109 results about "Fusion system" patented technology

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention discloses an SVG valve hall multi-source monitoring data fusion method and fusion system, and belongs to the technical field of power system equipment monitoring, and the method comprises the steps: carrying out the abnormal value and missing value data cleaning of initial data, outputting the preprocessed monitoring data, carrying out the algorithm matching of the preprocessed monitoring data to complete the time-space unification, and carrying out the fusion of the SVG valve hall multi-source monitoring data. Extracting associated features, forming a multi-source feature parameter set, quantifying data source reliability differences and locking key features by taking the multi-source feature parameter set as a reference, forming a dynamic fusion weight distribution scheme and key feature data, calling the multi-source feature parameter set, and eliminating feature conflicts; and generating a comprehensive fault diagnosis result and a health state evaluation index of the valve hall equipment, outputting early warning information and maintenance decision suggestions, and completing the whole data fusion closed-loop application. And a more efficient monitoring scheme is provided for safe operation of valve hall equipment.

The invention relates to the technical field of brain-computer interfaces, and particularly discloses a fusion system combining a brain-computer interface with transcranial electrical stimulation and a feedback stimulation method. The fusion system comprises a main machine part, an auxiliary machine part, a PCB substrate, a terminal integrated receiver and a feedback calculation module. The host part comprises an electroencephalogram signal acquisition device and a transcranial electrical stimulation device which are both installed on a PCB substrate. The auxiliary machine part comprises an MCU and a first wireless device which are both installed on the PCB substrate. The auxiliary machine part further comprises a battery which is connected to the MCU, the electroencephalogram signal collecting device and the transcranial electrical stimulation device. The terminal integrated receiver has a second wireless device. And the terminal integrated receiver is connected to the feedback calculation module. According to the fusion system, the electroencephalogram signal acquisition device and the transcranial electrical stimulation device are combined to acquire brain waves and feed back and adjust transcranial electrical stimulation parameters, so that nervous excitability can be better induced, and an electroencephalogram value is enhanced.

The invention relates to the technical field of deep mineral exploration, and discloses a deep metal mine multi-source geophysical data real-time fusion system. The system comprises a data access module which divides detection dimensions according to physical field types so as to receive original data; the space-time registration module is used for establishing mapping between original data and a unified space-time reference system through timestamps and space coordinates; the feature extraction module is used for extracting key features from the registration data, determining weights according to physical meanings and constructing a feature association network; the fusion decision module identifies an abnormal value and adjusts a network topology structure based on the credibility of the network evaluation data; and the real-time updating module is used for optimizing the fusion priority and algorithm parameters and generating a real-time fusion result library. According to the system, real-time fusion of multi-source geophysical data is realized, and systematicness and timeliness of data processing are improved.

The invention provides a communication and inductance integrated fusion system, an index determination method, electronic equipment and a medium, and relates to the technical field of communication, the communication and inductance integrated fusion system comprises a fusion base station, a preset terminal and a fusion server, the fusion base station is provided with a passive Internet of Things module, and the preset terminal is provided with an RFID tag; the fusion base station is used for sending a first signal and receiving a second signal through the passive Internet of Things module, the first signal comprises a first sensing signal and an RFID excitation signal, and the RFID excitation signal multiplexes a waveform and a time slot resource of the first sensing signal and sends the second signal to the passive Internet of Things module; the second signal comprises a second sensing signal obtained after the first sensing signal is reflected by the preset terminal and an RFID uplink signal obtained after the RFID excitation signal is backscattered by the RFID tag; and the fusion server is used for performing fusion calculation according to the received second sensing signal and the RFID uplink signal so as to determine a sensing performance index. The perception performance of the system can be evaluated more comprehensively and accurately, and the accuracy of perception performance indexes is improved.

The invention discloses a nine-axis linkage intelligent welding process optimization method based on analogue simulation and digital twinning. An existing welding process mostly depends on artificial experience and single equipment control, and an integrated processing system of simulation prediction, equipment cooperation, virtual-real linkage and iterative optimization is lacked. According to the nine-axis linkage intelligent welding process optimization method, a platform is built based on simulation software, after a three-dimensional model of a to-be-welded component is built, the three-dimensional model of the to-be-welded component is subjected to multi-physics field coupling simulation processing to obtain welding data, the welding data is input into an AI multi-target optimization algorithm to form a simulation data training model, and the simulation data training model is used for training the to-be-welded component. Screening according to the simulation data training model to obtain a processing process of an optimal welding sequence and process parameters; through deep integration of intelligent equipment integration, technological process simulation optimization and digital twin control, the problems of low efficiency, poor quality and large deformation in a traditional manufacturing mode are systematically solved.

The invention relates to the technical field of data processing, in particular to a cleaning fusion system for urban traffic multi-source heterogeneous data, which comprises a space-time correlation graph construction module used for creating a unique node identifier for each GPS data record, roadside camera data record and coil data record according to multi-source heterogeneous traffic data, and establishing a spatial adjacent relation edge by judging whether road segment identifiers to which multiple nodes belong are the same or not. According to the method, context awareness evaluation of data source reliability is achieved, meanwhile, when traffic time series data are processed, the data are decomposed into a low-frequency approximation coefficient representing a long-term trend and a high-frequency detail coefficient capturing a sudden change event, and the approximation coefficients of the multi-source data are fused through the obtained context awareness credibility score; meanwhile, a maximum value selection strategy is adopted for detail coefficients, key signal features caused by real traffic events can be reserved, and the fidelity of fused traffic data is improved.

The invention discloses an adversarial agent-based number-real consistency error adaptive prediction method and system, and belongs to the technical field of information physical system and artificial intelligence crossing. According to the method, a destructive agent for maximizing a number-real consistency error and a prediction agent based on a pre-training language model are constructed for continuous confrontation, in the interaction process, the destructive agent intelligently generates disturbance through reinforcement learning so as to explore a weak link of a system, and the prediction agent predicts a disturbance result; after the system obtains the real error, on one hand, a strategy of destroying the agent is awarded and updated, and on the other hand, the real error is utilized to carry out supervised learning type increment fine adjustment on the prediction agent. Through the closed-loop mechanism of double-agent coevolution, the prediction precision and generalization ability of the data-real consistency error can be significantly improved, and the system is endowed with efficient and online adaptive learning ability, so that the reliability and safety of the data-real fusion system can be comprehensively improved.

The invention discloses an energy structure planning method and system based on an entropy flow dynamic model, and relates to the field of comprehensive energy planning, and the method comprises a first model building module which is used for building a decision matrix and a state matrix, and building an evolution model based on a system dynamics principle; the second model construction module is used for constructing a target function which takes system entropy generation as an optimization target and takes policy implementation cost and system orderliness as constraint conditions according to the entropy balance equation and the thermodynamic force-flow relationship, and constructing a maximum entropy generation model based on the target function and an evolution model; and the path output module is used for performing iterative calculation by utilizing an outer layer nested optimization algorithm based on the maximum entropy generation model to obtain an optimal policy combination of each time node in a preset planning period, and outputting an energy planning maximum entropy path. According to the method, system dynamics and an entropy theory are fused, source-network-load-storage full-link interaction is described, and a scientific and reliable benchmark reference is provided for low-carbon transformation of an energy system.

The present invention relates to a spatio-temporal fusion system and method for time-series integration of multimodal data in which numerical data, image data and the like are combined. Provided are the spatio-temporal fusion system and an operating method therefor, the system comprising: a numerical data reception unit for receiving time-series numerical data for a plurality of variables; an image data reception unit for receiving sequentially captured image data; a sequence encoder, which encodes the time-series numerical data so as to generate numerical tokens; an image encoder, which encodes the image data so as to generate image tokens; a time encoder, which encodes data obtained by combining the time-series numerical data and the image tokens, so as to generate first intermediate tokens; a spatial encoder, which encodes data obtained by combining the image data and the numerical tokens, so as to generate second intermediate tokens; and a fusion unit for deriving integrated characteristic data by using the first intermediate tokens and the second intermediate tokens.

The application discloses a multi-radar point cloud weighted fusion system and method based on an entropy weight method, belongs to the technical field of intelligent stereoscopic warehouse perception and point cloud fusion, and aims to solve the problems of low fusion precision, poor real-time performance and unreasonable weight distribution of existing multi-radar point cloud fusion. The method comprises the following steps: synchronously collecting and pre-processing multi-radar point clouds to generate a set; separating dynamic and static points to obtain static point clouds; dividing fusion voxels and detecting changed voxels; extracting features and standardizing; combining prior weights and entropy weight methods to calculate fusion weights; and weightedly fusing to generate a fusion point cloud map. The application can significantly improve the precision and real-time performance of intelligent stereoscopic warehouse point cloud fusion, and provides a reliable three-dimensional perception basis for upper-layer operations of a warehouse.

The application discloses a kind of information physical fusion method and system of mechanical equipment health prediction, wherein the method comprises: collecting the working state data of mechanical equipment;Working state data is input to pre-trained information physical fusion system CPS hybrid model, and the health prediction result of mechanical equipment is obtained, wherein CPS hybrid model is obtained according to the fusion of signal processing physical modeling and deep learning data modeling;The health prediction result is mined, and the real-time state of mechanical equipment is evaluated according to the mining result, and the state evaluation result of machine equipment is obtained.The application aims to use the optimized physical modeling method to extract more rich prior knowledge, and then realize the improvement of hybrid modeling accuracy through the optimized data modeling method, which is beneficial to support predictive maintenance activities, so as to realize the monitoring and control of CPS from physical components to network components to physical components of mechanical equipment in network collaborative manufacturing and intelligent factory.

The invention discloses a bridge dynamic deformation monitoring method and system based on multi-mode joint calculation, and relates to the technical field of structural deformation monitoring, and the method comprises the steps: obtaining acceleration data and Beidou / GNSS data obtained through bridge response, and carrying out the parameter transformation; frequency domain parameter transformation and noise parameter solving are carried out on the time domain parameters obtained through parameter transformation; carrying out bridge structure real-time dominant frequency solution on the frequency domain parameters obtained through transformation; according to the frequency threshold value and the noise characteristics of the sensor, carrying out weight distribution on time domain parameters; and performing time domain parameter weight fusion according to a weight distribution result to obtain a bridge dynamic deformation monitoring result. According to the method, weight parameter fusion is carried out by considering the dominant frequency and noise which change in real time, the fusion strategy and parameters are dynamically and adaptively adjusted according to the signal quality, the carrier dynamic characteristics and the sensor error characteristics which change in real time, and the robustness and precision of the fusion system can be maintained under the complex deformation working condition of the bridge environment.

This invention relates to a method and system for mining potential user needs based on hierarchical temporal memory enhancement. First, a dynamic time window hierarchical slicing mechanism is used to decouple the cleaned multimodal user behavior sequence into long-term historical sequences and short-term real-time sequences. A semantically approximate nearest neighbor graph is constructed from the long-term historical sequence, and adaptive community discovery is performed. This is combined with a large language model to generate a high-semantic-density long-term memory abstract representation. Then, a hybrid prompt template integrating system instructions, long-term memory representation, and short-term sequences is constructed to drive the large language model to complete a three-stage thought chain reasoning process: focused denoising, scene instantiation, and function completion, generating a structured reasoning data package. A dual-tower semantic retrieval and scene-constrained filtering architecture is employed to achieve accurate mapping between the reasoning results and the product database, outputting a product list with recommendation reasons. This invention endows the recommendation system with deep logical reasoning and cross-domain association capabilities, improving the accuracy and interpretability of e-commerce recommendations.

The application provides a hybrid large language model modular fusion system, an implementation method and related devices, which comprises the following steps: firstly, receiving a task request and analyzing to generate a task characteristic vector describing the task demand; then, obtaining the ability characteristic vectors of each expert model and calculating the similarity between the ability characteristic vectors and the task characteristic vector to screen a target expert model group to realize accurate matching of the models; secondly, querying the real-time load state of the computing node where the target expert model group is located and dynamically distributing the task to improve the utilization rate of computing resources; then, controlling the target expert model group to perform multi-round iterative collaborative reasoning through a preset cooperation process, taking the output of a previous expert model as the input of a subsequent expert model to strengthen the collaborative linkage capability among the models; finally, performing fusion processing on the intermediate output result to generate a standardized task output result, ensuring the standardization and accuracy of the output result and improving the stability and reliability of the system operation.

The invention provides a virtual-real fusion system for periodontal subbone pocket teaching and a teaching method. The virtual-real fusion system comprises a learning task construction module, a virtual simulation module, an identification module, an entity operation module, a deconstruction module, an image acquisition module, an image processing module and an intelligent analysis module. A course is issued through a learning task construction module, and a user learns the virtual model under the guidance of an identification module; and then under the guidance of the deconstruction module, physical splicing is carried out by utilizing the entity operation module. The system collects operation data of a user in real time through an image processing or sensor technology, and comprehensively calculates a grasp degree score by combining the correct rate and time consumption of the user in the recognition stage and the splicing similarity, hesitation time and taking sequence of the user in the deconstruction stage. According to the system, through a virtuality and reality combined teaching mode, a user can more clearly master the structures of different types of periodontal subosseous pockets, and the learning efficiency is improved.

Digital object fusion techniques and systems are described. In one or more examples, a base object and an adornment object are received and anchor points of the base object and the adornment object are detected by a digital object fusion system. The digital object fusion system then identifies linked anchor points from the anchor points as supporting a link between the base object and the adornment object. A path is fused by the digital object fusion system that defines the base object and the adornment object based at least in part on the linked anchor points. From this, a fused object is generated by the digital object fusion system by propagating visual style data to the path from the base object or the adornment object.

This application provides a crystal oscillator compensation method and apparatus for a cellular positioning and communication fusion system, relating to the fields of communication and positioning technology. After the positioning module and / or communication module are started, this application determines the validity of historical information based on current environmental parameters. If valid, the historical error data is used as the current crystal oscillator error to reduce processing overhead caused by repeated estimation and shorten the preparation time after system startup. If the historical information is invalid and the positioning module is working, the crystal oscillator error is estimated through the communication module. This avoids the high time overhead, high processing complexity, and even malfunction caused by the positioning module directly performing large-scale time and frequency offset scanning within a large error range. This application allows the crystal oscillator error to converge to a smaller range before performing positioning processing, reducing the system's dependence on high-performance crystal oscillators while ensuring positioning and communication performance, which is beneficial for reducing terminal costs and power consumption.

This invention provides a dynamic weighted fusion system and method based on real-time error covariance closed-loop feedback. Addressing the contradiction between underlying physical coupling interference and the need for high-bandwidth dynamic response in non-stationary random environments for large-scale arrays, this invention utilizes a first-order Markov model to quantify the statistical coherence strength of individual observation units. Secondly, it implements physical-layer regularization constraints, eliminating bus capacitance interference and geometric phase deviation through a slope-constrained driving strategy and spatial consistency mapping. In high-frequency sampling environments of 208Hz and above, combined with transient discrimination logic based on full-axis spatial correlation, an analytical solution from a Lagrange optimized functional is used to establish a negative correlation mapping function between weight allocation and the real-time posterior error covariance trace, achieving dynamic gain suppression for non-stationary observation units. This invention achieves optimal fusion of large-scale redundant information while ensuring high-bandwidth real-time response, significantly improving the system's measurement accuracy and robustness.

The invention provides an upgrading method and system of a hyper-fusion all-in-one machine and a readable storage medium, and belongs to the technical field of hyper-fusion. The method comprises the following steps: acquiring current configuration information of a target hyper-fusion all-in-one machine; sending the current configuration information of the target hyper-converged all-in-one machine to a service management center of the hyper-converged system, wherein the service management center is used for returning upgrade version information suitable for the target hyper-converged all-in-one machine according to the current configuration information; and downloading and installing an upgrade installation package corresponding to the upgrade version information. The invention aims to upgrade each hyper-converged all-in-one machine in the hyper-converged system stably and reliably.

The invention relates to the field of radar data processing, in particular to a multi-area-array radar track fusion method based on a pre-starting mechanism. Fusion processing is carried out on the tracks of the multiple array planes, each array plane respectively processes respective data, the fusion system analyzes the data of each array plane, the fusion system transmits track information parameters of the tracks which are about to enter the area overlapped with other array planes to the corresponding array planes, and the corresponding array planes process the track information parameters so that the tracks can be batched in time. And carrying out flight path tracking, and then carrying out fusion so as to ensure that the flight path is coherent.

The application discloses a data center computing, network, security and desktop integrated system based on a unified virtualization base, and belongs to the technical field of digital integration. The application adopts super-integrated design, carries out unified resource pool management on computing, storage and network, supports elastic expansion, makes planning simpler, improves resource utilization and lowers overall cost. The application provides a virtualization security strategy, realizes fine isolation, full-flow visibility, and dynamic migration of the strategy with virtual machines, and solves the adaptation deficiency of a traditional security architecture in a virtualization scene. The desktop cloud has advantages of centralized control, high security, elastic supply and long-term cost optimization, and makes office systems and data management safer and more efficient. The technical architecture of the system is advanced, and has overall advantages in scalability, security, operation and maintenance efficiency and long-term input-output ratio.

The application discloses a kind of multi-channel sensing fusion systems for exposure stress judgment, it is related to sensing fusion technical field, for solving the problem of insufficient accuracy of stress exposure state judgment, by the monitoring equipment corresponding to the working individual, the respiratory rhythm, the heart beat interval, the skin electricity response and the action trajectory such as multi-dimensional physiological and behavioral parameters in the working process are continuously collected, the multi-channel stress state observation basis is constructed, the feature extraction and abnormal discrimination of each channel data are carried out, the state mode is classified modeling using pattern recognition method, combined with cross-channel consistency analysis and causal order constraint, the abnormal response is comprehensively evaluated, when the evaluation result meets the preset stress exposure determination condition, combined with action trajectory characteristics, the stress exposure grade of working individual is graded determination, and according to the determination result, continuous monitoring or event record and information upload operation is selected, realize the automation, real-time judgment of stress exposure state.

A lane edge fusion system for autonomous vehicles includes one or more controllers that execute instructions to receive perception data and map data of the road on which the autonomous vehicle is traveling, derive multiple map lane edge points from the map data, and derive multiple perceived lane edge points from the perception data, and optimize the registration transformation and fusion problem to simultaneously compute the registration transformation to align the multiple map lane edge points from the map data and the multiple perceived lane edge points from the perception data, and construct fused lane edges.

The invention provides a self-adaptive multi-source target fusion system and method, the system comprises a plurality of unmanned platforms and a simulation and simulation module, and each unmanned platform comprises a communication module and a fusion module; each communication module comprises an external control assembly, a data driving assembly and a data reporting assembly; each fusion module comprises a fusion data adapter and a fusion data pipeline; the simulation and emulation module is used for generating sensor data and sending the sensor data to the external message middleware; each external control assembly is used for generating an internal control instruction; each data driving assembly is used for obtaining internal sensor data; each data reporting component is used for sending the received internal fusion target data and the unmanned platform cluster fusion target to the external message middleware; each fusion data adapter is used for obtaining an internal sensor frame and a sensor frame after secondary analysis; each fusion data assembly line is used for obtaining internal fusion target data and unmanned platform cluster fusion targets.

A data processing method, device and system of a fusion system are disclosed, and relate to the field of data. The fusion system comprises a computing node and a storage node. The computing node is connected with the storage node through a network to construct a storage-computing separation architecture. The storage medium of the computing node and the storage medium of the storage node are uniformly addressed to constitute a global memory pool, that is, a global storage medium shared by the computing node and the storage node. When performing read and write operations on the system, processing request data is obtained, and a memory operation of the processing request data is performed on the global memory pool according to a memory operation instruction. Thus, based on the storage-computing separation architecture with the global memory pool, the computing and storage can be flexibly expanded as needed, the read and write operations on the system can be quickly performed, the end-to-end data processing time is shortened, the data transmission speed is improved, the system energy consumption is reduced, and the system performance bottleneck is improved.

The invention provides an underground pipeline geophysical prospecting data multi-source fusion system, method, device and medium, and belongs to the field of geophysical prospecting, and the method specifically comprises the steps: obtaining geophysical prospecting point table data of different formats from different sources; according to the geophysical prospecting point table data in different formats, constructing a pipeline layout three-dimensional model of the underground pipeline in different three-dimensional spaces; displaying the pipeline layout three-dimensional model so as to receive modification of underground pipelines in the pipeline layout three-dimensional model by a user, and performing real-time adjustment; the pipeline layout three-dimensional models of the geophysical prospecting point table data from different sources are uniformly fused and mapped to a standard three-dimensional space, and a three-dimensional fusion model is generated; through a space geometry algorithm, detecting the collision risk between different pipeline layout three-dimensional models in the three-dimensional fusion model, and generating visual early warning information in real time; and outputting the three-dimensional fusion model as a drawing file in a specified format according to a preset rule. Through the processing scheme provided by the invention, real-time linkage of data can be realized, multi-source information can be fused, and three-dimensional intelligent analysis can be provided.

The invention discloses a TOGAF enterprise architecture system based on a 5G special frequency private network, and belongs to the technical field of communication, and the system comprises a content meta-model optimization module, a key technology module used for adding a 5G network in a TOGAF content meta-model, an architecture development method ADM optimization module, and a module used for adding a 5G network demand analysis tool and integrated design and layered integration architecture in the ADM of the TOGAF. The method is used for deeply fusing a 5G network with an enterprise business layer, an information system layer and a technology layer. By expanding a TOGAF meta-model and standardizing the 5G core capability, the private network design can be organically integrated into an enterprise architecture blueprint, the complexity and risk of 5G deployment are reduced by an optimized ADM process, the fusion of IT and OT is promoted, the real-time and dynamic adjustment of network resources supported by the system is promoted, the service agility is remarkably improved, and by integrating a data governance framework, the data governance efficiency is improved. And the data quality and decision-making intelligence based on the 5G data are enhanced.

The invention discloses an aerospace equipment data-real fusion test cross-domain scene-task generation and evaluation method, and belongs to the technical field of equipment digital testing. The method comprises the following steps: constructing a cross-domain test definition model of a layered architecture; analyzing a natural language demand by using a large language model in combination with a domain knowledge base, and generating a cross-domain task flow and a variable scene instance; generating a scene image conforming to a physical rule through a fine tuning generative model and a visual language model closed-loop verification mechanism, and reconstructing the scene image into a dynamic simulation scene with physical attributes; and finally, driving a digital-real fusion system to operate, and completing automatic evaluation through a digital-real synchronous verification mechanism and a multi-dimensional performance evaluation model. According to the method, the problems that scene construction depends on manpower and the number-real consistency is poor in aerospace equipment testing are effectively solved, and high-credibility and automatic number-real fusion testing is achieved.

The invention discloses a BIM (Building Information Modeling)-based wind power project green construction method, which has the advantages that a complete BIM model of a wind power project is constructed based on collected geographical environment information and an engineering drawing drawn according to a design target, and is fused and applied with an Internet of Things monitoring system formed by laying Internet of Things monitoring equipment around a wind power project construction site; according to the method, a wind power engineering BIM and Internet of Things fusion system is formed, on the basis of the wind power engineering BIM and Internet of Things fusion system, a road and platform, a draught fan foundation and draught fan hoisting construction are digitally guided to form an association scheme of all steps, and in the construction process of the three steps, environment indexes are collected through Internet of Things monitoring equipment; and when the noise or PM10 of the monitoring data continuously exceeds a set corresponding threshold value for 3 minutes, part of high-noise or high-dust-raising mechanical operation is suspended, and an association scheme of the three steps is corrected, so that the noise and the dust raising are effectively controlled in real time, and pollution is avoided.