167 results about "Frequency data" patented technology

The invention discloses a data auditing method based on big data, and relates to the technical field of big data, and the method comprises the steps: obtaining multi-modal data in a big data platform form, extracting features of the multi-modal data to generate a feature set, creating a rule base based on the feature set, and constructing a cross-form dependency graph according to the feature set and the rule base; cascade auditing is executed through a cross-form dependency map, when auditing fails, an affected field is reversely positioned according to the connection direction of the map, and incremental state rollback and re-auditing are executed on the affected field; when it is detected that the high-frequency data is changed, an anti-rollback storm mechanism is started, a cascade load index is obtained, and a rollback instruction set is generated through cascade blocking based on the index; and executing distributed fault-tolerant control, constructing a node collaborative protection system through cascade loads, and outputting a fault-tolerant operation instruction.

The invention relates to a power construction deviation degree diagnosis method based on multi-modal time series data fusion, and the method comprises the steps: collecting voltage, current and frequency data through a multi-channel synchronous sampling technology, filling missing data through cubic spline interpolation, and constructing an initial data matrix; based on a hierarchical feature extraction technology, mapping the voltage frequency domain features and the current time domain statistical features to a unified feature space through canonical correlation analysis, and generating a multi-modal feature vector with a time sequence tag in combination with a sliding window; analyzing the dynamic trend of the electrical variable under multiple time scales by adopting a long short-term memory network, and capturing a key time point through an attention mechanism; a sudden change point and a stationary section are defined, an isolated forest algorithm is combined to detect an abnormal point location deviating from a trajectory, anomaly is classified as transient disturbance or continuous deviation through a multi-layer perceptron, the evolution trend of regional continuous deviation is predicted, and the key problem of the power deviation degree diagnosis capability is improved.

The invention belongs to the technical field of machine room power distribution, and provides a machine room power distribution equipment remote monitoring method and system, and the method comprises the steps: collecting operation data in real time through deploying edge equipment, fusing basic operation data, transient feature data and historical abnormal data, and constructing a state scoring model; utilizing a neural network training weight coefficient to realize equipment health degree dynamic evaluation; the sampling frequency is adaptively adjusted based on a scoring result, abnormal data are screened by combining multiple characteristic thresholds such as current sudden change intensity, energy sudden change quantity and temperature gradient, and only high-frequency data fragments in an abnormal time period are reserved; through an edge-cloud collaborative storage mechanism, abnormal data are locally cached after RAID redundancy check, normal data are uploaded to the cloud according to storage duration and access frequency in a grading manner, full-life-cycle traceability of the data is realized through tagging processing, anomaly detection sensitivity is improved, and storage efficiency and fault diagnosis reliability are both considered.

The invention relates to the field of sensor data processing, and discloses an MEMS drift correction method based on data dynamic sampling and multi-source fusion, and the method comprises the following steps: S1, collecting MEMS three-axis acceleration original data at a first sampling frequency, and synchronously collecting environment temperature, humidity and air pressure data at a second sampling frequency, the first sampling frequency is higher than the second sampling frequency, the basic drift correction model is established by using the low-frequency data to capture the macroscopic and slow-varying relationship between the environmental factors and the drift, then the residual refinement model is established for the high-frequency data, and the high-frequency residual is specially processed; high-frequency drift components related to the environmental change rate are captured, through the two-stage fusion strategy, the accuracy of the model in the macroscopic trend is guaranteed, high-frequency data are fully utilized to improve the instantaneous correction precision, information loss caused by simple downsampling in a traditional method is avoided, and false signals introduced by rough interpolation are also avoided.

The invention provides an oil well load indicator diagram prediction method and system, and the method comprises the steps: synchronously collecting an initial electrical parameter data set, a load data set, an accelerometer data set and a barometer data set of an oil well, the initial electrical parameter data set comprising an active power data unit, and then obtaining an active power data segment and a load data segment; obtaining a time lag based on the two, and further obtaining a final electrical parameter data segment; acquiring a reference displacement sequence through the accelerometer data set and the barometer data set; obtaining a dynamic stroke length based on the reference displacement sequence and the load data segment, and further obtaining a final displacement sequence; and constructing a prediction neural network, and obtaining an oil well load indicator diagram based on the prediction neural network and the real-time electrical parameter data segment. According to the technical scheme, the indicator diagram is obtained through continuous and high-frequency real-time electric parameter data segments, motion period information loss caused by low-frequency data collection is avoided, accumulative errors are avoided, and the accuracy of the indicator diagram is ensured.

The invention provides a cooling tower system optimal operation parameter self-adaptive regulation and control method, which comprises the steps of collecting multi-source high-frequency data in a system operation process, and obtaining a standardized variable sequence through normalization, abnormal value processing and time sequence consistency verification. A sliding window mechanism and a lightweight causal discovery algorithm are combined, a causal atlas is dynamically constructed and fused, parameters are corrected in real time through an incremental Bayesian network, a dominant causal path is extracted, lightweight CMA-ES rolling optimization is executed in an equivalent control subspace after dimension reduction, optimal control parameters are solved, system closed-loop adjustment is driven, and the optimal control parameters are obtained. Meanwhile, a map confidence degree evaluation and depth reconstruction mechanism is set, dynamic self-adaption of a causal structure and parameters is ensured, the response capacity of a cooling tower system to working condition changes can be improved, and energy efficiency and operation stability are optimized.

The invention relates to the technical field of power system frequency stability control, in particular to a power system inertia online evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining frequency deviation data and active power data of each node, carrying out the low-pass filtering of the frequency data, and calculating the amount of active unbalance; an inertia response model is constructed based on the rotor motion equation of the synchronous generator, and inertia and damping coefficients are estimated by using a least square method; constructing a transfer function and judging the stability of the transfer function, if the transfer function is stable, inputting an active unbalance amount to obtain a frequency deviation predicted value, and calculating a Pearson correlation coefficient with an actual frequency; different time periods are selected through a sliding window to repeat the process, finally, the inertia corresponding to the maximum Pearson's correlation coefficient is selected as a system inertia evaluation result, the inertia level of the system can be accurately evaluated on line through the method, and therefore data support is provided for frequency control, and the frequency stability and safety of the system are guaranteed.

The invention provides a CFRP plate elastic constant inversion method based on zero group velocity point wave number-frequency, and belongs to the technical field of nondestructive testing of carbon fiber composite plates. In order to solve the problems that in the nondestructive testing process of the CFRP plate, a complete elastic constant matrix of the composite material cannot be obtained and the obtained elastic constant has obvious errors, the method comprises the following steps: calculating frequency dispersion curves in different directions according to the elastic constant of the carbon fiber composite material plate; obtaining theoretical wave number-frequency data based on the wave number-frequency data; carrying out full wave field scanning on the surface of the carbon fiber composite material plate, carrying out three-dimensional Fourier transform on time domain data obtained by the full wave field scanning, and extracting to obtain actually measured wave number-frequency data; and constructing an objective function of an error between the wave number-frequency data corresponding to the theoretical data and the actually measured data ZGV frequency point, and performing multi-parameter optimization on the objective function by using a particle swarm algorithm to obtain an inversion result of the elastic constant of the carbon fiber composite material plate.

The invention relates to the technical field of ocean sample management, and discloses an ocean sample full-life-cycle digital management method, which comprises the following steps of: firstly, collecting characteristic data such as sample types, source positions and chemical component information of ocean samples, and calculating a classification scheme and storage parameters according to the characteristic data; evaluating a management demand and generating a target management strategy by combining the real-time management data of the sample library; making and optimizing a preliminary management plan by using algorithms such as k-means clustering and a decision tree in combination with historical management data and sample use frequency data, and generating a personalized management scheme; and finally, implementing the scheme, monitoring a sample preservation rate index through a sensor, dynamically adjusting a temperature threshold value and a storage position of classified storage equipment, introducing a real-time feedback mechanism to iteratively correct and adjust parameters, and generating a full-life-cycle management scheme. According to the method, full-process digital management of the ocean samples is realized, and the management efficiency and the sample storage quality are improved.

The invention relates to the technical field of information retrieval, in particular to an intelligent screening method based on multi-source data, which comprises the following steps: acquiring high-frequency time sequence data of a first data source, and dividing the high-frequency time sequence data into a state change interval and a state stable interval; extracting dynamic features from the state change interval, extracting statistical features from the state stable interval, and generating a pattern feature cluster sequence; acquiring low-frequency data of a second data source, acquiring a timestamp as an anchor point, acquiring a precursor node and a subsequent node of a mode feature cluster in combination with the mode feature cluster sequence, and generating an anchor point association map; executing bidirectional prediction verification based on the anchor point association graph, calculating a bidirectional consistency score, and generating an alignment confidence label; and monitoring incremental updating of high-frequency time sequence data, performing reverse prediction verification again, updating and aligning confidence labels, screening anchor point association records with high confidence, performing fusion processing on low-frequency data features and mode feature clusters, and outputting a screening result.

The invention relates to the technical field of data processing, in particular to a user and entity behavior modeling anomaly detection system, which comprises a timestamp generation module for receiving user power consumption data, equipment monitoring data and power grid monitoring data to generate data vectors; the dynamic association module interpolates the low-frequency data in the same time window to generate a virtual sampling point, and outputs a multi-dimensional association tensor; the behavior model generation module generates a joint behavior model and a confidence probability; the detection decision-making module adopts four parallel optimization channels to output three-dimensional decision-making indexes including abnormal probabilities; the simulation optimization module drives multi-algorithm simulation in the digital twin environment and writes back optimal parameters; and the feedback correction module generates a time reversal data flow reconstruction behavior model based on the simulation error, and outputs an equipment binding early warning signal to a power grid safety system. According to the method, the problem of dynamic association missing caused by multi-source data time sequence asynchronization is solved, and efficient collaborative diagnosis of composite anomalies such as electricity stealing is realized.

The invention discloses an electric power real-time dynamic carbon factor metering and monitoring system, which belongs to the technical field of electric power systems, and comprises a data acquisition module used for acquiring multi-source data of a power generation side, a power grid side and a user side of an electric power system in real time, verifying the multi-source data, generating high-frequency data streams, and transmitting the high-frequency data streams to a data processing module; the high-frequency data stream comprises renewable energy output fluctuation data, load change data and power grid topological structure data; the dynamic calculation module is used for generating time-sharing and partitioned dynamic carbon factors based on the high-frequency data stream; the monitoring feedback module is used for generating a carbon flow path track based on the dynamic carbon factors and generating a calibration instruction set in combination with deviation records in the historical dynamic carbon factors; and the optimization output module is used for recalculating the dynamic carbon factor based on the calibration instruction set and the real-time multi-source data, and outputting the optimized dynamic carbon factor of the time-sharing partition to an external carbon accounting system. According to the method, the problem that the traditional annual average factor cannot reflect the space-time dynamism is solved by calculating the carbon factor of the time-sharing partition.

The invention discloses a health detection instrument data processing system and method based on the Internet of Things, and relates to the technical field of data analys.The method comprises the following steps that sampling intervals are determined, a CGM sensor is controlled to collect data according to the corresponding sampling intervals, and a frequency mixing blood glucose data set is formed; constructing an input data set, and introducing a sampling interval weight to construct a basic trend model; based on the input data set, introducing a correction item to construct a scene correction model, and outputting a final prediction value at the current next moment; setting a future prediction time interval and a prediction moment number N, generating N prediction values based on the basic trend model and the scene correction model, and integrating the N prediction values to form a prediction sequence; setting a threshold value to analyze whether the prediction sequence is abnormal or not and the abnormal type; when an anomaly is detected, a feedback mechanism is triggered. The method can effectively improve the situation that in the prior art, dynamic blood glucose monitoring sampling frequency lacks scene adaptability and is insufficient in frequency mixing data processing capacity.

The invention discloses a frequency data analysis method and device based on self-supervised learning, and a storage medium. The method comprises the following steps: S1, constructing a standard spectrum tensor; s2, executing frequency change feature extraction operation to generate a frequency change feature map; s3, determining a frequency section of which the hopping intensity is greater than a preset hopping threshold value, constructing a hopping section mask, and generating a structure covering spectrum tensor; s4, inputting to an improved ConvTransform model, and generating a frequency spectrum reconstruction tensor and a residual feature tensor; s5, constructing a frequency spectrum reconstruction loss function and a residual error fitting loss function, executing a self-supervised training process, and constructing a frequency guidance feedback graph; and S6, applying to frequency data analysis, and generating a frequency spectrum reconstruction result, a residual error response result and a hopping section positioning result. According to the invention, the abnormal change identification capability and the frequency spectrum reconstruction precision in the frequency data analysis process are improved.

This invention discloses a high-frequency macroeconomic monitoring method and system based on an intelligent model. The method constructs a high-dimensional time-varying mixed-frequency dynamic factor model to establish a dynamic factor structure of time-varying parameters for daily, low-frequency flow, and stock indicators. It also constructs a real-time GDP tracking model, decomposing quarterly GDP into daily GDP and using it as a common factor, with other indicators providing updated estimates through time-varying regression. Joint parameter estimation is performed using a quasi-maximum likelihood method and Kalman filtering. In application, the model parameters are dynamically updated using real-time released mixed-frequency data, ultimately extracting the daily economic condition index and the daily GDP growth rate sequence, achieving multi-frequency real-time macroeconomic monitoring and early warning. This invention improves the timeliness, adaptability, and accuracy of macroeconomic monitoring.

The invention belongs to the technical field of arc detection, and relates to an aviation alternating current series arc fault detection method based on a variable coefficient, which comprises the following steps: collecting a current signal, dividing the current signal into a continuous time window, and forming a current frequency data set; performing frequency component analysis on the current frequency data set to obtain a frequency distribution vector, and calculating a standard deviation and a mean value of the frequency distribution vector to obtain a variable coefficient; forming a variable coefficient sample set based on historical current data collected under different loads, and performing dual Weibull distribution fitting; selecting a numerical value of a variable coefficient corresponding to a preset quantile level as an adaptive threshold value; and if the variable coefficient of the current time window is greater than the adaptive threshold, determining that an alternating current series arc fault exists, and outputting alarm information. According to the technical scheme of the invention, the current behavior is represented in a form capable of reflecting the characteristics of the frequency structure, and the stable recognition under the multi-load and disturbance working conditions is realized through the variable coefficient and the adaptive threshold.

The present disclosure provides a business test data extraction method applied to U shield testing, which can be applied to the fields of big data technology and artificial intelligence technology. The business test data extraction method applied to U shield testing comprises: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U shields according to the historical use frequency of each test U shield in the plurality of test U shields corresponding to a business category; updating the high-frequency data extraction rule corresponding to the business category at the current time through the high-frequency data features corresponding to the high-frequency data set and the low-frequency data features corresponding to the low-frequency data set; and extracting the corresponding business test data based on the high-frequency data extraction rule to realize the U shield testing. The present disclosure also provides a business test data extraction device, equipment, storage medium and program product applied to U shield testing.

The invention relates to an unbalanced traffic accident data analysis method based on a mixed diffusion model. The analysis method comprises the following steps: S1, preprocessing original accident frequency data; s2, performing marking processing on the accident frequency data; s3, constructing and training a variational auto-encoder model, and obtaining low-dimensional hidden space embedding representation; s4, constructing and training a mixed feature diffusion model; s5, performing oversampling on accident frequency data by using the trained mixed feature diffusion model; s6, training a traffic accident frequency model based on the enhanced accident frequency data set; and S7, outputting a result by applying the SHAP value interpretation model, and identifying key factors influencing traffic safety. Compared with the prior art, the method has higher mixed variable modeling and generating capacity, data with higher quality can be generated on the basis of keeping the diversity of accident frequency data and the consistency of variable relations, and the effect of accident frequency modeling and analysis is remarkably improved.

An object of a system according to an embodiment is to propose an optimal delivery timing based on a use frequency of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects use frequency data of a user. The analysis unit analyzes the data collected by the collection unit and calculates a delivery timing. The proposing section proposes a delivery timing to the user based on the analysis result obtained by the analyzing section.SELECTED DRAWING: Figure 1

The invention discloses a real-time data processing method and system based on frequency layering, and belongs to the technical field of computers, and the method comprises the steps: obtaining a layering configuration rule and a preset processing period of a data table according to attribute frequency; acquiring a corresponding change attribute according to the change of the real-time data, and acquiring a change plane and change time to which the change attribute belongs according to a hierarchical configuration rule; if the change attribute belongs to a frequent change plane in the hierarchical configuration rule, entering a frequent change increment queue and processing the change attribute and the change time according to the change time and a first period in a preset processing period, and if the change attribute belongs to a non-frequent change plane in the hierarchical configuration rule, entering a non-frequent change increment queue and processing the change attribute and the change time according to the non-frequent change plane in the hierarchical configuration rule; and processing the change attribute and the change time according to the change time and a second period in the preset processing period. The technical problem of low data processing efficiency caused by the fact that high-frequency data items and low-frequency data are not layered and distinguished in the prior art is solved.

This invention relates to the field of seismic source monitoring and discloses a dual-bubble seismic source monitoring method based on marine seismic detection. The method involves deploying a seismic detector array in a target sea area, recording seismic wave data generated by the dual bubbles, using the frequency data of the seismic waves as the first target monitoring data for seismic source identification, and using the propagation velocity of the seismic waves as the second target monitoring data. The method analyzes the arrival time of the seismic waves at different detectors to locate the seismic source. It also analyzes the amplitude attenuation characteristics and propagation path of the seismic waves during propagation to assess the earthquake magnitude and calculate the probability of earthquake occurrence based on the assessed earthquake risk level. This method improves the accuracy of seismic source data acquisition, enables accurate identification and location of the seismic source, and thus improves the accuracy of earthquake probability prediction.

The invention relates to the technical field of software running exception analysis, and discloses an AI-based software running exception analysis method and system, performance information and text information during software running are collected and matched, a judgment value P is calculated according to the performance information, and when the judgment value P is 0 or a negative number, the software running exception is analyzed. The duration data SJ and the occurrence frequency data CS are extracted for judgment, when a judgment value P is a positive number and the judgment value P is larger than or equal to a fault threshold GY, a detection instruction is sent, when the instruction is sent, the fault problem is analyzed, the fault position is positioned, a repair strategy is called, and fault isolation and repair are carried out; according to the method, the performance information and the text information during software operation are acquired, so that when the software operation has a problem, the fault can be known, and the fault can be found and processed in time in the early stage, and the situation that the software is directly and abnormally broken is avoided.

This application relates to a wheel detection method, apparatus, computer equipment, and storage medium. The method includes: acquiring noise time-domain data of the target wheel at a preset acquisition frequency using a noise acquisition component corresponding to the target wheel; determining the dominant noise frequency data of the target noise time-domain data in a preset frequency band based on the target noise time-domain data, and determining the target wear order of the target wheel based on the dominant noise frequency data; determining the target sound pressure level density threshold corresponding to the target wear order based on the target wear order and a preset first correspondence between the wear order and a sound pressure level density threshold; and generating detection information indicating wheel abnormality if the dominant frequency sound pressure level density in the noise frequency data is greater than or equal to the target sound pressure level density threshold. This solution allows for real-time detection of the target wheel.

The application relates to an intelligent data classification storage method and system based on deep learning, which comprises the following steps: obtaining an original data set, and obtaining a multidimensional data set after preprocessing; obtaining a frequency statistical set of data, and fitting the frequency statistical set by using a least square method to obtain an access frequency prediction value of each type of data; generating high-frequency or low-frequency data identification for corresponding data according to the comparison between the access frequency prediction value and a preset frequency threshold; and constructing a high-frequency data list and a low-frequency data list accordingly; obtaining a storage cost weight and an access delay index of a storage node according to a storage location index; calculating a storage efficiency score of each storage node; calculating an allocation proportion of each storage node, adjusting the allocation proportion through the storage efficiency score, and generating a multi-level storage allocation scheme; and after generating corresponding migration instructions according to the multi-level storage allocation scheme, migrating data to realize hierarchical classification storage of the data. The application can improve the utilization rate of system storage resources.

To provide an EMC countermeasure presentation system capable of efficiently extracting and presenting an emission countermeasure case in another field and another model with high similarity (countermeasure effectiveness) in consideration of a device configuration of an electronic apparatus.SOLUTION: A similar configuration extraction unit 3 configured to extract, based on device configuration data 11 at the time of noise measurement, device configuration data at a short distance by configuration learning data 9 obtained by learning a graph model representing a device configuration when the noise countermeasure and reduction amount frequency data is acquired; and a countermeasure estimation unit 4 configured to estimate, from data obtained from the reduction countermeasure extraction unit and the similar configuration extraction unit, a recommended countermeasure content having a high degree of similarity of a device connection configuration and expected to have an excessive noise reduction effect.SELECTED DRAWING: Figure 1

The invention relates to the technical field of economic measurement modeling, in particular to an economic prediction method and system based on weekly, monthly and quarterly big data, electronic equipment and a medium. The method comprises the following steps: acquiring multi-frequency multi-source economic time sequence data; performing frequency mixing alignment preprocessing on the multi-source economic time series data according to a preset frequency mixing data processing rule to generate a frequency mixing data set; constructing a connection model through a predefined modular architecture and a connection algorithm based on the frequency mixing data set; training the connection model through a parameter estimation algorithm; and inputting multi-source economic time sequence data obtained in real time into the trained connection model, executing high-frequency prediction, and outputting a real-time prediction result. By means of the mode, the technical problem that when an existing mixing dynamic factor model deals with multi-frequency mixing data, prediction precision is insufficient is solved, and the capacity of conducting high-frequency, accurate and real-time monitoring on the macroeconomic trend is improved.

The application belongs to the technical field of arc detection, and relates to an aviation alternating current series arc fault detection method based on a variation coefficient, comprising the following steps: collecting a current signal, dividing the current signal into continuous time windows, and forming a current frequency data set; performing frequency component analysis on the current frequency data set to obtain a frequency distribution vector, calculating a standard deviation and a mean value, and obtaining a variation coefficient; forming a variation coefficient sample set based on historical current data collected under different loads, and performing double Weibull distribution fitting; selecting a numerical value of the variation coefficient corresponding to a preset quantile level as an adaptive threshold value; if the variation coefficient of a current time window is greater than the adaptive threshold value, it is determined that an alternating current series arc fault exists, and alarm information is output. The technical scheme of the application characterizes the current behavior in a form capable of reflecting frequency structure characteristics, and realizes stable identification under multiple loads and disturbance working conditions through the variation coefficient and the adaptive threshold value.