59 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 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.

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 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 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 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 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 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.

This invention relates to the field of data parsing technology, specifically a method, system, and medium for parsing non-standard nested format data with zero code. First, sample data is prepared, obtaining non-standard format samples to be parsed. A unique marker is generated for each character in the interactive text area through a rendering module. Then, single-level parsing rules are generated, identifying the content field type marked by the user in the interactive text area and generating parsing rules. Next, nested parsing rules are configured, including a tree-like nesting strategy. Finally, production data is parsed, and the parsing rules are distributed to the data access module for batch parsing of the input production data. This allows users to simply mark the sample data visually, and the system automatically captures mouse events, eliminating the need for users to write or understand regular expressions. Simultaneously, sample fragments are instantly converted into executable rules, shielding underlying syntax and conversion details, achieving zero code and zero syntax requirements. Parsing rule definition can be completed independently through interactive word highlighting.

The invention relates to the field of digitization, and provides a cross-platform digital content creator integrated operation method, which comprises the following steps: capturing public anchor data of a target platform in real time through a unified data acquisition interface, and creating an anchor archive library; executing conflict detection when the scheduling saving operation is triggered, and dynamically adjusting the task priority based on a preset rule; establishing association mapping with anchor archives in an anchor archive library; associating the anchor video metadata with the anchor file in real time, and generating a multi-dimensional visual analysis report; generating an initial anchor tag based on a preset tag system of the multi-dimensional visual analysis report and the trend prediction result, and recording a tag version change history; creating an anchor task template based on the visual scheduling view, and triggering reward settlement calculation after the state verification is passed; and constructing a platform adaptation layer to uniformly manage an application program interface (API) and a crawler calling rule, and triggering the timed task scheduling system to retry a task based on an incremental updating mechanism so as to repair abnormal data.

The invention relates to the technical field of electronic commerce and information recommendation, in particular to a recommendation method and system based on user tags. According to the method, the importance of the user tags can be effectively determined by constructing the user hierarchical tag system and calculating the weight of the ungenerated multi-dimensional user tag set; periodically attenuating and updating the weights of the tags in the user tag set by using a time attenuation function, and capturing interest drift through dynamic updating; a commodity feature vector calculated by a commodity-label knowledge graph is constructed based on commodity data, so that commodity features can be effectively determined; the similarity between the commodity feature vector and the user feature vector is calculated, and the recommended commodity is determined based on the similarity, so that the recommended commodity can be accurately determined; and determining a recommendation state based on a comprehensive score determined by the click rate, the conversion rate and the stay duration of the user on the commodity recommendation page in a preset period, and adjusting corresponding parameters based on the recommendation state. The recommendation efficiency is improved.

This invention provides a method, apparatus, device, and storage medium for improving the connection establishment rate. It generates user feature vectors by fusing behavioral data and textual semantics through a pre-trained feature extraction model, constructs a multi-level user tag system using a tag generation model, and then extracts common features between the two parties using a tag clustering model. Subsequently, combining the target user's historical interaction data and tag system, a sentiment analysis model is used to obtain their current emotional state, which is then displayed as a pet emoji on the initiator's interface. Finally, the matching features, emotional state, and tag system are input into a copywriting generation model to generate a personalized greeting with appropriate emotion. The model parameters are dynamically optimized based on the response, thereby improving the relevance and persuasiveness of the information and ultimately increasing the connection establishment success rate.

The application discloses a member right management system and method based on data tags, and relates to the technical field of big data analysis.The application forms a structured analysis set by associating scattered member attributes and right use data according to unique identification by constructing a three-dimensional member tag system, and realizes fine classification of member groups.Based on standardization processing and quantitative analysis, the application calculates the right use heat and the correlation strength characteristics by weighting, constructs a scientific adaptive scoring model by combining Pearson correlation analysis, accurately describes the group right preference and core attributes, realizes accurate screening of target member sets, and improves the pertinence and rationality of right allocation.The application sequentially issues rights through a phased verification promotion mechanism and dynamically monitors abnormalities, simultaneously constructs a multi-dimensional effect evaluation system to form a dynamic closed loop, ensures the safe and stable updating of rights, responds to changes in member needs in real time, breaks the static and solidified configuration mode, and realizes the intelligentization and sustainable iteration of right management.

The invention belongs to the technical field of mine ventilation decision making, and aims to solve the problem that intelligent construction of a current ventilation system lacks structural modeling and inference question and answer ability support for ventilation knowledge. The invention provides an intelligent question and answer method for mine ventilation event capture and action reasoning. The method comprises the following steps: extracting entity, attribute and rule triples related to ventilation in regulation documents and technical corpora; carrying out structure analysis and context modeling on the triad, and constructing a semantic tag system; constructing regions, conditions, events and action nodes in the triple into a quintuple path with logic connectivity; performing construction of a ventilation knowledge graph and modeling of path mapping; and completing structure mapping and answer generation of the natural language question. Semantic understanding, condition recognition and response path reasoning of ventilation events can be achieved, a closed-loop supporting mechanism from knowledge modeling to intelligent question and answer is formed, and the intelligent level of a mine ventilation system is improved.

The invention relates to the technical field of sorting and tagging, in particular to a sorting and tagging system of a laser compound machine, which comprises a multi-modal dynamic visual acquisition module, a three-dimensional (3D) linear array scanner, a hyperspectral camera and an infrared thermal imager are integrated in the multi-modal dynamic visual acquisition module, and digital twins of plates are generated; and the intelligent optimization decision module executes a dual-objective optimization algorithm and outputs a cutting layout and a safety tagging area, and the distance function, the position of the QR code and the cutting line are represented. According to the application, through a dynamic calculation model of the safety tagging area and a nested contour layering cutting strategy, 100% retention of the two-dimensional code is ensured, and through real-time path avoidance, the retention rate is increased to 99.9%; and a bidirectional ACO-DP algorithm is fused with an inflection point factor to optimize a tool turning path, the idle stroke proportion is reduced to be smaller than or equal to 8% from traditional 22%-30%, the defect map is fused with acoustic emission crack risk and thermal stress data, and the cutting forbidden zone recognition precision is improved by 40%.

The application relates to a label system-based Internet of Things monitoring model construction method and belongs to the field of natural language processing. The method comprises the following steps: establishing an initial label system, manually labeling a structured data set based on the initial label system, and constructing a first training sample set; constructing a Bert model, training the Bert model based on the first training sample set to obtain a trained Bert model; inputting business data into the trained Bert model to obtain corresponding characteristic values, and obtaining a perfect label system based on the characteristic values; constructing an Internet of Things monitoring model, using the Internet of Things monitoring model to predict business data to obtain predicted business data; using the perfect label system to label the predicted business data to construct a second training sample set; and training the Internet of Things monitoring model based on the second training sample set to obtain a trained Internet of Things monitoring model. The method can automatically generate feature labels required by business, label business data by using a perfect label system, and improve the recognition effect and training efficiency of the model.

The invention discloses a text information processing method and device based on a large language model, equipment and a medium, which are used for improving the accuracy of text information processing so as to improve the accuracy of text classification. The method provided by the invention comprises the following steps: inputting to-be-processed text information into a preset cue word template for text classification, and generating an initial tag system of the text information by adopting a large language model based on a pre-classification result output by the cue word template; wherein the label system comprises classification labels obtained by performing text classification on the text information; based on the initial label system, determining a target text needing reinforcement learning from the to-be-processed text information; calculating an adaptation loss value of the initial tag system about the target text; and adjusting the cue word template based on the loss value, and optimizing the initial label system by using the adjusted cue word template.

The invention provides a dynamic electronic tag system for railway communication equipment and an implementation method of the dynamic electronic tag system, relates to the technical field of intelligent operation and maintenance of the railway communication equipment, and solves the technical problems that a traditional paper tag is not easy to modify and information updating is lagged. The system comprises a background management platform used for storing and managing equipment basic information and port service information of railway communication equipment; the two-dimensional code generation module is used for generating a two-dimensional code image; the physical two-dimensional code label is used for being pasted on a panel of the corresponding railway communication equipment; the information association and synchronization module is used for updating the port service information of the background management platform in real time to obtain real-time port service information; and the mobile terminal is used for scanning the physical two-dimensional code label, sending the obtained two-dimensional code information to the background management platform for verification and decryption, and receiving and visually displaying the returned real-time port service information. The method is used in the intelligent operation and maintenance management process of the railway communication equipment.