92results about How to "Solve redundancy" patented technology

The invention provides a liquid storage tank capable of adjusting the amount of stored liquid, a refrigerating system and a control method.The liquid storage tank comprises an inner cylinder and an outer cylinder, an outlet pipe is arranged at the bottom of the inner cylinder so that fluid in the inner cylinder can be guided out, and a valve structure is arranged between the inner cylinder and the outer cylinder; in the compression refrigeration mode, the valve structure can guide fluid in the outer cylinder into the inner cylinder when the frequency of the compressor is increased, and the amount of the fluid discharged from the outlet pipe is increased. In the compression refrigeration mode, the valve structure can guide the fluid in the inner cylinder into the outer cylinder when the compressor is subjected to frequency reduction, and the amount of the fluid discharged from the outlet pipe is reduced; in the fluorine pump refrigeration mode, the valve structure can guide fluid in the outer cylinder into the inner cylinder, and the liquid level in the inner cylinder rises. According to the invention, the problem of refrigerant redundancy caused by difference of refrigerant filling amounts required under different operation conditions of a fluorine pump compression refrigeration system can be solved, and the problems of retention and release of refrigerant liquid are solved, so that better circulation amounts required by the refrigeration system under various operation conditions can be met as much as possible.

The invention relates to the technical field of tumor image analysis systems, in particular to a multi-modal tumor image feature extraction and fusion analysis system and a method thereof.The multi-modal tumor image feature extraction and fusion analysis system comprises a data acquisition module, a multi-modal feature extraction and fusion module, a tumor prediction module and an optimization module; according to the method, the quality and robustness of feature extraction are effectively improved, and the problems of noise and artifacts in medical images can be better solved. In the feature fusion stage, an innovative weighted fusion strategy is adopted, complementary information between different modals is fully utilized, the problem of information redundancy is effectively avoided, the convolutional neural network and the bidirectional LSTM network are combined, spatial features can be captured, time sequence information can be effectively utilized, and the method has the advantages of being high in robustness and high in robustness. Therefore, the prediction accuracy of the tumor development trend is improved, the genetic algorithm is introduced to optimize network hyper-parameters, and the innovative optimization method enables the model to better adapt to specific data sets and tasks.

The invention relates to the technical field of photovoltaic prediction of a power system, in particular to a photovoltaic power short-term prediction method, device and equipment based on multi-level reverse error correction and a medium, and the method comprises the steps: obtaining original data, and carrying out the data interpolation and data selection; variational mode decomposition is used to decompose the selected data, and kernel principal component analysis is carried out to reduce dimensions; dividing the data after dimension reduction into a plurality of sub-data sets with the same size and a test set as input data, selecting a plurality of training algorithms, constructing a prediction model based on model stacking Stacking, and inputting the input data into the prediction model for training; performing prediction by using the trained prediction model, and evaluating a prediction result; according to the method, historical data or environmental factors are analyzed, photovoltaic stations with similar characteristics or prediction results are clustered, photovoltaic stations of different clusters are predicted according to the clustering results, and a prediction model is subjected to reverse correction.

The invention relates to the technical field of wide-temperature battery monitoring and management, in particular to a high-weather-resistance wide-temperature storage battery monitoring method and system based on a power transmission line online monitoring device, terminal equipment and a storage medium, and the method comprises the following steps: S1, collecting voltage, current, power and environment data in real time; s2, constructing a time sequence data matrix of the monitoring device, the environment and the battery characteristics; s3, dynamically adjusting a threshold value according to the joint entropy and the historical state; s4, fine wide-temperature battery feedback analysis is carried out; and S5, if the entropy value continuously increases, executing backtracking analysis. According to the invention, performance monitoring and service life management of the wide-temperature battery can be optimized, and the battery is prevented from being damaged due to excessive discharge or charging at an extreme temperature; the interactive influence of the environment and the monitoring equipment can be accurately evaluated, the accuracy of battery state prediction is remarkably improved, and the overall stability and efficiency of a power transmission line monitoring system are improved.

The invention discloses a database construction method and device for nonlinear data based on a large model. The method comprises the steps that a node is selected in an initial graph structure to input request data of a user, a retrieval result of the request data is retrieved and generated through an RAG system, and reply data of the request data is generated through the large model; writing the request data, the reply data and the retrieval result into a new node, determining a nearest node associated with the new node in the existing nodes, and forming a branch path with topicality by taking the nearest node as a father node of the new node; storing the new node in a database; wherein all the branch paths form a tree-shaped structure. According to the method, each dialogue node has a traceable father-son relationship by constructing a database storage mode of a multi-branch tree structure, and a user can branch a new dialogue path from any historical node. And the user can freely select different topic paths for dialogue, so that the dialogue context content of the large model is more accurate.

The invention discloses a method and a device for determining an association relationship of associated persons. The method comprises the following steps of: determining an association relationship hierarchy graph of the associated persons; performing character recognition processing on the received related party supervision information to obtain a specified association relation element; identifying existing association elements of an existing association relationship corresponding to the supervision organization identifier of the associated person; the existing association elements are matched with the specified association relation elements; taking the existing associated elements which are not successfully matched as to-be-corrected associated elements which do not conform to a specified associated party affirmation rule; according to the to-be-corrected associated elements, correcting one or more supervision organization identifiers of the associated person; and determining a hierarchical path step length between the associated person and the associated party corresponding to each corrected associated party supervision identifier, and modifying to obtain an association relationship hierarchical graph of the associated person. The method is used for improving the recognition accuracy and recognition efficiency of the related person supervision identifier and avoiding data redundancy.

The invention relates to a machine learning-based power grid operation data cleaning method and system. The method comprises the following steps of obtaining real-time operation data of a power grid structure; sequentially performing low-dimensional representation and data reconstruction on the real-time operation data by adopting a pre-trained auto-encoder model, and performing screening based on the reconstructed data to obtain abnormal data and normal data; based on the abnormal data, adopting an Apriori algorithm to classify the abnormal data into abnormal state data and dirty data, and removing the dirty data; and adopting a pre-trained GRU recurrent neural network model to complement the abnormal data from which the dirty data is removed to obtain the complemented abnormal data, and completing the data cleaning process. Compared with the prior art, the method has the advantages of realizing efficient and low-threshold power grid operation data cleaning and the like.

The present application relates to the technical field of medium and shallow coalbed methane development, in particular to a kind of medium and shallow coalbed methane well production dynamic prediction method, comprising the following steps: S1, target well data acquisition and preprocessing;S2, random forest algorithm filters production main control factor;S3, fusion attention mechanism's CNN-LSTM multimodal time series feature extraction;S4, physical constraint deep learning model construction;S5, mixed loss function construction and model training;S6, production dynamic prediction and result output.The present application fuses physical constraint and deep learning technology, both utilize the efficient feature extraction capability of deep learning, and also guarantee the physical consistency of prediction result by physical constraint, solve the problem that pure data driven model generalization ability is poor, long-term prediction error is big.

The application discloses a kind of hardware wallet offline operation method, system's working method and hardware wallet device, belong to hardware wallet technical field.The method stores the word element structure containing word element value and public-private key pair in hardware wallet, realizes the word element segmentation and merging operation under trusted offline environment.When carrying out word element operation, hardware wallet constructs load data inside security component, generates authentication information using corresponding private key for signature, and authentication data is recorded in word element conversion operation log file.After the hardware wallet is networked, complete word element conversion operation authentication data can be synchronized to background system for verification.The application solves the redundancy problem of hardware wallet in the prior art through the segmentation and merging mechanism of word element, avoids the accumulation of word element data in offline environment, and uses authentication mechanism to ensure the integrity and traceability of word element conversion operation, thereby improving the user experience and security of digital currency offline operation.

The invention discloses an industrial printed circuit board image defect detection method based on deep learning, which specifically designs a small data set expansion scheme to enhance data diversity. The method comprises the following steps: designing an early stage of a depth separable convolution reconstruction parameterization module to construct a backbone network, designing a non-adjacent window filling block improved multi-head self-attention module to construct a lightweight neural network Transform architecture to construct the backbone network, and designing a small target step length output improved module to construct a target detection head; a defect detection model capable of positioning an industrial printed circuit board image is designed, a small target detection strategy is designed, and the small target defect learning speed and precision of the model are improved; and a label duplicate removal scheme and an adaptive defect screening scheme are designed, so that the accuracy of model prediction is improved. According to the invention, the feature learning ability of an image with a small detection target can be improved, and the defect detection performance of an industrial printed circuit board image can be effectively improved.

The present invention discloses a method for predicting silicon content in molten iron based on TD-LightGBM, and belongs to the technical field of industrial process monitoring. The method comprises: first, using the decision tree model structure of a lightweight gradient booster to judge the importance of the feature according to the number of times the feature is used to segment training data; then, combining a time difference algorithm to establish a dynamic feature extraction model; finally, using a local weighted algorithm to establish a local model based on the Euclidean distance between samples. Predicting the silicon content in molten iron during the ironmaking process based on this model can effectively remove redundant items and drift phases in the blast furnace environmental parameters. Compared with the existing prediction scheme for silicon content in molten iron, the present invention further improves the accuracy of the prediction.

The present invention belongs to the field of power systems and automation thereof, and discloses a data modeling method, system, device and storage medium for control cloud applications, including: modifying a preset model data management structure according to the model data requirements of the control cloud application to obtain a target model data management structure; determining the screening conditions of the model data according to the use scope of the control cloud application, and acquiring the model data based on the screening conditions of the model data; adding the model data to the target model data management structure to obtain the structured model data of the control cloud application, and modeling the control cloud application based on the structured model data of the control cloud application. The structured model data of the control cloud application meets the structure and data requirements of the control cloud application for the model data, solves the problem of coexistence of redundancy and missing of the structured model data of the control cloud for different control cloud applications, and modeling the control cloud application through the structured model data makes the modeling of the control cloud application extremely simple.

The utility model relates to the field of LED display circuits, and discloses an interaction control circuit of an LED display screen. According to the method, N HDMI input interfaces, an MCU controller, an LED serial port and a relay control module are included; the N HDMI input interfaces are connected with the MCU controller, the MCU controller is connected with the LED serial port, and the MCU controller is connected with the relay control module; the N HDMI input interfaces are used for being connected with external conference equipment, the external conference equipment transmits CEC control signals to the N HDMI input interfaces, the N HDMI input interfaces transmit the CEC control signals to the MCU controller, the MCU controller converts the CEC control signals into display control signals and transmits the display control signals to the LED serial port, and the MCU controller converts the CEC control signals into power-on signals and transmits the power-on signals to the relay control module. In the embodiment of the utility model, the smooth display of the external LED display screen is realized with low cost.

The application discloses a system and method for accelerating reading compressed files based on A Virtual File System, and belongs to the technical field of time series databases; the technical problem to be solved by the application is how to realize column format compressed storage and reading of time series data types, reduce IO, memory and CPU consumption caused by decompression, and improve the ability of the system to process time series data; the technical scheme adopted is as follows: in the time series engine of the open database ZNBase, an execution engine generates an execution plan according to SQL semantic analysis, calls a storage layer interface of ZNBase, and queries accurate time series history partition compressed data; the storage layer completes fast execution of the query in the storage layer based on various compressed query interfaces provided by AVFS, and accelerates the query of the database; the specific steps are as follows: pre-analysis; compressed file pre-reading; reading compressed data.

The invention discloses a safety guarantee method for an intelligent driving vehicle. The safety guarantee method comprises the steps that S1, the control behavior of a driver is detected; s2, whether the vehicle breaks down or not is diagnosed according to the braking system and the motor torque; s3, further determining whether the intelligent driving vehicle is out of control due to vehicle machine faults; and S4, when the vehicle machine is judged to have the fault problem, forcibly switching the manual driving mode. The invention further discloses an intelligent driving vehicle safety guarantee device. According to the method, the intelligent driving vehicle can exit the vehicle automatic driving mode when system faults such as software and hardware occur in the automatic driving process, safety accidents caused by out-of-control of the vehicle can be avoided, and the out-of-control situation of the intelligent vehicle can be effectively handled.

The application relates to an elevator flow field multi-sensor targeted data acquisition method and system based on CFD, and relates to the technical field of elevators. The method first constructs a three-dimensional model based on elevator shaft geometric parameters and performs CFD simulation to obtain multi-physical field data such as pressure field and velocity field; a vortex identification algorithm is used to extract key flow structure features and accurately identify key flow field regions; then, a wisdom performance sensing point distribution algorithm is used to optimize sensor targeted distribution, effectively solving the problems of measurement blind areas and resource redundancy caused by traditional experience point distribution. Multi-source sensing data is collected through synchronous trigger control, and high-confidence standardized flow field data sets are generated through filtering and noise reduction, drift compensation and space-time registration fusion processing. The application guides sensor targeted deployment through CFD pre-performance, greatly improves the efficiency and pertinence of data acquisition, and fuses multi-dimensional data to construct high-quality data sets, thereby laying a solid foundation for elevator flow field characteristic research and intelligent sensing.

This invention provides a database optimization method and terminal for storing game items. The method involves obtaining general attributes of item data and extended attributes for each type of item data; constructing a general table based on the general attributes and an identifier field; and constructing an extended table based on the extended attributes and the identifier field. An extensible key-value pair field is added to the extended table to store variable attributes. This invention avoids the redundancy problem of distributing general attributes of different types of item data across multiple tables by storing them in a general table, thus improving retrieval speed. Furthermore, the addition of a key-value pair field to the extended table to store variable attributes allows for direct addition of new attributes using the key-value pair field without modifying the extended table structure, further reducing the impact on the original table structure, lowering the difficulty of extending attributes, and increasing the flexibility of modifying item data.

The invention provides an image-driven three-dimensional animatable asset generation method and device based on unified field representation and a program product. The method comprises the following steps: carrying out feature extraction on an obtained single RGB image to obtain a multi-view feature and a global semantic feature; generating sparse three-dimensional voxel representation based on the multi-view features and the global semantic features; generating, by a structured encoder, a unified field representation based on the sparse three-dimensional voxel representation, the unified field representation comprising a shape field, a bone field, and a skin field; generating three-dimensional animatable assets with geometric shapes, skeleton structures and skin weights based on the unified field representation; wherein the skeleton field adopts a confidence attenuation mechanism to process the fuzziness of skeleton connection, and the skin field adopts double skin feature fields to respectively associate geometric features and skeleton features. The effect of directly generating animatable three-dimensional assets with high-fidelity geometry, reasonable skeleton structure and accurate skin weight from a single image is achieved.

The application relates to an aluminum template installation precision control method, system, device and medium. The method comprises the following steps: extracting the actual local characteristics of the installed aluminum template, matching the actual local characteristics with the predefined key local characteristics, and giving corresponding semantic labels; calculating six-degree-of-freedom deviation parameters and weight coefficients to construct a multi-objective optimization function; based on the function, based on the physical connection relationship and the physical stroke limit value, a mathematical optimization problem is constructed; the problem is numerically solved to obtain the optimal adjustment amount of each aluminum template unit, generate and execute the adjustment instruction, obtain the verification point cloud data of the adjusted aluminum template, and verify and iteratively optimize until the new structured deviation data set meets the preset precision requirement. The method constructs a multi-objective optimization model, combines the physical connection relationship and the stroke limit value, realizes high-precision and intelligent adjustment of the aluminum template installation, significantly improves the precision and efficiency of the aluminum template installation, and enhances the global optimization capability and intelligent level of the adjustment process.

A high-redundancy liquid-cooling cold plate system comprises a vapor chamber, cold plate sets, a gas pipeline, a double-acting valve, a gas return main collecting pipe, a condenser, a pump, a liquid pipeline, a liquid separation main collecting pipe and heat exchange fluid, and further comprises at least one cold plate set, and each cold plate set is composed of at least two cold plates which are independently arranged in a crossed mode. And online maintenance of the high-heat-flow computing power chip cold plate system can be realized. In combination with the requirements of operation safety and energy conservation of the computing power chip, the device is high in safety and has the advantages of being high in heat dissipation capacity, compact in structure, high in temperature control precision, high in applicability, wide in application range and the like.

The invention belongs to the field of artificial intelligence, and relates to a video key frame extraction method and device based on self-supervision, equipment and a medium, and the method comprises the steps: obtaining a frame sequence of a target video, extracting the feature vector of each frame in the frame sequence through a pre-training feature extractor, and obtaining a feature vector sequence; determining graph nodes according to the frame sequence, calculating edge weights according to the feature vector sequence, and constructing an initial directed graph; calculating a graph Laplacian matrix, screening positive and negative sample frame pairs from the frame sequence by using a target sampler, and calculating time sequence perception comparison loss; circularly using parameters of the loss updating feature extractor to finely adjust the feature vector, update the edge weight, construct the graph and the like until the loss converges; reconstructing a directed graph, and calculating a Laplacian matrix of the graph to obtain a Laplacian matrix of a target graph; and performing Laplacian matrix spectral clustering on the target image to obtain a target video key frame set. The method can be applied to the business fields of financial science and technology, insurance, medical treatment and the like, and can improve the extraction accuracy of the video key frame.

The invention relates to the technical field of biology, and particularly provides a method for improving NGS sequencing accuracy and application thereof.The method comprises the steps that NGS sequencing is conducted on a sample library to obtain initial sequencing data, and the library content of the sample library is not lower than 0.5 ng; and analyzing the initial sequencing data to obtain a sample sequence. According to the method, NGS sequencing can be carried out on a sample based on sanger sequencing, and finally obtained sequencing data has remarkable advantages in the aspects of assembly success rate, redundancy rate, assembly result verifiability and the like.

The invention provides a landslide area extraction method and device, electronic equipment and a storage medium, and belongs to the technical field of image data processing. The method comprises the following steps: respectively inputting a first remote sensing image of a time phase before occurrence of a landslide, a second remote sensing image of a time phase after occurrence of the landslide and a slope map into a first feature extraction network, a second feature extraction network and a third feature extraction network of a landslide extraction depth model, a first feature map, a second feature map and a third feature map which are correspondingly output are obtained; inputting the first feature map, the second feature map and the third feature map into a feature fusion module of a landslide extraction depth model to obtain a fused feature map output by the feature fusion module; and inputting the fused feature map into a decoder of the landslide extraction depth model to obtain a landslide extraction result output by the decoder. According to the method, the accuracy, robustness and efficiency of landslide automatic detection and mapping are improved, the slope map is introduced as a supplement of topographic information, and the identification capability of the model for the potential landslide area is enhanced.

This invention provides a method and system for cross-variable long-term time series prediction that integrates linear and enhanced Transformer methods. The method includes: extracting an input sequence using a sliding window based on historical data and calculating the temporal statistical features of the sequence; generating scaling and bias factors for the window through a shared fully connected network to perform affine normalization on the input and eliminate distribution bias; performing a Fast Fourier Transform on the normalized tensor to extract the main frequency domain components and map them back to the time domain, then fusing positional encoding to form a time-frequency hybrid embedding; constructing an adjacency matrix based on the correlation between variables, using this matrix as a mask in cross-variable attention calculation to suppress interference between weakly correlated variables and enhance variable co-representation; mapping the representation to the prediction length through a time projection layer, and performing an inverse transform using previously generated normalization parameters to restore the original dimensions of the data and obtain the prediction result.

The invention provides a neural network training method and device based on time self-erasing. The training method comprises the following steps: for each time step length, inputting a training sample into a convolutional layer to generate a first feature map corresponding to the time step length; for each time step other than the first time step, generating a second feature map by inputting the first feature map to which an erase mask that varies with each time step is applied into the pooling layer; generating a first prediction map by inputting the second feature map into the first full connection layer; generating a loss based on the first prediction map and the real tag; and adjusting the parameters of the neural network based on the loss to obtain a trained neural network. Therefore, the overall prediction accuracy is improved, the calculation efficiency is improved, and the calculation overhead is reduced.

The invention discloses a flexible all-solid-state two-dimensional nanofluid pressure sensor based on direct driving of osmotic energy and application thereof, and belongs to the technical field of flexible wearable pressure sensors and artificial intelligence interaction. The pressure sensor is formed by packaging a GO film layer and a KGO film layer in combination with an MXene charge collection layer, a CNF isolation layer and conductive graphite paper. According to the invention, ions are driven to selectively migrate in the nano-channel by utilizing permeation energy, and external pressure is converted into an electric signal through interlayer spacing modulation. Due to the unique structural design, the sensitivity, the response speed and the like are improved. On the basis of excellent performance and wearable characteristics of the sensor, real-time man-machine interaction is realized by a single sensor in combination with a deep learning algorithm, and the gesture recognition accuracy is as high as 95.78%.

The application discloses an advertisement click rate prediction system, comprising a data acquisition module for real-time acquisition of user behavior data, attribute data, context data and biological characteristic signal data; a multi-modal feature extraction module for extracting multiple feature vectors; a dynamic feature fusion module for dynamically weighting and fusing multi-modal features through an adaptive attention fusion network; a graph neural network coding module for coding a heterogeneous graph structure to generate a graph embedding feature vector; an adaptive routing prediction module for dynamically selecting an expert subnetwork through a router network for prediction; a real-time feedback calibration module for online parameter updating based on real click feedback and generating a calibration signal; and a multi-objective optimization decision module for outputting an advertisement recommendation list based on multi-objective optimization. The application realizes accurate prediction and intelligent decision through adaptive routing and multi-objective optimization, and significantly improves the accuracy and scene adaptability of advertisement click rate prediction.

The application relates to a multi-source data fusion man-machine interaction task online acceleration method and system, and belongs to the field of man-machine interaction. The application provides multiple interaction modes through the arrangement of multiple sensors, improves the self-adaptive capability and high reliability; through a task self-adaptive multi-source data fusion method, feature parameters of acquired multi-source sensing signals are extracted, and data fusion is carried out according to a fusion discrimination criterion, the time for processing multiple information of multi-mode man-machine interaction tasks under uncertain conditions is reduced, interaction instruction redundancy is avoided, and the response speed is improved; in combination with a fusion parameter online updating method, the fusion parameters are predicted in multiple steps in advance, the fusion parameters are corrected and updated in real time, and the problems that multi-mode man-machine interaction data cannot be fused online and the prediction accuracy is low are solved. The application is suitable for the fields of man-machine interaction, automatic driving, medical diagnosis and the like, and is used for improving the accuracy of multi-source sensor data prediction results and the reliability of man-machine interaction under various uncertain conditions.

The invention discloses an aircraft battery thermal management system and method. Comprising an evaporator (5), a liquid cooling loop is connected between a hot edge medium inlet and outlet of the evaporator (5), and an evaporation circulation loop is connected between a cold edge medium inlet and outlet of the evaporator (5); the liquid cooling loop comprises a liquid storage tank (1), a booster pump (2), a cold plate A (3) and a cold plate B (4) which are sequentially connected between a hot edge medium inlet and a hot edge medium outlet of the evaporator (5); the evaporation circulation loop comprises a compressor (6), a condenser (7) and an expansion valve (8) which are sequentially connected between a cold edge medium inlet and a cold edge medium outlet of the evaporator (5); the cold plate A (3) is installed on an electric control system of the aircraft, and the cold plate B (4) is installed on a battery system and a motor system of the aircraft. According to the invention, comprehensive thermal management of batteries, motors and electric control of the large-scale all-electric aircraft and a thermal management strategy during short-time large-capacitor charging and discharging can be realized, and the requirement of comprehensive thermal control of the large-scale all-electric aircraft is met.