906results about "Other databases browsing/visualisation" patented technology

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention discloses a mass data display method, device and equipment based on a browser client side and a medium, relates to the technical field of computers, is applied to a server side and comprises the steps that a data obtaining request sent by the browser client side is obtained; the data acquisition request carries a request time range and a target sampling frequency; obtaining original time sequence data in the request time range from a preset time sequence database, and determining the size of a sampling window based on the data frequency of the original time sequence data and the target sampling frequency; the data frequency is greater than the target sampling frequency; grouping the original time series data based on the size of the sampling window so as to obtain a plurality of data windows, and extracting a target feature point for representing data trend change in each data window; and merging the target feature points of the data windows to obtain a downsampled data sequence, and sending the data sequence to a browser client for display. According to the method and the device, effective visual display of mass data on the browser client can be realized.

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional data visualization supply chain process optimization method and system based on big data, and provides a supply chain dynamic optimization thought based on multi-source heterogeneous data fusion and intelligent decision closed loop. By constructing an end-to-end multi-source heterogeneous data real-time cooperative processing and intelligent decision closed-loop architecture, dynamic integration, intelligent analysis and automatic response and update optimization of full-link data of the supply chain are realized, so that the real-time analysis capability and dynamic adjustment capability of the supply chain are remarkably improved.

The invention discloses a construction method and system of a power distribution network visualization platform based on SG-CIM, and relates to the technical field of construction of power distribution network visualization platforms. According to the construction method of the power distribution network visualization platform based on the SG-CIM, power text information of a power distribution network is obtained and input into a pre-trained semantic recognition model for analysis, an equipment attribute triple and an equipment relation triple of each piece of power equipment are obtained, topology reconstruction processing is carried out on the equipment relation triple, and the power distribution network visualization platform based on the SG-CIM is constructed. According to the method, a power equipment topological structure diagram is generated, graph modeling processing is carried out, a semantic knowledge graph is generated, then quality verification analysis is carried out, and a semantic structure coordination index is obtained. Therefore, the uniformity of semantic expression and topological connection logic of each power device in the power distribution network is guaranteed from the source, and the intelligent analysis capability, the operation visualization capability and the automatic verification capability of the power distribution network structure are improved.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and / or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

The present application belongs to the technical field of visualization, and provides a method and system for tracking and visualizing scientific research hotspots, which comprises the following steps: obtaining scientific research literature data in multiple dimensions; performing cluster analysis and heat mining on the scientific research literature data to obtain target scientific research literature data with potential hotspots; tracking and analyzing the heat persistence and heat stability of the target scientific research literature data, and screening to obtain hot scientific research literature data; and performing visual analysis on the hot scientific research literature data to obtain hot visual analysis results. The present application can improve the accuracy of obtaining scientific research hotspots and the visual effect by obtaining scientific research literature data in multiple dimensions, tracking and analyzing the heat persistence and heat stability of target scientific research literature data with potential hotspots, screening to obtain hot scientific research literature data, and finally performing visual analysis.

The invention discloses an automatic settlement path generation method fusing a knowledge graph and a tax rule, and relates to the technical field of financial science and technology and intelligent finance and tax compliance, and the method comprises the steps: S1, obtaining initial decision basis data and execution process details from a settlement path through a block chain network, employing a Hash chain structure to link each link record, s2, according to the obtained path chain, a graph database is adopted to store dynamic association attributes, and the dependency relationship of each node in the path is determined through knowledge graph reasoning, and S3, if an information fault is detected in the path chain, the matching degree of an execution result and a decision basis is compared through a timestamp verification mechanism, and if the information fault is detected in the path chain, the decision basis is determined. Judging a deviation position and marking a deviation point; according to the automatic settlement path generation method fusing the knowledge graph and the tax rules, the security and reliability of financial transactions and the tax management efficiency are effectively improved.

The invention belongs to the technical field of engine fault diagnosis, and provides an engine test fault diagnosis method, system and device and a storage medium, and the method comprises the steps: collecting and analyzing engine data, and obtaining a fault data set; converting the fault data set into a target format; dynamically updating the target knowledge graph based on the fault data set in the target format; and inputting the query elements into the target knowledge graph, obtaining fault data of the corresponding engine, and obtaining a fault reason. According to the method, the dynamic target knowledge graph is adopted, after fault detection, the target knowledge graph can be updated according to the data of the engine, the node relation can be automatically expanded in real time, manual intervention is reduced, and more accurate diagnosis is provided for subsequent fault query; according to the method, nodes and fault data can be visualized during query, and an interactive fault diagnosis and learning interface can help a user to quickly position a fault source and accumulate fault detection experience.

The invention relates to an artificial intelligence technology, can be applied to the technical field of finance, and discloses an artificial intelligence-based abnormal data identification method, device, equipment and medium, and the method comprises the steps: collecting investment data in real time, and inputting the investment data into an investment data semantic large model; performing anomaly identification on the investment data based on the investment data semantic large model to generate an abnormal data identification result; obtaining abnormal investment data in the abnormal data identification result, analyzing an abnormal condition of the abnormal investment data based on a graph neural network algorithm, and generating an abnormal reason and abnormal positioning information; and obtaining a repair suggestion based on the exception reason, and pushing the exception reason, the exception positioning information and the repair suggestion to a visual interface. According to the invention, investment data is scanned in real time through the AI algorithm, abnormal values are rapidly identified, and human errors are reduced; abnormal reasons can be automatically positioned, repair suggestions can be provided, real-time monitoring is achieved, and abnormal data recognition efficiency is improved.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The invention discloses a data asset visualization and collaborative governance method and system based on an AI agent, and the method comprises the steps: carrying out the mapping of attribute parameters, such as the type, scale, value and the like, of data assets, building a feature space, completing the nonlinear mapping from high dimension to low dimension through a t-SNE model, and achieving the visualization feature presentation of the data assets. Initializing a deep Q network agent, setting a reward function by taking visual features as a state space, governance operation as an action space and parameter change, selecting actions by the agent according to the reward function to execute collaborative governance, and storing empirical data into a buffer region to train the network; and training is repeated until network convergence, and a visual and cooperative treatment strategy is generated. The system is composed of a data asset feature mapping unit, a t-SNE conversion unit, an agent initialization unit and the like, all the units are mutually connected and cooperatively operate, data asset visualization and intellectualization and automation of cooperative governance are achieved, and the data asset management efficiency and the value mining capacity are effectively improved.

The invention, which relates to the technical field of printing production management, discloses a production processing tracking system and tracking method for a printing industry, and the method specifically comprises the steps: creating a corresponding production order in a preset printing production management system based on customer demand information, generating a unique identification code for each production order according to a dynamic random number algorithm, and associating and binding the unique identification code with each production order; obtaining a process node of each production order associated with the unique identification code to construct a process dependency graph of each production order; calculating the remaining delivery time of each production order associated with the unique identification code, and sorting each production order in real time to construct a self-adaptively updated priority scheduling queue; obtaining a current process node of each production order, and obtaining a state attribute of each process node of each production order and a corresponding execution progress; and carrying out full-link visual display on the state attribute of each process node of each production order and the corresponding execution progress, and inputting a unique identification code through a client to carry out query.

The invention discloses a multi-source sensing data fused rotary excavating hole-forming verticality online monitoring system and method, and the system comprises a drill bit inclination angle monitoring device which collects and stores the roll angle and the pitch angle of a drill bit, and transmits the roll angle and the pitch angle to a cloud server; the drill rod rotation angle monitoring device collects the rotation angle of the drill rod relative to the machine body and transmits the rotation angle to the drill bit azimuth angle resolving unit; the fuselage attitude monitoring device collects a roll angle, a pitch angle and an azimuth angle of the fuselage and transmits the roll angle, the pitch angle and the azimuth angle to the drill bit azimuth angle resolving unit; the drill azimuth angle resolving unit is used for calculating a drill azimuth angle and transmitting the drill azimuth angle to the cloud server; the cloud server calculates the track of the drill bit and the perpendicularity of the formed hole by combining the formed hole depth and the drill bit position data provided by the rotary drilling rig; and the visual terminal displays the track of the drill bit and the perpendicularity of the formed hole by accessing the cloud server. According to the invention, real-time monitoring of the perpendicularity of the formed hole can be realized, and the construction efficiency and quality of the rotary drilling hole are improved.

A system is disclosed herein for attributing user affective responses to discrete elements within an experience. Software agents extract token instances from sensor data, while a response decomposition module allocates portions of measured affective response to tokens based on user attention. A token library stores token-response associations across users and domains, enabling prediction of responses in new contexts. An orchestration module coordinates agents and library updates, with privacy managers ensuring local processing of raw data. The system distinguishes tokens of interest from background elements, supporting cross-domain learning and overcoming limitations of static, siloed affective measurement systems.

The invention discloses a localization-adaptive track point visual rendering method based on partitioning and hierarchical caching, and the method comprises the steps: S1, inputting a track data stream, carrying out the reading according to the source type of the data stream, and carrying out the data partitioning and LOD processing of the track point data; s2, performing data analysis and rendering on the data; s3, carrying out batch processing on GPU vertexes, according to the state of the circular buffer, if the vertexes are writable, incrementally writing vertex data, and if Wrap is needed, carrying out loopback writing and updating a pointer; and S4, finally, the descriptors and the styles are updated, rendering increment drawing is carried out on the track points, and visual display is carried out. The method supports real-time rendering and playback of ten-million-level track points, has visual and instantiated rendering of window dynamic loading, space-time partitioning, hierarchical caching and WebWorker decoupling calculation, and improves the space-time data rendering capability of the domestic autonomous controllable field by ten thousand times; and the user experience is greatly improved by a non-perceptual interaction and transition smoothing algorithm.

The invention relates to the technical field of industrial robot data acquisition and transmission, in particular to a welding robot behavior recording system integrating edge acquisition and cloud management, which comprises the following modules: an edge end data acquisition module for acquiring the state of a welding robot and sensor data in real time, and performing time alignment and validity processing, generating edge end data; the data preprocessing module is used for optimizing the edge end data and unifying the data format to form preprocessed data; and the local storage management module is used for carrying out hierarchical storage and index management on the preprocessed data according to the data access frequency and importance. According to the invention, through a breakpoint continuous transmission mechanism of cooperation of the edge end and the cloud end, fragmentation and recording of the transmission progress in the data transmission process are realized, continuous transmission is only performed on the uncompleted fragmentation after the network is interrupted, meanwhile, sorting and integrity verification are performed on the received fragmentation at the cloud end, and reliable uploading of large-capacity data in a complex network environment is realized.

The invention provides a self-defined graph node visualization method based on dynamic rendering of service attributes, which comprises the following steps of: receiving and verifying node style configuration in a JSON format, and analyzing the node style configuration into an internal model containing graph definition, style, rule and position; dynamically evaluating whether graphic elements are displayed or not according to rules in combination with node service data, and generating a to-be-rendered set; and finally, performing layered rendering: firstly drawing a basic shape and a style, and then rendering dynamic elements meeting conditions according to a sequence to finish node drawing. According to the method, a user only needs to set rules and styles through a configuration interface, drawing codes do not need to be written, the system can automatically and dynamically render nodes with rich visual elements according to the service data, and the expressive force and development efficiency of graph visualization are greatly improved.

The invention relates to the technical field of ocean observation, in particular to an intelligent quality control system and method for ocean survey data, which takes whole-process data quality closed-loop management and control as a core, proposes an element-standard-process three-dimensional quality control framework, constructs a real-time and timing dual-mode architecture, and integrates a whole-chain visualization and non-intervention automation technology. By extracting more than 40 differentiated quality control elements of more than 10 types of data, a four-layer quantitative standard of equipment state-parameter setting-data integrity-operation compliance is established, and closed-loop control of the whole process is standardized. The error condition detection rate of the system reaches 89% or above, the manual quality control time cost is reduced by 90% or above, the quality control time efficiency is compressed to the real-time / timing hour level, paradigm innovation from post-event error correction to real-time intelligent management and control is achieved, core support is provided for high-quality collection and high-reliability application of ocean survey data, and the method is suitable for various ocean comprehensive scientific investigation scenes.

The invention discloses a multi-dimensional graph series-parallel and visualization method for telecommunication network fraud cases based on a graph database. According to the method, intelligent series-parallel analysis across time-space cases is realized by constructing a knowledge graph containing various entities such as cases, personnel, accounts and equipment and deeply fusing a hard association matching algorithm and a soft similarity graph algorithm. The system has the functions of white list filtering, time sequence behavior analysis, NLP information extraction and the like, and can effectively identify criminal gangs, track fund flow directions and mine crime modes. And finally, dynamic visual display is carried out through a plurality of interactive views such as a force-oriented graph, a time axis and a mulberry-based graph, and clue intelligent pushing and report automatic generation are supported. According to the method, the case association mining depth, the serial-parallel analysis efficiency and the interpretability of a complex network are remarkably improved, and an efficient technical support is provided for the public security organization to fight against telecommunication network fraud.

The invention discloses a marine geological environment multi-dimensional data reconstruction and dynamic visualization mapping method and system, and belongs to the technical field of marine information technology and data visualization. In order to solve the problems that in the prior art, standard data asset structure is isolated, heterogeneous data deep fusion is difficult, and mass data visualization performance is bottleneck, the method mainly adopts the following technical scheme that standardized multi-source data is received, a data volume mapping rule is established, discrete observation data is reconstructed and fused into a unified and continuous three-dimensional data volume, and the three-dimensional data volume is obtained; and executing a three-dimensional space pattern recognition algorithm on the three-dimensional data volume to recognize a feature entity, and providing on-demand query and multi-resolution access services through a visual mapping interface. According to the method, efficient automatic modeling and high-performance interactive analysis of mass and multi-dimensional marine geological environment data can be realized.

A machine learning model for extracting information from web pages is prepared. The preparation includes generating respective representations of a first set of web pages, including embeddings from screenshots and bounding boxes of the web pages for multi-phase training of the model. In a first phase of training of the model, multiple loss functions associated with respective prediction tasks are optimized jointly, including a markup language element prediction task and a prediction of overlap between bounding boxes and screenshot subdivisions. In a second phase of training, using output of a hidden layer of the model (whose parameters were learned in the first phase) as input, a loss function is optimized to achieve a target web information extraction objective. The trained version of the model is stored.

A computing device parses a user-specified natural language command to form a first expression. The computing device determines that the first expression is ambiguous or underspecified. The computing device, in accordance with the determination, infers first information using one or more inferencing rules, where at least one of the inferencing rules is based on an attribute of data fields and / or data values in a data source. The computing device forms a second expression based on the first expression using and the first information. The computing device retrieves one or more data sets from the data source using according to the second expression. The computing device generates and displays a data visualization of the retrieved one or more data sets.

An information processing apparatus identifies a first path on the basis of relationship information indicative of data transfer relationships between two of functions in an AI system, management target data, and relevant persons. The first path indicates a route which follows a data transfer relationship from a start point to an end point with the start point set to one of the functions, the management target data, and the relevant persons and the end point set to another one. The information processing apparatus selects a first check item related to a first data transfer relationship on the route indicated by the first path from among a plurality of check items associated with any of the data transfer relationships indicated by the relationship information. Furthermore, the information processing apparatus displays information on the first check item.

Methods and apparatuses for virtualizing building management systems. An apparatus embodiment comprises a processor that receives a temperature adjustment command from a building application via a first application programming interface (API). The command specifies a target temperature for a designated space. The processor validates the access rights of the user for the command, translates the command into one or more device-specific control parameters, and communicates these parameters to an on-premise building management system via a second API. This enables execution of the temperature adjustment to achieve the desired temperature value for the building space.

A system and method for quantifying the impact of investor behavioral persona on investment outcomes. A Behavioral Risk Index (BRI) is calculated based on investor behavioral factors including investor type, behavioral biases, and financial literacy. The system simulates behavior-impacted investment returns by mapping exit and re-entry points of investor groups sharing a common behavior persona during historical market events. The simulated behavior-impacted investment outcomes is quantified by comparing to buy-and-hold strategy. Personalized visualizations, including the visualization of the BRI, behavioral factor breakdowns and performance comparisons, are displayed to assist financial professionals and investors in identifying behavioral risk exposure and demonstrating the value of behavioral coaching.

The invention relates to the technical field of water quality detection, and provides a fluorescence detection test paper visualization system and method based on deep learning, and the method comprises the steps: detecting a fluorescence detection test paper image obtained through shooting through a target detection network, and obtaining a test paper main body region, a colorimetric block region and a test paper key angular point coordinate; performing homographic perspective transformation on the main body area of the test paper according to the key angular point coordinates of the test paper, generating a test paper standard view after perspective correction, performing color space standardization correction, generating a mixed deep learning model, and outputting a fluorescence intensity concentration predicted value of the target detection area; generating an interpretable thermodynamic diagram based on the intermediate features of the mixed deep learning model; according to the method, shooting deviation is eliminated through multi-stage image correction, the fluorescence intensity is accurately quantified by fusing CNN and Transform models, the result credibility is improved in combination with a thermodynamic diagram, and the detection automation is comprehensively improved.

The invention provides a user portrait generation method and device based on a knowledge graph, and a storage medium, and the method comprises the steps: carrying out the event causal association recognition of a continuously collected user behavior record sequence, generating a user behavior causal event pair set, carrying out the entity relation extraction of the set, generating a triple set, and generating a user behavior knowledge graph. Carrying out multi-hop relationship propagation calculation on the entity, fusing multi-order neighbor feature information of each entity, generating an entity embedding vector set, carrying out path mining on the entity embedding vector set through a preset causal reasoning model, identifying potential causal conduction paths among different behavior entities, and calculating an influence weight coefficient between a behavior mode and a result state on the basis of the calculated influence weight coefficient. And generating a user behavior causal association feature, performing feature association fusion on the user behavior causal association feature and the entity embedded vector set, generating a user behavior pattern classification result, and generating a user portrait label system. The depth and the application value of the user portrait are improved.

The invention relates to the technical field of data processing, and discloses a multi-source data difference attribution system and method based on tensor decomposition, and the method comprises the steps: obtaining an original engineering quantity list file, and then carrying out decoupling to generate a standardized list item text sequence and an initial value set, constructing unit prices to form a tetrahedral geometric model set, mapping the tetrahedral geometric model set to a holographic feature vector space, and generating a multi-dimensional list feature vector set; constructing a standard cost reference vector field, identifying a difference vector set and generating a visual cost difference topological data model; a natural language query instruction is responded, a target difference attribution tensor is locked, a logic verification chain is constructed, and an intelligent diagnosis report is output; according to the method, a computable logic distance is established in multi-source heterogeneous data by reversely reconstructing a high-dimensional price-rate feature space, so that the problem of attribution blocking caused by dimension collapse in traditional cost auditing is effectively solved, and accurate traceability and compliance automatic diagnosis of cost differences are realized.