78results about How to "Good explanatory" patented technology

The invention relates to a Bayesian network (BN)-based rolling bearing fault diagnosis method. According to a common rolling bearing fault diagnosis method, a mathematical model is required to be established, and an initial diagnosis effect is unsatisfactory; problems of the selection of a wavelet base function are unsolved; and the interpretability of a deduction process is low. The method comprises the following steps of: sampling a vibration signal of a bearing, acquiring a sample, performing N-point rapid Fourier transformation processing to convert a time-domain signal into a frequency-domain signal, calculating a fault characteristic vector, discretizing the fault characteristic vector, establishing a fault diagnosis reasoning BN model, setting a fault sample to be diagnosed, acquiring an observational evidence of the bearing, finishing updating the reliability Theta of a fault diagnosis type node Bearing in the BN model, calculating a fault diagnosis type node, and outputting a result. A complex mathematical modeling process for the vibration signal is avoided, an obtained diagnosis reasoning model has the advantages of a few characteristic parameters, prominent fault characteristics, high interpretability and the like, and an effective way for solving the problems of the rolling bearing fault diagnosis is provided.

The invention discloses an event detection method based on Twitter, used to solve the real-time problem of news clue acquisition in the prior art. The event detection method based on the Twitter comprises the following steps: pre-treating tweets by using the natural language processing technology; performing online clustering on the tweets based on short text similarity; performing preliminary judgment on the event attributes of a tweet cluster by means of the characteristics of the tweets in each tweet class; and judging whether the detected event is a first event or not by means of the existing tweet event library, so as to prevent the event from repeating output. The event detection method based on the Twitter has low computational complexity and can be used for detection of sudden hotevents in large-scale flow data; and the event detection method based on the Twitter helps to obtain valuable information from a social media faster and more accurately, and can produce economic benefits directly or indirectly.

The invention discloses a method for recognizing superior shale gas reservoir by adopting conventional logging information. The method comprises the following steps: a, analyzing shale mineral composition; b, qualitatively recognizing the shale gas reservoir; c, quantitatively evaluating the shale gas reservoir; d, comprehensively evaluating the shale gas reservoir. According to the method, the shale gas reservoir is recognized by using a conventional logging curve, and a logging curve combination feature suitable for destination shale gas reservoir is summarized by comprehending mineral rock composition, clay type and deposition environment and analyzing common and personal corresponding features of the logging curve.

The invention disclosers a power grid fault diagnosis method based on topological graphic primitive information fusion; the method comprises the following steps: building a system various-bus, line, protection and breaker topology association matrix according to a power grid topology structure, and building a power grid fault diagnosis model by using said matrix as basic graphic primitive; fusing protection and breaker motion confidence probabilities according to the protection and breaker topology mapping relation; building rules to realize remote backup protection topology mapping according to remote backup protection cooperation logics and an outlet mode; selecting the maximum value to optimize the confidence probability according to the confidence probabilities of different protection inference paths; fusing the fault probability information according to topological graphic primitive probability distribution and port directions, thus obtaining the element integral fault probability; ranking the element fault probability result so as to determine the failure element, and carrying out protection and breaker motion evaluation.

The invention discloses a disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis. The disease prognosis prediction system comprises a data acquisitionmodule, a data preprocessing module, a prediction model construction module and the like. On the basis of a deep neural network model, a survival analysis problem is converted into a multi-task learning model composed of a semi-supervised learning problem of multi-time-sequence-point survival probability prediction; the model directly models the survival probability, does not depend on proportional risk hypothesis, can fit a time-dependent effect, and has better interpretability; a semi-supervised loss function and a sorting loss function are utilized to fit the data, complete data and deleteddata are fully utilized, and a traditional survival analysis problem and a survival analysis problem considering competition risks can be solved; according to the model, through multi-task learning of multiple time sequence points, data sharing among multiple prediction tasks is achieved, mutual constraint among the multiple prediction tasks is achieved, and the generalization ability of the model is improved.

The invention provides a redundancy removal feature selection method LLRFC (Locally Linear Representation Fisher Criterion) score+ based on LLRFC and correlation analysis. A DNA (Deoxyribonucleic Acid) microarray technology provides a new direction for clinic tumor diagnosis. Performance of gene expression data corresponding to different kinds of tumor is different; through the analysis on the tumor gene expression data, study personnel can realize the accurate recognition on the tumor and the tumor subtype in the molecular level; and important biological significance is realized on the diagnosis and the treatment of the tumor. The feature genes in LLRFC judging criterion descending sort gene expression data is used to be combined with the dynamic correlation analysis strategy for further eliminating redundant features; an LLRFC score+ algorithm is provided; and the optimum feature gene subset is selected. The feature selection method LLRFC score+ has the advantages that the classification precision of a classifier can be effectively improved; a sample data set does not need to meet the normal distribution; and the method is applicable to data in various distribution types. The feature selection method LLRFC score+ can help people to find the virulence gene of cancer, and the early-stage diagnosis, tumor staging and typing, prognosis treatment and the like of clinic tumor diseases are facilitated.

The invention discloses a rapid ship target detection method, a storage medium and computing equipment, and the method comprises the steps: establishing a feature pyramid full-convolution network anda double-branch module which sequentially comprise an input layer, a feature extraction layer, a feature fusion layer and an output layer, determining a ship data set, inputting a generated training set into the built feature pyramid full-convolution network, and generating a target detection result. According to the method, the ship in the image can be rapidly and accurately detected; and the detection result is accurate and rapid, the requirement on embedded equipment is low, and the method has very high practical application value in various aspects such as military affairs, civil use and the like.

The invention relates to a defensive distillation model construction method and device. The method comprises the steps of obtaining original training data in a training set and a real label of the original training data; Performing knowledge distillation on the deep neural network according to the original training data and the real label, migrating knowledge of the deep neural network to the decision tree model, and generating a soft decision tree model; And performing countermeasure training on the soft decision tree model through the original training data to generate a defensive distillation model. The technical scheme of the invention is adopted; the decision tree model has good interpretability; the limitation of a defensive distillation technology on the framework requirement is broken through; the defensive distillation technology can be applied among different model structures, the application range of defensive distillation is expanded, and the scheme performs adversarial training on the soft decision tree model, so that not only is the problem of overfitting of the soft decision tree model prevented, but also the robustness of the model for adversarial samples can be improved.

The invention provides a machine room sketch identification method and device based on a legend library, and the method is characterized in that the method comprises the following steps: formulating asketch drawing standard, building a standard legend library, and carrying out the regularization constraint of the drawing of a machine room sketch; obtaining sketch data; performing hierarchical segmentation on the whole image in the machine room sketch to obtain a component diagram of the machine room sketch; performing matching identification by utilizing a standard legend library so as to realize detection and identification of the machine room sketch components; carrying out reasonable position constraint on the machine room sketch component; and performing parameterized output of results. The machine room sketch recognition method and device based on the legend library have the advantages that the standard graph recognition result matched with the input sketch can be output quicklyand accurately in real time, therefore a more visual reference basis is provided for designers to conduct quick design after the designers combine a sketch exploration report.

The invention provides a cloud-edge collaborative industrial control network safety protection method based on a strategy library. The method comprises the steps: generating an abnormal condition recognition rule library through a generative adversarial network (GAN) of a generative model network, namely a long short-term memory (LSTM) network, and a novel discrimination model network, namely a DendriteNetworks DD, at a cloud end; and then deploying the rules to corresponding edge ends, matching a corresponding coping strategy for each rule, and finally forming a cloud-edge collaborative strategy library to meet the low-delay safety protection of the industrial control network. A side end of the method comprises a data collection module and a strategy library module, and a cloud end comprises a data processing module, a generative adversarial network module, a rule generation module and a rule library module. Meanwhile, the rule base and the strategy base further comprise prediction rules generated by time sequence features extracted by the long and short-term memory network and recognition rules generated by the dendritic network. Policy corresponding to different rules can be set by an administrator of the industrial control system.

The invention discloses a real-time risk control variable calculation method based on a distributed streaming calculation engine. The calculation method comprises the steps that a data source configuration module is adopted, wherein a user configures data sources of various calculation dependencies; The variable definition module can be used for developing various variables by using SQL (Structured Query Language); The variable calculation module is used for capturing data from a pre-configured data source according to the calculation request, constructing a memory table, executing all SQL ina related variable pool once, and outputting a calculation result; According to the real-time risk control variable calculation method based on the distributed streaming calculation engine, the variable calculation engine and system researched and developed by the invention are adopted, personnel (such as business personnel and developers) familiar with SQL language can define variables through the system, the variable development period is greatly shortened, and the cost is saved.

The invention relates to a degradation modeling and life prediction method considering performance index clustering in a dynamic environment, and belongs to the technical field of degradation modeling and life prediction. Firstly, test data are collected, then a performance index degradation model is established, unknown parameters in the model are estimated, and finally life and reliability prediction is carried out. The method comprises the following specific steps: 1, collecting test data; 2, establishing a degradation model; 3, estimating parameters, and updating the model in real time; and 4, predicting the service life and the reliability. For the phenomenon that each index of a multi-performance index product shows a clustering phenomenon due to the influence of public potential factors, mathematical modeling is carried out, so that degradation modeling for the product with the phenomenon is more practical, and a better explanatory is provided for the origin of correlation among the indexes.

The invention relates to the field of animal habitat evaluation, and discloses a method and device for evaluating the suitability of a seal habitat. The method comprises the following steps: acquiringa plurality of evaluation factors influencing the suitability evaluation of the seal habitat and data thereof; performing gridding processing on the data to form a plurality of evaluation factor griddistribution diagrams comprising a plurality of evaluation factor grid units; establishing a seal habitat suitability evaluation model on the basis; calculating according to the model to obtain the snoopard habitat suitability indexes of a plurality of evaluation factor grid units; grading the suitability indexes to obtain a grading result; and evaluating the reasonable degree of the grading result according to the plurality of evaluation indexes. The model established by the method provided by the invention is more reasonable in structure, so that a model evaluation result has a good practical application value, and the protection and planning of a seal habitat are facilitated; and the grading result of the suitability index of the seal habitat is reasonably evaluated according to a plurality of evaluation indexes, so that the comprehensiveness and the reliability of the evaluation can be improved.

The invention discloses a method and device for doubly judging bad short messages through a pre-training model and a short message address and relates to the technical field of information. The deviceis composed of a classified short message sample set, a pre-training model module, a short message collector, a text processor, an address extractor, a web crawler and a bad short message judger. According to the invention, the problem that traditional machine learning depends on features in bad short message recognition is solved; compared with deep learning, a large number of training sets arenot needed, judgment can be conducted through url short links in the short messages, and the short messages with sparse semantic information can be well recognized; meanwhile, the property of the short message judged by combining the text information and the short message address has better interpretability and more intuitive analysis effect than the property of the short message judged only according to the ip of the short message address.

The invention discloses a public opinion monitoring method and system using an aspect-based sentiment analysis method, and the method comprises the steps: constructing a crawler for crawling news comments, and crawling needed news and corresponding comment information; processing the news and the corresponding comment information, wherein stop words are removed, and word segmentation is conductedon sentences with the stop words removed; performing aspect-based sentiment analysis on the processed information by using a deep learning model, and analyzing sentiment of each aspect in the text; identifying a topic of each news text by using an LDA topic distribution model; and performing statistics on the theme of the news text and the emotion of each aspect in the news text. The emotion analysis can be carried out on the news comment text in a finer-grained manner, the emotion of each aspect in the news comment text can be judged, and the emotion of the news comment text can be judged more accurately.

The invention discloses an identification method and a system based on an integrated tree for false positive alarm signals in a ventilator. The identification method comprises the following steps: S1,data collection: collecting monitoring data of a patient from a ventilator and a monitor in a hospital; S2, data preprocessing: processing missing values and abnormal values in a dataset, conductingstandardization, and generating rules for identifying false positive alarm signals; S3, feature extraction: sorting features by using a random forest, and selecting the features with good recognitioncapability; and S4, false positive alarm signal identification: establishing a false positive alarm signal identification method for the ventilator and the monitor. Experimental results show that themethod of the present invention has excellent identification performance for false positive alarm signals and robust identification effect.

The invention discloses a mixed personalized recommendation method, which comprises the following steps that: obtaining film and television metadata capable of being recommended; carrying out similarfilm and television retrieval to obtain a candidate film and television; through sorting weight, carrying out similar sorting on candidate films and televisions; carrying out user behavior clustering,and generating a mixed recommendation candidate film and television according to a clustering result; and after all candidate films and televisions are sorted for multiple times, obtaining a recommendation result used for displaying. User behaviors are clustered to establish a user portrait, the user portrait obtained by clustering is used for generating the recommendation result, and personalized recommendation is carried out on the basis of the short-term watching historical behavior of the user and a hot broadcast film library, wherein films, television plays, variety shows and the like are mixed in the recommendation result. On the basis of content recommendation, the method is free from the influence of user-film incidence matrix sparseness problems, is explanatory and has a recommendation effect.

The invention provides a cross-domain heterogeneous large data multi-view clustering method and device based on tensor. The method includes: constructing sample tensor according to fused cross-domainheterogeneous feature space, and constructing feature space combination vector according to different context; accumulating the sample tensor to obtain a combined tensor; normalizing the order corresponding to each characteristic space of the merging tensor to obtain a connection tensor; obtaining the feature space scoring vectors according to the connection tensor, and obtaining a scoring tensorby outer product of the feature space scoring vectors; introducing the feature space combination vector and the scoring tensor into a high-dimensional space tensor distance to construct a combined scoring tensor distance; calculating sample similarity according to the combined scoring tensor distance, and constructing a view matrix according to the feature space combination vector; obtaining the multi-view clustering results under different views according to the view matrix. The invention solves the technical problem that in the prior art, different clustering results cannot be generated according to the requirements of different applications in different contexts, thereby providing high-quality clustering services for different applications.

Embodiments of the invention disclose a malicious script detection method and device, and a storage medium. The method comprises the step: obtaining a to-be-detected file; performing text recognitionon the to-be-detected file to obtain a to-be-detected text; detecting whether the to-be-detected text has at least one specific behavior feature or not; if the at least one specific behavior characteristic exists, carrying out association rule mining processing on the at least one specific behavior characteristic, wherein an association behavior characteristic combination meeting preset conditionsis determined, and the preset conditions are the minimum support degree and the minimum credibility of the behavior characteristic combination; obtaining a scoring result corresponding to the associated behavior feature combination; judging whether the to-be-detected file is a malicious script or not based on the scoring result. According to the method, text recognition is carried out on the to-be-detected file, the behavior that a malicious script falsifies a suffix is eradicated, association analysis is carried out on the behavior characteristics of the to-be-detected file, judgment of themalicious script has better interpretability, and the technical effect of false alarm is reduced.