31 results about "Content awareness" patented technology

The invention discloses a bar code scanning data rapid transmission method based on edge calculation, and the method comprises the following steps: S1, collecting bar code data, and packaging the bar code data into a bar code data package; s2, sending the barcode data packet to an edge computing node; s3, carrying out preprocessing by the edge computing node; s4, constructing and managing a multi-task scheduling queue in the edge computing node; s5, the edge computing node collects network state parameters and generates a network score value; s6, when the network score value is smaller than the network score threshold value, performing compression and encapsulation by adopting a content awareness compression coding mechanism; s7, when the network score value is greater than or equal to the network score threshold value, starting a delay synchronization mechanism; and S8, the cloud server receives, executes processing and returns to the edge computing node. According to the method, edge calculation and content perception compression are fused, efficient scheduling and reliable transmission of bar code data are realized, and the method has the advantages of high real-time performance, weak network resistance and efficient transmission.

The invention provides a vehicle target detection method based on lightweight YOLOv8, on the basis of a YOLOv8 model, related parts are innovated aiming at detection precision and speed, the method is named as HAF-YOLOv8, firstly, in order to avoid that a backbone network of a detector extracts redundant features, and then, the backbone network of the detector extracts the redundant features; the Faster Net containing the PConv is used for reducing redundant calculation and memory access and extracting spatial features more effectively at the same time; then, a focus modulation-based self-adaptive adjustment view field mechanism FocalNet is used for replacing an original SPPF method, and compared with a self-attention mechanism, the method has a simpler calculation process and lower complexity; and finally, selecting a converter Bi-Former of a double-layer routing dynamic sparse attention mechanism as a newly added module of the backbone network at the end of the backbone network, and filtering out the most irrelevant area to save the parameter quantity and the calculation quantity so as to realize more flexible and effective calculation distribution and content awareness.

The invention relates to the field of audio and video communication, and particularly discloses an audio and video communication demonstration method and demonstration device under an electronic countermeasure condition, and the method comprises the following steps: 1, carrying out the coding compression of an original audio and video signal through a perception compression technology, the perception compression technology is combined with a deep learning algorithm, ROI region detection and protection and coding parameter prediction based on content perception; through combination of deep learning, ROI region protection and content awareness coding, on the premise of keeping the visual image quality of human eyes, the 1080P video code rate is compressed from 2Mbps of traditional H.265 to below 400Kbps, the code rate is reduced to 1 / 20 of the original code rate, the channel bandwidth is remarkably saved, and the communication cost is reduced; by adopting adaptive multi-protocol hybrid transmission and forward error correction coding, the audio and video availability can still be maintained when the bidirectional packet loss rate is as high as 70%, the end-to-end delay is reduced to be within 500ms, and the inherent defects of a traditional single protocol are overcome.

The invention discloses a heart valve segmentation method based on edge and important content perception, which adopts an encoder and decoder architecture and comprises the following steps of: firstly, transmitting a heart CT (Computed Tomography) image into the first three layers of encoding stages of an encoder; in each coding stage, multi-direction edge information and local shape features are extracted through a mixed feature enhancement module constructed by a multi-direction learnable Sobel convolutional layer and a standard convolutional layer; then, the local features are transmitted to the last two layers of coding stages; and finally, respectively transmitting each coding feature into a corresponding decoding stage for decoding so as to generate a heart valve segmentation result. According to the method, a staged and progressive feature coding mode is designed, multi-direction edge and local shape features of the heart CT image in the shallow coding stage and important content information of the heart CT image in the deep coding stage are effectively learned, and important details of the heart valve are learned, so that the segmentation effect is better improved, and the segmentation efficiency is improved. And a certain technical support is provided for the diagnosis requirements of clinicians.

The invention provides a content awareness and intelligent routing document analysis method and device, equipment and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the steps of performing content perception on a to-be-recognized multi-modal document, and extracting a multi-dimensional feature vector of each page of the to-be-recognized multi-modal document; a preset routing decision model is adopted to route the multi-dimensional feature vectors, and a document analysis tool of each page of document is determined; carrying out statistics on document analysis tools of multi-page documents of the to-be-identified multi-modal document to obtain analysis page numbers of the document analysis tools; according to the analysis page number of each document analysis tool, calling each document analysis tool to analyze a document corresponding to the analysis page number to obtain a local analysis result; and integrating all the local analysis results to generate a target analysis result of the to-be-identified multi-modal document. According to the method, the selection and use threshold of a document analysis tool can be reduced, and the efficiency, quality and automation degree of document analysis are improved.

The invention discloses an edge computing-oriented ultra-high-definition video dynamic concentration and lightweight transmission system, and provides a lightweight transmission mechanism based on content awareness and closed-loop feedback. The system performs frame-level motion detection and semantic analysis on a video stream at an edge node, extracts motion intensity, object density and key event characteristics, dynamically calculates a concentration factor in combination with task priority, realizes intelligent retention of key frames and cutting of redundant frames, and generates a concentrated video stream; meanwhile, the coding rate is adaptively adjusted according to the real-time uplink bandwidth, and transmission parameters are optimized through a bandwidth-quality tradeoff function; a receiving end evaluates the actual quality and feeds back the actual quality to an edge node, and dynamically adjusts a concentration and coding strategy to form closed-loop control of sensing-concentration-transmission-feedback; the system is suitable for a large-scale intelligent video analysis scene, the bandwidth occupation and the storage overhead are remarkably reduced, and the system response efficiency and the key information retention rate are improved.

The invention discloses an automatic cross-platform advertisement putting system and method, and belongs to the technical field of advertisement putting, and the system comprises a multi-modal content analysis module which is used for carrying out the cross-modal theme aggregation of the graph, text and video contents of a social platform, and generating a unified content vector; the correlation scoring module is used for calculating correlation scores of the content vectors and the commodities in the advertisement library; the dynamic delivery strategy module executes a hierarchical strategy according to the score: high-time-sharing accurate delivery, middle-time-sharing associated extension delivery, and low-time-sharing exploratory delivery decision algorithm is started to perform opportunity exploration; and the strategy self-adaptive optimization module is used for automatically adjusting various parameters in the putting strategy by periodically backtesting the actual putting return efficiency of the advertisement. Through combination of deep content awareness and strategy closed-loop optimization, the accuracy and the return rate of advertisement putting and the utilization efficiency of advertisement inventory are remarkably improved.

This application discloses a WAL log compression method, device, and medium under PostgreSQL streaming replication, belonging to the database field. The method includes: reading configuration parameters and establishing a streaming replication connection when the master database starts up, obtaining network I / O load values ​​in real time, and calculating the predicted WAL log generation rate based on the transaction processing rate to estimate future bandwidth requirements. When the network I / O or predicted bandwidth requirements reach a preset threshold, samples of the original WAL log data are extracted and compared using multiple algorithms for rapid compression. The optimal compression algorithm is dynamically selected for adaptive compression processing, and the corresponding compression flag field is set; otherwise, the data is transmitted directly in an uncompressed manner. The standby database completes the corresponding decompression or direct writing by parsing the compression flag field. This invention achieves a dynamic balance between network bandwidth and CPU resources through a dual mechanism of load prediction and content awareness, effectively reducing master-standby replication latency and improving the adaptability and overall operating efficiency of the streaming replication system in complex production environments.

The invention belongs to the technical field of image processing, and particularly relates to a content awareness-based picture elastic compression method, which comprises the following steps of: designing a key point comparison method, and identifying key point changes of front and back frames; designing a redundancy elimination method, and carrying out background recognition and elimination; designing an elastic compression scheme, and automatically configuring the compression scheme according to a current equipment state and a hardware condition; through a key point comparison method, key point changes of front and back frames are recognized, only video image frames with key point changes are reserved, data screening is achieved, and data memory is reduced; background recognition and elimination are carried out through a redundancy elimination method for the reserved video image frames; through the elastic compression scheme, the compression scheme is automatically configured according to the current equipment state and the hardware condition, and the elastic compression scheme realizes intelligent compression of different equipment, so that the method has relatively high universality.

The invention discloses an industrial product surface defect detection method based on multi-scale content awareness, and belongs to the technical field of industrial product surface defects, and the method comprises the following steps: data acquisition and division; constructing a model; model training; and detecting defects. According to the method, an SARepVGG module is provided, a bimodal feature interaction architecture is constructed in Backbone and Neck networks, a self-attention mechanism and a structural re-parameterization method are innovatively and deeply fused, and the defect feature expression ability of the model is enhanced; an MFCARAFE module is designed, industrial product context defect semantic information is creatively fused in a cross-scale mode, and the detection performance of defects of different scales of industrial products is improved; an AIFIC module is also provided, multi-scale normalization processing is carried out on input through a dynamic feature calibration module, convolution unit processing is combined, the adaptability of the model to complex scenes is improved, and the generalization ability of the model is enhanced.

The invention discloses a content-aware adaptive image coding system and method for a VLM, and aims to solve the semantic cutting problem in high-resolution image processing and improve the efficiency. The system comprises a content awareness slicing module, a parallel visual coding module and a feature aggregation and fusion module, the content awareness slicing module recognizes a key semantic region in an image through a lightweight pre-analysis model and executes a self-adaptive slicing algorithm to generate image slices which are variable in size and length-width ratio and possibly partially overlapped, and the feature aggregation and fusion module carries out feature fusion on the image slices. The semantic integrity is reserved to the maximum extent; the parallel visual coding module uses a frozen pre-training standard visual encoder to process the slices, and efficient feature extraction is realized through size adjustment and slight interpolation position coding; the feature aggregation and fusion module integrates features of all slices and spatial position information thereof by using an attention mechanism, outputs globally unified image feature representation, and retains high-resolution details and overall layout.

A data protection system protects the data with the appropriate level of security and tiering for storage in data pools. The system includes a content awareness feature that is utilized to classify and analyze the data to be backed up and assigns the classified data to the appropriate data pool dynamically. It uses a classifying software development kit (SDK) or application program interface (API) to classify and analyze the contents. Based on this classification, data is segregated using specific data properties and is compared with a list of data pool properties. Then system then ranks and predicts the pool that is best suited for the classified data and dynamically assigns the data to the pool during backup.

The invention relates to the technical field of computers, discloses an adaptive resolution and code rate video transcoding control method, and aims to solve the problems of lagging, image quality reduction, overhigh energy consumption and the like caused by network response lag, parameter decoupling control and neglecting of equipment state in the prior art. The method comprises the following steps: periodically acquiring network state, video content and equipment physical state data; predicting a future bandwidth by using a linear Kalman filter; extracting video space domain and time domain complexity features in real time; mapping a resource constraint level according to the equipment temperature and the electric quantity; and in a pre-established code rate-resolution-quality multi-dimensional model, combining the multi-dimensional information to solve an optimal transcoding parameter combination, and driving a transcoding engine to execute. According to the method, dynamic balance of fluency, image quality and energy efficiency is realized through collaborative optimization of millisecond-level bandwidth prediction and content awareness and equipment adaptive protection.

The present application belongs to the technical field of industrial control system data storage and backup, and relates to an enterprise-level incremental backup method and system based on content perception and intelligent scheduling, comprising: loading a filtering driver component in the kernel input-output stack, intercepting data write requests and calculating content hash fingerprints; matching a business topology mapping table to establish data weights; constructing a backup task queue in combination with node load states; starting write back pressure logic when the load exceeds a threshold, intercepting physical disk writing actions and feeding back asynchronous confirmation messages; and completing incremental merging and disk writing by relying on fingerprints through a virtualization driver component. The present application effectively solves the resource mutual exclusion contradiction between backup tasks and real-time production instructions through the kernel state in-situ perception and back pressure feedback mechanism, guarantees the business logic continuity under saturated storage bus conditions, prevents write timeout from inducing retry storms, and improves the data availability of industrial control systems.

The invention discloses an image super-resolution method of a lightweight mixed path model based on content awareness, and the method comprises the following steps: providing matched recovery paths for different types of image degradation by introducing a grouped multipath strategy; in combination with a content awareness mechanism, processing branches are adaptively selected according to image region complexity, and refined reconstruction of a complex region and efficient processing of a simple region are realized; a progressive calibration module is further adopted, the fusion process of simple and complex region features in the content perception module is optimized, artifacts are reduced, and reconstruction consistency is improved. According to the method, the image super-resolution reconstruction quality in a real complex degraded scene can be remarkably improved on the premise of keeping relatively low calculation overhead, and the method is suitable for the fields of remote sensing image enhancement, medical image analysis, high-definition display and the like.

The invention relates to a file fragment type identification method based on context content perception, and aims to solve the problem that in data recovery of a hard disk, an existing recovery method cannot fully utilize a context relationship among file fragments. As the adjacent file fragments usually come from the same file or have similar file types, the adjacent relationship between the file fragments can provide key clues for data recovery. The BERT model has remarkable advantages in the aspect of capturing the dependency relationship among complex sequences due to the powerful context modeling capability in natural language sequence processing. According to the method, the feature of BERT is migrated to the field of file fragment sequences, and the context relevance of file fragments is further mined in combination with a BiLSTM model. According to the method, modeling and learning are carried out on the context relation of the file fragments actually existing in the storage environment, and the file fragments of different types are accurately recognized. And an efficient and reliable solution is provided for safe recovery of personal data.

The invention provides a multi-scale image convolution image classification method and system based on weight suppression, and the method comprises the steps: firstly introducing depth separable convolution to replace cavity convolution, and redesigning a combination mode of different scale features to obtain improved multi-scale features; secondly, a weight suppression mechanism is introduced to weaken class activation of irrelevant classes, and the class activation is multiplied by the improved multi-scale features to generate a new content awareness vector; and finally, inputting the content perception vector into a double-graph convolution fusion embedded network for final classification. Experimental results show that the precision of the provided model is improved on MS-COCO and VOC2007 data sets, and it is proved that the improved model has better performance.

The invention discloses an audit knowledge base construction method and device. The method comprises the steps that basic elements are extracted from audit data through an information structured processing method, and the basic elements comprise text elements, image elements and table elements; performing logic organization on the basic elements by using a content awareness method to form structured data blocks; and constructing an audit knowledge base based on the structured data blocks. The technical problems of low auditing efficiency and poor auditing decision are solved.

The invention relates to the field of computer vision and target detection, and particularly provides an improved method combining receptive field convolution and content perception features for rotating small target detection based on a convolutional neural network. According to the method, aiming at a traditional YOLOv11OBB network, an improved C3K2 module (C3K2-RE) and a content awareness feature recombination up-sampling module (CARAFE) are introduced, so that the small target detection precision and robustness are improved. Specifically, a convolutional layer in the YOLOv11OBB is replaced by a C3K2-RE module. The module fuses receptive field attention convolution (RFAConv) and efficient multi-scale attention (EMA), strengthens the feature extraction capability, and optimizes the detection effect of a rotating small target in a remote sensing image. Meanwhile, an up-sampling module of a network check part is replaced by a CARAFE module. As a lightweight efficient up-sampling scheme, the CARAFE generates the weight of each position through content awareness, the features are weighted and recombined by a predefined region, and up-sampling is completed through rearrangement of multiple groups of weights, so that details are effectively reserved, the reconstruction quality is improved, and the output is clearer and more accurate.

The invention discloses an image defogging model training method and an image defogging processing method and device. The image defogging model training method comprises the following steps: acquiring a training sample set and a sample label set; an initial network model is constructed, and the model at least comprises M Transformer modules which are used for carrying out global context feature extraction on the foggy image and are combined with content perception and recursive interaction, and a multi-scale frequency domain attention module which is used for carrying out frequency domain feature extraction on global context feature maps output by the M Transformer modules which are combined with content perception and recursive interaction, m is a positive integer; and performing iterative training on the initial network model by using the training sample set and the sample label set to obtain an image defogging model. The image defogging method and device solve the technical problem that the defogged image is poor in quality due to the fact that a related image defogging model is poor in capability of processing a low-frequency foggy part and recovering high-frequency details.

The invention discloses a user intention recognition method and system based on an outbound robot, and relates to the technical field of intention recognition. Comprising the following steps: acquiring voice data of known task intentions including confirmation, denying, query intentions and themes, extracting text features, and obtaining intention feature comprehensive vectors through a content awareness network model; a neural network model is utilized, the comprehensive vector is used as input, known intentions and themes are used as labels, and training is completed; extracting speech features such as speech speed, volume, fundamental frequency and the like from speech data to be recognized, and calculating a speech judgment coefficient; generating a comprehensive task intention judgment index in combination with the predicted task intention and the voice judgment coefficient, and comparing the comprehensive task intention judgment index with a threshold to confirm the user intention; and finally providing a response through the outbound robot in combination with the task theme. The method not only can optimize the final judgment of the user intention in combination with the dynamic change of the voice features, but also can significantly reduce the occurrence of misjudgment.

The invention discloses a shale pore type identification method based on an improved YOLOv8 model. The method comprises the following steps: acquiring a shale digital core image and annotation information thereof to construct a data set; carrying out improved YOLOv8 model training by utilizing the data set to obtain a trained improved YOLOv8 model; the improved YOLOv8 model comprises a backbone network, a neck network and a head network; and inputting a to-be-processed shale digital core image into the trained improved YOLOv8 model to obtain a corresponding shale pore type identification result. According to the method, through an improved YOLOv8 model, a C2fDCNv3 module and a content awareness recombination feature enhancement module are integrated, and the feature extraction and fusion capability of a shale pore complex structure is remarkably improved; and in combination with a dynamic detection head DyHead and an Inner-CIoU loss function, efficient and accurate shale pore type identification and bounding box regression prediction are realized.

The invention belongs to the technical field of video coding. The invention provides a video coding intelligent control method based on content awareness. According to the embodiment of the invention, coding intelligent control can be carried out according to different scene changes and region-of-interest division, and the video quality of the abrupt change frame and the region-of-interest is effectively improved. Intelligent control of video coding can be carried out according to a video content perception analysis result, and the collaboration of a content perception function and intelligent coding control is good. According to the method, more-dimensional perception analysis is performed on video contents, scene mutation frames are detected by dividing frame types, refined video region division is realized through a target detection algorithm, bit allocation of scene static frames and background regions is further reduced, saved bandwidth resources are allocated to the video mutation frames and regions of interest, and the video region division efficiency is improved. Therefore, the coding efficiency of the static frame scene and the background area is improved while the video quality of the dynamic scene and the interested area is ensured, so that the required total bit number is reduced.

The invention relates to a target detection method based on dynamic content perception and frequency domain information enhancement, and the method comprises the following steps: constructing a target detection model to carry out the target recognition of a to-be-detected image, and obtaining a target detection result; the target detection model comprises a backbone network used for performing multi-scale feature extraction on a to-be-detected image to obtain a plurality of backbone features of different scales; the neck network is used for constructing a dual-path aggregation structure to perform feature fusion on the trunk features of different scales, introducing an up-sampling module based on dynamic content awareness to perform up-sampling operation, and performing frequency domain information enhancement when deep fusion features are generated; and the head network is used for generating a target detection result according to the deep fusion features. According to the method, the small target feature expression capability can be effectively enhanced on the premise that computing resources are not remarkably increased.

Techniques for pseudo real-time content-aware auditory cleansing are described herein. A method for pseudo real-time content-aware auditory cleansing may include introducing a delay in the delivery of an audio file by an audio delivery system, collecting a sample of the audio file, predicting the presence of a sibilance and other unwanted sounds in the sample, cleansing the sample of the sibilance and other unwanted sounds, and outputting a cleansed version of the sample. The delay corresponds to a length of the sample. Cleansing the sample may include one or more of a gain reduction, an equalization adjustment, a replacement, and removal of the sibilance and other unwanted sounds.

The invention relates to a video coding method and device, electronic equipment, a storage medium and a program product. The video coding method comprises the following steps: sampling continuous frames in a video stream to be processed to obtain sampled frames and non-sampled frames; performing ROI (Region of Interest) detection on the sampling frame based on touch driving and / or content awareness, and identifying an ROI in the sampling frame; carrying out multiplexing or fine tuning on the ROI region through lightweight inter-frame difference analysis and ROI state judgment in a non-sampling frame; when video encoding processing is performed, the ROI region and the non-ROI region in the sampling frame and the non-sampling frame are encoded in a distinguished manner. According to the invention, the video quality of the key area can be guaranteed, and the overall calculation and coding overhead can be obviously reduced.

The invention discloses a clipboard synchronization method, system and device, a storage medium and electronic equipment, and relates to the technical field of cloud computing, firstly, when an application program in a cloud mobile phone instance calls a clipboard interface, the calling operation of the clipboard interface is intercepted; obtaining clipboard content corresponding to the calling operation and an identity label of the cloud mobile phone instance; querying a preset synchronization strategy based on the identity identifier, and determining one or more target devices; and synchronizing the clipboard content to the target device. Compared with the prior art, the method has the advantages that the synchronization coverage rate of clipboard content is increased by performing system-level interception on clipboard interface calling, querying the synchronization strategy in combination with the cloud mobile phone instance identity identifier and executing directional synchronization; and furthermore, through organic combination of identity recognition, strategy query and a content synchronization process, basic support is provided for introduction of mechanisms such as content awareness, intelligent filtering and network self-adaption, so that the network self-adaption capability in a clipboard synchronization process is improved.

The invention discloses a self-adaptive fixation point display method and system based on content awareness and user calibration, and the method comprises the steps: carrying out the calibration of a visual attention range when the system is started for the first time, and obtaining the individualized initial fixation area parameters of a user; the position of a fixation point is obtained in real time through an eye movement tracking module, and eyeball movement states are classified; identifying a content semantic category of the current display picture; according to the content semantic category and the initial fixation area parameters, dynamically determining geometric parameters of the high-fidelity rendering area; executing rendering operation for reducing power consumption outside the high-fidelity rendering area; and monitoring content change in the area, and when key non-periodic change is detected, performing temporary vision enhancement operation on the changed area. According to the method, the clear display area can be dynamically adjusted according to the screen content and the personalized visual features of the user, significant energy saving is achieved, meanwhile, it is ensured that the user does not miss important information through an intelligent visual enhancement mechanism, and display efficiency, power consumption control and user experience are considered.

The invention relates to the technical field of graph layout, in particular to a content-aware graph layout generation method based on an unsupervised domain adaptation technology, which comprises the following steps: acquiring a large poster graph layout data set; constructing a content awareness graph layout model PDA-GAN which is composed of a pixel-level discriminator PD and a layout generator G and is used for eliminating domain differences to generate poster high-quality content layout; an adaptive unilateral label is introduced to smoothly enhance the content aware graph layout model PDA-GAN; and quantitatively evaluating the layout generation performance of the PDA-GAN through three pre-established correlation indexes. According to the method, a pixel-level discriminator PD and a generator are designed for adversarial training based on a domain difference between a repaired image marked in a data set CGL-Dataset and a clean product image applied in reality in combination with an unsupervised domain adaptation technology, and an adversarial loss is calculated for each pixel point in an original input image; the problem of domain difference caused by pixels in a restoration area is eliminated at a pixel level.

The invention discloses a multi-dimensional data analysis task adaptive arrangement method supporting incremental calculation and resource awareness scheduling. Comprising the steps of receiving a multi-dimensional data analysis request, constructing a task dependency graph, realizing an incremental calculation identification mechanism based on content awareness, realizing a hierarchical scheduling architecture, realizing an intelligent rescheduling mechanism, realizing adaptive check point management, realizing a closed-loop self-optimization mechanism and the like. Through the synergistic effect of incremental calculation and self-adaptive scheduling, redundant calculation is reduced, the response time of a complex analysis task is shortened from the hour level to the minute level, the resource utilization rate and the system throughput of a large-scale calculation cluster are improved, and the method is suitable for application scenes needing frequent and repeated analysis such as industrial big data analysis, energy monitoring and business intelligence.