3631 results about "Report generation" patented technology

The invention discloses a multi-agent interactive efficient data analysis system, which comprises a natural language analysis unit, which is used for carrying out preliminary analysis on a natural language query request input by a user based on a natural language analysis model, and predicting a user intention to obtain the user intention; the task intelligent routing unit is used for exchanging information through a communication mechanism between the intelligent agents according to the intention of the user and distributing the data analysis tasks to the corresponding intelligent agents; the agent execution unit is used for executing various operations required by a data analysis task by an agent, including task disassembly and strategy evaluation, information storage and memory calling, external tool use and action execution, and obtaining an analysis result; and the agent report generation unit is used for generating a data analysis report based on the analysis result according to a preset report template and a formatting rule. And automation of a data analysis task is realized through efficient cooperation and a task allocation mechanism among multiple agents.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses an intelligent review system and method for a large model auxiliary compliance report. The system comprises a report processing module, a rule base management module, a knowledge graph engine, a large model reasoning module, an automatic review and feedback module and a problem publication and approval interface. The method comprises the steps of data acquisition and preprocessing, blocking and field extraction, rule matching and knowledge association, multi-dimensional inspection, problem identification and positioning, report generation and process triggering. Through deep fusion of the large model and the knowledge graph, intelligent and automatic compliance examination of the report is realized, the method has the advantages of high examination efficiency, high accuracy, good interpretability and the like, and efficient and reliable technical support is provided for compliance management of enterprises and examination work of supervision organizations.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

The invention provides an audit report automatic generation method based on natural language processing, and relates to the technical field of natural languages, and the method comprises the steps: obtaining audit document data passing the examination and approval, the audit document data comprising an audit manuscript, an audit problem, an audit evidence obtaining sheet and an audit verification sheet; performing semantic analysis on the audit document data through a natural language processing model, and extracting key information of an audit subject, a risk point and a compliance conclusion; inputting the key information into a structured processing algorithm to generate structured data matched with a preset auditing template; and inputting the structured data into a pre-training text generation model for language reconstruction, generating an audit report text, automatically filling the audit report text into a corresponding field of an audit template, and outputting a complete audit report.

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

The invention relates to the technical field of business process compliance inspection systems, in particular to a compliance inspection system and method for an enterprise business process, and aims to solve the problems that in the prior art, an enterprise business process compliance inspection system generally adopts a static risk assessment model and lacks automatic and intelligent data acquisition and processing means, so that the enterprise business process compliance inspection system is inconvenient to use. And the accuracy and timeliness of risk assessment are insufficient. The system comprises a data acquisition module, a risk assessment module, a monitoring and early warning module, a map construction module, an intelligent recommendation module and a report generation module. According to the invention, through automatic data acquisition, preprocessing, risk assessment and monitoring early warning, the efficiency and accuracy of compliance inspection are significantly improved; by utilizing artificial intelligence and big data technologies, the system can quickly identify potential compliance risk points and perform quantitative evaluation, so that errors caused by manual intervention and subjective judgment are reduced, and the efficiency and accuracy of compliance inspection are improved.

Embodiments of the present disclosure provide a report generation method and system, an electronic device, and a storage medium. The report generation method comprises: acquiring requirement text information of a report to be generated in a target application scenario; performing requirement intent recognition on the requirement text information, to acquire a report parameter matching the report to be generated; on the basis of the target application scenario, selecting and arranging a preset intelligent agent, to obtain an intelligent agent call chain; on the basis of the report parameter, performing chain-type calling on the preset intelligent agent in the intelligent agent call chain, to generate a report matching the requirement text information. The present method can automatically and quickly generate a report matching a user requirement, effectively increasing report generation efficiency. Using the present method to generate a report does not require manual data acquisition and analysis; efficiency is higher, and costs are lower.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

The invention relates to a power log intelligent analysis system based on a knowledge graph and a large language model. The power log intelligent analysis system comprises a data collection and preprocessing module, a knowledge graph construction and management module, a context understanding and correlation analysis module, a personalized report generation module and a multi-modal data integration module. According to the method, information such as equipment, events and operations in the power logs is structured through the knowledge graph and is associated with domain knowledge, so that deep understanding and association analysis of log contents are realized, and the problem of insufficient context understanding in a traditional method is solved. By utilizing the strong generation capability of a large language model and based on context information provided by a knowledge graph, a personalized report meeting roles and requirements of a user can be automatically generated, and the user experience and practicability of log analysis are improved; log data in various formats are integrated into the knowledge graph, a comprehensive summary report containing information such as charts and timelines is generated through a large language model, and the problem that heterogeneous data are difficult to integrate is solved.

The invention discloses a contract compliance examination method and platform based on multi-agent cooperation, relates to the field of artificial intelligence, and is used for improving the safety and accuracy of contract examination. The method comprises the following steps: acquiring latest rule data by a data collection agent; the data construction agent constructs retrieval data and a rule knowledge graph based on the rule data; the document analysis agent extracts key information of the target contract and retrieves related rules from the retrieval data according to the key information; the compliance review agent performs risk item assessment on the target contract according to the related rules and the rule knowledge graph; the risk management and control agent generates a response measure of the risk item based on the retrieval data; the report generation agent generates a compliance review report based at least on the evaluated risk items and corresponding countermeasures. According to the invention, the security, accuracy and efficiency of contract review are improved.

The invention relates to the technical field of medical informatization, and discloses a method for generating a medical record report based on a doctor-patient dialogue, and the method comprises the following steps: multi-modal data collection: collecting the multi-modal data of the doctor-patient dialogue, including voice data, video data and environment data; multi-modal data preprocessing: preprocessing the collected data, including noise reduction and feature extraction of voice signals, key point extraction of video data and time synchronization of the multi-modal data; and semantic analysis: converting the voice signal into a text by using a voice recognition technology, carrying out semantic analysis on the text, and extracting key information. Through multi-modal data acquisition, semantic analysis, emotion modeling and knowledge graph reasoning technologies, the comprehensiveness, logicality and dynamic correction ability of medical record generation are improved, meanwhile, the defects of patient psychological state analysis in the prior art are overcome, information orderliness and doctor decision-making efficiency are remarkably improved through generation of the visual medical record report, and the medical record report generation method is suitable for popularization and application. And powerful support is provided for precise medical treatment.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention relates to the technical field of PCB welding spot defect detection, and discloses a PCB welding spot defect detection system and method based on image recognition, and the system comprises an image preprocessing module which is used for obtaining an original image flow and dividing an interested detection area; the feature fusion module is used for extracting multi-modal features to form a fusion set; the defect judgment module is used for establishing a mapping index and obtaining a judgment result; the parameter calibration module is used for verifying the detection parameters and adjusting the mapping index; and the report output module is used for generating a defect detection report. The method comprises the steps of image preprocessing, feature fusion, defect discrimination, parameter calibration, report generation and the like. According to the system and the method, the PCB welding spot defects can be efficiently and accurately detected, the detection precision and stability are improved, a structured report is generated, and an effective solution is provided for PCB quality detection.

The invention discloses a medical image report generation method and system based on a large language model, and relates to the field of image report generation, and the method comprises the steps: firstly obtaining original image data and a clinical background text of a patient, and respectively extracting an image embedding vector and a background embedding vector; then, case retrieval based on priori knowledge is carried out by utilizing the embedded vectors, and K highly related historical case reports are screened out from massive historical data; and inputting the image embedding vector and the historical case report into an observation large language model, and outputting the image embedding vector and the historical case report in a structured JSON (JavaScript Object Notation) format. And finally, a large language model is written to integrate the visual evidence JSON, the clinical background text and the historical case report, and a final medical image report is generated. According to the mode, through a staged and multi-modal fusion mode, the problems of incoherent report logic, inaccurate information and the like are effectively solved, and the report quality and the generation efficiency are remarkably improved.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention discloses a multi-modal data-oriented multi-agent deep forgery attack detection system, and particularly relates to the technical field of electrical equipment image analysis and voice-assisted fault judgment, an image and voice semantic vector is constructed through a semantic extraction module, a semantic alignment module calculates an alignment score and a coverage ratio, and a multi-agent deep forgery attack detection result is obtained. The semantic judgment module recognizes semantic inconsistency, the agent fusion module outputs comprehensive fault judgment based on multi-dimensional scores, and the interpretation display module realizes risk area visualization and semantic deviation labeling and enhances the accuracy, interpretation and practicability of fault detection; structured semantic extraction and alignment judgment of the multi-modal power operation and maintenance data are realized, and the fault detection precision is improved based on dual-index consistency evaluation and multi-level conflict identification; a multi-agent confidence scoring and dynamic fusion mechanism is introduced, so that the judgment credibility is enhanced; and image-text linkage explanation output and report generation are supported, and operation and maintenance results of the power grid operation and maintenance scene can be rechecked and used conveniently.

The invention discloses a radiology report generation method based on semantic alignment, and belongs to the technical field of radiology report generation. The radiology report generation network constructed by the invention comprises a multi-scale feature extraction module, a cross-modal semantic alignment module and a Transform report generation module which are connected in sequence; the multi-scale feature extraction module comprises a convolutional neural network and a multi-scale local sparse attention module which are connected in sequence; the cross-modal semantic alignment module comprises an embedded layer, a multi-head attention module and a gating fusion module, wherein the multi-head attention module and the gating fusion module are connected with the embedded layer. The input end of the multi-head attention module and the input end of the gating fusion module are connected with the output end of the multi-scale local sparse attention module. The predicted radiology report generated by the radiology report generation method is good in quality.

The invention discloses a hardware basic service component test system, which comprises a test management module, a hardware simulation module, an automatic test module, a data acquisition module, a data analysis module, a report generation module and a feedback adjustment module, the hardware simulation module is used for simulating hardware environments under different load conditions; the automatic test module is used for executing predetermined test tasks; the data acquisition module is used for monitoring hardware states and performance data in real time; the data analysis module is used for analyzing the acquired data; abnormal behaviors are identified and performance is evaluated, a report generation module generates a test report, and a feedback adjustment module dynamically adjusts test parameters and resource allocation according to an analysis result. The hardware basic service component testing efficiency, accuracy and comprehensiveness are improved, and the method can be applied to the technical field of hardware testing.

The invention discloses a report generation system and method based on a legal knowledge graph, and relates to the technical field of artificial intelligence and law, and the method comprises the steps: obtaining a legal appeal of a user through a non-standardized domain corpus collection engine, carrying out the multi-dimensional intention deconstruction through a semantic slicing model, and obtaining a judicial element topology network; inputting the judicial element topology network into a law deduction engine of dynamic value perception ability, calculating a decision vector weight through a dynamic value evaluation network, and generating a judicial decision path by using a time sequence feedback mechanism; based on a judicial decision path, through an association rule mining engine of a multi-modal knowledge graph, a dynamic weighting law retrieval confidence matrix is generated in combination with semantic coupling degree analysis and logic path influence evaluation. According to the method, qualitative change of legal consultation from information retrieval to intelligent decision is realized through collaborative innovation of a legal deduction engine with dynamic value perception capability and a legal semantic constraint decoder.

The invention relates to the technical field of data processing, and discloses a financial report generation method and system based on artificial intelligence. The method comprises the steps of preprocessing multi-source financial data to obtain target data; performing semantic understanding through a capital extraction algorithm to obtain a knowledge base; intelligent matching template filling is carried out to generate a preliminary report; verifying and analyzing the logic relationship by applying a causal relationship; optimizing the content based on the verification result; and performing multi-level auditing and updating to obtain a final report. According to the method, intelligent processing, deep semantic understanding and logic verification of financial data are realized by introducing a knowledge-based capital extraction algorithm and a causal relationship extraction model, so that a high-quality financial report which is strict in logic and professional in content is generated.

The invention discloses a multi-modal data driven general report generation method and system based on a large model, and belongs to the technical field of intelligent report generation. Firstly, texts, images and sensor data related to a report theme are obtained and subjected to standardized preprocessing; analyzing the report generation instruction, and matching and querying a task modal mapping library matching modal configuration scheme according to a task demand; quantitatively evaluating the data quality of each modal, dynamically calculating the final decision weight of each modal in combination with the basic weight, and distributing the final decision weight to a corresponding processing path to form dominant, supplementary and reference data; inputting the dominant data and the supplementary data into a multi-modal model for analysis to obtain a preliminary conclusion with confidence score, performing consistency judgment, if no conflict exists, performing fusion to form a comprehensive conclusion, and if the conflict exists, combining a quality evaluation result and an arbitration rule to complete conflict judgment; and finally, inputting the comprehensive conclusion and the reference data into a large language model to generate a report text, and outputting a complete report after typesetting and proofreading.

The invention discloses an image report generation method, system and device based on multi-modal learning optimization, and the method comprises an image retrieval module which is used for carrying out the image feature extraction of an input medical image, and obtaining similar medical images and reports; the simulation generation module is used for guiding an image report generation model to generate an initial draft report by utilizing the medical image and the report; the personalized diagnosis module is used for carrying out personalized adjustment on the report generation model according to the historical report preference of the doctor or the historical medical data of the patient; the reinforcement learning optimization module is used for dynamically adjusting a text generation mode based on a reinforcement learning report optimization strategy and optimizing an image report generation model in combination with doctor scoring, automatic evaluation and clinical feedback; and the iterative refining module is used for performing text retrieval by using the initial draft report as a query, acquiring text fragments which are semantically similar to the initial draft report, and performing neural symbol reasoning optimization to generate a final report.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention discloses a BIM-based two-dimensional data and three-dimensional model verification matching method and system. The system comprises a 2D drawing processing module, a 3D model processing module, a difference detection module and a report generation module. In the 2D drawing processing module, a user imports a process pipeline plan and a profile map, and through preprocessing and image semantic analysis, pipeline elevation labeling information is extracted and numbered. And the 3D model processing module loads a target BIM model, locates a process pipeline area, analyzes pipeline parameters and a space state, analyzes a pipeline topological relation and performs conflict verification. And the difference detection module binds the 2D drawing with a corresponding pipeline object in the 3D model, establishes a mapping relation, compares differences, and performs labeling and classification. And the report generation module is used for carrying out visual annotation in the 2D drawing and the 3D model based on the annotation difference, automatically generating a difference comparison table and outputting a report, so that efficient and accurate verification and matching of the two-dimensional data and the three-dimensional data are realized, the examination period is shortened, and the rapid collaborative correction efficiency of the construction team is improved.

Provided in the present invention are a radiology report generation method and apparatus, and a terminal and a storage medium. The method comprises: inputting a radiology image to be processed into a pre-trained dynamics priori network model, wherein the dynamics priori network model comprises a dynamic knowledge graph network, a prior knowledge network and a decoder; in the dynamic knowledge graph network, obtaining a dynamic knowledge graph on the basis of the radiology image, and obtaining a visual representation on the basis of the dynamic knowledge graph; acquiring prior knowledge information corresponding to the radiology image, and on the basis of the prior knowledge information and the visual representation, using the prior knowledge network to obtain a prior representation vector; and inputting the visual representation and the prior representation vector into the decoder to generate a radiology report corresponding to the radiology image. In the present invention, the radiology report is generated by combining the dynamic knowledge graph with the prior knowledge information, such that visual and textual biases caused by limited data availability can be better handled, thereby improving the report generation quality.

The invention relates to a financial bill management system and an implementation method thereof. The financial bill management system comprises a data acquisition module, an AI identification module, an auditing module, a cost reimbursement module, a report generation module and a data storage and backup module. According to the financial bill management system and the implementation method thereof, by introducing the AI recognition module, automatic recognition and classification of bill types are achieved, the requirement for manual operation is reduced, the working efficiency and accuracy are improved, the AI recognition module can automatically process bill information, the whole process from image preprocessing to final key information extraction is highly automatic, and the efficiency is high. The system is not limited to bill identification and classification, but also covers a plurality of functional modules such as data acquisition, auditing, expense reimbursement processing, report generation, data storage and backup and the like, and provides a one-stop solution for users. The image preprocessing unit can effectively remove image noise, correct inclined images, cut redundant parts and enhance image contrast and brightness.