25 results about "Data binning" patented technology

An example operation may include one or more of storing an original data set in memory, splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set, converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation, generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.

The present invention relates to an embedded video recognition safety integrated control platform including an artificial intelligence-based video recognition system, and more particularly, to a video recognition system including: a video data receiving unit that receives video data of the vicinity of a construction or industrial site from a camera module; a CNN-based object recognition unit that recognizes a human object in the video data received from the video data receiving unit and detects the recognized human object by boxing it according to the recognized human object data; and an event information generating unit that generates a warning light or a warning alarm when the human object detected by the CNN-based object recognition unit is exposed to a preset danger radius and situation including the vicinity of heavy equipment; a false detection / undetected classification unit that receives event information detected from the CNN-based object recognition unit and classifies it into positive detection, false detection, or undetected, and a deep learning-based object recognition learning algorithm, YOLO (You Only Look Back), which classifies the false detection or undetected classification received from the false detection / undetected classification unit. the re-learning unit re-learns the data set generated by the re-learning unit in real time through a CNN-based object recognition unit (CNN-based object recognition unit), a learning model update unit uploads the re-learned data set generated by the re-learning unit to the CNN-based object recognition unit in online real time, and an AI video recognition platform including an LTE communication unit for communication between the video recognition system and an external device including an external server or an administrator terminal.

The invention discloses an on-site historical evolution behavior analysis method for aerodynamic performance of a large wind turbine generator, which takes a power coefficient as a basic index for on-site analysis of the aerodynamic performance, and overcomes the influence of two adverse factors, namely deviation and randomness, of SCADA (Supervisory Control And Data Acquisition) wind speed data by adopting data compensation and data filtering. The relation between the wind speed and the wind wheel rotating speed is regarded as an inertial system, and a data driving time constant determination method is provided. A practical calculation working condition region division algorithm is designed, and a data binning method and a conservative data comparison method are adopted to solve the problems of scatter data singularization and interval critical value determination which need to be solved during working condition division respectively. Then, power coefficients based on the instantaneous power and the cumulative power are calculated, respectively. The five-year deterioration degree of the two units is respectively given by observing the effectiveness of the algorithm in a multi-angle manner through four indexes, the historical evolution of the on-site aerodynamic performance of the units is described, and the result shows that the method is effective and feasible.

The invention pertains to a new computer-implemented method for processing an EEG signal, that makes it possible to extract information about cerebral damage in full-term babies, born in asphyxia context, for help in an indication for treatment of hypoxic-ischemic encephalopathy (HIE), by computing a power spectral density (PSD) of an EEG signal, transforming the PSD by a mathematical function, processing the transformed values by data binning, and creating a probability distribution of the durations of the binned values.

A computer system determines whether a selected data field matches a first bin concept of a plurality of bin concepts in a semantic bin lookup table. Each bin concept is associated with one or more bin options and each bin option includes a plurality of bin intervals. When the data field matches the first respective bin concept, the computer system selects, from the semantic bin lookup table, a bin option of one or more bin options associated with the bin concept. The selected bin option includes a first plurality of associated bin intervals. The computer system displays a data distribution for the selected data field according to the first plurality of associated bin intervals. When the data field does not match a bin concept, the computer system generates default bin intervals based on statistical properties of the distribution of data values for the data field.

The invention provides a peasant household monitoring assistance method and system, a storage medium and a program product, and the method comprises the steps: obtaining the basic information of a peasant household and the information of an industry department, carrying out the deep analysis, and constructing the structured personal information of the peasant household; calculating and converting the personal information of the peasant households to form portraits taking the households as units, and storing the portraits in a database and regularly updating the portraits; carrying out data preprocessing, data binning and feature engineering by taking historical farmer monitoring data as labels on the basis of portraits taking households as units, obtaining risk coefficients by utilizing an integrated model, determining risk thresholds and setting risk early warning; and carrying out vectorization coding on the peasant household personal information triggering risk early warning by using a natural language processing technology, matching similar historical assistance measures, retrieving and recommending assistance policies, and integrating and outputting the assistance policies. According to the invention, the condition of the farmer is dynamically monitored, and intelligent assistance recommendation is timely and accurately provided.

The invention provides a cluster privacy set intersection method and device, and is suitable for the technical field of data processing, and the method comprises the steps: in an offline state, employing a data binning parameter and a data redundancy sharing parameter to carry out the preprocessing of a set element held by a specified participant, so as to at least obtain a coding result; carrying out casual transmission with other participants so as to respectively obtain specified random numbers held by the specified participant and the other participants; and in an online state, on each computing node of the computing cluster, according to the coding result and the specified random number, interacting with other participants so as to perform intersection calculation on the set elements distributed to the computing nodes to obtain an intersection result. According to the scheme, the availability and performance improvement of the privacy computing cluster can be considered to a certain extent, and the computing efficiency of the privacy computing cluster is exerted and improved.

This specification provides a data sorting method and a data processing method based on privacy protection. Based on the above methods, computing nodes can, on the premise of protecting the data privacy of the first data party and the second data party, jointly use the ciphertext data of the first data group and the ciphertext data of the second data group through translation operations according to preset protocol rules for joint sorting, so as to efficiently obtain a target sequence group containing the ciphertext data of the first data and the ciphertext data of the second data arranged according to the preset sorting rules. Then, by using the above target sequence group, data binning is performed to obtain multiple data bins; and based on each data bin, an information value about the combined first data and second data is calculated to accurately determine the data quality of the first data and the second data, effectively avoiding the leakage of the data held by the first data party and the second data party during the above data processing process.

A method for performing real-time data binning of spectrometer data includes receiving, at a digitizer including hardware and software and from a mass spectrometer, first scan data associated with a first scan from a plurality of scans. The method also includes receiving, at the digitizer and from the mass spectrometer, second scan data associated with a second scan from the plurality of scans. The method also includes combining (e.g., summing) the first scan data and the second scan data via the digitizer, to produce first summed scan data. The method also includes modifying, via the hardware of the digitizer, the first summed scan data based on a predefined intensity threshold, to produce second summed data. The method also includes analyzing, via the digitizer, the second summed data to identify a composition of a sample associated with the plurality of scans.

The invention relates to the technical field of data processing, in particular to an automatic data binning method, device and equipment and a medium, and the method comprises the following steps: carrying out initial binning on original data according to an initialization parameter to obtain an initial binning result; initializing a tangency point position and an initialization speed based on the initial binning result; calculating the initial fitness of the initialized tangency point position based on the initialized tangency point position; judging whether the initial fitness meets a preset condition or not; if not, updating the initialization tangent point position and the initialization speed; a target binning result is obtained through continuous iteration until the maximum number of iterations is reached or convergence is achieved, the target binning result is used for data classification processing of machine learning before model training so as to obtain a regular data classification result, population iteration is carried out based on a particle swarm algorithm, and a target binning result is obtained. Therefore, the optimal box separation number and the box separation tangent point which meet the requirements of the user can be quickly found, the box separation complexity is reduced, and the later student ability portrait distinguishing and determining are facilitated.

The invention discloses a figure distribution construction method of numerical value type tags and related equipment thereof, and belongs to the technical field of big data. The method comprises the following steps: obtaining total data used for constructing portrait distribution, the total data being numerical value type data; using a kernel density estimation algorithm to calculate the density distribution condition of the total data; performing data binning on the total data based on the density distribution condition to obtain a plurality of data intervals; clustering analysis is conducted on the data intervals, a plurality of data groups are obtained, and the number of the data groups is smaller than or equal to the number of the data intervals; and performing data visualization conversion on the plurality of data groups by using a preset visualization tool to generate data portrait distribution. The invention further relates to the technical field of block chains, and full data is stored in a block chain network. Visual figure distribution of numerical value type labels can be generated, a user can check and analyze the figure distribution efficiently, and the problem of figure distribution construction for processing high-cardinal numerical value data is solved.

The invention discloses a peasant household credit evaluation pre-model based on data binning processing and an application thereof, data is collected and verified in essential key links (land contract / circulation, irrigation water and electricity and the like) in agricultural planting, and python data programming is adopted to carry out peasant household planting data self-defined WOE binning and multi-dimensional data fusion processing to form an IV (Information Value), so that the peasant household credit evaluation pre-model can be used for evaluating the credit of the peasant household. And obtaining a real and credible peasant household credit pre-evaluation grading model based on IV statistical grading. The method comprises the following steps: taking a grain planting cycle (year) as a node, obtaining an evaluation result of batch peasant household data according to an evaluation grading model, comprehensively matching the evaluation result with a credit and credit conservation result of a peasant household, dynamically adjusting a weighting coefficient of a WOE result according to a matching result so as to approach an optimal score and conservation matching effect, and obtaining updating of a pre-model. The application of the model can bring more honest common peasant households into the credit range of banks, and supports the development of the general financial business of banks in rural areas.

The invention discloses a user intention-oriented data binning interaction method and device and a storage medium, and relates to the technical field of data binning, the method comprises the following steps: when a data binning node is in a to-be-interacted state, displaying an interaction page according to an interactive binning instruction; box separation adjustment parameters corresponding to the interactive page are determined, the box separation adjustment parameters are generated by the user through box separation adjustment according to the feature tag of the interactive page, and the interactive box separation instruction and the box separation adjustment parameters are generated with the user intention as the guide; and performing data binning according to the binning adjustment parameter and the binning index to obtain a binning boundary, and displaying a binning result to a user based on box monotonicity. Due to the fact that the user is guided to conduct interactive box separation through the interactive page and directly participates in the data box separation process, the box separation indexes and the feature tags of box separation are dynamically adjusted through interaction with the data box separation nodes, the intention of the user is fully played, and various complex and changeable data conditions and modeling requirements can be achieved.

The invention belongs to the field of information security, and discloses a physical unclonable function modeling method based on repetition information, and the method comprises the steps: collecting multiple responses of a PUF to the same excitation, calculating repetition data, and constructing an excitation-repetition data pair; the value of the repetition degree data is converted into a limited category label by adopting a data binning strategy, so that the training complexity is reduced; and training a repetition degree model by using a multi-classification feedforward neural network, and finally obtaining a PUF response through model prediction label mapping. According to the method, response and stability information contained in the repetition degree is fully utilized, efficient modeling of the complex structure PUF is achieved, limitation of traditional reliability attacks is broken through, the method has the advantages of being wide in attack range, high in efficiency, free of knowing the internal structure of the PUF and the like, and the method is suitable for various strong PUF structures and suitable for evaluating the anti-modeling safety of the PUF.

The present specification relates to the technical field of big data processing, and provides a data binning method and device based on a big data cluster, equipment and a storage medium. The method comprises the following steps: reading a data binning configuration file to obtain user-defined information; the user-defined information comprises a bin number and an evaluation function; determining a mapping number and a reduction number of a mapping reduction task according to a node cluster state and a data volume of a feature table; in the mapping stage, each Map is caused to separate each row of data in the feature table according to columns, and an index value of each column of data in the corresponding row is determined; in the shuffle stage, a partitioner is caused to place features of a same complete column in a same partition according to the index value; in the reduction stage, each Reduce reads a column of data from a corresponding partition and calls the evaluation function to process the column of data, and the processing results of each Reduce are reduced according to the bin number. The embodiments of the present specification can improve the processing efficiency of data binning processing and reduce resource consumption.

The application relates to a driving control and data acquisition system of a digital ion trap. The application relates to the technical field of mass spectrometer control and data acquisition, and the system takes an FPGA main control unit as a core, internally has four modules of main control and timing scheduling, waveform generation, direct current control and data acquisition, is matched with a communication module, a radio frequency coupling and output interface module, a power module and an upper computer. The application binds waveform scanning parameter updating, direct current gate switching, sampling triggering and data binning operation to the same timing engine through the main control and timing scheduling module to construct a global unified clock domain and timing engine, realizes full-process synchronous linkage of driving control and data acquisition, supports configurable radio frequency driving, multi-mode and multi-channel direct current output, real-time signal processing and multiple safety protection, can eliminate alignment errors of scanning and acquisition, improves mass spectrometry precision, resolution and repeatability, and can flexibly adapt to application requirements of various ion trap structures and portable ion trap mass spectrometers.

The invention provides a model monitoring method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: loading model data of a network model from a preset data source end; performing data preprocessing on the model data of the loaded network model; performing data slicing processing on the preprocessed model data to obtain a plurality of data slices; for each data slice, carrying out data binning processing on the model data in the data slice to obtain a plurality of data buckets corresponding to the data slice; carrying out dimension reduction mapping processing of model data on the plurality of data buckets in parallel; a plurality of threads in a preset thread pool are utilized, monitoring index calculation is carried out in parallel according to the model data subjected to dimension reduction mapping processing in each data sub-bucket corresponding to each data slice, a monitoring index corresponding to each data sub-bucket is obtained, and the monitoring indexes are indexes reflecting the performance of the network model.

Embodiments of the present application disclose a power transmission line icing data processing method, device and equipment, comprising: acquiring an icing data sequence, preprocessing the icing data sequence; performing frequency spectrum analysis, determining a screening threshold based on the frequency spectrum analysis result; using the latitude and longitude position of the station to perform data binning on the icing data sequence; acquiring meteorological data of each station, generating a first evaluation result of each station by using a pre-established meteorological multiple regression prediction model, generating a comprehensive evaluation result of each station subsequence according to the first evaluation result; comparing the comprehensive evaluation result of each station subsequence with the screening threshold, determining abnormal data in the multiple subsequences according to the comparison result, and marking. The method can clean the icing thickness observation data based on the actual meteorological conditions without the need for substantial modification of the icing observation instrument, and improves the quality of the icing thickness data after processing.

The invention discloses an analysis method for automatically processing metagenome high-throughput sequencing data binning to obtain a viral genome, which comprises the following steps: acquiring second-generation sequencing original data, and performing data quality control to obtain Reads to be analyzed; a plurality of Reads are assembled into an overlapping group through overlapping of fragments, statistics is conducted on the overlapping group, and a length distribution diagram is drawn; reconstructing a viral genome based on contig binning, and generating a box after sequence clustering; constructing a three-dimensional matrix of all boxes according to the attributes of the sequence, and predicting and identifying the species category of each box; carrying out quality identification on the virus box bin, and detecting and rejecting virus host genes; counting the abundance of the virus box bin in each sample to generate a virus box bin abundance table; constructing a virus annotation database, completing species annotation, constructing a virus evolutionary tree, and finally generating a report. The invention further discloses an analysis system and application for implementing the analysis method.

A method for performing real-time data binning of spectrometer data includes receiving, at a digitizer including hardware and software and from a mass spectrometer, first scan data associated with a first scan from a plurality of scans. The method also includes receiving, at the digitizer and from the mass spectrometer, second scan data associated with a second scan from the plurality of scans. The method also includes combining (e.g., summing) the first scan data and the second scan data via the digitizer, to produce first summed scan data. The method also includes modifying, via the hardware of the digitizer, the first summed scan data based on a predefined intensity threshold, to produce second summed data. The method also includes analyzing, via the digitizer, the second summed data to identify a composition of a sample associated with the plurality of scans.

An example operation may include one or more of storing an original data set in memory, splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set, converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation, generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.

An example operation may include one or more of storing an original data set in memory, splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set, converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation, generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.

An example operation may include one or more of storing an original data set in memory, splitting the original data set into a subset of continuous-type data values and a subset of discrete-type data values based on variable types in the original data set, converting the subset of continuous-type data values into a second subset of discrete-type data values based on a data binning operation, generating a new subset of continuous-type data values based on the subset of continuous-type data values in the original data set, and combining a subset of discrete-type data values from a conditional contingency table within the new subset of continuous-type data values to generate a new data set.

The invention pertains to a new computer-implemented method for processing an EEG signal, that makes it possible to extract information about cerebral damage in full-term babies, born in asphyxia context, for help in an indication for treatment of hypoxic-ischemic encephalopathy (HIE), by computing a power spectral density (PSD) of an EEG signal, transforming the PSD by a mathematical function, processing the transformed values by data binning, and creating a probability distribution of the durations of the binned values.

The application relates to a data binning method, device, computer equipment and storage medium. The method comprises the following steps: acquiring business data; dividing the business data into discrete variables and continuous variables; automatically binning each continuous variable to obtain a plurality of variable binning results corresponding to the continuous variable; in response to the number of categories of the discrete variables being less than or equal to a minimum category number threshold, outputting the discrete variables; in response to there being at least one discrete variable with a category number greater than the minimum category number threshold, automatically binning each discrete variable to obtain a plurality of variable binning results corresponding to the discrete variable; according to a preset evaluation index, screening the plurality of variable binning results corresponding to each continuous variable and / or discrete variable to obtain an optimal variable binning result; and outputting the optimal variable binning result and a parameter range of a corresponding binning method. The method can reduce the system calculation pressure, and can comprehensively obtain a plurality of variable binning results of data and an optimal variable binning result.