18 results about "Data discrimination" patented technology

This invention belongs to the field of target detection and recognition technology, and proposes a quantum radar experimental device based on a non-classical optical field. The device comprises a transmitter, a receiver, and a transmission-receiver synchronization system. The transmitter includes a light source unit, a non-classical optical field control unit, and a signal collimation and transmission unit. The receiver includes a signal collection unit, a detection unit, and a data discrimination unit. The transmission-receiver synchronization system enables clock synchronization and real-time communication between the transmitter and receiver. This invention can effectively overcome the high dependence of existing quantum radars on quantum entanglement, improve the ease of operation and equipment of quantum radar, enhance the detection capability of dark and small targets, and provide key technical support for the large-scale application of quantum radar in the future.

To provide a game machine that allows a processing load to be reduced.SOLUTION: Control data for executing predetermined control on a game is stored in control data storage means. Control of the game is executed by control means according to the control data stored in the control data storage means. On the basis of establishment of a predetermined storage condition, the control data is stored in the control data storage means by storage control means. On the basis of establishment of the predetermined storage condition, at least one of control data stored in the control data storage means and data stored in the control data storage means is discriminated by data discrimination means. On the basis of a fact that a discrimination result of the data discrimination means becomes a specific discrimination result determined beforehand. Control is executed so that data according to the control data is stored only in a part of a storage region of the control data storage means. Thereby, an effect of reducing a processing load is produced.SELECTED DRAWING: Figure 1

The embodiments of the present disclosure disclose a model training method, a data discrimination method, equipment, a medium and a product. The method comprises: obtaining time series data; generating synthetic time series data through a time series data generation model; obtaining time series feature data, obtaining synthetic time series feature data; obtaining a data discrimination result; training the time series data generation model and the time series data discrimination model through an adversarial training method based on the data discrimination result; and determining the time series data generation model after at least one round of training as a target time series data generation model in response to the data discrimination result output by the time series data discrimination model after at least one round of training satisfying a synthetic time series data indistinguishable condition. The target time series data generation model determined based on the scheme can generate synthetic time series data with high similarity to real time series data.

The embodiment of the invention discloses a health assessment method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring physiological data of a target object; the method further comprises the step of determining physiological discrimination data of the target object based on the physiological data of the target object and physiological data discrimination conditions, and the physiological data discrimination conditions are dynamically adjusted at least based on historical data of similar objects. The method further comprises the step of determining historical doctor seeing data of the target object based on historical data of the target object. In addition, the method further comprises the step of outputting a health assessment result of the target object by the assessment model based on the physiological discrimination data and the historical treatment data of the target object. Through the mode, the physiological data detected by the target object in real time is judged based on the dynamically adjusted judgment condition, the physiological judgment result of similar objects can be accurately reflected, the physiological judgment data of the target object and the historical treatment data are combined, and the personalized health state of the target object is accurately evaluated.

The invention provides a pressure change memory. Each pressure change memory unit comprises a first electrode, a first dielectric layer, a memory function layer, a second dielectric layer and a second electrode which are sequentially arranged from top to bottom, the storage function layer has an amorphous characteristic and a defect state with the concentration higher than 10 < 14 > cm <-3 >, and the component is selected from a sulfur threshold switch material, an oxide, a nitride or a solar cell material. The device realizes controllable offset of threshold voltage under the action of forward and reverse electric fields, and takes the voltage difference as a read-write window, thereby realizing nonvolatile information storage. Through asymmetric structure design, storage function layer multi-layer design or PN junction introduction, a read-write window can be further expanded, and the reliability of data discrimination is improved. The memory is simple in structure and easy to integrate, has the characteristics of high speed and low power consumption, and is suitable for three-dimensional stacked high-density storage application.

The invention belongs to the technical field of analysis methods, and particularly relates to an animal behavior analysis method and device based on a multi-modal agent, computer equipment and a storage medium. The method comprises the following steps: receiving multi-view video data and a natural language cue word; extracting three-dimensional skeleton key point data of the animal from the multi-view video data; the behavior analysis agent generates semantic description and a calculation task list at least comprising one physical index according to video key frames and natural language cues in the multi-view video data; calling an independent calculation tool chain, and performing quantitative calculation on the three-dimensional skeleton key point data according to the calculation task list to obtain a quantitative result corresponding to the physical index; and fusing the semantic description and the quantitative result through a slot filling mechanism to generate an animal behavior analysis report. According to the method, the physical accuracy of calculation can be realized, the capability of generating a scientific and interpretable analysis report is also realized, and the crossing from data discrimination to intelligent analysis is realized.

The present invention provides an information processing device that can perform processing using weight measurement data more appropriately. [Solution] The information processing device 30 comprises a measurement information acquisition unit 331, a data discrimination unit 332, a data processing execution unit 334, and a notification unit 335. The measurement information acquisition unit 331 performs an acquisition process to acquire weight measurement data from a measuring device installed in the pet toilet. The data discrimination unit 332 performs a pattern discrimination process to determine which of one or more patterns the weight measurement data group corresponds to. The data processing execution unit 334 and the notification unit 335 perform processing according to the pattern determined by the pattern discrimination process.

The application provides a data security monitoring method, device, intelligent gateway, system and storage medium for coal mine gas emission reduction. The method comprises the following steps: acquiring first time series data collected by at least one sensor of a to-be-monitored device within a recent preset time period; generating a dynamic threshold according to the first time series data; acquiring second time series data collected by the at least one sensor of the to-be-monitored device; detecting whether the second time series data is within the dynamic threshold range; in the case that the second time series data is not within the dynamic threshold range, marking the second time series data as abnormal data; otherwise, marking the second time series data as normal data; and transmitting the second time series data marked as normal data to a national carbon market management platform. The embodiment of the application can transfer the task of abnormal data discrimination to the intelligent gateway side, greatly reducing the data transmission amount of the gateway and the national carbon market management platform, and avoiding waste of network bandwidth and storage resources.

The embodiment of the application discloses a kind of saliency target detection hybrid supervision training methods, by step, saliency target detection model of multiple different network structures is trained using labeled data image respectively, the discriminator of reliable pixel and unreliable pixel in unlabeled image is divided;Unlabeled data image and fine annotation data image are simultaneously loaded into the saliency target detection model to be trained as training data;For labeled data image, data augmentation method is used to increase the diversity of data samples, for unlabeled data image, the saliency prediction result corresponding to the image is obtained by calling the trained multiple discriminators respectively;Pixel reliability is divided;The reliable pixel in labeled data and unlabeled data is mixed supervision training to model, by data labeling, and by model corresponding data discrimination, the method for increasing the diversity of data samples mixes all data supervision training, improves the generalization ability of model.

The embodiment of the application discloses a method for obtaining the discrimination of financial service data, which comprises the following steps: obtaining a candidate financial service data set as a financial data sample, determining the first number of different values R1 of the financial data sample according to part of the data of the financial data sample, determining the second number of different values R2 of the financial data sample according to all the data of the financial data sample, and finally determining the discrimination of the financial service data according to the comparison relationship between the first number of different values R1 and the second number of different values R2, so that the related service data can be quickly inquired or retrieved according to the size of the discrimination of the data in the financial service data set.

The application provides a physical-data collaborative driving generated adversarial network (PIGAN) long-span bridge abnormal wind data self-adaptive completion system. The system comprises a long-span bridge abnormal wind data discrimination module 101, a long-span bridge abnormal wind data interpolation module 102 and a long-span bridge interpolated wind data evaluation module 103. The 101 utilizes a set priori criterion to discriminate the abnormal state of the measured wind data in real time, and completes abnormal marking and abnormal classification. The 102 interpolates the wind data of different abnormal types according to the abnormal classification result of the 101. If the abnormal wind data belongs to the accidental abnormal category, the weighted K-nearest neighbor (WKNN) algorithm is adopted. If the abnormal wind data belongs to the continuous abnormal category, the PIGAN is adopted. The 103 is used for evaluating whether the interpolated wind data reaches the preset quality requirement. If the preset quality requirement is not reached, the 102 is returned. If the preset quality requirement is reached, the long-span bridge abnormal wind data self-adaptive interpolation is completed.

The invention relates to a data processing method and device based on multi-modal contrast fusion and a graph neural network, and a medium. The method comprises the following steps: collecting multi-modal biomedical data set samples of mRNA expression data, DNA methylation data and miRNA expression data; embedded matrixes of three modes are obtained through an independent feedforward neural network encoder; adopting a contrast learning mechanism based on NT-Xent to carry out unsupervised contrast alignment on the embedded matrixes of different modalities, and generating aligned embedded matrixes of three modalities; embedding and stacking the aligned modalities into a sequence, and inputting the sequence into a multi-layer Transform encoder to obtain a unified fusion representation; normalizing the fusion representation, and constructing a sample similarity graph structure; and based on the graph structure and the fusion representation, node high-order adjacency features are extracted through a three-layer graph convolutional network, sample classification is completed in combination with two full-connection layers, and a sample classification result is output. Compared with the prior art, the method has the advantages of cross-modal data fusion optimization, medical data discrimination enhancement, high efficiency and the like.

The invention discloses an intrusion data discrimination method and device, electronic equipment, a storage medium and a vehicle, and relates to the technical field of vehicle information security, and the method comprises the steps: extracting multi-dimensional operation data features in the collection of first vehicle operation data of the vehicle through a preset discrimination model, and constructing an operation data feature matrix; determining the confidence coefficient of the operation data feature matrix based on context information in the vehicle operation process; and according to second vehicle operation data of a plurality of vehicles of the same type, in combination with the confidence coefficient, discriminating the operation data features, and determining an intrusion data discrimination result. Multi-source data fusion analysis is realized, the characteristics of a vehicle as a mobile physical entity are fully considered, real attack and normal behavior variation are effectively distinguished, and the false alarm rate is reduced.

The application provides a multi-modal private data generation model training method, data generation method and system, and belongs to the technical field of data privacy protection.The application realizes the generation of RGB images and table data in a unified framework, does not need to use additional public data and an end-to-end training mode, and is connected with each other in the generation process of various types of data, thereby improving the semantic consistency of the generated data, effectively fusing the key information of different modalities to realize accurate multi-modal data discrimination;multi-angle discrimination and multi-modal matching loss are proposed, the multi-modal discrimination loss dominates the training process of the GAN model, the single-modal discrimination loss is used to assist the model in balancing the learning efficiency of different modalities, and the matching loss promotes the learning of the model on the relationship between modalities.In order to reduce the privacy leakage risk of the generation model, the differential privacy mechanism is applied to the image and table data joint generation model based on GAN, so as to balance the availability and privacy of the synthesized data.

The invention discloses a computer fan grading regulation control system based on multi-sensor feedback, which relates to the technical field of fan grading regulation, takes a fan grading regulation control platform as a core, and cooperatively controls an execution decision unit, a regulation execution influence evaluation unit and a fault-tolerant analysis substitution unit. An internal hardware performance evaluation unit and an operation control unit are combined to form a closed-loop control link of perception-decision-execution-feedback; the aging coefficient dynamic evaluation mechanism is used for acquiring the operation deviation and data precision difference of the control main body and the monitoring main body in a continuous / single adjustment scene in real time, constructing an aging coefficient curve after de-dimensionalization processing, and accurately quantifying the hardware performance attenuation trend; the environment self-adaptive adjusting strategy is based on the matching relation between environment parameters such as air pressure and the graded adjusting wind speed range; and fault-tolerant analysis and anomaly suppression capability are realized, and the reliability of an adjustment instruction is ensured through monitoring data anomaly detection and accidental data discrimination.

A small sample relationship classification method and system based on a data-enhanced triplet network includes sample extraction: sampling boundary samples as a training task to generate samples that are easily confused at the classification boundary; sample data discrimination: using a triplet network to discriminate the sample data and then uniformly training to obtain the correct classification results of small sample relationships. It solves the problems of existing methods that are difficult to handle samples at the classification boundary and long-tail distribution of data, and can be widely used in the field of knowledge extraction in natural language processing.

The purpose of the present invention is to improve the comprehensiveness of teacher data. This data discrimination device stores a plurality of reference data items and additional data items to create a reference data group. The data determination device comprises a distance calculation unit, a grouping unit, a frequency calculation unit, and a selection unit. The distance calculation unit calculates a reference point of the reference data group and the distance from the reference point to the plurality of reference data items and the additional data items. The grouping unit groups the plurality of reference data items and the additional data items into a plurality of groups on the basis of the distance calculated by the distance calculation unit. The frequency calculation unit calculates the frequency of the plurality of reference data items and the additional data items for each of the plurality of groups. The selection unit selects a plurality of reference data items and additional data items to be included in the reference data group such that the frequency approaches a predetermined target level.

The invention discloses a wind power plant wind resource simulation method and simulation equipment, and relates to the field of wind resource simulation. The method comprises the following steps: collecting wind resource data in a wind power plant in a short time, wherein the collected data serve as real-time short-time wind data; s2, data discrimination and classification; the classification model is used for classifying the real-time short-time wind data, and according to classification, the judgment model is used for judging the wind condition type corresponding to the real-time short-time wind data and outputting the wind condition type; s3, generating data; and S2, the wind regime type and the real-time short-time wind data in the step S2 serve as input, simulated long-time wind data are generated through a generation model corresponding to the wind regime type, and the simulated long-time wind data are within the error range compared with wind resource data collected for a long time in the wind power plant. According to the method, validity judgment and wind regime classification can be automatically completed on the short-term wind resource data, and the long-term wind resource data matched with the short-term wind resource data are generated based on the classification result so that a wind power equipment simulation platform can carry out simulation testing.