129 results about "Hierarchical coding" patented technology

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention discloses a water conservancy project management method and system based on multi-source inspection data fusion and correlation analysis, and relates to the technical field of data processing. According to the method, hierarchical coding is performed on the inspection data in a unified acquisition period, and a three-layer envelope structure is constructed, so that time-space integrated organization of multi-source inspection information is realized, cross-source data is enabled to obtain structural consistency expression, and the accuracy and traceability of data fusion are improved; according to the method, a joint positioning point is established in a time envelope layer and a collaborative labeling identifier is distributed in a space envelope layer, so that collaborative labeling entries with synchronous semantics are formed by data of different sources, and the alignment capability and the contrastive analysis efficiency of inspection information are remarkably improved; by means of construction of a step-by-step linkage association link, continuous evolution of a water conservancy structure state can be presented on a time sequence, meanwhile, spatial-temporal characteristics can be synchronously captured in combination with generation of dynamic association paragraphs, and the recognition sensitivity of abnormal changes is enhanced.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses a progressive multi-modal medical image fusion method based on multiple scales. Relates to the technical field of multi-modal medical image fusion, in particular to a progressive multi-modal medical image fusion method based on multiple scales. Through the integrated design of multi-scale extraction, hierarchical coding, layer-by-layer fusion and adaptive modeling, efficient and accurate fusion of multi-modal medical images is realized, and reliable technical support is provided for clinical application. The method comprises the following steps: acquiring images of a mode A and a mode B, and preprocessing the images; performing multi-modal parallel coding on the preprocessed image, wherein the multi-modal parallel coding comprises four coding stages; the input of each coding stage passes through a self-adaptive visual RWKV module, an efficient cross-modal module and a progressive fusion module in sequence to obtain the output of multi-modal parallel coding; and carrying out image conversion to obtain an RGB fusion image.

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 wireless communication, in particular to a secure communication perception integrated system optimization method based on rate segmentation multiple access and a reconfigurable holographic surface, which comprises the following steps: constructing a secure communication perception integrated system model; constructing a multi-objective joint optimization problem; a multi-objective joint optimization problem is decomposed into three sub-problems, and solving algorithms are designed respectively; and sequentially updating the solutions of the three sub-problems in an alternate iteration mode until a convergence condition is met, outputting an optimal parameter, and carrying out multi-dimensional verification on the system performance. According to the invention, through a rate segmentation multiple access public stream-private stream layered coding mechanism, the system can intelligently convert part of signal energy into man-made interference to potential eavesdroppers, and at the same time, a high-dimensional beam forming degree of freedom provided by a reconfigurable holographic surface is utilized; accurate orientation of legal user signals and accurate control of null depth of an eavesdropping direction are realized.

The invention provides an image segmentation method and device for a blood vessel image, and the method comprises the steps: carrying out the step-by-step feature extraction of a to-be-segmented blood vessel image based on N hierarchical coding modules in a coding part of a blood vessel image segmentation model, and obtaining a bottleneck layer feature image; based on a global coding module of the coding part, performing global morphological feature extraction on the bottleneck layer feature image by using a self-attention mechanism to obtain a global feature image for representing a target blood vessel in the to-be-segmented blood vessel image; performing step-by-step feature fusion and step-by-step decoding on the global feature image and coded feature images output by the N step-by-step coding modules respectively based on N step-by-step decoding modules in a decoding part of the blood vessel image segmentation model to obtain a target decoded feature image; and generating a segmentation result for the target blood vessel according to the target decoding feature image.

The invention discloses a multivariable measurement lightweight space-time redundant data construction method for intelligent driving, and belongs to the technical field of intelligent driving and multi-sensor data fusion. According to the method, firstly, multi-source sensing data acquisition and space-time synchronization are carried out, then space-time redundancy measurement and feature compression modeling are carried out, and finally, lightweight space-time data structure construction is carried out. Efficient storage and rapid retrieval are realized through indexing and hierarchical coding strategies, and real-time support is provided for subsequent fusion and fault-tolerant control. According to the method, by introducing physical consistency constraint and a space-time redundancy measurement mechanism, lightweight and high-reliability fusion of multi-source sensing data is realized, data transmission and calculation burdens are effectively reduced, and the robustness and real-time response capability of an intelligent driving system in a complex environment are improved.

The invention discloses a hierarchical Mama and multi-modal fusion unknown network threat detection method based on physical boundary perception, and relates to the technical field of network space security and artificial intelligence crossing, and the method comprises the steps: carrying out physical boundary perception preprocessing on original network traffic, and extracting byte modal features and statistical modal features; constructing a layered Mama encoder, extracting a load semantic feature of each data packet, and extracting a time sequence interaction feature between the data packets; inputting the byte modal features and the statistical modal features into a multi-modal gating fusion module to generate an anti-confusion stream representation vector; and based on the mask reconstruction pre-training model, calculating a reconstruction error of the input flow. According to the method, the physical level of a network protocol is strictly aligned, stable detection performance can still be kept, extremely high reasoning speed and low video memory occupation are kept, the limitation that a traditional closed set classifier can only recognize known attacks is broken through, and the blank of an efficient flow detection model in the field of open set detection is filled up.

The present application relates to the technical field of aquaculture, in particular to a multi-source aquaculture data low-latency anti-interference communication method and system for unmanned feeding ship, comprising the following steps: multi-source aquaculture data acquisition; edge data fusion and instruction layered coding encapsulation, real-time fusion processing through a lightweight neural network at the unmanned feeding ship end to generate decision instructions corresponding to the aquaculture event; multi-node anti-interference networking; data transmission scheduling; data reception and state feedback. By deploying a MobileNet-SSD lightweight neural network at the unmanned feeding ship end, the present application realizes edge real-time cross-modal fusion of aquaculture water environment data and feeding state data, combines layered coding design driven by aquaculture events and channel quality change rate adaptive transmission strategy, avoids data redundancy, guarantees complete transmission of key instruction information when channel quality deteriorates, and effectively solves the problem of insufficient data fusion and transmission protocol adaptability.

The application discloses a time capsule expression structure reservation method based on deep learning and a knowledge graph, and aims at solving the problems that the expression and organization structure of a user's sealed multi-modal content in a digital time capsule service is difficult to be identified and recorded, and the display according to the uploading time or the fixed template of the media type in the opening stage is easy to cause the distortion of the expression intention and the fragmentation of the receiving experience. The multi-modal content input in the sealing stage is normalized and time-aligned, the content representation is obtained by using multi-modal hierarchical coding, the segmentation boundary and hierarchical relationship are determined and the key weight and object information are extracted by combining span boundary detection and a pointer network, the narrative rhythm parameters are obtained by using a neural time point process modeling based on a media switching event sequence, a timing knowledge graph is fused and constructed, and the arrangement sequence and the presentation control parameters are generated under the sequence constraint and the segmentation continuous constraint by graph representation learning, and the presentation data for the display in the opening stage is generated, so that the technical effects of reserving the user's expression structure and rhythm and differentially arranging and presenting in the future are realized.

This invention provides a lightweight knowledge base construction method based on hierarchical coding. The method includes processing documents in a knowledge document library using a lightweight semantic coding model on a cloud server to generate corresponding high-dimensional semantic vectors. Each high-dimensional semantic vector is mapped to a fixed-length binary fingerprint using a pre-defined Locality Sensitive Hash (LSH) function cluster, constructing a global fingerprint index library. On a mobile terminal, fingerprint index data from the cloud server is received and stored. Responding to user-inputted query text, a query fingerprint is generated using the same lightweight semantic coding model and LSH function cluster as on the cloud. By calculating the similarity between the query fingerprint and the fingerprints in the locally stored fingerprint index data, relevant document information is retrieved, and the results are returned. The beneficial effects of this invention are that it can reduce the knowledge base storage overhead by more than an order of magnitude while keeping the accuracy loss of semantic retrieval within 5%.

This invention relates to a beamforming and resource allocation method based on communication-aware joint optimization, applied to hierarchical coding communication scenarios. The method includes the following steps: Step 1, initializing the beamforming matrix and rate allocation vector; Step 2, solving the perception optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new beamforming matrix; Step 3, solving the communication optimization subproblem based on the current beamforming matrix and rate allocation vector to obtain a new rate allocation vector, and determining whether convergence has occurred. If not, proceed to Step 2; if yes, proceed to Step 4; Step 4, solving the communication-aware joint optimization problem based on the current beamforming matrix and rate allocation vector to obtain the final beamforming matrix and the final rate allocation vector, thus realizing beamforming and resource allocation. Compared with existing technologies, this invention has advantages such as large capacity, high speed, and low complexity.

The application provides an aviation part multi-process parameter collaborative surface treatment standardization optimization method, and relates to the technical field of aviation manufacturing surface engineering, which comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; S2, establishing a parameter solving method based on multi-objective collaborative optimization; S3, constructing a modular process chain; S4, establishing real-time monitoring and closed-loop control of multi-source data fusion; S5, establishing a digital simulation body and verifying; S6, constructing a full-process quality traceability database. Through parameter hierarchical coding and correlation mapping and cross-process chain intelligent decision-making model, the application realizes dynamic collaborative optimization of multi-process parameters, reduces parameter conflict rate, and improves process decision-making efficiency. At the same time, the adaptive optimization engine based on reinforcement learning can correct process parameters in real time, improve batch consistency of key performance indicators, and significantly improve process stability and product quality.

The invention discloses an unmanned station intelligent inspection system supporting multi-device cooperation, and the system comprises a device sensing layer which is used for the data collection of inspection devices; the equipment abstraction layer comprises a plurality of adapters; the edge calculation layer is used for carrying out data analysis; the cloud service layer comprises: a hierarchical coding system for performing multi-level identification on a service entity by using a pCode of a segmented structure; the data management service module is used for associating data acquired by a plurality of inspection devices through pCode; the task scheduling module is used for matching inspection equipment to execute an inspection task according to the inspection equipment capability description, and establishing a linkage rule through pCode to realize cross-equipment-type collaborative operation; an algorithm micro-service cluster, wherein each algorithm is deployed in an independent service form and is associated with routing inspection task configuration; and the data management service module is also used for configuring an associated algorithm according to the inspection equipment and the inspection task to complete multi-source inspection data set retrieval and defect identification. The inspection system provided by the invention is light in weight, extensible and capable of arranging services.

The present application relates to the technical field of digital circuit control, and particularly relates to a contact state monitoring and control method of a direct current contactor. The method comprises the following steps: monitoring the contact state of the direct current contactor and collecting the driving code signal of the direct current contactor, and extracting the characteristic code after filtering; performing spiral hierarchical identification on the characteristic code, analyzing the hierarchical information to distinguish the driving instruction, and obtaining the characteristic code identification result; distributing the characteristic code identification result to the corresponding driving circuit; outputting the rated driving voltage to the corresponding direct current contactor coil according to the characteristic code identification result, driving the coil to attract the armature, and closing the contact of the direct current contactor. The present application realizes fine management and control and abnormal processing of the direct current contactor by combining filtering and spiral hierarchical coding identification, and improves the instruction execution accuracy and the control whole-process stability.

This invention provides a UAV power emergency communication and resource collaborative scheduling system, relating to the field of wireless communication technology. Through system initialization, data acquisition and hierarchical coding, satellite-to-ground link establishment, NOMA access and scheduling modeling, resource collaborative configuration model establishment, joint optimization solution, online collaborative execution, and anomaly response, it achieves dynamic resource scheduling and optimization, offering advantages such as efficient collaborative resource scheduling, improved spectrum utilization, optimized energy use, guaranteed access to key nodes, and enhanced system reliability and response speed.

This invention discloses a method for identifying jasmine planting areas in remote sensing images based on texture features and global attention, comprising the following steps: Step S1. Dataset creation; Step S2. Image segmentation network construction: using a hierarchical encoder, a small convolutional kernel module, and a lightweight decoder as an image classification network; Step S3. Model training; Step S4. Model inference: loading the trained image segmentation network model parameters from step (3) into the image segmentation network from step (2), and sequentially inputting the remote sensing image data of jasmine planting areas from the test set in step (1) into the network for inference to obtain segmentation prediction image data. Based on the correlation characteristics of remote sensing images of jasmine planting areas, this invention improves the network structure of the algorithm model, enhances the speed and accuracy of segmentation and recognition, reduces model size, and improves model generalization.

The application discloses a double-branch Raio gating graph aggregation multi-modal meningioma segmentation method, belongs to the cross technical field of computer vision and medical image processing, is used for meningioma segmentation, and comprises the following steps: preparing a data set, constructing a deep neural network model, and performing neural network training; inputting the data set into the trained deep neural network model; first inputting a double-branch multi-modal input module to output a multi-modal fusion feature tensor; then inputting the multi-modal fusion feature tensor into a hierarchical Swin Transformer encoder to output three-dimensional segmentation masks of enhanced tumors, tumor cores and whole tumor regions of meningioma. Through double-branch multi-modal input fusion, hierarchical Swin Transformer coding, edge perception modulation and Laplace gating graph aggregation, high-precision three-dimensional segmentation of enhanced tumors, tumor cores and whole tumor regions of meningioma is realized.

The invention provides a static detection method for multi-type text files and script files, a storage medium and electronic equipment. The static detection method comprises the following steps: acquiring a to-be-detected file; mapping each character into a predefined character category to obtain a character category sequence; carrying out statistics on similar characters continuously appearing in the character category sequence, and carrying out classification according to the number of continuous appearing times obtained through statistics to obtain a hierarchical coding sequence; combining the character category sequence with the hierarchical coding sequence to generate a structured fuzzy feature character string of the file; and matching the structured fuzzy feature character string with a feature library in a static detection engine, and judging the security of the file according to a matching result. The feature extraction process does not depend on the specific grammar or semantics of the file, so that different types of text and script files can be uniformly processed, the universality is very high, and the method has certain resistance to common code deformation and simple confusion.

The application discloses a data-driven multi-objective radio frequency circuit layout optimization design method based on hierarchical coding and belongs to the field of radio frequency circuit layout optimization design. The application uses a data-driven multi-objective optimization method based on hierarchical coding to solve the radio frequency circuit layout optimization design problem, adopts a multi-level collaborative coding mode to code the circuit layout, and compared with a traditional mode of directly optimizing the XY coordinates of each component, fundamentally avoids component overlapping, simplifies the optimization process, exponentially reduces the search space, and improves the optimization efficiency; a large language model (LLM) is used to collaboratively generate offspring, realizes the transformation from "random variation" to "intelligent creation", helps the optimization algorithm to jump out of the local optimum point, significantly accelerates the convergence speed of the algorithm, and can quickly find an excellent circuit layout scheme meeting the multi-objective requirements.

The invention discloses an orchard multi-robot task allocation optimization method based on hierarchical path reconstruction, and the method comprises the steps: S1, building a multi-target mathematical model, optimizing the maximum completion time and total energy consumption, including robot load, speed, energy consumption and battery capacity constraints, and employing VLDIM to generate an initial solution population; s2, optimizing the task sequence of each path by using DRRM in the microscopic layer; s3, robot-level DRRM optimization, TRRM task redistribution, CRRM charging path reconstruction and environment selection are executed iteratively; and S4, splitting the longest path by using the SRRM, redistributing the longest path through the MILP1, and outputting a non-dominated solution set. Through hierarchical coding, multi-stage collaborative optimization and a special reconstruction mechanism for core constraints, the task allocation efficiency and scheme quality of a multi-robot system under complex constraints are significantly improved, and the method is suitable for agricultural automation and warehouse logistics scenes.

The invention provides a data acquisition method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, entity information of a to-be-acquired book is acquired, the entity information is coded according to a preset hierarchical coding rule, and a unique identification code corresponding to the to-be-acquired book is generated; based on a plurality of preset acquisition dimensions and the parameter range of each dimension, generating an acquisition parameter combination, and generating an acquisition task according to the unique identification code and the acquisition parameter combination, the acquisition task including the unique identification code of the target book and the corresponding acquisition parameter combination; and executing an acquisition task to obtain a book image, combining the unique identification code and the acquisition parameters as metadata in the acquisition process, and performing associated storage on the metadata and the acquired image to construct a book image data set. And the generalization ability of the image processing model trained based on the data set is obviously improved.

The invention relates to the technical field of underwater energy storage system optimization, and discloses a cluster distributed underwater flexible compressed air system topology optimization method comprising the following steps: establishing a cluster distributed gas storage system mathematical model; coding the individuals into a capacity allocation coding part, a spatial layout coding part and a topological connection coding part by adopting a hierarchical coding strategy; optimizing the air bag capacity distribution vector by adopting a capacity distribution method based on pressure loss weighting; executing an improved multi-objective evolutionary algorithm, and performing synchronous optimization on the capacity allocation coding part, the spatial layout coding part and the topological connection coding part through a co-evolutionary strategy; and outputting a Pareto optimal solution set. According to the method, an air bag capacity distribution vector, a space coordinate matrix and a connection topology matrix are used as joint design variables, so that three highly-coupled sub-problems of capacity distribution, space layout and pipeline topology connection are synchronously optimized.

The application discloses a kind of dynamic controllable video encoding method and device, method includes introducing controllable coding module in video acquisition end, control is carried out to the real-time switching of video stream in combination with encryption control signal;Using encrypted video platform, the coding control instruction including non-encryption encoding instruction and encryption encoding instruction is issued through network control interface;Video encoding is carried out using layered coding strategy;When video does not need to be encrypted, non-encryption encoding mode instruction is issued by platform;When video needs to be encrypted, encryption encoding mode instruction is issued by platform, and video acquisition terminal switches to encryption mode;In encrypted video platform client, corresponding session key is obtained from key management module according to session identification in video frame header, received encrypted video stream is decrypted and played, and decryption end adopts frame-level decryption strategy, only complete decryption is carried out to key frame, and prediction frame is reconstructed by referring to decrypted frame, effectively solve the problem of video security and video sharing.

The invention discloses a multi-dialect large model adaptation method and system oriented to a Gluggin language, and the method comprises the steps: obtaining corpus data and model constraint information of each Gluggin dialect region, building a dialect mapping matrix, and positioning key semantic difference points; characterization offset features are extracted, contrast training sample pairs are constructed, and vector space analysis is implemented to determine a priority adjustment layer; identifying dialect difference features in the model constraint information to generate layered coding parameters, generating a layered representation structure through collaborative matching of a priority adjustment layer and the layered coding parameters, and determining a weight adjustment boundary; determining a parameter distribution density by combining a weight adjustment boundary and a priority coefficient, generating an optimal migration weight, extracting a negative migration node, converting the negative migration node into a dialect boundary mark, and constructing a gradient propagation path; and detecting the confusion expression set, generating representation decoupling configuration, executing progressive collaborative optimization based on the representation decoupling configuration and the gradient propagation path to output a multi-dialect adaptation scheme, and realizing accurate adaptation and collaborative optimization of the multi-dialect of the Gu-language family.

A method of signalling a signal adjustment when encoding an input signal using a hierarchical coding scheme to manage a display aspect ratio. The hierarchical coding scheme comprises encoding a downsampled version of the input signal using a first encoding method to produce a first encoded signal. When the downsampling operation of the hierarchical coding scheme is a non-square downsampling operation, the method signals an adjustment to cause a pixel aspect ratio of the first encoded signal to be adjusted by a scaling factor, wherein the scaling factor is determined from the non-square downsampling operation. Additionally, correction is applied in an enhancement level of the hierarchical coding scheme to ensure that the signalled change of pixel aspect ratio does not lead to unintended consequences in the enhanced reproduction at a decoder-side.

The application relates to the technical field of artificial intelligence, and discloses a multi-modal large language model intelligent decision-making method and system fusing cross-domain information. The method comprises the following steps: performing hierarchical coding processing on cross-domain multi-modal input data corresponding to a target decision event, generating observation evidence data and conditional constraint data, and performing modal confidence estimation processing, cross-domain conflict relationship construction, gating fusion processing and historical decision feedback updating on the basis of the observation evidence data and the conditional constraint data. The method can distinguish the category tendency of the corresponding mode from the reliability of the evidence, identify real conflicts and pseudo conflicts, reduce the interference of low-quality modes and invalid differences on the fusion decision result, and simultaneously perform closed-loop correction on the modal confidence estimation parameters and the gating fusion parameters based on the historical decision feedback data, thereby improving the accuracy, stability and self-adaptive ability of the multi-modal large language model intelligent decision-making fusing cross-domain information in a time-varying environment.

The invention relates to the technical field of operation and maintenance management, and discloses an operation and maintenance task-oriented automatic template management system, which comprises the following steps of: uniformly expressing an equipment tree in a fixed sequence of projects, branch companies, machine room sub-parts / sub-items and first-level / second-level / third-level equipment through node indexing-hierarchical coding-alignment to generate a stable index vector; cross-project naming differences and hierarchy missing or redundancy are converted into recoverable index offset, the same object is positioned consistently in different tree versions, candidate screening is not affected by hierarchy jitter, a type extraction module executes alias merging on a type table to obtain type mapping, then type vectors are extracted from a task list according to the type mapping, and the type vectors are extracted from the task list. According to the method, alias crossing and granularity inconsistency are converged to a unified key space, ambiguity is eliminated in an extraction link, and template matching is accurately restrained, so that near-synonym error locking is reduced, the accuracy and stability of template automatic locking are improved, and manual proofreading and backspacing are reduced.