207 results about "Asynchronous data" patented technology

The invention relates to the technical field of intelligent monitoring of petroleum equipment, and particularly provides an industrial equipment state diagnosis method based on multi-modal fusion deep learning. The method comprises the following steps: synchronously acquiring load, vibration and current data through an explosion-proof RTU (Remote Terminal Unit), and transmitting the data to an edge node through an industrial LoRaWAN (Load RaWide Area Network); aligning a multi-source signal time sequence by adopting Kalman filtering, and fusing to generate data streams with consistent time domains; features are extracted by using wavelet packet energy entropy, and a lightweight ResNet18 model is input to realize high-precision diagnosis; based on the diagnosis result, a dynamic optimization strategy is generated through an LSTM-Transform parallel model; and finally, issuing a control instruction to the equipment for execution, and collecting feedback data to update the cloud model. Through the multi-rate signal time sequence alignment and feature-decision two-stage fusion technology, the problem of diagnosis errors caused by data asynchronization in a traditional method is solved, the fault recognition precision and the equipment energy efficiency are remarkably improved, and meanwhile the requirements for explosion prevention and real-time performance of the industrial environment are met.

The invention relates to the field of production line state monitoring, and particularly provides a production line state monitoring and MES integration notification method and system, and the method comprises the steps: collecting multi-modal data, and constructing a unified state context object; performing space-time alignment and dynamic weight fusion based on a process stage on the multi-source asynchronous data to obtain a feature vector; reasoning a semantic current state of the production line through a time sequence mode recognition model; generating an enhanced data set based on the state context and the current state of the production line, and outputting a structured state event through anomaly detection and root cause analysis; outputting an optimal response strategy through a predefined decision rule in combination with MES service information; and packaging the strategy into an operation instruction which can be identified by the MES and issuing the operation instruction, and driving the MES to execute work order creation, billboard updating or message notification. According to the method, semantic diagnosis, global decision and automatic response of the production line state are realized, so that the production transparency, the decision accuracy and the operation and maintenance automation level are improved.

The invention discloses a method for evaluating piglet feed digestibility based on excrement indexes, and particularly relates to the field of piglet feed digestibility evaluation.The method comprises the steps that a piglet excrement sample under target feed intervention is obtained, and multiple biochemical index data in a preset time sequence are collected for the excrement sample; the biochemical index data comprises moisture content, ammonia nitrogen concentration and short-chain fatty acid concentration, and a corresponding excrement index original data sequence is formed according to the biochemical index data. Continuous aggregation of discontinuous sampling data is realized by constructing a sample index structure, and in combination with frequency domain feature extraction, interpolation reconstruction and a collaborative tensor modeling mechanism, an absorptivity inversion path having a robust adaptability to missing and asynchronous data is established. The key problems that in the prior art, a current evaluation method seriously depends on data integrity, and discontinuous information in a real sampling environment cannot be processed are solved.

In some implementations, the techniques described herein relate to a method including: receiving a data log entry associated with a driver that includes a service provider location and a timestamp; identifying a vehicle associated with the driver based on the data log entry by identifying the vehicle includes applying a machine learning model to the data log entry and a vehicle database; loading a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry includes applying a rule-based optimization algorithm to a historical service provider database; and transmitting a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry.

The invention discloses an intelligent monitoring and early warning system and method for project progress and cost, and relates to the technical field of constructional engineering management. The streaming data fusion module adopts an Apache Flink engine and applies a dynamic time warping algorithm to carry out time alignment on multi-source asynchronous data to realize semantic unification; the space-time diagram neural network prediction module constructs a space-time association diagram, integrates global features such as weather and supply chain fluctuation, and adaptively learns an influence weight by using a graph attention mechanism; the self-adaptive early warning module adopts a Bayesian online learning framework, an early warning threshold value is dynamically adjusted according to a historical false alarm rate and a missing report rate, the method comprises the steps of data acquisition, fusion, mapping, joint prediction and self-adaptive early warning, the problem of data islands is solved, complex space-time association is accurately captured through a space-time diagram neural network, and real-time early warning is achieved. Joint prediction of progress and cost risk is realized, false report and missing report are reduced, and real-time performance, accuracy and decision-making efficiency of engineering management and control are improved.

The invention discloses a heart sound, electrocardio and echocardiogram multi-modal data phase synchronization method, which belongs to the technical field of medical image processing, and comprises the following steps: A1, obtaining an original echocardiogram video and independently recorded heart sound audio data information; a2, preprocessing the acquired data and performing sample automatic construction; a3, a double-path neural network architecture is constructed, electrocardiogram waveform extraction is carried out, and a digital electrocardiogram signal sequence is obtained; a4, periodic feature points of the heart sound and the electrocardio are recognized based on the digital electrocardio signals; and A5, aligning the asynchronous data based on linear phase resampling. According to the method, the video frames, the electric signals and the heart sound signals under different sampling rates are forcibly mapped into the unified phase coordinate system through a linear interpolation algorithm, absolute synchronization of the video frames, the electric signals and the heart sound signals in cardiac pulsation physiological logic is finally achieved, and the influence of individual heart rate differences on data alignment is eliminated.

The invention belongs to the field of industrial control, particularly relates to a workshop production line whole-process data processing and intelligent monitoring method and system, and aims to solve the problem that the whole production process is difficult to effectively monitor in related technologies. The method comprises the following steps: collecting asynchronous data streams from different sources in the whole production process; mapping each data point in the asynchronous data stream to a unified space-time reference framework; constructing a knowledge graph; inputting the knowledge graph into the space-time diagram convolutional network model to obtain an abnormal probability score of each node in the knowledge graph; and determining an abnormal probability node, carrying out reverse backtracking along a relation edge in the knowledge graph, positioning a source abnormal node, carrying out feature attribution analysis on state data of the source abnormal node, and generating a root cause interpretation report of an abnormal working condition. According to the method, the knowledge graph with the work order as the entity and the process flow as the relation chain is constructed, digital mapping and unified representation of the whole production flow are achieved, and the whole production flow can be effectively monitored.

The invention discloses an efficient data flow transceiving controller applicable to a multi-core storage and calculation system, which comprises two transmitting controllers and two receiving controllers, and the transmitting controllers and the receiving controllers are arranged on the peripheries of cores and are used for performing data packaging and transmitting and data flow judgment work; wherein the sending controller comprises a configurable asynchronous FIFO (First In First Out), a data packaging unit and an error correction code generating unit; the receiving controller comprises an error correction code decoding unit, a data packet unpacking unit and a microchip detection unit; the flit detection unit is used for detecting information in a head flit to judge whether data meet the calculation requirement of the current core particle or not, and if the data do not belong to the task range of the current core particle, the data are redirected to the sending controller through a bypass transmission mechanism. According to the application, the transmission speed of the data stream among different core particles is improved, and the data stream can be dynamically planned and transmitted according to the preset rule by constructing the efficient data transceiving controller.

The invention relates to a multi-channel air pressure monitoring method, an adaptive monitoring circuit and computer equipment, and the method comprises the steps: synchronously collecting an original air pressure signal set in a target region through a multi-channel air pressure sensor, and carrying out the signal optimization processing, thereby obtaining an air pressure signal set; performing data analysis on a signal cluster divided by the air pressure signal set according to a channel grouping relationship to obtain an air pressure monitoring value of each channel, and calculating a differential pressure value between the channels in the cluster; and inputting the air pressure monitoring value and the differential pressure value into a two-dimensional data anomaly monitoring model of a dual-threshold system established based on historical data and environment auxiliary parameters, and outputting an anomaly judgment result and an anomaly weight ratio, so as to solve the problems of asynchronous monitoring data and high anomaly misjudgment rate in the traditional single-channel monitoring method, and improve the accuracy of the monitoring result. And the environmental stability of the target area is ensured.

The invention provides an AI multi-agent and digital twinborn fusion production scheduling process visualization method, medium and system, and belongs to the technical field of AI multi-agent production scheduling. Large-scale parallel computing is achieved by constructing a GPU three-layer CUDA processing architecture, a first layer data preprocessing grid executes data cleaning in parallel, a second layer data preprocessing grid executes data cleaning in parallel, and a third layer data preprocessing grid executes data cleaning in parallel; the second layer of negotiation analysis grid operates an agent interaction recognition model based on Transform to perform parallel mode recognition, the third layer of visual calculation grid operates an LSTM-CNN fused production scheduling process mapping model to generate visual data, and calculation resource allocation is dynamically adjusted through a multi-head attention mechanism. The calculation performance is optimized by adopting a pipeline parallel and data parallel strategy, and the memory access delay is hidden by combining an asynchronous data transmission and calculation overlapping technology, so that the technical problem that the real-time parallel processing of the multi-agent high-frequency negotiation data cannot be realized is solved.

The invention discloses a dynamic error correction system and method for an OTP (One Time Programmable) memory, a computer and a storage medium, and the system comprises a cross-clock synchronization module which is used for synchronizing asynchronous data of an OTP interface to a system clock domain and generating a control signal according to a data format; the double-buffer-area module is used for alternately writing data and reading a control signal by utilizing two independent buffer areas, and correcting and outputting the written data; the CRC decoder module is used for carrying out error detection on the synchronous data; the error position searching module is used for positioning an error position candidate item of each channel; the error position matching module is used for finding out a uniquely matched error position; and the read control module is used for judging whether data reading needs to be paused or not and starting error correction operation, and sending a control signal to the double-buffer module. According to the invention, a modular architecture design is taken as a core, and a complete processing flow from data receiving, error detection, error positioning to error correction output is constructed.

The invention provides an IMU sensor space-time fusion industrial defect detection system and method based on an SORT algorithm. The method comprises the steps that S1, system initialization and space-time calibration are carried out, a time synchronization model is established, the single pixel size dpixel is calculated, and IMU and camera coordinate system registration is completed; s2, collecting asynchronous data, and obtaining working parameters of a camera and the speed vt of a conveyor belt; s3, constructing an SORT state vector and predicting an IMU enhancement state, detecting defects of a current frame by adopting YOLOV8, and generating a detection set Dt; s4, executing EKF space-time fusion, and predicting and updating a defect state covariance and a position; s5, quantifying the position uncertainty sigma pos, calculating an optimal acquisition period Topt, controlling the line frequency of the camera, and carrying out confidence verification and misjudgment elimination; s6, cloud federated filtering fuses the edge and the cloud state, IMU zero offset is estimated, and cooperation of multiple detection devices and system expansibility enhancement are achieved.

The invention provides a front-end high-frequency data security interaction method and system based on a Wasm, and relates to the technical field of network programming, network front ends and brain-computer interfaces, and the method comprises the following steps: securely storing protocol decoding parameters of Internet of Things equipment in a Wasm isolation environment; a composite authentication mechanism of JWT verification logic, domain name IP check and API authentication is provided in the Wasm through the closed characteristic of network assembly; parallel iterative processing is carried out on the multi-channel biological data in the wasm virtual environment, and analysis operation is executed; dynamically calculating a data time scale through a timestamp generation function, wherein the function comprises an index rewinding compensation mechanism; and each piece of parsed plaintext data is returned to the JavaScript calling end through an asynchronous data stream. According to the method, an authentication and authorization mechanism of the Wasm and the rear end, authentication and authentication in a Wasm isolation environment, analysis and a bridging architecture of the Wasm and the JavaScript are provided, so that the Wasm module is not called by the unauthorized Javascript.

The invention relates to the technical field of hydropower station intelligent operation and maintenance, in particular to a hydropower station operation and maintenance system based on the Internet of Things, which comprises an intelligent sensing layer, an edge computing layer, a cloud analysis layer and a decision execution layer, and data interaction is carried out among the layers through a special industrial network protocol; the intelligent sensing layer comprises a multi-mode sensing array and an environment self-adaptive acquisition unit; the edge calculation layer comprises a multi-source asynchronous data alignment unit and a fault feature extraction unit; the cloud analysis layer comprises a digital twinborn updating unit and a collaborative decision-making unit; the decision execution layer comprises a self-adaptive control execution unit; cavitation erosion and mechanical vibration signals are monitored at the same time through the multi-mode sensing array, and the system can diagnose faults more accurately and reduce the misjudgment rate in combination with the compensation of the environment self-adaptive acquisition unit for external factors.

The invention discloses a digital art exhibition space display interaction method and system based on a VR technology, and relates to the technical field of exhibition man-machine interaction, and the method comprises the steps: carrying out the time sequence alignment processing of a multi-source asynchronous data flow, and constructing an emotional state confidence curve, an interest concentration curve and a cognitive load curve; constructing a user emotion space vector based on the tangential direction of the curve and the instantaneous value; and calculating a difference value between the user emotion state and the exhibit semantic emotion confidence coefficient, and respectively adopting different strategies according to the difference value: when the difference value is large, directly generating display content, and when the difference value is small, judging whether to execute deepening interaction or correct an emotion space vector through an interest maintenance coefficient. Through multi-modal data fusion and dynamic sentiment analysis, the problem that the existing VR exhibition content is static and fixed and cannot adapt to the user state in real time is solved, intelligent content display based on user sentiment change is realized, and the immersion and individuation level of exhibition viewing experience are remarkably improved.

The invention discloses a high-end memory chip asynchronous test system based on a distributed architecture, which relates to the technical field of chip testing, and comprises a state modeling module, a master control end initialization and test end access, memory chip test task pool construction, test end state analysis in combination with historical test load and historical fault information, and memory chip asynchronous test. Generating an optimized scheduling strategy; the scheduling module is used for performing task division and asynchronous scheduling on the memory chip test task pool by adopting an intelligent scheduling algorithm based on an optimized scheduling strategy to generate an asynchronous data flow queue; according to the issuing module, the test end receives the asynchronous data flow queue in an asynchronous mode to carry out protocol unpacking and task mapping, and a memory chip function test task is obtained and issued; according to the method, the test throughput rate and the resource utilization rate are improved by asynchronously sequencing and scheduling tasks, the task scheduling delay is reduced, the load of each test end is balanced, and meanwhile, the horizontal expansibility and the fault-tolerant capability are enhanced.

The invention provides an MCP2515CAN controller virtual simulation and integration method oriented to a hardware-in-the-loop test. The method comprises the following steps: constructing an MCP virtual model; a virtual CAN bus is constructed; constructing an external interface; for the MCP virtual model, creating an independent event, and realizing asynchronous data transceiving and interrupt processing by using an event triggering mechanism; the MCP virtual model, the virtual CAN bus and the external interface are integrated in a unified simulation environment. The technical problems that a traditional controller communication test depends on real hardware, the cost is high, debugging is complex, and compatibility is poor are solved.

The invention relates to the technical field of cable tunnel intelligent monitoring, and discloses a monitoring method and device for a cable tunnel. The method comprises the following steps: acquiring monitoring data of a cable tunnel, and constructing a circular queue according to the monitoring data; segmenting the cable tunnel according to the circular queue to obtain a plurality of segmented monitoring areas; sorting the monitoring data in each segmented monitoring area according to time to generate a monitoring value sequence; determining similarity values of different monitoring value sequences in the segmented monitoring areas; determining the area type of the segmented monitoring area according to the similarity value; processing the segmented monitoring area according to the area type to obtain a corresponding processing result; and determining a data acquisition and transmission task of the cable tunnel according to a processing result. The technical problems of low monitoring precision, serious communication resource waste and high potential abnormal missing detection risk caused by the fact that cable tunnel monitoring cannot adapt to time-space correlation characteristics and dynamic change values of multi-source asynchronous data are solved.

The invention discloses a high-end memory chip asynchronous test system based on a distributed architecture, which relates to the technical field of chip testing, and comprises a state modeling module, a master control end initialization and test end access, memory chip test task pool construction, test end state analysis in combination with historical test load and historical fault information, and memory chip asynchronous test. Generating an optimized scheduling strategy; the scheduling module is used for performing task division and asynchronous scheduling on the memory chip test task pool by adopting an intelligent scheduling algorithm based on an optimized scheduling strategy to generate an asynchronous data flow queue; according to the issuing module, the test end receives the asynchronous data flow queue in an asynchronous mode to carry out protocol unpacking and task mapping, and a memory chip function test task is obtained and issued; according to the method, the test throughput rate and the resource utilization rate are improved by asynchronously sequencing and scheduling tasks, the task scheduling delay is reduced, the load of each test end is balanced, and meanwhile, the horizontal expansibility and the fault-tolerant capability are enhanced.

This invention discloses a method and system for identifying safety hazards in power equipment, specifically relating to the field of intelligent operation and maintenance technology for power equipment. It addresses the hidden operational risks caused by the mismatch between equipment aging characteristics and generalized strategies in existing technologies. By collecting multi-source operating status data of power equipment and constructing an asynchronous dataset, it dynamically identifies spatial electric field distortion gradients and contact resistance fluctuations as abnormal parameters. Based on equipment structural characteristics, it assigns differentiated weights to these abnormal parameters, quantifying the risk levels of different areas. It analyzes the temporal correlation between contact resistance and electric field response to determine the breakdown risk caused by insulation degradation. Combining the risk levels, it generates maintenance cycle optimization schemes and electric field compensation parameters, and dynamically updates the aging model based on execution feedback. Through dynamic adaptation of equipment status and control strategies, it achieves early warning and precise intervention for potential hazards, effectively solving the control mismatch problem caused by neglecting material degradation characteristics in traditional methods.

This invention discloses a testing device for the operating performance of a lever mechanism simulating load, belonging to the field of high-end equipment performance testing. The core innovation of the device lies in the sequential connection of the lever, crank, connecting rod, slider, and frame assembly to form a crank-connecting rod-slider core transmission and load simulation mechanism. This mechanism integrates adjustable load simulation and integrated synchronous measurement functions. By configuring counterweights, replacing elastic elements, and adjusting connection angles and lever arms, it can accurately simulate nonlinear, multi-dimensional coupled actual operating loads. Simultaneously, a sensor network composed of force sensors, tilt sensors, and acceleration sensors respectively set at key nodes such as the lever and crank synchronously collects multi-dimensional force / torque, displacement, and acceleration parameters throughout the entire operating process. This invention solves the problems of load distortion, single measurement, and asynchronous data in traditional testing, achieving accurate and universal testing of the operating performance of lever mechanisms.

This invention discloses a cloud-edge-device collaborative intelligent panoramic perception and target tracking system, including an image processing layer comprising an NPU acceleration module and an image stitching module. The NPU acceleration module is used to extract multi-scale defect features from the panoramic video data using a lightweight convolutional neural network and output feature vectors. This invention achieves real-time edge processing of multiple video streams from the data acquisition layer via the NPU acceleration module and the key-value binding-based image stitching module at the image processing layer. This enables precise alignment and seamless fusion of images from multiple cameras, effectively solving the problems of poor stitching quality caused by large image distortion and asynchronous data frames in existing technologies. Furthermore, the system utilizes YOLO11m as the detection engine and an incremental learning interface at the server layer to perform high-precision target recognition and continuous cross-lens tracking of the panoramic image stream, allowing the system to dynamically expand its recognition capabilities and achieve low-latency intelligent perception.

The application provides a meteorological anchor point-based cross-modal asynchronous data alignment method, system, device and storage medium, which comprises the following steps: acquiring multi-modal data of a target area, and performing spatial alignment on the multi-modal data; constructing a unified anchor point time axis based on the meteorological predicted water depth grid sequence; based on the unified anchor point time axis, performing nearest time point matching and time decay on a remote sensing grid observation sequence and a GIS risk grid observation sequence respectively to obtain a time-aligned remote sensing grid observation sequence and a time-aligned GIS risk grid observation sequence; based on the unified anchor point time axis, performing time window aggregation on a social crowdsourcing point observation sequence to obtain a time-aligned social crowdsourcing point observation sequence; generating metadata of each modal data for each anchor point timestamp on the unified anchor point time axis, and outputting the time-aligned multi-modal data and the metadata.

This invention discloses a combined shear-resistivity testing device and method for soil-rock mixed fillers, belonging to the field of geotechnical engineering testing technology. It addresses the shortcomings of existing combined testing methods, such as unstable contact resistance, fluctuating electrical signals, and asynchronous data. The device includes a direct shear test, resistivity test, and data acquisition and control system. The shear test chamber has a multi-layered composite structure integrated electrode embedded in its inner wall, coupled with an array of spring needles, and features a stress-driven electrical connection and hardware synchronous triggering structure. The method completes the test through steps including sample preparation, system initialization, stress-driven electrical connection establishment, and synchronous triggering of the combined test. This invention achieves synchronous and accurate acquisition of shear mechanics and resistivity data, enabling real-time non-destructive characterization of the internal structural damage evolution of the sample, significantly improving testing accuracy and stability, and providing reliable technical support for the mechanical research and engineering applications of soil-rock mixed fillers.

The invention relates to the technical field of manufacturing data processing, and discloses a production business data processing system for a discrete manufacturing workshop, which comprises an asynchronous data acquisition module for acquiring an asynchronous event pulse sequence of a discrete execution node; the mapping transformation module establishes an addressing mapping relation between an event source and a memory logic bit plane and generates an original differential bitmap; the bitmap filtering module is used for hedging paired logic flipping signals by utilizing an exclusive-OR logic self-reversal principle and filtering jitter interference at the edges of the signals so as to generate a net value difference bit plane; the state synchronization processing module performs in-situ upset updating on the state global bitmap through address offset addressing according to the bit identifier, and through a bitmap differential synchronization mechanism, asynchronous state updating without transaction lock constraint is achieved, calculation blocking generated by high-frequency pulse impact is relieved, and the real-time performance and stability of system data throughput are enhanced.

Backup systems and methods create consistent backups across multiple data sets, while integrating ADC technology. To accomplish this, an application group ADC configuring function (APG-ADC configuring function) configures the ADC to ensure that backup data sets remain consistent with those at the original source. The APG-ADC configuring function configures ADC such as to update backup data sets in the same order as the original data sets.

This invention provides a fault identification method for test equipment based on diagnostic semantic adaptation, belonging to the field of test equipment operation status monitoring technology. It includes: S1, collecting multi-measurement channel sensor signals and image information generated during the test and performing preprocessing; S2, performing measurement channel mapping, unit conversion, range matching, and normalization processing on multi-source asynchronous data; S3, determining the fault probability of the test equipment, obtaining the fused fault confidence level, and generating a structured test equipment fault diagnosis record package; S4, analyzing the fault types of the test equipment and executing the test equipment fault identification alarm program. This invention uses an edge-side temporal fault discrimination network to perform real-time fault screening on a unified state vector, and employs a multimodal transfer diagnostic network for fault verification and incremental learning, forming a parameter optimization closed loop to improve the accuracy and robustness of fault identification.

Methods and systems for managing operation of a deployment of data processing systems are disclosed. The operation may be managed by determining a synchronicity classification of data that is to be transmitted between the data processing systems, and performing the transmission based on the synchronicity classification. The data may be classified as synchronous and / or asynchronous. If the data is classified as synchronous, then the data may be transmitted through a synchronous channel immediately and / or at a predetermined future time when the synchronous channel is operational. Otherwise, if the data classified as asynchronous, then the data may be appended to a queue of data to be transmitted at a future time.

The invention provides an asynchronous data distribution method, computing equipment and a storage medium. The method comprises the steps that a first node applies an erasure code algorithm to original data to generate n data fragments; wherein the first node is any network node with original data; the first node applies an error correction code algorithm to the n data fragments to generate n verification fragments; the first node broadcasts the first specified data fragment and the first specified verification fragment to other nodes, and sends the n verification fragments to the corresponding network nodes according to the first corresponding relation, so that the second node determines original data according to the received multiple data fragments and multiple verification fragments; wherein the first corresponding relation is used for indicating the corresponding relation between the data fragment and the verification fragment and the network node; the second node is any network node without original data. According to the scheme, data can be efficiently and reliably distributed in an unreliable asynchronous network.