38 results about "Structured reporting" patented technology

This application relates to the field of network situational awareness technology, and discloses a method and device for network security situational awareness. The method includes: identifying regulatory entities and collecting abnormal events; identifying interactive and operational behaviors as risk characteristics; constructing a first risk identification model and deploying it on an edge terminal; collecting the type and sensitivity attributes of the regulatory entities; dynamically forming regulatory assistance pairs based on the attributes; generating a second risk identification model by weighted averaging of the parameters of the first model based on the regulatory assistance pairs and deploying it in the cloud; real-time interactive data is preferentially screened by the cloud-based second model; if a risk is identified, a structured report is generated and distributed; if not identified, it is reviewed by the first model at the edge terminal. This application achieves distributed intelligent perception with cross-domain collaboration and data not leaving the domain, solving the model bias and centralized delay problems caused by insufficient samples of a single entity, and significantly improving detection accuracy, response efficiency, and collaborative defense capabilities in low-sample scenarios.

This application discloses an automated underwriting assessment method, apparatus, and system, solving the technical problem that existing underwriting systems cannot directly perform semantic understanding and automated decision-making on unstructured medical examination report images, resulting in an inability to form a complete closed-loop processing flow. The method includes: acquiring medical examination report image data and associated insurance application information; performing character recognition on the images to obtain text data; converting the text data into formatted input data according to underwriting medical terminology mapping rules; inputting the formatted input data into a large language model to obtain structured medical examination indicator data containing examination items and their values ​​or descriptions; and logically matching the structured medical examination indicator data with a pre-configured underwriting rule knowledge base to generate underwriting conclusion data. This application achieves a complete, systematic closed-loop processing flow from image input to conclusion output, improving the system's automated parsing and decision-making capabilities for unstructured report data.

This invention discloses a three-color probability assessment method and system based on high-precision clause mapping, comprising the following steps: Step S1: Receive text input containing facts to be assessed, and perform structured preprocessing to extract core entities, time elements, and request types to generate a structured case representation; Step S2: Based on the structured case representation, plan a mapping path from the starting point of the case to potentially relevant clauses in a preset clause knowledge network. The mapping path consists of multiple sequentially connected nodes and directed edges, where each node corresponds to a clause to be assessed, and each directed edge corresponds to a verification operation based on logical rules. This invention, through high-precision clause mapping, probability calibration, versioned threshold management, and structured report generation, overcomes the shortcomings of existing technologies and achieves standardization, traceability, and efficiency throughout the entire assessment process.

This invention relates to the field of anti-misoperation interlocking systems and provides an intelligent diagnostic method for anti-misoperation interlocking systems in new energy power plants. The method includes: constructing and dynamically updating a topology model representing the real-time electrical connections of the power plant by real-time access and fusion of multi-source state information from the power grid data acquisition system, relay protection devices, intelligent grounding wires, and environmental sensors; based on this, using a knowledge graph reasoning model to traverse and detect conflicts in a static anti-misoperation rule base to diagnose logical vulnerabilities; combining historical operational data with a machine learning model to identify potential risk patterns and rule blind spots to improve diagnostic results; analyzing the time-series data of locks and sensors using a predictive model to identify hardware performance degradation trends and fault risks; and finally, synthesizing all the above diagnostic and identification results to generate a structured report. This effectively improves the safety level and adaptive reliability of new energy power plants in complex operating environments.

This invention discloses a method for identifying safety hazards in the hazardous chemicals industry based on a multimodal large model, belonging to the interdisciplinary field of hazardous chemicals safety and artificial intelligence. The method automatically constructs a three-layered knowledge base of "equipment—inspection items—standard clauses," supporting hot-swappable updates of standards. It employs a three-stage cascaded reasoning approach: equipment identification introduces visual-semantic dual-channel verification; inspection checklist matching introduces visual inspectability assessment and environmental adaptive adjustment; and hazard assessment introduces a three-stage reasoning approach of "observation—comparison—conclusion." A structured report containing hazard descriptions, standard tracing, and rectification suggestions is generated. This effectively realizes a new paradigm for safety hazard identification: "standard-driven, model-executed."

The application provides a medical image structured report generation method and system, the method comprising: extracting the base feature map of standardized image data by using a multi-scale feature extraction network, and performing multi-level down-sampling to obtain a multi-scale feature map sequence, and calculating the corresponding adaptive fusion weight based on the semantic richness of each scale feature map, and performing weighted fusion on the multi-scale feature map sequence based on the adaptive fusion weight to obtain a target semantic feature vector; performing semantic deduction on the target semantic feature vector based on a medical knowledge graph to generate image semantic description information, matching a target structured report template based on the image semantic description information, and generating a medical image structured report based on an adaptive template filling strategy. The application can fully capture the multi-scale semantic information of the lesion area in the medical image, and simultaneously realizes the dynamic adaptation of the report template and the image semantic content, thereby improving the consistency and clinical practicability of the report and the image semantic content.

This invention discloses a mechanism- and data-driven root cause diagnosis method and system for industrial production lines. The method includes: acquiring and preprocessing multi-source heterogeneous industrial data from the production line; performing anomaly detection on the preprocessed multi-source heterogeneous industrial data, identifying abnormal time windows, and extracting multimodal anomaly data; constructing a three-level hybrid diagnostic model and generating a preliminary root cause candidate set based on the model's diagnostic output for the multimodal anomaly data; performing source tracing analysis on the preliminary root cause candidate set and generating a structured natural language diagnostic report based on the source tracing analysis results. This invention significantly improves the accuracy and generalization ability of root cause diagnosis across a wide range of scenarios by constructing a three-level hybrid model architecture. Furthermore, through source tracing analysis and natural language extension, it provides a structured report supported by clear physical logic and data evidence, thereby improving operational decision-making efficiency, reducing reliance on scarce domain experts, and significantly reducing unplanned downtime.

The application discloses a kind of based on the design method of structured report template of semasiology association, comprising the following steps: step S1, based on historical medical record big data semantics construction obtains first diagnosis and treatment knowledge graph;Step S2, based on the construction of second diagnosis and treatment knowledge graph of disease diagnosis and treatment guideline big data;Step S3, the first diagnosis and treatment knowledge graph and second diagnosis and treatment knowledge graph are fused to obtain the diagnosis and treatment structured knowledge graph of fusion representation diagnosis and treatment practical experience and diagnosis and treatment expert experience, and based on diagnosis and treatment structured knowledge graph constructs structured report template for disease category;Step S4, according to structured report template, uniform diagnosis and treatment is carried out to disease category to improve diagnosis and treatment standardization.The application makes it in accordance with structured report template to carry out disease diagnosis and treatment, that is, it is in accordance with diagnosis and treatment practical experience of doctor also in accordance with diagnosis and treatment expert experience, realizes diagnosis and treatment standardization and maneuverability, breaks through the limitation that computer-aided diagnosis method only uses diagnosis and treatment guideline driving.

The present application relates to the technical field of data processing, and especially relates to a data collection and report generation method, medium and equipment, a general access strategy is obtained by analyzing a reference collection node, target collection nodes are screened based on the general access strategy and text data is collected, irregular nodes are removed from the source, and corresponding measures are taken in time when irregular behaviors of the nodes are found, so that the compliance of the collection nodes and the continuity of the collected data are guaranteed; a pre-trained large model is used to analyze the text data to generate an attribute information set, so that the analysis efficiency and accuracy are improved; according to target network site information update time, the multi-source attribute information of a data subject is verified, multi-site data conflicts are accurately identified, and the subject information consistency and the integrity of the feature portrait are ensured; the feature description data of the target identification code and the feature portrait are subjected to similarity matching, multi-source network data is converted into a structured report available for a third-party system, and reliable data collection and report generation are realized.

This application relates to the field of digital operation and maintenance technology for substations, and in particular to a method, system, and related equipment for intelligent generation of verification reports. The method includes: parsing the model values, identification values, and field wiring status of basic verification object information, and matching the corresponding verification point type based on the basic verification object information; comparing the model values ​​and identification values ​​for consistency based on the verification point type, and outputting processing suggestions according to scenario when the comparison results are inconsistent; constructing a circuit hierarchy system based on the comparison results and field wiring status, and outputting report priority and problem attribute information; performing data merging and optimization of the problem attribute information according to preset rules; and outputting a structured report by combining the report priority and the optimized problem attribute information.

The present application relates to the technical field of environmental monitoring, and particularly relates to an air-ground multi-source spatio-temporal information fusion perception method and system based on artificial intelligence, comprising: constructing a three-source data acquisition architecture, using an intelligent calibration model to dynamically calibrate the three-source data in space and time, and obtaining calibrated data; correlating and verifying the calibrated data and quantifying the weight of interference factors through a causal reasoning model, dynamically adjusting the field constraint parameters through a time sequence feature coding model, completing deep fusion through an intelligent fusion model, and outputting high-precision fusion results; constructing a field knowledge graph, mining potential business rules and identifying unknown abnormal patterns through rule mining and abnormality identification, and obtaining an iteratively updated knowledge graph dynamic; based on a collaborative learning framework and combined with privacy protection technology, realizing cross-region model collaborative evolution, constructing a scenario-based value conversion engine to generate a business structured report. The present application can improve the data fusion accuracy and the full-link traceability accuracy.

The application discloses a finite element analysis result data automatic processing and screening method for proxy model construction, comprising the following steps: creating a data extraction script of a single finite element analysis result file, configuring data extraction parameters, executing automatic data extraction on the single finite element analysis result file, automatically screening out effective integral point data, and outputting a single structured report file; constructing a batch automatic control program script; collecting all eigenvalues and corresponding effective integral point data obtained through the step processing; and collating the collected data according to a preset data structure to generate a standardized data set file, which is used for training a proxy model. Through scripting of data extraction rules, automation of effectiveness judgment, batch processing of a processing flow and standardization of output results, efficient automatic processing of the whole flow of finite element simulation post-processing data preparation is realized, and the efficiency is greatly improved.

The application discloses a kind of sensor manual automatic analysis and ROS2 generation system and method, to sensor PDF manual as input, by AI multimodal analysis and integrity evaluation, extract three elements and complete credibility score;Low credibility manual is carried out to multi-source information completion;Generation standardized format structured protocol intermediate representation IR, after checking, automatically generate ROS2 drive code package, complete compilation verification and AI code repair;Start hardware-independent simulation verification closed loop, carry out multidimensional automatic verification and output structured report;For manual version iteration, by IR difference comparison realizes incremental update and incremental verification.The application can shorten the development cycle of drive, adapt to incomplete manual scene, guarantee the quality of drive, reduce version iteration cost, suitable for laser radar, camera, IMU and other sensor ROS2 drive automation development.

This invention discloses an intelligent countersigning method, system, and storage medium, belonging to the field of electronic signature technology. The method includes: receiving a countersigning request; loading organizational structure data from an enterprise HR system and querying encrypted historical countersigning configurations; generating a countersigning path recommendation list based on a graph neural network model; establishing a multi-person signing pool by department through smart contracts, where any member's signature signifies the completion of that step; pushing multi-channel signing notifications based on members' network environments; calculating the hash of the countersigning information after signing and submitting it to a consortium blockchain for notarization; and finally generating a structured report containing a notarization number. This invention optimizes recommendation accuracy through graph attention networks and utilizes smart contracts and a consortium blockchain to solve the problems of difficult personnel matching, easy process stalls, and poor data security in traditional countersigning, significantly improving the efficiency and reliability of multi-level countersigning.

The application discloses a molecular mechanism automatic prediction method and system based on a complex compound system, relates to the technical field of bioinformatics analysis, and comprises the following steps: constructing an integrated analysis platform, configuring a visual workflow, performing full-process automatic analysis, and generating a result and a structured report. The application forms a unified integrated analysis platform by integrating a multi-source data interface, multiple independent analysis modules, a dynamic module adaptation mechanism and multi-source data collaborative verification logic, realizes end-to-end process connection from compound information input to structured report generation, does not need manual intervention in data transmission and format conversion, solves the problems of dispersion, process fragmentation and complicated operation of traditional tools, improves the overall efficiency of molecular mechanism prediction in early drug research and development, and adopts a drag-and-drop visual workflow configuration interface to support users in combining independent analysis modules and flexibly configuring data transmission paths, so that the configuration difficulty of a complex analysis process is reduced.

This application discloses a method for model distillation solidification and parameterized deployment for industry data analysis equipment. The method includes: a cloud-based teacher agent receiving industry analysis task requests and forming a teacher trajectory library; performing a reliable gating score on each trajectory sample in the teacher trajectory library to generate a gating distillation training set; training a local student model using a multi-objective loss function for gating distillation; outputting a student model weight parameter set and capability package after training; loading the student model weight parameter set and capability package on the industry data analysis equipment side; initiating an industry analysis task request on the industry data analysis equipment side; and automatically completing multi-source data collection, arrangement, and structured report generation under constraints, outputting a due diligence report and corresponding trajectory and log records. This application can reduce overall usage costs and improve the reusability and implementation efficiency of cross-customer deployment.

This invention relates to the field of intelligent analysis and data processing technology, specifically to an intelligent operation and maintenance report generation method based on a large model. This method addresses the problems of low report generation efficiency, high data query thresholds, weak data analysis capabilities, and limited interaction methods in traditional operation and maintenance management. The method encapsulates database operations into standardized tool interfaces through the MCP tool layer, enabling secure and regulated access to operation and maintenance data by the large model. It dynamically generates SQL permission filtering conditions based on user organizational permissions to ensure data access security and prevent unauthorized access. The method employs SSE streaming transmission to achieve real-time response from the large model, supporting both HTTP and NL2SQL data sources to enhance the user experience. The report generation layer supports multiple report types, automatically calculates month-on-month time periods, and aggregates multi-dimensional operation and maintenance data to generate structured reports. The multi-dimensional analysis layer integrates intelligent data querying, data analysis, trend prediction, and intelligent recommendation functions to achieve comprehensive intelligent analysis of operation and maintenance data.

The present application relates to the technical field of knowledge graph, in particular to a steel manufacturing knowledge graph construction method based on multi-source heterogeneous data, comprising: converting message data into standardized semantic triples through semantic mapping rules, and using physical and chemical component balance logic to complete missing attributes to form standardized entities; at the same time, using a semantic processor to extract the logic of unstructured report text, and performing cross-modal coupling with SEM image features to generate failure mechanism enhanced entities. Subsequently, collect full-process dynamic parameters and regularize them into equidistant time sequence parameters. Finally, taking the standardized entity as the index feature, taking the mechanism enhanced entity as the correlation constraint, using a topological feature mapping engine to perform semantic fusion, and combining a dynamic probability distribution correction operator to iterate parameter weights, a dynamic weight graph structure is constructed. The present application realizes efficient correlation and semantic organization of multi-modal data in the whole manufacturing process, greatly improving the retrieval accuracy and knowledge discovery ability of steel manufacturing data.

The application relates to the technical field of large model retrieval, in particular to a text generation method based on a large model, an electronic device and a storage medium, the method comprises the following steps: rewriting an initial question input by a user, performing sorting processing on a plurality of relevant documents obtained based on the rewritten question, screening out target documents, performing key information extraction on each target document, performing semantic clustering processing on the extracted key information segments based on a dynamic clustering quantity, obtaining a plurality of clustering clusters, obtaining cluster representative contents corresponding to each clustering cluster after deduplication processing, to generate pretreatment texts, matching a target report template corresponding to the initial question from a preset report template library, and generating a target report text in a format corresponding to the target report template according to the pretreatment texts; the application realizes intelligent generation of a whole process from fuzzy input of a user to a precise and structured report, and significantly improves the accuracy, relevance and format standardization of report contents.

The application discloses a kind of sensor manual automatic analysis and ROS2 generation system and method, to sensor PDF manual as input, by AI multimodal analysis and integrity evaluation, extract three elements and complete credibility score;Low credibility manual is carried out to multi-source information completion;Generation standardized format structured protocol intermediate representation IR, after checking, automatically generate ROS2 drive code package, complete compilation verification and AI code repair;Start hardware-independent simulation verification closed loop, carry out multidimensional automatic verification and output structured report;For manual version iteration, by IR difference comparison realizes incremental update and incremental verification.The application can shorten the development cycle of drive, adapt to incomplete manual scene, guarantee the quality of drive, reduce version iteration cost, suitable for laser radar, camera, IMU and other sensor ROS2 drive automation development.

This invention relates to the fields of medical image processing and artificial intelligence, and discloses a method and system for rapid localization and information extraction from smart medical images. The system includes an image preprocessing module, a three-level cascaded localization module, a dynamic processing strategy module, a precise segmentation and information deconstruction module, a multimodal feature fusion module, and a structured report generation module. Specifically, the three-level cascaded localization module achieves progressive localization from coarse to fine; the dynamic processing strategy module determines subsequent processing paths based on candidate regions generated by coarse localization; the precise segmentation and information deconstruction module segments and extracts features from the target region; the multimodal feature fusion module generates a unified feature representation; and the structured report generation module outputs structured diagnostic results. This invention can improve the efficiency of medical image localization and information extraction.

This invention discloses an intelligent evaluation method and system for software testing course experiments. It adopts a hybrid C / S and B / S architecture, supports offline and online operation, and is based on a layered architecture design. An experiment resource integration module centrally manages experiment resources and toolchains; an evaluation rule configuration module supports customized scoring rules for different teaching objectives; a test execution evaluation layer automatically executes and analyzes tests; an AI intelligent evaluation module integrates large language models to intelligently analyze and provide feedback on code and reports; a dynamic monitoring module collects experimental process data; and finally, a test report and result feedback module generates a structured report containing automatic scoring and multi-dimensional quality analysis. This invention achieves automated execution and intelligent evaluation of software testing experiments, solving problems such as low evaluation efficiency, inconsistent standards, poor environmental compatibility, and uncontrollable processes, thus improving teaching adaptability and feedback quality.

The invention discloses an intelligent report generation method and system based on two-stage penetration processing, and relates to the crossing field of insurance finance and artificial intelligence. According to the method, through a collaborative architecture of a non-intelligent calculation tool, a multi-system interface and a rule adjustment large model, firstly, the non-intelligent calculation tool automatically calculates multi-source cost data based on a preset actuarial template, and outputs standardized cost difference integration data; pushing to a large model which is subjected to prompt engineering and small sample learning adjustment and calibration through a RESTful API (Application Program Interface) protocol; and finally, the large model executes multi-dimensional penetration analysis according to an insurance financial fee difference business rule knowledge base, accurately locates an abnormal fee difference cause and generates a structured report containing a natural language conclusion. According to the method, the problems of high data processing time consumption, insufficient penetration dimension and manual report interpretation in the prior art are solved, and the cost difference analysis efficiency and precision are greatly improved.

This invention discloses a method and system for multimodal data fusion and analysis for intelligent substation inspection, relating to the field of intelligent operation and maintenance of power systems. The method includes: acquiring multimodal data such as lidar point clouds, dual-light images, and IMU attitude data; preprocessing data through adaptive electromagnetic interference denoising and federated Kalman filtering; constructing a three-level fusion architecture of "physical layer - feature layer - decision layer" to achieve spatiotemporal unification, geometric-semantic association, and semantic-rule reasoning; performing full-scene analysis based on models such as temporal attention CNN-LSTM; and outputting real-time alarms, 3D SLAM maps, and structured reports. This invention is adaptable to the special operating conditions of substations, has high spatiotemporal synchronization accuracy, covers the entire scenario of "equipment-personnel-construction," and significantly improves analysis accuracy and decision-making practicality, effectively supporting intelligent inspection and operation and maintenance decisions for substations.

The application discloses a kind of TAVI postoperative automatic analysis and visualization system based on medical image, including data import, automatic analysis, valve leaflet function evaluation, valve stent geometry evaluation, interactive alignment and report generation module.System imports time series medical image data covering at least one cardiac cycle, segments aorta, coronary artery, left ventricle, valve stent and calcified structure, extracts stent central axis and establishes stent local coordinate system, generates transverse, longitudinal, bottom, coronary artery opening and walsh sinus reconstruction plane.Based on multi-plane reconstruction display, the system evaluates HALT, SFD and PFD, and calculates perimeter, area, long-short diameter, AED and PED and other parameters at stent inflow end, valve leaflet nadir and commissure height plane.Interactive alignment module maps valve structure to standard observation plane and superimposes simulated valve model, and report generation module integrates results to output structured report.

This invention discloses a method for zero-shot interpretable anomaly detection in the chemical industry, relating to the field of anomaly detection technology in the chemical industry. The method includes: constructing a training set and a structured reporting protocol; optimizing the model through 2D-3D comparative pre-training, supervised fine-tuning, and reinforcement fine-tuning with verifiable rewards; collecting and preprocessing multimodal data of the object to be detected; extracting features through a frozen encoder; fusing features through cross-source interaction, modality alignment, and bidirectional cross-attention; parsing evidence slot features and spatial support graphs to generate a structured report containing fields such as defect type and location; extracting anomaly-related statement features; and generating pixel-level anomaly masks and evidence tracing chains through language-guided two-hop grounding. This invention requires no target anomaly annotation data, achieves zero-shot generalization, combines detection and location accuracy with report interpretability, adapts to industrial quality inspection processes, and has a high degree of automation.

The application discloses a kind of based on function call stack backtracking and defect management method of crash sample clustering, comprising: subscribing and collecting Java exception, ANR and Native crash signal, record time stamp, thread name, device fingerprint, APK / SDK version meta information, it is encapsulated as "crash sample unit";Load corresponding version mapping file and NDK (Native Development Kit) symbol, restore method name and source code line number, and execute path normalization and noise frame clipping;Splice scattered call segment, generate complete trigger path;Based on exception type, TopK key frame, trigger path hash and environment abstract, generate robust signature;Similarity calculation and configurable threshold clustering are carried out in signature space, and same source or similar crash is merged into "crash cluster";Representative sample is selected to each cluster, combined with event sequence, page state and life cycle track, automatically generate replayable PoC script and structured report.The application can improve the accuracy of defect deduplication and aggregation and enhance defect reproduction and positioning ability.

Embodiments of the present application provide a SIS system trusted policy consistency verification method and related equipment, and relate to the technical field of industrial control safety. The method comprises: starting a verification tool; inputting target device related information and trusted management platform related information; selecting objects to be compared and trusted policy modules to be compared; automatically obtaining the total number of strategies and the strategy content of the target trusted policy module of each object to be compared; comparing the strategies according to the trusted policy module, and outputting the comparison process; generating an analysis result report, which contains the comparison result and the problem positioning direction. Through Python and Selenium, the whole process is automatically realized, the single verification time is significantly shortened, and the efficiency is significantly improved. The combination selection mode of 2-3 objects and any module is supported, different operation and maintenance scenes are adapted, unnecessary redundant comparison is avoided. The comparison process and the structured report are output in real time, the operation and maintenance personnel do not need to manually record, can quickly master the inconsistent module and the approximate abnormal direction, and the difficulty of troubleshooting is reduced.