40 results about "Sample number" patented technology

The invention discloses a report checking method capable of automatically feeding back a checking result based on a snapmake language. The method specifically comprises the following steps: step 1, file preparation: preparing a question settlement report and an analysis confirmation sheet excel table; 2, performing report basic information checking, quality control data checking, intra-group sample difference checking, report sample number and display sequence checking, report integrity checking and / or report picture checking in parallel and / or in series on the basis of the question settlement report obtained in the step 1 and an analysis confirmation single excel table; and step 3, generating a webpage version checking report according to one or more pieces of checking log information obtained in the step 2, and automatically sending feedback through a mail. The invention further discloses a system and application for implementing the report checking method, and the system and the application have wide application value.

The invention discloses a model increment training method and device based on dynamic storage, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the model increment training method based on dynamic storage comprises the steps: obtaining at least one piece of original sample data, and determining an information amount score corresponding to the original sample data; determining the original sample data with the information content scores higher than a preset score threshold as training sample data, storing the training sample data in a preset memory, and accumulating the information content scores of the training sample data in the preset memory to obtain a corresponding accumulated information content score; and performing incremental training on the to-be-trained model by utilizing the training sample data in the preset memory to obtain a target model in response to the condition that the accumulated information amount score or the sample number corresponding to the training sample data in the preset memory meets a preset triggering condition. The technical problem that the training effect of a traditional field small model is affected due to data distribution offset of training data is solved.

The present disclosure relates to a text processing method and device, equipment and storage medium, and belongs to the technical field of computer, which can improve the accuracy of text processing. The text processing method comprises: obtaining a to-be-modified text; determining a target text according to the to-be-modified text, a preset number of words and a pre-trained text modification model; the number of words of the target text is the preset number of words; the text modification model is obtained according to a sample text pair and a sample number; the sample text pair comprises a to-be-modified sample text and a modified sample text; and the sample number is used to represent the remaining number of words after the byte corresponding to the sample number in the modified sample text.

The present invention discloses a method for automatically and uniquely corresponding nucleic acid detection sample numbers to experimental plate numbers, which comprises the following steps: a. test plate design; b. test number design; c. sample collection; d. sample collection with plate number replacement; e. generation of original nucleic acid test reports. In the sample collection link of the nucleic acid detection test tube laboratory of the present invention, the test tube holes in 96 plates on the test plate correspond one by one to the test tube numbers, and in the system, a nucleic acid detection sample number is automatically and uniquely corresponding to the experimental plate number, effectively reducing the error rate in the sample detection link, and at the same time, it is possible to clearly distinguish between personnel nucleic acid samples and environmental samples on the same test plate.

The invention relates to the technical field of data processing, in particular to a processing method for task classification and subclass sample enhancement of a corner plastic data set, which comprises the following steps of: S1, acquiring a label set of the corner plastic data set, judging whether each label in the label set conforms to a classification task or a regression task, and if yes, judging whether each label conforms to the classification task or the regression task; taking samples corresponding to the same label in the corner plastic data set as a label category; s2, a learning model is constructed for the labels conforming to the classification task, the learning model adjusts the sample weight of a sample corresponding to each label category through a dynamic category weight method, and then the sample number of the angle plastic data set is adjusted through a self-adaptive mixed sampling method to form an adjusted data set; s3, adjusting the attention degree of the samples in the adjusted data set; and S4, performing prediction fusion on the adjusted data set processed in the step S3 through a subclass gating fusion method, and further executing a classification task on each sample in the adjusted data set. According to the invention, the sample processing accuracy is improved.

An apparatus, a method, and a computer program product for sidelink positioning are provided. An example method may include receiving a set of sidelink control information (SCI), where the set of SCIs reserves a set of resources for transmission of a set of sidelink positioning reference signals (SL-PRSs) in at least one same slot, where the set of resources is periodic. The example method may also include measuring the set of SL-PRSs during the measurement period based on the set of SCIs satisfying at least one of the set of conditions.

This invention provides a method, apparatus, electronic device, and storage medium for identifying credit card fraud. The method includes: determining target numbers corresponding to behavioral characteristics from a set of numbers to be identified based on indicator data of a sample number set; and determining fraudulent numbers based on the target numbers. The behavioral characteristics include joint fraudulent behavior characteristics, and the target numbers corresponding to these joint fraudulent behavior characteristics are numbers corresponding to a joint fraudulent group. The joint fraudulent group is obtained by comparing the number trajectory similarity of the set of numbers to be identified with a similarity threshold corresponding to the number trajectory similarity of the set of numbers to be identified. The similarity threshold is obtained through iterative training based on the number trajectory similarity of the sample number set, the indicator data, and a loss function of a greedy algorithm. This invention improves the accuracy and efficiency of credit card fraud identification.

The invention relates to the technical field of model construction, and discloses a motor multi-physics field agent model construction method and a related device, the motor multi-physics field agent model construction method comprises the following steps: obtaining field operation condition data of a target motor; dividing the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample three-dimensional space, setting a first sampling number for the key sampling intervals, and setting a second sampling number for the non-key sampling intervals; sampling the field operation condition data in the key sampling interval and the non-key sampling interval according to the first sampling number and the second sampling number by using a Latin hypercube sampling method to obtain a first condition sample set; and performing model training on the initial agent model based on the first working condition sample set until an average error meets an error requirement, and obtaining a motor multi-physics field target agent model. The method can improve the sample rationality and improve the accuracy of the agent model.

The present invention relates to the technical field of fintech, and discloses a clustering method for samples, a server, a client, a device, and a readable storage medium. The clustering method for samples includes: the server receives the sample numbers of the samples in the data set sent by each client and the distances corresponding to the sample numbers, where the distance is the distance between the normal sample corresponding to the sample number and the central sample in the data set; determines each first sample number corresponding to each second sample number according to each distance, the sample numbers include the first sample number of the normal sample and the second sample number of the central sample, and the data set includes multiple central samples; sends each first sample number corresponding to each second sample number to each client respectively, wherein the client determines the normal sample corresponding to the second sample number and the central sample as samples in the same cluster. The present invention avoids the leakage of data during the sample clustering of the client.

The invention discloses a minority class sample generation method and system based on a large language model and a storage medium, and the method comprises the steps: carrying out the preprocessing and analysis of emotion data, so as to determine a minority class platform sample data set and a majority class platform sample data set; determining the number of majority class samples corresponding to the majority class platform sample data set; inputting the minority class sample data set into a large language model based on the minority class sample amplification factor and the majority class sample number to generate a minority class model sample data set; and combining the minority class platform sample data set and the minority class model sample data set to perform sentiment text classification through a sentiment classification model. Through data sample processing and minority sample data generation, a minority sample synthesis part based on a large language model samples a step-by-step data synthesis framework, and minority sample data is synthesized through dynamic optimization to efficiently reduce a sample data distribution gap, so that the accuracy of an emotion classification model is improved.

The invention relates to the technical field of fishery resource sample management, in particular to an intelligent network library retrieval and management method and system for fishery resource samples. Multidimensional sample information is collected, and a label coding layer is constructed to paste a unique retrieval label for each sample; a category search tree is constructed by combining a cabinet body structure of a sample storage cabinet and a classification system, three-level codes of cabinet number-column number-box number are formed, and standard classified storage of fishery resource samples is achieved. Sample information is input into an Excel table data network library based on inverted indexes, and multi-condition rapid retrieval of places, time, sample numbers and the like is achieved. Before warehousing, three samples are randomly extracted from each box to verify the consistency of the labels and the database, and no less than 5% of samples are regularly backed up and randomly checked, so that long-term accuracy of the data is ensured. According to the method, the fishery resource samples can be classified, stored and taken for rapid positioning and retrieval, the cost is low, the operability is high, and the sample positioning, retrieval and management efficiency is remarkably improved.

The present application discloses a data sampling method, device and computer-readable storage medium, the method comprising: sampling data of a target type at a first sampling rate in a first time window to obtain target sampling data; merging the target sampling data with historical sampling data in a sampling database to obtain merged sampling data, wherein a third sampling rate of the merged sampling data is determined according to the first sampling rate and a second sampling rate of the historical sampling data, when the amount of the merged sampling data is greater than a sampling amount threshold, sampling the merged sampling data based on an adaptive sampling parameter to obtain updated historical sampling data, the sampling amount threshold being related to the data of the target type; the adaptive sampling parameter is used to control the amount of the merged sampling data within the sampling amount threshold; and replacing the historical sampling data in the sampling database with updated historical sampling data. By adopting the present application, system capacity can be saved when sampling data.

The invention discloses a contact window detection method and device based on dynamic density clustering decision. The method comprises the following steps: collecting multi-source physical signals, and performing weighted summation on the multi-source physical signals based on a compensation coefficient to obtain a synthetic Tsyn signal; dividing the continuous synthetic Tsyn signal into a plurality of equal-width intervals and generating a histogram; extracting a maximum count value from the histogram and multiplying the maximum count value by a signal-to-noise ratio correlation coefficient k to serve as a main density threshold value; comparing the main density threshold value with a 0.05 quantile value of the synthesized Tsyn signal, and taking the larger value as a dynamic density threshold value; extracting a section which meets a dynamic density threshold and has a duration exceeding M in the continuous synthetic Tsyn signal as a feature sequence; judging the sample number of the signal amplitude in the feature sequence, and if the sample number is not greater than 5, processing and identifying the contact section by adopting a statistical mode; and if the number of the samples is greater than 5, processing and identifying the contact segment by adopting a clustering mode. According to the invention, real-time accurate identification of the contact window can be realized.

The invention discloses a rock slope soil sample collection method, and relates to the technical field of geological and soil scientific research, and the method comprises the following steps: 1, preparing before sampling; 2, arranging and collecting sampling points; 3, storing and transporting the soil sample; and 4, sample numbering and file recording. According to the method for collecting the rock slope soil sample, sampling points are reasonably arranged, limitation of a traditional sampling method is avoided, and the representativeness and accuracy of a sampling result are improved; the sampling process is standard, the influence of human factors on the sample is reduced, and the purity and integrity of the sample are ensured; the sample storage and transportation method is scientific, and the stability and the reliability of the sample in the storage and transportation process are ensured; perfect sample archives and databases are established, and powerful support is provided for follow-up analysis and research.

The invention relates to a biological detection data analysis method, which belongs to the technical field of data analysis, and comprises the following steps: obtaining inspection data, generating a unique sample number, calling a standard template and matching a standard detection item, generating a sample preparation guide, receiving a triggered preparation confirmation instruction, obtaining a sample preparation completion instruction, and triggering a sample detection instruction. The method comprises the following steps: acquiring original data of equipment, judging according to a judgment standard to obtain a detection result, calling a multi-dimensional data analysis engine to perform statistical analysis on the detection result based on the detection result, and calculating an epidemic disease positive rate and comparing the epidemic disease positive rate with a preset positive rate reference range to obtain an analysis result, so that intelligent upgrading in the field of biological detection is realized; the traceability and the processing normalization of sample information are ensured, the problems of large personal error and data analysis lagging in the traditional detection process are effectively solved, and the comprehensive purpose of improving the detection accuracy, the detection efficiency and the risk early warning capability is achieved.

The invention relates to a maximum time interval error analysis method and system in the technical field of digital communication, and the method comprises the following steps: obtaining a time error sample sequence, and carrying out the sample cutting processing of the time error sample sequence, and obtaining a time error cutting sample sequence of which the sample number meets the index number of binary decomposition; binary decomposition is carried out on the time error clipping sample sequence according to the dynamic sliding window length, and the decomposed time error clipping sample sequence is constructed into an extreme value matrix; and calculating the length of the target observation window and the maximum time interval error of each level according to the extreme value matrix, and constructing a relation curve of the length of the target observation window and the maximum time interval error, thereby solving the problem that the calculation efficiency and the calculation accuracy are not compatible when the existing maximum time interval error is calculated.

The invention relates to the technical field of medical data processing and intelligent analysis, and discloses a breathing alarm monitoring system and a processing method thereof. The method comprises the following steps: setting a sampling period and monitoring duration, calculating a sample number and forming an original sequence, calculating a mean value and a standard deviation, performing complete sequence standardization, and setting a numerical value stable constant; if the sample number is zero or one, an empty result is output. Then setting a segment number range and a minimum segment length, generating candidate segments based on an equidistant reference and recursive constraint, and calculating a mean value and a variance of each segment; constructing a coding length containing scale penalty and fitting error, and superposing the segment number and the combination item to obtain an overall coding length; and selecting an optimal one among the candidates according to a parallelization rule. And standardizing the mean difference of the adjacent segments, setting a threshold value for alarm judgment when the number of the segments is enough, mapping segment points into time, and outputting a structured alarm.

The application is suitable for the technical field of bearing processing data processing, and provides a minimum sample number determination and interruption processing method for rolling bearing matching, which comprises the following steps: determining the grouping number based on the probability density function of bearing inner and outer rings and the equal-probability grouping method, then calculating the batch bearing matching failure probability based on the waiting area bin channel number of the bearing matching equipment, and finally calculating the minimum sample number of the bearing matching by measuring the matching failure probability of different waiting area bin channel numbers and grouping numbers, so as to optimize the design and provide an implementation scheme for the bearing matching selection process. When the waiting area bin channel number is occupied by different groups and causes the interruption of the matching process, the application can use the intermediate group components in the group to cross-group match under the condition of meeting the optimal clearance, and use the bearing rolling body size deviation method to make up for the bearing matching selection stop problem, so that the rolling bearing matching process does not stop under the conditions of the optimal bearing service clearance and requirements.

The invention relates to a multi-party data processing method, a multi-party joint recommendation method, equipment and a readable medium, and relates to the technical field of multi-party security computing and privacy computing. According to the method, the intersection potential of a first sample set of a requester and a second sample set of a data party under characteristic value screening of an initial threshold value is determined through hidden tracing privacy intersection, plaintext information of all parties is not leaked, and privacy leakage is avoided; and performing business index calculation according to the intersection potential and the first sample number of the first user sample, evaluating the screening effect of the initial threshold through the screened intersection number to determine the target threshold, and determining the characteristic value distribution fully meeting the business demand of the requester according to the target threshold on the basis of not leaking the plaintext information. And the data party performs division of different fourth sample sets based on the target threshold value and the characteristic value, and different business index levels corresponding to the requester can be realized, so that the data party can provide support for differentiated business requirements of the requester in subsequent multi-party combination.

A device determine that a neighboring block associated with a current block is associated with a plurality of intra prediction modes. The device may obtain a sample number associated with a first intra prediction mode. The device may update a histogram of intra prediction mode occurrence associated with the current block based on the sample number associated with the first intra prediction mode and the first intra prediction mode. The device may encode and / or decode the current block based on the histogram of intra prediction mode occurrence for the current block. The neighboring block may include a plurality of partitions. The device may identify a first partition of the neighboring block associated with the first intra prediction mode and a second partition associated with the second the first intra prediction mode, and update the histogram of intra prediction mode occurrence based on the sample numbers of the two partitions.

1. Name of the product in this design: Operation area of ​​a graphical user interface for inputting sample information in an electronic device. 2. Intended use of this design: for use in electronic devices, with the operating area for inputting information required for measuring samples such as bacteria. 3. The key design features of this product are the solid line drawings in the graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Since the key design feature of this design is the graphical user interface, the rear view, left view, right view, top view, and bottom view are omitted. 6. Uses of the graphical user interface: to input information needed to measure samples such as bacteria, or to display images of petri dishes used as containers for culturing bacteria. 7. Graphical User Interface: Users can select the desired measurement type by choosing options such as "Examine" or "Prevent Measurement," and input various information into input fields such as "Sample Number," "Measurement Object," and "Cultural Plate" for the sample being measured. When the "Start Measurement" button at the bottom of the information input area is clicked, the measurement of the sample begins, and an image of the culture dish, which serves as the container for culturing bacteria, is displayed on the image display area on the left side of the interface.

The invention discloses a big data cleaning method and system for an intelligent electric energy meter. The method comprises the following steps: loading original test data of the intelligent electric energy meter; performing three-layer grouping and encoding on the original test data according to a test scheme, manufacturers and sample numbers, and encoding applied electric stress; decomposing the coded input time data into different time dimensions and carrying out normalized coding; carrying out abnormal value detection and elimination on the input time data after normalization coding by adopting a weighted isolation forest algorithm based on feature importance; filling missing values by adopting an improved K-nearest neighbor decision tree algorithm; and storing the processed data according to a three-layer grouping mode of a test scheme, a manufacturer and a sample number. The invention aims to realize automatic and standardized data cleaning and coding, provide ideal input for a deep learning model, and improve the accuracy of operation state evaluation and degradation prediction of the intelligent electric energy meter.

The invention relates to the technical field of network security, and discloses an outbound C2 detection method based on a flow level risk score and destination end entity aggregation, which comprises the following steps of: firstly, acquiring outbound packet-by-packet records containing timestamps, quintuples and packet lengths; maintaining a session state according to a quintuple, and generating a session sample when a packet number triggering condition and a time window triggering condition are met; semantically extracting an arrival interval and a packet length sequence according to an internal network side direction, and executing negative value clamping, threshold cutting and log1p compression on the arrival interval to form session features; inputting a risk scoring model to obtain a flow-level risk score; aggregating the multi-stream scores according to target end entity keys to obtain entity-level risk scores; and obtaining a calibration set, triggering alarm according to a fixed false alarm rate constraint when the number of samples is not less than a threshold N, triggering alarm according to an alarm budget constraint when the number of samples is less than the threshold N, and outputting an evidence chain. According to the invention, the alarm quantity is controllable, the threshold value is adjustable and the processing cost is reduced under the non-decryption condition.

The invention aims to provide a synchronous correction preprocessing acquisition method for test data of a reinforcing steel bar testing machine, which comprises the following steps of: acquiring the test data of the testing machine, and cleaning and converting the data; displaying the collected testing data of the testing machine in real time, and performing curve fitting on the data; analyzing the sample number, and positioning a database file corresponding to the sample number; matching the testing machine database curve with the generated real-time testing machine test data curve by taking the interval residual error as a basis; and verifying an acquired data result, and synchronizing the acquired data into a database.

This image is a test selection image for setting the test details for a sample in an automatic analyzer that tests samples (e.g., blood or urine). A list of sample numbers is displayed on the left side of the screen, corresponding to their positions in the sample rack within the automatic analyzer. Detailed information, such as the type of sample selected from the list of sample numbers on the left, is displayed in the upper center section of the screen. The middle center section of the screen displays the test items that can be measured for the selected sample, and the test details are set by selecting a test item. An area for displaying operation buttons, such as a save button, is provided in the lower center section of the screen.

The application provides a sample quantity determination method, device and equipment, wherein the method comprises: determining an estimated sample quantity according to a sample lower limit quantity and a sample step quantity; obtaining sample information of a preset number of groups from total sample data; wherein each group of sample information in the sample information of the preset number of groups comprises sample information of the estimated sample quantity; determining a sample quantity identification value corresponding to the estimated sample quantity according to the sample information of the preset number of groups; and determining the estimated sample quantity as a target sample quantity or redetermining the estimated sample quantity according to the sample quantity identification value.

The invention provides a sample optimization method and device for a microseismic data set and electronic equipment. The method comprises the following steps: determining reference sample characteristics of micro-seismic data; micro-seismic events of the mining area are monitored, a micro-seismic data set is determined, and sample features corresponding to the micro-seismic data set are determined based on the micro-seismic data set of the mining area and the reference features; based on the number of the samples and a set number threshold value, determining minority samples and majority samples in the microseismic data set; determining the total number of to-be-synthesized samples, and determining the proportion of majority-class samples based on neighbor samples of minority-class samples; standardizing the proportions of most types of samples; and determining the number of target new samples to be generated according to the total number of the samples and the proportion of the standardized majority of samples, and generating the target new samples to optimize the microseismic data set. Therefore, according to the scheme, the unbalanced micro-seismic data set can be optimized into the balanced micro-seismic data set, so that the accuracy of rock burst type prediction is improved.

The invention relates to an analytical machine arrangement (100). A method comprises obtaining (A10) N time domain measurement samples at a time T (n), where n traverses the number of samples N, and the distance between adjacent samples in the time domain is T. The method further comprises determining (A12) updated, normalized time-domain measurement samples having a timing Tadapt (n) for each sample based on the associated T (n) and T adjustment, further based on an actual frequency factual (n) of the machine arrangement (100), and further based on an undesired behavior and / or error-related frequency F. The method further comprises determining (A12) the updated, normalized time-domain measurement samples having the timing Tadapt (n) for each sample based on the associated T (n) and T adjustment, further based on the actual frequency factual (n) of the machine arrangement (100). The method further comprises determining (A14) M reconstructed, normalized time domain measurement samples having a constant sampling period T2 at a time T (m), where m traverses the number M of samples. Furthermore, the method comprises performing (A16) a frequency analysis on the reconstructed time-domain measurement samples.

The invention relates to the field of strain management, in particular to a strain library management system, an application method and a storage medium, and the system comprises a sample information module, a strain library list module, a strain preservation record module, a process batch approval module, a strain data rapid retrieval module, an information collection module and an application test module. Sample numbers, original bacterial strain numbers and central bacterial strain numbers are set for the bacterial strains; then setting an original bacterial strain number and a central bacterial strain number; associating the sample with the strain according to the sample number; therefore, the sample information is effectively associated with the strain information, and the strain information and the corresponding sample information can be inquired according to the original serial number of the strain and the central serial number of the strain, so that the management efficiency is improved.

The application discloses a kind of method, device, electronic equipment and storage medium for predicting website access volume, related to artificial intelligence technical field.The method comprises: obtaining access volume sample sequence, determining the access volume gray sequence corresponding to access volume sample sequence;According to the gray parameter of access volume prediction determined according to access volume gray sequence;According to access volume sample sequence, gray parameter, preset prediction parameter vector and gray prediction equation, determine initial access volume prediction sequence;According to preset adjustment sequence and initial access volume prediction sequence, determine access volume prediction sequence, and access volume prediction sequence includes future date and the website access volume of the future date.The technical scheme of the application can quickly and accurately predict future access volume even if the sample number is small, and the prediction result can achieve high accuracy, avoiding the problem of inaccurate manual prediction, and solving the problem of narrow application range of the scheme of hiring experts to estimate access volume.