57 results about "Continuous data" patented technology

A data quantization method and device, a computer readable storage medium and a chip, the data quantization method comprising: obtaining a data block to be quantized, dividing the data block to be quantized into one or more data groups to be quantized according to a preset grouping method; performing quantization calculation on each data group to be quantized according to a target quantization format, to obtain quantization data corresponding to data in each data group to be quantized and quantization metadata corresponding to each data group to be quantized, wherein the quantization metadata is used for dequantization of the quantization data; and storing the quantization metadata corresponding to the data block to be quantized and the quantization data as a continuous data structure.

Embodiments of the present application disclose a touch data transmission and acquisition method and device, equipment and a storage medium. In the case of obtaining an initial data stream, a first target data in the initial data stream is replaced by a preset value to obtain a target data stream. The initial data stream is one frame of data generated according to a touch operation on a touch screen of an electronic device, and the first target data is data whose absolute value in the target data stream is within a first value interval. The target data stream is compressed to obtain compressed data, and the compressed data includes first compressed data for indicating a plurality of continuous data in the target data stream whose values are the preset value. The compressed data is sent to an operating system of the electronic device. According to the embodiments of the present application, invalid data is discarded and valid data is retained by means of lossy data compression, so as to reduce the data amount of touch screen change data to be transmitted, and further reduce data transmission time and transmission power consumption.

The present application provides a kind of data processing method and data processing device, electronic equipment and non-transient computer readable storage medium, the method includes in response to the decompression instruction of data, the check of received compressed data is executed;In the case where check passes, according to the preset continuous data length, the compressed data is divided into multiple data segments, wherein the compressed data is compressed according to the preset continuous data length;The decompression operation of the multiple data segments in the preset address space is respectively executed, and the decompressed data is obtained;Whether the decompressed data is consistent with the data amount of the original data corresponding to the compressed data is compared;In the case where data amount is consistent, the decompression task of the compressed data is ended.According to embodiment, by dividing compressed data into multiple data segments and performing double check on decompressed data, not only the accuracy of data decoding is improved, but also the speed and efficiency of decompression are improved.

This invention discloses an autonomous data processing system based on a deep mining framework, relating to the field of data processing technology. It includes: a semantic judgment module that extracts the target missing field, associated fields, and historical homologous field sequences from input records; determines the field constraint relationships between the target missing field and associated fields; and constructs a field constraint graph accordingly; calculates the missing position value, constraint break value, and historical offset value; and determines the missing semantic code based on these values; and writes the field constraint graph, the missing semantic code, and the missing position value, constraint break value, and historical offset value into a record-level state payload. This invention constructs a continuous data processing link around the target missing field, associated fields, and historical homologous field sequences; and establishes clear data flow and control flow relationships between vectors such as field constraint relationships, field constraint graphs, missing semantic codes, original data tracks, and completed data tracks.

The application provides a road section travel time prediction method and system, the method comprising: obtaining two continuous data points in vehicle trajectory data, and determining path travel time based on the two continuous data points; determining path free flow travel time and path delay time based on the path travel time, wherein the path free flow travel time comprises at least one road section free flow travel time corresponding to at least one road section; determining at least one road section static delay time and at least one road section dynamic delay time corresponding to the at least one road section in the path delay time based on the scene where the two continuous data points are located; determining road section delay time of each road section based on the at least one road section static delay time and the at least one road section dynamic delay time; and determining road section travel time based on the road section delay time and the road section free flow travel time. The application achieves the purpose of obtaining more accurate road section travel time of each road section through low sampling frequency vehicle trajectory data.

The application provides a disc area structure level determination method and device and electronic equipment. A first area containing a disc area is scanned line by line to obtain OCT volume data. A center point of the first area is taken as a center to star-type sample the OCT volume data to obtain a first number of first BScan two-dimensional section image data. An initial center point of the disc area is determined, and the OCT volume data is star-type sampled with the initial center point as the center to obtain a second number of second BScan two-dimensional section image data. The structure level of the disc area is determined. The OCT volume data is obtained in a line-by-line scanning manner, which ensures a high sampling rate. The sampled data is uniform and continuous. Star-type sampling is performed on the continuous data, which can avoid jumping and errors. In addition, the initial center point of the disc area is determined through star-type sampling, and then star-type sampling is performed with the initial center point as the center, which can obtain a more accurate disc area structure level.

The application is suitable for the technical field of artificial intelligence and medical health, and provides a postpartum depression intelligent screening and early intervention system and method, which comprises a whole life cycle management module for managing continuous data flow and state transition; a physical entity layer for collecting physiological data of puerpera, mother-infant behavior interaction data and environmental semantic data; a digital twin layer for constructing a dynamic virtual mapping model, the model comprising a causal graph model based on a structural causal model and a time sequence evolution mechanism; an application service layer for generating and pushing a risk early warning, an attribution analysis and a self-adaptive intervention scheme based on causal reasoning in stages; through causal reasoning instead of correlation analysis, the early warning accuracy is improved, the digital twin layer constructs a puerpera-baby binary group dynamic mapping based on a structural causal model, and through a directed acyclic graph to quantify the causal relationship between variables and combine Monte Carlo tree search to deduce the future risk probability, compared with a traditional scale, high-risk events such as self-harm thoughts can be early warned.

This invention discloses a method and system for parallel loading of meteorological data based on sliding window data locality, relating to the fields of artificial intelligence and high-performance computing. The method includes: allocating continuous data file segments to each parallel process based on a global data file path list, sliding window length, the number of time steps contained in each data file, and the total number of parallel processes; determining the data file path where the sample is located and its time step offset within the file; constructing an in-memory cache pool based on an ordered dictionary for each parallel process, setting an upper limit for the cache pool size; reading the time step data from the corresponding data file in the cache pool according to the sliding window length, concatenating them to form the sequence data required for model training, and outputting it to a computing device for model training. This invention achieves efficient and scalable parallel data loading by constructing a data locality-aware caching mechanism and an adaptive multi-process partitioning strategy.

The present application relates to the technical field of mine geophysical monitoring, in particular to a continuous parallel electric method intelligent monitoring system based on deep learning; the underground monitoring hardware system realizes full-field synchronous acquisition by parallel electric method AM / ABM mode through monitoring master station, substation and electrode array, and overcomes the defects of traditional high-density electric method; the underground acquisition control software and the uphole control processing module trigger acquisition and archiving regularly, form continuous data sequence, and meet the long-period monitoring demand; the intelligent inversion model is ERT-TransNet, the input embedded layer of which maps point cloud sequence, the encoder extracts global features, the decoder outputs predicted resistivity, realizes end-to-end mapping, avoids boundary ambiguity, improves boundary definition and positioning accuracy, and single sample prediction only needs 0.2 seconds, realizes efficiency order of magnitude improvement, and meets real-time processing demand.

A method for determining a convolution kernel for an iterative statistical algorithm based on a continuous-to-continuous data model. The method is used for image reconstruction from radiation measurements obtained in emission tomography, specifically in a Time-of-Flight Positron Emission Tomography (TOF PET) scanner. The presented method is a non-list-mode solution that reduces the computational complexity of the reconstruction problem, improves the resolution of reconstructed images, reduces the radiation dose absorbed by patients during TOF PET examinations, and / or shortens the measurement acquisition time without significantly compromising the quality of the diagnostic images. Notably, the quality of the functional images remains at the same level. These improvements are achieved by designing the convolution kernel to account for the statistical properties of the measurement signals registered during the TOF PET scanner's acquisition process.

The application discloses a traditional Chinese medicine large variety project information management method and system based on multi-source data, and relates to the technical field of multi-source project data management. The method obtains project execution data of a traditional Chinese medicine large variety project in project establishment, drug management, subjects, visits, quality control and statistical analysis, carries out standardized mapping processing on different source data, establishes corresponding mapping relationships, and forms a standard transaction data set; the standard transaction data set is subjected to time sequence compensation and reconstruction, candidate time sequence segments are generated, the candidate time sequence segments satisfying preset connection conditions are subjected to chain merging, and a continuous data chain for the same project transaction is constructed; finally, based on the continuous data chain, state loss and state jump abnormality in the project advancement process are identified, and whether to output final dynamic management information is judged. The continuous correlation integration of multi-source project data is realized, and the identification accuracy of the real advancement state and abnormal node condition of the traditional Chinese medicine large variety project is improved.

This invention discloses a method for identifying primordia of Agaricus bisporus in industrialized cultivation, relating to the field of industrialized edible fungi cultivation technology. By continuously acquiring images of the culture bed surface and establishing a unified micro-region division system, the same spatial location forms a corresponding continuous data structure at different times. This transforms the originally scattered image information into traceable micro-region temporal data, providing a stable data foundation for subsequent identification. This addresses the problem in existing methods where inconsistent image positions or lack of continuous correspondence hinders evolutionary analysis. By introducing position alignment processing in the image preprocessing stage, images at different times are unified to the same spatial reference system, avoiding positional errors caused by slight camera offsets or changes in acquisition angle. This ensures the consistency of data for the same micro-region in the temporal dimension, allowing grayscale changes to accurately reflect changes in the culture bed surface structure.

This application discloses a supply chain intelligent risk management method based on DLT and LLM, including the following steps: A. Continuous data perception and dynamic maintenance of DCRG; B. Risk identification, assessment, and verifiable interpretation generation; C. Smart contract early warning and adaptive strategy generation; D. Decision making and authorization confirmation; E. Automated execution and recording of response strategies. This application tightly integrates the deep insights of DCRG, the intelligent response of LIA-SCR, and the credible interpretation of VEP through DLT as a trusted infrastructure and smart contracts as an automated link, forming a complete closed loop from data perception, intelligent analysis, dynamic decision-making, automated execution to credible verification. DLT is not only a trusted carrier of data, but also an undeniable recording platform for collaborative behaviors such as strategy approval and result confirmation, effectively solving the problems of information silos and low collaborative efficiency in existing technologies.

This invention discloses a simulation method, apparatus, equipment, and medium for an electromagnetic railgun. Compared to constant load parameter values, it employs a finite element method combined with equivalent circuit analysis. The rail is divided into N segments, and the armature is positioned at the end of the i-th segment. The current equivalent load parameters of the rail are updated based on the circuit parameters at each discrete time point, thereby approximating the true value of the equivalent load parameters segment by segment. The current equivalent load parameters are then substituted into the circuit and mechanical equations to calculate the discrete data when the armature is positioned at the end of the i-th segment. If i < N, let i = i + 1, and iterate the above simulation steps; if i = N, then fit all the calculated discrete data between the armature velocity and discrete time points to obtain continuous data between the armature velocity and time. It is evident that this invention corrects the equivalent load parameters whenever the armature moves a certain distance, thus simulating an electromagnetic railgun firing process that more closely approximates the real situation.

This invention discloses an intelligent detection system and method for rotor bar breakage faults in asynchronous motors, belonging to the field of intelligent motor fault diagnosis technology. By deploying multi-dimensional sensors on the motor and aligning them over time, the system divides the continuous data stream into fixed-duration analysis windows. It calculates the characteristic average values ​​of each physical quantity within each window, using the average current intensity as a benchmark, and calculates the correlation ratios with torque, noise, temperature rise, and vibration. This quantifies the energy conversion efficiency and the response characteristics of each physical quantity to current, and monitors short-term fluctuations in each correlation ratio between adjacent windows in real time. When these fluctuations exceed a threshold, an instability warning is triggered. Simultaneously, the real-time correlation ratios are compared with pre-stored baseline data on bar breakage status to calculate single-dimensional differences, which are then fused to generate a comprehensive health index, enabling a quantitative assessment of the rotor's mechanical integrity degradation trajectory. Based on the comprehensive health index classification, a closed-loop hierarchical management system is completed, from early identification to severe alarms.

This invention provides a method for measuring the matrix content in nodular limestone. It involves obtaining a core sample from a nodular limestone stratum at a specific depth in a single well. By measuring the area ratio of the matrix in the roll scan image of the core sample and the area ratio in the corresponding static electro-optical imaging image from the same location in the well, the difference between these two area ratios reaches a designed standard value. An empirical threshold for the static electro-optical imaging image is then obtained, and the matrix content in the nodular limestone is determined using this empirical threshold. The area ratio is primarily determined by adjusting the color threshold to identify the pixel values ​​of the matrix and the overall image. The matrix area ratio in the image is then calculated using these pixel values. This method eliminates the need for separate descriptions of core samples from each nodular limestone stratum at each depth in each well. Continuous data can be obtained and a quantitative description generated in one go using the empirical threshold. The method is simple to operate, highly efficient, and cost-effective.

This invention discloses a fermentation control system and method based on multi-parameter collaborative control, belonging to the field of fermentation control technology. The system includes the following steps: collecting continuous data on temperature, humidity, airflow velocity, and heat flow distribution within the fermentation chamber and overlaying them with time markers; simultaneously recording the heating and ventilation response times to form a dynamic reference; tracking heat flow changes based on the time difference between heating and ventilation stages and extracting the convergence point, extending the heating cycle to form a time misalignment; adjusting the ventilation trigger interval in conjunction with the time misalignment buffer to couple and match air inflow with heat energy attenuation, constructing a stable heat exchange state; and implementing temporal perturbations based on this state, introducing delay differences to maintain asynchronous balance. This invention, through multi-parameter time correlation and temporal control, enables stable misalignment between temperature control and ventilation, suppressing heat flow resonance and stabilizing the temperature field; and maintains long-term asynchronous balance through temporal perturbations, ensuring stable microbial metabolism and improving the conversion efficiency of distiller's grains protein and the overall stability of the fermentation process.

The application discloses a kind of safety state monitoring method and system of urban slow traffic, it is related to traffic space-time data processing technical field, including to the electronic data of urban slow traffic, constructs slow scene topology graph and generates time-varying visibility matrix.Slowness scene topology graph and time-varying visibility matrix are used to establish double memory state and generate candidate continuous trajectory.Based on candidate continuous trajectory, identity correlation score is calculated, identity corresponding relationship is determined, and subject level safety state value is output.The application constructs continuous data processing chain of synchronous modeling, double memory maintenance, associated output around urban slow traffic electronic data, realizes the unified organization and unified update of topological constraint, visibility constraint, negative evidence constraint and state value calculation.Compared with the distributed processing mode, the application can convert multi-source space-time objects into indexable, associated, accumulative digital data objects.

The utility model provides a kind of unattended astronomical observatory data collection system, it includes: manned base, field data collection node, astronomical telescope array, data transfer device between astronomical telescope array and field data collection node carries out data transmission, with the liftable communication platform with optical fiber that field data collection node and manned base are communicated simultaneously connection.The system of the utility model replaces traditional wireless unmanned aerial vehicle by the liftable communication platform of optical fiber tethering, or it is used in cooperation with data transmission line, four-foot mobile platform, can stably establish data communication link in the area of complex terrain, without unmanned aerial vehicle frequent take-off and landing, overcome endurance problem, greatly reduce artificial dependence, improve system stability, security and data transmission efficiency, meet the continuous data collection needs of field astronomical observatory under unattended state, especially applicable to long-period observation task, high security data transmission demand field astronomical observatory environment.

The present application relates to the field of tool life prediction, and more particularly to a kind of life prediction and repair control method and device for CNC tool.The method comprises the following steps: according to fixed sampling frequency, continuous data acquisition is carried out to CNC machine tool machining process, and standard monitoring data set is generated;Phase classification is carried out based on the multi-source monitoring data, and tool use stage is generated;The tool use stage is input to LSTM time series prediction model for data training, and time series prediction model is generated;Current prediction sequence is output based on time series prediction model;Threshold comparison and tool residual life prediction are carried out to current prediction sequence, and residual life prediction value is obtained;Self-adaptive life repair decision is made according to residual life prediction value, and life repair strategy is output.The present application realizes the accurate analysis of tool wear state and the early prediction of residual life, and improves tool utilization and processing stability.

The application discloses a spacecraft intelligent anomaly detection method supporting multi-source mixed heterogeneous data types, first, for discrete data, the discrete data dictionary is used to detect data anomalies by using residual reconstruction; then, for continuous data, the analysis result of discrete data is used to prune the search space to detect anomalies; finally, for mixed type data, the above two methods are combined to adjust the algorithm optimization target, and fast and accurate detection of anomalies is realized. Compared with the traditional method, the application patent fully explores the relationship between data sparse representation and abnormal characteristics, and establishes an abnormal detection method supporting discrete signals, aiming at the discrete data processing needs of the autonomous health management and intelligent precise operation and maintenance scene of the spacecraft. In addition, based on the idea of discrete and continuous data fusion, the application designs an abnormal detection algorithm based on sparse representation, which supports the simultaneous detection of abnormal characteristics of two types of heterogeneous data.

This application provides a data acquisition method, a self-contained seismograph, equipment, and medium for a self-contained seismograph, relating to the field of earthquake monitoring technology. The method, by integrating a low-energy-consumption first sensor and a high-precision second sensor into the self-contained seismograph, employs a method where the first sensor continuously acquires initial vibration signals and compares them with a preset vibration signal threshold to determine the presence of abnormal events. Only when an abnormal event is detected is the power module activated to supply power to the second sensor to acquire target earthquake data. Power is then deactivated after acquisition. This approach ensures continuous data acquisition using the low-energy-consumption first sensor when no abnormal events are detected, reducing overall energy consumption, extending equipment runtime, and lowering monitoring costs and maintenance workload. Simultaneously, it enables timely activation of the high-precision second sensor to acquire accurate target earthquake data when an abnormal event is detected, providing a reliable basis for subsequent earthquake analysis and improving the accuracy and effectiveness of earthquake monitoring.

The present application relates to relay conduction detection technical field, specifically relates to a kind of relay conduction fault data tracing method and system, comprising: multi-parameter synchronous acquisition configuration: for each relay test station deployment independent multi-parameter acquisition unit, synchronous acquisition conduction voltage drop, current parameter, environmental temperature parameter and load state parameter, set acquisition interval.The present application is by for each test station deployment with the multi-parameter synchronous acquisition unit of unique hierarchical identification, same synchronous clock trigger and dynamic sampling interval design, realize the accurate binding of acquisition data and station, solve the problem that data and fault station are not accurately associated;Rely on the cyclic coverage of hierarchical cache and double-mode timing synchronization mechanism, cooperate with the dynamic threshold calibration algorithm based on historical data trend analysis, freeze fault related temporary data, ensure that continuous data is not lost in specific time period before fault occurs, solve the limitation that only fault instantaneous data can be obtained.

This invention relates to the field of medical data processing technology, specifically to a method and system for risk assessment of gastroenterological diseases based on big data. The method includes the following steps: extracting biochemical parameters using a terminal interface to construct a feature time-series matrix; inputting this matrix into a mining network to calculate cross-weights and generate a dynamic node graph; extracting fluctuation frequencies to determine feature drift rates; mapping these drift rates to adjustment factors; updating engine intervals to output early warning thresholds; and collecting parameters for differential comparison to generate risk probabilities. In this invention, by constructing a coupled time-series matrix and mapping it to the underlying dynamic topology graph, the individual feature drift rate is calculated to adaptively adjust the core early warning threshold. This allows for fine-grained classification of continuous data, directly outputting disease probabilities. This effectively overcomes the significant drawback of traditional fixed decision branches that forcibly sever the connection between indicators, comprehensively improving the system's ability to identify early, minor lesions and ensuring the reliability of screening applications in complex environments.

The application relates to the technical field of oil and gas exploration, and discloses a near-surface transverse wave velocity modeling method, device, equipment and medium based on node instrument continuous data, which comprises the following steps: acquiring seismic data continuously collected by a plurality of node instruments; based on the seismic data and the positions of the node instruments, interval range division is carried out on the pseudo surface wave channels corresponding to the node instruments, and the vertical velocity structure of the transverse wave corresponding to each node instrument is determined; based on all the vertical velocity structures of the transverse wave, a three-dimensional transverse wave velocity model of a target region is constructed; and based on the three-dimensional transverse wave velocity model, the converted wave static correction amount of the target region is determined. According to the scheme, the seismic data collected by the node instruments is used, the positions of the node instruments are utilized, interval range division is carried out on the pseudo surface wave channels, and a corresponding transverse wave velocity model is constructed for each node instrument corresponding point position, and a three-dimensional transverse wave velocity model is established, so that more accurate converted wave static correction is realized, and a foundation is laid for more accurate anisotropy migration imaging.

The application provides a kind of method, system and device for monitoring abnormal change of vehicle torque and rotating speed, comprising the following steps: continuously collecting the running data of vehicle, and recording the collection time corresponding to the running data, according to terminal equipment number N, the index data belonging to the same vehicle is partitioned;For the data in the partition, the throttle pedal opening degree data A and torque data T, rotating speed data R of M continuous data in each partition are sorted according to the generation time of data, whether the power is abnormal is judged, if so, the collection time and position corresponding to the abnormal data are determined, the application can automatically detect the torque and rotating speed change of vehicle during vehicle operation, so as to monitor the abnormality of vehicle power, so as to help operation personnel track the torque limiting problem in vehicle operation process, better serve customers, effectively reduce the operation cost of company and improve management quality.

A method for determining a convolution kernel for an iterative statistical algorithm based on a continuous-to-continuous data model for image reconstruction from radiation measurements obtained in emission tomography, specifically in a Positron Emission Tomography (PET) scanner. The method improves the resolution of reconstructed images, reduces the radiation dose absorbed by patients during PET examinations, and / or shortens the measurement acquisition time without significant loss in the quality of the diagnostic images obtained. Specifically, the quality of the functional images remains at the same level. These improvements are achieved through the design of the convolution kernel, which takes into account the statistical properties of the measurement signals registered during the acquisition process in the PET scanner.

The application discloses a Hive-based data updating method and device and a readable storage medium. After updating data records in a target data table, version information of the target data table is recorded. When the version information meets preset conditions, valid data records that are not deleted are acquired, and a unique identification value (UUID) and a latest operation version number of each row of the valid data records are inquired. Target data that needs to be reserved is further determined, and the operation version number is reset. A temporary data table is generated to replace historical data in the target data table, and the version information of the target data table is reset. Since the version information of the data table is monitored in real time after a data updating operation is performed, once the preset conditions are met, redundant data in the data table is automatically cleaned, and the version information of the data table is initialized. Therefore, in the case of long-term and continuous data updating, the calculation complexity can be ensured to be always within a reasonable range, so that data updating is realized while the data processing efficiency is ensured.