38results about How to "Reduce memory overhead" patented technology

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention discloses a fine tuning method and system for precise parameters of a pre-training language model. The method comprises the following steps: constructing a robust adapter; a plurality of constructed robust adapters are connected in series to form a robust adapter chain, and the maximum chain node is determined; preparing a weight of a pre-training language model and a data set of a corresponding task label; firstly, each robust adapter in the robust adapter chain is subjected to preliminary training, optimization and approximate fine tuning of parameters of the robust adapters; after the parameters of the robust adapter are approximately fine-tuned, the parameters are combined with the weight of the preorder frozen pre-training language model to complete calculation of a current robust adapter chain node, residual information used for next iteration is generated, and the parameters of the current robust adapter are updated at the same time; and carrying out iterative training, updating residual information until the number of iterations is equal to the maximum node of the robust adapter chain, and obtaining the optimal weight of the pre-training language model and the optimal parameters of the robust adapters, so that more stable convergence and simpler hyper-parameter adjustment can be provided.

This application provides a method, apparatus, device, and storage medium for processing decision trees. The method includes: acquiring stored data of a decision tree, the stored data of which includes records of multiple logical nodes, each logical node record containing an identifier of the next node pointed to by the logical node under a positive branch, a negative branch, or an abnormal branch; constructing a decision tree organized in a ternary tree structure based on the stored data of the decision tree; recursively executing the logical nodes in the decision tree from the root node until a leaf node is reached, and outputting a decision result. This method reduces the time complexity and memory overhead of decision tree execution, significantly improving the execution efficiency of the decision tree.

The present disclosure provides a message processing method and device, equipment and storage medium, comprising: in response to receiving a to-be-processed message, extracting quintuple information from the to-be-processed message; matching the quintuple information with the index key of the session table to determine the type of the to-be-processed message, wherein the index key of the session table is constructed based on the quintuple information of the established session; in response to the type of the to-be-processed message being a first packet, matching the quintuple information with the index key of the association mapping table to determine whether the to-be-processed message is an associated session of the main session, wherein the index key of the association mapping table is constructed based on the IP address and port information obtained by parsing the application layer of the control channel message of the multi-channel protocol, and the main session is the session of the control channel; in response to the to-be-processed message being an associated session of the main session, performing forwarding processing on the to-be-processed message.

Embodiments of the present specification provide a method for question and answer reasoning of image-text content based on a target model, the target model comprising a plurality of decoding layers, and the image-text content comprising a plurality of images and a plurality of text segments; the method comprising: for a first image, performing first representation compression to obtain L visual compressed representations corresponding to the L decoding layers; for a first text segment, performing second representation compression to obtain L text compressed representations corresponding to the L decoding layers; replacing the images and the text segments in the image-text content with placeholders respectively, and inputting the plurality of decoding layers for decoding operation, comprising: in each decoding layer of the L decoding layers, superimposing the visual compressed representation and the text compressed representation corresponding to the decoding layer on the current hidden representation of the corresponding placeholder, and then performing fusion processing to obtain a decoding representation; and generating an answer text corresponding to the image-text content based on the decoding representation output by the last decoding layer.

The invention relates to the technical field of computer data processing, in particular to a personnel information offline processing method based on Android. According to the personnel information offline processing method realized by using the Android platform, the personnel information is stored on the mobile equipment by using the local database, so that the defects of a traditional mode are overcome. The method comprises the following steps: acquiring personnel information input by a user; storing the personnel information in a local database; and querying and displaying personnel information according to user requirements. According to the method and the device, offline processing of the personnel information is realized on the Android platform, so that the processing efficiency and the safety are improved, and meanwhile, the user experience is also improved.

The invention provides a blind area early warning optimization acceleration method, which comprises the following steps: acquiring an image around a vehicle in real time, and identifying a target object according to the image; when a plurality of target objects are identified, dividing the plurality of target objects into a plurality of tracking target groups according to the target category of each target object; parallel computing is carried out on tracking matching of the multiple tracking target groups to obtain object data of each target object; dividing the plurality of target objects into a plurality of target object groups based on a first preset number of target objects; according to the object data of each target object, motion directions of the multiple target object groups are calculated in parallel; according to the object data of each target object, the motion speed of the target object with the motion direction being the vehicle approaching direction is calculated in parallel; calculating the predicted collision time of the target object in parallel according to the movement speed; and calculating an alarm accumulation value of the target object in parallel according to the predicted collision time. In addition, the invention further provides a computer readable storage medium and a vehicle.

The present invention discloses a keyword / sequence extraction method through feature set dimensionality reduction, including processing a text set to obtain a feature set; obtaining a vector representation of the feature set; reducing the dimension of the feature set to obtain a feature set after dimensionality reduction and a mapping relationship between the feature set and the feature set after dimensionality reduction; keyword / sequence extraction. The present invention can not only improve the quality of keyword / sequence extraction, and reduce the computing power and memory overhead required for vector calculation in the keyword / sequence extraction model training process of massive data, but the dimensionality reduction algorithm module therein can also be well applied to the dimensionality reduction of high-dimensional feature sets (similarity cannot be calculated at one time).

The invention provides a firmware upgrading method and device, electronic equipment, a storage medium and a product. The firmware upgrading method comprises the steps of obtaining firmware program information and firmware mirror image distribution information of firmware; wherein the firmware mirror image distribution information is used for representing mirror image distribution of firmware; performing information grouping on the firmware program information according to mirror image distribution of the firmware mirror image distribution information to obtain target firmware grouping information; determining firmware upgrade package information according to the target firmware grouping information and the basic attribute information of the firmware; wherein the basic attribute information refers to information for describing the property of the firmware; and determining a firmware upgrading result of the firmware according to the firmware upgrading package information. According to the technical scheme, the firmware is subjected to grouped updating and upgrading in an information grouping mode, upgrading of different firmware packages is supported, and the memory overhead of the firmware in the upgrading process can be reduced through grouped updating and upgrading.

The present disclosure relates to a layered parallel inference acceleration system, method and computer program product. The system comprises a text content analysis module for analyzing and processing user input text content to obtain a vector sequence; a candidate token sequence generation module for determining a corresponding set of to-be-predicted positions according to the current position of each of the plurality of vectors, and determining the feature information corresponding to each to-be-predicted position in the set of to-be-predicted positions; and predicting at least one candidate token according to the feature information, and combining each corresponding at least one candidate token in the set of to-be-predicted positions to generate a plurality of candidate token sequences; and a token sequence verification module for screening a candidate token sequence with the most number of candidate tokens that are continuously verified from the start position from the plurality of candidate token sequences as a target token sequence. In this way, the diversity and quality of the generated content can be ensured while reducing the model inference delay.

This invention discloses a message processing method and related apparatus, relating to the field of message processing. When the current message cache is not full, the main thread receives live messages and submits them to a HandlerThread for asynchronous parsing to obtain different message types. Corresponding caching strategies are then applied, retrieving image cache data from the ImageSpan cache and text cache data from the plain text cache, assembling them into a complete live message, and inserting it into a RecycleView queue for message aggregation caching. The complete live message is then inserted into a scrolling control in the RecycleView list for display. Based on a preset scrolling strategy, the complete live message is directly scrolled and displayed using a SpannableString cache. This invention utilizes HandlerThread for asynchronous message processing, a three-layer caching mechanism to optimize performance, and strategy parsing to decouple rendering logic, ensuring the main thread maintains a high frame rate.

The application discloses a channel prediction method based on multi-scale complex time-frequency feature fusion, and relates to the technical field of wireless communication, comprising the following steps: acquiring a historical complex-valued channel state information sequence of a target communication link; estimating a channel coherence time and a coherence bandwidth from the historical complex-valued channel state information sequence to obtain physical parameters; constructing a learnable multi-scale complex time-frequency base function based on the physical parameters, and transforming the historical complex-valued channel state information sequence to obtain a complex time-frequency feature map; calculating an activation confidence of each scale according to the complex time-frequency feature maps of multiple scales, and selecting complex time-frequency feature maps of partial scales according to the activation confidence to form a sparse multi-scale feature set; extracting a query vector, a key vector and a value vector from each scale complex time-frequency feature in the sparse multi-scale feature set, and calculating a cross-scale attention weight in a complex domain through a conjugate inner product of the query vector and the key vector.

An elevator fault diagnosis method based on a short sequence time convolutional network belongs to the technical field of elevator fault diagnosis and vibration fault diagnosis. First, a one-dimensional convolution method is used to reduce the dimension of the original time domain vibration signal of the elevator; then, a time convolutional neural network is used in combination with a neural ordinary differential equation to realize the extraction of local fault features of a short sequence; finally, a self-attention mechanism is introduced to extract richer global fault-related feature information for elevator fault diagnosis. The present invention provides an elevator fault diagnosis method based on a short sequence time convolutional network that effectively improves the accuracy and reliability of fault detection.

This application discloses a method and apparatus for visualizing video instance segmentation masks. The method includes: receiving instance mask data from a server; parsing the compressed mask string character by character, recovering the corresponding run length count values ​​sequentially during the parsing process; writing the corresponding mask pixel value into a pixel buffer based on the currently recovered run length count value to obtain the corresponding pixel buffer data; generating the corresponding image data based on the pixel buffer data; and rendering the image data onto the browser canvas. This method, during the character-by-character parsing of the compressed mask string, directly writes the corresponding mask pixel value into the pixel buffer after recovering each run length count value, eliminating the need to store the complete run length count sequence during the decoding process. This reduces intermediate data memory usage, lowers memory overhead in the browser environment, and improves the smoothness of mask visualization rendering.

The invention belongs to the field of motors, and discloses a harmonic blocking type consequent-pole permanent magnet hub motor and a harmonic group differential vibration suppression method. The harmonic wave blocking type consequent pole permanent magnet hub motor provided by the invention adopts a harmonic wave trapping stator and a local magnetic short circuit magnetic barrier rotor structure under consequent poles to actively suppress the vibration source harmonic wave amplitude. The harmonic group differential vibration suppression method provided by the invention comprises the following steps: actively identifying a main vibration source under a current working condition, extracting a characteristic frequency of a superior harmonic group as a core optimization object, obtaining a Pareto leading edge and a solution set thereof under each working condition by adopting a multi-objective optimization method, taking an intersection of parameters of different working conditions, screening out a parameter set with optimal comprehensive performance, and carrying out differential vibration suppression on the Pareto leading edge and the solution set of the Pareto leading edge. The basic harmonic waves of the vibration source are classified into a monitoring layer, and monitoring temporary inactive compensation is carried out, so that differentiated accurate control is realized. According to the method, the limitation that a traditional method is fixed and single is overcome, and active suppression of the dynamic change main vibration source in the full working condition range of the consequent-pole permanent magnet hub motor is achieved.

The invention relates to a high-speed data packet classification method based on self-adaptive tuple replacement and application, and belongs to the technical field of data packet classification in the field of computer networks. By constructing the mixed data structure, the decision tree and the tuple are combined, and bottleneck branches are detected and replaced in the rule updating process, so that the data packet classification efficiency is improved. According to the method, a low-efficiency branch in a decision tree is replaced by a lightweight hash table tuple structure, so that the stability of the classification speed is kept under the condition that the rule is frequently updated. According to the method, the classification performance can be remarkably improved while memory use is optimized, and the method has good hardware deployment adaptability and is suitable for rapid packet classification in a large-scale network environment.

The present application relates to a kind of data stream integrity detection method, device, equipment and medium, its method includes: receiving the source code to be detected input by user generates original assembly file with line number information;According to read instruction and write instruction in original assembly file, define sdDFIx instruction and ldDFIx instruction using the EUI attribute of preset data;Again, code insertion is carried out to original assembly file, and new assembly file is generated;Using original instruction set architecture, the new instruction set architecture of the new assembly file containing sdDFIx instruction and ldDFIx instruction after compiling is extended to execute;Using the new instruction set architecture after extension, the new assembly file after compiling and linking is executed, and the inspection result to the code to be checked is obtained;The purpose of the present application is to solve the defects that performance overhead is large in prior art, cannot more efficiently monitor program abnormal behavior, cannot effectively protect the problems of program runtime.

A kind of multi-outlet neural network design method and device for edge scene dynamic resource, its method includes: (1) given pre-training neural network, generate effective branch library, and screening is carried out;(2) further reduce memory occupation to the branch in branch library by self-merging, the accuracy of branch is quickly recovered by retraining through few-sample knowledge distillation after self-merging, and form branch candidate library;(3) based on priority, the optimal branch combination that satisfies memory and time requirement is found out to branch candidate library by depth-first search;(4) after selected branch combination is received by on-device scheduler, the branch of pre-training neural network is updated in real time by SBRAN component.The multi-outlet neural network with heterogeneous and dynamic characteristics is converted from traditional single-outlet neural network, and neural network can more flexibly adapt to the change of memory resource, so as to improve running efficiency and accuracy on edge device.

The application discloses a chip function safety verification system based on hierarchical explainable graph learning, comprising: a feature extraction module for parsing a gate-level netlist to extract a cell-level feature; a hypergraph construction module for dividing fan-out-free regions based on a dominating node and aggregating into super nodes to construct a hypergraph structure; a hierarchical graph learning module for constructing a gate-level and super node-level double-layer network to capture local topology dependence and global fault propagation and output a fault critical region prediction; and an explainability analysis and reinforcement module for locating the core gate unit with the highest contribution in the critical region and generating a hierarchical safety reinforcement scheme. The application realizes accurate prediction and explainable positioning of the fault critical region through hierarchical graph learning, and significantly improves verification efficiency and accuracy.

The invention provides a function call tracking method and system, a storage medium and electronic equipment. The method comprises the steps of obtaining a target stack top and determining a target stack according to the target stack top; carrying out stack backtracking on the target stack to obtain the content of the target stack; obtaining call chain data of a function according to the content of the target stack and the range of the code segment, wherein the call chain data comprises address information of the function; and obtaining a corresponding function name according to the call chain data and the instruction storage address of each function. The method provided by the invention can improve the processing speed of the equipment and reduce the overhead of storage resources.

The invention discloses a picture generation method and device, a storage medium and equipment. The method comprises the steps of obtaining sparse voxel illumination data corresponding to a highest detail level model in a virtual scene; determining a cascade coordinate space corresponding to the virtual scene according to the spatial position and the observation depth of the virtual camera, and mapping the world coordinates of each detail hierarchy model into the cascade coordinate space; determining a boundary region of the adjacent detail hierarchy models in the cascade coordinate space; obtaining original voxel data of the current detail hierarchy model corresponding to the boundary region; the sparse voxel illumination data is loaded in the boundary region, and the sparse voxel illumination data and the original voxel data are mixed to generate a rendering picture of the virtual scene, so that smooth visual transition of the illumination effect between adjacent detail hierarchy models is realized, and the memory consumption is effectively reduced.

The present application discloses an encrypted file acquisition method, device, computing device and storage medium, belonging to the field of copyright protection technology, the method comprising: receiving a content request sent by a user, the content request including user identity information and requested content information; after the user identity information is verified, obtaining a content block according to the requested content information and loading it into a memory; retrieving the symmetric key and asymmetric key of the content block in the memory, decrypting the content block based on the symmetric key and asymmetric key, and outputting the decrypted content block to a client. This solution can not only increase the confidentiality of the content, but also save storage space and improve the user reading experience.

The application discloses a multi-modal three-dimensional target detection method based on structural feature and semantic feature fusion, and belongs to the technical field of target detection.The application solves the problems of low precision, poor real-time performance and insufficient robustness of the existing method.The application constructs an explicit matching fusion mechanism of low-layer structure guided initialization, high-layer semantic optimization and time sequence expansion, projects 3D boundary boxes of a laser radar and a camera to multi-view image planes, calculates geometric similarity and category consistency constraints of 2D projection regions, and constructs an explicit matching graph in combination with low-layer structure information and high-layer semantic features.The application guides weighted aggregation of cross-modal target features with high confidence through a sparse matching graph, aligns historical frame targets to a current frame coordinate system to construct a space-time matching graph, aggregates historical information through expansion to a time sequence dimension, and realizes efficient and accurate three-dimensional target detection of the laser radar and the visual multi-view camera.The application method can be applied to multi-modal three-dimensional target detection.

The application provides a log collection method, system, device and medium based on an MQTT protocol, relates to the technical field of data collection, and comprises the following steps: acquiring a plurality of original log data; storing each original log data to a ring buffer area according to a non-blocking coverage strategy, wherein the ring buffer area comprises a preset number of continuous storage blocks; reading a plurality of target log data from the ring buffer area; performing hierarchical filtering on each target log data according to a preset filtering rule to obtain a plurality of filtered log data; performing binary encoding compression on each filtered log data to obtain a plurality of compressed log data; storing each compressed log data in a preset memory pool based on a dynamic memory allocation strategy; and transmitting each compressed log data in the preset memory pool to a remote server based on a lightweight processed MQTT protocol. The application reduces the memory overhead required for log collection while improving the log transmission efficiency and transmission stability.

The embodiment of the present invention provides a kernel page migration method, device, equipment and computer program product, wherein the kernel page migration method includes: obtaining the kernel page to be migrated in the physical database; determining the virtualization database for storing the migrated kernel page; when the kernel page is a cold page, using the memory allocation function vmalloc function to migrate the cold page from the physical database to the virtualization database. In this embodiment, when the kernel page is a cold page, in order to reduce the memory overhead caused by the kernel page, the vmalloc function can be used to migrate the cold page to the virtualization database, which can not only greatly improve the success rate of the data migration operation, reduce the memory overhead caused by the kernel page, and thus help reduce the application cost of the physical database; and can also avoid the page migration operation affecting the online operation of the data, further improving the practicality of the method.

The invention discloses a feature reconstruction method and device based on Token scores, and belongs to the technical field of computer science and artificial intelligence. The method comprises the following steps: performing word segmentation processing on a binary file to obtain a plurality of Tokens; obtaining a text feature and a visual feature of each Token; the text feature is obtained by mapping the Token into a unique ID and then carrying out vector conversion; the visual features are extracted based on a coded image converted from a binary file; performing importance scoring on the corresponding Token by using the text feature and the visual feature of each Token to obtain a score of each Token; calculating attention weights for the K Tokens with the highest scores, and performing feature weighted fusion on the K Tokens with the highest scores according to the attention weights to obtain attention features; performing feature reconstruction on the attention features and the pooling features to perform task processing by using the reconstructed features; the pooling feature is obtained by performing pooling fusion on the features of the remaining Token. According to the invention, the accuracy of the detection result can be ensured while the calculation complexity is reduced.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a prototype memory enhanced video key frame extraction method, device, equipment and medium, the method comprises the following steps: carrying out time sequence pulse convolution on each video frame in a time sequence pulse sequence converted from original video data; performing correlation matching on the pulse feature sequence and differentiable prototype features in a preset prototype memory bank to obtain a related memory feature sequence, and performing dynamic fusion on the pulse feature sequence and the related memory feature sequence to obtain memory enhanced pulse features; and performing key analysis on the video frames with the memory enhanced pulse features to obtain key frame discrimination scores, and screening out video key frames. According to the method, overfitting is effectively suppressed in a few-sample scene, video dynamic time sequence information is accurately captured, and the efficiency and accuracy of key frame recognition are improved while the calculation complexity and memory overhead are reduced.

The present application discloses a fire detection method, device and equipment, which relates to the field of fire image processing. The fire detection method includes: obtaining an image to be detected; inputting the image to be detected into a pre-trained fire detection model to obtain a target frame and a target category label of the detection target in the image to be detected; wherein the detection target includes flames and / or smoke; the pre-trained fire detection model includes a feature extraction network and a Transformer network. A feature suppression module is added after the feature extraction module to suppress features with low significance, which can reduce redundant calculations and memory overhead; a multi-scale deformable attention mechanism is adopted to sparsely sample key positions of features from multi-scale feature maps, realize detection of targets of different scales, and then accurately segment different targets to improve detection accuracy; at the same time, the use of FPN is avoided, which improves the simplicity and efficiency of the model.

The application provides a user environment scene recognition method, system and electronic equipment, comprising: sequentially completing multi-modal original data acquisition and alignment, noise reduction and normalization, multi-scale feature extraction, time sequence structure decision and dynamic network structure adjustment, through constructing time sequence consistency gating mechanism and dynamic structure interpolation control signal, realizing adaptive smooth adjustment of channel activation number and attention head number of a lightweight mixed backbone network, and then driving a learnable soft mask to sparsely schedule backbone network level calculation on demand, finally completing efficient and accurate environment scene recognition, the application effectively improves the calculation efficiency and inference accuracy of the scene recognition model under the condition that the terminal device resources are limited, and has good structure continuity and hardware adaptability.

The invention relates to the technical field of image processing, in particular to a feedforward type large-scale scene reconstruction method based on three-dimensional Gaussian splashing. The method comprises the following steps: acquiring a multi-view image set and constructing an initial camera token carrying view angle identification information; carrying out feature interaction between the intra-frame and the cross-view through an alternating attention geometry Transform, and outputting an updated camera token and an updated image feature token; the pose, the depth map and the depth confidence map of each frame are predicted through decoding branches; the pixels are projected back to a three-dimensional space by using the prediction parameters, and Gaussian attributes and confidence coefficients are regressed by combining image features; and finally, realizing redundancy elimination of Gaussian primitives through voxelization aggregation, and outputting a target view. According to the method, high-efficiency and high-precision three-dimensional reconstruction of a large scene can be realized without the prior of internal and external parameters of a camera and a complex sparse reconstruction process, the calculation overhead is remarkably reduced, and the problems of geometric drift and video memory overflow which are easy to occur in the large scene in a traditional method are solved.