52 results about "Sample number" patented technology

The invention discloses a fast convergence reliability algorithm based on an improved AK-MCS model, mainly relates to the technical field of reliability analysis, and comprises a weight clustering algorithm, a multi-trend selection method and a sample number control method adopted for constructing the AK-MCS model, and the three algorithms can be combined for use or can be used independently; according to the method, the number of times of experimental point calculation can be greatly reduced while the model precision is ensured, and the failure rate calculation efficiency is improved.

The invention relates to an automatic optimization method and device of a model, a medium, electronic equipment and a program product, and relates to the technical field of computers.The method comprises the steps that in response to feedback operation of a user on an answer text output by a target model, a question text corresponding to the answer text is obtained; a model optimization step is automatically executed, and the model optimization step comprises the steps that the quality type corresponding to the answer text is determined according to the feedback operation, the answer text and the question text; determining a target optimization strategy corresponding to the target model according to the quality type; and optimizing the target model according to the target optimization strategy. Therefore, on one hand, the dialogue data of the user is automatically collected and analyzed in an actual application scene to serve as a basis for optimizing the target model, and the sample number and sample diversity of model optimization are increased. On the other hand, human intervention is not needed in the whole process, the data analysis efficiency is greatly improved, and then the model optimization efficiency is improved.

To provide a sample inspection management system which can uniquely specify sample inspection between a plurality of inspection institutes and a plurality of clients, can correctly grasp the sample inspection, and can be also applied to pathological diagnosis management.SOLUTION: A sample inspection management system reads out unique information uniquely specifying a main body stored in storage means, wherein first sample number issuing means combines a client management number of a client and unique information of the client by SQL and issues a first sample management number, sample number issuing means combines an inspector management number of an inspector and unique information on the inspector by SQL and issues a sample management number, management number issuing means associates the first sample management number with the sample management number by SQL on a one-to-one basis, issues a management number while being uniquely associated with the numbers, and a management code issuing means issues a two-dimensional code associated with the management number, and makes the two-dimensional code stuck to a sample container, and code read-out means reads out the two-dimensional code and uniquely specifies sample inspection.SELECTED DRAWING: Figure 3

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 present application discloses an interface misuse defect detection method, device, readable medium and electronic device, the method comprising: based on first sample data and preset interface call data, obtaining second sample data through a generative adversarial network; the first sample data is an interface call sequence corresponding to real code data; forming a call sequence library according to the first sample data and the second sample data; the call sequence library includes normal sample interface call sequences without interface misuse defects; extracting the interface call sequence to be detected from the code data to be detected; matching in the call sequence library according to the interface call sequence to be detected to determine whether the code data to be detected has an interface misuse defect. The technical solution of the present application greatly enriches the number of samples in the call sequence library and improves the coverage of API misuse defect detection, thereby reducing the occurrence of false positive detections and improving the detection accuracy when detecting the interface call sequence to be detected.

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 discloses a multi-terminal management system, method and equipment for field sampling data and a medium, and the system comprises an initialization configuration module which is used for generating a detection scheme according to an environment quotation list; the sampling task arrangement module is used for arranging a sample number list according to the environment quotation list; the WEB end processing module or the APP end processing module is used for outputting a to-be-filled sampling record form on a web end management page or an app end management page, and performing sampling data self-adaptive analysis, sampling sub-form association output and detection time period self-adaptive verification according to filling of the to-be-filled sampling record form; according to the invention, sampling whole-process digital monitoring and management can be carried out on the stages of sampling initial configuration, sampling scheme generation and distribution and recording and filling at two ends, so that the authenticity, correctness and effectiveness of sampling data are effectively improved, and a more persistent storage mode is provided for the sampling data; and sampling personnel can be helped to complete sampling process recording more efficiently.

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 utility model discloses a portable trolley for storage and transportation of reinforcing steel bar samples, belongs to the technical field of sample transportation tools in the metallurgical industry, and solves the problems that in the prior art, the temperature of the samples cannot be dissipated in time, sample labels are prone to being scratched, and the samples are prone to being mixed. The trolley comprises a sample storage frame used for storing samples, groove breast boards which are symmetrically arranged are arranged on the left side and the right side of the storage frame, a plurality of grooves used for containing the samples are formed in the groove breast boards, and a plurality of heat dissipation grating boards which are arranged in parallel are arranged in the middle of the storage frame. A rolling assembly used for transportation is arranged at the bottom of the storage frame, and the rear portion of the storage frame is connected with two extension parallel handles which are correspondingly arranged in parallel and have inclined angles. The portable trolley for storing and transporting the reinforcing steel bar samples is convenient to operate, it is guaranteed that the samples are rapidly cooled under the hollowed-out heat dissipation condition, and the numbers of the reinforcing steel bar samples are not prone to being confused.

The invention discloses a sample imbalance data processing diagnosis system and method, the system comprises a data import module, a sample classification module, a sample number averaging module, a sample balance module and a data analysis module, the data import module is connected with the sample classification module, the sample classification module is connected with the sample number averaging module, and the sample number averaging module is connected with the sample balance module. The sample number averaging module is connected with a sample balance module, and the sample balance module is connected with a data analysis module; according to the invention, through the use of the sample classification module, automatic classification of data is realized, and errors during group-by-group import are prevented; meanwhile, through the cooperation between the sample number averaging module and the sample balancing module, better data balancing is realized, and offset during model establishment is avoided; meanwhile, through cooperation between the data analysis module and the data prediction module, a data model can be generated on the basis of data analysis, and the application range of the system is widened.

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 number recognition model training and recognition method and device, a storage medium and a product, and the method comprises the steps: building a first sample number set based on a CDR call ticket of a user in a first scene, and enabling the features of a sample in the first sample number set to comprise a first number of call features corresponding to the CDR call ticket; training a preset number recognition model based on the positive samples and the negative samples of the first sample number set to obtain a trained first number recognition model; constructing a second sample number set based on the CDR call ticket of the user in the second scene and the supplementary features of the second scene, wherein the features of samples in the second sample number set comprise a first number of call features corresponding to the CDR call ticket and a second number of supplementary features corresponding to the CDR call ticket; and carrying out secondary training on the first number identification model based on the positive samples and the negative samples of the second sample number set to obtain a trained second number identification model and taking the trained second number identification model as a number identification model in a second scene.

The invention discloses a data generation method and system for member privacy risk detection of a credit investigation model, and the method comprises the steps: estimating the average prediction confidence of member data and non-member data in each credit investigation category, and taking the average prediction confidence as a judgment threshold; randomly sampling from the sample space of the target credit investigation model to be tested to obtain a sample, randomly sampling a new feature value from the feature value space of the feature dimension to replace the current feature value for each feature dimension of the sample, and generating a seed sample; inputting the seed sample into a to-be-tested target credit investigation model to obtain posterior probability prediction output; when the prediction probability of the seed sample on the target label c meets a judgment threshold condition of member data or non-member data, the seed sample is correspondingly used as a member detection sample or a non-member detection sample; and generating detection data meeting the sample number. According to the method, the expression difference of the credit investigation model on two types of different data can be more accurately captured, and the validity of the generated detection data is guaranteed.