46results about How to "Reduce processing latency" patented technology

The application discloses a video processing method, device, electronic equipment, storage medium and program product. The video processing method comprises: in the decoding stage, reading video frames in a to-be-processed video frame by frame, decoding a video frame to obtain a frame data in an original format after reading each video frame, and transmitting the frame data in the original format to a conversion stage; in the conversion stage, converting the frame data in the original format into frame data in a target format after each time, transmitting the frame data in the target format to an encoding stage; in the encoding stage, encoding the frame data in the target format to obtain semantic representation data, and transmitting at least one frame of semantic representation data to an inference stage; and in the inference stage, inputting at least one frame of semantic representation data to a multi-modal large model for inference to obtain an inference result after obtaining at least one frame of semantic representation data. The application can reduce the occupation of a storage area, reduce processing delay, and improve resource utilization.

The invention discloses an uplink service message identification processing method and device, a terminal and a medium. The method comprises the following steps: acquiring core feature information corresponding to an uplink service message; obtaining a target message core data structural body according to the core feature information; obtaining a PDU session identifier corresponding to an uplink service message based on the target message core data structure body; obtaining a QoS flow identifier based on the PDU session identifier and the target message core data structure body; based on the QoS flow identifier, obtaining an uplink service message after scheduling processing; performing query processing on the QoS flow identifier to obtain a target packet data convergence protocol instance; and completing a QoS identification processing flow of the uplink service message based on the target packet data convergence protocol instance. The invention aims to realize rapid QoS identification, classification and processing of the uplink service message, improve the message processing efficiency and accuracy, and reduce the system resource overhead.

This invention discloses a unified management method and system for provincial and municipal station dispatching, relating to the field of integrated operation and management technology. The method includes: receiving data from provincial and municipal station monitoring equipment, including timestamps, event codes, and associated parameters; analyzing the data to identify data fluctuations; and filtering potential fault signals by comparing historical patterns to obtain initial fault screening results. The unified management method for provincial and municipal station dispatching provided by this invention achieves early identification of fault signals and accurate classification of abnormal patterns by receiving and analyzing data from monitoring equipment. It improves the efficiency and accuracy of data analysis by comparing real-time and historical data, accelerates fault detection and enhances the handling capability of complex abnormal situations by identifying data changes in different time periods, sets alarm response times and processing priorities for different anomaly types to improve emergency response efficiency, optimizes network traffic management strategies and data transmission paths, reduces processing latency, improves network performance and stability, and effectively enhances operational efficiency and user experience.

This invention discloses a visual monitoring system and method for arc additive manufacturing equipment, relating to the field of visual monitoring. In this system, the host computer is used by the operator to calibrate the CCD camera, acquire monitoring data, and provides a unified interface for interaction. The PS terminal receives start commands and CCD camera calibration commands from the host computer, and sends program functions to the host computer to obtain the molten pool width, molten pool center point coordinates, molten pool collapse amount, and collapse direction obtained through CCD camera monitoring. It is also used to store the original molten pool image at a fixed location in the off-chip DDR3 memory. The PL terminal receives control commands from the PS terminal to the IP cores in the PL terminal. The CCD camera is used to acquire the original molten pool image. The LCD screen communicates with the PL terminal through a TFTLCD interface. This invention can improve the real-time performance of the monitoring system and reduce costs.

The invention discloses a multi-source heterogeneous data pollution traceability and management system based on end-edge-cloud collaboration. The system comprises a multi-source heterogeneous data acquisition module, a cloud-edge collaboration data management and control module, an environment traceability graph embedded sensing module, an interpretable traceability analysis module, a closed-loop regulation and control decision module and a pollution abatement feedback module. A sensing end multi-dimensional sensor collects various types of data, edge end preprocessing and cloud deep analysis cooperation are carried out, an abnormal sensing algorithm is embedded based on an environment traceability graph, a dynamic pollution source identification mechanism is constructed by an interpretable environment pollution traceability analysis model, and a strategy is adjusted in combination with a data pollution closed-loop regulation and control decision model. And the links of early warning, patrol, traceability, disposal and feedback are covered. According to the system, the robustness and maintainability of a multi-source data system are improved, rapid response, accurate traceability and intelligent closed-loop management of pollution events are realized, the problems of insufficient monitoring data collaboration and poor traceability treatment closed-loop performance are effectively solved, and the fine management requirement of environment monitoring is met.

This invention relates to a robust image semantic perception adaptive masking transmission method for vehicle-to-everything (V2X) networks, comprising: Step 1: The transmitting end performs semantic analysis on the original input image, extracts high-precision semantic features, and identifies the regions of interest (ROI) and non-ROI regions of interest (NROI); Step 2: A Boolean mask is dynamically generated based on the semantic segmentation result and SNR; Step 3: The ROI features are directly masked based on the Boolean mask; Subsequently, the processed feature map is fed into a semantic encoder to extract multi-scale semantic features; Step 4: Power normalization is performed, dynamic feature encoding is performed, and then the encoded signal is transmitted through a typical analog wireless channel, and noise is superimposed before reception and recovery; Step 5: The receiving end receives the noisy feature signal, performs channel fading compensation and preliminary denoising, and sends the decoded features into a semantic decoder; Through progressive upsampling and multi-level semantic reconstruction, multi-scale features are fused, and finally, a high-quality reconstructed image is recovered and output.

The application provides a kind of based on edge computing and big data's wisdom campus safety early warning method and system. Through edge computing node, real-time collection is carried out to multiple video data streams, frame sequence segmentation and space-time feature extraction are carried out, initial behavior feature set is generated, initial behavior feature set is carried out multidimensional correlation analysis based on preset behavior semantic label, space-time behavior feature vector corresponding to target monitoring scene is extracted, and is transmitted to central server, space-time behavior feature vector is input into target behavior identification model, behavior semantic description sequence corresponding to video data stream is generated, and according to its matching result with preset abnormal behavior rule library, real-time behavior monitoring result is determined, real-time behavior monitoring result is received by edge computing node, and based on dynamic priority strategy, the collection parameters of video data stream are adaptively adjusted.The application can improve the real-time performance, accuracy and system sustainability of campus behavior monitoring.

This invention discloses a parallel optimization system for massive video processing. The system adopts a four-layer integrated parallel and collaborative processing architecture, comprising, from top to bottom: a parallel task layer for video stream access, grouping, splitting, encapsulation, and queue management; a resource abstraction layer for unified hardware modeling, status acquisition, topology construction, and capability assessment; a parallel scheduling layer for task-hardware matching, load balancing, priority scheduling, and dynamic adjustment; and an execution optimization layer for data stream localization, parallel read / write, cache optimization, and zero-copy processing. The parallel task layer, resource abstraction layer, parallel scheduling layer, and execution optimization layer form a complete parallel processing link: task input, resource awareness, accurate scheduling, and optimized execution. This invention implements concurrent rate limiting and smooth access for the input video stream to prevent traffic surges; and sets synchronization points and timing control for parallel tasks to ensure orderly output.

This application discloses a data-processing-based intelligent management method and system for thermal power fuel, comprising the following steps: collecting multi-dimensional data of the entire life cycle of thermal power fuel; cleaning, standardizing, and fusing the collected multi-dimensional data to obtain pre-processed fuel data; constructing a multi-dimensional analysis model to analyze the pre-processed fuel data, comparing the analysis results with preset condition information, generating procurement adjustment instructions, inventory scheduling instructions, coal blending execution instructions, and equipment operation and maintenance early warning instructions, and pushing the instructions to the corresponding execution terminals, collecting feedback data in real time during the instruction execution process; obtaining instruction execution status data based on the feedback data, visualizing the instruction execution status data, fuel data, and analysis results, and generating multi-dimensional statistical reports; this invention establishes a unified data collection and integration mechanism, breaks down data barriers at each stage, and forms a complete data link for the entire fuel life cycle.

The application relates to the technical field of Internet of Things and edge computing, and discloses an Internet of Things data access and processing system based on edge computing, which comprises a data acquisition module, an environment sensing module, a data preprocessing module, a transmission strategy analysis module, an edge distributed analysis module, a multi-source data fusion module, a deep intelligent analysis module, a resource scheduling analysis module and a global resource optimization module; through the arrangement of the nine functional modules, the whole-process processing of Internet of Things data from access, preprocessing, transmission, edge analysis, multi-source fusion, deep intelligent analysis to resource scheduling optimization is realized; compared with the prior art, the data processing delay is significantly reduced, the resource utilization rate and the data processing accuracy are improved, the application is suitable for various Internet of Things application scenarios, and has wide popularization value.

The application discloses a kind of for full duplex sensing integration moving target signal detection method and system, the method includes: including the following steps:S1.in full duplex sensing integration platform, OFDM sensing integration transmitter is transmitted OFDM continuous signal by antenna;S2.transmitted OFDM continuous signal is transmitted to communication user, and communication function is completed;After the transmitted OFDM continuous signal encounters dynamic sensing target and static object, echo signal transmission reaches OFDM sensing integration receiver, and the direct coupling self-interference signal leaked by OFDM sensing integration transmitter also reaches OFDM sensing integration receiver;S3.OFDM sensing integration receiver is received by receiving antenna, the signal received is handled and is transformed back frequency domain, establishes frequency domain signal representation model and carries out frequency domain difference self-interference suppression.The application does not need channel information, does not need to carry out channel estimation, and the realization complexity is low.

This invention relates to the field of vehicle collision detection technology, and specifically discloses a vehicle collision detection method and system based on an improved dual-stream convolutional network, comprising the following steps: Step S1: Real-time acquisition of an original video stream containing the target vehicle via an onboard camera; Step S2: Preprocessing the original video stream to extract spatial frame sequences and optical flow frame sequences; Step S3: Inputting the spatial frame sequences into the spatial flow branch of the improved dual-stream convolutional network. This vehicle collision detection method and system based on the improved dual-stream convolutional network achieves a significant improvement in detection accuracy and extremely low end-to-end processing latency, providing sufficient warning and response time for active safety systems. Its dual-stream parallel processing and adaptive feature fusion design effectively balance computational efficiency and feature integrity, enabling the system to maintain a low false alarm rate in complex driving scenarios and clearly distinguish between emergency braking and real collision risks.

This invention relates to a lock-free I / O scheduling method, apparatus, and storage medium for cloud PCs. Responding to a client session request initiated by a client, the method controls a metadata coordinator to allocate storage nodes to the client and, within those storage nodes, allocates I / O shards for the client, obtaining allocation information. The metadata coordinator then returns the allocation information to the client, and the client caches this allocation information to facilitate the use of the allocation information to fulfill I / O requests. Each I / O shard includes an I / O thread bound to a first CPU core, a data plane development toolset driver, a storage performance development toolset driver, a first lock-free circular buffer, and a second lock-free circular buffer. By binding the CPU core and using lock-free circular buffers, thread switching, lock contention, and most memory copying are eliminated, reducing I / O processing latency from milliseconds to hundreds of microseconds and significantly reducing latency jitter.

The present application relates to the field of wisdom agricultural technology, and discloses a reflection reasoning multi-modal intelligent question and answer method for vegetable production management, comprising the following steps: receiving an agricultural image to be processed and an initial production management question text, inputting a multi-modal base model to generate a plurality of initial prediction answer texts, calculating an initial prediction information entropy and comparing it with a threshold value, if the information entropy is less than or equal to the threshold value, directly extracting the answer with the highest experience probability as the result output; if it is greater than the threshold value, a candidate option subset is generated, the model is re-input in combination with the image and the question, key visual sub-question texts and observation evidence texts are generated in turn, the observation evidence texts are then spliced into a composite reasoning sequence to generate a verification answer, if they are consistent, the verification answer is output, if there is a difference, an iteration control logic based on a maximum iteration round threshold value is triggered. The present application quantifies model uncertainty and introduces a visual feature retrieval and closed-loop iteration mechanism, thereby improving the accuracy of agricultural decision-making.

This invention relates to the field of image processing technology, and in particular to a hardware implementation algorithm for image connected component labeling. The hardware implementation architecture of the connected component labeling algorithm adopts a pixel-based approach for connected component labeling. The equivalence table structure uses a one-dimensional array. The computational architecture includes an initial labeling and equivalence table storage module, an equivalence table copying and updating module, a coordinate recording and updating module, and an adjacent row label storage and comparison module. Based on the characteristics of connected components in binary images, this invention performs copying and updating operations on the equivalence table according to the image behavior cycle, effectively reducing processing latency and forming a high-efficiency computational architecture for connected component labeling based on FPGA.

The application discloses a virtual anchor real-time driving system based on facial motion capture, aims to solve the problems of insufficient precision, lack of naturalness and weak scene adaptation of virtual anchor facial motion in the prior art, and through multi-thread parallel collection of audio and video streams, optimizes data quality through adaptive preprocessing; adopts multi-dimensional facial feature collaborative extraction, double-branch phoneme recognition, face function area semantic segmentation and confidence dynamic weight adjustment technology, realizes accurate representation and collaborative fusion of expression and lip feature; finally, through the lip-expression collaborative driving mechanism, the real-time driving characteristics of the adaptive virtual image are output; thereby effectively improving the precision and naturalness of virtual anchor facial motion, enhancing the complex scene adaptability, giving consideration to real-time response and low-cost deployment, and being applicable to virtual live broadcast, online education and other scenes, and having good application value.

This invention provides a 100G coherent acquisition closed-loop demodulation method and system integrating TIADC and demodulation, belonging to the field of digital signal processing. The method includes: TIADC mismatch correction, frame synchronization and symbol synchronization, resampling clock recovery, frequency domain full dispersion compensation, three-level polarization demultiplexing (CMA+LMS+DDLMS), joint carrier frequency offset estimation and compensation, Viterbi-Viterbi carrier phase recovery, soft decision and full performance monitoring. This invention is the first to use TIADC front-end correction as the first stage of demodulation, achieving deep integration of acquisition and demodulation. The three-level cascaded equalization architecture balances convergence speed and compensation accuracy. A joint carrier recovery closed loop is established through phase residual feedback, significantly improving demodulation robustness and accuracy. This method can output full test indicators in real time, uses FPGA parallel pipelines to reduce processing latency, and is suitable for engineering applications such as 100G coherent acquisition cards and optical communication test instruments.

The image motion deblurring method based on a hybrid expert network belongs to the technical field of image processing and computer vision, and particularly relates to an image motion deblurring method based on a hybrid expert network; solves the technical problems of coarse-grained routing mechanism, sparse activation detail loss, imbalance between lightweight and precision, single feature fusion strategy, and difficulty in balancing precision and efficiency in the prior art; the method comprises the following steps: inputting a single-frame blurred image to be repaired into a hybrid expert deblurring model that has been trained, and reconstructing and outputting a clear image after deblurring. The image motion deblurring method based on a hybrid expert network is suitable for the fields of lightweight image visual perception, image restoration and deblurring processing.

This disclosure relates to the field of data storage technology, and in particular to a data storage method, apparatus, electronic device, and computer program product. By acquiring data information of a node corresponding to a project name, the method writes sampled value information from the data information into a sub-table corresponding to a first super table based on the project name and the node; and writes status information from the data information into a sub-table corresponding to a second super table based on the project name and the node. Each sub-table stores data for one node. This storage method, using both super tables and sub-tables, significantly reduces data transmission overhead and lowers data processing latency.

This invention discloses a frequency offset correction method and system based on fixed-point computation, belonging to the field of wireless communication technology. It includes: an absolute value calculation and comparison module, an adaptive truncation module, a reciprocal lookup table module, a multiplier module, an arctangent lookup table module, a quadrant correction module, a phase increment calculation module, a phase accumulator module, sin and cos lookup table modules, and a complex multiplication module. The absolute value calculation and comparison module calculates the absolute values ​​of the real part *a* and the imaginary part *b* of the cross-correlation value, outputting *max_ab* and *min_ab*. The adaptive truncation module truncates *max_ab* to an L-bit unsigned number, and truncates *min_ab* by the same proportion. This invention significantly reduces algorithm complexity and consumption of FPGA and DSP resources by replacing complex division and CORDIC operations with lookup table operations and simple shift and multiplication operations, making it suitable for implementation on resource-constrained hardware platforms.

The application discloses an improved ELM speech enhancement method and device for noise reduction, and belongs to the field of speech signal processing; specifically, first, a noisy speech signal and a clean speech signal are acquired to construct training samples; meanwhile, an improved ELM speech enhancement network is constructed, the training samples are used for training, and a network weight matrix is saved; then, a new noisy speech signal is acquired, input into the trained enhancement network to output a noise-reduced enhanced speech signal; the device comprises an audio acquisition module, an offline training module, an online running module and an audio playing module; the audio acquisition module acquires speech signal samples used for training and inputs the offline training module to train the improved ELM speech enhancement network and save the network weight matrix; a new noisy speech signal is acquired and input into the online running module to directly output a noise-reduced enhanced speech signal; and the audio playing module is used for playing. The application reduces training cost and has lower processing time delay.

The embodiment of the invention provides a message processing method and device, electronic equipment and a storage medium, and relates to the technical field of networks, the method comprises the following steps: applying for a memory space for an original message, reserving an extension head area in the memory space, storing data of the original message in the memory space, and storing the data of the original message in the extension head area; the initial position of data storage is identified by a data initial pointer, and the extended header area is located in front of the initial position pointed by the data initial pointer, then adding an outer-layer message header to the original message in response to the need, and if the extended header area meets the length requirement of the outer-layer message header, adding the outer-layer message header to the original message. If not, moving the data start pointer towards the start direction of the memory space by the length of the outer-layer message header, filling the outer-layer message header in the expanded header area vacated after moving, and packaging the original message into the target message, thereby remarkably reducing memory access operation, ensuring that enough space is available in the whole life cycle of the message, and improving the message access efficiency. And the certainty and efficiency of the message processing flow are improved.

The invention discloses a quadruped robot perception positioning method oriented to a visual degradation environment, and the method comprises the steps: collecting a depth image and IMU data, inputting the depth image into a feature extraction network, and obtaining feature points and descriptors; inputting the feature points of the current frame and the previous frame and the descriptors of the feature points into a feature matching network, performing iterative optimization on feature point matching pairs, and retaining reliable feature point matching pairs; calculating an updated pre-integration quantity according to IMU data between two adjacent frames of depth images, constructing an objective function according to visual rotation and the pre-integration quantity to correct the consistency of gyroscope rotation, and optimizing speed, gravity and scale factors; and storing the latest frame state by adopting a sliding window, calculating an IMU measurement residual error and a visual residual error according to a minimum pre-integration error of two adjacent frames and a feature point matching pair, and optimizing the poses of all frames. According to the method, the environmental adaptability is improved, and the problem that the performance is obviously influenced in a scene with insufficient illumination or single environmental texture is solved.

The invention discloses a virtual anchor real-time driving system based on facial motion capture, and aims at solving the problems that in the prior art, virtual anchor facial motions are insufficient in accuracy, lack of natural sense and weak in scene adaptation. Audio and video streams are collected in parallel through multiple threads, and the data quality is optimized through self-adaptive preprocessing; the technologies of multi-dimensional facial feature collaborative extraction, double-branch phoneme recognition, face functional region semantic segmentation and confidence coefficient dynamic weight adjustment are adopted, and accurate representation and collaborative fusion of expression and mouth shape features are achieved; and finally, outputting real-time driving characteristics matched with the virtual image through a mouth shape-expression cooperative driving mechanism. Therefore, the accuracy and the natural sense of the face action of the virtual anchor are effectively improved, the adaptability of complex scenes is enhanced, real-time response and low-cost deployment are both considered, and the method is suitable for scenes such as virtual live broadcast and online education and has good application value.

The invention relates to the technical field of machine vision and coal mine intellectualization, in particular to a miner-bolter dynamic image sensing system based on a storage and calculation integrated framework. Comprising the following steps: a multi-modal data acquisition step: synchronously acquiring visual image flow and kinematics state data of an imaging platform; an in-memory mapping processing step: mapping the motion data into a displacement offset vector, and writing the image into an in-memory calculation chip; a reverse characteristic integration step: performing reverse displacement superposition on the continuous multi-frame image based on the displacement offset vector; a differential enhancement filtering step: utilizing the statistical difference between the static structure and the dynamic noise to enhance the static characteristics and suppress the noise; an environment semantic reconstruction step: performing threshold segmentation and topology analysis on the enhanced features, and resolving a three-dimensional structure and an obstacle; a closed-loop feedback control step: adjusting a motion state or a sensing strategy according to a resolving result; according to the invention, the signal-to-noise ratio is improved by utilizing mechanical vibration, and perspective perception under the working condition of high-concentration dust is realized.

This application discloses an audio processing method, device, and storage medium, belonging to the audio field. The method includes: filtering the left and right channel signals of the original audio using a first low-pass filter to obtain low-frequency left and right channel signals; the first low-pass filter is a finite impulse response low-pass filter with minimum phase property; filtering the left and right channel signals of the original audio using a first high-pass filter to obtain high-frequency left and right channel signals; and mixing the high-frequency left and right channel signals with the low-frequency left and right channel signals after sound field adjustment to obtain a stereo output signal. This solution achieves flexible sound field adjustment while completely solving the problem of blurred sound image positioning caused by nonlinear phase distortion in traditional solutions, as well as the problem of auditory imbalance caused by changes in low-frequency energy distribution, thereby achieving a high-fidelity and high-reproducibility stereo sound field processing effect.

The present application relates to the technical field of water environment treatment, in particular to a multi-source heterogeneous water quality data real-time fusion and dynamic monitoring method based on a domestic AI remote sensing cloud platform, comprising the following steps: integrating multi-source heterogeneous water quality data collected by domestic high-resolution satellite remote sensing, unmanned aerial vehicle remote sensing and ground sensors, after standardization preprocessing, extracting spatial and time sequence characteristics through convolutional neural network and long short-term memory network respectively, introducing an attention mechanism to realize real-time deep fusion of multi-source data; based on the fused data, a dynamic monitoring model combining a hybrid kernel function support vector machine and a hidden Markov model is constructed to realize accurate water quality inversion and trend prediction. The whole process of this method adapts to the domestic software and hardware ecology, significantly improves the accuracy, real-time performance and comprehensiveness of water quality monitoring, and provides independent and controllable technical support for water resource protection and water environment treatment.

The application provides a land-air collaborative agricultural patrol system and method based on vision and path planning, which comprises a path planning module and a data analysis module running in a cloud computing center, a collaborative control module and a target identification module running on an edge computing node, and an image acquisition module running on a terminal device; the terminal device comprises a robot dog, a drone and a camera, which are used for collecting farmland images from the ground at close range and local monitoring, collecting farmland images from the air in a wide range and large-scale patrol, and continuously collecting farmland images from fixed points, and converting light signals of farmland and its surrounding environment images into first image information; the edge computing node is used for receiving and processing the first image information from the terminal device and data from the cloud computing center; and the cloud computing center is used for performing global scheduling and data analysis, solving the problems of low bird and beast invasion identification precision and limited coverage in agricultural monitoring, and improving the intelligent level and efficiency of agricultural monitoring.

The invention relates to a hydropower station load shedding test actual measurement data processing method based on a block chain technology, and is suitable for the field of water conservancy and hydropower engineering. According to the technical scheme, the hydropower station load shedding test actual measurement data processing method based on the block chain technology comprises the steps that encrypted data are obtained from a block chain network, the encrypted data are decrypted, and load shedding test actual measurement data are obtained and preprocessed; based on a preset decomposition mode number k, carrying out VMD decomposition on the preprocessed load shedding test actual measurement data to obtain k IMF components; on the basis of a Pearson correlation coefficient between each IMF component and a load shedding test actual measurement data original signal, the IMF components with the correlation coefficient greater than a preset coefficient are reserved; noise, pressure pulsation, and average pressure are determined based on the retained IMF components and corresponding correlation coefficients.

The present invention relates to the technical field of automated maintenance operations in power systems. Specifically, it relates to a complete set of operation systems and methods for power verification and grounding wire hanging and removal based on an unmanned aerial vehicle (UAV). The method includes: determining whether the transmission wire is charged, and generating a permission signal after confirming that there is no electricity; under the trigger of the permission signal, controlling the UAV to perform the operation of hanging or removing the grounding wire, and identifying and discriminating the state of the grounding wire device during the operation; monitoring the operation process in the steps of hanging and removing the grounding wire in real time, and transmitting the operation status and associated operation site images to the ground terminal. Through the present invention, a systematic process integrating power verification, state identification, and operation monitoring is achieved, realizing full-process automation and intelligence from power verification to hanging and removing operations and then to process monitoring, improving operation safety and efficiency.