9results about How to "Small data size" patented technology

The embodiments of the present specification provide an anonymous identity authentication method and device. According to the method, a first device generates a binary indication vector and an auxiliary vector having a predetermined relationship with the indication vector according to the sequence index of a first account in a target account set; and generates a signature value C according to the indication vector, the auxiliary vector and a private key of the first account. A first public key vector is also calculated according to a first random number selected by a verification device and a public key sequence of the target account set, and a blinding value is calculated based on the first public key vector and a random number array selected by the first device. The first device further generates a first and second group of commitment values according to the indication vector, the auxiliary vector and a challenge array and a variable value selected by the verification device. The first device provides the signature value C, the blinding value and the first and second group of commitment values to the verification device, and the verification device verifies whether the first account belongs to the target account set according to the foregoing.

The application belongs to the technical field of Internet, and particularly relates to an industrial Internet cross-domain metadata trusted fusion method and system, comprising the following steps: collecting structured metadata, time series metadata and relationship metadata of each industrial domain node, and constructing an intra-domain metadata graph based on field attributes and call relationships; performing semantic coding and time series coding on the intra-domain metadata graph, obtaining corresponding semantic representations and time series representations, and calculating inter-field consistency; identifying stable field clusters, drift field clusters and abnormal nodes according to the inter-field consistency in each industrial domain node; and performing segmented aggregation on the stable field clusters and the drift field clusters by each industrial domain node to generate anchor field summaries and drift trajectory summaries. The application combines metadata graph coding, field clustering and federal dynamic weighting, and realizes industrial cross-domain metadata private trusted fusion through abnormal compensation correction.

This invention discloses a face recognition method and apparatus based on millimeter-wave radar phase features. The method includes: acquiring face radar echo signals; selecting target points in the frequency domain; using the target point data as data to be processed containing face information; extracting phase features from the data to be processed; performing a deconvolution operation on the phase features to obtain the phase change difference of the target points; performing 3D convolution and max pooling on the phase change differences of all target points to obtain dimensionality-reduced phase features; downsampling the dimensionality-reduced phase features using a convolutional autoencoder; and inputting the downsampled data into a convolutional neural network to achieve face recognition. Unlike vision-based face recognition, this invention uses millimeter-wave radar, which has higher resolution, is not limited by lighting conditions, and can effectively protect user privacy.

A front-end and back-end combined hidden danger analysis system and method thereof, the system comprising: a front-end camera module, a video target recognition module, an image definition recognition module, an image similarity recognition module, a front-end slice compression module, a back-end image analysis module, a back-end hidden danger analysis result module and a front-end loudspeaker module, the method comprising: one: image acquisition; two: hidden danger target recognition; three: image definition recognition; four: image similarity recognition; five: video slice compression; six: image analysis; seven: hidden danger analysis; eight: hidden danger broadcast reminder; the video frame is preliminarily detected through the front-end video target recognition module, only the frame containing the hidden danger target is reserved for subsequent processing; combined with the image definition recognition module and the image similarity recognition module, further eliminate the fuzzy and repeated frames, generate high-quality video segments, effectively reduce the image data amount required to be processed by the back-end multi-modal large model, thereby greatly reduce the consumption of token.

This invention relates to the field of artificial intelligence interactive data processing technology, including at least one smart terminal. The smart terminal has a built-in dual-partition storage module, a permission management module, a data compression module, and a cross-device migration module. The invention includes the following steps: S1, dual-partition storage system initialization stage; S2, hierarchical permission control rule configuration stage; S3, lightweight migration data package generation stage; S4, end-to-end offline cross-device migration stage; S5, data synchronization and process closure stage. This invention, through a dual-partition isolated storage architecture design, achieves physical layer isolation between personalized user private data and general AI model algorithms. Combined with a hierarchical and independent permission control mechanism, it effectively avoids the risk of user privacy data leakage due to unauthorized access. Furthermore, the end-to-end encrypted transmission mode eliminates the need for third-party cloud servers, reducing the risk of leakage of user private data during transmission.

The invention discloses a three-dimensional modeling method and device, electronic equipment, a storage medium and a program product, and belongs to the technical field of three-dimensional modeling. The three-dimensional modeling method comprises the following steps: based on multiple frames of original images corresponding to a target scene, obtaining an initial three-dimensional model corresponding to the target scene and camera parameters corresponding to the original images; inputting the initial three-dimensional model and the camera parameters into a neural rendering model to obtain target texture data output by the neural rendering model; and processing the low-surface-number three-dimensional model corresponding to the initial three-dimensional model based on the target texture data to obtain a target three-dimensional model corresponding to the target scene. According to the three-dimensional modeling method, secondary creation does not need to be carried out manually to improve the image quality, the labor cost is saved, the volume of data needing to be processed by the neural rendering model is reduced, and the high-fidelity rendering effect is achieved on the basis of ensuring the image fluency.

This invention belongs to the field of data management technology, specifically relating to a method and system for managing geographic information mapping data based on multi-source spatiotemporal features. The method includes: acquiring mapping data containing spatiotemporal coordinate attributes and generating time-granularity codes; selecting the optimal child node based on comprehensive cost when inserting data; when a node is overloaded, evaluating the spatiotemporal variability cost of candidate splitting strategies to select the optimal strategy; for newly generated leaf nodes, determining the reference object and differential accuracy level based on the spatiotemporal distribution of internal data objects, and storing the differential values ​​of the data relative to the reference object; extracting query feature vectors and matching historical query patterns to cluster centroids for data prefetching; and asynchronously updating the centroids and adjacency pointer transition probabilities in batches after the query. This invention improves the retrieval response performance of mapping data while effectively compressing data storage space by constructing an index structure and combining differential storage with predictive prefetching.

The invention relates to the technical field of unmanned aerial vehicle cluster cooperative control, and discloses a method, a device, equipment and a medium for cooperative sensing between unmanned aerial vehicles, and the method comprises the steps: obtaining the multi-channel heterogeneous sensor data of a target unmanned aerial vehicle, and carrying out the time synchronization of the multi-channel heterogeneous sensor data, and obtaining the first sensor data with the aligned time sequence; based on the first sensor data, generating a structured description of an environment where the target unmanned aerial vehicle is located, and compressing the structured description to obtain compressed sensor data; the compressed sensor data are transmitted to other unmanned aerial vehicles, and the structured data aligned in time sequence can ensure that all the unmanned aerial vehicles make decisions based on the same time reference and environment model, so that collaborative errors caused by data time difference or format difference are avoided, and collaborative consistency is ensured; moreover, the lightweight data transmission delay is lower, the dynamic environment change can be quickly responded, and the real-time cooperative task of the unmanned aerial vehicle cluster is supported.

The invention relates to the technical field of information processing and personalized recommendation, and discloses an attention weight positioning recommendation method and system for searching and browsing footprints. According to the method, the original historical behavior data is deconstructed and compressed into the standardized search and browsing feature vectors, and the corresponding resource utility score is obtained through the dynamic resource utility evaluation model based on the feature vectors, so that accurate recognition and screening of high-value parts in mass data are realized; therefore, the scale of data participating in core calculation can be directly reduced, the size of a memory working set and I / O pressure are reduced, and the problem of calculation delay caused by storage and processing of a large amount of low-value and high-noise data in a traditional method is solved. By constructing an anti-circulation label topological relation graph and implementing a weight propagation and pruning method under strict resource constraints, it is ensured that memory occupation and calculation delay in the feature construction stage are controllable, and the aim of conducting accurate recommendation within millisecond-level delay is achieved.