14results about How to "Guaranteed recognition effect" patented technology

The application discloses an event reporting method, a setting device, a target device and a system based on MCTP, and the method comprises the following steps: the setting device sends a setting event message with the MCTP message type code being 0x77 to the target device, the message at least containing an event label, a data length and an event condition described by a formatted string key-value pair; the target device receives and stores the event condition; when the event condition is met, the target device actively sends a reporting event message with the code being 0x77 to the setting device. By using the event notification mechanism, the setting device does not need to poll frequently, the monitoring condition is configured on the target device by one time setting, the target device actively reports when the condition is met, repeated information transmission is avoided, the MCTP bus communication flow is greatly reduced, and the system load is reduced; meanwhile, the key information and the change information can be sent in real time, the information updating delay problem caused by reducing the polling frequency is solved, and the balance between the system load and the real-time response is realized.

The application discloses a communication risk detection method, system, device and storage medium, relates to the technical field of network security, and solves the problem of difficult data tracing by standardizing communication data and associating the standardized communication data with an abnormal list library to construct a dynamic network; through multi-time scale feature extraction combined with a parallel analysis architecture, fast response is realized by using a real-time rule matching layer, dynamic evolution rules are captured by using a hidden Markov model of a behavior pattern analysis layer, and group risks are identified by using a space-time weighted model of a correlation network analysis layer, so that the defects of poor adaptability and single dimension of traditional methods are overcome; finally, through multi-engine decision and hierarchical disposal mechanism, the recognition accuracy is guaranteed while the disposal efficiency is optimized, so that high-precision and low-delay identification and control of cross-border abnormal communication behaviors are realized.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The invention discloses a dialogue intention information determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring target speaking information of a target user in a communication dialogue; and performing complexity analysis processing according to the target speaking information, and determining a comprehensive complexity value corresponding to the target speaking information. And screening and determining a target reasoning module for intention analysis from a pre-constructed reasoning module set according to a pre-constructed complexity level threshold value and the comprehensive complexity value. And performing intention recognition processing on the target speaking information according to the target reasoning module, and determining dialogue intention information of the target user. According to the technical scheme, different resources are called according to the complexity of the user dialogue information, so that stable recognition of the intention category and intention intensity of the user is achieved, and the computing power and the cost are controlled while the recognition effect is guaranteed.

The invention discloses a sample screening method and device based on a generalization optimization algorithm, and the method comprises the steps: collecting to-be-screened target data, and constructing a to-be-screened data set; inputting the to-be-screened data set into a pre-trained intelligent recognition model to obtain sample categories output by the intelligent recognition model and the statistical number of the sample categories; wherein the intelligent identification model is obtained by training a machine learning network by using a training data set, the training data set is constructed after marking screened training data, and the training data is obtained by screening collected original data in batches through a high-value sample selection system. According to the method, the optimal recognition effect of the model can be achieved with the minimum labeling workload, the samples with the highest information value are scientifically selected for labeling, redundant samples in a data set are effectively removed, and the time needed by expert labeling is greatly shortened.

The invention relates to the technical field of fault identification, and provides a wind power plant unmanned aerial vehicle inspection data fault identification system based on artificial intelligence, which comprises an unmanned aerial vehicle, a server, a data acquisition module, an interaction module, an analysis module, a decision module and an inspection control module, the data acquisition module acquires appearance image data of a wind driven generator in a wind power plant, the interaction module interactively transmits the appearance image data acquired by the data acquisition module and ground data receiving equipment, and the inspection control module acquires the position of the wind driven generator and real-time position data of an unmanned aerial vehicle. The data acquisition module acquires the position of the wind driven generator, evaluates the inspection route of the unmanned aerial vehicle according to the position of the wind driven generator and the real-time position data of the unmanned aerial vehicle, adjusts the acquisition posture of the data acquisition module according to the evaluation result, and analyzes the wind driven generator according to the appearance image data acquired by the data acquisition module to form an analysis result. And the decision module evaluates the state decision of the wind driven generator according to the analysis result.

This invention provides a method and system for online automatic wire layup defect detection using a robot based on 3D vision. The method includes: constructing and calibrating a 3D vision imaging system based on a Sham lens and a line laser; synchronously moving with a wire layup robotic arm to acquire two-dimensional images of the layup and reconstruct three-dimensional point cloud data; fusing and analyzing the two-dimensional images and three-dimensional point cloud data; using a lightweight deep learning network to perform real-time defect detection on the two-dimensional images; simultaneously locating and segmenting defect regions based on point cloud normal vectors and curvature information; fusing two-dimensional texture features and three-dimensional geometric features through a collaborative representation model to achieve accurate defect identification and location; transforming the defect information in a unified coordinate system; and controlling multiple laser projection devices to project onto the layup surface in real-time using a segmented relay method. This invention achieves high-quality imaging under low reflectivity, large depth of field, and short-range conditions by fusing Sham lens imaging and line laser scanning; achieves a recognition rate of over 95% in high-speed wire layup by combining dual-modal fusion and lightweight few-sample learning; and utilizes multi-device collaborative calibration and relay projection to achieve real-time defect feedback.

The invention relates to a sustainable Internet of Things equipment identification method and system based on traffic time sequence characteristics, and the method comprises the steps: capturing traffic data packets of Internet of Things equipment according to a set time interval, and carrying out the feature extraction of each traffic data packet, and obtaining a time sequence characteristic construction data set; constructing an Internet of Things equipment identification model, and training by using the data set to obtain an Internet of Things equipment identification deployment model; obtaining the time sequence characteristics of the newly-added Internet of Things equipment to obtain a newly-added data sample and a new data set; performing continuous comparative learning based on the new data set by using parameters of the deployment model to obtain an updated deployment model; inputting the to-be-identified Internet of Things equipment into the updated deployment model, and outputting to obtain an identification result; the efficient and accurate identification of the Internet of Things equipment is realized by using the time sequence characteristics of the traffic, the sustainable identification of the Internet of Things equipment is realized by using the continuous comparison learning framework, the types of the newly added equipment are accurately classified, and the identification accuracy of the known equipment is kept.

The present disclosure provides a sensitive information identification method and device, electronic equipment and storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and information security. A specific implementation scheme is: obtaining entity information of a target webpage text; using a preset sensitive information identification model to identify and process the entity information to obtain an identification result of the entity information, the preset sensitive information identification model is obtained by performing knowledge distillation processing on a pre-training model, the pre-training model includes a pre-training network, a first text feature extraction network and a first classifier, and the preset sensitive information identification model includes a second text feature extraction network and a second classifier; and obtaining a sensitive information identification result of the target webpage text according to the identification result of the entity information.

The application discloses an electronic case generation method, an electronic device and a storage medium. In the method, initial dialogue text corresponding to doctor-patient dialogue voice is acquired; for a plurality of case sub-topics, each case sub-topic is spliced with the initial dialogue text to determine corresponding updated dialogue text; each updated dialogue text is provided to a preset topic content generation model to determine answer content corresponding to each case sub-topic; and an electronic case is generated based on the answer content of each case sub-topic. Thus, the user does not need to locate the sub-title content in the doctor-patient dialogue, the content extraction and generation of different sub-titles can be realized based on a single model, the cost of generating the electronic case is reduced under the premise of ensuring the recognition effect.

The application provides a protein-metal ion binding site recognition method, device and equipment, and belongs to the field of protein detection. The method comprises the following steps: extracting features of known metal ion binding proteins to obtain a plurality of sample evolutionary information features; for an unknown protein sequence, candidate distant homologous metal ion binding proteins are determined through multiple sequence comparison and cosine similarity screening, and a training set is constructed; a composite framework of a bidirectional long short-term memory network and a full connection neural network is adopted, input features include evolutionary information features and physicochemical property features, and output is probability values of different metal ion binding sites; the composite framework is trained through the training set to obtain a site prediction model; and the prediction model is used to predict the binding sites of the unknown protein sequence. Stable and efficient prediction of metal ion binding sites is realized.

This invention discloses a surface acoustic wave (SAW) tag structure using a single mask and its encoding and recognition method. All coded tags are made using the same mask, and the tag is encoded by different external capacitors. The SAW tag includes multiple pairs of reflective gratings. Each pair consists of a reference reflective grating and a load reflective grating with an external load capacitor. The capacitance value of the load capacitor has multiple options, corresponding to the possible codes for each reflective grating pair. Since the reflectivity of the load reflective grating changes with the external capacitor, the encoding value of the reflective grating pair is obtained by the amplitude ratio of the two echo pulse signals corresponding to the load reflective grating and the reference reflective grating, and the encoding of the SAW tag is finally identified. The advantages of this invention are that it requires only one mask and can dynamically expand the encoding capacity. Its recognition method not only eliminates the influence of distance but also avoids the coupling effect between the encodings of different reflective grating pairs.

The invention discloses a cooperative deformation monitoring point laying method and system for existing subway parking base upper cover development engineering, and belongs to the technical field of structural deformation monitoring. The method comprises the following steps: determining a cooperative deformation influence area; identifying a structural joint and a force flow transmission path in the area; dynamically laying settlement monitoring points based on a structural joint settlement threshold value, and continuously laying strain, corner and three-dimensional strain monitoring points along a force flow transmission path to form a linear candidate monitoring point set; generating a two-dimensional grid candidate point set in the influence area; and carrying out topological overlay analysis based on a Delaunay triangulation network and a Voronoi diagram on the two types of candidate point sets, deleting redundant grid points through a distance threshold value, supplementing monitoring blind area points through uniformity check, and outputting a final monitoring point coordinate set. According to the method, the problem that point distribution is not systematic or the cost is high in a traditional method is solved, and unification of systematicness, economical efficiency and space coverage uniformity of monitoring point distribution is achieved.

The application provides a radar working mode recognition method and device with a self-checking function, and the method comprises the following steps: using a configured shortest path method to pre-recognize the working mode of a radar state sequence; determining a mode estimation result of matching between a candidate string and a verification string based on at least one of a configured active search matching algorithm or a normal matching algorithm; and recording a mode recognition result and a corresponding sliding window moving rule. The embodiment of the application can effectively avoid recognition errors caused by improper mode division, has strong recognition ability for the anti-reconnaissance means of 'one word with multiple modes', and the method provided by the embodiment of the application does not need to create training samples for each mode, can automatically correct recognition deviation by adaptively adjusting a matching strategy through a feedback loop, improves the calculation efficiency on the premise of ensuring the recognition effect.