5 results about "Run-length encoding" patented technology

A compressor for compressing a control signal includes residual compression circuitry configured to sequentially receive a first control signal and a second control signal and perform compression based on a difference between the first control signal and the second control signal, coding mode determination circuitry configured to determine a coding mode based on a type of control signal, analog signal compression circuitry configured to perform compression based on indexing, and control signal compression circuitry configured to perform compression based on run-length encoding (RLE).

This invention relates to the field of electricity meter data transmission technology, and particularly to a method and system for compressing and transmitting data from smart meters in a distribution area based on edge computing. The method includes: deploying an edge computing gateway on the transformer side of the distribution area to periodically receive raw electrical energy data sequences uploaded by all smart meters within the area. The gateway employs improved run-length encoding, dynamically adjusting segmentation thresholds based on the current electricity consumption pattern to complete initial compression. Then, it combines adaptive dictionary encoding to dynamically construct a local dictionary based on the statistical characteristics of data blocks for secondary compression, generating high-compression-ratio data packets. The gateway locally maintains a meter behavior feature database, screening and marking abnormal data packets. Abnormal data packets are transmitted in real-time without loss, while normal data packets are periodically batch-aggregated and transmitted. This invention can reduce transmission bandwidth usage, optimize transmission resource allocation, and shorten data processing and response latency.

The application discloses a kind of satellite-borne image compression method and system based on generative adversarial network, wherein the method comprises: obtaining compressed code stream and binary mask chart;Get logic gate control signal;Using neural network to train training data set obtains network weight parameter;The JPEG-LS lossy compressed code stream of real-time transmission on satellite is decompressed to obtain preliminary recovery image and satellite binary mask chart;Primary recovery image and satellite binary mask chart are input into neural network, and network weight parameter is used to repair the pixel value of the pixel value of satellite run-length encoding area to obtain intermediate recovery image data;Residual chart data is obtained;According to residual chart data and maximum allowable error value, satellite run-length encoding area optimization image data is obtained;According to satellite run-length encoding area optimization image data and preliminary recovery image, the final recovered image data is obtained.The application improves the quality of reconstructed image.

This invention provides a photovoltaic module operation monitoring method and system based on AI vision, belonging to the field of photovoltaic module condition monitoring and fault diagnosis technology. This invention synchronously acquires surface images and electrical time-series data of photovoltaic modules through hardware, constructing a sub-millisecond spatiotemporal alignment mechanism for multi-source heterogeneous data, laying a reliable foundation for the correlation analysis of electrical anomalies and visual representations. It utilizes Radon transform and a one-dimensional convolutional neural network to extract run-length encoding features of the grid lines, transforming grid line continuity into quantifiable and comparable structured strings, significantly improving feature robustness under complex scenarios such as light variations and dirt interference, while reducing the computational complexity of subsequent processing. Based on a neighborhood similarity comparison strategy using the state matrix, it fully exploits the spatial consistency constraints of adjacent modules in the photovoltaic array, enabling rapid location of modules with abnormal grid line structures, effectively avoiding the high false alarm rate caused by traditional single-point detection.

The application provides a time-based bipartite graph butterfly counting index construction and query method and a query system, acquires time-based bipartite graph data, and performs vertex priority ordering preprocessing; enumerates a wedge-shaped structure, calculates a butterfly life cycle, and constructs a basic index TBCI; uses run-length encoding and difference storage technology to compress and optimize the index; receives a query request, performs fast query and aggregation based on the optimized index, and returns a result. Through innovative index structure design and two-stage compression technology, the index volume is reduced by 2 to 3 orders of magnitude compared with existing advanced index methods, so that the index only needs a small storage space when processing large-scale graph data, and has strong practicability and deployability. The index construction and query process has good parallelism, exhibits near-linear speedup in a multi-threaded environment, and can effectively utilize modern multi-core hardware resources.