112 results about "Data density" patented technology

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention provides an operation and maintenance performance assessment data management system based on dynamic weight optimization, and relates to the technical field of operation and maintenance management, and the system comprises a quantification module which is used for obtaining an original operation and maintenance data sequence through a data collection interface, and mapping the operation and maintenance data sequence to a multi-dimensional feature space; in the multi-dimensional feature space, quantitative analysis is carried out on spatial distribution of data points, and data density distribution characteristics of all areas are determined; the calculation module is used for executing unsupervised clustering operation based on the density distribution characteristics and the proximity relation of the data points to obtain a group of data sets with internal consistency; core statistical features are calculated for each generated data set, and a specific calibration coefficient is derived for the data set based on the core statistical features. According to the invention, precision, scenario and high efficiency of operation and maintenance performance assessment are realized.

The present application relates to the technical field of storage, and discloses a data attribute-aware storage system and data management method for a key-value database. The method comprises two parts, i.e., data hotness attribute-based writing and data density attribute-based merging. The data hotness attribute-based writing comprises classifying key-value pairs into cold key-value pairs and hot key-value pairs, to realize separate storage of cold data and hot data by means of a data hotness attribute-based writing strategy. The data density attribute-based merging comprises: when key-value pairs in an SSTable are extracted during a merging operation and form a sorted key-value pair sequence, extracting the key-value pairs one by one, and calculating a binary difference between every two adjacent key-value pairs; and if the difference is greater than a set threshold, stopping filling the current SSTable, and creating a new SSTable. In the present invention, by analyzing data hotness and data density, data having different attributes are classified to different data partitions, thereby reducing I / O amplification caused by repeated reading and writing in a compression process.

The invention discloses a geological mineral data intelligent modeling method and system based on multilevel space-time intrinsic coding, and the method comprises the steps: generating a 9-level adaptive grid through employing an improved HEALPix subdivision algorithm for the geological complexity and target data density of a to-be-detected region; based on a 9-level adaptive grid, encoding space time-varying grid units of existing geological mineral resource data to obtain a geological feature space matrix; constructing a multi-modal data register based on Transform, inputting the 9-level adaptive grid and the geologic feature space matrix into the multi-modal data register, and outputting an associated feature matrix; using a hybrid interpolation model to perform hybrid interpolation on the correlation feature matrix to obtain a full-grid correlation correlation interpolation matrix; and a CNN error correction module is used to optimize the full-grid correlation degree correlation interpolation matrix to obtain a geological mineral resource space-time intrinsic code, and geological mineral resource data intelligent modeling is realized based on the geological mineral resource space-time intrinsic code.

The invention discloses a virtual space-time environment automatic completion method and system based on data density dynamic perception and a medium, and relates to the field of computer data processing. The method comprises the following steps: monitoring a data activeness index (such as spatio-temporal information entropy) of a spatio-temporal grid in real time in a distributed index database of a virtual spatio-temporal environment; when the index meets a preset data sparseness condition, generating a completion request; analyzing the space-time attribute tag of the target grid and acquiring associated multi-modal reference data from an external heterogeneous data source; carrying out feature recombination on the reference data by utilizing a generative neural network model, and generating discrete spatio-temporal data slices containing spatio-temporal anchor points and scene content attributes (such as environment illumination and material mapping); and finally, injecting the slices into the database to fill data holes. According to the method, a passive trigger mechanism and a structured reconstruction technology of unstructured data are introduced, so that the computing power cost of a large-scale virtual environment is effectively reduced, the problems of data sparseness and cold start of a long-tail region are solved, and the historical authenticity and time-space consistency of a virtual scene are ensured.

The invention discloses a genome variation cold and hot spot region prediction method and device, and the method comprises the steps: S1, carrying out the slicing of a target genome region according to a preset sliding window length, and constructing a multi-modal input tensor corresponding to a window; s2, inputting the multi-modal input tensor into a pre-trained deep learning prediction model, and outputting a cold and hot spot prediction score of each site in the window through a full connection layer; and S3, according to the cold and hot spot prediction scores, identifying a variation cold spot region and a variation hot spot region in the target genome region. According to the technical scheme provided by the invention, the dependence on the existing variation data density is eliminated, and non-blind area coverage in the whole exon group range is realized; meanwhile, the structured output based on the preset transcript coordinates can directly support clinical variation interpretation, a quantitative basis is provided for PM1 and cold spot evidence, and the proportion of unclear significance variation is effectively reduced.

The invention relates to the technical field of data storage, in particular to a distributed parallel data management method and system based on hierarchical storage, and the method comprises the steps: obtaining a distributed data set, numbering each data object in the distributed data set to obtain a first data object to an Nth data object, acquiring data type sequences, data size sequences and access frequency sequences of the first data object to the Nth data object, performing data density analysis to obtain first data density to Nth data density, and performing reference-dependency conjoint analysis on the first data object to the Nth data object to obtain a first weight matrix, the first correction density to the Nth correction density are obtained through density correction according to the first weight matrix, and hierarchical storage is performed according to the first correction density to the Nth correction density to obtain a data storage hierarchy allocation result, so that more accurate and efficient storage management of the distributed parallel data is realized.

The invention discloses a method for identifying a public charging infrastructure boundary in combination with Internet of Vehicles data and an artificial intelligence algorithm. The method comprises the steps of S1, data acquisition; s2, constructing a buffer area; s3, carrying out buffer region fusion; s4, decomposing and splitting a plurality of regions; s5, area calculation and small region elimination; s6, convex hull construction and softening treatment; and S7, determining the boundary, and determining the boundary of the charging infrastructure according to the softened boundary after the step S6. The technical scheme of the invention has the following advantages: (1) automatic identification is realized, and boundary information is extracted from actual behaviors without depending on manual delimitation or operator declaration; (2) the adaptability is high, the method can be suitable for different cities and different time periods, and different data densities and distributions can be flexibly adapted by adjusting parameters; (3) high-precision positioning is carried out, and real range identification can be realized by combining desensitized latitude and longitude data (4-bit precision) with a buffer zone strategy; and (5) the expandability is good, and boundary types and grades can be refined by combining other characteristics.

The invention relates to the technical field of data processing, in particular to a portable terminal with an electric power payment service intelligent management function, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: obtaining an index combination for grouping users; clustering the users according to any index combination to obtain a cluster; obtaining an effect evaluation factor of any index combination according to the correlation of the historical data of the user behavior index of each user in each cluster, the data density in each cluster and the distance between every two clusters, obtaining an effective index combination according to the effect evaluation factor of each index combination, and obtaining the effective index combination according to the effective index combination. The users are grouped according to each effective index combination, and the user portraits are generated for providing personalized payment reminding services for each user, so that the accuracy of pushing the personalized payment reminding services for the users is improved.

The invention discloses a power consumption data abnormity identification method and system, a medium and a product, and belongs to the field of power data abnormity identification, and the method comprises the steps: collecting the current original power consumption time sequence data of a plurality of loads in a target region, and obtaining the corresponding historical power consumption data and load flow calculation data; correcting the original power consumption time sequence data based on the historical power consumption data to obtain power consumption time sequence data; performing multi-scale time sequence feature extraction and prediction on the historical power consumption data by using a preset P-T imemi x algorithm to generate prediction data with higher data density; performing bidirectional alignment fusion on the power consumption time sequence data and the prediction data to obtain fused power consumption data; and combining the fused power consumption data with the power distribution network load flow calculation data, calculating a preset deviation index, judging whether each data point in the fused data is abnormal or not, and outputting an abnormality identification result. According to the invention, the problem that the abnormal power consumption data of low-cost acquisition equipment is difficult to identify accurately in the prior art can be solved.

The application discloses a cloud API service quality prediction method based on three-dimensional tensor high-order feature interaction, and belongs to the field of service quality prediction.The prediction method comprises the following steps: step 1, obtaining a cloud API service quality dataset; step 2, designing an abnormal point detection module to detect and screen out outliers of the cloud API service quality dataset; step 3, designing a spatial position information optimization module to fully extract information generated by latitude and longitude coordinates; step 4, constructing a self-adaptive three-dimensional tensor network model, inputting processed user features and cloud API features into the three-dimensional tensor network model to obtain new feature information generated after high-order feature interaction; and step 5, inputting the new feature information into a deep neural network layer for sufficient learning, and finally obtaining a cloud API service quality prediction value through a full connection layer.The method designed in the application has more accurate prediction effect and higher robustness in various data density scenes.

The invention discloses a joint compression method for building point cloud data based on geometric features, and the method comprises the steps: firstly carrying out the sampling and statistical filtering preprocessing of the building point cloud data under an octree, so as to remove noise and reduce the data density; secondly, accurately identifying and retaining key contour and structural features of the building through an edge point extraction algorithm based on curvature calculation, and then performing redundancy removal on non-edge points by adopting an adaptive multi-scale normal differential segmentation technology (DoN) which can adaptively adjust scale radius parameters according to geometric complexity of the surface of the building; and structure key points are effectively distinguished and reserved. And finally, merging the edge points and the points subjected to normal differential segmentation to form compressed point cloud data. Through verification of a precision evaluation index, the method can remarkably improve the quality of reconstructed point cloud while keeping a low compression ratio, and provides powerful support for efficient storage and transmission of building point cloud data.

The application discloses a database range filter design method and device based on a binary decision tree, and belongs to the technical field of database query. The method considers that data sets to be processed in a database are not uniform in most application scenarios, and an initial binary decision tree is constructed offline according to the distribution characteristics of the data sets. Each node of the initial binary decision tree is a range, and a root node represents a range interval of keys in the entire data set. Simulated online query is performed by using the initial binary decision tree, and pruning is performed according to the access frequencies of leaf nodes. Finally, compression coding is performed to obtain a database range filter. In the process of constructing the binary decision tree, the binary tree is grown according to data density and skew degree, so that the problem of high false positive rate in the case of uneven data distribution can be solved. The pruning operation of the binary tree according to the access frequency can reduce the space occupation. Finally, the database range filter can efficiently process data in the case of uneven data.

The invention relates to the technical field of financial transaction data visualization, in particular to a transaction data analysis method and system based on a regular polygon block thermodynamic diagram. By constructing a unified time-price two-dimensional data matrix, adaptive selection and rendering of various regular polygons such as rectangles, hexagons, parallelograms, triangles, linear views and the like are supported, and on this basis, information of untransacted buyers and sellers is superposed. According to the transaction data analysis method and system based on the regular polygon block thermodynamic diagram provided by the invention, multi-shape rendering is supported through the unified matrix, repeated calculation of data for different shapes is avoided, and the calculation complexity is reduced; the optimal shape is automatically selected according to the data density, the visual definition is improved, the problems of grid shape solidification, insufficient transaction granularity distinguishing, low multi-view linkage efficiency, missing of non-transaction data and the like in the prior art are solved, and the adaptability, precision and comprehensiveness of transaction data visualization are remarkably improved.

Embodiments of the present application provide a key point recognition device and method based on wireless radar signals. The method comprises: performing feature extraction on point cloud data obtained within a period of time to obtain first spatial feature data, Doppler velocity feature data, reflection energy feature data, density distribution feature data and time distribution feature data of the reflection point cloud; using a neural network-based fusion feature extraction model to detect the cascaded feature data to obtain fusion feature information; and using a neural network-based key point detection model to detect the fusion feature information to output key point data of the object. Thus, the key points of the object (such as a human body) can be detected according to the radar point cloud without limiting the action category, the required computing resources are less, and the detection accuracy is high; in addition, it is easy to implement, simple to operate, has strong noise resistance, and high privacy protection.

The invention provides a data transmission method, device and system, electronic equipment and a storage medium, and relates to the technical field of data communication, and the method comprises the steps: obtaining to-be-transmitted data, carrying out the packaging of the to-be-transmitted data according to a unified data transmission protocol, and obtaining a data package; wherein the unified data transmission protocol at least comprises transmission quality requirement parameters; dynamically determining a target transmission protocol according to the transmission quality requirement parameter and / or historical transmission data; according to the storage information and the data density of the equipment sending end and the cloud server, adjusting a data transmission parameter of the equipment sending end to obtain a first data transmission parameter; sending the data packet to a cloud server based on the target transmission protocol and the first data transmission parameter; the complexity of data processing is reduced through a unified data transmission protocol, the transmission layer protocol is dynamically determined in combination with the transmission quality requirement parameters and the historical transmission data, and the suitability and the data transmission quality during data transmission are improved.

The application provides a high-speed motor operation data intelligent analysis method and system, and relates to the technical field of data processing.The method comprises the following steps: performing a preprocessing operation on basic operation information and surrounding working condition factors to form initial integrated information; performing boundary contour calculation on a multidimensional data point set for the initial integrated information, and extracting an outer convex boundary point set of data distribution; determining a coverage range of data distribution based on the outer convex boundary point set, and establishing a minimum convex polygon verification interval containing all data points; performing adaptive hierarchical cutting on the verification interval according to a data density gradient and a working condition change amplitude to form a plurality of cutting sub-regions with different characteristic densities; extracting geometric features, distribution densities and boundary curvatures of each cutting sub-region as characteristic parameters, and mapping and converting the characteristic parameters into stability evaluation parameters and abnormality evaluation parameters of corresponding verification points.The application realizes accurate evaluation, real-time abnormality identification and dynamic adaptation of the running state of a high-speed motor.

The application discloses a bearing fault diagnosis method and system based on a CS-SHAP model, bearing operation signals are collected through acceleration, temperature and acoustic emission multi-sensor, multi-dimensional fault features are extracted from time domain, frequency domain and time-frequency domain after wavelet transform or EMD algorithm denoising to complete preprocessing, feature data is divided into similar clusters by K-means clustering, SHAP values are calculated cluster by cluster and weighted according to cluster data density or diagnosis influence degree to obtain feature importance score, a SVM classifier based on RBF kernel function is trained with screened key features, model parameters are optimized through 5-fold cross validation, new collected data is preprocessed and input into the trained model to obtain diagnosis results, feature SHAP values are calculated and visualized by using the CS-SHAP model, and key factors of faults are analyzed. The application realizes accurate diagnosis of bearing faults and interpretability of diagnosis results, and provides technical support for intelligent operation and maintenance of industrial equipment.

The invention discloses a battery ZCV curve adaptive arrangement method. High-precision and high-adaptability curve arrangement is realized through full-link design. The method comprises the following steps: collecting and preprocessing multi-source data, and screening reliable data through dynamic voltage interval demarcation and multi-dimensional validity verification; sOC interval subdivision and multi-mode missing point intelligent completion are carried out, and data are completed by combining battery characteristics of different SOC intervals and adopting a matching algorithm; performing two-dimensional deep purification on the physical data, and removing abnormal data through duplicate removal and physical rule verification; performing scene-based intelligent interpolation expansion, and adjusting data density as required; performing two-factor dynamic calibration in an aging environment, and correcting a curve in real time to adapt to the state change of the battery; self-adaptive DOD remapping is carried out, and the resolution of a key interval is improved; and dynamic precision optimization and resource adaptation are realized, and precision and equipment resource occupation are balanced. The method is compatible with various battery types, and can be widely applied to wearable equipment, electric automobiles, industrial internet of things and other scenes.

The invention relates to the technical field of acoustic holographic transmission, and relates to a high-data-density acoustic holographic transmission method and system under a limited space bandwidth product. The method comprises the steps of obtaining a target image and generating an amplitude hologram; based on a physical model of sound wave propagation, establishing a coupling relation model between the complex sound pressure amplitude and the phase of the amplitude hologram; inversely inverting the amplitude hologram based on a time inversion principle, and inversely solving focusing phase distribution corresponding to the low-information-density amplitude hologram through a horny lizard optimization algorithm according to the coupling relation model; coupling the amplitude hologram with low information density with focusing phase distribution to generate a coupling amplitude hologram with high information density; and reconstructing a high-data-density sound field based on the high-information-density coupling amplitude hologram. According to the method, the diffraction limit of sound waves on the wavelength scale is successfully overcome, and super-focusing sound field reconstruction under the condition of low information density is achieved. In addition, the method can realize high data density sound field transmission even under the condition of low information density. The research provides a new insight for far-field acoustic super-resolution imaging and low-density information transmission, and has huge application potential in the fields of medical ultrasonic imaging, remote acoustic communication and the like.

The invention discloses a regional crowdsourcing ionospheric scintillation monitoring and early warning method and system based on low-cost equipment, and belongs to the field of ionospheric scintillation monitoring, and the method comprises the steps: recording reference station observation data and ISMR data as an accurate data set; recording observation data acquired by the low-cost equipment as a fuzzy data set; performing flicker intensity assignment of ROTI and GFTI by using ISMR data in the accurate data set; interpolation is carried out on the flicker index of the position where the puncture point coordinates in the fuzzy data set are located by adopting common Kriging interpolation; subtraction is carried out on the interpolation result and the index of the corresponding position in the fuzzy data set, and an abnormal difference value in the subtraction result is calculated; and dividing the obtained ionospheric scintillation detection result according to a scintillation generation range, and analyzing a possible scintillation generation position so as to carry out early warning. A large amount of low-cost equipment observation data in the area is introduced to fill the blank area of professional equipment monitoring data, and the monitoring data density and the resolution of the monitoring result are improved.

The invention provides a track data compression method and system for power grid material sampling monitoring, and belongs to the technical field of power internet of things and intelligent terminal data processing, and the method comprises the steps: collecting original track data including a timestamp, longitude and latitude, and speed through a positioning module; sampling the original trajectory data based on a preset time interval to preliminarily reduce the data density; performing data cleaning on the sampled trajectory data, and removing speed abnormal points and position abrupt change points; compressing the cleaned trajectory data on the basis of a time ratio distance model in combination with a length loss rate threshold, and retaining key trajectory points; and outputting the compressed trajectory data. Through time ratio distance compression and length loss rate control, on the premise that the trajectory form is guaranteed, the data volume is greatly reduced, abnormal point cleaning is carried out by adopting speed and position double criteria, and the data quality is improved.

A multi-dimensional time series data dynamic evolution analysis method, device, medium and electronic equipment, the method comprises the steps of: obtaining and structuring pre-processing time series data; each of the time series data is quantified and attributed in multiple dimensions, and a three-dimensional data node containing spatial coordinates, initial weight and timestamp is constructed; based on the set of three-dimensional data nodes in a specific time window, a three-dimensional data density field is constructed which can represent the data hotspot distribution and intensity in the time window; in the three-dimensional data density field, the density peak area is identified as the core data cluster, and the cluster quality is calculated according to its density and coverage, and the association strength between different core data clusters is calculated; the sequence of the state snapshots of the continuous multiple time window data fields is serialized to form a dynamic evolution view which can show the birth and death of data hotspots, the fusion and splitting process of data clusters. The abstract multi-dimensional data is converted into intuitive three-dimensional density field.

The application discloses a kind of general hydrological multi-element data mapping method, system and medium, method includes configuring a hydrological multi-element data source, setting data source url and the authentication information of access, after being configured, administrator is bound in data source site binding page according to demand Batch binding operation;After being associated well with station and data source, start the hydrological test equipment of the station binding, enter to online data management configuration page;According to hydrological test type grouping, respectively to each test type is added equipment, select the data source corresponding to the equipment, set the equipment data density, according to requirement whether to the equipment is opened or closed.The application real-time and accurate monitoring data source transmission health state situation, can directly in platform configuration data source, data source push frequency is independently configured, each hydrological element can dynamically show data interval.

The present application discloses a kind of soft rock zone high spectral remote sensing identification method for railway engineering geological survey, comprising: obtaining railway survey area hyperspectral image and constructing data set;Subsample set is constructed by random sampling, and the space division of multiple groups of isolated hyperspheres and residual space is generated based on the distance of pixel and its nearest neighbor;According to the position of pixel falling into each space division, generate isolated kernel feature vector;With all or part of pixel as background pixel set, calculate the average value of its isolated kernel feature vector as isolated distribution kernel mean model;The inner product of the isolated kernel feature vector of the pixel to be measured and the mean model is calculated to obtain the initial abnormal similarity, and the contiguous area is extracted as soft rock zone after spatial neighborhood regularization.The present application reduces the computational complexity from quadratic to linear, has data density adaptive ability, and can efficiently and accurately identify soft rock zone under complex geological background along railway by fusing spatial continuity constraint.

The invention belongs to the technical field of image processing, and particularly relates to a point cloud up-sampling method and system based on a Mama model, and the method comprises the steps: obtaining a sparse point cloud, dividing the point cloud into at least two voxels, serializing the at least two voxels according to a space filling curve, obtaining a one-dimensional sequence, carrying out the feature extraction of the one-dimensional sequence, and obtaining a point cloud up-sampling result. The method comprises the steps of obtaining multi-scale voxel features, inputting the multi-scale voxel features into a Mamba module to carry out time sequence modeling, outputting enhanced time sequence features and hidden states corresponding to each voxel, predicting an interpolation weight matrix of each voxel according to the hidden states, generating prediction points in each voxel, and outputting the prediction points. The method comprises the steps of obtaining a prediction point, predicting the coordinate offset of the prediction point relative to a voxel center, finally fusing the newly added point with the point cloud, and outputting a dense point cloud, and in conclusion, the invention provides a point cloud up-sampling scheme which can effectively improve the sparse point cloud data density and geometric accuracy and considers the processing efficiency.

The application discloses a kind of mass time series data distributed storage and fast retrieval method and system, it is related to distributed time series data storage technical field, including to the original time series data generated by edge device is received and preprocessed in stream;According to data age calculation result and query heat forecast result, the original time series data is divided into real-time hot data, recent warm data and historical cold data, and the time-space density of device identification corresponding time series data is calculated, to obtain the classified time series data;To the classified time series data, adaptive dynamic partition decision of time series perception is used, and according to the load of distributed storage node, query frequency and data density, partition key is determined and partition operation and micro-migration operation are executed, to obtain the partitioned time series data;To the partitioned time series data, hierarchical mixed compression processing and distributed node write mode are used to generate compressed segment file and complete writing and redundant storage, while global metadata directory is updated.

The invention discloses an SRAM (Static Random Access Memory) device capable of executing signed multi-bit multiplication operation, which comprises two types of SRAM units, the two types of SRAM units are respectively sign bit operation units, namely 11T SRAM units; and a numerical value operation unit, namely a 10T SRAM unit. The 11T SRAM unit comprises 11 MOS (Metal Oxide Semiconductor) transistors; the 10T SRAM unit comprises 10 metal oxide semiconductor (MOS) transistors; a transmission gate is arranged between the two types of SRAM units and is used for transmitting a sign bit operation result generated by the 11T SRAM unit to the 10T SRAM unit; through the combination of the two types of SRAM units, signed multi-bit multiplication and accumulation operation is realized. The device can efficiently and quickly execute the signed multi-bit multiplication operation, the operation of the sign bit is realized without depending on an additional auxiliary circuit module, the balance of the area and the operation time overhead is realized, and the device is suitable for a scene with high operation data density.

A kind of nuclear function bandwidth optimization mechanical structure load extrapolation algorithm device, comprising: data processing model, data clustering model, load extrapolation model;The present application is clustered according to data density, and the bandwidth of different clusters is respectively obtained according to the result of clustering when carrying out kernel density estimation, to solve the problem of global fixed bandwidth, improve the accuracy of load extrapolation.The mean, standard deviation and maximum of the extrapolated load are more close to the test load, and the corresponding error is less than the fixed bandwidth method.The distribution of the extrapolated load data and the measured load data has high similarity, can effectively simulate the data distribution characteristics and rules of measured load, prove the feasibility of the extrapolation device.The pseudo-damage ratio of the present application is lower than the fixed bandwidth method, and has high consistency with the original load.

The invention provides an eye pattern generation method based on stack data, electronic equipment and a storage medium. The method comprises the steps of obtaining time domain waveform data of a digital signal; determining the period of the digital signal according to the time domain waveform data, and performing period alignment on the time domain waveform data; cutting the time domain waveform data after period alignment into a plurality of waveform segments with the same time length, and performing data homing on the data of all the waveform segments on the same time axis to form stack data; performing interpolation processing on the stack data to increase data density; carrying out two-dimensional binning statistics on the interpolated stack data, and generating a two-dimensional statistical matrix; and generating an eye pattern based on the two-dimensional statistical matrix. According to the method and the device, the time domain waveform data is subjected to periodic alignment, cutting, stack data formation and two-dimensional binning statistics, so that quantitative statistics and eye diagram generation of the time domain waveform data are realized, and the precision and efficiency of signal integrity evaluation are remarkably improved.