219 results about "Class information" patented technology

This invention discloses a multimodal data heterogeneous Transformer-based asset recognition method, system, and device, the method including: collecting various-modal information of an asset, including text information and image information; building an ALBERT model, a ViT model, and a CLIP model; by the ALBERT model, extracting a text information feature; by the ViT model, extracting an image information feature; by the CLIP model, extracting image-text matching information feature; by different channels, applying asset type recognition to information in different modalities; outputting classification information from the different channels; by the CLIP model, generating asset void information; and discriminatively fusing the classification information from the different channels with the matching degree between the image information and the text information obtained by the CLIP model, and outputting final asset class information. This invention realizes comprehensive discrimination by drawing from multiple modalities to improve the accuracy of asset recognition.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

A high-definition (HD) map-related map construction method, electronic device, and storage medium are provided. The method includes: extracting a bird's-eye view (BEV) feature map based on the data; determining map information through a hybrid decoder based on the BEV feature map and a hybrid query; and constructing an HD map corresponding to the data based on the map information, wherein the map includes a plurality of map elements each including an area formed by a plurality of coordinate points in the map, the map information comprises coordinate information and class information of the plurality of map elements, and the hybrid query includes a plurality of hybrid features each corresponding to one map element and including a point feature and an element feature. Optionally, the method may be executed using an artificial intelligence (AI) model.

The invention provides a rotating machine transfer learning fault diagnosis method fusing semi-supervised contrast learning in the field. In the method, an intra-domain semi-supervised contrast learning (SSCL) algorithm is designed, the SSCL takes category information as supervision, discriminative learning of different categories in each domain is guided to eliminate fuzzy classification boundaries, convenience is provided for domain adaptation of cross-domain features carried out by adopting local maximum mean difference (LMMD), and then cross-working-condition diagnosis performance is improved. Meanwhile, domain confrontation is introduced in a mode of dynamically limiting related contrast learning loss and transfer learning loss gains, so that negative effects of target domain pseudo labels with poor quality on SSCL and feature transfer learning are reduced, and the stability of a diagnosis model is improved.

The invention provides a drug temperature and humidity digital monitoring and quality tracing system based on a full life cycle, and relates to the technical field of digital monitoring, and the system comprises the steps: firstly collecting the temperature and humidity data of the whole link of drug production, storage, transportation to terminal sales, and obtaining a standardized data set through the verification and packaging of an industrial standard library; performing secondary screening through primary screening in combination with a dynamic temperature and humidity screening model to obtain a precise abnormal data set; associating drug full-cycle multi-class information, and constructing a temperature and humidity-drug state mapping relation based on a time sequence association algorithm to form a full-dimension association data set; based on the data set, combining with a quality risk assessment model for early warning, realizing full-period traceability through a reverse traceability algorithm, and generating an assessment result and a traceability link; and finally, relevant data are converted into a dynamic temperature and humidity monitoring curve, a quality risk early warning report and a full-life-cycle tracing atlas, and an interactive report is assembled, so that the temperature and humidity state of the medicine can be checked conveniently.

The invention discloses a low-voltage uninterruptible operation site visual monitoring optimization method based on an edge computing architecture, and the method comprises the following steps: constructing an operation scene graph analysis module, obtaining the distribution of operators, the operation type and the equipment voltage class information of each monitoring region, generating a risk class mapping graph, and carrying out the operation scene graph analysis module; positioning and initial identification of a high-risk area are realized; generating a risk perception matrix; constructing a priority queue of computing resources and risk levels; acquiring identification precision, processing delay and behavior capture integrity indexes, and establishing a task feedback mechanism; constructing a scheduling logic credibility evaluation module, analyzing a scheduling instruction, marking and recording an abnormal path, generating a scheduling behavior record library, and predicting and early warning a priority inversion risk; and iteratively optimizing scheduling model parameters, and dynamically updating risk scores and scheduling logic. According to the method, high-risk task resources are guaranteed preferentially, priority reversal is avoided, and the stability, robustness and safety response timeliness of the system in a complex operation environment are remarkably improved.

The invention provides a multi-dimension-based information identification method and device and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the data analysis processing of a plurality of pieces of information data transmitted by an identified risk transmitting end, determining a receiving end, and obtaining the information interaction data between the risk transmitting end and the receiving end; performing classification processing on the multiple pieces of information data according to information interaction data to obtain one-way information data and interaction information data; inputting the one-way information data, and / or the interaction information data and the interaction information into a trained information identification model for information identification processing to obtain an information probability value; and determining the information data with the information probability value greater than a preset probability threshold as abnormal information data. The condition that a single piece of information is normal but can be determined as abnormal information after multiple pieces of information are integrated can be effectively identified, so that the accuracy and comprehensiveness of information identification are improved.

A computer acquires training data including first text, first class information indicating a class mapped to a single word contained in the first text, first position information indicating a position of the single word in the first text, and first range information indicating a range of a first named entity that includes the single word in the first text. The computer executes, based on the training data, machine learning of a machine learning model which is used to estimate, from text, class information, and position information, range information of a named entity included in the text.

The invention provides a code peculiar smell detection method and system based on a large language model, and relates to the technical field of code peculiar smell detection.The method comprises the steps that context code information of a to-be-detected code is extracted, and the context code information comprises a method code and a class code; respectively extracting key information of the method codes and the class codes, and constructing a method information set and a class information set; constructing a large language model; on the basis of a thinking chain and a thinking tree, constructing cue words of the large language model; constructing a hierarchy-based code replacement strategy; dynamically adjusting the context code information by using the method information set and the class information set according to the code replacement strategy; and inputting the cue word and the dynamically adjusted context code information into the large language model for code odor detection, and outputting a code odor detection result of the to-be-detected code.

The invention provides a camouflage object semantic segmentation method and device based on an adaptive candidate strategy, and a medium, and the technical scheme of the invention obtains the category code of a to-be-segmented image through a coarse feature extractor and a classifier, achieves the transmission of semantic category information, and enhances the semantic perception capability of a subsequent segmentation task. Initial prediction of a camouflage object is carried out through a target detector and an adaptive candidate strategy, an optimal target attention box is generated, and robust space guidance is provided for a core segmentation task; according to the method, category coding and a target attention box are combined, multi-source information is fused and reconstructed through a multi-guide feature fusion module and a multi-task perception decoder, and an accurate segmentation result is output.

An image matching apparatus acquires a ground-view image, an aerial-view image, and class information. The class information indicates a distribution of classes of objects on the ground-view image, the aerial-view image, or both. The image matching apparatus-extracts features from the ground-view image to compute a ground image feature, extracts features from the aerial-view image to compute an aerial image feature, and extracts features from the class information to compute a class feature. The image matching apparatus determines whether or not the ground-view image and the aerial-view image match each other based on the ground image feature, the aerial image feature, and the class feature.

The embodiment of the invention discloses a detection device and self-moving equipment, and the detection device comprises a laser assembly which is used for transmitting at least one beam of first-type line laser to a first region and transmitting at least one beam of second-type line laser to a second region; wherein the energy of the first type of line laser is greater than that of the second type of line laser; and the collection assembly is used for collecting first-class information, corresponding to the first-class line laser, of the first area and collecting second-class information, corresponding to the second-class line laser, of the second area.

An example operation may include at least one of producing, by a class-conditioned sample generator executing on at least one processor communicatively coupled to a memory on a host platform, a synthetic feature set based on a label sequence and class information derived from received data, transmitting, by the host platform, a finalized synthetic sample to a computing device when the synthetic feature set satisfies a fidelity threshold, generating, by the computing device, a fidelity score based on a comparison of the finalized synthetic sample to the label sequence and the class information, retraining, by the computing device, the class-conditioned sample generator based on the fidelity score, and validating, by the computing device, the class-conditioned sample generator by transmitting a test prompt to the host platform, receiving a synthetic response generated by the class-conditioned sample generator, and comparing the synthetic response to previously stored synthetic data to validate the class-conditioned sample generator.

The invention discloses a warehouse management method and system based on the Internet of Things, and the method comprises the steps: determining a target material and the demand data of the target material according to a production task issued by a manufacturing execution system; obtaining attribute information of the target material based on the demand data, and distributing priority for the target material according to the attribute information to obtain priority information of the target material; wherein when the target materials comprise various materials, the priorities of different types of materials in the target materials are distributed according to first-class information, and the priorities of the same type of materials in the target materials are distributed according to second-class information; obtaining layout information of a current platform, wherein the layout information comprises storage position information and transportation path information; and according to the priority information of the target materials, the storage positions of the target materials on the current platform are adjusted, so that the target materials with higher priorities can be delivered out of a warehouse in a shorter transportation path. The storage position and the transportation path of the material can be adjusted according to the priority information of the material, and effective operation of a production link is ensured.

The invention relates to a test method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a test request, wherein the test request comprises at least one piece of step description information; determining at least one target test step from a test step set based on the at least one piece of service function class information; for each target test step, determining a step code corresponding to the target test step based on the step description information to obtain at least one target step code; and for each target step code, distributing the target step code to the corresponding target equipment according to the execution environment class information, so that the target step code is executed in the target equipment. The invention provides a more efficient and more adaptive test scheme. The embodiment of the invention can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, intelligent entertainment and the like.

The invention relates to an information extraction method and device and a storage medium. The information extraction method comprises the following steps: acquiring a first image, and identifying text information included in the first image; the text information is matched with a text dictionary library, the text dictionary library is a text detailed information library which is constructed in advance and comprises detailed information of specified class information, and the specified class information comprises one or more pieces of detailed information. And in response to the situation that the detailed information corresponding to the text information is not matched in the text dictionary library, extracting the detailed information, corresponding to the specified type of information, of the text information in the first image based on a language model and the first image. According to the invention, the detailed information of the specified class of information existing in the image can be accurately extracted.

The invention provides a knowledge base construction method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the screen capture processing of a first graphical user interface of an application program in the operation process of the application program, and obtaining a screen capture image; acquiring class information corresponding to the first graphical user interface; and constructing a corresponding relationship between the screenshot image and the class information, and carrying out storage processing on the corresponding relationship to obtain a knowledge base of the application program. Based on the knowledge base of the application program constructed by the invention, for any type of information of the application program, a user can obtain the screenshot image corresponding to the type of information, so that the user is helped to understand the function effect realized by the type of information.

The invention provides a multi-class machine abnormal sound detection method based on a diffusion model, and the method comprises the following steps: collecting normal sound data which comprises class information, and constructing a training data set; preprocessing normal sound data in the training data set to generate a Mel spectrogram, and generating labels corresponding to categories; training a multi-class abnormal sound detection network based on a diffusion model according to the labels corresponding to the classes and the Mel spectrogram; using the trained multi-class abnormal sound detection network to make mean-GMM features, and training a Gaussian mixture model; and inputting to-be-detected sound data into the multi-class abnormal sound detection network and the Gaussian mixture model to obtain a detection result. According to the invention, an abnormal score algorithm based on a Gaussian mixture model is designed, and the continuity and change characteristics of sound data in a time domain are uniformly considered. And an abnormal score with finer granularity is provided, and the detection accuracy and the adaptability to a complex industrial environment are remarkably improved.

The invention discloses an electromechanical system heterogeneous domain generalization fault diagnosis method and device based on digital twinning, and belongs to the technical field of electric data processing. The method comprises the following steps: constructing a digital twin model to simulate entity characteristics of an electromechanical system, generating virtual fault data and structured interconnection knowledge distributed across working conditions through the digital twin model, and reconstructing a K-class information domain according to the virtual fault data and entity data of the electromechanical system; the information source is divided into a source domain and a target domain, the source domain represents visible operation conditions, and the target domain represents invisible operation conditions; performing structured interconnection knowledge embedding on the source domains to obtain a knowledge embedding data set of the K-class source domains; and constructing a heterogeneous domain generalization network, and training the heterogeneous domain generalization network by utilizing the knowledge embedding data set of the K-type source domain and the K-type target domain data set to obtain a fault diagnosis model so as to perform fault diagnosis on invisible operation condition information to be diagnosed. According to the invention, fault diagnosis can be carried out on the electromechanical system under the limitation of invisible operation conditions.

Provided are a target detection network training method and apparatus, an electronic device, and a storage medium. The method comprises: segmenting each image sample in a global sample set to obtain a plurality of image blocks; using a label of each image sample, adding a label to each image block containing a target to obtain a local sample set, any label comprising class information and position information of the target; taking each image block in the local sample set and a descriptive statement of class information corresponding to each image block as an input and the label of each image block as an output, training a target detection network for multiple rounds; and when the number of rounds of training reaches a first set value, taking each image sample in the global sample set and a descriptive statement of class information corresponding to each image sample as an input and the label of each image sample as an output, training the target detection network for multiple rounds until when the number of rounds of training reaches a second set value, the training is stopped.

The invention provides a data returning method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: carrying out the classification processing according to the working condition data of to-be-returned equipment, and obtaining the class information of the working condition data of the to-be-returned equipment; identifying the to-be-returned equipment state data to obtain identification information of the to-be-returned equipment state data; generating a plurality of initial to-be-returned data sets according to the return time information, the to-be-returned equipment working condition data category information and the to-be-returned equipment state data identification information; screening the plurality of initial to-be-returned data sets to obtain a target to-be-returned data set; and performing to-be-returned data extraction processing on the target to-be-returned data set to obtain target to-be-returned data so as to return the target to-be-returned data. The method and the device are used for filtering invalid data in the equipment working condition data and the equipment state data, the data return efficiency and accuracy are improved, and therefore the strict requirement of the industrial Internet of Things for equipment full-life-cycle data management is met.

A multi-lead electrocardiogram (ECG) signal classification method based on self-supervised learning relates to the technical field of ECG signal classification. The method includes: processing an original signal through different data augmentation methods, designing an appropriate encoder module, extracting a feature of an ECG signal through a large amount of easily available unlabeled data such that an encoder learns more class information of the ECG signal, fine-tuning the model encoder with a small amount of labeled data for feature optimization, and continuously optimizing a parameter of a feature extractor by training a model such that a generated feature well reflects a structure and information of input data. Through self-supervised learning, the method reduces obstacles caused by performing ECG signal classification through a large amount of expensive manually labeled data, improving the generalization ability of the model.

The invention discloses a super-resolution combined remote sensing image rotation small target detection method, which comprises the following steps: receiving a to-be-detected remote sensing image, and inputting the remote sensing image into a pre-established and trained small target detection network model to obtain a target bounding box and category information; the model comprises a backbone rotation small target detection network, and the network introduces a feature enhancement reconstruction module on the basis of a backbone basic rotation target detection network for improving the feature characterization capability of the backbone rotation small target detection network for weak and small targets; in the training stage, the model further comprises an auxiliary super-resolution network and a multi-scale feature association and alignment module deployed between the trunk rotation small target detection network and the auxiliary super-resolution network. An auxiliary super-resolution network provides richer high-frequency features for a trunk rotation small target detection network branch so as to improve the detection precision of a weak and small target; and the multi-scale feature association and alignment module constructs deep information interaction between the super-resolution and the target detection task.

The invention discloses a mixed-flow water turbine runner surface cavitation point detection method. The method comprises the following steps: acquiring a mixed-flow water turbine runner surface cavitation point image to be detected and a trained cavitation point detection model; the method comprises the following steps: scaling a to-be-detected turbine surface cavitation point image of a mixed-flow water turbine to a preset specification, then sending the image to a backbone network, performing shallow feature extraction through a convbn module in the backbone network, and then performing deep feature extraction through a uib module so as to gradually extract image features of each level through the backbone network; inputting the image features of each level into a Neck network in which a BiFPN module is introduced and a high-resolution detection layer is added, and performing cross-scale feature interaction fusion on the image features of each level through the Neck network to obtain a multi-scale to-be-detected cavitation point feature map; the multi-scale to-be-detected cavitation point feature map is sent into a detection head, the positions and confidence coefficients of cavitation points in the map are predicted, accurate positioning and classification of cavitation points of different sizes are achieved through a dynamic self-adaption mechanism of the detection head, and a detection result containing bounding box coordinates, confidence coefficient scores and category information is output. According to the invention, water turbine runner surface cavitation point detection under different scales can be realized.

Disclosed herein is a method of training deep learning networks resistant to adversarial attacks, which includes inputting data into a pre-trained feature extractor to extract feature information, inputting the extracted feature information into a neural ordinary differential equation to output denoised feature information, and inputting the denoised feature information into a classifier to output estimated class information for the data.

The present invention belongs to the technical field of image tampering detection, and particularly relates to a mutually aggregated copy-and-move remote sensing image tampering detection method based on contour guidance, comprising step 1, inputting a copy-and-move tampered image; step 2, similar region detection; step 3, tampered edge detection; step 4, information fusion; step 5, contour detection; step 6, class information fusion; step 7, contour-guided region modeling; step 8, outputting a tampered detection image. The present invention solves the problem of information redundancy in the feature extraction process and solves the problem of global modeling feature confusion.

Use of behavior graphs is disclosed. Information is acquired related to datacenter activity comprising entry point information associated with a client entering a datacenter from an external entry point, a user on a machine class information, information on launched processes, child processes, and / or interactive processes, and information related to addresses with which processes communicate. Various tiers of nodes are generated based on the acquired information. A baseline graph is generated and used for comparison with subsequent behavior graphs. Selective remediation can be performed.

The invention provides a target detection method and device based on a meta universe, electronic equipment and a storage medium, and the method comprises the steps: obtaining an initial image, carrying out the AI creation of the initial image in the meta universe to obtain an enhanced image with a label, training a preset teacher model according to the enhanced image and the initial image, and obtaining a target detection result; and optimizing the weight of the teacher model through a gradient descent algorithm to obtain a trained target teacher model and a teacher model weight, performing weight updating on a preset student model through an index moving average and the teacher model weight to obtain an initial student model, inputting the initial image to the initial student model, and obtaining the target teacher model. Obtaining a prediction bounding box and prediction category information, calculating distillation loss based on the prediction bounding box and the prediction category information, performing knowledge distillation on the initial student model according to the distillation loss to obtain a target detection model, and performing target detection through the target detection model; the target detection method based on the meta universe is more practical and more accurate.

The invention provides a recommendation method and device and electronic equipment, and the method comprises the steps: determining a first-class word matrix based on first-class information in user historical behavior data and first-class information of the electronic equipment; determining a second class word matrix based on the second class information in the user historical behavior data and the second class information of the electronic device; determining an equipment information matrix based on the first type of information of the electronic equipment and the second type of information of the electronic equipment; determining a fusion matrix based on the first type of word matrix, the second type of word matrix and the equipment information matrix; inputting question data input by a user and the fusion matrix into a recommendation model to obtain recommendation information for the performance of the electronic equipment; wherein the first type of information and the second type of information represent different attribute information of the electronic equipment.

The present application relates to the technical field of medical image recognition, in particular to a semi-supervised medical image classification method and system based on similarity threshold pseudo label, more unlabeled data is used by using similarity threshold pseudo label, the representative probability distribution of each class is calculated according to labeled data, and labeled data can provide more reliable class information;Unlabeled data is selected according to the confidence threshold to give pseudo label, then the probability distribution of the low confidence part of unlabeled data and the similarity between the representative probability distribution of the corresponding class is greater than the similarity threshold, and the unlabeled data is given pseudo label, so as to select more high-quality unlabeled data to participate in model training.The present application can utilize more high-quality unlabeled data on the confidence threshold method without reducing the accuracy of pseudo label, and further improve the robustness of model training and the accuracy of medical image classification.