57 results about "Data compression ratio" patented technology

The invention discloses a compression ultrafast imaging device based on time domain discrete illumination. The device is composed of a time domain discrete illumination compression ultrafast imaging system, a time sequence control system and an image reconstruction system. Time domain discrete illumination is provided for a to-be-measured scene through a pulse sequence generator, dozens of time slices of the to-be-measured scene are uniformly intercepted, spatial random coding is carried out, then the time slices are transmitted into a streak camera for space-time deflection and superposition imaging, and finally the to-be-measured scene is restored by adopting an image reconstruction algorithm (TV-CD) combining a total variation noise reduction model and a cascade noise reducer. The invention relates to a time domain discrete illumination compression ultrafast imaging technology (DI-CUP) for short. According to the invention, not only can single multi-image ultrafast imaging be realized, but also the data compression ratio can be reduced, the spatial resolution can be improved, and the observation time window is flexible and adjustable. The above advantages provide an accurate and effective measurement tool for non-repeatable ultrafast processes such as light pulse propagation and plasma evolution, and are of great significance to the development of related research and technology of natural science.

The invention discloses a network data transmission method and system for a network virtual surgery, and the method comprises the steps: obtaining a work online feature of the network virtual surgery, and inputting the work online feature into a pre-trained back-propagation neural network to obtain four monitoring output features; calculating to obtain a data compression rate, a prediction compensation window size, redundancy retransmission times and a local interpolation smoothing factor according to the four monitoring output characteristics; data transmission is executed after working data of the network virtual surgery is compressed according to the data compression rate; in the data transmission process of the network virtual surgery, delay is compensated based on a Kalman predictor predicting the size of a compensation window, redundant retransmission is triggered according to the number of redundant retransmission times to resist packet loss, and a local interpolation smoothing factor is substituted into an interpolation filter to smooth network jitter; a back propagation neural network is combined with a virtual operation force feedback system, and adaptive adjustment of delay, jitter and packet loss problems in a networked virtual operation is realized.

The invention relates to the technical field of electrical variable measurement, in particular to a self-adaptive power quality monitoring method for a power cabinet, which comprises the following steps of: starting a low-frequency acquisition unit arranged at a monitoring node of the power cabinet, and acquiring a low-frequency coarse scanning voltage signal of the power cabinet in a current monitoring period at a preset low-frequency sampling rate; according to the method, compressed observation vectors are transmitted, sparse basis inversion calculation is executed by using an orthogonal matching pursuit algorithm, high-precision power quality time domain waveform data are reconstructed, rich high-frequency transient details are restored under the condition of low-data-volume transmission, distortion feature analysis is carried out based on reconstructed waveforms, disturbance types are identified, and a monitoring analysis report is generated and uploaded. The real-time dynamic adjustment of the sampling scale and the matrix structure along with the signal fluctuation state is realized, the optimal balance of the data compression ratio and the signal reconstruction quality is achieved, the contradiction between the storage and transmission bandwidth limitation of massive high-frequency data in a complex power grid environment is solved, and the sensitivity for transient abnormality is improved.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.

The invention relates to the technical field of unmanned aerial vehicle interaction, in particular to a low-altitude unmanned aerial vehicle virtual reality fusion management and control system based on digital twinning. The full-fusion low-altitude unmanned aerial vehicle management and control system comprises a model building module, an acquisition module, a data transmission module, a calculation module, an analysis module and an instruction control module, the model building module accurately builds a digital twin virtual model according to entity unmanned aerial vehicle parameters and a force action principle, and the acquisition module periodically acquires unmanned aerial vehicle information and transmits the unmanned aerial vehicle information to the instruction control module. The data transmission module transmits the data to the calculation module for processing, the analysis module evaluates the simulation degree according to a result and sends an adjustment instruction, and the instruction control module optimizes a positioning difference value and a data compression ratio or sends a notification accordingly. According to the method, the real-time simulation precision of the system is remarkably improved, and the high consistency of the three-dimensional model and the actual scene is ensured, so that a more scientific and accurate basis is provided for the low-altitude unmanned aerial vehicle management and control decision, and the reliability and effectiveness of the decision are effectively enhanced.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle edge cooperative computing method and system for an unmanned aerial vehicle airport cluster, and the method comprises the following steps: analyzing the type of an unmanned aerial vehicle and a real-time network data flow based on an unmanned aerial vehicle communication network, evaluating the congestion condition of the current network, and determining the congestion condition of the current network according to the network condition and the data type; and adjusting a data compression ratio and transmission priorities of the plurality of data packets, optimizing a data transmission path, and generating a transmission efficiency optimization record. According to the method, the transmission efficiency and the network response speed are improved by evaluating the network congestion condition, adjusting the data compression proportion and priority and optimizing the data transmission path, the stability of the network and the reliability of task execution are guaranteed by combining real-time monitoring of node activity and rapidly recognizing fault nodes, the processing task is refined into a plurality of processes, and the efficiency of the network is improved. And the processing capabilities of the plurality of nodes are analyzed, so that the scientificity and the high efficiency of task allocation are ensured, and the cooperative efficiency of the unmanned aerial vehicle is optimized.

An embodiment of this specification provides a data processing method in a blockchain and a blockchain node. The method is executed by a first node in a first blockchain and includes: sequentially executing transactions in N transaction sets in chronological order, and the execution of the transactions in the N transaction sets causes the states of M first accounts to change; obtaining the signature information of each first account for its state; generating a first proof according to the root of the first world state, the root of the second world state, and the M signature information, where the first proof is used to prove that the first world state is generated after executing the transactions in the N transaction sets based on the second world state, and the second world state is the world state before executing the transactions in the N transaction sets; storing the root of the first world state and the first proof in a second blockchain. When improving the overall expansion of the blockchain network, this method can not limit the number of participants and has an infinite data compression ratio.

The invention discloses a data compression method adaptive to a data change trend. The method comprises the following steps: sequentially obtaining original values in a data stream to be compressed according to a time sequence; writing the obtained first original numerical value into a compressed data area through a preset coding mode, and taking the first original numerical value as a reference value; determining a data change trend, and dividing the data stream into N sub-data streams; calculating a threshold value of each sub-data stream; calculating a difference value between a subsequent original numerical value and an adjacent numerical value of the sub-data stream, or taking a value calculated through a fluctuation amplitude function as an intermediate value; performing corresponding calculation on the difference value or the intermediate value and a corresponding threshold value to obtain a target value; coding the plurality of target values of each sub-data stream by adopting a preset coding mode; and organizing and storing the reference value and the coded target value. The method can significantly improve the data compression ratio.

This invention discloses a parallelizable adaptive lossless matrix compression method. It involves dividing a large first matrix into smaller second matrices, assigning a splitting process to each second matrix, and having these splitting processes divide all second matrices into multiple bit matrices in parallel. Compression threads are then assigned to each bit matrix, and these compression threads adaptively select the optimal compression strategy from a set of compression strategies. Finally, the compression threads compress all bit matrices in parallel according to the determined compression strategy, forming a compressed matrix and a flag array as the final compressed data of the first matrix. By hierarchically and parallelizing the compression process, the memory overhead of the compression process is effectively reduced and the compression speed is improved. Adaptive compression is achieved by selecting the optimal compression strategy through trial compression, effectively improving the data compression ratio. Lossless data compression is achieved by constructing a flag array.

This specification provides a data compression method and apparatus. The data compression method is applied to an intelligent storage device and includes: acquiring at least one piece of business data to be processed; compressing each piece of business data to be processed according to a preset storage sector size to obtain a storage block to be compressed corresponding to each piece of business data to be processed, wherein the storage block to be compressed includes at least one storage sector; compressing the fill data in each storage block to be compressed to obtain a target storage block, wherein the target storage block includes compressed business data corresponding to each piece of business data to be processed, and the fill data is used to fill the free space in the storage sector. This method compresses the original business data at a preset storage sector size granularity, aligns the compressed business data at the storage sector granularity, and fills the empty space with fill data. In the hardware compression stage, the fill data in each storage sector is compressed, improving the data compression ratio.

The embodiment of the invention provides a data compression method and device, a logic chip and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the hash transformation of a to-be-processed data segment, and obtaining a first hash value; a second number of first initial positions recorded in the data dictionary are inquired with the first hash value as an index, the first initial positions are the positions of the data segments with the hash dictionary value being the first hash value in the original data, and the second number is larger than or equal to 2; if each first initial position is an invalid position, encoding the byte to be encoded, and updating any first initial position to a second initial position of the data segment to be processed in the original data; and if the valid initial position exists, performing byte-by-byte comparison from the valid first initial position and the second initial position to the data tail direction to obtain a continuous repetition length, and based on continuous repetition length coding, updating the position to be updated in the first initial position by using the second initial position. The data compression ratio can be improved.

The invention provides a deep learning data compression method and device for image storage, and the method comprises the steps: extracting a plurality of high-dimensional image features from a subset for each subset in a plurality of similar data subsets, and classifying and screening the image data in each subset according to the distance between each high-dimensional image feature and the similarity to obtain a new similar data subset, decoding the new similar data subset, and sequentially performing image fusion and image splicing on the images with the similarity greater than a second distance threshold in the decoded similar data subset to obtain a first to-be-coded image, carrying out image splicing on image pairs with the distance smaller than a second distance threshold in the decoded similar data subsets to obtain a second to-be-coded image; and compressing the first to-be-coded image and the second to-be-coded image to obtain a compressed data set. According to the method, the data redundancy is effectively reduced, the coding efficiency and the image compression accuracy are improved, the image data compression ratio is improved, and the data storage and transmission cost is reduced.

The embodiment of the invention provides a universal component adaptation system and method for edge equipment and cloud collaboration, and the system comprises a standard interface layer and a dynamic adaptation layer which are deployed at the edge side, and a cloud service component deployed at the cloud, the standard interface layer encapsulates the isomerism of bottom hardware and software through a unified calling interface, a data format and configuration parameters, and realizes cross-platform deployment of components; the dynamic adaptation layer is in communication connection with the standard interface layer and comprises an environment sensing module and a parameter adjusting module, and the environment sensing module is used for collecting local parameters such as equipment computing power and network bandwidth in real time; the parameter adjusting module is used for dynamically optimizing operation settings of the components according to the parameters, such as a data compression ratio and a calculation task splitting strategy; and the cloud service component interacts with the edge side standard interface layer and is used for receiving the processed data or issuing an instruction.

The invention discloses a regular building three-dimensional model data lightweight method. The method comprises the following steps: S1, reading data; s2, detecting a plane capable of being simplified; s3, simplifying the triangulation network; and S4, the simplified effect is detected and adjusted. According to the lightweight method disclosed by the invention, on the premise of ensuring that the structure of the three-dimensional model is complete and a pyramid is not deleted, the data compression ratio is obviously improved by planning the triangulation network and simplifying the triangulation network and utilizing the aggregation and movement of the internal vertexes of the triangulation network. Meanwhile, the method does not involve the reading and compression of the image, so that the requirements on the GPU and the image processing computing power are greatly reduced, and the cost is effectively reduced.

The invention discloses a method for dynamically determining data scanning and calculation in a time sequence database, and relates to the technical field of time sequence database processing. Comprising the steps that 1, real-time statistical information is collected, and the statistical information comprises entity data volume distribution, a query time range and maximum and minimum timestamp information in a database; 2, calculating the average data volume of the entities based on the collected statistical information, and dynamically estimating the queried data volume in combination with a data compression ratio, data distribution and the size of a query window; 3, calculating the optimal parallelism degree according to the query complexity, the available resources of the system, the query time range, the quantity of entities and the quantity of entity data, and 4, allocating data processing tasks to calculation nodes or threads according to the calculated parallelism degree, and executing parallel data processing; and 5, monitoring the use condition of system resources in real time, querying execution time and a load state, and dynamically adjusting the degree of parallelism and a resource allocation strategy by utilizing a feedback mechanism according to a monitoring result.

The invention discloses a video analysis method and system for coal mine multi-service scenes, and relates to the technical field of video analysis. The method comprises the following steps: performing multi-device semantic segmentation and feature region clipping on an original video stream of a coal mine production region to obtain a clipped video stream; calculating an initial operation risk coefficient of the equipment based on the operation state monitoring data and the expected work task; correcting the initial operation risk coefficient based on the equipment operation association mapping, and adjusting the initial data compression proportion of the corresponding equipment to obtain an adaptive data compression proportion; and calling a corresponding video compression plug-in to carry out data dimension reduction on the clipped video stream based on the equipment type and the adaptive data compression proportion to obtain dimension-reduced video data, and carrying out state abnormity identification according to the dimension-reduced video data to obtain an abnormity identification result of each equipment. According to the method, dynamic adaptation of video analysis resources and equipment risks in a complex coal mine scene is realized, and the data transmission and storage efficiency is optimized while the anomaly recognition precision is guaranteed.

The embodiment of the invention provides a data compression method and a related device, and the method comprises the steps: obtaining first data which is table data composed of multiple rows of row data and multiple columns of column data; processing the first data to generate first intermediate data, the sequence of a plurality of row data included in the first intermediate data being different from the sequence of a plurality of row data included in the first data; and performing compression coding processing on the first intermediate data to generate second data. On the basis of replacement processing of row data, the long-tail distribution characteristic of data in table data can be effectively utilized, and the compression rate of the data is improved.

The invention provides a data grading sharing interface device and method in a vehicle and road cloud three-level cloud architecture, and belongs to the technical field of data security, and the device comprises an authority control module which is used for receiving a data access request, and carrying out the authentication of the request based on a preset three-level authority matrix, the three-level permission matrix is used for defining data access ranges of the edge cloud, the region cloud and the center cloud; the format conversion module is used for performing format conversion on the authenticated data and dynamically adjusting the data compression ratio based on the real-time network bandwidth; and the protocol interaction module is used for transmitting the data subjected to format conversion and compression through a request-response-acknowledgement mechanism based on an MQTT-SN protocol. According to the invention, the security and reliability of data access are improved, the data transmission efficiency is optimized, and the high requirements of vehicle-road cooperation, intelligent traffic and other application scenes on data security, real-time performance and reliability are met.

The invention discloses an intelligent cloud collaborative geological disaster monitoring and early warning platform in the field of geological disaster monitoring and early warning. Comprising a cloud collaborative monitoring architecture, an edge computing optimization front-end processing module, an intelligent video monitoring and behavior analysis system, a multi-level information interaction and linkage mechanism, a unified access standard and a centralized power supply solution. The cloud collaborative monitoring architecture is used for cloud storage, analysis and three-dimensional geological modeling of multi-source data; and the edge calculation optimization front-end processing module is deployed at a port of the mobile acquisition equipment and executes data screening and compression. Quantitative control is realized through a data compression ratio formula, an anomaly recognition accuracy formula and an early warning response time model, and transmission and security are optimized in combination with 5G communication and a block chain technology. According to the invention, in mountain landslide monitoring, the investigation efficiency can be improved by 60%, the early warning response time can be shortened by 83%, the hardware cost can be reduced by 40%, and the method is suitable for engineering investigation, three-dimensional visualization and emergency response scenes in a complex geological environment.

The present application provides a data compression method and an apparatus for the data compression method. The data compression method includes: obtaining a plurality of data to be compressed from a data object to be compressed based on different preset ranges; respectively generating a plurality of hash values based on the plurality of data to be compressed; looking up a hash table corresponding to each hash value to obtain the position information of the compressed data corresponding to the hash value, wherein the hash table is used to store the position information of the compressed data and the data to be compressed in the data object to be compressed, and the position information corresponds to a hash node; obtaining the matching length of the data corresponding to the hash node based on the data to be compressed and the compressed data; and compressing the data in the data object to be compressed based on the hash node and the matching length. This data compression method can improve the data compression ratio.

The invention relates to the technical field of data communication, in particular to a data fusion communication method and device, electronic equipment and a storage medium. Then according to a preset data length, sequentially segmenting a plurality of first data segments from the plurality of data queues, and sending the plurality of first data segments obtained by each segmentation into a first fusion model to obtain a plurality of first fusion data; segmenting each piece of first fusion data, and modulating the subcarriers according to the segmented data to obtain a plurality of modulated subcarriers; and finally, performing waveform synthesis on the plurality of subcarriers, and performing up-conversion on the obtained synthesized waveform to a predetermined wave band for transmission. According to the embodiment of the invention, the data compression ratio and loss are controllable, the influence on audio and video data and audio and video expression is small, the problem of multi-channel synchronization time slice division does not exist, and the synchronization effect is good.

The invention provides a data transmission optimization method and device, network equipment, a medium and a program product, and relates to the technical field of wireless communication. The data transmission optimization method comprises the following steps: in response to an obtained data transmission request of a user, determining a corresponding transmission scene and a network slice configured for the transmission scene; determining a transmission target corresponding to the transmission scene; configuring a transmission strategy based on the transmission target, the monitored network working condition information and the cache information of the edge node; and based on the transmission strategy, indicating the network slice to transmit target semantic data obtained based on the compression ratio to the user and / or indicating the edge node to transmit target cache data to the user, so as to generate target request data of the data transmission request based on the target semantic data and / or the target cache data. Through the technical scheme of the invention, semantic communication and / or cache are dynamically selected, and data transmission optimization is realized by utilizing the advantage of high data compression ratio of semantic communication and the advantage of low delay of cache.

The triangular mesh processing method, device, electronic equipment and storage medium provided by the present application relate to the field of oral digital technology. The method obtains an original triangular mesh; based on the geometric structure and topological connection relationship of the original triangular mesh, the curvature of each vertex and the characteristic mark of each edge in the original triangular mesh are obtained; according to the curvature of each vertex and the characteristic mark of each edge, each triangle face to be subdivided and each edge to be folded in the original triangular mesh are determined; each triangle face to be subdivided is subjected to subdivision processing, and each edge to be folded is subjected to folding processing to obtain a target triangular mesh, so that an optimal balance is achieved between geometric fidelity, topological rationality and data compression ratio, and the practicability and robustness of the triangular mesh in three-dimensional reconstruction, real-time rendering, physical simulation and downstream AI-driven mesh processing tasks are improved.

This application relates to a method, apparatus, computer equipment, and storage medium for the safe operation and maintenance of distributed photovoltaic (PV) systems. The method includes: acquiring electrical quantity data and environmental parameters of PV modules in an edge system; evaluating latency sensitivity using a trained support vector machine based on the electrical quantity data and environmental parameters to obtain a target latency sensitivity level; determining a decision action space using a trained decision network based on the target latency sensitivity level, communication bandwidth resources, and equipment load rate; the decision action space includes sampling frame rate, data compression ratio, and model index; receiving a fault location identification model matched to the model index from a cloud server; acquiring image data and electrical quantity sequences transmitted at the sampling frame rate and data compression ratio; and using the fault location identification model to identify fault locations, obtaining fault location identification results. This achieves efficient fault identification in distributed PV edge systems.

The invention discloses a self-adaptive remote communication control system and method based on a large model. The system comprises a dynamic weight channel evaluation module, a multi-mode communication intention analysis module, a self-adaptive TopK gradient sparse module, a distributed feeder automation 5G cooperative control module, a federal communication knowledge distillation module and a self-adaptive remote communication control module. The dynamic weight channel evaluation module combines parameters of the 5G cooperative control module to calculate a channel weight, the multi-modal communication intention analysis module processes terminal multi-modal data and outputs a result, and the adaptive TopK gradient sparse module screens a large model key gradient and transmits the large model key gradient to the federal communication knowledge distillation module; and model parameters after distillation, the weights and analysis results are all transmitted to the self-adaptive remote communication control module. According to the method, the communication link, the transmission rate and the data compression ratio are adjusted, the communication stability and the self-adaptive capability are improved, and the distributed feeder automation 5G scene requirements are met.

The invention provides an age-aware routing and unloading joint optimization method for a satellite network, and the method comprises the steps: enabling each satellite node to broadcast a lightweight HELLO beacon to a neighbor at the beginning of each decision time slot, and receiving the beacon of the neighbor; starting an independent optimization process for each observation task newly arriving at a satellite node in the time slot to determine a joint decision tuple; each satellite node enters a local execution stage, and after each time slot is finished, the queue value and the real battery energy of the virtual energy queue are updated according to the actual energy consumption and the energy collection amount of the satellite node in the time slot; according to the invention, a virtual energy queue is introduced to realize long-term energy balance constraint conversion, a block coordinate descent method is adopted to solve an optimal routing path, unloading nodes and a data compression ratio are calculated, and a self-adaptive energy sensing routing mechanism and a scheduling strategy based on an information age priority are combined to obtain an optimal routing path. And dynamic optimal balance among information freshness, data processing precision and energy sustainability is realized.

The present invention suppresses delay of drawing or operation even if a demagnification ratio becomes small when a graph is reduced and displayed for a time-series signal.　This display device that displays digital signals in time series comprises: a signal history acquisition unit that acquires a signal history of the digital signals; a demagnification ratio reception unit that receives input of a demagnification ratio related to time-series display of the signal history of the digital signals; a display width / compression ratio determination unit that determines a display width ratio and a data compression ratio on the basis of the received demagnification ratio and a threshold of the demagnification ratio; and a data compression unit that compresses the signal history of the acquired digital signals at the determined data compression ratio. The display device performs the time-series display of the compressed signal history on the basis of the display width ratio.

The invention provides an offshore yield increase operation dynamic monitoring optimization method based on data interface conversion, and the method comprises the steps: collecting original collection data during yield increase operations such as offshore acidification, fracturing, sand prevention, and the like, so as to calculate an interference index of a simulation channel and an RS485 channel; respectively carrying out standardization processing on the data of the analog channel and the RS485 channel, and generating a compensated analog signal sequence and an effectiveness score of the RS485 channel; calculating the state score of each channel, and arranging the state scores of each channel in a descending order to form a high-priority uploading queue; and inputting the high-priority upload queue into the MCU for caching and estimating the channel quality, adaptively adjusting the data compression ratio, the redundancy overhead and the transmission interval, and automatically downshifting the transmission power in a low-voltage state. The method can be directly deployed in various scenes of offshore yield increase operation, the wiring and maintenance cost is remarkably reduced, and the monitoring safety and the decision-making efficiency are improved.

[Problem] To provide a method for analyzing a target organism, which can analyze data from a compression ratio without normalization, and a method for acquiring a graph from which various types of biological information can be analyzed. [Solution] A method for analyzing a target organism, comprising compression ratio fluctuation examination step of obtaining a compression ratio of sequence data on the target organism for each variable related to the target organism, wherein the compression ratio fluctuation examination step includes: a sequence data acquisition step of obtaining a plurality of pieces of sequence data based on the target organism for each of the variables; and a compression ratio calculation step of compressing the plurality of pieces of sequence data to obtain a data compression ratio.

The present invention provides a lossy compression method for seismic data based on the blind inversion theory. The lossy compression method for seismic data based on the blind inversion theory includes: Step 1, inputting seismic data corresponding to the current shot number; Step 2, extracting local seismic data within the spatial window of the current gather based on the preset spatial window ranges in the X and Y directions; Step 3, performing compression on the local seismic data based on the blind inversion theory; Step 4, determining whether the sliding window has traversed the current gather; Step 5, determining whether all shot gathers have been processed. The lossy compression method for seismic data based on the blind inversion theory utilizes the local time-space information of multi-channel seismic signals to achieve the compressed storage of local characteristic reflection event axes, and the data compression ratio is higher than that of general lossless compression technologies, which can meet the requirements of real-time data processing.