119 results about "Information space" patented technology

An intelligent analysis method based on medical document structure perception and multi-modal fusion comprises the following steps: carrying out structure topology modeling on a medical document, extracting visual layout, text meta-information, space coordinates and semantic keyword features, constructing a semantic topological graph and dynamically shielding irrelevant contents; selecting an extraction path according to a document type, performing deep semantic analysis and entity recognition on a text-type document, and performing visual enhancement OCR recognition on a scanning-type document; the features are injected into a medical knowledge graph, and feature fusion, semantic verification, relation reasoning and information completion are achieved through a graph neural network; a three-stage strategy optimization model of basic pre-training, domain adaptation and online reinforcement learning is adopted; and large-scale processing is realized through a dynamically aggregated distributed architecture. The method is used for intelligent analysis and structured conversion of documents of hospitals, medical insurance and medical scientific research. The problems that heterogeneous medical document analysis adaptability is poor, multi-modal fusion is difficult, medical knowledge utilization is insufficient, and large-scale processing efficiency is low are solved.

The invention discloses a patent disclosure auxiliary writing method and system based on multi-agent collaboration. The method comprises the steps that a multi-agent system composed of a collaboration agent, an agent agent and an expert agent is constructed, a knowledge base and a decision module of each agent are configured, and a collaboration architecture of a task scheduler, an arbiter and a shared information space is established; the method comprises the following steps: receiving an original technical scheme of a user, and constructing a structured intermediate representation through multi-agent parallel semantic analysis and feature extraction; based on the representation, a writing task sequence is generated through dynamic task decomposition, intelligent agent division is coordinated through a contract network protocol, and content generation and verification are completed based on a multi-round debate mechanism; performing multi-dimensional consistency detection on the generated draft, and eliminating logic conflicts and expressing defects through iterative revision; and finally, an interest portrait is generated based on the user technical scheme and the optimized draft, interest retrieval is performed, a technical content report is output, and the efficiency and quality of disclosure book writing are improved.

The invention discloses a weak light image enhancement method and device, equipment and a storage medium, and relates to the technical field of image enhancement, and the method comprises the steps: obtaining a to-be-enhanced image and a target normal illumination image, and optimizing an initial image enhancement model to obtain a target image enhancement model; performing feature extraction and manifold distribution estimation on the target normal illumination image by using the initial image enhancement model to obtain first distribution manifold information; performing feature extraction and manifold distribution estimation on the to-be-enhanced image by using the target image enhancement model to obtain second distribution estimation information; fusing the first distribution manifold information and the second distribution estimation information by using a target image enhancement model, and establishing a mapping relation between target high-dimensional feature information vectors and feature information vectors by using the target image enhancement model based on a fused target high-dimensional manifold information space; and obtaining a target projection and target manifold distribution according to the mapping relationship, and decoding the target manifold distribution by using a decoder to obtain a target image enhancement result. And image adaptive enhancement is realized.

A video compression system and method integrates geometric compression with cognitive understanding through a persistent cognitive machine interface. The system employs a hierarchical encoder generating multi-scale compressed representations organized within a Lorentzian manifold structure. A geometric processor maintains temporal causality through time-like geodesics and light cone constraints while organizing video content according to semantic relationships. A cognitive interface creates thought bundles as navigable submanifolds, enabling semantic access to compressed content beyond traditional temporal indexing. The system supports real-time processing through progressive refinement, streaming coarse representations immediately while adding detail in parallel. Symbolic anchors mark semantically significant points, enabling concept-based navigation through compressed video. Federated learning capabilities allow distributed systems to share geometric patterns while preserving content privacy. The architecture enables improved compression ratios while maintaining both temporal causality and semantic navigability, transforming video from sequential media into an intelligently accessible information space.

The invention relates to the technical field of geographic information big data processing, and discloses a geographic information space-time big data real-time aggregation and cleaning method based on edge computing, and the method comprises the following specific steps: S1, deploying edge computing nodes in a geographic information collection terminal coverage area, constructing an edge computing network, an acquisition terminal pushes original geographic information spatio-temporal data including geographic coordinates (x, y, z), an acquisition timestamp t and attribute information to an edge node through a protocol, and the edge node establishes a data buffer queue and preliminarily classifies and stores the data according to a spatio-temporal dimension. According to the geographic information space-time big data real-time aggregation and cleaning method based on edge computing, efficient and low-delay transmission of geographic information space-time big data is achieved through edge computing node deployment and a multi-algorithm fusion aggregation cleaning strategy, and the bandwidth and delay problems of a traditional centralized architecture are solved; multi-dimensional collaborative cleaning (geographic constraint, statistics and machine learning) is combined with an intelligent algorithm, and abnormal data are accurately recognized and processed.

The invention provides a textile flexible production line multi-material scheduling system, which comprises a physical execution layer and a multi-material scheduling layer, wherein the physical execution layer comprises reconfigurable textile production equipment; the intelligent sensing layer is arranged at key nodes of the physical execution layer; the central scheduling and control layer is in communication connection with the physical execution layer and the intelligent sensing layer; and the digital twin layer is a virtual mapping of the physical execution layer in an information space, and forms a closed loop with the central scheduling and control layer and the physical execution layer. According to the method, the perspectiveness and the global optimization capability of a scheduling decision are realized through a digital twinning technology, the system places an initial production scheduling scheme in a high-fidelity virtual production line model for millisecond-level concurrent simulation deduction, and potential process bottlenecks and quality risks during blending production of different materials are accurately predicted; and by taking the production efficiency, the energy consumption and the quality qualification rate as multi-target reward functions, completing iterative optimization of massive strategies in a virtual space, thereby obtaining an approximately globally optimal scheduling instruction before the physical production is put into use.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention discloses a multi-branch power distribution network fault positioning method and system based on multi-dimensional fault characteristics. The method comprises the following steps: acquiring fault voltage traveling wave waveform data; performing phase-mode transformation on the waveform data to extract a line-mode component, extracting wave head coordinates, amplitude and slope information of a fault traveling wave in a set time window, and after redundancy elimination, determining a feature matrix, an adjacent matrix and an edge feature tensor based on a wave head information space so as to construct a multi-dimensional fault feature space; constructing a section identification model and a precise positioning model, and performing hyper-parameter tuning on the models by adopting a parameter-setting-free harmony search algorithm; and whether fault section positioning is carried out is judged based on the multi-dimensional fault feature space, if yes, the multi-dimensional fault feature space is input into a section recognition model to obtain a section recognition result, and if not, the multi-dimensional fault feature space is input into a precise positioning model to obtain a positioning result. And dual targets of power distribution network fault section identification and accurate positioning are realized from two aspects of multi-dimensional fault feature space extraction and a power distribution network fault positioning model.

The invention provides an abnormal event positioning model training method, an abnormal event positioning method, an abnormal event positioning device and abnormal event positioning equipment. The abnormal event positioning model training method comprises the following steps: acquiring a sample monitoring image and corresponding text description information; inputting a preset chain frame reasoning template, the sample monitoring image and the text description information into the initial visual language model for reasoning to obtain multiple groups of reasoning results corresponding to the sample monitoring image, and determining format reward information of each group of reasoning results according to a preset reasoning output format and an output format of the prediction bounding box; determining space reward information of each group of reasoning results according to the prediction bounding box and the real bounding box; determining semantic reward information of each group of reasoning results according to the prediction region features and text features of the text description information; according to the format reward information, the space reward information and the semantic reward information of each group of reasoning results, parameter adjustment is carried out on the initial visual language model, and a target visual language model is obtained and used for abnormal event positioning.

The invention provides a BIM-based electromechanical pipeline intelligent collision detection and automatic optimization adjustment method, which comprises the steps of analyzing a BIM model database, and extracting component information of electromechanical components; establishing a spatial index and an adjacency relationship between the electromechanical components, constructing a component spatial relationship database, and storing the component information, the spatial index and the adjacency relationship in the component spatial relationship database; calling data in the component space relation database to carry out multi-level collision detection; performing collision optimization adjustment on the electromechanical component based on a result after multi-level collision detection, and generating an optimized BIM model to be fed back to constructors; according to the method, the optimized BIM model is subjected to 3D display, meanwhile, a figure roaming scene is constructed, the moving view angle of a constructor in a building space is simulated, collision points are secondarily checked in the roaming process, the collision recognition precision can be improved through multi-level collision detection, collision conflicts are eliminated through optimization adjustment, design errors are reduced, and the engineering efficiency is improved.

The invention provides a prefabricated building component intelligent docking system based on a BIM model, and the system comprises a BIM model processing module which is used for importing the BIM model of a prefabricated building, and extracting the three-dimensional model information, geometric parameters and preset docking position information of each prefabricated component; the component sensing module is used for collecting actual position information and space attitude information of the to-be-butted component in real time; the data processing and analysis module is used for performing comparative analysis on the actual position information and the space attitude information of the to-be-docked component and preset docking position information in the BIM model, and calculating position deviation and attitude deviation; and the docking control module is used for generating an adjusting instruction according to the position deviation and the attitude deviation. According to the invention, precision limitation of traditional manual docking is broken through, intelligent management and control of the component docking process are realized, and docking precision and efficiency are improved.

The invention discloses a multi-intersection traffic signal control method fusing non-neighborhood information space-time perception. Traffic signal control of multiple intersections is optimized by adopting a reinforcement learning strategy, each intersection is used as a reinforcement learning agent, and space and time sequence characteristics of each intersection are extracted as state information. Non-neighborhood information spatial-temporal characteristics of multiple intersections are realized by using a non-neighborhood spatial characteristic enhancement graph transformer and a two-way multi-head cross attention mechanism to perform action decision, optimal control of a multi-intersection traffic network is realized, and spatial-temporal association of traffic flow and global road network situation awareness are realized.

The invention belongs to the technical field of distributed computing and data twinning, and provides a digital twinning method and system for the industrial Internet of Things, and the method comprises the steps: obtaining a multi-dimensional dynamic mapping model and a multi-modal time series data flow of a physical entity in the industrial Internet of Things in an information space; edge-cloud collaborative processing is carried out on the obtained multi-modal time series data flow, and fusion data used for driving the multi-dimensional dynamic mapping model is generated; performing hierarchical architecture deployment on the generated fusion data based on containerized micro-service; performing closed-loop control of industrial Internet of Things interruption on an edge side close to a physical entity by combining the obtained hierarchical architecture deployment and fusion data; and performing cloud data processing of the physical entity according to a closed-loop control result, and establishing a maintenance mechanism in which the edge and the cloud are synchronous and consistent, thereby completing digital twinning oriented to the industrial Internet of Things.

The invention relates to the technical field of data security management, and discloses a security management system for data access control. The system comprises an access request receiving unit, a hierarchical analysis unit, a space mapping unit, a permission decision unit and a permission execution unit. The access request receiving unit obtains a data access request of a user, and analyzes a request main body identifier and a request resource identifier. The layering analysis unit carries out layering processing on the resource identification and extracts resource level feature information. And the space mapping unit retrieves the historical access log according to the main body identifier, and constructs an access behavior space distribution model. And the authority decision-making unit generates a dynamic access authority strategy in combination with the resource level characteristics and the access behavior space model. And the permission execution unit controls the access operation of the user to the resources according to the strategy. The system can adapt to a complex data resource structure and realize fine-grained dynamic access control.

The invention relates to the technical field of soil remediation, in particular to a contaminated site soil remediation amount optimization determination method based on multi-source monitoring data fusion, which comprises the following steps: acquiring a drilling position and in-situ data, extracting a pollution boundary, identifying a low-lying area and a pollution overlapping area, calculating a pollution cumulant, and dividing remediation types. According to the method, the in-situ concentration and apparent resistivity data are collected, a unified depth splicing structure is constructed, pollution information space fusion is achieved, the recognition precision is improved, mutation points are extracted, a three-dimensional grid is generated, and the integrity and adaptability of boundary reconstruction are enhanced; the method combines elevation and slope direction data to identify a low-lying catchment area, judges a migration trend, accurately captures a high-risk block, calculates cumulant according to pollution concentration and volume, determines different pollution levels, fuses terrain and pollution load features to extract sorting indexes, optimizes resource allocation efficiency, and promotes conversion from restoration to precise treatment.

The invention discloses a rural sewage treatment station inspection management control system based on an intelligent wearable terminal. The system generates a first preset path based on inspection task requirements and environment distribution information through a path generation module, and guides an inspector to move according to the path through a sending module; the obtaining module collects position information and behavior characteristics of inspection personnel in real time. And the prompt module triggers a preset warning event according to the inspection data and sends prompt information. Meanwhile, the system optimizes path generation by adopting a preset multi-dimensional data analysis model, and realizes image information space positioning through a multi-view stereoscopic vision algorithm and the like. The system effectively solves the problems of difficulty in personnel monitoring, single equipment function and the like in rural sewage treatment station inspection, and can monitor the state of inspection personnel in real time, provide efficient communication and emergency support, improve the inspection efficiency and standardization, guarantee personnel safety and enhance the emergency response capability of the system in a complex environment.

The invention provides a remote sensing image road extraction method based on deep learning, a remote sensing image is processed through a trained generator to obtain a road segmentation map, and the generator comprises an encoder module, a decoder module, a multi-scale feature fusion module and an output module; wherein an enhanced decoder module is constructed in a decoder structure, multi-level semantics and information space are fused through an up-sampling path and a transverse connection path, and a channel attention and space attention mechanism is introduced into the decoder to enhance the attention capability to a road area; and unified alignment, channel compression and fusion are carried out on the output of each stage of the decoder through the multi-scale feature fusion module, so that the modeling capability of road structures with different widths and changeable forms is improved. According to the method, high precision and robustness are still kept under the conditions of complex background and weak supervision, the problems of road breakage, adhesion and wrong segmentation can be effectively relieved, and the method has good engineering application value and popularization potential.

The present application provides a kind of village area spatial function characteristic measure method based on multi-source geographic information data, comprising: obtaining the geographic information space data of regional space, pre-processing geographic information space data;Participatory evaluation method is used to data analysis to the pre-processed geographic information space data;The data after analysis is counted, and multi-source geographic information data is obtained;Multi-source geographic information data is fused;The accurate identification of regional production-life-ecological space is carried out to the data after fusion;According to the identification result, the spatial function measure index system based on production-life-ecology-vitality is constructed;Based on the spatial function measure index system, the function of the region is measured, and the space resource is distributed according to the measurement result;The present application focuses on village scale, and carries out household survey, and this research scale and method selection has unique advantage.

The application provides a goaf overburden strata settlement calculation method based on internal damage characteristics of overburden, and the method comprises the following steps: obtaining a detection image; determining a fractal description of a damage variable by using a digital image processing technology and a fractal box covering method according to the detection image; calculating a crack area distribution weight of overburden at different depths, and determining an optimal fractal box size according to a relationship between an information space dimension and a box covering fractal dimension; determining a fractal damage constitutive model by determining an overburden body damage factor according to the fractal description of the damage variable and the optimal fractal box size; calculating additional stress of overburden at different depths according to a stress balance principle, and calculating the settlement of overburden at different depths by using a layer summation method. By using the fractal damage constitutive relationship, the application constructs a goaf overburden strata settlement calculation model, so that the settlement of overburden at different depths is calculated, the calculation precision is improved, and the purpose of calculating the goaf overburden settlement by using the fractal theory is achieved.

The invention discloses an intelligent interactive door control system, and relates to the technical field of door control systems. An image acquisition module is used for acquiring image information in front of a door in real time; the space processing module is used for processing and analyzing the collected images and human body movement tracks, automatically identifying the identity of the visitor and judging behaviors; the voice interaction module is used for generating a voice conversation with a visitor; the control module is used for calling the voice interaction module to communicate with the visitor according to the recognition result of the space processing module; according to the invention, active and intelligent noise suppression is realized, and the interaction reliability in a severe acoustic environment is ensured. The core of the system is to break the barriers of vision and hearing, and the system can understand the meaning of speech by performing feature-level fusion on the visual information and the voice acoustic features, so as to make distinct and highly personalized responses. By bypassing strong dependence on a final text recognition result, fusion is creatively carried out on a feature level.

The application discloses a parameterized model construction method and device of a power distribution line and an application system, wherein the method comprises the following steps: acquiring account information, spatial position information and topology information of each power distribution device in the power distribution line; extracting model information of each point device from the account information, and constructing a three-dimensional model of the corresponding point device according to the model information; performing step-by-step segmentation on the power distribution line based on the topology information, obtaining a plurality of line sections, and generating section registration information corresponding to each line section; acquiring actual position information of each tower, and calculating actual position information of a wire hanging point corresponding to each tower according to the actual position information of the tower and hanging point information; calculating wire T junction position information on a branch tower, and obtaining actual position information of a wire corresponding to each line section. The parameterized modeling of the power distribution line and the visual presentation of the power distribution line based on the parameterized modeling are realized.

The invention discloses a multi-level territorial space planning index monitoring system and method, and relates to the technical field of territorial space planning, and the method comprises the steps: completing the table creation of a database and the input of initial index basic information and space coordinate data, building an incidence relation between an index and a space unit, obtaining an initialized database and associated data; based on data synchronization parameter configuration, initializing a database and associated data, calculating and updating index values, generating index business data and historical record data, and through traceability analysis, displaying obtained index traceability analysis results in multiple forms to generate an index multi-dimensional comprehensive result set; and according to the checking request and the checking rule, checking the index multi-dimensional comprehensive result set, and performing integration to generate a territorial space planning index monitoring result. According to the method, index full-process monitoring is supported through double databases, the problem that the territorial space planning effect is poor is solved, and the effects of monitoring process standardization and result display visualization are achieved.

The invention discloses an unmanned aerial vehicle image small target detection method based on shallow guidance and local attention, and the method comprises the steps: extracting the features of a given input unmanned aerial vehicle image through employing a backbone network, and generating a down-sampled multi-level feature map; a multi-scale feature Mout containing global semantic information is obtained through an SFEM; the SGFM is used for receiving the output Mout of the SFEM and the hierarchical feature Stagei of the backbone network to serve as input, and finally Gi is obtained to serve as the output of the SGFM; target information, space attention attention local information and channel attention adjustment weight are further enhanced through CLAM, and finally F'is obtained to serve as CLAM output; respectively predicting the category and the detection frame of the target from the enhanced feature map by a classification and positioning decoupling detection head; according to the method, target classification is carried out by using cross entropy loss as classification loss, and Distribrute Focus Loss and Complete IoU Loss are selected as regression loss and are used for bounding box regression of a target. According to the method, modeling of the relation between the object and the background in the unmanned aerial vehicle image is facilitated, the loss of target information details caused by down-sampling operation is reduced, and the detection accuracy is improved.

This application relates to a method for synchronizing information during group travel among car enthusiasts. The method includes: constructing a team data structure based on a team creation command and sending a team invitation; creating a shared information space for the team based on team identifiers and location authorization information and entering initial data; generating unified navigation and custom navigation route planning data, determining spatiotemporal convergence points, and storing them in association; processing and classifying member voice stream data, responding to voice playback requests and adapting playback attributes; detecting member route deviations and classifying deviation states, obtaining relevant status data, responding to travel description editing commands and updating information, integrating various data to generate a status synchronization message and pushing it to all member terminals. This method enables comprehensive synchronization of navigation, voice, status, and travel descriptions during group travel among car enthusiasts, ensuring team collaboration and improving travel experience and safety.

The present invention discloses a method for calculating enterprise nitrogen and phosphorus emission flux based on multi-source heterogeneous data fusion, comprising the following steps: S1, collecting attribute information of enterprises from multiple sources and uniformly classifying it according to industry standards; S2, calling multiple map APIs, obtaining and standardizing the longitude and latitude coordinates of the enterprises, and realizing the fusion of attribute data and spatial location information; S3, integrating multi-source spatial data, matching it with the enterprise location information, and extracting regional environmental characteristics; S4, using a general large language model and a batch scanning tool to respectively identify and structure the heterogeneous format data, and performing fusion comparison and data correction on the extracted results to obtain the enterprise nitrogen and phosphorus emission permit data; S5, integrating the enterprise's attribute information, spatial characteristics and the enterprise nitrogen and phosphorus emission permit data, using a machine learning method to supplement the missing information, calculating the enterprise nitrogen and phosphorus emission flux, and performing rasterization processing.

The application provides a set forecast precipitation information space-time error correction method and system based on image super-resolution, and relates to the technical field of precipitation forecast.The application corrects the space-time error of set forecast precipitation information based on the image super-resolution idea and a deep learning algorithm, improves the spatial resolution of set forecast precipitation information, and improves the forecast precision.The method has outstanding advantages in improving the set forecast precipitation space-time structure similarity and the precision of rain or no rain certainty classification forecast compared with the traditional statistical post-processing method, and provides high-precision data support for the application of set forecast precipitation information in flood forecast business.

The application provides a change detection method and system based on feature interaction and space-time correlation, and relates to the technical field of remote sensing image change detection. The specific steps are as follows: first, a double-time change detection data set is obtained and preprocessed. Second, a change detection model based on a double-encoding double-decoding structure is constructed. The model includes a mixed effective channel module, which mainly enables the double-time features to maintain spatial information similarity and retain more effective channel information when interacting; a spatial denoising attention module, which mainly strengthens position information such as details and edges on the shallow layer features; and a feature fusion up-sampling module, which is mainly used for model training and data transmission in the up-sampling stage. Finally, the effect of the trained model is verified, and the result is saved for use. The application can effectively solve the challenges faced in remote sensing application tasks due to the limitations of remote sensing images themselves, such as fog, shadow, complex targets, etc., and improve the accuracy of the change detection task.

The application provides a point track level cooperative anti-interference method based on multi-source information space-time consistency judgment, which is applied to a multi-source information fusion system, and implementation steps include: receiving n radar point track data, and unifying point track coordinates; judging a common view area for each point track; judging a time window for the point track; judging the point tracks of multiple different radars as being homologous; sending the point track after point track arrangement to a composite tracking module, and forming a target tracking track through multi-radar data correlation filtering; judging whether the current point track is a target source of an existing track, if the current point track is a target track target, point track arrangement is performed, otherwise, the current point track is regarded as an interference point and is removed. Through the method, the cooperative anti-interference problem in a multi-radar information source fusion system is solved without sacrificing radar detection performance, a more stable anti-interference effect is provided, and the starting of radar target tracking is more stable and reliable.

The present application relates to the technical field of equipment energy efficiency management, and discloses a driving energy efficiency and safety virtual-real fusion closed-loop management method based on digital twinning. The method comprises the following steps: acquiring multi-source heterogeneous data of a physical environment, and constructing a digital twinning model of a physical space; then extracting equipment operation and environment state characteristic parameters, and constructing an information space behavior simulation model by fusing historical operation data; performing dynamic situation awareness simulation on equipment operation behavior based on the model, and generating a virtual behavior simulation result. The result is mapped to the digital twinning model, and a static safety evaluation value is obtained in combination with real-time safety monitoring data; an environment disturbance variable is acquired, and a dynamic safety prediction value is obtained by inputting the prediction model. Driving energy efficiency optimization parameters and safety control instructions are generated based on the fusion result of the two, the physical space equipment operation state is corrected in a closed loop, and effective linkage between the physical and virtual spaces is realized.