127results about How to "Improve abilities" patented technology

The invention provides a power demand transaction assessment method, device and equipment considering the action of event factors, and the method comprises the steps: collecting the related information of a target event, and extracting the event entity information related to the power demand from the information; determining an event type according to a preset event classification system based on the event entity information, and determining an influence industry of the target event and influence coefficients of the target event on different industries based on event classification and an influence quantification knowledge base according to the event type; taking the influence industry as a processing group, adjusting the basic weight of each control unit in the control group corresponding to the processing group according to the influence coefficient, and obtaining the dynamic weight of each control unit; according to the dynamic weight, constructing an anti-factual power utilization path of the processing group under the condition that no target event occurs; and calculating the difference between the actual power consumption data of the processing group and the anti-fact power consumption path, and obtaining the net influence quantity of the target event on the power consumption demand of the processing group. According to the invention, the influence of emergencies on power demands can be accurately identified and quantified.

The invention discloses a multi-layer semantic perception, distillation and semi-supervised cooperative training target detection method, and the method specifically comprises the following steps: constructing a data set: extracting a first part of images from image data, marking the first part of images to construct a supervised target detection data set, and taking the remaining images as an unmarked image data set, the data volume of the unlabeled image data set is greater than that of the supervised target detection data set; teacher model optimization: performing supervision training on the teacher model on the supervised target detection data set; constructing a teacher model, and executing self-distillation training on the teacher model; pseudo labels are generated for the unlabeled images through a teacher model, and adaptive screening is carried out based on confidence distribution; mapping a pseudo label to a strong enhanced sample through enhanced geometric transformation, carrying out semi-supervised training by using the strong enhanced sample and the pseudo label, and introducing a feature layer distillation constraint at the same time; optimizing a student model; and outputting the target detection model obtained through training.

The application relates to the technical field of catalysts, and discloses a limited type iodine-based homogeneous immobilized catalyst as well as a preparation method and application thereof. The catalyst comprises a carrier and iodine ions, magnesium ions and quaternary ammonium ions loaded on the carrier, wherein the molar ratio of the magnesium ions, the quaternary ammonium ions, the iodine ions and the carrier is 0.01-1:4-15:1-30:1. The limited type iodine-based homogeneous immobilized catalyst disclosed by the application enhances the electrostatic interaction between iodine ions and quaternary ammonium ions through the molecular sieve limiting effect to construct a limited type structure. The catalyst has significantly enhanced CO2 adsorption and activation capacity, can activate an epoxide compound, and accelerates the ring opening of the epoxide compound. In a cycloaddition reaction catalyzed by the catalyst under the conditions of no solvent and no auxiliary agent, the conversion rate of propylene oxide can reach more than 73%, and the propylene carbonate selectivity can reach more than 85%.

This invention discloses a muscle-relaxing and meridian-clearing health powder and its preparation and application methods, belonging to the technical field of traditional Chinese medicine external health care products. The health powder is composed of fifteen raw medicinal materials: safflower, angelica, asarum, kudzu root, red peony root, scutellaria, dragon's blood, saposhnikovia root, frankincense, myrrh, cinnamon twig, mugwort leaf, coptis root, *Lycopodium clavatum*, and *Clematis armandii*. These materials are dried, pulverized to 80-120 mesh, and then evenly mixed. This invention, through a unique formulation design, integrates multiple effects such as promoting blood circulation and removing blood stasis, dispelling wind and cold, relieving muscle tension and clearing meridians, and clearing heat and drying dampness. Combined with a refined pulverization process, it significantly improves the dissolution rate and transdermal absorption efficiency of the drugs. During use, a hot compress applied via steam allows the medicinal properties to directly reach the affected area, effectively relieving pain and discomfort in the neck, shoulders, waist, legs, and joints. This invention's product has a scientific formulation, advanced technology, is convenient to use, and highly safe, possessing broad market application prospects.

The embedded wireless sensing test wafer is characterized in that the embedded wireless sensing test wafer comprises an upper silicon wafer, a lower silicon wafer and a circuit board, the upper silicon wafer and the lower silicon wafer are clamped on the two sides of the circuit board, the surfaces, facing the circuit board, of the upper silicon wafer and the lower silicon wafer are subjected to laser engraving or mechanical fine carving to form containing grooves, and the circuit board is embedded in the containing grooves; the thickness of the upper silicon wafer is 0.7-0.9 mm, the thickness of the lower silicon wafer is 0.3-0.4 mm, and the overall total thickness of the upper silicon wafer and the lower silicon wafer clamping the circuit board is smaller than or equal to 1.2 mm. The thickness similar to that of an actually produced wafer is realized, the authenticity of measurement is improved, the internal stress of the wafer is effectively eliminated by the heat treatment process, the reliability of a product is improved, the service life of the product is prolonged, the risk of processing fragments is reduced by the overall processing process, and the production cost is reduced.

The present disclosure relates to a pellet containing an oxide additive to improve a nuclear-fission-gas-adsorption ability of a uranium-dioxide pellet used as nuclear fuel and increase the grain size thereof, and to a method of manufacturing the same. A La2O3-Al2O3-SiO2 sintering additive is added to uranium dioxide so that mass movement is accelerated due to the liquid phase generated during sintering of the uranium-dioxide pellet, which promotes the growth of grains thereof. Further, since less volatilization occurs during sintering due to the low vapor pressure of the liquid phase, efficient additive performance is exhibited, so the liquid phase surrounding the grain boundary effectively adsorbs cesium, which is a nuclear fission gas.

The application provides a kind of metering device remote calibration system and method, wherein the system is composed of Beidou satellite, ground satellite station, Beidou master station board card and at least one Beidou measurement board card, Beidou master station board card sends encrypted information to Beidou measurement board card through Beidou satellite and ground satellite station, then Beidou measurement board card decrypts the encrypted information to form decrypted data, and then outputs corresponding analog signal, wherein the output signal includes voltage, current and frequency. The Beidou master station board card and Beidou measurement in the system and method use two-way timing principle, obtain timing by receiving satellite signal, then communicate with ground satellite station to obtain timing calibration, so that the clock is more accurate, thereby realizing accurate synchronization of remote multi-channel data acquisition and control, and having broad engineering application and achievement popularization space.

This application provides a hybrid magnetic circuit and intelligent flux distribution current transformer energy harvesting device and method to solve the problems of low sensitivity for small current energy harvesting and weak resistance to large current saturation in current transformer energy harvesting devices. The device includes a hybrid magnetic circuit unit, a magnetic state detection unit, and an intelligent flux distribution unit. The hybrid magnetic circuit unit includes a first magnetic circuit and a second magnetic circuit connected in parallel and magnetically coupled. An energy harvesting winding and an auxiliary control winding are wound on the first and second magnetic circuits, respectively. The magnetic state detection unit monitors the magnetization state of the first magnetic circuit, and the intelligent flux distribution unit injects controllable current into the auxiliary control winding to adjust the flux ratio flowing through the first and second magnetic circuits. By constructing a parallel hybrid magnetic circuit and implementing active flux distribution, this application ensures that the first magnetic circuit operates in the ideal linear region under any transmission conductor current, thereby simultaneously achieving ultra-high sensitivity for small current energy harvesting and extremely strong resistance to large current saturation.

The utility model belongs to the technical field of power cables, and particularly relates to an IE-grade K3-class radiation-resistant power cable, which comprises a protective sleeve, ventilation and heat dissipation ports arranged at equal intervals are arranged on the outer surface of the protective sleeve, a connecting sleeve is arranged in the protective sleeve, a power cable is arranged on the inner wall of the connecting sleeve, and two groups of support rings are fixedly connected to the outer surface of the connecting sleeve. The side faces, close to each other, of each set of supporting rings are fixedly connected with supporting plates arranged at equal intervals, the side face, away from the connecting sleeve, of each supporting plate is fixedly connected with two sets of telescopic springs, and the ends, away from each other, of each set of telescopic springs are fixedly connected with connecting plates. Particularly, an elastic buffering system composed of a supporting ring, a supporting plate, a telescopic spring and a connecting plate can convert mechanical impact into spring deformation energy, the external force resisting performance is greatly improved, and meanwhile an efficient heat dissipation path can be formed by matching equidistant ventilation heat dissipation openings in the surface of the protective sleeve with an air channel between the protective sleeve and a connecting sleeve.

This application discloses a method, apparatus, device, and medium for item recommendation across multiple user groups, scenarios, and tasks, relating to the field of artificial intelligence technology. The method involves acquiring user-side information, item-side information, and corresponding scene environment information in different scenarios; calculating weight dimensions, shared feature vectors, and environment dimensions to obtain a first dimension, a dense vector, and a second dimension; performing attention mechanism feature cross-calculation to obtain a third dimension; calculating item recommendation scores using the first dimension, dense vector, second dimension, and third dimension to obtain an item recommendation value; determining the target item recommendation value; identifying the target item information corresponding to the item recommendation value; and recommending the target item information to the user. This application can construct a three-dimensional perspective model encompassing multiple scenarios, user groups, and tasks, thereby improving the ability to represent differentiated user groups, meeting diverse user needs, and achieving comprehensive item recommendation across multiple user groups, scenarios, and tasks.

The application discloses a kind of man-machine collaborative multi-modal data intelligent labeling and quality checking method, it is related to man-machine collaborative technical field.The method first obtains at least containing two modalities of original data of image, text, audio, generates multi-modal joint data sample after pre-processing and modal alignment;Input pre-trained multi-modal AI pre-labeling model generates pre-labeling result with confidence;Through cross-modal consistency check calculation consistency score, sample is divided into high, medium, low confidence three categories;High confidence sample is directly adopted and enters the sampling pool, and medium and low confidence sample is based on labeling personnel ability portrait and AI model dynamic capability evaluation and carries out intelligent task distribution.The application effectively solves the problem that efficiency and quality are difficult to balance in multi-modal data labeling, significantly improves labeling accuracy and automation rate, reduces labeling cost, and can meet the construction demand of large-scale high-quality multi-modal data set.

This invention discloses a DNA polymerase mutant and its applications, relating to the field of biotechnology. The DNA polymerase mutant developed by this invention significantly enhances performance in Mg2+ compared to wild-type DNA polymerase. 2+ Under catalysis, it enhances the efficiency and capability of DNA extension amplification on non-natural substrates, especially achieving highly efficient extension amplification of DNA fragments longer than 48.5kb. It effectively fills the gaps in the compatibility of related polymerases with non-natural substrates, efficiency of long fragment extension, and stability in existing technologies, providing a key tool for non-natural substrate-mediated long fragment DNA amplification technology.

The application belongs to the technical field of teaching practical training scene simulation control, and specifically discloses a teaching practical training scene simulation control method based on PLC technology, which comprises the following steps: constructing a fault mode library containing low, medium and high levels, randomly distributing faults from the low fault level, loading a simulation scene, collecting fault identification response time and adjustment operation sequence, verifying diagnosis accuracy and evaluating fault recovery degree after executing the sequence, generating a practical operation qualification degree according to different conditions, then judging promotion and dynamically adjusting the fault level according to the qualification degree, and generating a student comprehensive ability evaluation report based on the qualification degrees in the previous iterations; the application constructs a fault mode library containing low, medium and high fault levels based on historical power operation data, and dynamically adjusts the fault level to be distributed subsequently according to the practical operation qualification degree, thereby overcoming the defects of static preset fault situations, and realizing a personalized and gradual skill upgrading path.

This invention discloses a specular removal method based on grouped enhanced convolutional attention feature fusion, belonging to the field of image processing technology. The method constructs a specular removal model based on a CycleGAN network, which consists of a generator, a discriminator, and a loss function optimization module. The generator includes detail enhancement depth downsampling, upsampling, and grouped enhanced convolutional attention fusion modules, effectively capturing image details, reducing checkerboard artifacts, and enhancing feature fusion and capture capabilities. The discriminator uses a PatchGAN structure to improve local detail capture capabilities. The optimized loss function integrates multiple losses to improve model performance. Experimental results show that compared with various traditional and deep learning methods, the method of this invention outperforms in PSNR and SSIM metrics, exhibits strong adaptability, and can provide high-quality image data for subsequent detection of elevator door components.

The invention discloses a carbon nitride composite material as well as a preparation method and application thereof, and designs and prepares an activated carbon loaded modified carbon nitride nanosheet, K ions are doped between layers of the modified carbon nitride nanosheet to form local geometric distortion caused by KN bridging and K + intercalation, and the local geometric distortion and a P gap state generate synergy, so that the performance of the carbon nitride composite material is improved. The adsorption and activation capabilities of the material on O2 molecules are jointly optimized, so that the reaction from * OO to * OOH is obviously accelerated. The adsorption and enrichment effects of the activated carbon and the oxidative degradation and sterilization capabilities of the photocatalytic material can be fully utilized.

The invention discloses a high-dimensional seismic data denoising method and device. The method comprises the following steps: segmenting a high-dimensional seismic data volume into a plurality of data blocks by using a sliding window; taking the data block with the corresponding de-noised data as a sample to obtain a sample set, and training a Transform model to obtain a de-noising model; and inputting a plurality of data blocks obtained by segmenting the high-dimensional seismic data volume into a denoising model, and obtaining a denoised seismic data volume of the high-dimensional seismic data volume according to an output result of the model. According to the method, intelligent suppression of high-dimensional seismic data noise can be realized by using high-dimensional features of seismic data based on a deep neural network Transform model, and the denoising precision is high.

This application provides a tumor classification method based on convolutional neural networks. It constructs a feature extraction module to extract features from the tumor image to be identified, generating a tumor feature map; a magnification adaptation module to extract multi-layer detail features from the tumor feature map, where each layer of detail features includes multi-scale features, to generate an output feature map, enhancing adaptability to different magnification levels; and a classification module to identify the tumor based on the output feature map, generating a classification result for the tumor image to be identified. This application utilizes the magnification adaptation module to collect important information from tumor images from multiple scales and multi-layer contexts, better understanding the content of tumor images, enhancing adaptability to different magnification levels, and improving the accuracy of tumor image classification.

Relates to a method for classifying traffic signs in an image. A computer-implemented method includes obtaining an image captured by a camera of a vehicle, the image depicting at least a portion of an environment of the vehicle; identifying a region in the image corresponding to the traffic sign by processing the image via a first machine learning model configured to output a detection of the traffic sign in the input image; extracting a cut corresponding to the identified region from the image, where the cut has a native resolution based on a size of the identified region with respect to the obtained image; and determining classification data of the traffic signs by processing the cropping at the native resolution via a second machine learning model, where the second machine learning model is an attention-based neural network trained to process input images of the traffic signs at different resolutions and to generate corresponding classification data, where the classification data are classified by processing the cropping at the native resolution via the second machine learning model. The second machine learning model applies attention at the pixel level of the input image.

Disclosed are bacterial strain HSCY 2073, isolation and screening thereof, and application thereof in improvement on flavor quality of vinegar. Bacterial strain HSCY 2073 is identified as Lactobacillus sp. and is deposited in China General Microbiological Culture Collection Center, where deposit date is 21 November 2019, and deposit number is CGMCC No. 18982. Bacterial strain HSCY 2073 in the present disclosure is Lactobacillus sp. that has strong acetic acid and ethanol tolerance and excellent effect of producing non-volatile acid, and can produce high contents of 5 aroma substances including acetaldehyde, ethyl acetate, isobutyl acetate, isoamyl acetate and ethyl 3-methylvalerate that have fruit aromas. The lactobacillus can adapt to high-acid, high-alcohol and oligotrophic harsh environments, etc. of vinegar brewing, and has strong ability of high acid tolerance.

The application discloses an insect exoskeleton morphological layup composite panel, a sandwich structure and a preparation method. The sandwich structure comprises two pieces of bionic layup composite panels and a piece of aramid honeycomb core layer. The bionic layup composite panel draws lessons from the spiral stacking microstructure of the insect exoskeleton, the layup design considers the symmetry and balance requirements of the composite material, a fixed rotation angle is introduced between adjacent fiber layers, and the rotation angle is taken as a period of 180 degrees, and after a plurality of periods are laid and heat-pressed and cured, the bionic layup composite panel is obtained. The bionic layup design effectively reduces the interlaminar shear stress of the composite material, weakens the inherent anisotropy of the composite material, and improves the transverse bearing capacity and impact resistance. The bionic layup composite panel and the aramid honeycomb core layer have the advantages of high specific strength, high specific stiffness, impact energy absorption, vibration and noise reduction, electromagnetic shielding and the like, and a structure and function integrated material with more excellent comprehensive performance is obtained, and the structure and function integrated material is expected to be widely used in main load-bearing structures of various spacecrafts.

Embodiments of this invention include computer-implemented methods for use of haar wavelet transforms (hwt) to pre-process video frames that can then be compressed using a codec to produce a compressed video frame. A compressed video frame can then be tr

The application provides a catalytic filter element and a preparation method and application thereof, the catalytic filter element comprising a filter element main body, a denitration catalyst being loaded on an outer surface of the filter element main body and having a loading depth to the inside of the filter element main body, and a VOCs removal catalyst being loaded on an inner surface of the filter element main body and having a loading depth to the outside of the filter element main body. The obtained catalytic filter element can meet the demand for simultaneously removing NO x and VOCs in complex flue gas in the same catalytic reaction unit under the same temperature zone condition, the removal rate reaches more than 90%, and the catalytic filter element has strong stability and can simultaneously remove NO x and VOCs for a long time and with high efficiency.

The application provides an information recommendation method, system, device and storage medium, comprising: by associating a special graph structure for different expert networks, the physical and logical decoupling of modal information is realized, and the embedding feature information of the target user and the candidate item in different semantic dimensions can be more accurately described. By calculating the data routing distribution with the user-item interaction instance as the granularity, and combining the progressive routing strategy with the prior routing distribution to obtain a hybrid routing distribution, accurate expert scheduling can be realized in different context environments, that is, it can automatically identify which modal features play a key role in user decision-making in a specific scenario, thereby accurately capturing the fine-grained preferences of users. Through the gating selection mechanism, the feature contribution of key experts is retained, effectively filtering the noise interference generated by irrelevant modalities, and further improving the accuracy of the prediction score and the reliability of the recommendation result.

This invention discloses an Ag-modified hydrogenation catalyst, its preparation method, and its application. The preparation method of the Ag-modified hydrogenation catalyst of this invention includes: (1) sulfiding an oxidized hydrogenation catalyst to obtain a sulfidated hydrogenation catalyst; (2) desulfurizing the sulfidated hydrogenation catalyst obtained in step (1); and (3) introducing an organic solution containing Ag into the catalyst treated in step (2) to carry out a reaction, thereby obtaining the Ag-modified hydrogenation catalyst. The Ag-modified hydrogenation catalyst of this invention still exhibits good stability under low-sulfur conditions, and is particularly suitable for long-term processing of low-sulfur or sulfur-free raw materials.

The application discloses a kind of control method, device, equipment, storage medium and program product of mechanical arm, involve the robot control technical field of artificial intelligence technology.The method comprises: obtaining the desired pose sequence and the predicted pose sequence of the end effector of machine arm in prediction period, which includes multiple sampling time after current time;Based on the desired pose sequence and the predicted pose sequence, the predicted control equation of the end effector under current time is constructed;The predicted joint speed of the end effector under current time is obtained by solving the predicted control equation;According to the predicted joint speed of the end effector under current time, control end effector to follow the desired pose under current time moves.The embodiment of the application obtains the predicted joint speed that makes the end effector can follow the desired pose movement by the desired pose and the predicted pose in prediction period, improves the following accuracy of end effector.

The application relates to the field of high-speed communication and information network technology, in particular to a space expandable photoelectric information processing device, which comprises a device body, the device body comprises a power board module, an information processing board module, an interface board module, an expansion information processing module and a processing board heat dissipation top cover, a shielding block is arranged in the interface frame, and the device body comprises the processing board heat dissipation top cover. The application can not only improve the complex processing technology, overcome the deficiencies of the existing technology in the aspects of efficiency, cost, precision, material utilization rate, environmental protection and the like, improve the modularization and expandability of the system, improve the photoelectric data processing capacity, realize high integration and small and light design, help improve the heat dissipation performance and environmental adaptability of the device, and help unify the information processing of multiple types of photoelectric loads.

A positive temperature coefficient ceramic thermistor element having strong reducing atmosphere resistance and a preparation method therefor, the thermistor element comprising a sintered thermosensitive ceramic piece that uses lead barium titanate as a base, as well as metal ohmic electrodes which are positioned on two side surfaces of the thermosensitive ceramic piece. The thermistor element has the following features: the thermistor element further comprises a microporous channel barrier layer, and the microporous channel barrier layer comprises a glass sealing layer or an organic matter sealant; when the microporous channel barrier layer is the glass sealing layer, the glass sealing layer integrally wraps the outer surface of the thermosensitive ceramic piece, and the metal ohmic electrodes are combined on two side surfaces of the glass sealing layer; and when the microporous channel barrier layer is the organic matter sealant, the organic matter sealant fills and blocks micro-pores in the surfaces of the metal ohmic electrodes combined on the two side surfaces of the thermosensitive ceramic piece and, at the same time, blocks gaps in the surfaces of areas, that do not have the metal ohmic electrodes, of a peripheral edge of the thermosensitive ceramic piece. Thus, resistance to harsh environments is improved, service life is prolonged, and the reliability and expected service requirements of related heating groups are improved.

The present application belongs to the technical field of nuclear power plant physical simulation and simulation, and particularly relates to a nuclear power plant core instrument bay equipment simulation body and a simulation method. The simulation body comprises a driving unit, a group selector assembly and a path selector assembly, the driving unit is connected to the path selector assembly through the group selector assembly, the cable outlet end of the driving unit is connected to the group selector inlet end through a stainless steel pipe and a sleeve joint, and the group selector outlet end of the group selector assembly is connected to the path selector inlet end of the path selector assembly through a stainless steel pipe and a sleeve joint. The present application has the beneficial effect of improving training effect and efficiency, the RIC system simulation body can simulate the real operating environment of the nuclear power plant core, so that the trainees can learn and operate in a work scene close to the actual one, thereby more intuitively understanding the working principle and operation process of the core measurement system.

The invention discloses a self-generating multi-agent confrontation system and method based on quantum random entropy injection, belongs to the technical field of artificial intelligence and quantum computing, and aims to replace a traditional pseudo-random number with quantum random entropy injection, so that an agent decision-making path is essentially unpredictable, malicious attacks based on historical data are effectively resisted, and the service life of the agent decision-making path is prolonged. The application security of the system in high-sensitivity fields such as finance and security is greatly improved; the quantum mutation mechanism can randomly trigger evolution of agent strategies, human settings, view angles and the like, simulate evolution and selection processes, can spontaneously generate innovative analysis and insight except a traditional fixed strategy pool, and has breakthrough in solving complex problems; the intelligent agent can be dynamically adjusted and optimized according to real-time performance and quantum random triggering in the confrontation process, continuous improvement of the overall capacity of the system is achieved, the whole process from demand analysis, intelligent agent generation to confrontation judgment is automatic, new fields and new tasks can be rapidly adapted, and development and maintenance cost is greatly reduced.

The invention discloses an EEG (electroencephalogram) emotion recognition method based on distribution progressive domain adaptive diffusion modeling, which comprises the following steps of: firstly, acquiring an EEG (electroencephalogram) signal, and forming a source domain feature and a target domain feature; secondly, constructing a feature extraction model, and obtaining corresponding source domain potential representation and target potential representation according to the source domain features and the target domain features; then, based on the potential representation of the source domain and the potential representation of the target, the distribution from the source domain to the target domain is aligned through DDEEG, and target domain classifier training is carried out; and finally, using the trained target domain classifier in a test stage, and based on the input EEG signal, identifying the emotional state of the target domain. According to the method, more stable cross-domain feature alignment is realized, mode collapse is effectively avoided, a category structure is kept, and the emotion recognition accuracy under cross-subject and cross-session conditions is remarkably improved.