34 results about "Incremental encoding" patented technology

The invention provides a construction engineering information management method and system, and belongs to the technical field of construction engineering information, and the method comprises the steps: responding to a received information updating instruction, and determining a source component unit from a plurality of component units based on the updating component unit information carried in the information updating instruction; recursively identifying an associated component unit of the source component unit based on the target dependency graph; the target dependency graph is used for representing a dependency relationship among the plurality of component units; at least one associated component unit is provided; performing incremental coding on the difference set of the source component unit and the associated component unit to form incremental data; and in response to the situation that conflict processing is not executed on the incremental data, uploading the incremental data to the cloud to obtain an updated building information model. According to the construction engineering information management method and system provided by the invention, efficient and accurate model updating can be realized, and the overall efficiency and performance of construction engineering information management are improved.

The invention discloses a block compression method, device and equipment based on a block chain, and a readable storage medium. The method comprises the steps of obtaining M to-be-packaged transactions; one to-be-packaged transaction comprises transaction attribute values of N transaction attribute types; according to the transaction attribute type priorities corresponding to the N transaction attribute types and the M transaction attribute values corresponding to the N transaction attribute types, performing position sorting processing on the M to-be-packaged transactions to obtain M position sorted transactions; adding transaction attribute values with the same transaction attribute type in the M position sorting transactions to the same to-be-compressed attribute value set to obtain N to-be-compressed attribute value sets; performing incremental coding compression processing on the N to-be-compressed attribute value sets to obtain N attribute value incremental codes; and performing block encapsulation processing on the N attribute value increment codes to obtain a to-be-chained block. By adopting the method and the device, the storage space required by the block can be reduced, and the performance of the block chain system is improved.

The invention discloses a gas detection alarm method and system for mining safety equipment, and relates to the technical field related to gas detection, and the method comprises the steps: obtaining basic gas detection related equipment information of a target mine, and constructing a mine topological graph; performing incremental coding identification on the node gas correlation detection data sequence of the M equipment topology nodes in the preset detection window; performing multi-dimensional structured interaction on the M signal token sequences to obtain a local association sub-graph set; and in combination with the M signal token sequences, carrying out local gas detection anomaly identification on the local association sub-graph set, and obtaining gas detection alarm information according to an identification result set. The technical problems that in the prior art, the semantic relation between signals is difficult to accurately understand in the complex and heterogeneous mine environment, local gas abnormity is difficult to recognize in time, and consequently the accuracy and timeliness of gas detection alarming are poor are solved, and the technical effects that abnormity accurate positioning is achieved, and the accuracy and timeliness of gas detection alarming are improved are achieved.

The present application relates to the technical field of audio codec, and particularly relates to a fast coding and decoding method and system for real-time audio.The method comprises the following steps: obtaining real-time sampling data of an input audio stream, performing multi-time point sliding window division and coding preprocessing, and generating coding preprocessing enhanced audio; performing adaptive quantization strategy analysis on the coding preprocessing enhanced audio, and performing multi-level residual quantization modeling to construct a real-time incremental coding mapping table; performing multi-module decomposition and execution based on the real-time incremental coding mapping table, and performing multi-thread parallel coding optimization to construct a real-time audio parallel coding strategy; performing real-time coding processing according to the real-time audio parallel coding strategy, and performing task-by-task rate-distortion optimization to obtain optimal coding parameters of each coding task.The present application realizes high-fidelity rate and low-delay audio coding and decoding, and improves user auditory experience.

The invention provides a multi-agent reasoning optimization method and system based on Token calculation acceleration, and relates to the field of intelligent decision making, and the method comprises the steps: generating and maintaining a Token-level directed acyclic graph on line; constructing a reasoning path consistency sharing strategy, an approximate semantic sharing strategy and a forced sharing strategy to perform KV cache sharing; local change features are processed in an incremental coding mode, and context difference detection, incremental coding calculation and KV cache splicing are carried out; defining a contribution degree function for each Token so as to carry out Token contribution degree evaluation, and introducing a dynamic pruning mechanism based on the contribution degree; based on the Token-level directed acyclic graph, the parallel execution of the independent Token is realized through topology analysis and dynamic scheduling. According to the method, generation of redundant Token can be avoided, and the communication bandwidth and computing resources of the spacecraft are saved; the delay of the critical path is shortened, the decision-making efficiency of a multi-agent cooperation system can be improved, and the strict requirements of spacecraft tasks on the decision-making efficiency and the decision-making safety are met.

The application relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data; specifically, hydrogeological data is collected in real time, data denoising, space-time calibration and error correction are carried out in combination with edge computing, noise and drift error are eliminated by adopting variational mode decomposition, geological model constraint and wavelet transform, and the storage and transmission efficiency is optimized by increment encoding and Huffman encoding, and a quadratic form check code is generated to ensure data reliability; a space-time deep learning model and a self-attention mechanism are fused to predict a hydrological evolution trend, adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; water resource allocation strategies are optimized through reinforcement learning, risk levels are evaluated in layers, and management schemes are generated, and finally, monitoring data, abnormal scores and decision results can be visually displayed. The application integrates multi-source data fusion, space-time modeling and intelligent decision-making, and improves hydrological monitoring precision and response efficiency.

The invention provides a double-track absolute position detection method and system, computer equipment and a storage medium, and the method comprises the steps: detecting a to-be-detected object through a double-track magnetic railing ruler, enabling the to-be-detected object to be connected with a reading head, enabling the reading head to move along with the to-be-detected object, and enabling the reading head to read the to-be-detected object when the to-be-detected object linearly moves relative to the double-track magnetic railing ruler. Incremental magnetic track information of the incremental coding magnetic track is read through an incremental coding reading head; a level signal is output through a plurality of switch position detection chips in an absolute coding reading head based on the polarity of an absolute coding magnetic track position closest to the switch position detection chips, and absolute magnetic track information is determined according to the level signal; and performing combined calculation according to the incremental magnetic track information and the level signal to obtain a double-magnetic-track absolute position detection result. According to the method, the defect that an existing incremental displacement sensor needs to make a zero position again or search the zero position after being powered off or restarted is overcome, and the application scene of non-contact displacement detection is expanded.

A method of database operations includes receiving a user query, generating a query vector embedding representative of the user query, querying a vector database using the query vector embedding, retrieving a first database vector of the plurality of database vectors based on the query and representative of a first data file corresponding to a first time and belonging to a first time-series data set, receiving a first plurality of delta encodings describing differences between vector representations of temporally-adjacent data files of the first time-series data set, identifying a second data file of the first time-series data set having a second vector representation that differs from the first database vector and corresponds to a second time, and retrieving the second data file from a database.

A computer-implemented method for optimizing encoding video frames from a video is provided. In an embodiment, the method comprises receiving a video frame to be encoded. The method further comprises using one or more machine learning models to generate an encoding parameter value for encoding the video frame. The method further comprises comparing a first set of delta encoding values, based on the encoding parameter value, representing differences between groups of pixels of the video frame to a second set of delta encoding values, based on an alternative encoding parameter value, representing differences between the groups of pixels of the video frame, and in response to determining that the first set of delta encoding values is less than the second set of delta encoding values, selecting the encoding parameter value. The method further comprises based on the encoding parameter value, encoding the video frame to generate an encoded video frame.

According to the data set processing method and device provided by the embodiment of the invention, a three-layer storage architecture is innovatively constructed, and efficient collaboration of edge caching, center storage and memory grids is realized through heat value calculation and dynamic allocation. And designing a small file processing mechanism based on file similarity, and establishing a logic large file generation strategy by combining dynamic partitioning and incremental coding. And an access mode prediction mechanism is introduced, and data prefetching and training optimization are realized through feature vector analysis and zero-copy transmission. According to the method, the defects of the traditional technology in the aspects of storage architecture, small file processing, data prefetching and the like are effectively overcome, and the data set processing efficiency and performance are remarkably improved.

This invention proposes an integer-order differential analog-to-digital converter (ADC) for intracranial neural potential acquisition. First, an integer-order differential operator is designed to calculate arbitrary integer-order incremental codes, which more effectively utilizes the sparsity of intracranial neural potentials, significantly improving the compression rate of intracranial neural potentials. It also offers greater flexibility, with adjustable differential order, adapting to a wider range of intracranial neural potential acquisition applications. Second, a multi-level buffer matrix module is designed to generate arbitrary integer-order incremental code values ​​in a concise and efficient manner. Finally, a serialized bitstream generator is designed to serialize and package integer-order incremental data acquired from multiple parallel channels. This not only offers high scalability but also allows for reliable interfacing with subsequent synchronous clock wireless transmission systems, avoiding the overhead and latency of synchronous-asynchronous interface coordination, further improving signal acquisition efficiency.

Provided are a blockchain-based block compression method and apparatus, a device, and a readable storage medium. The method comprises: acquiring M transaction data packets, each of the transaction data packets comprising transaction attribute values of N transaction attribute types; on the basis of the transaction attribute values and type priorities respectively corresponding to the N transaction attribute types, determining a positional order of the M transaction data packets, so as to obtain a transaction data packet sequence; for the N transaction attribute types, on the basis of the transaction attribute values corresponding to the same transaction attribute types in the transaction data packet sequence, respectively generating attribute value sets according to the positional order, so as to obtain N attribute value sets; respectively performing delta encoding processing on the N attribute value sets to obtain N attribute value delta encodings; and performing block encapsulation processing on the N attribute value delta encodings to obtain a block pending for blockchain uploading.

A digital tape measure is provided including a reel located within a housing and a tape wound on the reel and extendable from the housing. The tape further includes incremental and absolute encoding markings on a surface thereof, and a sensor assembly is located within the housing to read the incremental and absolute encoding markings. A controller is in communication with the sensor assembly and receives output from the sensor assembly to perform incremental sensing by incrementally counting the incremental encoding markings, absolute sensing by interpreting a period of the absolute encoding markings as an absolute position of the tape, and to combine the incremental sensing with the absolute sensing to determine a current length of the tape extended from the housing.

The embodiment of the invention provides an overseas cloud mobile phone AI-driven elastic synchronization method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: collecting a user operation sequence and transnational network link quality data; predicting a session interruption risk value R by using an AI model in which GNN and Transform are fused; when R exceeds a threshold value, selecting an optimal target node from the compliance node list to trigger hierarchical elastic scheduling; layering state changes according to high and low priorities through a semantic-level differentiated state synchronization engine, and performing incremental coding on high-priority operation to generate minimum instruction set synchronization; a data compliance strategy is automatically executed during cross-border transmission; and the global strong consistency of the metadata is guaranteed by adopting a Raft protocol optimized by a wide area network. Through the scheme disclosed by the invention, the problems of high delay, easy interruption and data compliance of overseas cloud mobile phones in a complex network environment are solved, and high continuity, low delay synchronization and reliable operation of sessions are realized.

Provided in the present invention is a fractional-order difference analog-to-digital converter for collecting a neural spike potential. Firstly, a fractional-order difference operator is designed to calculate arbitrary fractional-order delta encoding, such that the sparsity of a neural spike potential can be more effectively utilized, thereby significantly improving the compression ratio of the neural spike potential; moreover, the fractional-order difference analog-to-digital converter has higher flexibility by means of tunable differential orders, and can adapt to a wider range of neural spike potential collection scenarios. Secondly, a fractional-order buffer module is designed, such that an arbitrary fractional-order delta encoding value can be generated in a simple and efficient manner. Finally, a serialized bitstream generator is designed, such that serialized packaging can be performed on fractional-order delta data collected by a plurality of parallel channels, which not only exhibits high scalability and is applicable to multi-channel collection, but also enables reliable interfacing with a subsequent synchronous clock wireless transmission system, thus avoiding overheads and latency of synchronous-asynchronous interface coordination, and further improving the efficiency of neural spike potential collection.

A mechanism for processing video data is disclosed. The mechanism includes determining that a supplemental enhancement information (SEI) message is included in a bitstream, in which the SEI message carries danmaku information. A conversion is performed between a visual media data and the bitstream based on the SEI message carrying danmaku information.

Provided are a decoder unit, a decompressor unit, and method for a decoder of multiple stages of adders to decode delta encoded data. A decoder unit implemented in a cache memory having a cache memory cell array comprises a first stage of adder circuits to add, in parallel, pairs of encoded items transformed using a delta encoding, wherein the encoded items include a plurality of deltas of neighbors of sequential source items. The decoder unit further comprises at least one successive stage of adder circuits to add, in parallel in each stage, pairs of outputs from a previous stage of adder circuits, wherein each successive stage has fewer adder circuits than the previous stage, and wherein output from a last of the at least one successive stage comprises the sequential source items.

The present application relates to the technical field of positioning and speed measurement of super-speed maglev train, in particular to a super-speed positioning and speed measurement method and system based on laser coding, mainly including a plurality of laser code readers with unique numbers which are uniformly distributed along the track of the train, and the laser signals read each time are coded; the activation state of the laser code reader is determined according to the signal vector, and the state information of the laser code reader is transmitted to the positioning and speed measurement algorithm model. The laser propagation speed is fast, the photoelectric response speed is fast, and the positioning and speed measurement algorithm is simple, so that the delay of the speed positioning method can reach microseconds; through the combination of absolute coding and incremental coding, the problem of initial positioning loss is solved, the design of the ID code plate can make the laser coding not affected by the vertical floating of the train, and the redundant setting of the response of the two laser code readers increases the reliability and safety of the positioning and speed measurement algorithm results.

This invention provides a novel, reversible data transformation method that, while employing a simple algorithm, offers the potential for data scrambling while simultaneously producing output of the same length as the original data. [Solution] The method calculates the Hamming weight of the input symbol sequence and reverses the symbol at the Hamming weight position from the start or end until the symbol sequence reaches a predetermined convergence state (e.g., all zeros). The history information obtained during this reversal process is encoded using delta coding and Huffman transform based on occurrence intervals, etc. Finally, padding is applied to the encoded data based on the original data length, and converted data with the same length as the original input symbol sequence is output. This makes it possible to achieve data transformation that eliminates statistical bias in the data and has both confidentiality and diffusion capabilities, despite being a simple algorithm.

This invention provides a method and apparatus for analyzing incremental encoder output signals. The method includes: acquiring a first set of pulse signals and a second set of pulse signals from the incremental encoder output signal, wherein the first set of pulse signals includes amplitude, phase difference, and clutter; simulating and reproducing a Lissajous image fitted by the first and second sets of pulse signals to obtain the roundness of a standard circle; calculating a corrected Lissajous image to obtain the corrected roundness; comparing the corrected roundness with the roundness of the standard circle to confirm the failure weight of the Lissajous image; the corrected roundness includes a first corrected roundness, a second corrected roundness, and a third corrected roundness, which are calculated using clutter, amplitude, and phase difference, respectively. The incremental encoder output signal analysis method and apparatus provided by this invention improves the accuracy of the detection signal by comparing the corrected roundness with the roundness of the standard circle to confirm the failure weight of the Lissajous image.

The application provides a fractional order difference analog-to-digital conversion device for neural spike acquisition. First, a fractional order difference operator is designed to calculate arbitrary fractional order delta coding, which can more effectively utilize the sparsity of neural spikes, significantly improve the compression rate of neural spikes, and have higher flexibility, adjustable difference order, and can adapt to more neural spike acquisition application scenarios. Secondly, a fractional order buffer module is designed, which can generate arbitrary fractional order delta coding values in a simple and efficient way. Finally, a serialization bit stream generator is designed, which can serialize and pack the fractional order delta data collected by multiple parallel channels. It not only has high scalability and can be applied to multi-channel acquisition, but also can be reliably connected with the subsequent synchronous clock wireless transmission system, avoiding the overhead and delay of synchronous- asynchronous interface coordination, and further improving the neural spike acquisition efficiency.

The invention discloses a super-resolution imaging method and system based on a laser confocal microscope, and particularly relates to the technical field of laser confocal microscopes, and the method comprises the steps: writing a point detection signal matrix and a frame index into a video memory at a collection end through a shared cache in a zero-copy manner; reasoning the real-time data stream by using a residual network of an integrated point spread function convolution kernel to generate a reconstructed voxel stack; calculating the signal-to-noise ratio, the resolution and the drift, converging the signal-to-noise ratio, the resolution and the drift into a quality index vector, and comparing the quality index vector with a threshold value to obtain a dynamic scanning parameter vector; the scanning step pitch and the laser power are adjusted in real time according to the synchronous mark, and an acquisition-reconstruction closed loop is formed; performing inter-frame difference on the reconstructed voxel stack to obtain a difference voxel stream, generating an incremental code through adaptive entropy coding, packaging the incremental code and control metadata into a compressed packet, and directly mapping the compressed packet to a rendering buffer to realize synchronization of data compression, parameter recording and three-dimensional visualization; and the calculation time delay and the storage pressure are remarkably reduced while the high-resolution image quality is kept.

Provided in the present invention is a total variation analog-to-digital converter for collecting an electroencephalogram. Firstly, a total variation operator is used to calculate second-order delta encoding, such that the sparsity of an electroencephalogram signal can be more effectively utilized, thereby significantly improving the compression ratio of the electroencephalogram signal. Secondly, a non-switching double-buffer module is designed, such that a second-order delta encoding value can be generated in a simple and efficient manner. Finally, a serialized bitstream generator is designed, such that serialized packaging can be performed on second-order delta data collected by a plurality of parallel channels, which not only exhibits high scalability and is applicable to multi-channel collection, but also enables reliable interfacing with a subsequent synchronous clock wireless transmission system, thus avoiding overheads and latency of synchronous-asynchronous interface coordination, and further improving the signal collection efficiency.

The application provides a verifiable resource transfer method and device based on incremental encoding, which comprises the following steps: pulling certificate information from a key server, and determining the mapping relationship between each communication entity and a public key; determining a target communication entity and an intermediate communication entity on a communication path based on the forwarding strategy of each communication entity and the certificate information; transmitting resource information on a source communication entity to the intermediate communication entity on the next path, verifying the incremental encoding contained in the resource information by the intermediate communication entity; if the verification is successful, attaching the verifiable encoding of the intermediate communication entity to the incremental encoding, sending the resource information to the intermediate communication entity on the next path, and repeating the verification process and the attachment process until the target communication entity receives the resource information. The incremental encoding generated hop by hop and verified hop by hop can prove the verifiable trust of the path nodes to the resource, and can solve the problem of trusted transfer of the resource in the network.

The application discloses a sepsis organ dysfunction early warning method based on sparse data incremental coding, comprising the following steps: obtaining event sequence data and non-event sequence data of a target object, wherein the event sequence data comprises at least one of clinical diagnosis data, treatment record data and laboratory history detection data, and the non-event sequence data comprises at least past medical history data and target sign data; performing incremental coding on the event sequence data to obtain a sparse mode of the event sequence data; processing the sparse mode, the event sequence data and the non-event sequence data according to a pre-constructed early warning model to obtain a sepsis risk index of the target object; and outputting target early warning information in the case that the sepsis risk index meets preset early warning conditions.

A mechanism for processing video data is disclosed. The mechanism includes determining that a number of syntax elements representing motion vectors for vertices is not present in a bitstream and that the syntax elements have a value of 0. A conversion is performed between a visual media data and a bitstream based on the syntax elements.

The invention provides a traceability log storage and query method based on deep embedding and single-layer RNN prediction, and relates to the technical field of traceability graph compression, and the method comprises the steps: converting a system bottom layer audit log into a traceability graph, carrying out the coding mapping of path class and identification class attribute fields through deep learning embedding representation, and obtaining a traceability graph; coding prefixes are distributed for the path type attribute fields and the identification type attribute fields through self-adaptive dictionary coding, and numerical attribute fields are recorded through incremental coding; preprocessing and learning the traceability graph through a single-layer recurrent neural network (RNN) unit; performing local adjustment on the traceability graph during data updating based on incremental coding and adaptive mapping; and mapping the query condition into an attribute coding prefix, reconstructing an attribute sequence based on the attribute coding prefix, and performing correction through the adjustment table. For redundant information and structural similarity, a deep learning embedding representation technology and an adaptive dictionary coding strategy are adopted, and shared codes of similar attribute fields are effectively learned and extracted, so that redundant data storage is reduced.

A method of encoding (e.g. compressing) data is disclosed. The data comprises a sequence of values. It is encoded by delta-encoding the sequence of values to generate a sequence of delta values, and encoding each delta value as an offset into a lookup table of delta values, thereby generating a sequence of offsets. The sequence of offsets, or data representative thereof, is stored or output as encoded data.

A method of vector database compression includes receiving first time-series vector data comprising a first plurality of vectors representative of data corresponding to a first time point, receiving second time-series vector data comprising a second plurality of vectors representative of data corresponding to a second time point, generating a plurality of delta encodings, discarding the second time-series vector data after generating the plurality of delta encodings, and modifying database data to store the plurality of delta encodings. Each vector of the second plurality of vectors corresponds to one vector of the first plurality of vectors. The plurality of delta encodings is generated by, for all corresponding vectors of the first plurality of vectors and the second plurality of vectors, generating one delta encoding that describes differences between values of corresponding elements of the vector of the second plurality of vectors and the corresponding vector of the first plurality of vectors.

The application provides an absolute value encoding method, system and program product of an optical magnetic encoder. The method is applied to an encoder system, and the method comprises the following steps: in the case that a magnetic absolute value encoder and an optical incremental encoder are both in a non-fault state, obtaining a fused absolute position value based on a magnetic absolute position value of the magnetic absolute value encoder and an optical incremental value of the optical incremental encoder, the fused absolute position value serving as an output absolute position value of the encoder system, wherein the magnetic absolute position value of the magnetic absolute value encoder is used to determine an optical reference position of the optical incremental encoder; the magnetic absolute position value has a first precision, and the fused absolute position value has a second precision, and the second precision is higher than the first precision. Therefore, the absolute position of the movement of a mechanical component can be reliably and accurately obtained.