16results about How to "Reduce data redundancy" patented technology

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

This invention discloses a broadband oscillation disturbance source localization method based on compressed sensing and graph convolutional neural networks (GCNs), comprising two stages: offline training and online localization. In the offline training stage, electrical quantities under subsynchronous / supersynchronous broadband oscillation modes are obtained using measurement data from actual systems or simulation examples, constructing a broadband oscillation offline sample library. Compressed sensing is used to encode and compress the electrical quantities in the offline sample library, obtaining a training set and a test set. The constructed GCN localization model is trained using the training set until the test set reaches the target localization accuracy, resulting in a broadband oscillation localization model. In the online localization stage, a substation collects electrical quantity data and compresses and encodes it using compressed sensing technology; the substation data is uploaded to the master station; at the master station, the feature matrix and system adjacency matrix are input into the trained GCN localization model, and the oscillation source location is output. This invention's method is applicable to broadband oscillation disturbance source localization under various operating conditions.

This invention relates to the field of cable inspection and maintenance technology, and discloses a robot for the operation and maintenance of power transmission lines in mountainous areas. The robot includes a main body, a sliding buffer assembly, a camera inspection assembly, a positioning assembly, and a drive assembly. The sliding buffer assembly decouples the camera mechanism from the robot body through a sliding groove and a slider, allowing the robot to move forward continuously at a constant speed while the camera mechanism remains stationary, eliminating motion blur and channel misalignment. Driven by the drive assembly, the positioning assembly first stretches the cable section to be tested axially and then pushes it axially, forcibly opening circumferential and axial micro-cracks to facilitate the visualization of early damage. Four sets of multispectral high-definition cameras in a ring array employ an intermittent static shooting strategy, requiring only one key image to be acquired for each detection section during the stretching and pushing states. This invention achieves high-precision, low-miss-rate detection of micro-cracks in cables in mountainous areas, simplifies the system structure, and improves the robot's portability and operational stability.

The invention discloses a medical multi-modal large model intelligent system and method based on federated learning, relates to the technical field of medical information, and solves the technical problem that existing federated learning still has defects in the aspects of processing heterogeneous data quality, ensuring model interpretability and promoting multi-party effective cooperation. The system comprises a federated architecture module, a federated reasoning module and a model collaboration module, the federal architecture module is used for adjusting modal weight in real time according to data quality; the federated reasoning module is used for constructing a knowledge graph; introducing the knowledge graph into a medical multi-modal large model; setting an atlas constraint loss function; the model collaboration module is used for optimizing the medical multi-modal large model through distributed training and localized deployment; according to the method, multi-modal data can be effectively integrated, and the model training and reasoning process is optimized by utilizing medical knowledge, so that the accuracy and reliability of medical diagnosis are improved.

This invention provides an artificial intelligence-based internet property rights transaction risk assessment and decision-making system, relating to the field of data processing technology. The system includes: a data acquisition and preprocessing module for real-time acquisition of raw transaction data, followed by cleaning and standardization; a multi-dimensional feature construction module for modeling by time windows and constructing a multi-level interaction relationship graph to obtain time-series feature data and graph-related feature data; and for normalizing and weighting the features; a risk identification module for extracting suspicious transfer behavior chains and tracing the flow of benefits, performing composite risk discrimination; and an intelligent decision-making module for performing hierarchical adaptive discrimination and case migration comparison, summarizing and generating risk assessment results and pushing early warnings; and receiving feedback from the management terminal, which is then used to optimize identification and decision-making parameters. This invention improves the autonomy and accuracy of property rights transaction risk assessment and decision-making.

The invention relates to the technical field of unmanned driving, and particularly discloses an obstacle detection system for unmanned driving, which is characterized in that a multi-modal acquisition module acquires vehicle surrounding environment data according to near, middle and far layers, a data preprocessing and synchronizing module performs standardization processing on the acquired data and then outputs a standardized heterogeneous data set with a detection interval identifier; two types of feature libraries are arranged in the dynamic weighted fusion judgment module, after obstacle features are extracted from a data set, an obstacle judgment result is output through hierarchical weight distribution, dynamic correction, feature fusion and two-dimensional judgment, and the master control terminal receives the judgment result and outputs normal driving, constant speed deceleration, steering avoidance or emergency braking instructions. The obstacle detection accuracy, the response timeliness and the environmental adaptability of the unmanned vehicle under the complex road condition are improved, and reliable guarantee is provided for safe driving of the unmanned vehicle.

The application discloses a heterogeneous parallel material removal calculation method and system. The method comprises the following steps: constructing a heterogeneous parallel calculation environment comprising at least a first processing unit and a second processing unit, wherein at least one processing unit supports data parallel execution for a large number of discrete elements; constructing a discrete representation of a workpiece, and generating state data for representing a material state based on the discrete representation of the workpiece; generating space constraint information based on tool geometric parameters, relative motion information of the tool and the workpiece, and the state data; decomposing material removal simulation calculation into a preprocessing task and a relationship evaluation task; mapping at least two calculation tasks to the first processing unit and the second processing unit for execution, so that the first processing unit and the second processing unit concurrently or flow water overlap execute in at least part of a time period; updating the material state based on the space relationship quantity and calculating the material removal quantity, and iterating until the trajectory is completed. Therefore, the calculation efficiency is significantly improved, and the real-time simulation requirement is met.

The application provides a data processing method and device for a virtual scene, equipment, a computer readable storage medium and a computer program product, relates to game technology in the cloud field, and the method comprises the following steps: connecting multiple clients to multiple single games; allocating static resource masks corresponding to static resources to the multiple clients respectively, wherein the static resource masks allocated to the multiple clients are the same; allocating dynamic resource masks corresponding to dynamic resources to the multiple clients respectively; sharing static resources among the multiple single games based on the static resource masks corresponding to the static resources allocated to the multiple clients respectively; and isolating dynamic resources among different single games based on the dynamic resource masks corresponding to the dynamic resources allocated to the multiple clients respectively. The application can effectively reduce data redundancy of a server by realizing isolation of dynamic resources and sharing of static resources among different single games.

The present application relates to the technical field of computer, and provides a disease risk prediction method, device, equipment, medium and product, the method comprises the following steps: constructing N characteristic matrices based on metagenomic data of a to-be-tested sample; constructing sample correlation graphs based on the N characteristic matrices respectively, and performing graph feature extraction on the N characteristic matrices based on the corresponding sample correlation graphs to obtain N characteristic representations; performing cross-type fusion on the N characteristic representations to obtain a fusion representation of the to-be-tested sample; performing disease risk prediction based on the fusion representation to obtain a prediction result; wherein N is a positive integer not less than 2. The method can perform prediction based on the fusion representation which retains statistical characteristics and topological structure characteristics of biological information at each level, effectively improves the utilization degree of the original metagenomic data, and improves the accuracy and robustness of disease risk prediction.

The invention, which relates to the technical field of intelligent sensing, discloses a protection area multi-modal sensing fusion wild animal intelligent identification system comprising an intelligent sensing module used for receiving and obtaining a multi-modal data set; the fusion analysis module is used for processing the multi-modal data set, identifying all predation interaction events in a current sensing unit in a preset time window by adopting a target detection algorithm, generating a structured behavior semantic tag, and calculating an instant risk value of a generation event level of each predation interaction event so as to construct a food chain stability index; the first judgment module is used for executing first judgment to obtain a first evaluation result; aggregating the jth sensing area, calculating an average value of foraging efficiency coefficients FEC of all predation interaction events, and executing second judgment to obtain a second evaluation result; and the decision module is used for combining the first evaluation result and the second evaluation result to generate a corresponding strategy.

The invention discloses an abnormal log determination method and device, equipment, a storage medium and a program product. The method comprises the steps that multiple first logs generated in the system running process are obtained, each first log comprises a host name, a process name, a timestamp and original log content, and the original log content comprises dynamic parameters; mapping dynamic parameters in the original log content into corresponding semantic categories to obtain target log content; determining a first log vector based on the host name, the process name, the timestamp and the target log content; sorting the plurality of first log vectors based on the timestamps to obtain a log sequence; determining an abnormal log vector in a plurality of first log vectors based on a normalized log sequence corresponding to the system and a context dependency relationship of the plurality of first log vectors in the log sequence; and determining the first log corresponding to the abnormal log vector as an abnormal log. According to the embodiment of the invention, the efficiency and accuracy of determining the abnormal log can be improved.

The invention discloses a communication simulation system for a 4G / 5G and satellite fusion scene. The communication simulation system comprises a simulation deduction system, a simulation computer, a scheduler and a Kafka message bus. The simulation deduction system generates and issues scene information including a base station, a user and a satellite link on a time axis; the simulation computer subscribes information, executes link budget, access control, resource scheduling and cross-domain transmission mode selection algorithms, and outputs communication performance indexes; and the scheduler coordinates the simulation period, generates a strategy based on a result and issues a control instruction to drive the scene to be dynamically updated. According to the system, rapid entity construction is achieved through model library multiplexing and Query ID binding, six self-adaptive transmission modes are supported on the basis of multiple types of adjacent matrixes, and co-simulation of the ground and a satellite link is achieved. The system architecture supports module parallelism and message decoupling, the expansibility, real-time performance and result credibility of large-scale simulation are improved, and efficient simulation support is provided for planning and optimization of a satellite-ground fusion network.

The invention relates to the technical field of intelligent sensing, and provides a dynamic event sensing method, system and equipment, and the method comprises the steps: obtaining an electric response signal outputted by a synaptic device in a preset time period, so as to obtain an electric signal sequence; calculating the change rate of the electric signal sequence in the preset time period according to the electric signal sequence; obtaining a preset event discrimination threshold value, and comparing the change rate with the event discrimination threshold value to obtain a comparison result; if the comparison result is an effective dynamic event, recording event information corresponding to the electric response signal; based on the electric signal sequence, event information is packaged to generate event signals, the characteristic that an artificial synaptic device responds to signal evolution along with time under external stimulation is utilized, self-triggering sensing of dynamic events is achieved, and the robustness of a system to complex environment changes is improved.

The application discloses a crop classification method of a self-adaptive SAM and ConvxLSTM hybrid model, and relates to the technical field of remote sensing image processing. The method comprises the following steps: constructing a time series dataset facing crop phenology, filtering and screening an optimal classification time phase band combination through J-M distance; generating unmarked samples according to the optimal classification time phase band combination, generating crop pseudo-labels with category annotations based on a self-adaptive SAM model; processing the time series dataset based on a ConvxLSTM network to classify crops and output crop classification results. The application can ensure automatic acquisition of high-quality crop pseudo-labels, provide crop category annotations for the pseudo-labels, greatly reduce the excessive dependence of a model on artificially annotated samples, more effectively mine the space-time features of data, and thus improve the crop classification precision.

The application provides a kind of edge side industrial data lightweight pretreatment cache method, belong to industrial data technical field, the application is through Gray code anti-interference coding to improve data acquisition quality, adaptive sampling rate adjustment and weak abnormal feature weight vector realizes dynamic sampling, wavelet decomposition and high frequency fault feature sparse matrix accurately locates fault position, semantic perception compression encoder cooperates with dispersion evaluation matrix and carries out differential coding, and network transmission graph model optimizes transmission path, this overall systematic technical scheme from signal acquisition, adaptive processing, intelligent compression, dynamic cache management to network optimization each link, solves the technical problems that the cache occupancy rate is too high and the data transmission delay is large caused by the difficulty of efficient pretreatment of multi-source signals of industrial equipment at edge side.

This application relates to the field of computer data processing technology, providing a method and system for soil classification prediction based on multi-source environmental and remote sensing data. The method includes: constructing a soil feature dataset containing spectral features, physicochemical features, and environmental features; calculating the contribution of each feature to soil classification and the correlation between them, generating an initial weight configuration; updating the weight coefficients based on the contribution of spectral features and / or the correlation between physicochemical features and environmental features, generating an adaptive weight configuration; weighted fusion of feature subsets whose contribution exceeds a contribution threshold based on this configuration to generate a soil classification feature vector; constructing a soil classification decision tree based on this vector and determining the optimal classification path; predicting the classification result of the soil sample to be classified based on the optimal classification path; if the confidence level of the classification result does not meet the confidence threshold condition, updating the weight coefficients, and returning to the step of generating the adaptive weight configuration, until the confidence threshold condition is met, obtaining the final classification result.