287 results about "Asynchronous data" patented technology

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

The invention discloses an adapter life prediction system based on digital twinning and a dynamic early warning method. The system comprises a multi-source data acquisition module, a digital twinning model construction module, a data coordination module, a life prediction module and a calibration module. According to the method, the adapter full-life-cycle digital twins are constructed, the limitation of one-way static analysis of a traditional life prediction technology is broken through, and dynamic health assessment under multi-dimensional data driving is achieved; a cross-dimension feature fusion and closed-loop calibration mechanism is innovatively proposed, and the industrial problems that multi-source asynchronous data is weak in relevance and sudden abnormal response lags behind are effectively solved; through the synergistic effect of the generative adversarial network and the attention model, the stability and credibility of a prediction result are remarkably improved under a complex working condition; the technology can be adapted to a harsh use environment of an industrial adapter, and quantifiable and traceable decision support is provided for intelligent operation and maintenance of power electronic equipment.

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention discloses a hydraulic motor fault diagnosis method and system based on heterogeneous asynchronous data fusion, and the method comprises the steps: collecting heterogeneous asynchronous data of a sensor network, carrying out the preprocessing of the data, inputting the data into a parallel dynamic pruning residual network, extracting features, and carrying out the preliminary fusion, thereby obtaining an initial feature plane; and inputting the initial feature plane into a multi-connection neural network, fusing data, extracting features, obtaining a final feature plane, using the fused feature data as a training set, and building a feature classification model to test and evaluate the performance of the feature classification model. According to the method, the limitation of a traditional fusion model in asynchronous processing of high-frequency vibration signals and low-frequency thermodynamic data of a hydraulic system is effectively overcome, the robustness of key fault features in a strong noise environment is remarkably improved, and rapid virtual-real mapping of bench test simulation data and online monitoring data is realized; typical faults such as plunger pair abrasion and valve plate cavitation of the hydraulic motor can be accurately supported, and safety guarantee is provided for equipment life prediction and safety control.

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is realized.

A system and method for ΔI-powered engine diagnostics combine OBD-II data, high-frequency sound, and vibration analysis to detect engine wear and mechanical faults. A detachable puck sensor in the engine bay captures sound and vibration signals, while an OBD-II module collects engine performance metrics. A smartphone app collects and uploads data to a backend AI engine, where Dynamic Time Warping (DTW) aligns event-driven or asynchronous data and time series data from multiple sources, ensuring accurate feature fusion. Machine learning models then detect engine wear, belt degradation, knocking, and bearing faults, generating a diagnostic report with severity assessments and predictive maintenance recommendations. By integrating multi-modal sensor data, this system enhances early-stage fault detection beyond traditional OBD-II diagnostics, offering greater accuracy in assessing physical wear conditions and optimizing vehicle maintenance.

The invention discloses a hotspot acquisition equipment adaptive control system based on multi-modal data fusion, and relates to the technical field of intelligent sensing and adaptive control, the hotspot acquisition equipment adaptive control system comprises a heterogeneous sensor array and a space-time alignment engine, each sensor clock is synchronized through an event trigger mechanism, and asynchronous data is processed by adopting B-spline interpolation; the feature pyramid fusion network extracts multi-scale features, and key information is enhanced through a channel and a space attention mechanism; the self-adaptive hot spot tracking module predicts a trajectory by adopting volume Kalman filtering; the dynamic resource allocation module is used for optimizing equipment scheduling according to a hotspot priority evaluation result; the multi-agent cooperative control layer is used for realizing autonomous negotiation among equipment through reinforcement learning; according to the edge-cloud cooperative computing architecture, computing resources are dynamically allocated according to task complexity. According to the method, the omission ratio is reduced through multi-modal data fusion, space-time alignment, temperature measurement errors and a self-adaptive resource allocation mechanism, and the reliability and practicability of the system are remarkably improved.

The invention relates to the technical field of data visualization, and discloses a visual analysis method and system for security and protection monitoring data, and the method comprises the steps: carrying out the asynchronous collection of a video monitoring data stream, an infrared sensor data stream, an access control system data stream and an audio collection data stream in a security and protection monitoring system, and obtaining a multi-source asynchronous data stream set; generating a potential causal event pair set based on the multi-source asynchronous data stream set, and creating a time window allocation table according to the potential causal event pair set; performing causal probability calculation on the multi-source asynchronous data stream set to obtain a multi-dimensional causal probability propagation matrix; and asynchronous time delay compensation and causal chain visualization are performed on the multi-dimensional causal probability propagation matrix based on the time window allocation table to obtain a dynamic causal relationship graph, so that visual dynamic display of the causal chain of the asynchronous data stream is realized, and brand new monitoring situation awareness experience is provided for security personnel.

The invention discloses a state monitoring system for a railway electric switch machine. The state monitoring system comprises a multi-mode sensor module, a data preprocessing module, an intelligent analysis and decision module and a visualization and interaction module. The multi-mode sensor module is distributed at a key part of the switch machine and is used for acquiring multi-dimensional data such as the rotating speed of a power transmission shaft, displacement and vibration of an actuating rod, an expression notch image, temperature and humidity, air pressure and the like; the data preprocessing module performs noise reduction through a Kalman adaptive filtering algorithm and fuses asynchronous data by using a space-time alignment technology; the intelligent analysis and decision-making module constructs a hierarchical index system, determines a weight in combination with an analytic hierarchy process and an entropy weight method, dynamically evaluates a health level through a fuzzy grey clustering algorithm, predicts and represents a gap size trend by adopting an ARIMA model, performs monthly early warning on jamming faults by applying a random forest model, and optimizes and maintains resource configuration according to historical data; the visualization and interaction module develops a Web mobile terminal interface and supports multi-level health state display, real-time alarm and maintenance work order generation.

The invention provides a single instruction stream multi-fiber programming model and a construction method thereof. The method comprises the following steps: constructing a task, thread and fiber three-level concurrent unit; different code logic units are configured for each task, and one task is mapped to a plurality of threads; mapping the threads and processor cores one by one, and mapping one thread to a plurality of threads; the threads within the thread are caused to share all registers on the processor core. According to the single-instruction-stream multi-fiber-thread programming model and the construction method thereof, the programming model is constructed based on the fiber threads, concurrent execution of asynchronous data streams is achieved, the resource utilization rate is effectively increased, and redundant calculation is eliminated.

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 provides a component configuration method which can be applied to the technical field of cloud computing and the technical field of big data. The method comprises the steps of obtaining configuration information used for configuring a target interface component from a server side based on an interface request; packaging the interface request into an asynchronous data acquisition object, and storing the asynchronous data acquisition object as a global shared instance; registering a callback processing function based on the global shared instance for the target interface component; and responding to the configuration information returned by the server side and triggering the callback processing function, and executing component rendering on the target interface component based on the configuration information.

The invention provides an unmanned aerial vehicle formation collaborative navigation method based on an open information fusion architecture, belongs to the field of unmanned aerial vehicle navigation, and aims to solve the navigation information fusion problem of asynchronous updating of multi-sensor data and available dynamic change of measurement information in a traditional formation collaborative navigation method. In the prior art, when asynchronous data fusion and measurement information availability change are coped, the defects of precision loss, poor flexibility and expansibility and the like exist. According to the method, a state variable and a measurement information set are constructed under an open architecture, a probability model of collaborative navigation state estimation is established based on Bayesian reasoning, and on this basis, a factor graph model is constructed by combining an incidence relation between a measurement model in posterior probability and prior information. By designing an independent measurement factor for each sensor, when sensor data is updated, flexible response to asynchronous data fusion and dynamic change of measurement information is realized by optimizing changed measurement factor nodes in real time and dynamically adding and deleting the nodes. And gradually minimizing an error cost function of each factor node through a nonlinear least square optimization method to realize efficient fusion of multi-sensor data and real-time updating of a navigation state. According to the method, the efficiency, flexibility and expansibility of asynchronous information fusion of the collaborative navigation system are remarkably improved, and reliable technical support is provided for unmanned aerial vehicle formation collaborative navigation.

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.

A lead-sync log record is used to synchronize the replication logs of follower shards to the leader shard. In response to a failure to determine that there is a consensus for a database transaction commit operation after a shard server becomes a new leader, the new leader shard performs a sync operation using the lead-sync log record to synchronize replication logs of the follower shards to the replication log of the new leader. A shard server identifies a first transaction having a first log record but not a post-commit log record in the replication log, defines a recovery window in the replication log starting at the first log record of the identified first transaction and ending at the lead-sync log record, identifies a set of transactions to be recovered, and performs a recovery action on the set of transactions to be recovered.

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 multi-agent dynamic access and cooperation method for an air-space-ground Internet of Vehicles integrated architecture, provides a dynamic access decision scheme based on the air-space-ground Internet of Vehicles integrated network architecture, and aims to solve the problems of vehicle sensing range limitation and communication delay. Through multi-agent deep reinforcement learning, channel quality and load conditions of a ground network and a satellite network are evaluated in real time, and an optimal access mode is selected for a vehicle node. Meanwhile, in order to solve the problem of data asynchronization caused by communication delay between vehicles, clock inconsistency and transmission delay difference between a ground network and a satellite network, a Transform-based multi-agent cooperation mechanism is provided on the basis of a dynamic access architecture, time sequence coding is performed on asynchronous delay, sparse features of the vehicles are extracted, and the data asynchronization problem is solved. Effective processing of asynchronous data and accurate reconstruction of a global environment are realized at a core network end. According to the invention, more flexible and efficient dynamic access management and cooperation are realized.

The invention provides a replication method based on a consensus protocol. For each change operation performed by the leader server on the copy of the database, the leader server creates a copy log record and returns the result to the client. The leader does not wait for a consensus for the change operation from the follower. For submission, the leader creates a submission log record and waits for a consensus. Thus, the leader asynchronously performs database transactions, asynchronously performs replication of change operations, and synchronously performs replication of transaction submissions.

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 discloses a data processing method for improving a customized tourism recommendation algorithm model, relates to the technical field of big data processing, and effectively solves three core problems of a tourism recommendation system: for data sparsity, multi-source data fusion is realized through time sequence data set construction and cross-modal contrast learning of a time window segmentation interval 1h / 24h / 7d; for individual deficiency, an attention mechanism is adopted to calculate a user interest weight Ayh, and three types of interest attenuation models are established; and aiming at poor dynamic adaptability, an asynchronous data stream framework with a construction response smaller than or equal to a fixed time length and an environment perception mechanism, a dual matching degree threshold is set to realize real-time adjustment, and finally a closed-loop optimization system is formed through reinforcement learning, so that the recommendation accuracy and the user experience are remarkably improved.

The invention relates to the technical field of cognitive impairment noninvasive screening, and discloses a cognitive impairment noninvasive screening system and method.The system comprises a physiological sensing end, a feature collaboration end, an intelligent decision-making end and a visual report end, by arranging the physiological sensing end, 5-meter-distance non-contact detection is achieved, the feature collaboration end is connected with the intelligent decision-making end, and the visual report end is connected with the feature collaboration end; discomfort and signal interference caused by a traditional contact type electrode are avoided; the problem of asynchronous data fusion is solved by setting a feature collaboration end; according to the method, the dependence of traditional screening on contact equipment and professionals is broken through, high-precision and low-cost cognitive impairment general screening is realized, and the method is suitable for community medical treatment and family health monitoring scenes.

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.