18results about How to "Enhance aggregation ability" patented technology

The application discloses a federated learning method and system based on modulus component homomorphic encryption and response verification, relates to the technical field of information security, and comprises the following steps: obtaining initial global model parameters and generating a pre-shared key; a client generates a participation key and a calculation key; a plurality of clients are randomly selected as participation clients and a challenge pair is sent; a participation client generates a challenge response value based on the pre-shared key and the challenge pair, performs local training based on the initial global model parameters, encrypts local model parameters based on the participation key and a modulus component homomorphic encryption algorithm, and obtains model parameter ciphertext; trusted verification is performed based on the challenge response value, and encrypted global model parameters are obtained by aggregating trusted model parameter ciphertext based on the calculation key; the participation client updates the local model based on the encrypted global model parameters, the above process is repeated until the model converges, and a trained global model is obtained. The encryption calculation efficiency, response speed and security robustness of the federated learning are improved.

The invention relates to an exception monitoring method and device, equipment and a storage medium. The method comprises the following steps: acquiring monitoring data of a plurality of cloud servers in response to an exception monitoring request for the plurality of cloud servers in a target area; processing the monitoring data of each cloud server by adopting an alarm type identification model to obtain an alarm type of each cloud server; the alarm type identification model is obtained by training a two-dimensional image formed by a plurality of historical monitoring data in advance; the two-dimensional image comprises time domain feature information and amplitude information of a plurality of historical monitoring data; the plurality of historical monitoring data are monitoring data of a plurality of candidate cloud servers acquired based on a plurality of different time scales; and outputting an exception monitoring result of each cloud server based on the alarm type of each cloud server. By adopting the method, the accuracy of a monitoring result can be improved when abnormity monitoring is carried out on the cloud computing platform.

The invention discloses a salt brick based on negative ion release and a preparation device thereof, and belongs to the field of building materials, the salt brick based on negative ion release comprises a main material and an auxiliary material, the main material comprises a ground raw material salt by mass fraction; the auxiliary materials comprise a heat conduction agent, a binding agent and a reinforcing agent, the raw material salt is subjected to dry mixing with the heat conduction agent, the binding agent and the reinforcing agent, the invention further discloses a preparation device of the salt brick based on negative ion release, and a first synchronous belt wheel and a second synchronous belt wheel are in transmission connection through a transmission belt; the two groups of discharging assemblies are symmetrically arranged at the top of the mixing barrel in a floating manner; the stirring assembly which is horizontally arranged in the mixing cylinder and can turn over not only has a high-strength polymerization effect, but also has the effects of resisting high temperature, preventing a prepared product from deforming and improving the stability of the salt brick acting on a wall surface, ensures that raw materials are uniformly distributed during subsequent extrusion forming of the salt brick, and avoids deformation of a finished product of the salt brick.

The invention relates to the field of olefin polymerization catalysts, and provides a metal-silane composite catalyst as well as a preparation method and application thereof. The metal-silane composite catalyst comprises a diazacyclo-dialkoxy silane compound, an organic compound containing C-O and / or C = O, a mixture of metal halides and / or a reaction product of the above components, and further comprises a superfine inorganic oxide, and after the metal-silane composite catalyst and an organic aluminum compound are mixed, the mixture is subjected to heat treatment, and the metal-silane composite catalyst is obtained. The catalyst can be used for olefin polymerization. The polymer prepared by adopting the catalyst has high melt index and low oligomer content.

This invention discloses a lightweight image super-resolution reconstruction method, system, and storage medium, relating to the field of image processing technology. Specifically, it includes: acquiring high-resolution and low-resolution images of the target as training sample pairs; constructing a lightweight image super-resolution reconstruction model comprising a shallow feature extraction module, a deep feature extraction module, a multi-layer feature fusion module, and an image reconstruction module. The shallow feature extraction module performs shallow processing on the low-resolution image; the deep feature extraction module consists of multiple cascaded enhanced separable residual feature refinement blocks, extracting shallow features to obtain multiple deep features; the multi-layer feature fusion module is used to obtain fused features; and the image reconstruction module is used to output the high-resolution image. The trained model is used to reconstruct the low-resolution image to be processed. This invention has a simple network structure and achieves accurate reconstruction of image texture and details using only a low number of parameters and computational cost.

The application provides a foreground-guided cross-scale memory attention self-supervised monocular depth estimation method and system, and relates to the technical fields of computer vision, three-dimensional visual perception and self-supervised depth estimation.The foreground-guided self-supervised monocular depth estimation network is constructed, and an encoder, a self-generated foreground prior branch, a foreground-guided cross-scale memory grouping query attention bottleneck module and a depth decoder are used as main construction modules.In the self-supervised monocular depth estimation task, the self-generated foreground prior branch is used to learn to predict the foreground prior under the supervision of the offline generated binary pseudo foreground mask, so that the network can generate the binary foreground prior according to the single target image in the inference stage without an external segmentation model.The foreground-guided cross-scale memory grouping query attention bottleneck module is used to fuse deep layer encoding features and intermediate layer encoding features, construct cross-scale shared key-value memories, and construct a foreground selective attention bias according to the binary foreground prior, so as to enhance the information aggregation in the foreground region, and at the same time, not to explicitly suppress the attention path between the foreground and the background, thereby improving the depth prediction stability and integrity of the moving foreground region, the occlusion region and the depth discontinuous boundary.

The invention discloses an alarm data aggregation method and device, equipment and a medium, and belongs to the technical field of computers. According to the method, the importance of condition attributes relative to each equivalence class corresponding to a preset decision attribute set is introduced, and according to the difference between the importance of each condition attribute in the preset condition attribute set relative to the equivalence class and the importance of the whole preset condition attribute set relative to the equivalence class, the equivalence class is determined. And determining an attribute weight of each condition attribute corresponding to the equivalence class, thereby identifying important attributes in respective associated condition attributes under different equivalence classes, and further judging whether the related alarm data is associated with the same attack classification according to the important attributes under each equivalence class. And the alarm data associated with the same attack classification is aggregated.

The invention relates to the field of olefin polymerization catalysts, and discloses an olefin polymerization catalyst system as well as a preparation method and application thereof. The olefin polymerization catalyst system comprises an organic aluminum compound and a metal composite component containing an acyl phenyl group, and the metal composite component containing the acyl phenyl group comprises a mixture of a tetraacyl phenyl silane compound, magnesium halide, titanium halide, an oxygen-containing organic compound and an ultrafine inorganic oxide and / or a reaction product of the components. The acyl phenyl group and the metal component in the catalyst system enable the obtained catalyst to have higher activity, and in addition, the superfine inorganic oxide carrier added in the catalyst component enables the catalyst to have better dispersity and more excellent catalytic effect. The polymer prepared by adopting the catalyst has high melt index and low oligomer content.

The present application relates to the field of olefin polymerization catalyst, and provides a metal-silane composite catalyst and a preparation method and application thereof.The metal-silane composite catalyst comprises a mixture of diazabicycloalkyldialkoxy silane compounds, organic compounds containing C-O and / or C=O, metal halides and / or reaction products of the above components, and further comprises superfine inorganic oxide; after mixing the metal-silane composite catalyst and organic aluminum compound, the mixture can be used for olefin polymerization reaction.The polymer prepared by using the catalyst has a high melt index and low oligomer content.

The invention relates to the technical field of thick oil emulsification and viscosity reduction, and discloses an emulsification and viscosity reduction agent composition, a preparation method and application thereof, and a super thick oil emulsification and viscosity reduction method, the composition comprises an anionic sulfate surfactant, an ampholytic surfactant and a solvent; the anionic sulfate surfactant has a structure as shown in a formula (1), # imgabs0 #, wherein R1 is selected from C8-C22 alkyl groups; r3 and R4 are independently selected from H or-SO3M, and at least one of R3 and R4 is-SO3M; each R2 is independently selected from H, 2-hydroxy propyl and methyl; m is selected from any one of Li, Na, K and NH4; m is selected from any integer of 1-10, n is selected from any integer of 1-10, z is selected from any integer of 0-9, and the sum of m, n and z is any integer of 5-11. The super heavy oil emulsifying and viscosity reducing agent composition has excellent emulsifying and viscosity reducing effects and good salt resistance under the condition of weak shearing.

The invention discloses a preparation method of a trimeric surfactant, and belongs to the technical field of surfactant preparation, the method comprises the following steps: mixing chloroacetyl chloride and tris (2-aminoethyl) amine, reacting to obtain trichloro acetylated tris (2-aminoethyl) amine, reacting the obtained trichloro acetylated tris (2-aminoethyl) amine with N, N-dimethyl formamide, and reacting to obtain the trimeric surfactant. And reacting with N, N-dimethyl dodecylamine to obtain the trimeric surfactant. When the prepared trimeric surfactant is used in an industrial cleaning agent, the use amount of the surfactant in the industrial cleaning agent is greatly reduced, the COD value of the industrial cleaning agent is greatly reduced, the environmental protection treatment cost of industrial cleaning waste liquid is greatly reduced, the preparation process is simple, the reaction condition is mild, the reaction period is short, and the method is suitable for industrial production. The raw materials are low in cost and simple in source, and the obtained product is high in purity and easy for industrial production.

The application relates to the technical field of artificial intelligence, in particular to a multi-scale causal shunt prediction method and system for an online soft measurement of a cut tobacco process. Multivariate process data and outlet material moisture data of the cut tobacco process are acquired; the data is subjected to time synchronization, missing value processing, abnormal value processing and standardization preprocessing, and a sliding time window sample is constructed; input features are divided into local path features and global path features along a channel dimension, the local path features are subjected to local mapping, and the global path features are subjected to attention calculation with a causal mask; the shunt modeling result is input into a multi-scale causal coding backbone network to extract hierarchical features, and the different hierarchical features are subjected to time alignment and cross-layer fusion; local transient disturbance and high-frequency change information are extracted through a high-frequency residual branch; the main road fusion features and the high-frequency compensation features are jointly input into a prediction head to output an outlet material moisture prediction result. The application aims to solve the problem of how to improve the online soft measurement precision of the cut tobacco process and reduce the calculation overhead.

The invention discloses a metal compound containing a silafluorene group, an olefin polymerization catalyst containing the metal compound as well as a preparation method and application of the olefin polymerization catalyst. The metal compound contains a main compound which is a metal compound containing a silafluorene group, and the metal compound is a mixture of metal halide, a dialkyl silafluorene compound and an electron donor compound and / or a reaction product of the components, wherein the contained silafluorene group enables the catalyst containing the silafluorene group to have higher activity. The main body compound is loaded on an inorganic oxide carrier in a specific particle size range to obtain a high-dispersity catalyst, the catalyst can be used for olefin polymerization reaction, and a polymer obtained by polymerization has a relatively high melt index and low oligomer content.

The invention belongs to the technical field of boron isotope separation, and particularly relates to a process for preparing boron-10 acid by separating boron isotopes. According to the process, a chromatographic column taking a ferroferric oxide / resin microsphere material as a stationary phase is adopted, a boric acid-monosodium borate aqueous solution with the pH value of 6-7 is taken as an adsorption solution, a sodium hydroxide aqueous solution with the pH value of 10-11 is taken as a desorption solution, a 10B-rich boric acid-monosodium borate aqueous solution is obtained through iterative separation, and boron-10 acid is further prepared; according to the process, 10B in an aqueous solution with boric acid as a main component can be enriched to a relatively high degree; the chromatographic column has relatively stable boric acid adsorption capacity and 10B and 11B separation performance, is relatively long in service life and has a certain application prospect.

The present application discloses a metal complex containing silicon-containing heterofluorene group, an olefin polymerization catalyst containing the metal complex, and a preparation method and application thereof. The metal complex contains a main complex which is a metal compound containing silicon-containing heterofluorene group, the metal compound is a mixture of metal halide, dialkyl silicon-containing heterofluorene compound, electron donor compound and / or reaction product of the above components, and the silicon-containing heterofluorene group contained therein makes the catalyst containing the same have higher activity. The above main complex is loaded on an inorganic oxide carrier with a specific particle size range to obtain a high-dispersity catalyst, and the catalyst can be used in olefin polymerization reaction, and the polymer obtained by polymerization has higher melt index and lower oligomer content.

The invention discloses a business content processing method and device, a computer program product and electronic equipment, and relates to the technical field of data processing. The business content processing method comprises the following steps: acquiring business content data, and clustering the business content data to obtain a plurality of clusters; according to the target business content data in each cluster, judging whether each cluster has a content risk or not; the target business content data is content data of a clustering center; determining a content publishing user corresponding to the service content data in the first cluster as an illegal user; the first cluster is a cluster with content risk in a plurality of clusters; and pushing the service content data in the second cluster. The second clustering cluster is a clustering cluster without content risk in the plurality of clustering clusters. According to the method and the device, the business content data is clustered, risk identification is carried out on the cluster, the risk content with high aggregation can be continuously sensed and locked, and meanwhile, the risk identification efficiency is improved.

The application discloses a medical time sequence classification method based on time-aware multi-instance learning, and belongs to the technical field of signal processing. The method comprises the following steps: acquiring a medical time sequence collected by a wearable sensor; and inputting the medical time sequence into a medical time sequence classification model, wherein the medical time sequence classification model comprises a time-aware module, a multi-granularity block embedding module, a multi-granularity attention module and a classification output unit. The method is based on time sequence input, sequentially processed through the time-aware module, the multi-granularity block embedding module and the multi-granularity attention module, and finally outputs a classification label result, so that automatic evaluation of Parkinson's disease is realized.

The invention discloses a browning additive and an IC carrier plate browning method using the same, and relates to the technical field of IC carrier plate production and manufacturing. Every 100 parts of the browning additive comprises the following components in parts by weight: 1.0-3.0 parts of a first corrosion inhibitor, 1.0-3.0 parts of a second corrosion inhibitor, 0.5-2.0 parts of a cross-linking agent, 0.5-2.0 parts of a plasticizer, 0.2-1.0 part of an oxidizing agent and the balance of water, the mass ratio of the first corrosion inhibitor to the second corrosion inhibitor is 1: (0.5-2.0). The browning additive can form a layer of organic metal film on the uneven micro rough copper surface with activity, the uniformity and binding force of the browned copper surface are effectively improved, and the copper surface has low roughness, high peel strength, low insertion loss and good heat resistance.