34 results about "Original data" patented technology

The invention discloses an obstacle feature extraction method based on a millimeter wave radar and a laser radar. The method includes the following steps that: step 1, radar raw data are acquired; step 2, the radar original data are solved according to a radar protocol, so that obstacle position information can be obtained; step 3, the solved millimeter wave radar data are screened; step 4, time and space synchronization is performed on the millimeter wave radar and the laser radar; step 5, regions of interest (ROI) are established in a laser radar coordinate system according to millimeter wave radar information and are denoted as Ri, wherein i is equal to 1, 2, ... n, wherein n is the number of the effective targets of the millimeter wave radar; step 6, the point cloud data of the i-th ROI are processed according to a scattered point cloud data simplification processing method; and step 7, a two-dimensional image is generated on the basis of the point cloud data, edge detection is performed on the image, and obstacle features are extracted. With the method adopted, obstacle edge information can be obtained, and therefore, more references can be provided for a driverless decision-making layer.

The invention discloses a serum specificity metabolite spectrum for a patient with a lung cancer and a building method thereof, and particularly discloses a method for carrying out liquid-phase/gas-phase chromatography mass spectrometry metabonomics analysis based on the patient suffering from the lung cancer. The method comprises the following steps: collecting fasting blood samples of a healthy and normal detected person and the patient suffering from the lung cancer 7 days before and after an operation, separating by a chromatogram column after processing; extracting and aligning original data of an instrument after adopting mass spectrometry analysis; building a mathematical model to analyze the metabolites spectrum by adopting multivariable data statistical software; comparing with the difference between an average person with the metabolites spectrum of the patient with the lung cancer, so as to determine the specificity metabolite spectrum. By adopting the method disclosed by the invention, the mutation status of the metabolite of the patient with the lung cancer can be generally and comprehensively reflected, and the serum specificity metabolite spectrum can be applied to early diagnosis of the lung cancer or a beneficial technical support is supplied for prognosis.

The invention relates to an improved particle swarm-optimized neural network-based transformer fault diagnosis method. The method includes the following steps that: related data of dissolved gases in transformer oil and transformer fault information are obtained so as to be adopted as sample data, and a drop half-normal distribution scoring model is adopted to pre-estimate the data of the dissolved gases in the transformer oil; the network structure of a neural network is determined; and the parameters of the neural network are optimized by using an improved particle swarm algorithm; pre-estimated sample data are adopted to train the parameter-optimized neural network, so that a final neural network model can be obtained; and the neural network model is adopted to process transformer data to be evaluated, so that the fault type of a transformer can be obtained through diagnosis. With the method of the invention adopted, the interference of original data redundancy information can be reduced, and the validity of data evaluation can be improved; and convergence speed in the training of the neural network can be increased, and the search ability of parameter optimization can be improved, and the accuracy and reliability of transformer fault diagnosis can be improved finally.

The invention discloses an odor identification method based on gas sensors and deep learning, which is characterized in that a response curve cluster of odor to be detected is obtained through a gas sensor array, original data is directly used as an input sample of an odor identification deep neural network, data preprocessing and data amplification are performed on the input sample, time sequenceresponse data hierarchical features are automatically extracted by adoption of deep learning, global feature extraction and long-range dynamic feature extraction are performed at the same time, and an odor label is output through a classifier, so that high-sensitivity and specific odor identification is realized. The odor identification method of the invention has high sensitivity and high reliability, and can be widely applied to the fields of industrial production, medical treatment, environment, safety and the like.

The invention discloses a bridge progressive drift data cleaning method based on a data difference value. The bridge progressive drift data cleaning method comprises the steps of obtaining the sensor original data; calculating a difference value of the adjacent data between the original data; extracting the trend data from the original difference value data through a Super Smart algorithm to obtain a difference value between the original difference value data and the trend data; obtaining a change range of a normal value of a difference value of the original difference value data and the trend data by adopting an interval estimation theory according to a mean value and a standard deviation of the difference value of the original difference value data and the trend data; searching a data position where the difference value of the original difference value data and the trend data is outside the confidence interval, determining the position of the drift abnormal value in the original data, and cleaning the drift abnormal value. According to the present invention, the progressive drift phenomenon in the data with slower signal fluctuation can be accurately identified, the processing efficiency of the original data is improved, the real monitoring data can be restored to the maximum extent, and the effective data is provided for the subsequent structure state analysis.

The embodiment of the invention provides a data obtaining method and device and an electronic device. The method includes the steps of receiving a live broadcast service request, converting the live broadcast service request into a logic description sentence, and obtaining service data required by the live broadcast service request from a database storing the service data according to the logic description sentence, wherein the database receives and stores original data generated by service nodes in real time. By means of the data obtaining method and device and the electronic device, the timeexpenditures in the data obtaining process can be reduced.

The invention discloses a gastric cancer risk prediction method and system, computer equipment and a readable storage medium, and relates to the technical field of cancer risk detection. The method comprises the following steps: collecting original data; preprocessing the original data; establishing a risk assessment model and training the risk assessment model; and utilizing the trained risk assessment model to predict a result based on the to-be-tested set. According to the technical scheme provided by the invention, the risk of suffering from gastric cancer can be accurately predicted for reference of doctors.

The invention discloses a domain-specific Chinese event extraction method. The method includes: preprocessing, removing redundant tags, clauses and participles form original data information; dictionary building, namely performing artificial analysis of large-scale news corpus, summarizing domain-specific common characters and building a character dictionary; summarizing common expressions of reporting domain-specific events and building a trigger word based sentence pattern dictionary; sentence pattern matching, namely performing sentence pattern matching on a syntax tree according to the sentence pattern dictionary; event element extraction, namely performing character extraction on the syntax tree according to the character dictionary and performing time and place extraction on the syntax tree according to syntactic features of time and place elements. With the method, the difficulty of quickly acquiring Chinese news information in the big data environment is solved, users can get news events related to keywords by entering the keywords themselves through automatic processing, and great convenience is provided for information acquisition.

The invention relates to a method for processing mass concentration data of a suspended sediment based on a laser particle size analyzer. The method mainly comprises the following steps of acquiring volume concentrations of suspended particles with different particle sizes at each site of an observation sea area by use of the laser particle size analyzer; performing mass control on the original data of the acquired volume concentrations of the suspended particles with different particle sizes; performing linear fitting according to the mass concentration of the suspended sediments measured bya chemical weighing method at partial sites and the total volume concentration of the suspended particles at corresponding positions by use of a least square method to acquire a fitting coefficient and a correlation coefficient; and fitting the mass concentration of the sediment at each site according to the total volume concentration of a suspended matter at each site of the surveyed sea area byuse of a fitting relation acquired by linear fitting.

The invention discloses a heart rate data points extraction algorithm based on a piezoelectric sensor. Firstly, data preprocessing to original data S is conducted, after the processing, a new optimized data sequence A is obtained, then a local extremum value points sequence B is extracted from the new sequence; through the local extreme value points sequence B, in the collection of data, average time interval I between adjacent heart rate data points is estimated. Then, according to the above average time interval I, the algorithm can extract a heart rate data points sequence C from the localextremum value points sequence B. Finally, according to characteristics of human heart rate and characteristics of original data, the algorithm determines the loss of heart rate data, if a loss exists, then data from the sequence A is selected to complement the sequence C, a final heart rate data points sequence D is obtained. The algorithm has a good extraction effect for the piezoelectric sensorwith multi signal fusion and large signal disturbance and the like.

The invention discloses an automatic generation method of a PPT data report, and belongs to the technical field of automatic office. The method comprises the following steps of: (1) acquiring a target original data record of a use scene, wherein the target original data record comprises an information field and a file resource field; (2) designing a data report template, defining the data report template as a general PPT template style, wherein the general PPT template style comprises an area corresponding to the information field and an area corresponding to the file resource field; (3) performing data marking in the general PPT template style, wherein the data marking is used for establishing a relation between the general PPT template style and to-be-displayed data; (4) setting parameters of the general PPT template style; and (5) reading the data report template, identifying a data mark of the data report template, automatically performing searching in a database, and then filling the data to a specified position.

The invention relates to the field of anomaly detection and new high-risk alarm identification by adopting a One Class SVM (Support Vector Machine), and particularly provides an anomaly alarm identification method and device based on incremental learning, a server and a storage medium, the method comprises the following steps: firstly, training a One Class SVM model by utilizing an impure original data set to identify an anomaly alarm; and then, enabling a safety operator to regularly mark abnormal alarms obtained by the model, and iteratively optimize the model based on incremental learning, so that the model detection performance is improved, and missing report and false report are reduced.

The invention discloses a touch control method and a mobile terminal, and relates to the communication field. The method includes the steps that a mobile terminal monitoring module monitors whether a mobile terminal touch control module is touched, when the mobile terminal touch control module is touched, a CPU of the mobile terminal reads original data of a touch point of the mobile terminal touch control module, the CPU carries out operation on the original data of the touch point, so that coordinate data of the touch point are obtained, the coordinate data of the touch point are sent to the monitoring module, the monitoring module compares and judges whether the coordinate data of the touch point are the same as those of a last touch point and accordingly the monitoring module controls sampling frequency of the touch control module to change correspondingly. The touch control method is improved, processing capacity of the CPU is made full use of, and cost is reduced; a user can regulate data sampling accuracy according to different use occasions, use requirements of the user for high-accuracy data sampling are met during drawing, handwriting input and the like, and accordingly mobile terminal use experience of the user is improved.

The invention discloses an intelligent monitoring system and method for the service life of a cutter, the monitoring system comprises a workshop field layer, a unit general control layer, a workshop general control layer and a visual management and control layer, and the workshop field layer is used for providing real-time field data for the system; data acquisition lines are arranged on field machine tools, online tool positions and tool states of the machine tools are acquired in real time, original data are provided for visual management of tool monitoring, and the unit general control layer is used for realizing logic control among equipment in each unit of the workshop field layer and realizing information acquisition of bottom-layer equipment and information feedback to upper-layer equipment. According to the invention, the full-chain systematic management of the cutter flow is realized; according to the method, the residual life of the tool is predicted by adopting a mode of accumulating the number of machined workpieces configurable by a strategy or accumulating machining time, and then an online monitoring visual management function with a more reliable and more scientific prediction result and a more accurate numerical value for the residual life of the tool is realized by utilizing an early-warning value of the service life of the tool set by the system.

Methods of encoding and decoding data are described wherein the encoding method comprises: receiving a data file and dividing the data file or data stream into one or more data blocks, each data block having a predetermined size N and comprising a sequence of data units, e.g. byte values; and, iteratively encoding the data file into a data key based on a first permutation function and a first dictionary of permutation indices, preferably the encoded data file having a total size that is equal to or smaller than the original data file and preferably the data key having a size that is equal to or smaller than size of a data block. Iteratively encoding the data file comprises one or more encoding iterations, wherein each encoding iteration includes: determining a first permutation index defining a permutation to generate the first input data block from a first ordered data block, the generating including providing at least the first input data block to an input of the first permutation function, and the first ordered data block being obtainable by ordering the first input data block; determining a first permutation dictionary index representing a location in the first dictionary in which the first permutation index is stored; generating a first frequency data block defining the number of occurrences for each potential data value in the input data block, preferably determining the number of occurrences for each potential data value in the input data block and ordering the determined occurrences in a sequence of values in a hierarchical order, e.g. increasing or decreasing order of the data value; processing the frequency data block; and determining an encoded data block, the encoded data block comprising the first permutation dictionary index and the processed frequency data block. The encoding method further comprises outputting the data key comprising the one or more encoded data blocks and, optionally, iteration information.

The invention relates to the technical field of communication, and discloses a webpage live broadcast method, a device and system of a mobile terminal, wherein the method comprises the following stepsof: sending a TCP connection request to a first mobile terminal and establishing a TCP connection with the first mobile terminal; according to a preset rule, packing the data to be sent, sending thepacked data to the first mobile terminal through the TCP connection so that the first mobile terminal unpacks the data according to the preset rule, and analyzes the data to obtain the original data,and rendering display is carried out according to the original data. The method can solve the problems of package sticky and package breaking generated when the TCP connection is used to transmit data, and the received image data is rendered through a graphics processor by utilizing the characteristics of the mobile terminal.

The invention discloses a method for accurately estimating a spatial channel system function based on a sparse theory, which comprises the following steps of: establishing a sparse regular least square model based on a mixed l0 norm when channel estimation is carried out by using received data in consideration of the fact that the number of main channels in a scene is limited; in consideration ofthe non-convexity of the problem, using a convex optimization theory to carry out convex relaxation on the non-convexity, and giving a sparse regular least square model of a mixed l1 norm; and then reconstructing the received data according to a solving result, and adjusting a weight factor between a fitting error and channel sparsity in the optimization model according to an error between the received data and the original data until the weight factor meets a convergence condition. According to the method, the priori information of spatial channel sparsity can be fully utilized, so that the receiving data fitting error and the sparsity of the channel coefficient vector are optimized, the method better conforms to the actual situation, and the deviation of channel coefficient estimation caused by only considering the fitting error when a traditional method is used for estimating the spatial channel is effectively avoided.

The invention provides a fault analysis method, system and device based on an IT product and a storage medium. The fault analysis method comprises the steps that after structural processing is conducted on health examination data and health examination result data of multiple sources, storage is conducted according to corresponding attribute information, and incremental data of the health examination data and the health examination result data are obtained; based on incremental data of the health examination result data, calculating a fault parameter in a first predetermined time period, a fault number in a second predetermined time period and a fault number average value in a third predetermined time period; and determining that the operation and maintenance environment is abnormal when the ratio of the fault number in the second preset time period to the fault number average value in the third preset time period exceeds the range limited by the fault parameters. According to the fault analysis method provided by the invention, the stability of the operation and maintenance environment can be determined by performing centralized management and analysis on the health examination original data from multiple sources in the time dimension and analyzing the health examination original data in different fields according to the universal indexes.

The invention relates to a fuel cell service life rapid prediction method based on a clustering algorithm, and belongs to the technical field of fuel cells. Firstly, a collected high-dimensional operation data set of the fuel cell system is projected to a three-dimensional feature vector space through a principal component analysis (PCA) method; and inputting the three-dimensional feature vector after dimension reduction into a clustering algorithm, such as K-means and density-based noise application space clustering DBSCAN (Density-Based Spatial Clustering of Applications with Noise). According to a clustering result, the fuel cell voltage data with similar operation conditions can be classified. Finally, the selected voltage data may be used to accurately represent the true performance degradation of the on-board fuel cell stack. Compared with unprocessed original data, the method can obtain more accurate and more reliable fuel cell performance degradation.

The invention discloses a comprehensive data augmentation method based on small sample medical images, and relates to the technical field of medical image processing. The method comprises the following steps: S1, analyzing a CT image, and converting a pixel matrix value of the image into an HU value; s2, carrying out preprocessing on the CT image; s3, performing random rotation on the CT image in an angle range; s4, scaling the CT image in a multiple range; s5, performing mirror image changes of different dimensions on the CT image; s6, random Gamma transformation is carried out on the CT image; and step S7, inputting the augmented CT image into the U-Net network, and training to obtain a U-Net model. According to the method, after the CT image is preprocessed, the preprocessed image is subjected to rotation, zooming and mirror image expansion to obtain an original data set, so that CT data under various clinical conditions are simulated, and the trained model has better generalization and accuracy.

The invention relates to an e-commerce platform customer segmentation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining customer information, commodity information and customer order information, and taking the customer information, the commodity information and the customer order information as original data; preprocessing the original data to obtain preprocessed data, and performing data conversion and data integration on the preprocessed data to obtain data after data integration; and clustering the data after data integration to obtain a customer category. According to the e-commerce platform customer segmentation method provided by the invention, the segmentation of e-commerce platform customers can be realized.

The invention provides a data transmission method and equipment for a vehicle-mounted intelligent cabin. The method comprises the steps: receiving data from at least one data source and attribute information of the data at a sending end; segmenting the original data based on the attribute information to obtain a plurality of data parts; calculating a check code of each of the plurality of data portions according to each of the plurality of data portions, and transmitting the plurality of encoded blocks; receiving a plurality of coding blocks at a receiving end, and checking data parts included in the plurality of coding blocks by using check codes included in the plurality of coding blocks; and at least providing the successfully verified data part for the user. Correspondingly, the invention further provides a data transmission system, a computer readable storage medium, a computer program and a vehicle. Through the technical scheme of the invention, the data transmission efficiency is favorably improved.