102 results about "Information dynamics" patented technology

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

The invention provides a novel traffic information dynamic time sequence diagram topological structure, which relates to the technical field of dynamic time sequence diagrams, and comprises the following steps: configuring a namespace and time granularity system of a traffic information dynamic time sequence diagram, setting a global timestamp rule and a propulsion strategy, defining an information identification bit structure, and setting a visibility and consistency control strategy. And a unified time window scheduling and snapshot life cycle management mechanism is configured. A dynamic extensible time sequence naming system is formed by establishing a unified naming space, time granularity delta t and a global timestamp rule, windowed organization and snapshot chain management of traffic information are achieved, the structure can automatically advance snapshot in a time driving mode and a data driving mode, time sequence consistency and real-time performance are guaranteed, and the time sequence naming efficiency is improved. The problems that node time management is disordered and data cannot be traced in a traditional traffic map model are solved.

The invention provides an electric vehicle charging demand prediction method and system and electronic equipment, and relates to the technical field of deep learning prediction. Comprising the following steps: acquiring historical observation data and corresponding time characteristic data of an electric vehicle charging station; an MIFM prediction model is constructed, an initial input information coding module receives initial data and carries out parallel embedding processing, and unified input representation containing node specific adaptive embedding is constructed; the spatial-temporal information dynamic fusion module obtains the unified input representation, captures explicit geographic space dependence and implicit function correlation in parallel through a two-channel dynamic graph learning mechanism, dynamically integrates multi-layer spatial-temporal characteristics by using an adaptive Kalman filtering fusion strategy, and generates an enhanced fusion signal; and the space-time dependency modeling and prediction module learns the spatial relationship representation based on the enhanced fusion signal by using a stacked graph attention network, captures time dynamics through a time sequence decoding module, and generates a charging demand prediction result of a future time step.

The invention discloses a natural resource monitoring multi-source heterogeneous spatio-temporal data fusion verification system and method, relates to the technical field of geographic information systems and spatial data processing, and is used for solving the problems of low fusion precision of multi-source heterogeneous data and difficulty in conflict processing. The system performs standardized packaging on the same geographic element of different data sources through an evidence packaging module to generate a standard evidence unit containing element type claim and data source credibility weight; the intelligent judgment module corrects the evidence probability based on the DS evidence theory in combination with the positioning precision, calculates the conflict degree and outputs a high-low confidence coefficient judgment result, so that the fusion reliability is improved; the semantic coupling module performs spatial topology and service semantic verification on the judgment result to generate a type recommendation sequence; the knowledge updating module dynamically adjusts data source credibility and semantic rule weight according to user confirmation information, a self-learning mechanism is established, and efficient conversion from multi-source heterogeneous data to high-credibility monitoring information is achieved.

The invention provides a low-altitude traffic service information fusion and interaction system and method, and relates to the technical field of low-altitude traffic management, and the system comprises a multi-source information collection module which is used for obtaining multi-dimensional data from different information sources; the dynamic weight information fusion module is used for calculating the dynamic weight of each information source based on the data credibility coefficient and the updating frequency factor of each information source, performing weighted fusion and generating low-altitude traffic comprehensive data; the traffic service information generation module is used for matching the low-altitude traffic comprehensive data with a preset service rule base to generate traffic service data; and the multi-terminal information interaction module is used for transmitting the traffic service data to different terminals by adopting different communication protocols. According to the system and the method provided by the invention, by integrating multi-dimensional information, the weight of the information source is dynamically adjusted, and the fusion accuracy is improved; the system adapts to various communication protocols, seamless interaction with different terminals is realized, and the service quality of low-altitude traffic service is remarkably improved.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention discloses a video target tracking system based on historical information dynamic propagation and future frame prediction collaborative optimization, which belongs to the field of visual target tracking and comprises a dynamic attention token module, a multi-constraint future prediction module, a real-time bidirectional calibration module, a tracking decision module and a scene adaptive memory module. The dynamic attention token module is used for receiving continuous video frames and generating a multi-scale token sequence dynamically adjusted along with target states, and the target states comprise a shielding area, a movement speed and an appearance scale; the target tracking robustness problem in a complex scene is solved by dynamically fusing the historical trajectory features of the target and future state prediction, and the method can be applied to the fields of intelligent monitoring, automatic driving visual perception, unmanned aerial vehicle inspection, robot visual navigation and the like. A bidirectional optimization mechanism based on historical token propagation and future frame prediction breaks through the limitations of insufficient utilization of time sequence information and insufficient dynamic adaptability in the prior art.

The invention provides an OCR (Optical Character Recognition) information dynamic verification method and system based on multi-modal fusion and a medium. The method comprises the following steps: firstly, acquiring OCR identification data and scene environment data, extracting a multi-modal feature set, then training a multi-modal quality evaluation model based on historical data, performing field correctness, format compliance and logic relevance scoring on each modal, calculating confidence to position a problem modal, and finally obtaining a multi-modal quality evaluation result. The method comprises the following steps of: obtaining an OCR (Optical Character Recognition) identification result, performing traceability analysis on a problem mode, implementing mode enhancement, recalculating an enhanced quality score and a confidence score, dynamically distributing each mode weight according to enhanced confidence, obtaining a global quality score after weight fusion, and finally judging whether the OCR identification result is qualified or not based on a global score threshold value. According to the method, the accuracy and the robustness of OCR information verification are remarkably improved through collaborative verification and dynamic weight fusion of the multi-modal information, and identification interference in a complex scene can be effectively coped with.

The invention relates to the technical field of network fault diagnosis, in particular to an automatic network fault positioning system and method based on multi-source data fusion and an artificial intelligence algorithm, and the method comprises the steps: collecting network flow, equipment states, logs and topological structure data in real time; cleaning, denoising, standardization processing and feature extraction are carried out on the collected original data; extracting network health state key features, generating node embedding vectors, aggregating neighbor features, and splicing the aggregated neighbor features with equipment states and log semantic vectors; a fault diagnosis model is constructed based on deep learning and a knowledge graph technology, reinforcement learning and current network topology, equipment state and historical fault information are combined, a fault point is dynamically positioned, and a fault type is identified; and displaying the fault diagnosis result and the positioning information, and pushing alarm information to an operation and maintenance system. According to the invention, intelligent, accurate and efficient positioning of network faults is realized, the real-time performance and reliability of fault diagnosis are improved, and the operation and maintenance dependence and cost are reduced.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

The invention relates to the technical field of hybrid electric vehicle energy management, in particular to a target SOC track generation method and device based on road information dynamic optimization, and the method comprises the steps: carrying out segment processing on path data in a current navigation path of a vehicle, constructing a path information matrix through employing a plurality of obtained segment paths, and obtaining a path information matrix; calculating a dynamic basic SOC value of the power battery according to a target input characteristic matrix determined by the path information matrix, performing target correction processing to obtain an initial target SOC value of the power battery, performing adaptive correction processing, smoothing processing, compensation processing and dynamic limiting processing in sequence to generate a processed target SOC value, and outputting the processed target SOC value. And generating a target SOC track of the power battery by using the processed target SOC value. Therefore, the problem that in the related technology, dynamic adjustment near a fixed target is limited, so that target SOC control lacks global adaptability, and energy consumption optimization requirements under complex road conditions cannot be fully matched is solved.

The application discloses a kind of dynamic ring vision splicing method and system based on image overlap area feature perception, method includes based on the depth information and the feature information dynamic planning splicing path;According to the splicing path of planning, image splicing is executed, including the multi-band image fusion based on depth perception.The present application extracts local and global features of the overlapping area and calculates pixel-level depth information, constructs a geometric transformation model optimized by feature-depth combination, so that panoramic stitching can accurately identify the outline of close-range objects, and dynamically plan the optimal stitching line to bypass prominent objects;By searching the distance splicing template based on the real distance and automatically unifying the focal length of each camera, the field of view range is kept consistent;Through the multi-scale depth estimation network guided by features and joint reprojection error optimization, high-precision registration can still be maintained in scenes with few feature points or low overlap areas.

The present application relates to a kind of layered reinforcement learning driven car-road cooperation automatic driving longitudinal following control method and system, belong to the field of automatic driving vehicle control.For the problem that the generalization ability of existing following control method is insufficient, strategy update is easy to lose stability, easy to fall into local optimum, adopt layered architecture, upper layer adapts intersection, high-speed straight road and ramp multiple typical traffic scenes, based on real-time car-road cooperation communication information dynamic planning follow target, after normalizing processing to multi-source heterogeneous traffic characteristics, generate environment state vector;Lower layer uses proximal policy optimization algorithm, outputs continuous accelerator or brake command through actor-critic double network, and updates network parameters by combining strategy entropy with pruning loss with Jacobian correction.The method improves the cross-scene generalization ability and perception robustness, effectively guarantees the stability and monotonicity of strategy update, avoids instruction step mutation, breaks the exploration deadlock, and balances driving safety, traffic efficiency and driving comfort.

The application provides an image generation method based on federated learning and an optimal time allocation strategy, comprising the following steps: determining the sparsity according to the client communication budget and the data volume, and randomly initializing the diffusion model parameters and the personalized sparse mask; determining the neighbor clients of each client according to different optimal time allocation strategies, and determining the mask intersection parameter aggregation mode of the current client and the neighbor clients according to the current round and the aggregation probability, to obtain the aggregated initialization model; performing gradient update on the initialized model parameters after aggregation according to the data batch and the parameter mask state, to obtain the updated model parameters; and calling a gradient information dynamic search algorithm to update the sparse mask in each client based on the updated model parameters. The application can protect the training data privacy of users of each party, can jointly use the data of each party to improve the generalization ability of the model, and can efficiently allocate limited communication resources under the condition of bandwidth limitation.

The invention belongs to the technical field of cross-platform information processing, and relates to a cross-system data management method based on dynamic protocol analysis and metadata driven rendering, which comprises the following steps: a cross-system general data operation request comprises a data object type identifier and an operation parameter; querying a service registration center by using the service name identifier to obtain a target service instance; assembling through a unified data management protocol and sending a remote calling request to the target service instance; executing business data operation by using the operation parameters to generate original business data; packaging the original business data into a standard data transmission object, and returning the standard data transmission object; combining the standard data transmission object with the metadata configuration information to generate a comprehensive response packet; and driving the client to dynamically generate an interface control based on the metadata configuration information, and filling data in the standard data transmission object. The problems that cross-system data interaction is poor in flexibility, and codes need to be frequently modified for adaptation in front-end rendering are solved.

The application provides a grouting pressure and flow integrated optimization method based on diffusion inversion and gas grading, and relates to the technical field of grouting pressure and flow optimization. It comprises the following steps: constructing a multi-field coupling prior model and, based on the final multi-field coupling prior model, collecting on-line field data and performing data preprocessing to obtain a time series data set; taking the final multi-field coupling prior model as a forward modeling framework and taking the time series data set as a constraint, constructing a joint objective function and determining a parameter posterior distribution; based on the parameter posterior distribution and gas state information, dynamically evaluating risks and performing multi-objective optimization, and outputting a joint control curve and a corresponding execution plan. The application solves the problem that, in the prior art, real-time closed-loop control cannot be realized under uncertain conditions to achieve parameter distinguishability, dynamic gas grading linkage and opportunity constraint multi-objective optimization.

The invention relates to the technical field of network data security management, and discloses an internet data security multi-dimensional evaluation and analysis system, which comprises a data acquisition and preprocessing module used for acquiring security data from a plurality of heterogeneous internet data sources and performing standardization and entity relationship extraction on the security data; the core asset and risk map construction module is used for constructing a multi-dimensional attribute graph model based on the extracted entities and relationships, nodes in the multi-dimensional attribute graph model represent the entities, edges in the multi-dimensional attribute graph model represent the interaction relationships between the entities, and the nodes and the edges carry attribute information; and the dynamic risk assessment engine is used for distributing dynamic weights for edges in the multi-dimensional attribute graph model. According to the invention, by constructing the core asset and risk map fusing multi-source data, the system breaks the information island of a traditional assessment tool, and associates isolated vulnerabilities, abnormal behaviors and attacked assets into an organic whole.

The invention belongs to an aircraft management system technology, and particularly relates to a high-safety aircraft management system based on a switched time-triggered Ethernet. Sensors, computers and execution mechanism equipment of all subsystems are efficiently interconnected through a network, so that the problems of excessive equipment redundancy, complex cable connection and low cooperative interaction efficiency existing in the design of a traditional aircraft management system are solved. According to the system, data sharing is enhanced, repeated deployment of equipment is reduced, openness is improved, non-safety key information dynamic expansion is supported, meanwhile, the safety level needed by an aircraft management system is not sacrificed, integration of the functions of main flight control, auxiliary flight control, thrust, undercarriage and front wheel steering under a unified network can be achieved, and the system is suitable for large-scale popularization and application. And a basis is provided for further comprehensive design of a future aircraft management system.

A problem extraction and answer generation method, device, medium and equipment are disclosed, which segment and vectorize text information in a bank data document to obtain multiple text vectors of each page; summarize each text vector to obtain multiple target summaries; use a preset question format for each target summary and input the target summary into a natural language processing model to obtain multiple initial questions and answers; and remove duplicates and sort the initial questions and answers to output a target question and answer corresponding to each target summary. The present application can analyze the bank data document, automatically generate relevant questions and answers, help users quickly understand the document content, and timely grasp industry information dynamics.

The application discloses a kind of collaborative screen information dynamic aggregation methods of fusing user intention prediction, it is related to data analysis technical field.The method includes obtaining intention performance index based on first time series interaction data;If the intention performance index is greater than first preset value, then obtain a plurality of second time series interaction data based on hospital information service platform;Based on each second time series interaction data, the operation guide of the first user is generated.The application calculates the intention performance index that can represent the possibility of user operation reflecting real intention by first time series interaction data, only in intention performance index greater than first preset value (that is, the intention of user is clear) when calling the historical second time series interaction data of other users in platform to generate operation guide, effectively solve the technical problem that operation guide is separated from actual operation demand of user caused by scene as core, fixed process push in traditional hospital information display.

The application relates to the technical field of data processing, in particular to an intelligent report data visualization management method and system, medium and product, the method comprising the following steps: obtaining initial access demand and initial access tendency information of a user, and determining an initial dashboard interface based on the initial access demand and the initial access tendency information; obtaining real-time drag operation data of the user on the initial dashboard interface in a preset analysis time period, and determining attention access data and current access tendency information based on the real-time drag operation data; and dynamically adjusting the initial dashboard interface based on the attention access data and the current access tendency information to obtain a target dashboard interface. The application facilitates improving the intelligent report data visualization management precision, thereby improving the use experience of the user.

This application provides an intelligent dialogue Q&A method and system for operation and maintenance technical services based on a large model knowledge base. It relates to the field of large model operation and maintenance dialogue technology. The method involves receiving operation and maintenance consultation requests, capturing the dynamic evolution characteristics of the problem, and associating them with the real-time updated knowledge modules of the large model knowledge base to obtain corresponding relationships. Based on these relationships and phased descriptions, dynamic scenario adaptation processing is performed. An operation and maintenance Q&A generation module with knowledge iteration functionality is invoked, combining the adaptation results and incremental knowledge to generate multiple rounds of preliminary Q&A content. The preliminary Q&A content is dynamically optimized and adjusted based on feedback information to obtain dynamically optimized Q&A content. The content is fed back to the initiator, and relevant information is associated with the knowledge iteration module to drive incremental updates, completing the intelligent dialogue Q&A process and improving the accuracy and practicality of operation and maintenance Q&A.

The invention relates to the technical field of data processing, and particularly provides a talent information dynamic management method, system and device and a storage medium, and the method comprises the steps: collecting community talent data from a plurality of heterogeneous data sources; data cleaning and entity recognition are carried out, a unique identifier is used to solve personnel entity conflicts, and a skill dictionary and a semantic similarity model are used to realize skill entity standardization; building a knowledge graph in a graph database based on the identified entities, and defining character nodes, skill nodes and relationships thereof, the relationship attributes including credibility scores; training a graph neural network model by using the graph to discover talents and assessment skills, and dynamically calculating a credibility score of a skill relationship through a hybrid assessment system; the score is used for performing priority ranking and recommendation on talents with the same skill. According to the method, a static talent pool is upgraded into a dynamic knowledge network, quantitative evaluation is realized through a hybrid model, and the recommendation accuracy and the data value are remarkably improved.

The invention belongs to the technical field of digital simulation, and discloses a plug-in-based model and scene task generation method and system. The method comprises the following steps: constructing a plug-in type framework, wherein the plug-in type framework comprises a plug-in framework, a plug-in contract and a plug-in component; wherein the plug-in framework is used for managing the life cycle of the plug-in; the plug-in contract is used for defining a unified interactive interface specification; the plug-in component is loaded into the digital simulation system through the plug-in framework and interacts through the plug-in contract; the plug-in component comprises a model editing plug-in and a scene task editing plug-in; dynamically editing the model based on the model editing plug-in and input first parameter information, and generating a target model; and dynamically editing the scene task based on the scene task editing plug-in and the input second parameter information, and generating a target scene task. According to the invention, the development flexibility of the digital simulation system and the reusability of the simulation model can be improved.

The invention discloses a method and a system for predicting the remaining mileage of an electric vehicle driven by fine driving data. The method comprises the following steps of: A, constructing and processing a remaining mileage and related factor data set; step B, multi-element feature extraction based on a random forest and an ALE algorithm; and step C, residual mileage prediction based on complete information feature fusion. According to the method, the influence of multiple factors on the remaining mileage is considered, the battery state, the driving environment and the driving behavior are dynamically and adaptively fused into an existing remaining mileage prediction method, and the remaining mileage of the electric vehicle under various environments and working conditions can be accurately predicted. According to the method, feature parameters are dynamically and adaptively fused through full information, the residual mileage is jointly predicted through a time convolutional neural network TCN-Transform double model, and data smoothing processing is conducted on an initial prediction result so as to improve the accuracy and stability of the finally output residual mileage value.

The invention provides a method, a device, equipment and a medium for implementing financial domain data governance and data-in-platform cooperation, and the method comprises the steps: constructing an enterprise-level data governance system, and determining a data management framework and a standard specification; based on a full-stack information creation technology stack, deploying a data middle platform supporting a lake-warehouse integrated framework to form a unified data storage, calculation and governance capability core; injecting an output rule of the data governance system into a data platform by taking the financial business domain as a governance template, governing the financial domain data, and generating financial data assets and services; the managed financial data assets are packaged into data services through the data middle platform, and operation of a financial sharing service system and financial data analysis application is supported; in the application process of financial data assets, feedback information is collected, rules of a data governance system and a governance flow of a data-in-platform are dynamically optimized, and closed-loop control is formed, so that the problems that the data governance system and the data-in-platform are disjointed, and a data-in-platform solution always faces challenges are solved.

An information processing device that dynamically reconfigures an information structure according to history and external information is provided. [Solution] The information processing device includes a score evaluation unit that calculates link scores between phrase nodes, a structure reconstruction unit that reconstructs a presentation structure based on the link scores, and a storage unit that stores the reconstructed phrase network structure. The score evaluation unit calculates a history score based on at least one of co-occurrence frequency, temporal proximity, and selection order. The structure reconstruction means deletes the corresponding link when the calculated link score falls below a predetermined threshold T, and reconnects the corresponding link when the link score of the link again exceeds threshold T.

The application discloses a virtual enterprise collaborative decision-making method and system for team heterogeneity identification and management strategy recommendation, and the method comprises the following steps: collecting team member basic information, dynamic cooperation behavior and periodic psychological measurement data; constructing task and relationship heterogeneity indexes based on the collected data; establishing and training a machine learning model; deploying the model, predicting future high-intensity conflict probability based on the heterogeneity indexes; and generating conflict management strategy suggestions in combination with the heterogeneity indexes and the predicted probability. The application integrates multiple data sources, constructs quantitative task and relationship heterogeneity indexes, and accurately portraits the team state. On this basis, the trained machine learning model is used to dynamically predict the future high-intensity conflict probability. This changes the passive situation of the existing team management system in responding after the event, improves the collaborative decision-making efficiency and overall management efficiency of the virtual team, and realizes the scientization and foresight of team management.

The application discloses a kind of low air space complex environment under unmanned aerial vehicle cluster target long-time robust tracking method, comprising: by the target detection model of pre-training to the unmanned aerial vehicle target is detected and is combined with environmental state information dynamic optimization;The detection result after optimization is parallelly input light graph convolution network and bidirectional LSTM module, respectively extract the spatial interaction feature and time sequence dynamic feature of target predicted position;Two kinds of features are fused using spatial-time sequence attention mechanism, to obtain more accurate predicted position information;Based on the confidence score of target detection frame, the predicted position of target and the actual detection result of target are two-stage data association.The method effectively improves the tracking accuracy and robustness of unmanned aerial vehicle cluster target in complex dynamic environment, especially in target occlusion, dense formation and background interference scene has strong adaptability.