442 results about "File size" patented technology

The invention discloses a multi-source three-dimensional model lightweight processing and lossless compression supporting method, system and device and a storage medium, and relates to the three-dimensional technical field, and the method comprises the following steps: carrying out format compatibility check on an imported three-dimensional model, and analyzing the geometric structure, texture characteristics and material attributes of the model; the geometric structure of the three-dimensional model is simplified by adopting a quadratic error measurement algorithm and edge folding, the simplified model is optimized through edge segmentation operation, and a lightweight model is obtained; and compressing geometric data of the lightweight model by using a data compression algorithm, and compressing texture data through predictive coding to complete lossless compression of the three-dimensional model. According to the method, the loading speed and the real-time rendering efficiency are remarkably improved by reducing the size of the three-dimensional model file and optimizing the texture compression algorithm. The lossless compression technique ensures that a high-quality visual effect is kept in the lightweight process, the lightweight model reduces the storage space requirement, and the energy consumption of data transmission and processing is reduced.

A real-time anomaly detection system for high-volume managed file transfers (MFT), consisting of: a secure, hardware-accelerated monitoring unit configured to interface with a managed file transfer server and intercept file transfer session data at wire-speed; a metadata extraction engine embedded in the hardware-accelerated unit, the metadata extraction engine configured to analyze protocol-specific session attributes, including, but not limited to, file size, transfer duration, encryption status, source and destination endpoints, transfer frequency, and payload entropy; a contextual AI inference engine communicatively coupled to the metadata extraction engine, the AI inference engine comprising a deep learning model trained on labeled historical MFT activity logs to detect contextual deviations from normative behavior; a federated learning architecture with a plurality of edge nodes, each hosting a local anomaly detection model trained on localized transmission metadata and configured to synchronize with a central aggregator using differentially private gradient updates; an Explainable AI (XAI) subsystem integrated into and configured to generate human-readable anomaly justifications, feature importance maps, and threat categorization labels; and a policy orchestration module configured to dynamically execute pre-configured or AI-based security responses, where the security responses include selective session termination, quarantining of transferred files, generation of alerts, or redirection of MFT workflows.

The invention discloses an SSD (Solid State Disk) testing method, which relates to the technical field of SSDs, and comprises the following steps: an SSD is connected to testing equipment, and the testing equipment comprises a controller, a data generation module, a data transmission module, a data storage module and a performance monitoring module; specific types of test data are generated through a data generation module, the specific types of test data comprise sequential read-write data, random read-write data and mixed read-write data, the file size of the sequential read-write data is within the range of 1GB-10GB, the operation block size of the random read-write data is within the range of 4KB-64KB, and the file size of the mixed read-write data is within the range of 4KB-64KB. The ratio of sequential read-write to random read-write in the mixed read-write data ranges from 1: 1 to 3: 1, the test data further comprises multiple file formats including but not limited to text files, image files, video files and audio files, and the data size of each file format accounts for 10%-30% of the total test data size; the SSD performance can be accurately evaluated, and the performance evaluation standard is perfected.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention provides a data dragging method, electronic equipment and a storage medium. The electronic equipment comprises a first application and a first dragging operation identification module. The first application receives a first operation for first data in the first application, and the first operation is a dragging operation; the first application sends a first message to a first drag operation identification module, wherein the first message is used for indicating that the first operation is a drag operation; in response to the first message, the first dragging operation identification module obtains and stores first metadata information of the first data, the metadata information of the first data comprises any one or more of the following items: the first data type of the first data, the creation time of the first data, the file size of the first data, the application identifier to which the first data belongs, and the storage position of the first data. Through the method, after the application recognizes the dragging operation, the application submits the dragging operation to the dragging framework for execution, the application does not need to execute the dragging operation, and the workload of application development can be reduced.

An AI-based audio compression method for use with audio formats, alone or in combination with other audio compression and enhancement approaches. A combination of audio pre-processing, sound to visual transcoding of audio, and a sequence of AI-enabled methods enabling maximal entropy order extraction applied within the sound and dimensionally extended visual domain projection of the audio significantly increases the degree of compression of audio files, thereby reducing storage, transmission and processing overhead associated with audio. A unique AI-driven domain conversion is leveraged together with domain-specific AI processing stages to reduce file size, while supporting optional use of standard and proprietary audio encoding, decoding, compression, and other methods. Support for native mode photonic computer processing of the higher-dimensional order representation of media enables further optimization via photonic computing methods that would not be possible if the audio was not extended into higher order visual domain space.

The invention discloses a large file concurrent uploading method and system based on a dynamic fragmentation strategy and resource optimization, and the method comprises the steps: a client generates a file fingerprint according to the file size of a large file and metadata, and dynamically calculates the fragmentation size of the large file; the client carries out fragment cutting on the large file according to the calculated fragment size, establishes a concurrent uploading queue and uploads a plurality of fragments to a server side in parallel; the server selects an optimal storage node to store each fragment based on a consistent hash ring and a multi-dimensional weight model; the client side and the server side track a fragment uploading state through a fragment serial number and a cyclic redundancy check (CRC) value, and after all fragments are uploaded, integrity check and logic recombination are carried out; and when network interruption or uploading failure is detected, the client side and the server side execute an intelligent retry strategy or a splitting rescheduling strategy of breakpoint resume and failure fragmentation.

The invention provides a remote upgrading system and method for energy storage equipment of an energy storage power station, which comprises a cloud platform, an energy management control unit (EMU) and the energy storage equipment, and is characterized in that the cloud platform is used for receiving an upgrading file uploaded by a user and issuing a downloading instruction containing a file downloading URL, a file size and a CRC32 check code to the EMU; the EMU is used for downloading an upgrading file from the cloud platform, performing format, size and CRC32 triple verification on the file, executing system self-inspection before upgrading, executing staged shutdown protection strategies in sequence, issuing an upgrading instruction to the energy storage equipment and recovering a system state; and the energy storage equipment is used for downloading the upgrading file from the EMU and finishing firmware updating.

The invention discloses a method, a system, a medium and equipment for extracting unified aggregated text content of all types of files, and relates to the technical field of file processing, the method comprises the following steps: system initialization and configuration, signature verification, extraction judgment, proxy forwarding, content extraction, and result storage and return. A single and stable service entry point (Endpoint) and a standardized API (Application Program Interface) specification are provided for all upper-layer businesses; through a standard S3 signature verification mechanism, the security and reliability of each call are ensured; in combination with an AK / SK system, unified authority control, flow limitation and post auditing can be conveniently carried out; a service-oriented architecture is adopted, and the extraction capability of different types of files is provided by an independent micro-service module. When a new file type needs to be supported, only a new content extraction module needs to be developed and registered into the mapping table, a main process and an upper layer service do not need to be changed, and the system expansibility is extremely high. And in combination with file size judgment, executing logic of combining synchronous extraction and asynchronous extraction of the file.

The invention relates to the technical field of image compression processing. The invention provides a video compression processing method and device. The method comprises the following steps: performing video feature extraction on a to-be-processed video, wherein video features comprise space complexity, time complexity and color complexity; configuring the size of a target file corresponding to the to-be-processed video, and determining the size of each target file according to a specific scene and a user demand; dynamically generating compression parameters through weighted calculation based on the extracted video features and the determined size of the target file; and performing dynamic evaluation according to the video compression effects under different compression parameters to determine the range of the optimal compression parameter, selecting the optimal compression parameter from the determined range of the optimal compression parameter, and performing video compression on the to-be-processed video according to the selected optimal compression parameter. According to the invention, the optimal compression parameter corresponding to the to-be-processed video can be determined, and more efficient and accurate video compression can be realized.

The invention discloses a vehicle fault file distributed uploading method and device, equipment and a medium. The method comprises the following steps: receiving a fault signal of a target vehicle through a remote information processing control unit, and extracting a part fault code and fault occurrence time according to the fault signal; collecting working condition data within a specified time range of fault occurrence time and generating a fault file; the method comprises the following steps: dividing a fault file into a plurality of sub-files according to the size of the fault file and the transmission capability of an SIM card, naming each sub-file according to a subpackage sequence, carrying out compression processing on each sub-file by adopting a compression algorithm, and sequentially uploading each compressed file to an Internet of Vehicles platform according to a sequence of file serial numbers in file names from small to large. According to the embodiment of the invention, each sub-file is named, compressed and uploaded by adopting a file subpackage strategy, so that the problem of large file transmission is solved, and the transmission efficiency is optimized; and special characters are added in the last sub-file, so that the integrity and accuracy of the fault file are ensured.

The invention provides a three-dimensional model data visualization processing method and device for railway engineering, and relates to the technical field of data processing, and the method comprises the steps: obtaining three-dimensional model data, and obtaining attribute information corresponding to the three-dimensional model data; obtaining the number of vertexes; dividing the three-dimensional model data based on attribute information of the three-dimensional model data and the number of vertexes to obtain a plurality of target data sets; performing tiling processing on the multiple pieces of three-dimensional model data in each target data set to obtain multiple pieces of three-dimensional sub-data; and rendering all the three-dimensional sub-data based on a preset rendering engine to obtain a target three-dimensional image. According to the method, the uniform size of the tiled file is ensured by recombining the three-dimensional model data structure, so that the network transmission smoothness is improved. Meanwhile, index association is established based on the attribute information and the tiled file, and a visual management structure tree conforming to railway engineering management logic is constructed, so that the overall data processing and visualization efficiency is improved.

Methods and systems are provided for a histogram model configuring a computing system to derive an indicator of compromise signature based on a sliding window index of identified malware samples, and a matching rule constructor configuring a computing system to generate matching signatures by selecting statistically relevant n-grams of an unidentified file sample. A matching rule constructor configures the computing system to construct a matching rule including, as a signature, 32 n-grams found in the unidentified file sample which occur most frequently, and another 32 n-grams found in the unidentified file sample which occur least frequently amongst records of the threat database across 32 discrete file size ranges. These functions can configure backend operations to a sample identification operation performed by a user operating a client computing device, in a fashion that does not require a user to manually discern strings from the unidentified file sample to derive a signature for the matching engine to search against the threat database.

The invention provides a file protection method, relates to the technical field of network security, and can be applied to the technical field of finance. The method comprises the following steps: determining a target file based on a file updating model, wherein the file updating model is generated by self-learning based on a system file updating condition; determining a generation rule of a bait file based on the file size and the file name of the target file; generating a bait file corresponding to the target file according to the generation rule; placing the bait file in a specified file path; monitoring the bait file in real time to record process information of accessing the bait file; and performing alarm operation according to the process information and the white list. The invention further provides a file protection device, equipment, a storage medium and a program product.

The embodiment of the invention discloses a file fragment uploading method and device, electronic equipment, a computer program and a storage medium. According to the embodiment of the invention, a to-be-uploaded file and file information are obtained, wherein the file information at least comprises a file size; segmenting a to-be-uploaded file according to the initial network speed of the current network to obtain a plurality of initial file fragments; uploading the initial file fragments to the server in sequence, and recording uploading time consumption and uploading rate corresponding to the initial file fragments; adjusting the size of the next initial file fragment according to the uploading time consumption and the uploading rate of the uploaded initial file fragment to obtain a target file fragment; and uploading the target file fragments to the server. Therefore, in the file uploading process, the real-time network state is determined according to the uploading condition of the uploaded fragment by adopting a mode of'dynamic fragment uploading + network self-adaption ', and the size of the fragment is dynamically adjusted according to the real-time network state, so that the file uploading success rate and the user experience are improved, and the method is flexibly applied to various large file uploading scenes.

The invention provides a base identification method and system. The base recognition method comprises the steps that input data of a plurality of amplicons are processed through a base recognizer based on a neural network, alternative representation of the input data is generated, the input data comprise position information and intensity information of the amplicons on images collected by the (n-m1) th round to the (n + m2) th round of sequencing, and the position information and the intensity information of the amplicons on the images collected by the (nm1) th round to the (n + m2) th round of sequencing are obtained; the file size of the input data is smaller than that of the image corresponding to the input data, n is a natural number greater than or equal to 1 and greater than m1, and m1 and m2 are independently integers greater than or equal to 0; processing the alternative representation by an output layer to produce an output identifying a probability that bases introduced into each of the amplicons are A, C, T, and G in the nth round of sequencing; and determining the base type introduced into each amplicon by the nth round of sequencing based on the output. According to the method, the input data can be efficiently processed, and an accurate base recognition result can be obtained.

The embodiment of the invention provides a core dump file generation method and device, electronic equipment and a medium, and relates to the technical field of data processing.The method comprises the steps that in response to abnormity of a target process, a thread causing the abnormity of the target process is determined as a target thread from threads of the target process; retrieving a virtual memory address from a register occupied by the target thread and a stack memory area of the target thread; determining a target address of a data page which belongs to a heap memory area and has mapping from the retrieved virtual memory address; and generating a core dump file containing the data page mapped by the target address. By applying the scheme provided by the embodiment of the invention, the validity of the core dump file can be improved while the size of the core dump file is reduced.

The invention relates to an attachment certificate dynamic form processing method and system, and belongs to the technical field of enterprise financial certificate intelligent processing, and the method comprises the steps: receiving an attachment certificate uploaded by a user, and extracting metadata features such as a file size, a format type and a hash value to generate an attachment object; carrying out fragmentation storage on the object by adopting an Erase Coding algorithm, and generating a voucher type identification cue word based on metadata; analyzing the voucher through a hybrid recognition model fusing a convolutional neural network and a Transform encoder, and outputting structured data containing a voucher type identifier; mapping the data into a database entity and triggering a voucher life cycle state change event, and driving a business rule engine to dynamically generate a form mode definition associated with a voucher type; and in response to user query, generating an interactive form through a responsive rendering engine in combination with a voucher type, state and authority dynamic combination form mode and instance data.

The invention discloses a distributed document analysis method and device, equipment and a medium. The method comprises the steps that a target to-be-analyzed document is acquired; extracting content feature data of the target to-be-analyzed document; the content feature data is used for reflecting the content complexity of the target to-be-analyzed document; according to the content feature data, determining estimated time consumption for analyzing the target to-be-analyzed document; based on load balancing, according to the estimated time consumption, distributing a target parsing task of the target to-be-parsed document to a target computing power node; and the target computing power node executes the target analysis task to obtain an analysis result. Therefore, the analysis duration is pre-estimated based on the content feature data of the document instead of determining the pre-estimated time consumption singly depending on the size of the document, the method is adaptive to the time consumption fluctuation characteristics of different documents, the precision of the pre-estimated time consumption is improved, calculation power stage distribution is performed based on load balance according to the pre-estimated time consumption, and the calculation efficiency is improved. The resource utilization rate and the load balancing effect of the system are improved.

The invention relates to a big model-based forensic analysis plug-in generation method and system, and belongs to the technical field of forensic analysis, and the method comprises the following steps: obtaining a to-be-forensic file, and carrying out the file format recognition, preprocessing and structural feature extraction; the structural features comprise structural complexity, field type distribution, field naming rules and file size and data volume; based on the forensic analysis plug-in, generating a task demand and a to-be-forensic file structure feature, and constructing a forensic intelligent prompt word framework; selecting an adaptive large model based on the structural features of the to-be-evidence-taken file, taking the structural features of the to-be-evidence-taken file and the evidence-taking intelligent cue word framework as input of the selected large model, and outputting an evidence-taking analysis plug-in code snippet by the selected large model according to an evidence-taking demand; the generated code snippets are integrated into a complete forensic analysis plug-in code through a code merging tool, the forensic analysis plug-in is verified through a plug-in platform, and the verified forensic analysis plug-in is deployed and applied.

An AI-based audio compression method for use with audio formats, alone or in combination with other audio compression and enhancement approaches. A combination of audio pre-processing, sound to visual transcoding of audio, and a sequence of AI-enabled methods enabling maximal entropy order extraction applied within the sound and dimensionally extended visual domain projection of the audio significantly increases the degree of compression of audio files, thereby reducing storage, transmission and processing overhead associated with audio. A unique AI-driven domain conversion is leveraged together with domain-specific AI processing stages to reduce file size, while supporting optional use of standard and proprietary audio encoding, decoding, compression, and other methods. Support for native mode photonic computer processing of the higher-dimensional order representation of media enables further optimization via photonic computing methods that would not be possible if the audio was not extended into higher order visual domain space.

The invention provides an intelligent file merging method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and cloud computing, in particular to the technical direction of big data. According to the specific implementation scheme, first-class files and second-class files are recognized from a to-be-processed file set, the file size of the first-class files is smaller than a target size, and the file size of the second-class files is larger than that of the first-class files; splitting the second class file into a plurality of sub-files; determining a to-be-merged file set based on the plurality of sub-files and the first type of files; and carrying out merging operation on the to-be-merged file set.

The present disclosure relates to a distributed data storage control method and apparatus, a readable medium, and an electronic device. The method includes: after receiving a write lock and modifying a file size according to the write lock, caching, by a worker node, the modified file size in the worker node until a write lock request sent by a distributed lock management and control node for taking back the write lock is responded to, and then transmitting the modified file size to the distributed lock management and control node, so that the distributed lock management and control node updates the file size.

The invention relates to the technical field of data storage and synchronization of intelligent equipment, and provides an off-line data storage synchronization method and system, comprising a data and file downloading storage method in an off-line mode, a data synchronous uploading method in an on-line mode, and a data and file downloading storage method in the off-line mode. Comprising the following steps: a front end sends a downloading request for acquiring data, files and a file-data relation table to a rear end through a calling interface: the rear end locks the data requested to be downloaded according to the downloading request, and then downloads the data so as to realize exclusive downloading of the front end; and the back end determines the number of files downloaded at a single time according to the sizes of the files aiming at the corresponding files in the file-data relation table, and performs fragmented downloading on the files. Storage and synchronization of complex data structures are effectively achieved, and the method is particularly suitable for storage and offline query of large data volumes.

The disclosure presents systems and methods for hierarchical classification of input data across a plurality of categories. A machine learning model processes various data formats, starting with dimensional reduction using tokenization techniques, such as Bert-tiny tokenization, to create model-readable representations. The system predicts super-categories, sub-categories, and granular categories through selective activation of sub-layers tied to identified super-categories, optimizing computational efficiency. Label smoothing during training mitigates overconfidence in predictions, while softmax normalization refines inference outputs. Synthetic data generation using Large Language Models (LLMs) supplements training datasets, and an automated data labeling pipeline efficiently generates hierarchical labels. Modifications to the model, such as stop word removal and file size limitations, further reduce latency. Inference analyzes logits to predict hierarchical paths, providing detailed classifications with clear outputs. The method is adaptable for multimodal formats, ensuring scalable and accurate predictions across diverse data types while minimizing computational costs and improving reliability.

The invention provides a firmware rapid loading method and circuit of a photoelectric DSP chip and an optical module.The method is applied to a DSP unit, the DSP unit is used for being installed in the optical module and executing a starting program, the DSP unit is electrically connected with an MCU of the optical module and obtains loading information of the MCU, and the step of obtaining the loading information of the MCU comprises the substeps that first information is obtained, acquiring second information according to the first information; the file size of the first information is smaller than that of the second information, and the first information comprises communication parameter information used for obtaining the second information; the second information comprises target firmware of the DSP unit. On the basis of saving the hardware cost, the loading speed of the DSP chip firmware is improved, the development work of the control program of the MCU is reduced, and the method has good compatibility.

The invention relates to a Json file parallel analysis and data writing method and device based on DataX. The method comprises the following steps: acquiring a source end Json file set, and dividing the source end Json file set into small file subsets and large file subsets according to file sizes; the small file aggregation path is configured with a json reader of the DataX, and parallel reading and analysis are carried out; splitting a root array of the large file into a plurality of independent small Json files through a Python script, aggregating paths, and reading and analyzing by using a DataX parallel channel; and finally executing the DataX Job to write the data into a target end. Through a classification processing strategy, the large-scale Json data analysis writing efficiency is remarkably improved, the large file memory occupation risk is reduced, the file scale is automatically adapted, a mature DataX frame and a Python script are reused, high performance and low maintenance cost are both considered, multi-type target end storage is supported, and the problems that a traditional tool is weak in parallel, high in resource consumption, poor in flexibility and the like are effectively solved.

The invention discloses a file access method, device and equipment and a computer readable storage medium, and belongs to the technical field of storage. The method is applied to a first node in a file system and comprises the steps that a first write request for a file is received, the first write request comprises first file data and IO information, and the IO information represents an address range for writing the first file data; in response to the first write request, metadata of the file is obtained, and the metadata comprises an extension attribute and a file size; according to the IO information and the file size, under the condition that it is determined that the written file size does not exceed the reference threshold value, the first file data is written into the extension attribute, and the written file size is the file size after the first file data is written. The file data is embedded in the metadata of the file, and the file data can be accessed while the metadata of the small file is accessed, so that the step of accessing the file data is reduced, the response time delay of the file system to the access request is reduced, and the purpose of improving the access performance of the system is achieved.

The invention relates to a DNA detection data synchronization and classification method based on a decision tree model, and belongs to the technical field of DNA data classification processing. And finally, generating a first synchronization strategy or a second synchronization strategy according to the file size information of the detection data after DNA classification and the real-time network communication data of the communication channel, and performing data synchronization according to the first synchronization strategy or the second synchronization strategy. According to the method, the Bayesian network is fused in the decision tree model, the situation that the decision tree model is not high in precision in the DNA detection data classification process can be optimized, the data transmission situation of the DNA detection data in the data synchronization process can be optimized, the DNA detection data can be synchronized to a medical platform in time, and the accuracy of the DNA detection data is improved. And the query efficiency of the DNA detection data is improved.

The invention discloses a processing method for exporting partition table data, which is used for solving the technical problems of low cross-partition query efficiency, inconsistent file size and lack of concurrent processing capability in the prior art. The method comprises the following steps: firstly, reading configuration information from a task detail table to generate an export task, counting the data volume of each partition, and distributing data to an output file with a fixed line number according to a sequential filling strategy; an innovative coding scheme is adopted to record a distribution result, different partition data containing conditions are represented through characters'-'and'. 'and numbers, and compressed coding is carried out on continuous empty partitions; sending a distribution result to a message queue to realize multi-server concurrent processing; the consumer decodes the distribution information and then executes restrictive query according to a partition sequence, and data is exported concurrently by using streaming query in cooperation with multiple threads; and a perfect state monitoring and exception recovery mechanism is established, and an MD5 verification file is generated to ensure data integrity. According to the method, the processing efficiency of partition table data export and the system reliability are effectively improved.