14 results about "Result Category" patented technology

The present disclosure belongs to the technical fields of computers and defect detection. Provided are a defect detection method and a related device. The method comprises: obtaining an image to be processed; processing said image by means of a feature extraction module in a deep-learning defect detection model, wherein the feature extraction module comprises an intermediate feature layer, the intermediate feature layer comprises a dropout layer, and the dropout layer is configured to randomly drop features which are extracted by means of the intermediate feature layer processing said image, such that a plurality of features subjected to random dropout are obtained after said image is processed by means of the feature extraction module; by means of a detection head in the deep-learning defect detection model, processing the plurality of features subjected to random dropout, so as to obtain a plurality of category prediction results and a plurality of position prediction results of defects in said image; and obtaining category detection results, position detection results, category uncertainties and position uncertainties of the defects in said image according to the plurality of category prediction results and the plurality of position prediction results of the defects. (FIG. 1)

The invention provides a defect detection method and related equipment, and belongs to the technical field of computers and defect detection. The method comprises the steps of obtaining a to-be-processed image; a to-be-processed image is processed through a feature extraction module in a deep learning defect detection model, the feature extraction module comprises a middle feature layer, the middle feature layer comprises a discarding layer, and the discarding layer is used for randomly discarding features extracted after the middle feature layer processes the to-be-processed image. After the to-be-processed image is processed by the feature extraction module, a plurality of features after random discarding processing are obtained; processing the plurality of features after random discarding processing through a detection head in a deep learning flaw detection model, and obtaining a plurality of category prediction results and a plurality of position prediction results of flaws in the to-be-processed image; and obtaining a category detection result, a position detection result, category uncertainty and position uncertainty of the flaw in the to-be-processed image according to the plurality of category prediction results and the plurality of position prediction results of the flaw.

The present application discloses a processing method, device, equipment and storage medium for the prediction results of a classification model, and relates to artificial intelligence, machine learning and big data technology in computer technology. The specific implementation scheme is: by performing ciphertext calculation based on the classification prediction results of the ciphertext, the category label data and statistical information of the ciphertext, the index information of the ciphertext of the classification prediction results is obtained, and the calculation participants do not need to obtain the plaintext of the classification prediction results, category label data and statistical information. Based on the classification prediction results, category label data and statistical information of the ciphertext, the ciphertext calculation can complete the calculation of the index information. During the calculation of the index information, the classification prediction results, category label data and statistical information of the ciphertext will not be decrypted, and these data will not be leaked. Under the premise of ensuring that the classification prediction results, classification label data and statistical information are not leaked, the evaluation index information of the model can be calculated to improve data security.

A query modality recommendation system provides recommendations to use a particular query modality based on one or more categories of search results for a search query. Upon receiving a search query in a first query modality at a search engine, the query modality recommendation system determines to recommend use of a second query modality based on one or more categories of the search results. For example, the first query modality may be a textual query and the second query modality may be an image query. In aspects, recommending use of the second query modality comprises comparing a first search performance of the one or more categories for the first query modality in historical search queries to a second search performance of the one or more categories for the second query modality in the historical search queries.

The invention discloses an enterprise humanized management optimization method and system based on machine learning. The method comprises the steps of data classification and collection, employee satisfaction prediction, employee loss risk prediction, employee personalized development prediction and enterprise humanized management optimization. The invention relates to the technical field of enterprise management machine learning, in particular to an enterprise humanized management optimization method and system based on machine learning, and the scheme adopts three predictions of employee satisfaction prediction, loss risk prediction and personalized development suggestion prediction, and improves the data dimension and result category of enterprise personalized management; employee satisfaction prediction is carried out by adopting a graph convolution gating circulation unit network combining dimension graph generation and three demand dimension data parallelism; employee loss risk prediction is carried out by using a bidirectional long-short-term memory neural network combined with a bidirectional encoder; and a graph convolution gating circulation unit network of a bidirectional encoder is adopted to carry out employee personalized development prediction.

The invention provides a large language model reinforcement learning system and method and related equipment, and the system comprises a management module which is used for carrying out the unified management and calling of a multi-class value function, and specifically comprises an environment registration unit which is used for building a global registry, and storing a mapping relation between an environment function and corresponding meta-information and a mapping relation between the environment function and the value function; the environment running unit is used for positioning an environment function according to the unique identification information, instantiating a running environment, calling the environment function to obtain a running result, inputting an answer generated by a large language model into a value function for evaluation, and obtaining a reward result and a result category; the integration module is used for generating a reward signal adaptive to the reinforcement learning process based on the reward result of the value function and the reward result of the value model; and the training module is used for updating strategy parameters of the large language model based on the reward signal. According to the method, unified calling of various heterogeneous operating environments is realized, and the cross-task generalization ability of the model is improved.

The invention relates to a method for inspecting containers (2) made of transparent or translucent material in order to classify the containers, comprising a phase of use comprising:-for each container, a step of inspecting the containers (2); acquiring at least a first and a second image (Ic) of at least one given portion of the container in two different viewing directions and using at least one modality; providing, for each container, a record of at least a first and a second image of at least one portion of the container using at least one modality and in two different viewing directions as input to a deep learning model (NN); and for each container, analyzing the record by the deep learning model to determine from the list of categories the result category to which the container part belongs.

The application provides a large language model reinforcement learning system, a reinforcement learning method and related equipment, the system comprises: a management module for unified management and calling of multiple category value functions, specifically comprising: an environment registration unit for establishing a global registration table, storing environment functions and corresponding meta information and mapping relationship with value functions; an environment running unit for locating environment functions according to unique identification information and instantiating running environment, calling environment functions to obtain running results, and inputting the answers generated by the large language model into the value function for evaluation, obtaining reward results and result categories; an integration module for generating reward signals suitable for the reinforcement learning process based on the reward results of the value function and the reward results of the value model; a training module for updating the policy parameters of the large language model based on the reward signals. The application realizes unified calling of multiple heterogeneous running environments and improves the generalization ability of the model across tasks.

This invention relates to monitoring, and provides a method, apparatus, device, and storage medium for locating program execution problems. The method receives user requests, wherein the user request includes user identity information and execution information; generates an execution task based on the user identity information and the execution information; when the execution task is detected to be completed, obtains the response result of the execution task; performs multi-dimensional analysis on the response result to obtain the result category of the execution task in each dimension; if the result category includes a preset category, identifies the user who made the request, and obtains the input parameters of the user request from a preset message system based on the user; generates alarm information based on the input parameters and the preset category, which can accurately locate the operational problem encountered by the user. Furthermore, this invention also relates to blockchain technology, and the alarm information can be stored in the blockchain.

The invention relates to a behavior category identification method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring a to-be-detected image sequence in power grid production operation; and inputting the to-be-detected image sequence to a target behavior category recognition model, and obtaining a category recognition result output by the target behavior category recognition model, the category recognition result being used for determining a power grid operation behavior category of a target detection object in the to-be-detected image sequence, the target behavior category recognition model is obtained based on sample image sequence training in power grid production operation. By adopting the method, the accuracy of a category identification result can be improved.

Method and device for inspecting glass containers according to at least two modalities in order to classify the containers according to glass defects. The invention relates to a method for inspecting glass containers (2) comprising the following steps: - inspecting each container using an inspection system (10) in order to obtain at least one analysis image according to a first modality corresponding to an absorption image (Ia) and at least one analysis image according to a second modality corresponding to a birefringence image (Ib) or a refraction image (Ir), - defining a list of classes (D1, D2,...Dk,…Dp) including at least glass defects, - ensure a matching of at least a portion of the analysis images according to the first modality and according to the second modality, - from at least one analysis image according to the first modality and at least one analysis image according to the second modality, matched, classify the analysis images using an image classifier (Cl) that determines membership in a result class from the list of classes, the image classifier having been trained by supervised learning, - classify the container according to the result class. Figure for the abstract: Fig. 1.

The present application relates to the field of computer technology, and in particular to a text classification method, device, electronic device and storage medium for quickly and accurately labeling samples. The method includes: obtaining multiple texts to be labeled, and determining the initial labeling information of each of the texts to be labeled according to the category of the corresponding search results and the relevant feedback behavior information; constructing multiple query samples and multiple reference samples based on the texts to be labeled and the initial labeling information; determining the target labeling information of each query sample based on the initial labeling information of the query samples and the reference samples; constructing a training sample data set based on the query samples and the respective target labeling information, and training a text classification model. Since the present application adjusts the initial labeling information of the corresponding query sample through the reference sample, and constructs the training sample data set based on the adjusted query sample, it can improve the accuracy of sample labeling, thereby improving the accuracy of text classification.

The invention relates to a method for inspecting glass containers (2), comprising the following steps: inspecting each container using an inspection system (10) in order to obtain at least one analysis image according to a first modality corresponding to an absorption image (Ia) and at least one analysis image according to a second modality corresponding to a birefringence image (Ib) or a refraction image (Ir), -defining a category list (D1, D2,... Dk,... Dp) comprising at least glass defects,-matching at least partial analysis images according to the first modality and according to the second modality,-from the at least one analysis image according to the first modality and the at least one analysis image according to the second modality which are matched together, determining a category list (D1, D2,... Dk,... Dp), classifying the analysis image by means of an image classifier (Cl) that determines which result category of the list of categories the analysis image belongs to, the image classifier having been trained by supervised learning,-classifying the container according to the result category.

Provided are a content search method, apparatus, and device, and a storage medium. The present disclosure enables: receiving a search content; and displaying a plurality of answer viewpoints and first contents in a search result interface, wherein each answer viewpoint corresponds to one category of search results, the search results are results obtained by searching the search content, the first contents comprises keywords, the keywords are used for indicating reasons for displaying a target answer viewpoint among the plurality of answer viewpoints, and the keywords are extracted from a target category of search results corresponding to the target answer viewpoint.