176 results about "Interval data" patented technology

The invention provides a performance optimization method and system for cross-data-source paging query, and relates to the technical field of data processing, and the method comprises the steps: calculating a data distribution weight and a data source routing table, determining an optimal data access path, and splitting a paging query request into sub-query tasks; dynamically adjusting the number of parallel query threads based on the load state and distributing sub-query tasks; establishing a local index and adaptively adjusting an index structure; dynamically adjusting the connection number of the connection pool and acquiring data in batches; performing feature analysis according to a query result, determining the size of a data fragment, and selecting an optimal merging strategy to merge the data; constructing a cache access mode graph, and aggregating and storing related data based on the access association degree; and optimizing a performance strategy set through reinforcement learning, adjusting a cache updating interval, a data access path and a feature matrix, and finally obtaining a query result. According to the method, the cross-data-source paging query efficiency can be effectively improved, the query delay is reduced, and the overall performance of the system is improved.

The invention provides a silent upgrading method for an air conditioner, and relates to the technical field of air conditioner upgrading, the method comprises the following steps: receiving an incremental upgrading package to obtain upgrading resource demand estimation information, and monitoring key environment parameters; judging whether to trigger a refrigeration interval period identification mechanism of the air conditioner or not according to the key environment parameters; after a refrigeration interval period identification mechanism is triggered, multiple refrigeration interval periods are identified to obtain historical interval data, and the prediction duration of the refrigeration interval periods under multiple different temperature difference ranges is predicted according to the historical interval data; and when the air conditioner enters the current refrigeration interval period, the duration of the current refrigeration interval is determined according to the key environment parameters, and the duration of the current refrigeration interval period, the key environment parameters and the upgrading resource demand estimation information are compared to judge whether silent upgrading operation is executed or not in the current refrigeration interval period. According to the method, whether the air conditioner has enough resources to complete silent upgrading or not is judged by utilizing the pre-cooling release time of the refrigeration interval period of the air conditioner, and non-stop silent upgrading of the air conditioner is achieved.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

The invention belongs to the technical field of gas pipelines, and particularly relates to a gas engineering construction quality evaluation system based on big data, which comprises the following steps of: firstly, constructing a defect feature tensor, introducing a space-time grid analysis technology to analyze defect disposal aging interval data and location defect reproduction frequency data, and constructing a defect feature tensor on the basis of a preset quality-efficiency reliability quantification rule; defect features are converted into quantifiable quality-effect scores, then deep fusion of multi-source heterogeneous data is achieved by establishing a space-time incidence matrix of welding defects, welding process parameters and environment monitoring parameters, and on this basis, a fuzzy correlation analysis technology and a preset process compliance quantification rule are adopted to achieve quality-effect evaluation of the welding defects. Complex process parameters and environmental conditions are converted into quantifiable process scores, the process scores and quality-effect scores are integrated to generate a gas pipeline welding quality comprehensive evaluation value of a target contractor, and a quantifiable intelligent decision support system is provided for gas pipeline construction quality management and control of the contractor.

The invention relates to the technical field of urban rail transit operation optimization, and provides an energy consumption peak value optimization method and system for a peak period of an urban rail train group, and the method comprises the steps: obtaining a historical train operation timetable, real-time position data and an energy consumption curve, and generating a training sample set; classifying the operation intervals in the sample set based on the operation interval difference, and determining the peak type of each interval; training a time sequence classification model by utilizing interval data and peak types to obtain an energy consumption feature classification model, and training a multi-objective optimization neural network model to obtain an energy consumption peak value optimization model; and inputting real-time operation data into the classification model to identify a current peak type, and inputting the data into the optimization model according to the type to generate a dynamic timetable adjustment strategy so as to smooth an energy consumption peak value. According to the method, the energy consumption peak value of the urban rail train group can be reduced, the energy utilization efficiency is improved, departure interval fluctuation and delay are reduced, and the operation stability is improved.

The invention relates to the technical field of single-photon detection, in particular to a single-photon detector performance parameter measurement method, device and equipment and a storage medium, and the method comprises the steps: building a statistical histogram of time domain interval data, and building the statistical distribution characteristics of the photon counting rate and dark counting of a single-photon detector; the probability of no photon detection event in the duration of the optical pulse and the ideal probability of no dark pulse in a preset time window are defined based on the statistical distribution characteristics, and then the probability of generating a single photon signal by the periodic optical pulse is calculated based on the obtained parameters; and finally, determining a periodic statistical peak count value based on the statistical histogram and the probability of generating the single photon signal by the periodic light pulse, and calculating an average detection photon number and a dark count rate so as to obtain the dead zone time, so that information such as the average detection photon number, the dark count rate and the dead zone time of the single photon detector is simultaneously represented through one-time data acquisition, and the detection accuracy of the single photon detector is improved. The problems that in a traditional method, the data storage amount is large, and cost is high are solved.

The invention provides a ceramic card production data state control method and system. The method comprises the following steps: obtaining production data in a ceramic card production process; by adopting the data acquisition module with a distributed architecture, sensor self-diagnosis and self-adaptive adjustment functions and dynamic self-adaptive time interval data acquisition, and combining with the preprocessing and standard storage mode of the data processing and storage module, the problems of inaccurate production data recording and transmission delay errors are solved; the data state updating module monitors in real time, updates the state based on a machine learning model, and realizes data state real-time tracking and anomaly discovery by matching with a visual platform of the data state monitoring and early warning module, dynamic threshold early warning and multi-mode early warning; a multi-dimensional analysis result is generated through a data analysis module by applying a professional tool and an algorithm, and an intelligent decision-making module constructs an intelligent decision-making model and provides an effective data state control mechanism, so that reasonable production scheduling and decision-making are performed according to a production data state.

The application discloses a kind of intelligent anchor rod system early warning method and threshold determination method, belong to anchor rod intelligent early warning field, including S1: monitoring data preprocessing, S2: data trend regression analysis and S3: early warning method and index;Step S1 includes substep, S11: detection and correction of singular point, S12: obtain equidistant data and S13: data denoising smoothing;Step S2 includes substep S21: regression model establishment and S22: determine regression coefficient;Step S3 includes substep, S31: the determination method of limit stress static threshold of each monitoring point, S32: the determination method of stress change rate static threshold, S33: sharp end mutation model, the determination method of section stability threshold and S34: multi-factor joint early warning method.The application is by monitoring data preprocessing, data trend regression analysis and division early warning method and index. According to this, the overall trend and law of stress change of roadway surrounding rock can be accurately analyzed, and the anchor rod early warning system early warning threshold is accurately determined.

The invention relates to the technical field of construction early warning, and discloses a visual early warning system based on highway construction environment monitoring. Comprising a monitoring node marking module used for marking monitoring nodes in a construction period, an interval data acquisition module used for acquiring comprehensive construction data of a standard construction interval and a diffusion construction interval, an environment diagram construction module used for constructing a node environment diagram, and an early warning prompt judgment module used for judging whether to send out an environment early warning prompt. According to the invention, an environment pollution area directly or indirectly caused by highway construction on a crossing area can be accurately represented, multi-source construction data of the highway can be comprehensively collected, and pollutant generation and diffusion rule information can be accurately analyzed in combination with a combination mode of field monitoring and experimental simulation, so that the method is simple and convenient to operate. The monitoring and early warning depth of the highway construction on the environmental pollution and harm of the crossing area is improved, and the healthy and stable progress of the highway construction progress is ensured.

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

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

The invention discloses a remote secure connection method and system for an industrial internet of things, and belongs to the technical field of remote secure connection, and the method comprises the steps: obtaining the polling interval data of target industrial equipment, analyzing a polling mode characterization factor, judging a polling interval execution scheme of the remote connection of the target industrial equipment, and carrying out the remote connection of the target industrial equipment; synchronously analyzing a polling mode characterization factor deviation value, executing dynamic polling interval adjustment of remote connection of the target industrial equipment, analyzing dynamic polling interval adjustment effect information, analyzing a network state characterization factor of remote connection, and executing dynamic protocol timeout adjustment of remote connection of the target industrial equipment. And analyzing the dynamic protocol overtime adjustment effect information to obtain a protocol overtime execution scheme of remote connection of the target industrial equipment. According to the invention, the dynamic polling interval is dynamically changed, the inherent rule is broken, the protocol timeout time is dynamically adjusted, the security and real-time performance of remote secure connection are improved, and the compatibility and standardization of the system protocol are enhanced.

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

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

The invention relates to the technical field of plastic particle cutting control, and discloses a high-precision plastic particle cutting equipment control method and system capable of automatically rectifying deviation, and the method comprises the following steps: obtaining a to-be-cut material image, a projection line image and the cutting amount of the current cutting stage; calculating the distance between adjacent projection lines according to the projection line image to obtain spacing data; performing image preprocessing on the to-be-cut material image to obtain an initial sectional area of the to-be-cut material and a corresponding sectional image coordinate; calculating the optimal section coordinate of each to-be-cut material section according to the initial section area and the section image coordinate, and adjusting the initial section area according to the optimal section coordinate to obtain the section area of the to-be-cut material; performing least square fitting on the sectional area of the to-be-cut material and the coordinates of the sectional image to obtain an optimal sectional area; the optimal sectional area is substituted into a preset cutting speed formula to calculate cutting speed data, and the optimal cutting speed is obtained; and the cutting device is controlled to conduct cutting work according to the interval data, the optimal cutting speed and the optimal cutting amount.

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

The present application relates to the technical field of data processing technology and discloses a method for analyzing the reliability of avionics products using multi-source interval data, comprising: summarizing and classifying the multi-source interval data of a specified avionics product to obtain multiple groups of interval data, substituting the multiple groups into an exponential distribution model, and constructing corresponding quality functions according to a unified identification framework; obtaining conflict coefficients and credibility coefficients between the quality functions corresponding to different groups of the interval data, and determining fusion weight coefficients of the respective quality functions; obtaining a weighted quality function according to the fusion weight coefficients, fusing the weighted quality functions based on the D-S evidence theory to obtain a fusion quality function, and transforming the fusion quality function according to the Pignistic probability transformation method; obtaining the reliability of the avionics product at any time according to the transformation result, fitting and obtaining a reliability curve with respect to time changes to perform reliability analysis.

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

A model generation unit (17) generates, on the basis of change interval data obtained from log data stored in a log data storage unit (12), a change interval model obtained by modeling change interval data representing an interval between a start signal that causes operation of a device and a completion signal that appears as a result of the operation of the device. An abnormality degree calculation unit (18) calculates the degree of abnormality of the operation of the device from an actual measurement value, which is an interval between the start signal and completion signal obtained from the device, and a predicted value, which is an interval predicted using the change interval model generated by the model generation unit (17).

The invention provides a self-starting control chart sequence rebound parameter updating method based on warning limit triggering, and relates to the field of statistical process control. Different from a traditional product quality characteristic distribution model based on normal distribution, the method mainly takes product quality monitoring in a high-quality manufacturing process as a target, modeling is performed on an event occurrence interval variable Xt in the process by using gamma distribution, a pivot vector # imgabs0 # is constructed based on the modeling, and mapping from the Xt to self-starting event occurrence interval data St is completed. Further constructing a monitoring statistic Qt of the self-starting control chart by using an exponential weighted moving average method, dynamically switching a parameter updating process based on a time sequence and a sequence bounce according to position information of the Qt in a safety domain, a warning domain and an out-of-control domain of the control chart, delaying the progress that an out-of-control sample flows into parameter estimation, and calculating the out-of-control sample according to the parameter updating process. Data pollution caused by out-of-control samples to parameter estimation is reduced, and the monitoring efficiency of the self-starting control chart is improved.

The invention discloses a real-time data compression method for a focal plane array performance test, and the method comprises the following steps: S1, connecting a test instrument with a to-be-tested focal plane array, and carrying out the calibration of the test instrument; s2, determining the distance between the laser light source and the focal plane array to be measured, setting a data compression template according to the characteristics of the focal plane array, and performing non-uniform interval division on output data; s3, estimating the occurrence probability of each interval data, and generating an output data interval mapping table; s4, on the basis of the interval division and the mapping table, Huffman coding is carried out on each output data interval, and an original output data coding mapping table is generated; and S5, in a data acquisition process, performing real-time compression on the acquired data by utilizing the generated original output data coding mapping table. According to the invention, the storage space of the test data is saved, the transmission time of the test data is shortened, and high-efficiency processing of the test data in the later period is facilitated.

The invention provides a diesel generator monitoring data analysis processing method and a control system, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a target combination in different server combinations according to the distribution data of servers in different server combinations in different dates during the simultaneous operation time period, and determining a starting processing strategy of the diesel generator in the simultaneous operation time period corresponding to the target combination according to the historical interval data between the simultaneous operation time periods of the target combination and the similar condition of the energy consumption data of other target combinations in the simultaneous operation time period, and performing active starting processing of the diesel generator according to the starting processing strategy. According to the method, the starting processing strategies of different time periods in the current date are determined according to the active starting data of the diesel generator in the preset time period and the similar condition of the server starting data of different time periods in the current date and the target combination, so that the reliability of monitoring processing is improved.

A method, system, and apparatus including a computer program encoded on a computer storage medium for determining a customer lifetime value of a user of a client application. The method can include receiving first user data characterizing an interaction between a first user and a client application executing on a client device of the first user, first time interval data characterizing a first time interval associated with the first user, and second time interval data characterizing a second time interval associated with the first user, wherein the first time interval characterizes a length of time shorter than a length of time characterized by the second time interval.

The invention discloses a sensor data super-resolution reconstruction method based on long and short time sequence characteristic assimilation, and relates to a sensor data super-resolution reconstruction method. According to the invention, the problem of standardized processing of irregular sampling interval data is effectively solved. Secondly, a double-engine super-resolution reconstruction framework is innovatively constructed: on one hand, a convolution-deconvolution neural network is utilized to mine long-time scale periodic features, and a deep feature representation capability is obtained through historical data training; and on the other hand, a Shenchang differential equation is innovatively introduced, a continuous time dynamic state is modeled from a micro gradient difference of data change, and short-time transient characteristics are accurately captured. On the basis, weight fusion is carried out on double-engine output through a particle filtering algorithm, and assimilation of long-time and short-time data features is achieved. According to the invention, through cooperative mining and fusion of spatial-temporal characteristics, the precision and robustness of super-resolution reconstruction are significantly improved. The invention belongs to the technical field of sensor data super-resolution reconstruction.

The invention discloses an efficient PLC process data high-speed acquisition method, and belongs to the field of industrial automation control. Aiming at the problems of insufficient stability, poor real-time performance, low processing efficiency and the like of the existing PLC data acquisition communication, an array queue and a time variable are created in the PLC, a data transmission structure is improved, a data acquisition subprogram is arranged in a high-speed operation cycle program block, and 10ms interval data acquisition is realized by matching with a 10ms timer. Edge equipment and fog computing are utilized to cooperate, a machine learning algorithm is introduced to evaluate data quality, and an upper computer adopts a self-adaptive communication strategy to encrypt key data. In the implementation process, a Siemens 1500 series PLC is selected and matched with TIA Portal software and CMS upper computer software, and through the steps of hardware and software preparation, PLC configuration, upper computer software CMS configuration and the like, 10 ms data high-speed collection is achieved, data processing efficiency is improved, real-time performance is enhanced, data integrity is guaranteed, and powerful support is provided for industrial automatic production.

The invention provides a modification method and device for a grid index of a spatial data set, equipment and a medium, and the method comprises the steps: obtaining a first spatial data set, and determining a grid division parameter based on the first spatial data set, constructing indexes for a plurality of first intervals divided by the first spatial data set based on the grid division parameters to obtain a plurality of first B-tree indexes; performing data updating on the first spatial data set to obtain a second spatial data set, and determining a second interval data volume; and based on any first interval, performing index modification on the first B-tree index based on the second interval data volume and the third interval data volume to obtain at least one second B-tree index, and performing interval adjustment on the first interval based on the second interval data volume and the third interval data volume to obtain at least one second interval. According to the technical scheme of the embodiment of the invention, the interval number of the grid index and the number of the data items in the interval can be automatically adjusted, and the indexing efficiency and the indexing performance of the spatial data set in the updating state are improved.

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

The invention discloses a monitoring method for a trolley breast board of a roasting machine, which comprises the following steps: taking the upper edge of the breast board of a lower trolley as a detection target of a distance sensor, continuously acquiring real-time interval data between the distance sensor and the detection target through the distance sensor, and taking a difference value between the real-time interval data and a standard distance as a judgment factor by a PLC (Programmable Logic Controller); when the judgment factor is smaller than the first parameter, a first signal, a second signal and a third signal are output between the first parameter and the second parameter and larger than the second parameter respectively, and the breast board bends outwards under the influence of hot air until a material loss accident exactly occurs and is separated from the distance sensor by a boundary point distance; the first parameter is a difference value between the standard distance and the boundary point distance, the second parameter is not less than two times of the thickness of the detection target, and the upper computer respectively displays the safety operation information, the first alarm information and the second alarm information according to the signals. In the application of an actual production field, multiple potential breast board risks are successfully detected, and accidents are avoided.

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

The embodiment of the invention provides a video lagging identification method and device, a storage medium and a computer program product, and relates to the technical field of communication, and the method comprises the steps: obtaining an RTP signaling data packet of a target RTP video stream; when the video frame serial numbers corresponding to the sequentially acquired RTP signaling data packets have discontinuous sequences, determining that the video frames where the RTP signaling data packets corresponding to the missing sequences among the discontinuous sequences are located are bad frames; and when the time interval data between the starting time of the bad frame and the starting time of the next key video frame which is a good frame is greater than a preset sensing delay threshold, it is determined that the target RTP video stream is lagged, and the video sequence number corresponding to the RTP signaling data packet of the good frame does not have a missing sequence. The video lagging identification method provided by the embodiment of the invention has nothing to do with the external environment, so that the video quality can be evaluated more accurately, and the user experience feeling of the video business service is improved.