86 results about "Interval data" patented technology

The invention relates to the technical field of state monitoring of numerical control machine tools, in particular to an embedded monitoring method for cutter wear of a numerical control machine tool. The method comprises the following steps: performing embedded monitoring and synchronous multi-modal data acquisition and preprocessing on a tool in a machining process to form a real-time working condition data frame queue; cutting effective interval data interception is carried out on the real-time working condition data frame queue to obtain an effective cutting data sequence; performing current and cutter feeding rate discretization processing on the effective cutting data sequence to form an energy-effect reference fingerprint; extracting an expected healthy feed rate from the energy-effect reference fingerprint; and performing performance deviation absolute amount calculation according to the expected healthy feed rate, and performing cutting performance attenuation analysis to obtain the cutting performance attenuation degree. According to the method, a complete process from detection to parameter adaptive closed-loop control is realized on a resource-limited embedded system, so that the service life of a cutter is prolonged, the machining quality is guaranteed, and the production efficiency is improved.

The invention discloses a processing method for a terrain model invading a bridge deck range, and belongs to the technical field of bridge engineering BIM modeling, and the method comprises the steps: searching a bridge deck space range based on a BIM model; a plurality of independent invasion intervals are positioned through intersection of a terrain model and a bridge floor, the invasion side direction of each invasion interval is independently judged, and only interval data of the invasion side are reserved; in the reserved invasion side interval, taking the central line of the route as a reference, iteratively generating a terrain model cutting surface according to a step length: determining a space point by vertically extending downwards to set a length and combining with a bridge floor section edge lateral level, iteratively intersecting with the terrain model on the basis of the space point to obtain an intersecting polyline, connecting all adjacent intersecting polylines in the invasion segmentation interval to form a cutting surface; and performing Boolean operation on the terrain model by using a cutting surface to eliminate an intrusive part. According to the method, rapid, accurate and automatic processing of the terrain model within the invasion bridge floor range is realized, and the accuracy and design efficiency of the bridge BIM model are remarkably improved.

A method for identifying a potential supercooled water zone through a satellite-borne multi-parameter ensemble includes: acquiring reference data on potential supercooled water zone identification, collecting parameter data, and pre-processing the reference data and the parameter data; grouping based on distribution features of the parameter data to obtain interval data, and performing feature selection of the parameter data based on the reference data and the interval data according to a cumulative frequency crossover method to obtain an influencing parameter; voting based on the influencing parameter to identify a potential supercooled water zone, and calculating a probability of supercooled water to obtain identification data; establishing a potential supercooled water zone identification model based on the identification data, and optimizing the potential supercooled water zone identification model using the reference data; and inputting data into the potential supercooled water zone identification model to obtain identification results.

The embodiment provides a wind turbine yaw-to-wind error calculation method and system based on goodness of fit, a terminal and a medium, and belongs to the technical field of wind power generation. The embodiment first preprocesses the original data to exclude the interference of variable pitch, shutdown, power limitation and the like on power generation; then performs interval data set division based on wind speed intervals and yaw angles; then uses a hypothesis testing method to check the power distribution of each wind speed interval sub-set, selects a corresponding fitting method through the actual distribution of each sub-set, and determines the yaw wind direction of the maximum power in the interval; and finally comprehensively considers the calculated yaw error angles in each wind speed interval. If the yaw error angles obtained in each wind speed interval have high consistency, and the goodness of the fitting result meets the preset requirement, it is considered that there is a significant yaw error angle in the wind farm, and the yaw error angle is calculated. The present application improves the accuracy of yaw error calculation.

A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention discloses a wide area measurement system abnormal data identification method, system and related device, and the method comprises the steps: carrying out the single electrical interval data abnormity identification of a single PMU at a plant station end, and obtaining a first abnormal data identification result; according to the first abnormal data identification result, when a certain measurement data in a single electrical interval is abnormal, comparing and identifying data anomalies of a plurality of PMUs at a plant-station end to obtain a second abnormal data identification result; if the same PMU is accessed to a plurality of electrical intervals on the same bus, performing wide-area level correlation comparison analysis on wide-area measurement system data at a master station end, and performing wide-area measurement system abnormal data identification through a power grid topology connection relation to obtain a third abnormal data identification result; and integrating the obtained abnormal data identification result, and obtaining an abnormal data identification result of the wide-area measurement system through collaborative analysis of a main station and a plant station. According to the method, the identification accuracy of the WAMS abnormal data is improved, and the work of diagnostic analysis of a master station is reduced.

The invention provides a full-automatic soil shrinkage tester and a control method thereof, and belongs to the technical field of control. Determining the test control sequence of the screening rotation speed intervals by combining the correlation degree between the test process of which the volume change curve meets the requirement and the volume change curve of other test rotation speed intervals, and performing test control processing of different screening rotation speed intervals according to the test control sequence; based on the consistency degree of the volume change curve in the screening rotation speed interval and the screening rotation speed interval data after the existing test is completed, the test control method of the full-automatic soil shrinkage tester in the screening rotation speed interval which is not tested is determined, and the reliability degree of deformation monitoring processing is improved.

The invention provides an automatic identification management system for a high-consequence area of an oil pipeline, and the system comprises a data management module which is used for managing pipeline data, pile data, valve chamber data, interval data, emergency data, and high-consequence area identification standard data; the high-consequence area automatic identification management module is used for performing high-consequence area engineering management, high-consequence area identification object information maintenance, high-consequence area automatic identification, high-consequence area emergency disposal management, high-consequence area result display and high-consequence area maintenance record management; the mobile terminal module is used for filling and reporting pipeline high-consequence area identification object information, displaying the high-consequence area information and displaying emergency disposal information; and the system management module is used for performing user management, role management and menu management. According to the automatic identification and management system for the high-consequence area of the oil pipeline provided by the invention, efficient and accurate automatic identification and management of the high-consequence area of the oil pipeline can be realized.

The invention discloses an industrial time series data storage and analysis method and device based on an array array format and a mixed gis format, and belongs to the technical field of industrial big data storage and processing. The method comprises the steps of constructing a database storage structure, receiving original data, aggregating the original data into an array array and writing in the array array, generating a geometry line string through a high-frequency single-measurement-point sequence, storing the geometry line string into the array array, and storing the geometry line string into the mixed gis format. And querying and utilizing a space function to combine and dilute and output data to an AI analysis model. According to the method, the problems of large storage space, low query efficiency and difficulty in processing non-uniform interval data of industrial ultrahigh-frequency time series data are effectively solved, and a high-quality and structured data basis is provided for subsequent AI time series analysis and prediction.

A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention discloses a line loss segmentation monitoring method and system for a power distribution master station and a fusion switch, and particularly relates to the technical field of power distribution network line loss, and the system comprises a line segmentation module, a segmentation interval data collection module, a segmentation interval line loss calculation module, a segmentation interval line loss correction module, and a segmentation interval abnormity processing module. Fusion switches are installed on a line according to a certain distance, measurement data of the fusion switches at the two ends of a line segment are collected through a main station, the basic line loss value and the line loss rate of the line segment are calculated, for a section containing a distributed power supply, the line loss influence coefficient and multi-scene compensation of the distributed power supply to the line segment are introduced, and the line loss value is calculated and corrected in real time. Based on the real-time corrected line loss value, in combination with the preset floating value, the threshold interval is constructed, the line loss abnormity of each line segment on the target to-be-monitored line is judged, the abnormal line segment is positioned, the line segmentation electric quantity metering is realized, the calculation precision under all working conditions is improved, the abnormity management is realized, and the operation and maintenance management complexity is reduced.

The invention discloses a square and rectangular pipe production control method based on speed measurement self-compensation, particularly relates to the technical field of saw cutting control, and is used for solving the problem that the saw cutting fixed length control precision is reduced due to contact dynamic characteristic change during pipe specification switching in the prior art. A reference speed signal and a speed measuring roller speed signal of a production line are obtained in real time, a signal correlation coefficient in a preset time window is calculated to judge a pipe fitting specification switching event, the production line is controlled to operate in at least two different preset speed intervals in sequence, and time sequence data are synchronously collected; performing dynamic modal decomposition on the multiple groups of speed interval data to extract a dominant modal energy proportion, calculating an equivalent transmission radius based on an attenuation relation, reconstructing a theoretical speed signal by using the equivalent transmission radius, and evaluating a contact state change degree through residual energy; speed compensation model parameters are dynamically updated based on the change degree, speed signals of the speed measuring roller are corrected in real time, and the accuracy and stability of saw cutting fixed length control are ensured.

The invention provides a personalized voice interaction control method for an intelligent toy IP pet, and the method comprises the steps: determining a familiarity judgment result according to an encounter frequency value and time interval data in a recorded complete data set; according to a familiarity judgment result, topic associated entries matched with the high familiarity level are filtered out from the recorded complete data set, historical interaction keywords in the topic associated entries are obtained, and a keyword set after filtering is obtained; performing sequence processing on the filtered keyword set to generate a dialogue template draft; determining a dialogue structure after transition according to the generated dialogue template draft; a generation model is applied to the transited dialogue structure, user experience feedback data extracted from the continuous interaction file is fused, and personalized voice content is generated; and extracting index parameters of emotion connection from the personalized voice content to realize personalized voice interaction control of the intelligent toy IP pet.

The application provides a motor control parameter data generation method based on conditional flow matching, comprising the following steps: S1, collecting motor physical data of a target motor, obtaining an electromagnetic torque-stator current-electric angle data set through a no-rack static jitter method test, and collecting a rack standard test data set of a motor of the same series as the target motor; S2, converting the electromagnetic torque-stator current-electric angle data set and the rack standard test data set into three-dimensional point clouds and processing the three-dimensional point clouds to obtain pretreated data; S3, constructing a conditional flow matching model based on the motor physical data and configuring a loss function, and training by using a low-rank adaptation technology; and S4, inputting the pretreated data into the conditional flow matching model to obtain a calibration data set containing an extreme working condition control parameter combination and an unsampled interval data point. The motor control parameter data generation method based on conditional flow matching has the beneficial effects that the motor control parameter data generation method based on conditional flow matching can realize low-cost acquisition of test parameters, improve model adaptation speed, ensure physical rationality, and improve motor research and development efficiency.

This invention belongs to the field of cross-linked polyethylene insulated power cables and proposes an automatic processing method and system for flywheel energy storage cables. The method includes the following steps: obtaining the cable stress and resistance magnitudes; calculating the cable stress-resistance offset based on the cable stress and resistance magnitudes; selecting cable stress-resistance tilt intervals based on the cable stress-resistance offset; calculating the stress edge concentration fluctuation difference and resistance edge concentration fluctuation difference of the cable stress-resistance tilt intervals, and calculating the stress balance using these fluctuation differences; determining whether the current cable shows signs of breakage based on the stress balance, and performing automatic processing. According to the automatic processing method of this invention, multi-time-point, multi-interval data acquisition and multi-parameter feature modeling within historical feedback cycles can improve the ability to identify the local health status of cross-linked polyethylene insulated power cables in flywheel cable systems.

This invention provides a method, apparatus, and vehicle for constructing a KD-tree from a vehicle-mounted LiDAR point cloud. The method includes: determining the first median of the point cloud data; performing KD-tree construction steps based on the left and right interval data until the unprocessed data variable is 0, and outputting the KD-tree. Thus, by obtaining the median using a quicksort algorithm, a full sort of the point cloud data is not required. Instead, the data is partitioned into two parts: less than a benchmark value and greater than a benchmark value. The median is then recursively searched, improving the efficiency of finding the first median. Since the first median divides the point cloud data into independent left and right interval data, multi-threaded parallel searching of left and right subtree nodes can be used during KD-tree construction, thereby improving the efficiency of KD-tree construction for the vehicle-mounted LiDAR point cloud and ensuring the response speed and operational sensitivity of the autonomous driving system.

The invention discloses a multilevel remote upgrading method for an electric power internet of things, which relates to the technical field of internet of things equipment management, and comprises the following steps: receiving a logic segment data packet issued by a main control platform, extracting a logic segment number and logic segment data, retrieving a target storage address in an address mapping table according to the logic segment number, and sending the target storage address to the main control platform; performing consistency verification on the logic segment data, writing the logic segment data into a corresponding address of a main firmware space of the target equipment after the verification is passed, and recording a writing timestamp of the logic segment data; on the basis of the running time interval data and the write-in timestamps, logic segment time intervals between any adjacent logic segments are calculated, the number of interruption times of the logic segments is counted, and a logic segment behavior feature set is built. And the stability of a remote upgrading task under the environments of unstable communication, heterogeneous power grid distribution and the like is enhanced.

The invention discloses a time sequence data adaptive processing method and system, electronic equipment and a medium, and relates to the technical field of industrial control systems and database storage. The method specifically comprises the following steps: entering a discharge mode in response to an experiment start trigger signal, pausing persistent writing and directly writing time sequence data into a memory buffer area in a binary stream manner; entering a sorting mode in response to the ending trigger signal, reading data and calculating a change rate index; comparing the rate of change indicator with a decision threshold to identify a transient interval and a steady interval; and performing lossless processing on the transient interval data, performing lossy compression on the steady-state interval data, and finally writing the data into persistent storage equipment. The invention aims to eliminate write jitter through a dynamic and static separation mechanism, and realize lossless hierarchical storage of key data and high compression ratio of stable data based on physical characteristics.

The invention discloses an embedded device low-power-consumption predictive wake-up method based on task characteristics, and relates to the related field of power management, and the method comprises the steps: collecting task characteristics in a system operation process, including recording historical execution interval data, historical time consumption data and dependent peripheral information of a task; on the basis of the historical execution interval data, predicting the triggering time of the next task to obtain a predicted interval, and determining advanced wake-up time; and waking up the system at the advanced wakeup time, and initializing the peripheral according to the dependent peripheral information. The technical problems of energy waste and task execution delay caused by difficulty in accurate on-demand wakeup of the existing embedded equipment are solved, and the technical effects of accurate on-demand wakeup, reduction of equipment power consumption and reduction of task delay are achieved.

The invention relates to the technical field of tunnel data processing, and discloses a pipe-jacking construction grouting method and system based on anti-drag slurry detection data, a terminal and a medium, and the pipe-jacking construction grouting method based on the anti-drag slurry detection data comprises the steps that detection data of anti-drag slurry around a jacking pipe and target interval data are obtained; according to the detection data and the target interval data, a control strategy of pipe jacking construction is determined; according to the control strategy, the grouting control operation of anti-drag slurry in pipe jacking construction is generated; and according to the grouting control operation, the control operation of drag reduction slurry is executed. Real-time data analysis is carried out to generate the grouting control strategy according to the thickness distribution state of the drag reduction slurry obtained through detection, so that the grouting control operation is determined according to the grouting control strategy, the corresponding operation is executed, a stable and uniform lubricating slurry sleeve is formed on the periphery of the jacking pipe, and the jacking resistance is reduced.

The application provides a boiler pipeline model construction management method and system for vibration suppression processing, and belongs to the technical field of model construction, and specifically comprises the following steps: performing update processing on the construction target based on an update method, determining the monitoring results of the self-excited vibration data of different construction targets in different flow interval, combining the construction target data and the matching flow interval data of the boiler, and determining the operation and maintenance management strategy of the vibration monitoring equipment of the boiler, determining the operation and maintenance processing of the vibration monitoring equipment of different boilers based on the operation and maintenance management strategy, determining the construction management method of the vibration analysis model of the pipeline of the boiler according to the matching degree of the operation and maintenance processing data of the boiler and the self-excited vibration data in the construction target and the self-excited vibration data of the model, and improving the reliability of the self-excited vibration processing.

The application provides a decoding method for ARINC specification flight data, wherein the ARINC specification at least includes ARINC717 specification and ARINC767 specification, the decoding method includes reading original flight data file to form binary data stream, searching all synchronization words and obtaining position data of each synchronization word; obtaining position difference data of each synchronization word according to the position data, and comparing the position difference data with corresponding specification interval data, when the position difference data is consistent with the specification interval data, corresponding synchronization word is confirmed as a checked valid synchronization word; for each valid synchronization word, parameter information associated with the valid synchronization word is obtained, and the parameter information is matched with parameter acquisition information contained in a decoding parameter library, when the matching is successful, the parameter information is decoded in combination with attributes of each parameter in flight data contained in the decoding parameter library, so that parameter values of each parameter contained in the flight data are obtained.

The invention provides a medium and long term electricity price prediction method based on causal TCN-HSMM duration perception, and relates to the technical field of electricity market analysis, and the method comprises the steps: obtaining first multi-source data needed by electricity price prediction, and carrying out the preprocessing of the first multi-source data, the first multi-source data at least comprises electricity prices, power system loads, renewable resource output, meteorological elements and holiday and festival identifiers at all time points in a first historical time period; inputting the first multi-source data into a neural network model, performing baseline prediction on the electricity price in the high-fluctuation scene, and generating a first prediction result; the fluctuation state of the first prediction result is recognized through a statistical model, the first prediction result is layered according to the fluctuation state, and a structured second prediction result is generated and comprises low-bit interval data, middle-bit interval data and high-bit interval data; and high-precision prediction of medium-and-long-term electricity price in a high-fluctuation scene is realized.

The invention discloses an ischemic heart disease risk prediction method based on continuous physical examination data, and belongs to the technical field of biological information. Continuous physical examination data and time interval data of each physical examination are vectorized, and characterization learning is carried out on the continuous physical examination data and the time interval data through a time perception attention mechanism; different attention scores are given to each physical examination, physical examination index data and time interval data of the physical examination index data are fused, then the most significant fusion features are imported into a prediction layer for prediction, a cross entropy loss function is adopted to measure the relation between a prediction value and an actual measurement value, and the method is provided for improving the interpretability of classification. Mendel randomization analysis is used for analyzing the causal relationship between part of physical examination indexes and the ischemic heart disease so as to enhance the reliability of identifying disease risk factors by the model, and compared with a traditional method, the performance of the ischemic heart disease risk prediction model is improved, and the risk prediction accuracy of the ischemic heart disease is improved. And meanwhile, the interpretability of the model is enhanced.

The embodiment of the invention discloses a data stream processing method and device, electronic equipment, a medium and a program product, and the method comprises the steps: dividing an obtained data stream through a tracking identifier, obtaining a data group, segmenting the data group according to a preset time window, obtaining interval data, building a data call tree based on the interval data and the corresponding tracking identifier, and obtaining a data call tree; or placing the data in the original data calling tree. According to the embodiment of the invention, the method achieves the restoration of the calling relation of each data in the mass data flow, reflects the calling relation of the data in the data flow through the data calling tree, and facilitates the improvement of the processing efficiency of the data through the restoration of the calling relation of the data in the unbounded data flow.

The invention relates to the technical field of natural resource planning, in particular to a Markov chain calculation method and system for multi-target planning of natural resources, and the method comprises the steps: obtaining initial indexes of the multi-target planning of the natural resources, and removing redundant indexes with correlation coefficients meeting a threshold value from the initial indexes through Pearson correlation analysis; screening a core index set through analytic hierarchy process; the positive indexes are processed by using an extreme value standardization method, the negative indexes are processed by using a reverse extreme value standardization method, and all the indexes are converted into [0, 1] interval data; dimensionality reduction is carried out on the indexes, and principal components with cumulative variance contribution rates meeting a threshold value are selected; clustering the indexes, and defining a Markov chain finite state according to a clustering result; performing mutual exclusion verification, completeness verification and key information verification; a state time sequence is generated through multi-period data, the number of state transition times is counted, the transition probability is calculated, a matrix is constructed, and a Markov chain is formed for natural resource multi-target planning.

A method for detecting an anomaly, which is performed by a computing device including one or more processors according to some embodiments of the present disclosure may include: collecting a CAN message generated in a controller area network (CAN); generating first traffic time interval data and first payload data based on the CAN message; obtaining a first latent vector generated by inputting the first traffic time interval data into a first neural network model; obtaining a second latent vector generated by inputting the first payload data into a second neural network model; generating merged data in which the first latent vector and the second latent vector are merged; and determining whether the CAN message is anomalous by inputting the merged data into a third neural network model.

The invention relates to the technical field of bridge structure state monitoring and safety evaluation, in particular to a live load cable force-based cable-stayed bridge stay cable unbalanced force calculation method, which comprises the following steps of: establishing a finite element model of a whole cable-stayed bridge, and configuring a health monitoring system on the cable-stayed bridge, and carrying out cable force monitoring on a stay cable of the cable-stayed bridge by the health monitoring system; performing iterative optimization on the finite element model to obtain a finite element model with errors meeting requirements; two groups of representative live load cable force data in measurement interval data of the health monitoring system are selected, the selection standard at least comprises the cable force change rate and the inhaul cable position, and meanwhile the reasonability of the cable force data is verified; carrying out backstepping calculation on the remaining unmeasured stay cable force through the selected actually measured live load cable force data; and the unbalanced force of the inhaul cables is obtained by combining the cable forces of all the inhaul cables of the cable-stayed bridge. The method has the advantages of high precision, high efficiency and the like, and makes breakthrough progress in engineering efficiency, cost control and safety guarantee dimensions.

The invention relates to a frame rate control method, system and device for live course supervision and a storage medium, and the method comprises the steps: generating target frame time interval data according to a frame rate demand of a course supervision scene; acquiring a current timestamp in response to an event triggered by capturing a new graphic frame; according to the current timestamp and the recorded last effective frame processing timestamp, calculating to obtain a time interval difference; if the time interval difference is smaller than the target frame time interval data, dynamically executing a discarding operation on the graphic frame, the discarding operation including releasing graphic resources occupied by the graphic frame; if the time interval difference is larger than or equal to the target frame time interval data, effective processing operation is executed on the graphic frame, and the effective processing operation comprises the steps that the recorded last effective frame processing timestamp is updated to the current timestamp, format conversion and encoding processing are executed on the graphic frame, and encoding frame data used for stream pushing are generated. According to the invention, the resource overhead of a graph capture level and a subsequent preprocessing link can be reduced.

The application belongs to the field of electrocardiosignal sampling and pulse coding, and particularly relates to a low-power electrocardiosignal sampling and coding device. The device utilizes the waveform characteristics of each waveband of the electrocardiosignal to realize the classification processing of the electrocardiosignal through the coding mode. Two different conversion modes are adopted through serial-parallel conversion to improve the data processing efficiency. The differential coding of the coding module removes the invalid data in the electrocardiosignal, and solves the problem of serious noise interference. When the R peak interval coding is performed, the R peak interval data is compressed by 5.5 times through the one-hot coding mode. In combination with the processing of 0 data in the differential coding of the amplitude data, the classification processing speed of the entire data is greatly improved, and the operation involved in the entire processing process is extremely small. Compared with the prior art, compared with the traditional analog-digital converter based on the time uniform sampling mechanism, the invalid data caused by the increase of a large number of amplitude data points with 0 after the transmission speed is solved.