus area and the fluctuation value of the gastric acid value are calculated, and comprehensive abnormal value analysis is combined, so that the structural and functional abnormalities of the digestive tract can be comprehensively evaluated, and a more accurate diagnosis basis is provided; by calculating the abnormal difference value between the patients, the group associated diseases can be effectively identified, automatic judgment and calibration are performed, and the accuracy and efficiency of diagnosis are improved.

The present invention belongs to the field of video surveillance technology, and discloses a video splicing method and system for multi-camera surveillance, and a processor, comprising: dividing a surveillance area into a plurality of partition units, arranging at least one camera in each partition unit; and creating an interactive interface for a user to input a preset surveillance target; obtaining the partition video data of each partition unit in real time, performing frame number synchronization processing on the plurality of groups of partition video data, so as to align them on a time axis; cutting each group of partition video data respectively according to a plurality of time periods, and capturing and splicing the preset surveillance target, obtaining the marked video units corresponding to the preset surveillance target, and then connecting them in series to form a collection, and creating a corresponding video directory on the interactive interface; through fine time management and efficient video splicing, the continuity and relevance of the surveillance video are improved, so that the user can quickly access the relevant video clips, and the efficiency of the surveillance system and the user experience are greatly improved.

The invention relates to the technical field of semantic analysis, and discloses an intelligent semantic analysis system and method based on a data large model, and the method comprises the steps: converting an audio file into a text, dividing the text into a character group and a word group, numbering the character group and the word group, and extracting repeated content; training a deep learning model to process the character group and the word group to form a standard text; analyzing the audio to determine a language region, obtaining a dialect database and extracting dialect content; combining the dialects, generating a to-be-analyzed text, and performing semantic analysis and classification; and finally, obtaining the expression meaning of the standard text according to the analysis result. The existing marked dialects are combined and substituted into the standard text, so that potential multiple explanations caused by dialect differences in the text can be covered and revealed, all expression meanings existing in the standard text can be obtained, and the process can ensure that any possible expression meaning is not missed, so that the accuracy of the expression meanings is improved. And the final semantic analysis is more comprehensive and accurate.

The embodiment of the invention provides a data processing method and device based on a natural language model and electronic equipment, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring financial data information and to-be-answered question information; performing knowledge representation on the financial data information, and creating a first data structure and a second data structure; creating an insurance data set according to the first data structure and the second data structure; training a preset natural language model through the insurance data set to obtain an automatic question and answer language model; in response to a user retrieval instruction, performing text analysis on the to-be-answered question information to obtain an analysis result; performing answer retrieval on the analysis result based on an automatic question and answer language model and an insurance data set to obtain a retrieval set; and when the retrieval set is empty or the preset answer statement does not exist in the retrieval set, converting the user retrieval instruction into a manual service conversion instruction. According to the embodiment of the invention, the problem processing efficiency and the service quality can be improved.