183 results about "Visual tool" patented technology

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

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 the field of legal service management, and discloses a legal service management system based on a block chain, and the system comprises the following modules: a block chain network, an intelligent contract module, a data storage module, a user interface module, a feedback and monitoring module and a user management module, the non-tampering property and the transparency are ensured; the intelligent contract module automatically executes contract terms and supports intelligent management; the data storage module adopts a hierarchical structure, and on-chain data and off-chain data are stored in a distinguished manner, so that the storage and access efficiency is optimized; and the user interface module provides user-friendly operation experience through a visual tool. By using the block chain technology, all contract execution and compliance audit data are encrypted and stored in a distributed mode, it is ensured that tampering is avoided and whole-course traceability is achieved, and a user and related mechanisms can verify the authenticity and integrity of the contract data at any time.

The invention discloses a remote automatic quality control and calibration method and system for a pH meter, and the method comprises the following steps: S1, carrying out the initial temperature compensation correction of a pH value, and generating a corrected pH value; s2, environment parameters collected by an environment sensor and the corrected pH value are combined and formatted; s3, cleaning the standardized data set at an edge computing node, and generating an optimized measurement data set; s4, performing confidence evaluation based on a committee query algorithm, and triggering a calibration process for a detected low-confidence sample; s5, generating an enhanced calibration curve by using a double-neural network regression model, and optimizing measurement data in real time by combining particle filtering; s6, displaying the final data in real time through a visual tool, and uploading the final data to a cloud end; and S7, dynamically adjusting the calibration period, the acquisition frequency and the communication strategy of the sensor through the edge computing node. According to the method, double neural network regression, particle filtering, cloud edge cooperation technologies and the like are utilized, and accurate measurement and remote intelligent calibration of the pH meter are achieved.

The invention discloses a dangerous rock mass shear zone quality evaluation method based on machine vision, and relates to the technical field of dangerous rock mass monitoring and evaluation, and the method comprises the following steps: S1, arranging a high-resolution camera, adjusting the angle and focal length, and regularly and automatically collecting and transmitting image data to a server; s2, de-noising by using a filter, adjusting a contrast ratio, equalizing a histogram, and enhancing image definition; s3, extracting key features and covering key areas by applying edge detection and feature point detection algorithms; canny edge detection and an SIFT algorithm are applied to perform feature extraction, a convolutional neural network (CNN) is adopted to perform model training and cross validation, shear band changes are monitored in real time, early warning is triggered, analysis results are displayed through visual tools such as a thermodynamic diagram, a standardized report is generated, normalization and consistency of the monitoring results are ensured, and the monitoring accuracy is improved. Therefore, the monitoring efficiency and accuracy of the dangerous rock mass shear zone are improved, and the geological disaster risk is effectively prevented.

The invention relates to a carbon emission prediction method based on electric power big data, and the method comprises the following steps: S1, collecting electric power production and consumption data, and constructing a data pool; s2, statistical features are extracted and generated through statistical analysis and an automatic encoder technology, spatial features are extracted by using a geographic information system technology, periodicity and tendency of time sequence data are analyzed to obtain time features, a causal inference method is introduced to identify key causal features, and finally a comprehensive feature set is constructed; s3, a regression model is adopted as a basic model, LSTM is introduced to process complex and long-sequence data, the nonlinear fitting ability of the model is enhanced, the generalization ability and accuracy of the model are improved through integrated learning, a carbon emission prediction model is constructed, and training is carried out based on a comprehensive feature set; s4, on the basis of the trained carbon emission prediction model, carbon emission prediction is carried out according to real-time data; and S5, displaying a prediction result through a visual tool, and supporting user-defined view and analysis. The accuracy of carbon emission prediction is improved.

The invention discloses an urban traffic intelligent management method based on multi-source data, and relates to the technical field of traffic data management, and the method comprises the steps: collecting multi-source traffic data, firstly carrying out the preprocessing, then carrying out the matching and integration, and building a unified traffic data warehouse; carrying out batch processing and stream processing on warehouse data by applying a distributed computing framework and a stream processing technology; on the basis of multi-source data, various traffic models are constructed and optimized, and a visual tool is developed to visually display an analysis result; by combining real-time traffic data, a model prediction result and historical data, traffic signals are dynamically adjusted, route planning and navigation services are provided for users, and routes and scheduling of public traffic are optimized; a traffic monitoring center is constructed, and real-time monitoring and timely early warning are carried out based on the traffic signal dynamic adjustment result and the real-time traffic data; establishing an emergency plan; and establishing a data sharing platform, and sharing the related traffic data. The system is used for monitoring, analyzing, managing and applying the traffic data.

The invention relates to the technical field of water quality monitoring and drainage basin water environment management, in particular to a drainage basin water quality prediction method and system based on a spatial flow river network topological structure, and the method comprises the steps: firstly, determining a monitoring region, obtaining drainage basin digital elevation model (DEM) data and drainage basin boundary data, and then obtaining water quality data in a drainage basin through setting a monitoring point; the method comprises the following steps: firstly, mainly monitoring an area with relatively large water quality change, then obtaining an environment variable in a drainage basin, analyzing and generating an accurate prediction river network and a prediction point in combination with a spatial flow river network topological structure, then predicting water quality parameters by constructing a spatial flow river network topological structure model and combining water quality data of a monitoring point and the environment variable, and finally, predicting the water quality parameters by constructing a spatial flow river network topological structure model. A prediction result is displayed through a geographic information system (GIS) visual tool, real-time monitoring and water quality early warning are facilitated, the technical scheme is suitable for water quality prediction of a large-scale drainage basin, the change of the water quality of the drainage basin can be monitored in real time, and a scientific basis is provided for drainage basin management and pollution prevention and control.

The invention discloses an asset use analysis method and system based on an intelligent prediction and optimization algorithm, and aims to solve the problems of low efficiency, insufficient prediction capability and unscientific purchase decision in existing asset management. According to the invention, asset use records, maintenance information and purchase history are obtained through the data acquisition module; constructing an asset use prediction model by using time sequence prediction, regression analysis and machine learning algorithms, and predicting future asset demand quantity and use trend; generating an optimized purchase plan and an asset allocation scheme in combination with an inventory state, an asset life cycle and a budget constraint; and the prediction result and the optimization suggestion are displayed through a visual tool, and support is provided for decision making. According to the invention, efficient planning and purchase optimization of asset use are realized, and the method can be widely applied to the field of enterprise asset management and supply chain optimization.

The invention relates to a computer-implemented system for the management of product development projects. The system (100) includes a processor that operates based on instructions from a non-transitory computer-readable medium to manage and process project requirements. Utilizing a natural language processing (NLP) interface, the system (100) captures user inputs, converting them into structured project data, which is then automatically updated and synchronized across various components such as system models and operational conditions. The system (100) includes a memory that supports the execution of program instructions for handling multiple, distinct projects within a unified platform. This allows for the reuse of common functional elements across different projects. A graphical user interface (GUI) is connected to the processor, providing interactive visual tools for real-time project tracking and adjustments. The security module enforces role-based access controls to protect data integrity and regulate access based on user roles within the project framework.

The invention provides a construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

The invention belongs to the field of scientific and technological achievement conversion, and discloses an achievement conversion supply and demand matching system and method, and the system comprises a feature extraction unit which is used for converting collected technical achievements and demand information into high-dimensional vectors; the supply and demand association degree mining unit is used for dynamically adjusting the matching priority of the result feature data in different fields by using an entropy weight algorithm and mining the supply and demand association degree between the technical result and the demand information; the optimal matching result acquisition unit is used for defining a multi-objective function and acquiring an optimal matching result through a genetic algorithm; the matching result verification and optimization unit is used for optimizing a matching process through user feedback data; and the matching result display unit is used for displaying the supply and demand matching result by utilizing a visual tool. According to the method, the technical achievements and the demand information are converted into the high-dimensional vectors easy to process, so that the information processing efficiency is improved.

Described herein are examples of interfaces for designing and / or configuring custom assisted defect recognition (ADR) systems and / or workflows. In some examples, the ADR systems and / or workflows may be designed and / or configured using visual tools, such as, for example, node based processing tools. In some examples, the ADR workflows may be used to analyze two dimensional (2D) and / or three dimensional (3D) image scans, such as might be generated by industrial X-ray scanning systems.

The invention relates to the technical field of aquatic product resource management, in particular to a protection area management planning method and system based on aquatic product resource survey data, and the method comprises the steps of data collection and preprocessing, data fusion and feature extraction, aquatic product resource evaluation and analysis, protection area management planning making, dynamic monitoring and adjustment and decision support and visual display. The method comprises the following steps: deploying aquatic product resource investigation equipment, remote sensing equipment and environment monitoring equipment, acquiring aquatic product resource data in real time, identifying key feature variables, performing partition evaluation on aquatic product resources by combining a clustering analysis method, formulating a protection area management plan, and performing resource change trend prediction through dynamic monitoring and a machine learning algorithm. Dynamic adjustment of protection area management is realized, a visual tool is used to display a resource change trend and a management state, and real-time decision support is provided for a decision department. According to the method, the management capability of the aquatic product resource protection area can be effectively improved, and a scientific guarantee is provided for ecological protection and sustainable utilization of resources.

The invention relates to a conveying belt tearing positioning and tracing system based on RFID and multi-sensor fusion, which effectively overcomes the influence of belt slipping and elastic deformation through multi-sensor fusion and motion compensation, and realizes accurate positioning of a tearing point. Meanwhile, through cross validation of vibration, vision and infrared data, the recognition accuracy is improved to 99.5% or above, false alarms caused by environmental factors (such as dust and moisture) are greatly reduced, stable operation in a severe industrial environment is ensured, and high reliability and low false alarm rate are achieved. And thirdly, the system supports long-term data tracing and intelligent operation and maintenance, and a user can analyze a tearing trend and identify a high-frequency fault area by storing historical data of at least 180 days and providing a visual tool, so that a maintenance plan is optimized, predictive maintenance is realized, and faults are reduced from the source.

The invention relates to a gas accident analysis system and method based on a dynamic Bayesian network. The method comprises the following steps: constructing a dynamic Bayesian network model; gas equipment operation state data and environment parameters are obtained; processing data, and performing gas accident risk assessment and accident evolution path analysis by using an inference algorithm; and generating an accident analysis result and outputting the accident analysis result through a visual tool. According to the gas accident analysis system and method based on the dynamic Bayesian network, multiple risk factors of a gas accident are comprehensively analyzed through the dynamic Bayesian network technology, the possibility of accident occurrence can be effectively predicted, the severity of the accident can be effectively evaluated, and reasonable prevention and response measures are put forward. Compared with a traditional risk assessment method, the method is higher in accuracy, real-time performance and flexibility, can provide more scientific safety management decision support for gas enterprises, remarkably improves the gas accident prevention and emergency processing capability, and has a wide application prospect.

The invention discloses a power enterprise production data quality analysis and optimization method and system, and the method comprises the following steps: S1, obtaining the production data of each production link of a power enterprise, and carrying out the preprocessing of the production data; s2, performing quantitative evaluation on the production data to obtain a comprehensive evaluation score; s3, in a preset time range, if the comprehensive evaluation score of the production data is smaller than a preset threshold value, identifying the production data, identifying an abnormal data type in the production data, marking the position of the abnormal data type, and judging factors generated by the abnormal data according to the position of the abnormal data; s4, optimizing and repairing the types of the abnormal data; and S5, displaying the comprehensive evaluation score of the production data and the production data optimization result through a visual tool. By analyzing the type, the position and the generation factors of the abnormal data, the efficiency of the production process is effectively improved, and the fault occurrence probability is reduced.

The invention discloses a method for systematically managing and publishing a machine learning model, which comprises the following steps of: constructing a resource service system in three modes of containerized resources, physical machine / virtual machine resources and big data cluster resources, and dynamically selecting configuration according to business requirements; registering a model file developed by multiple programming languages to a unified model warehouse, automatically generating a version identifier, and binding a mapping relationship between a feature field and a data set; model deployment is verified through a multi-role collaborative approval process, one-key automatic deployment is realized based on a containerization technology, and version information is recorded to support rapid rollback; issuing an online / offline prediction service interface, collecting service data in real time, and monitoring and evaluating model performance, service traffic and decision logic through a visual tool; according to the invention, a model unified management, rapid deployment and release and visual effect evaluation management system constructed by multiple types of programming languages is supported, and models generated under different technology stacks can be conveniently and rapidly maintained through the system.

The invention discloses an overhead transmission line point cloud classification method and system based on rule improved RandLA-Net, and a storage medium, and belongs to the field of three-dimensional laser point cloud processing and power system automation. The classification method comprises the following steps: collecting original point cloud data of the overhead transmission line, and performing preprocessing after data conversion and data classification to obtain a standardized point cloud data set; constructing and training an improved RandLA-Net point cloud classification model, performing feature enhancement through a channel attention mechanism, and outputting a preliminary classification result; performing optimization processing on the preliminary classification result based on a prior rule to obtain a final classification result; and outputting the final classification result and displaying the final classification result through a visual tool for supporting power transmission line inspection. Through improvement of a channel attention mechanism, data preprocessing optimization, combination of four types of prior rules and the like, high-precision classification of power transmission equipment is realized, and reliable data support is provided for power inspection.

The invention discloses a cloud wisdom digital information network transmission system, and relates to the field of information network transmission, and the key points of the technical scheme are that the method comprises the steps: collecting the original information and transmission path information of an initial data packet, carrying out the preliminary preprocessing of the initial data packet at a transmission starting end point through employing a forward error correction technology, and obtaining an actual data packet; extracting transmission path information of each actual data packet, constructing a transmission directed graph, and obtaining an optimal transmission path of the actual data packet in the transmission directed graph by using an optimized particle swarm algorithm; controlling the number of actual data packets at each transmission starting endpoint and transmission ending endpoint by dynamically adjusting the sizes of the starting window and the ending window, and sequentially performing node transmission on the actual data packets according to the optimal transmission path until the transmission ending endpoint is stopped; a visual tool is provided for checking the transmission process in real time, interface interaction is carried out, and optimized transmission of the cloud intelligent digital information network is achieved.

Sampling operations enable a computer vision tool to regulate downstream tasks. The sampling operations can indicate which frames of a video sequence should be processed by different downstream tasks. For example, a computer vision tool receives encoded data for a given frame and uses the encoded data to determine inputs for machine learning models in different channels. The computer vision tool provides the inputs to the machine learning models, respectively, and fuses results from the machine learning models. In this way, the computer vision tool determines a set of event indicators for the given frame. Based at least in part on the event indicator(s) for the given frame, the computer vision tool regulates downstream tasks for the given frame (e.g., selectively performing or skipping downstream tasks for the given frame, or otherwise adjusting how and when downstream tasks are performed for the given frame).

The invention discloses a concrete filled steel tube service life prediction method and system based on machine learning. According to the method, sensor data, external environment data, unmanned aerial vehicle images and satellite remote sensing data are collected to serve as original data and real-time monitoring data, fusion, denoising, normalization and missing value filling are conducted on the original data, and a feature matrix is generated. And training the feature matrix by using a machine learning model to obtain an initial life prediction model, and continuously receiving real-time data through an incremental learning algorithm for dynamic updating to obtain a dynamically optimized life prediction model. According to the structure aging characteristics, life prediction is divided into an initial aging stage, a middle-term damage stage and an advanced-age stage, and accurate prediction is carried out by applying customized models. And finally, integrating the prediction result to an intelligent decision support platform, and displaying the prediction result, the structure health state and the maintenance suggestion through a visual tool. The precision and real-time performance of service life prediction of the concrete-filled steel tube structure are remarkably improved, and the method has a wide application prospect.

The invention provides an auditing text auxiliary compiling method and system based on a large language model, and belongs to the technical field of intelligent office. Efficient reuse of experience is realized by constructing a structured knowledge base and performing word segmentation, entity recognition and theme clustering on historical documents by utilizing a natural language processing technology; dynamically generating an adaptive template, automatically filling fields in combination with service data, and supporting conditional logic to adjust contents; calling a large language model to generate a standard text, generating a table and a chart in combination with Pandas and a visual tool, and realizing multi-element mixed arrangement and format automatic verification; a user is guided to edit through an interactive interface and is stored in a knowledge base, a closed-loop evolution mechanism of auditing knowledge from reuse, generation to feedback is formed, and the normalization and efficiency of text writing are improved.

The invention discloses an alarm clustering topology method and system based on multi-dimensional features, belongs to the technical field of network security, and aims to solve the technical problems of poor timeliness, multiple repeated false alarms and low association degree of network alarm information processing. Comprising the steps of collecting original alarm information; performing multi-dimensional aggregation on the pre-processed alarm information based on a set alarm dimension; calculating the alarm weight of each alarm set based on the weight of each reference factor; clustering each alarm set based on the new alarm dimension; screening the alarm information of alarm convergence based on a predefined rule base; calculating the alarm number and the alarm weight sum between each pair of IP node addresses, and calculating the thickness and the color depth of a side line according to the normalized weight sum; an IP node topological graph and side boundary thickness and color depth information are constructed through a visual tool, and a topological relation graph between IP nodes is drawn; and visually displaying the alarm information.

The invention provides a construction system for realizing a business intelligent data question-answering system based on semantic recognition, which belongs to the technical field of information and comprises a user interaction layer, a semantic analysis layer, a data access layer, a data analysis layer and a visual presentation layer. A user can input a query request through a natural language, and the system analyzes the intention of the user by using an advanced semantic recognition technology and quickly extracts related information from mass business data. And then, the system visually presents an analysis result to the user in forms of charts, reports and the like through a visual tool, so that a business department is assisted to quickly understand a data trend and make an efficient decision. According to the system, the data query and analysis process is simplified, the dependence on professional data analysts is reduced, the working efficiency is greatly improved through automatic and intelligent means, the operation and maintenance cost is reduced, good expansibility and integration are achieved, and diversified business requirements of business departments are met.

The present invention is directed to a system and method for displaying interior design proposals for a given room according to known design principles and user preferences. The system is operative to convert user-input sketches and / or blueprints of two-dimensional space geometry into digital floor plans and identify and propose blocks of appliances and / or articles of furniture as design elements defined by their dimensions and any limiting characteristics. Each block is functionally and dimensionally appropriate for the floor plan based on user preferences, as filters, and floor plan limitations and may be defined by minimum space requirements, anchor point restrictions, and configuration restrictions. The system and method may further comprise visual tools for conveying a user's creative taste, style, and aesthetic for each floor plan, and be operative to display hyperlinked shopping lists in accordance with filtered blocks of design elements.

The invention discloses a method for constructing an offshore wind turbine fault sensitivity evaluation knowledge graph. The method comprises a method for avoiding limitation caused by lack of rapid diagnosis and advanced prediction of similar faults of a wind turbine generator. The structured marine meteorological data and the fan SCADA operation data are processed, fault sensitive feature factors are extracted, and feature factors with high fault correlation are comprehensively extracted; abstraction fault sensitivity characteristic factors are used as nodes, unstructured information in operation and maintenance data is abstracted into entity nodes or attribute nodes, the relation between the nodes is established, the incidence relation of fan faults is understood and analyzed, a graphic structure is presented through a visual tool, and the fault diagnosis efficiency is improved. A user can visually carry out query and reasoning operation, and the situation that personnel lack rapid diagnosis and advance prediction on similar faults of the wind turbine generator through an SCADA system and working experience of the personnel, and limitation occurs is avoided.

The invention discloses a social group discovery system based on fused semantic information. The system comprises a presentation layer, a presentation layer, a service layer and a data layer. The presentation layer supports checking of a group division result; the display layer displays a user structure network, group dynamic division and group characteristic indexes through a visual tool; the service layer is composed of a semantic tag generation module, a user structure analysis module, a social group division module and a social group analysis module, semantic and structural features are fused through a graph attention network, and accurate group division is realized in combination with an adaptive embedded graph aggregation algorithm; and the data layer stores the original data set of the social platform and the processed data. The system inputs user texts into a large language model to generate semantic tags, node embedding is generated by calculating node association weights, group division is completed by combining an adaptive embedding graph aggregation algorithm, and finally group characteristics are quantitatively analyzed from the aspects of cohesion, conductivity and the like. The system is suitable for scenes such as vertical domain community mining.

The invention discloses a diabetic intestinal preparation quality prediction method based on SHAP, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: collecting clinical feature data of a diabetic, and carrying out the preprocessing of the clinical feature data; constructing a diabetic intestinal preparation quality prediction model, taking the preprocessed clinical feature data as model input, and taking the intestinal preparation qualification rate as output; and an SHAP interpretable artificial intelligence framework is introduced to analyze and attribut the prediction model. According to the method for predicting the intestinal preparation quality of the diabetic patient, the prediction model is constructed by integrating multi-dimensional clinical data and applying an ensemble learning algorithm, so that the prediction accuracy and stability are remarkably improved, the limitation that the traditional method depends on doctor experience is broken through, the judgment inconsistency caused by individual difference and diagnosis and treatment pressure is reduced, and the prediction accuracy and stability are improved. The interaction between the features is displayed through a visualization tool, and the interpretability and transparency of the model are enhanced.

The invention relates to the technical field of low-code development platforms, in particular to a simulation process testing method based on a low-code platform, which comprises the following steps: based on the low-code platform, adopting a K-means clustering algorithm and a decision tree classification method to analyze historical process data, performing mode and exception recognition, and generating a process mode and an exception condition. According to the method and the device, through a low-code platform and a machine learning algorithm, the flow mode and the abnormal condition can be identified more accurately, and the test efficiency and accuracy are improved. And test parameters are optimized by using reinforcement learning, and the strategy is adjusted in real time. And the subtasks are executed in parallel, and the parallel processing capability is improved by message transmission coordination. Visual tools and interfaces are provided, and the design and execution process is simplified. And a real-time editing and debugging function is realized, and the flow chart is dynamically modified. AI and NLP technologies are utilized to analyze a process model and requirements, an intelligent test strategy and result analysis are generated, and deep process insight and understanding are provided.