83 results about "Tag system" patented technology

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention relates to the field of digital emergency management, and provides a plan digitization method and system based on a large model technology, and the method comprises the steps: creating a multi-stage label system of a pre-configuration industry; when the user instruction has the tag features in the multi-level tag system, generating a cue word template guided by the domain knowledge corresponding to the tag features; receiving a cue word template through a preset cue word information large model, executing structured analysis of the unstructured plan, and generating a first digital instruction set; and obtaining error correction backflow data of the first digital instruction set, updating the cue word large model and the corresponding model interface fine tuning through the error correction backflow data, and generating a second digital instruction set after model structure fine tuning.

The invention provides an authentication document form generation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining an authentication document; converting the authentication document into an XML (Extensible Markup Language) file so as to map authentication document data to an XML tree node of the XML file; standardizing the XML file through the global unique identifier and the tag system to obtain a standardized XML file; in combination with the standardized XML file, template candidate items conforming to different manageable data templates in the authentication document are extracted respectively; in combination with the template candidate item, mapping the authentication document to a target form; and establishing a bidirectional mapping relationship between the target form and the authentication document to realize bidirectional conversion between the target form and the authentication document and complete the form processing of the authentication document. Through establishment of the bidirectional mapping relation, synchronous updating of the form and the document content can be achieved, errors caused by manual operation are avoided, and automation, accuracy and practicability of document conversion are remarkably improved.

The invention provides a multi-modal medical image real-time labeling and collaborative browsing method and device based on adaptive DeepZoom and a readable storage medium thereof. A multi-resolution pyramid is constructed, a medical image is downsampled into multiple resolution levels layer by layer, each layer is divided into standard size blocks, dynamic loading is carried out based on a window, and cache management is carried out through an LRU algorithm. The problems of loading lagging and memory overflow of the GB-level image are solved; real-time synchronization of multi-user labeling operation is achieved through a WebSocket protocol, a three-level semantic label system containing lesion areas, cell types and tissue grading is supported, labeling conflicts are solved in combination with a timestamp priority strategy, and version backtracking is supported; a medical compliance log system is designed, information such as cases is recorded, and the whole operation process can be traced through synchronization of local Sqlite cache and cloud HIPAA encryption. According to the method, the medical image browsing efficiency and the labeling collaboration are remarkably improved, and the medical data compliance requirement is met.

Techniques for constraining a language model's generation / decoding process using a finite state machine are described. A finite state machine may represent information corresponding to APIs, arguments and argument values that are available / supported by the system. In some embodiments, a language model (LM) is constrained to generate tokens representing valid API calls based on the finite state machine. The system may enable generation of argument values from a defined set or free-form generation of argument values. The system may also enable unconstrained generation of a response by the LM. In some embodiments, a trie data structure is used to determine the possible next tokens that the LM can generate from.

The invention provides an open domain label system construction method and device based on a large language model, and relates to the technical field of data processing. The method comprises the following steps: constructing a label association pool according to different to-be-labeled contents in a first time period and initial labels output by labeling the to-be-labeled contents by using a first large language model; determining an initial tag system and a tag cluster of any initial tag based on the tag occurrence frequency of each initial tag; based on a preset semantic understanding task description, determining a normalized tag of each initial tag by using the zero sample capability of the second large language model; constructing a label directed graph set; and if it is detected that the first tag in the first tag directed graph is a normalized tag of the second tag in the directed graph and the normalized tag of the first tag in the second tag directed graph is a third tag, constructing a target tag system. According to the method, the time from emerging topic appearance to label system response is shortened, and the semantic accuracy of the label system is improved.

The invention discloses a file disassembling method based on multi-dimensional file association analysis and automatic interpretation, and relates to the crossing field of natural language processing and big data analysis. Comprising the steps of (1) carrying out multi-source heterogeneous data collection, (2) carrying out intelligent cleaning on the collected data and constructing a knowledge graph, (3) carrying out deep correlation analysis, (4) carrying out automatic disassembly and file simulation, and (5) carrying out intelligent application and feedback: automatically generating a Markdown document containing a file abstract, clause comparison and an enterprise portrait through a structured report engine, and carrying out intelligent application and feedback on the Markdown document. GIS map overlay analysis is supported, clauses in an applicable file are matched based on an enterprise tag system, declaration materials are pre-checked through an OCR + rule engine, user feedback cases are collected, model parameters are incrementally trained monthly, it is ensured that adaptation is completed within 24 hours after a new file is released, and a collection-analysis-application-optimization closed-loop iteration mechanism is formed.

The invention discloses a mass data automatic label generation method based on a large model and a rule engine, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring data through a unified data interface, and then performing data cleaning, data standardization processing and data format conversion to obtain standard input data; s2, constructing a label system framework; s3, generating a large model label; s4, performing intelligent clustering and label refining; s5, rule engine constraint and optimization; s6, evaluating and optimizing label quality; and S7, automatically expanding and updating the tag system. According to the method, automatic label generation of mass multi-source heterogeneous data is realized through a large model and rule engine technology under the condition of no personnel intervention, a large-scale label system containing thousands of labels is constructed, meanwhile, the label system can be rapidly updated along with product iteration and keep timeliness, and the generated labels are accurate and extensible.

The invention relates to the technical field of information, in particular to a personalized search information recommendation method based on user behavior analysis, which mainly comprises the following steps: acquiring interactive behavior data of a user in real time; the method comprises the following steps: automatically extracting keyword tags of various contents through a natural language processing technology, and constructing a content keyword tag system; according to the content keyword tag system, extracting keyword tags associated with user behaviors from the interactive behavior data; in combination with the accumulated value of each type of behavior and the preset weight value, calculating the matching degree between the keyword tag and the user, and generating a matching degree set; performing variance operation on the matching degree set corresponding to the keyword tag to obtain sensitivity corresponding to the user content; and generating recommendation information of the search content of the user according to the sensitivity. According to the method, high-precision and real-time response personalized search content recommendation can be realized by analyzing user behaviors.

The invention relates to the technical field of artificial intelligence and human resource informatization, in particular to an intelligent resume analysis method based on a large model, which comprises the following steps: constructing a multi-format analysis engine, constructing a hierarchical attention network by using the large model to extract resume feature information, generating text fingerprints by using an improved SimHash algorithm, and analyzing the text fingerprints. Establishing a multi-dimensional label knowledge base containing a three-level post classification system, and designing a three-dimensional talent cube data model; the method has the beneficial effects that the information extraction accuracy is remarkably improved compared with a traditional method; the semantic-level duplicate checking recognition rate is improved, and the false alarm rate is reduced; the post matching efficiency is improved through a dynamic label system; the quantitative evaluation model is used for providing an interpretable evaluation report; the data processing efficiency is obviously improved; and an enterprise exclusive talent map is established, and digital management of talent assets is realized.

Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.

The invention provides a method, a device, equipment and a storage medium for improving a link establishment rate. Behavior data and text semantics are fused through a pre-trained feature extraction model to generate user feature vectors, a multi-level user tag system is constructed by using a tag generation model, and common features of both parties are extracted through a tag clustering model. And then, in combination with historical interaction data and a label system of the target user, obtaining a current emotion state by using an emotion analysis model, and displaying the emotion on an initiator interface in a pet expression form. And finally, inputting the matching features, the emotional state and the tag system into a copywriting generation model, generating an emotion-adaptive personalized recruitment, and dynamically optimizing each model parameter according to the response, thereby improving the correlation and the infectivity of the information, and further improving the success rate of establishing the recruitment.

The disclosed methods include: detecting data associated with a first tag comprised in the tag network; resolving the data to generate resolved data associated with a plurality of tags in the tag network including the first tag and a second tag; determining the second tag based on the resolved data; and extracting or determining, using the resolved data: first telemetric data associated with the first tag, first location data associated with the first tag, second telemetric data associated with the second tag, and second location data associated with the second tag. The methods also include formatting the first telemetric data, the first location data, the second telemetric data, and the second location data to generate an inventory map associated with the first mobile or stationary cargo and the second mobile or stationary cargo; and transmitting the inventory map to a display device configured to visualize the inventory map.

The invention discloses a new media operation optimization method and system based on a data closed loop. The system comprises a data acquisition module, a three-dimensional label system module, an intelligent analysis module and a closed loop optimization module. The data acquisition module synchronizes core operation data through an API, and captures unstructured comment data through an RPA; the three-dimensional tag system module is used for carrying out tagging processing on platform characteristics, content forms and user behaviors on the data; the intelligent analysis module generates and outputs optimization suggestions through content diagnosis, flow prediction and strategy; and the closed-loop optimization module feeds back an analysis result to an external content generation system, and new content data is transmitted back to form a closed loop. According to the system, the problems of operation data dispersion, insufficient analysis and optimization lag are solved, automatic data acquisition, structured analysis and dynamic optimization are realized, and the operation efficiency and the traffic conversion effect are improved.

The invention discloses a label system based on a relational database, and relates to the technical field of database management. Comprising a data resource management module, a label rule management module, a crowd rule management module, a task scheduling module, a data authorization query module and a system security module. According to the method, information such as dispersed object data, management label rules and scheduling strategies can be integrated to realize labeling, classification and query analysis of the object data, corresponding user portraits and business object label systems are established, and by combining an actual government affair service scene, through analysis and association retrieval of user portrait data, the user portraits and business object labels can be obtained. And precise and personalized services are realized.

The invention relates to the technical field of property right project management, in particular to a dynamic management system and method for property right project state tags. The method comprises the following steps: a data acquisition and processing unit acquires project data and preprocesses the project data; a state label system construction unit constructs a multi-level state label system by using a property right project full life cycle process based on the preprocessed project data; and the state reasoning and dynamic updating unit is used for identifying the change trend of the project state based on the multi-level state tag system by adopting a level state machine model, generating a state tag set, dynamically updating the tag set when a project business event occurs, and generating a dynamically evolved state tag set. According to the method, a hierarchical state machine model and a link correction mechanism are introduced, a label set is dynamically updated when a service event is triggered, and structure-level compensation is carried out on inference interruption caused by data missing or cross-system inconsistency.

The invention belongs to the technical field of artificial intelligence, and relates to an evaluation method, system and device of an intelligent cabin scene multi-modal model and a medium, and the method comprises the steps: 1) a user questions a vehicle-mounted image to obtain questions, and a multi-modal large language model is used to classify each question according to a pre-designed question label system; 2) inputting the vehicle-mounted image and the question into an intelligent cabin scene multi-modal model to generate a question answer; 3) determining evaluation dimensions and importance scores of the dimensions based on the question types; 4) using a chain reasoning mechanism to construct a scoring process and formulating a scoring rule in advance; and 5) inputting the vehicle-mounted image, the question, a reference answer formulated in advance and the generated question answer into a multi-modal large language model, and performing comprehensive scoring according to the evaluation dimension and the importance score of each dimension based on a scoring process and a scoring rule. According to the method, comprehensive, reliable and automatic evaluation of the intelligent cabin scene multi-modal model can be realized.

The invention provides a multi-label path reasoning method and system, a medium and a terminal. The method comprises the following steps: constructing a label system; dynamically updating a keyword set in the tag system; and based on the tag system and the keyword of the tag node, allocating a final tag to the to-be-classified text. According to the multi-label path reasoning method and system, the medium and the terminal, multi-label attribution can be efficiently and accurately completed under a complex label system through label system structured grouping, upper and lower layer path collaborative judgment and an embedded semantic fusion and path scoring mechanism.

The invention relates to the technical field of reading behavior recognition, in particular to an artificial intelligence-based reading behavior analysis system, which comprises a behavior acquisition module, a skip reading recognition module, a path construction module, a label generation module and a cognitive modeling module. According to the invention, through high-frequency collection of rolling and clicking events, the integrity of a reading operation chain is improved, and the analysis granularity of student reading behavior data is enhanced; by utilizing a chapter structure threshold value and non-rolling area analysis, the jump reading recognition dimension is expanded, and the accuracy of behavior judgment is improved; the residence time change in the return path is collected, a comparison system with the first access is established, and the recognition capability of the deep reading behavior of the student is enhanced; constructing a learning label system with distinct hierarchy according to an increasing trend of residence time; and fusing double indexes of behavior frequency and time fluctuation, mapping to a knowledge graph based on a clustering result, strengthening dynamic comparison and difference analysis of chapter reading behaviors, and realizing accurate positioning and analysis of student reading behaviors.

The invention belongs to the technical field of spacecraft product quality and reliability analysis modeling, and particularly relates to a fault tree generation method and device based on a knowledge graph and a large model fusion technology. The method comprises the following steps: constructing a spacecraft product fault domain ontology model, establishing a tag system comprising entity tags and relation tags, and extracting related knowledge from a technical document, a SysML model and an FMEA table by using a large language model to form a triple; according to entity types and relation types of different source knowledge, fusion is carried out, and a fault knowledge graph is constructed; obtaining a function dependence sub-graph, a fault behavior sub-graph and a redundant sub-graph of a function or a composition structure from the fault knowledge graph, and retrieving a specific sub-graph from the fault knowledge graph according to a user question; analyzing fault propagation relation and redundant structure information based on function dependence in the specific sub-graph to construct an OR gate and an AND gate, and mapping nodes in the specific sub-graph to a basic event, an intermediate event, a top event and a gate to generate a fault tree.

The invention relates to a method, a system, a medium and equipment for automatically labeling resume data. The method comprises the following steps of: constructing a label system; obtaining new resume data, and performing fault knowledge extraction according to the tag system through the large language model to obtain extracted fault knowledge; judging whether the extracted fault knowledge is matched in a label library or not, and if yes, newly adding the extracted fault knowledge in the fault mode; and if not, generating a new tag through the large language model. According to the method, the label system of the resume is constructed, after the new resume data is generated, the resume data is automatically structured, the extracted label data is standardized through comparison of the knowledge base and is standardized in the resume, and new labels are automatically generated for labels which do not exist in the existing knowledge base; an effective fault knowledge base continuous updating mechanism is established, automatic analysis of resumes and fault knowledge extraction, accumulation and utilization are achieved, and the problems of data islands and the like are effectively solved.

The application provides a method for improving the artificial auditing efficiency of text content, which comprises the following steps: detecting a text generation result, calculating the grouping hash to classify similar content, calculating a unique identifier to remove duplicates, discarding the unique identifier if it exists, otherwise trying to inherit the historical artificial auditing result, automatically inheriting the auditing state, type and level, and marking the system auditing, storing the result and the hash, identifier and auditing result, displaying and batch auditing according to the grouping hash deduplication, and finally updating the auditing state of the whole group; the application can intelligently merge the massive text content to be audited through the grouping hash mechanism, so that the auditors do not need to process the repeated or highly similar content one by one, but only need to audit the representative data after deduplication according to the grouping; when processing the data of the order of 100,000, the auditing time can be shortened from dozens of hours in the traditional way to several hours.

The invention discloses an intelligent disease course record generation method and system based on hierarchical label dynamic mapping, and belongs to the technical field of medical information. According to the method, intelligent standardization processing of medical terms is achieved by constructing a three-level label architecture, and the method comprises six links of term collection, standardization processing, hierarchical mapping, label updating, content generation and auditing feedback. The system firstly obtains a clinical description text from an electronic medical record system and performs standardization processing, then associates terms to a standard tag library through a three-level semantic mapping process, dynamically maintains a tag system and generates a standard disease course record based on standardized tags. The technical problems that existing disease course records are high in repetition rate, low in semantic standardization degree, poor in clinical adaptability and the like are solved, and the medical record writing efficiency and quality are remarkably improved. The method is mainly applied to automatic generation and management of disease course records in a hospital electronic medical record system.

The invention discloses a comprehensive energy cooperative control method and device and a medium, and the method comprises the steps: carrying out the semantic mapping of a preset comprehensive energy association relationship in a node-edge manner, constructing an energy field ontology library, converting the comprehensive energy parameter data of an edge side into JSON data through a protocol conversion device deployed at the edge side, and carrying out the JSON data, the method comprises the steps of obtaining JSON data, performing semantic annotation on the JSON data according to a preset metadata tag system to generate a unified data stream, performing data cleaning on the unified data stream according to an energy field ontology library to generate a standardized data stream, and finally storing the standardized data stream to a preset multi-modal data lake according to a preset storage rule. And obtaining comprehensive energy scheduling data according to the standardized data flow in the multi-modal data lake. Through a multi-source heterogeneous data fusion mechanism, the data islanding phenomenon in the prior art is solved, and the overall response speed of the integrated energy system is improved by processing numerous multi-source heterogeneous data through Kafka + Flink.

The invention relates to the technical field of industrial label construction, in particular to a dynamic label system construction method and device, a terminal and a storage medium, and the method comprises the steps: determining an analysis scene based on a classification demand, and obtaining a classification entity corresponding to the analysis scene; obtaining all features of the classified entity, and determining at least one key feature corresponding to the classified entity from all the features; and generating a feature tree corresponding to the classification entity based on the at least one key feature, and verifying the feature tree, so as to construct a tag system corresponding to the classification entity based on the feature tree under the condition that the feature tree passes verification. Therefore, the problems that in the related technology, due to the fact that subjective differences exist in different expert perspectives, subjective differences exist in content concerned by industry analysis, and labels are generated manually, errors exist in construction of a label system to a certain degree, and efficiency is low are solved.

The invention discloses a germplasm resource electronic tag system which comprises an electronic tag which adopts an RFID tag or a two-dimensional code tag and stores a unique identification code of a coded germplasm resource. According to the method, the germplasm resource management efficiency is improved; through combination of the electronic tag and the data management platform, rapid acquisition and updating of germplasm resource information are realized; a worker does not need to manually look up a large number of paper records or switch query in a plurality of systems, and can instantly obtain comprehensive germplasm resource information only by scanning the electronic tag through the handheld terminal, so that the time and the labor cost are greatly saved; for example, in germplasm resource warehouse-in and warehouse-out management, detailed information of each batch of germplasm resources needs to be manually recorded in a traditional mode, time consumption is long, and errors are prone to occurring; when the system is used, a worker scans the electronic tag by using the handheld terminal, the recording and updating of the warehouse-in and warehouse-out information can be quickly completed, the whole process only needs several seconds, and the working efficiency is greatly improved.

A system for generating a labeled dataset is provided. The system includes a processing component configured to: receive first data, wherein the first data comprises one or more frames, and wherein the first data comprises data defining an object of interest (203) within a predetermined area (202); receive second data, wherein the second data is associated with the object of interest within the predetermined area; analyze the one or more frames of the first data to identify the object of interest present in the first data based on the second data; label the one or more frames of the first data based on the analysis to generate a labeled dataset; and output the labeled dataset. A method for generating a labeled dataset, a system for training a machine learning model, and a detection system for detecting one or more objects of interest are also provided.

A tagging system appends supplemental information to an original sequence of items, to produce a supplemented sequence of items. The tagging system includes a transformer-based encoder neural network that maps the supplemented sequence into hidden state information. The tagging system includes a post-processing neural network that transform the hidden state information into a tagged output sequence of items. That is, each item in the tagged output sequence includes a tag that identifies its entity class or some other characteristic. The tagging system can increase the accuracy of the tags it produces by virtue of the inclusion of the supplemental information added to each original sequence. A training system trains the tagging system to perform plural tasks, which further increases the accuracy of the tags it produces. The training system may commence training of the tagging system using a pre-trained model for the encoder neural network.

The present application proposes a method and device for determining tags of text data. The method includes obtaining text data; extracting text features of the text data; fusing the text features and tag embedding features into fused features, where the tag embedding features are determined according to the hierarchical relationship of multiple tags to be predicted; and determining at least one tag associated with the text data from the multiple tags based on the fused features. The solution of the present application can fully consider the influence of the mutual relationship of tags in the tag system and improve the accuracy of tag determination and classification of text data.