172 results about "Class information" patented technology

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 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 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.

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.

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.

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.

To allow results of measuring physical fitness of students to be continuously recorded in an educational institution.SOLUTION: A computer executes: acquiring student identification information identifying a student; generating authentication information to be associated with class information of a class that the student belongs to; acquiring the student identification information and the authentication information from a student terminal of the student; associating the class information with the student identification information, based on the student identification information and the authentication information acquired from the student terminal; acquiring, from the student terminal, measurement information obtained by associating the student identification information with results of measuring physical fitness; and updating history information indicating a history of results of measuring physical fitness of the student for multple grades and associated with the student identification information, based on the measurement information and the class information corresponding to the student identification information.SELECTED DRAWING: Figure 1

The disclosure provides a camouflage object semantic segmentation method and device based on an adaptive candidate strategy, and a medium. The technical scheme of the disclosure obtains the class code of a to-be-segmented image through a coarse feature extractor and a classifier, realizes the transmission of semantic class information, and enhances the semantic perception ability of a subsequent segmentation task. An initial prediction of a camouflage object is performed through a target detector and an adaptive candidate strategy, and an optimal target attention box is generated to provide robust spatial guidance for a core segmentation task. The class code and the target attention box are combined, multi-source information is fused and reconstructed through a multi-guided feature fusion module and a multi-task perception decoder, and an accurate segmentation result is output.

To implement quick and smooth parking assistance close to a human sense without being affected by an environment of a parking space. A 3D semantic segmentation image having a plurality of pixel units, each pixel unit of the plurality of pixel units including the depth data and class information, are generated, an available parking space is searched for on the basis of the 3D semantic segmentation image, and a path to the searched parking space is planned and a vehicle is controlled. The present disclosure can be applied to a parking assistance system.

The invention discloses a double-classification head voiceprint recognition training method based on federal learning. The process of the training method comprises the steps that a client provides local speaker category information, a server integrates global speaker categories, the server initializes a global classifier and a global encoder, the server distributes the global classifier and the global encoder, and the client receives the global classifier and the global encoder and then carries out training together with a local classifier. In the local training process, global classifier parameters are updated through a moving average algorithm, cooperative training of the global classifier and a client local classifier is provided, and the model performance is remarkably improved. In the model fusion process, in order to solve the influence caused by data distribution deviation, a global classifier carries out weighted averaging according to categories contained in each client. According to the method, the local model with better performance can be obtained, so that the global model performance loss during model aggregation is reduced, and a forward feedback mechanism is formed for subsequent model training.

The application discloses a kind of edge cloud cooperation human activity recognition modeling methods based on heterogeneous multi-modal data, and is divided into two stages of centralized pre-training and multi-modal semi-supervised fine-tuning.In the first stage, by dynamic mask contrast learning, pre-train the basic model based on converter using the joint dataset with super-class information, so that it has the ability to extract robust features from any modal combination.In the second stage, the local unlabeled data is used by the end-side client to generate pseudo-labels through weak mask view and calculate losses based on strong mask view for local update;Cloud side aggregates heterogeneous model parameters from different clients, and fine-tunes the global model using a small amount of labeled data.The application effectively alleviates the data heterogeneity problem by separating single-modal feature encoding and cross-modal information fusion, and can efficiently train a high-performance multi-modal human activity recognition model using a small amount of labeled data and unlabeled data while protecting data privacy.

The invention relates to an underwater vehicle intelligent navigation large model pre-training method and system and a medium. The method comprises the following steps: M1, multi-source data collection and format unification: collecting control of an unmanned underwater vehicle, a remote control underwater vehicle and a manned underwater vehicle in a normal navigation state, control in an abnormal navigation state, multi-device cooperative navigation control and control in different navigation environments; data information of control under different navigation depths and control in different navigation time periods is acquired, and preprocessing is carried out; m2, constructing a pre-training task: dividing sample points at each moment into state information and control information according to an execution process; according to the method, the generalization performance and the multi-task processing capacity of the model can be remarkably enhanced, more reliable technical support is provided for UV autonomous control, the problem of error transmission in a traditional multi-stage model assembly line is effectively solved, the maintenance cost is reduced, and meanwhile the robustness of the model is improved.

The invention discloses a target detection method based on homomorphic encryption, and the method comprises the steps: generating a pair of encrypted public and private keys at a local client through a homomorphic encryption algorithm, encrypting original image data through the public key to obtain an encrypted image, and transmitting the encrypted image to a server; the server preprocesses the encrypted image, calculates the preprocessed encrypted image data through a pre-trained convolutional neural network model, extracts an image feature value in an encryption domain, locates a target area, and generates an encrypted target detection result; the server returns the encrypted target detection result to the local client; and the local client decrypts the encrypted target detection result returned by the server by using the private key to obtain the final target position and category information. When the convolutional neural network is used for target detection, the data is encrypted during transmission and processing, so that the security of the data is improved, privacy information is protected, and meanwhile, the model performance is not influenced.

The present application relates to a kind of unmanned aerial vehicle micro target identification inspection method and system, belong to computer vision technical field.The present application utilizes the powerful computing power of cloud computing, based on DETR original model constructs micro target detection model, micro target detection model utilizes the query guide feature sequence generated by dynamic anchor frame module to initialize the position query of decoder, simultaneously, the present application will static learnable query vector as the input of decoder, so that model can utilize accurate position information, guide decoder to extract more pure, more comprehensive micro target content features, effectively improve the flexibility and accuracy of target detection.Finally, the class information and position information are output by prediction head.The present application fully releases the potential of cloud computing power while solving the problem of missed detection in dynamic scene, suitable for high-precision intelligent supervision task in complex scene, provides effective technical support for safety hazard management.

The application discloses a small sample face recognition method based on prototype calibration and adaptive fine-tuning, comprising the following steps: 1) pre-training the encoder on a large-scale face database, capturing rich semantic information and class distribution, constructing an evaluation set, and ensuring that the training identity information does not overlap with the constructed evaluation set; 2) initializing the classifier weight through the weight imprint method, ensuring smooth transition, and inducing the discriminative structure of the encoder; 3) calculating the cosine similarity between the fine-tuning class prototype and the pre-training prototype, fusing the pre-training class information through weighted average, and calibrating the biased fine-tuning class prototype; 4) introducing an adaptive margin loss function, adjusting the sample penalty strength, and selectively fine-tuning the BatchNorm layer and the classifier weight; and 5) using the field-aware similarity NAC to calibrate the unknown class rejection, enhancing the similarity relationship with the field sample, and obtaining the final matching score. The method can improve the recognition accuracy and rejection ability of the model in the small sample learning scene.

The application discloses a specific behavior recognition method in a dense crowd environment, comprising the following steps: S1, data set acquisition, wherein the data set comprises a pedestrian detection data set and a behavior recognition data set; S2, data set preprocessing; S3, inputting images in the preprocessed pedestrian detection data set into a feature map pyramid network to extract corresponding features, generating a candidate region and class information of the candidate region through a region generation network; S4, removing overlapping targets by using a maximum value suppression algorithm; S5, performing specific behavior target recognition based on a classification recognition network of a residual network; S6, training grid parameters of the classification recognition network; S7, obtaining optimal grid parameters through step S6, and importing the optimal grid parameters into the classification recognition network, and testing through a behavior recognition data set. The method realizes the detection and recognition of specific behaviors in a dense crowd environment by fusing two stages of detection and recognition tasks for the special environment of the dense crowd.

The invention provides a method, a device, equipment and a medium for generating a code call chain based on a graph database, and the method comprises the steps: carrying out the static analysis of a target source code, so as to extract related information, which comprises class information, method information and a call relation; target nodes and target relationships are constructed in the initial graph database according to the related information to obtain a target graph database, the target nodes comprise class nodes, method nodes and interface nodes, and the target relationships comprise a first relationship between the class nodes and the method nodes and a second relationship between the method nodes; a third relationship between the class nodes and a fourth relationship between the class nodes and the interface nodes; and constructing a visual code calling chain according to the target node and the target relationship. Therefore, the related information of the target source code can be accurately and intuitively indicated based on the graph database, and the visual call chain view is generated.