10048 results about "Test data" patented technology

The invention provides a storage battery pack performance evaluation method and system based on big data, and relates to the technical field of storage battery performance evaluation, and the method comprises the steps: obtaining the charging and discharging data of a to-be-evaluated storage battery under different working conditions, carrying out the denoising processing of the charging and discharging data, carrying out the adaptive normalization processing, and obtaining the standard charging and discharging data, equalization processing is carried out, time domain features and frequency domain features are extracted respectively, and high-dimensional feature vectors are obtained through combination; performing dimension reduction operation on the high-dimensional feature vector to obtain low-dimensional embedded representation, generating an initial data set, dividing the initial data set into a training data set and a test data set, constructing an initial prediction model and performing hyper-parameter optimization to obtain a first prediction model; and acquiring real-time charging and discharging data, constructing a real-time feature vector, adding the real-time feature vector into the first prediction model to generate a real-time performance parameter, filtering the real-time performance parameter, constructing a health state prediction model for prediction, and obtaining a comprehensive performance evaluation result.

A sensor data evaluation device may include an artificial neural network, configured to receive and process first testing data, and output a first testing data output, wherein the first testing data represent sensor data of a first sensor detecting a sensor input; and one or more processors, configured to receive sensor deficiency information representing a deficiency of a second sensor; generate second testing data, wherein the second testing data are based on the sensor deficiency information; implement the artificial neural network to receive and process the second testing data; and output from the output layer a second testing data output; determine a difference between the first testing data output and the second testing data output; and if the difference is outside of one or more predetermined ranges or does not satisfy one or more predetermined criteria, send a signal representing a result of the determined difference.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

A surface defect detection method for multi-scale and small target features comprises the following steps: step 1, acquiring a surface defect data set, converting the format of a labeling file into a required format, and dividing an image into a training set, a verification set and a test set; step 2, constructing a DMS-YOL O surface defect detection model; 3, training the constructed DMS-YOLO surface defect detection model, and training the surface defect detection model by using the training set and the verification set to obtain an optimal surface defect detection model; 4, performing evaluation on the test data set through the trained model; 5, fully testing and debugging the model trained in the step 3 to ensure the accuracy and stability of the surface defect detection method in different scenes; and step 6, deploying the trained DMS-YOLO surface defect detection model in an actual industrial production environment, performing defect detection on the surface of a real product, and recording the accuracy rate, the false detection rate and the missing detection rate of a detection result. And 7, summarizing the whole detection process, forming a standardized solution, and popularizing the standardized solution to different industrial fields for application verification to ensure that the method has practicability and stability.

The invention discloses an integrated circuit chip test system based on an AI algorithm, and relates to the technical field of integrated circuit chip testing, the integrated circuit chip test system comprises a chip test platform, the chip test platform is in communication connection with the following modules: a data acquisition module, which is used for collecting historical test data from existing integrated circuit chip test equipment, and sending the collected historical test data to the chip test platform; carrying out preprocessing and integration on the chip test data set to obtain a chip test data set; and the model training optimization module is used for analyzing the chip test data set, learning the relationship between the chip performance and the test vector, and training a test vector AI optimization model. According to the method, by analyzing historical test data and automatically generating the optimized test vector, a redundant item-by-item test process in a traditional test is avoided, key test nodes are accurately positioned through the test vector AI optimization model, and unnecessary test steps are reduced, so that the test time is remarkably shortened, and the test efficiency is improved.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The present invention relates to the technical field of deep learning and computer vision. Disclosed is an unmanned aerial vehicle image-based small object detection method for target areas. The present invention crops images of obvious small objects in certain target areas, and annotates the small objects of different categories to form a raw training and testing dataset, so as to ensure the accuracy of data required in the early stage of the algorithm and further ensure the scientificity of the algorithm; uses the computing capability of an improved YOLOv7 detection model to collect image features of different degrees in the dataset, the improved YOLOv7 detection model using YOLOv7 as a basic model and adding to a neck network an MS-CET module, which is constituted by an improved self-attention mechanism and convolution module SPPCSP, and a BHC-FB module, which is constituted by bidirectional mixed convolution modules NConv and RPConv connected in parallel; and finally fuses different feature layers as a final judgment basis of an unmanned aerial vehicle for small object detection in the target areas, to further check the accuracy of the algorithm and criteria for dataset selection, thereby improving recognition accuracy.

The invention relates to the technical field of chip testing, in particular to a chip aging test and analysis method, which comprises the following steps of: constructing an aging parameter matrix containing process deviation, electrical parameters and thermodynamic parameters on the basis of fusion of wafer level and packaged test data, multi-stress cooperative loading is realized through a programmable power supply and a multi-zone temperature control array, transient electric signals, thermal imaging data and leakage current frequency spectrums are acquired in real time, an electrical-thermal coupling coefficient and a process deviation sensitivity factor are extracted in a cross-dimension manner, a non-linear recession model is established, differential aging paths of chip functional blocks are evaluated, and the reliability of the chip functional blocks is improved. And based on the dynamic life prediction model and a fault early warning mechanism, generating and executing an adaptive test optimization strategy to realize early prediction and stress adjustment of multiple failure modes. According to the invention, the efficiency and accuracy of the aging test can be obviously improved, and technical support is provided for high-reliability evaluation of deep submicron and post-mole times of chips.

A computer system may have one or computers and one or more data storage devices storing instructions, which when executed by the one or more computers implements a characterization manager, comprising: a test data database storing a plurality of test data sets; the characterization manager configured for selecting one or more test data sets from the test data database and apply the selected test data sets to a Generative AI system to derive from the Generative AI system an output; an output analyzer for processing the output to generate characterization data describing one or more facets of the output.

The invention discloses a multivariable time series prediction method based on an NMF multi-scale lightweight space-time convolutional neural network, and belongs to the technical field of machine learning and deep learning application. The method specifically comprises the following steps: (1) collecting and processing multivariable time sequence data, and unifying the scale of the data; (2) decomposing the time sequence data by using NMF, extracting basis matrixes and coefficient matrixes of different scales, and constructing a multi-scale feature pyramid; (3) applying a space-time convolution layer on the basis of the output of the feature pyramid, introducing a Bayesian optimization algorithm, and adopting a feature fusion layer fused with a multi-scale attention mechanism; (4) dividing a training data set and a test data set, and carrying out model training; and (5) predicting future time sequence data by using the trained model and outputting an expected value. According to the method, a more powerful and more efficient multivariable time sequence prediction model is effectively constructed, and the method is suitable for analysis and prediction of multivariable time sequence data.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

The invention discloses a system for testing an electromagnetic shielding coupler of a testing transformer based on intelligent sensing, and relates to the technical field of electrical equipment testing, and the system comprises a flexible distributed intelligent sensing array which is composed of a plurality of high-density miniature electromagnetic sensors and a temperature-vibration composite sensing unit, a non-uniform topological structure is embedded into a seam and an insulating interface area on the surface of the electromagnetic shielding coupler. According to the test system of the test transformer electromagnetic shielding coupler based on intelligent sensing, through dynamic electromagnetic field reconstruction of the flexible distributed intelligent sensing array and multi-band interference coupling modeling, the limitation of a traditional static test is broken through; and real-time capture and multi-dimensional decoupling of shielding effectiveness attenuation characteristics in a complex alternating electromagnetic environment are realized. Compared with a traditional method, the method has the advantages that the shielding effectiveness dynamic quantization error is reduced, and the engineering guidance value of test data is improved.

The invention discloses an automatic anchor point searching and processing method for grid-connected test data of a photovoltaic inverter, and belongs to the technical field of automatic test of a power system. According to the method, a three-phase voltage and current signal output by a power grid simulator and an inverter power instruction signal are aligned through a high-precision time synchronization device; wavelet transform multi-scale noise reduction and moving average filtering combined preprocessing is adopted to improve the signal-to-noise ratio; identifying a voltage zero crossing point candidate set, a drop starting point candidate set and a recovery termination point candidate set based on a self-adaptive dynamic threshold value; transient energy characteristic verification is introduced for a voltage drop starting point; effective anchor points are confirmed through time window association of power instruction step changes. According to the method, the problems of low efficiency of manual key event point identification, misjudgment caused by noise interference, grid event and inverter response time sequence correlation missing and the like are solved, and the automation degree of test data analysis, anchor point positioning precision and control response time sequence analysis reliability are remarkably improved.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention belongs to the field of aircraft simulation, particularly relates to a flight simulator test data state identification method, system and equipment based on multi-modal learning, and aims to solve the problems of low verification efficiency, poor compliance and difficulty in comprehensive identification of equipment risks. The method comprises the following steps: acquiring a time sequence operation curve image, a standard document text and an equipment time sequence, and generating a multi-dimensional equipment state representation vector through a feature extraction network after standardization processing; analyzing a standard document, constructing a structured knowledge base, extracting an initial allowable deviation range, fusing environmental parameters, and calculating a health index by using a health state decision tree to adaptively adjust the upper and lower limits of a deviation interval; and performing intelligent compliance judgment in combination with the compliance probability output by the state classification model and the dynamic deviation range. According to the invention, through collaborative decision-making of multi-modal data analysis, deviation range optimization and equipment health state quantitative evaluation, the accuracy and environmental adaptability of flight simulator test verification are improved.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention discloses a software test result analysis system which comprises a multi-source test data acquisition module, an intelligent anomaly detection engine, a cross-project association analysis module, a visual decision support terminal and a self-adaptive feedback optimization module. The multi-source test data acquisition module integrates heterogeneous test tool data and performs standardization processing; the intelligent anomaly detection engine realizes dynamic anomaly detection by using an LSTM model and a reinforcement learning algorithm; the cross-project association analysis module completes cross-project data association by means of code tracking, a knowledge graph and the like; the visual decision support terminal visually displays an analysis result in the form of a three-dimensional thermodynamic diagram and the like; and the adaptive feedback optimization module forms closed-loop control and dynamically adjusts a test strategy. The system effectively solves the problems that according to a traditional method, multi-source data cannot be fused, anomaly detection is not sensitive, and visual analysis is lacked, efficient analysis of software test results and optimization of the test process are achieved through cooperative work of all the modules, and the test efficiency and the software quality are remarkably improved.

The invention provides a security risk detection method and device for text graph large model generation content, and the method comprises the steps: selecting a test category according to a test parameter based on a preset test database, and matching a corresponding data set from the test database according to the test category; inputting the keywords in the data set into the tested large model in batches according to the format of forward cue words; performing content security detection on a corresponding picture or video image generated by the detected large model according to the input cue word; performing watermark identification detection on a corresponding picture or video image generated by the detected large model according to the input cue word; and analyzing to obtain a detection report according to the content security detection result and the watermark identification detection result. According to the test database, the risk of the large model system is externally assessed based on the characteristics of the input prompt word, the content annotation and the output video content, and the accuracy and effectiveness of the detection and assessment method are improved.

A system and method for simultaneously running a plurality of air quality sensor units using a centralized station and software to test for field performance upon factory return from field installations, automatically identify preventative maintenance requirements, and automatically calibrate the sensor units and generate quality metric reporting. The centralized station and software include a computer, a manifold, tubes, and cables. A test gas is supplied, wherein the manifold distributes the test gas. Once powered and supplied with test gas, the sensor units are tested simultaneously. The computer runs software that compares the test data collected from the sensor units to determine if any of the sensor units fail standards or produce data that deviates from a mean average range. Sensor units that do not require maintenance are calibrated using test gasses to generate calibration coefficients which are stored within the sensors' firmware.

The invention belongs to the field of data processing, and discloses a test method for pedestrian trajectory prediction and emergency braking in an automatic driving platform. Comprising the steps of segmenting a real road environment in advance, obtaining grades of small road segments according to calculation of segmentation coefficients, carrying out resource allocation on the small segments according to the grades, and collecting multi-source heterogeneous data; importance optimization is introduced into a Transform model, possible prediction trajectories of individuals are generated, graph data are constructed by using the possible prediction trajectories, an Autoencoder anomaly detection mechanism is introduced, high-risk trajectories are screened out, and high-risk scenes are output; combining the road geometric model with the high-risk scene to form a scene library, loading the scene in a simulation platform, carrying out automatic driving test, carrying out virtual-real data cross validation in combination with real vehicle test data, and obtaining performance evaluation of an algorithm; pedestrian track prediction and emergency braking in the automatic driving process are achieved.

The invention provides a credibility measurement method and system for an unmanned multi-mode perception algorithm, a data-driven virtual simulation test platform is constructed, and feature distribution is counted on the basis of actually-collected actual measurement data. Through dynamic model modeling and state transition, an end-to-end closed loop sensing process is described. Afterwards, for the problem that a traditional combined test method is low in efficiency due to the high dimension of scene parameters, a black box substitution model between the scene parameters and a perception test result is established, historical test data is evaluated by using a Bayesian optimization algorithm to generate the most antagonistic scene parameters in a next iteration test, and the efficiency of the traditional combined test method is improved. In this way, the confrontation of the test environment is continuously improved, and the test efficiency is effectively improved. And finally, a perception algorithm credibility measurement module is constructed, a perception failure scene is extracted through playback test data, a credibility evaluation index system is constructed, and comprehensive credibility evaluation of the tested perception system is realized.

The invention provides a compliance test data generation method and device fusing differential privacy and a readable storage medium. The compliance test data generation device comprises a data acquisition module, a data sensitivity analysis module, a differential privacy parameter adjustment module, a data generation module and a test data verification module. The data acquisition module acquires and unifies a data format from multi-source heterogeneous data; the data sensitivity analysis module divides data into high, medium and low sensitivity levels by adopting a K-means clustering algorithm and integrating data types, sources and purpose quantitative weights; a differential privacy parameter adjustment module dynamically adjusts a privacy budget epsilon and a noise scale parameter lambda based on the sensitivity level and the test purpose; the data generation module selects a Laplace or exponential mechanism to generate test data according to the data distribution characteristics; and the verification module feeds back optimization parameters through statistical test. According to the method, the problem of test data deviation caused by insufficient privacy protection and static differential privacy parameters of a traditional anonymization method is solved.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The embodiment of the invention provides a model evaluation method and device, electronic equipment and a storage medium. The electronic equipment can obtain an evaluation test data set; based on the evaluation test data set, the to-be-evaluated model is evaluated through a preset evaluation mode, an evaluation result is obtained, and the preset evaluation mode comprises at least one of a consistency evaluation mode, an understanding ability evaluation mode and a knowledge mastery degree evaluation mode; the consistency evaluation mode is used for evaluating the consistency between prediction answers output by the to-be-evaluated model for the same test data based on different cue words, and the understanding ability evaluation mode is used for evaluating the accuracy of the prediction answers output by the to-be-evaluated model for multiple pieces of rewriting test data; the knowledge mastery degree evaluation mode is used for evaluating the accuracy of prediction answers output by the to-be-evaluated model for the in-depth test data corresponding to the test data. Therefore, the consistency, the understanding ability and the knowledge mastering degree of the large language model can be evaluated.

The invention relates to the field of electric power material quality detection, and discloses a test scheduling system for detection task collaboration and data acquisition, which comprises a task construction module, a state collaboration module, a graph reasoning module and a data acquisition module. And the task construction module generates a standardized test task packet including a task identifier, a project code, a target equipment identifier, an environment requirement parameter and a two-dimensional code according to the test rule base and the resource configuration state, and pushes the standardized test task packet to corresponding test equipment through a Web Service interface. And the state collaboration module receives an equipment state feedback event, constructs an event time sequence flow graph based on the task identifier and generates a task state sequence with a timestamp. The atlas reasoning module takes the state sequence and the environmental parameters as input, constructs a test atlas structure and generates an optimization execution path. And the data acquisition module controls the test equipment to complete a detection task according to the path, acquires test data and environmental parameters, and encapsulates the test data and the environmental parameters to form a structured task data packet, thereby realizing data collection and task tracing.

The invention provides an automatic software testing method and system based on artificial intelligence. The method comprises the following steps: analyzing a software demand document by using an NLP technology, converting the software demand document into structured data, extracting a key function point, and identifying a software function point; generating a test case covering all function points through a deep learning model in combination with historical test data training; automatically executing a test operation, analyzing a test result, analyzing a test log by using a machine learning algorithm, identifying and classifying potential defects by using a data mining and pattern recognition technology, and generating a repair suggestion report; and analyzing feedback data in the test process, dynamically adjusting a strategy according to a test result by using a reinforcement learning algorithm, and optimizing a test case and the strategy. According to the invention, intelligent analysis of test data, automatic generation of test cases and automatic execution of a test process are realized; flexible configuration and expansion can be carried out according to the actual condition of software; the test efficiency and quality are improved, and the test cost and manpower consumption are reduced.

The invention belongs to the field of big data, and relates to a data migration method, which comprises the following steps: obtaining storage cluster pressure test data, and constructing a performance model, the pressure test data being obtained by testing under different loads. And acquiring real-time monitoring data, and evaluating the current pressure state in combination with the performance model. And if the threshold value is exceeded, extracting a historical data change rule, and generating a load prediction model. And calculating a target migration concurrency number based on the real-time performance index and the prediction model. And adjusting the current data migration concurrence number and the bandwidth limiting parameter to target values. And finally, carrying out data migration of the storage cluster according to a preset protocol and the target parameter. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the pressure measurement data and the real-time monitoring data can be stored in a block chain. The data migration efficiency can be improved.