42results about How to "Scale up" patented technology

The invention relates to the technical field of cloud computing, in particular to a cloud tenant bearing scale optimization method and device, equipment, a medium and a product, and the method comprises the steps: recognizing a sender and an access purpose of an access request, and determining a data flow direction corresponding to the access request; in response to the condition that the sender is the virtual machine, address conversion and message encapsulation are performed on the access request through the virtual switch, and the encapsulated access request is sent to the cloud gateway; and in response to the fact that the sender is the Underlay network or the public network, the cloud gateway is determined based on the sender, the cloud gateway confirms the virtual private cloud based on the access purpose, the access request is subjected to message packaging, and the access request is sent to the virtual switch. According to the technical scheme of the invention, the original address translation is sunk to the virtual switches corresponding to the virtual private clouds from the cloud gateway for processing according to the data in the cloud-out direction and the cloud-in direction, so that the memory space occupied by the NAT table items can be released, the detailed routing capacity of the virtual machines is improved, and the expansion of the cloud tenant scale borne by the cloud data center is facilitated.

The application provides a photonic neural network system based on a multi-task neural network, which comprises: an optical computing module, taking a photonic artificial intelligence chip as a core, modulating an optical signal, and completing multiplication, summation and nonlinear operation of high-precision and high-speed optical analog quantities; a multi-task neural network module, having a main branch neural network and multiple secondary branch neural networks, and capable of simultaneously processing multiple classification tasks and regression tasks; and an optical computing communication and control module based on FPGA, connected with the optical computing module and the multi-task neural network module, and used for realizing real-time and high-speed communication of the optical computing module and the multi-task neural network module. The system has the characteristics of high bandwidth and low energy consumption, utilizes high-dimension parallel computing to greatly reduce the time of original neural network serial operation, has the advantages of large network scale and fast parallel operation speed, and can improve recognition accuracy and shorten training time on the basis of a single task.

The RWKV-based novel translation model inference method and system includes the following steps: 1) Collecting novel translations and extracting parallel corpora, and using dynamic MicroBatch concatenation technology for sequence compression; 2) Introducing a lightweight group query attention mechanism on the basis of the RWKV architecture, directly obtaining KV information from the Embedding layer to build the model; 3) Employing a sublinear complexity hybrid parallel training mode, combined with a global scalar scaling FP16 mixed precision strategy for training; 4) Applying a hierarchical distributed heterogeneous architecture, offloading the optimizer to low-performance devices and performing gradient compression transmission; 5) Outputting the translation using a joint decoder and dynamic batch inference technology. This invention, through the above method and system, effectively reduces the computational complexity and memory usage in the long text translation process, improves model training efficiency and inference throughput, and significantly improves the translation efficiency and contextual coherence of ultra-long texts.

The utility model provides a thrombin determination kit fixing frame convenient to expand, which relates to the technical field of medical instruments and comprises a fixing base. A fixing frame is inserted into the top end face of the fixing base, a partition plate is slidably connected into the fixing frame, a control rod is rotatably connected to the middle of the top end face of the partition plate, walking gears are symmetrically and rotatably connected to the left and right sides of the upper and lower end faces of the partition plate, and a locking pin is slidably connected into the partition plate in the middle of the top of the control rod. According to the thrombin determination kit fixing frame convenient to expand, the fixing frames are rapidly spliced through the connecting combination blocks, the connecting combination grooves and the butterfly nuts at the top and the connecting lug blocks and the connecting lug grooves at the bottom, a user can conveniently and rapidly expand the scale of the fixing frame according to actual requirements, and it is ensured that the overall structure is stable; therefore, the problems that a fixing frame is fixed in structure, single in mounting position, difficult to adapt to quantity change, low in space utilization rate and difficult to flexibly adjust a partition plate are solved.

The present disclosure provides an updating method and device of an intent template library, an electronic device and a storage medium, relating to the technical field of computers, in particular to the technical field of artificial intelligence such as deep learning, natural language understanding, voice interaction. The specific implementation scheme is: obtaining a target sentence with an empty intent recall result from a historical dialogue library; determining the word slot label corresponding to each word group in the target sentence; based on a preset word slot arrangement rule, sorting the word slot labels corresponding to the target sentence to obtain a semantic template corresponding to the target sentence; performing intent recognition on the semantic template or the target sentence to determine a target intent corresponding to the semantic template; and storing the semantic template and the target intent in the intent template library. Thus, the automatic updating of the intent template library is realized, which not only expands the size of the intent template library and reduces the cost of updating the intent template library, but also improves the accuracy and reliability of the intent template library and optimizes the performance of the machine in the human-computer interaction process.

This invention provides a Pinyin error correction method that simplifies the noise channel model used in most current Pinyin error correction algorithms by employing a real-time frequency counting method, effectively improving the efficiency and lightweighting of the algorithm. Furthermore, this invention uses a direct character substitution method instead of the traditional edit distance calculation method, avoiding frequent edit distance calculations in the Pinyin error correction algorithm. The dictionary database itself is localized to the individual user, resulting in a highly personalized, targeted, and small-scale dictionary that provides accurate candidate words. Compared to existing algorithms, this invention offers significant improvements in error checking rate, candidate word accuracy, and execution efficiency.

The application provides a vitiligo lesion area segmentation method and device based on image editing and electronic equipment, and belongs to the technical field of image processing. The method comprises the following steps: contrast learning pre-training of dual-mode vitiligo data; data set expansion based on Poisson image editing mode; pre-processing of the expanded data set is input into a segmentation neural network for training; the model parameters based on the above segmentation training are used as the baseline segmentation model of the semi-supervised framework of the student-teacher model, and the unmarked data and the marked data are jointly trained; the prediction result of the unmarked data input into the teacher model is refined to obtain a pseudo label as the corresponding label to train the network; and the segmentation network obtains the segmentation result of the vitiligo lesion area. Thus, the pre-trained segmentation network is used, and a reliable neural network model is obtained by using image editing to automatically segment the vitiligo lesion area. This process does not need to rely on manual work, and the segmentation efficiency is improved.

The invention relates to a learning state monitoring method, system and device based on artificial intelligence and a medium. The method comprises the following steps: collecting signals such as physiological and behavior images of a target object through a multi-source heterogeneous sensor, and performing normalization processing on aligned features to generate a standardized feature vector; calculating a comprehensive cognitive state score of the feature vector by adopting a dynamic weighted fusion algorithm, and judging and outputting a soft label according to a preset threshold value; applying constraint random disturbance based on statistical distribution characteristics of the feature vectors to generate synthetic data, and associating the synthetic data with the soft labels to construct a soft label data set; by taking the feature vector of the data set as input and the soft label as a supervision signal, iteratively calculating loss through a preset loss formula, updating model network parameters to convergence, and obtaining an optimized neural network model; during real-time monitoring, processed sensor signals are input into the model, and quantitative learning state scores are output through end-to-end reasoning. The method can break through the limitation that traditional monitoring is high in subjectivity and insufficient in real-time performance.

The invention discloses a state estimation network construction method and device of an electric power system and an operation state evaluation method, and belongs to the field of operation and maintenance of the electric power system.The method comprises the steps that feature vectors of all buses are constructed according to historical measurement data of all the buses of a target electric power system and historical measurement data of operation equipment associated with all the buses; according to topological structure data of a target power system and the historical measurement data, constructing a plurality of original power diagram data; performing feature learning on the plurality of original electric power diagram data by using a generative adversarial network to generate a plurality of enhanced electric power diagram data; and performing iterative training on a graph convolutional network according to the enhanced power graph data, and taking the trained graph convolutional network as a state estimation network. By implementing the application, the problem of low efficiency of evaluating the operation state of the power system in the prior art can be solved.

This application discloses a model building method, apparatus, electronic device, and storage medium, relating to the field of smart home technology. The method includes: acquiring a first noise sample set, which is collected from different types of fluid power equipment under different operating conditions; expanding the first noise sample set based on preset sound quality feature constraints to obtain a second noise sample set; and constructing a sound quality prediction model for the fluid power equipment based on the second noise sample set. The technical solution provided by this application can effectively expand the sample size and reduce the cost of sample acquisition, solving the pain points of insufficient training data and low prediction reliability under small sample conditions, and providing support for the sound quality optimization of range hoods.

The utility model relates to battery voltage detection, specifically relates to a kind of multi-channel battery voltage detection circuit with equalization function, multiplexer module is connected between the battery pack to be measured and analog front-end sampling chip AFE, under the control of controller MCU, corresponding channel is selected to pass battery voltage signal to analog front-end sampling chip AFE, while corresponding channel is selected to carry out passive equalization and active equalization to the battery pack to be measured;Analog front-end sampling chip AFE is connected between multiplexer module and controller MCU, battery voltage signal is sent to controller MCU, realizes battery voltage detection, while under the control of controller MCU, the passive equalization of the battery pack to be measured is realized using the discharge channel of itself;The technical scheme provided by the utility model can effectively overcome the defects that the voltage of each lithium battery in parallel battery module cannot be accurately and quickly detected, and the function of battery voltage detection circuit is single.

The invention relates to the technical field of robot double-arm operation and learning control, and discloses a generalization method for a double-mechanical-arm operation strategy in a complex scene, and the method comprises the steps: controlling double mechanical arms and clamping jaws installed at the tail ends of the mechanical arms to execute operation tasks through teleoperation equipment in a simulation environment, and collecting demonstration data; segmenting, classifying, optimizing and augmenting the demonstration data to obtain a large-scale demonstration data set; the large-scale demonstration data set is used as a training set, and the operation strategy network is supervised and trained, so that the operation strategy network can control the double mechanical arms to complete the operation task according to the operation task instruction; and performing reinforcement learning post-training on the operation strategy network in a simulation environment. According to the method, the task success rate and the generalization ability of the strategy in a complex environment are remarkably improved.

This invention relates to the fields of natural language processing and digital healthcare, providing a method, system, device, and medium for determining drug-target matching. The method includes: acquiring the drug molecule sequence and the target protein sequence; inputting the drug molecule sequence and the target protein sequence into a first model, performing multimodal processing on the drug molecule sequence and the target protein sequence to obtain a first confidence score; inputting the drug molecule sequence and the target protein sequence into a second model, performing feature extraction and feature splicing on the drug molecule sequence and the target protein sequence to obtain a second confidence score; weighted summing of the first confidence score and the second confidence score to obtain a total confidence score; and determining that the drug matches the target when the total confidence score is greater than or equal to a preset confidence threshold. This method achieves rapid and accurate determination of the targeting relationship between a new drug and the target protein to be identified, effectively improving the efficiency of drug screening.

The invention provides a data collection system oriented to the network field. The system performs data collection based on a plurality of distributed heterogeneous computing resource nodes. In the system, a user only needs to define a task pipeline and a node type which need to be used for data collection according to a data collection intention through a declarative experiment specification, a data collection category to which the task pipeline belongs is determined according to the task pipeline, and the system receives the experiment specification submitted by the user; and an instruction set for environment configuration and atomic task execution is compiled and generated according to the experimental specification, the instruction set is issued to a target node in the data collection module, the whole process of resource node scheduling, environment configuration and atomic task execution is automatically completed according to the instruction set, and sample data under the category to which the task pipeline belongs is obtained. According to the system, experiment specifications are automatically compiled into executable instructions for specific nodes and environments, the experiment script compiling complexity is reduced, and the data production efficiency, scale and reliability are improved.

This invention relates to a method for preparing a Ni metallized interface layer on a SnSe surface. The method utilizes an electrodeposition apparatus to prepare the Ni metallized interface layer on the surface of a SnSe single crystal. The process involves pretreating the single-crystal SnSe sample; injecting an electroplating solution into an electrolytic cell and heating it to a preset temperature; clamping the pretreated SnSe single crystal onto the cathode of the electrolytic cell; clamping a Ni wire electrode onto the anode of the electrolytic cell; and performing low-speed DC deposition to deposit a dense, tightly bonded initial Ni layer on the pretreated SnSe single crystal; then disconnecting the electrical connection of the constant current source; and finally performing high-speed pulse deposition to deposit a sufficiently thick Ni metallized layer on the initial Ni layer at a high rate. By combining DC and pulse deposition methods, the method balances the requirements for deposition rate and metallized interface layer quality, resulting in a metallized interface layer with good mechanical stability and adhesion.

The application relates to a speech translation model modeling method and device based on speech synthesis data, and belongs to the technical field of natural language processing; the method solves the problem that, in the prior art, a speech translation model is inaccurate in translation results due to insufficient training data and insufficient utilization; the modeling method comprises the following steps: acquiring a general speech synthesis data set, and training a general speech synthesis model; acquiring a speech translation data set of a target field; fine-tuning the general speech synthesis model in the speech translation data set to obtain a special speech synthesis model; inputting source language labeled text into the special speech synthesis model, generating a plurality of pieces of speech synthesis pseudo data according to a pre-set proportion, and obtaining a pseudo speech data set; constructing an initial speech translation model, training the initial speech translation model by using the speech translation data set of the target field and the pseudo speech data set, and obtaining a speech translation model through loss function iteration and updating.

The application discloses a wild animal video target detection method based on a feature fusion network, and comprises the following steps: collecting and labeling wild animal image and video data; performing morphological transformation, color transformation and contour destruction preprocessing; sequentially inputting the preprocessed image and video data into a feature extraction network to obtain multi-scale features; performing feature fusion on the extracted multi-scale features, fusing multi-scale features and same-scale features of adjacent image frames for the video data, and only fusing multi-scale features for the image data, finally obtaining multi-scale fusion features; inputting the multi-scale fusion features into a feature prediction network to obtain a prediction result; comparing the prediction result with a label in the training stage, calculating a loss function, performing gradient back propagation, completing model parameter updating, and until the network converges; in the inference stage, the prediction result is post-processed to obtain the final output result.

The invention provides a sample expansion method and device for a geographic regression model, equipment and a storage medium, and the method comprises the steps: carrying out the pairing of samples in an initial sample set of the geographic regression model, and obtaining an expansion sample set containing a plurality of paired samples; constructing a plurality of panel data entries based on the plurality of pairing samples; for any panel data entry, performing interpolation simulation on the independent variable variable quantity of any panel data entry, and generating a plurality of pieces of simulation input data in combination with the independent variable state quantity of any panel data entry; inputting the plurality of pieces of analog input data into a trained machine learning regression model to obtain a plurality of pieces of analog output data; and adding the plurality of pieces of analog input data to the independent variable state quantity of any panel data entry, and adding the plurality of pieces of analog output data to the dependent variable state quantity of any panel data entry to obtain a plurality of analog geographic samples. Therefore, the training sample scale can be effectively expanded, and the training effect of the geographic regression model is improved.

This invention discloses a well-seismic combined seismic data overlay method, relating to the field of geophysical exploration processing and interpretation technology in oil and gas field exploration. The method includes the following steps: S1, acquiring acoustic and density logging data, calculating the wave impedance curve and converting it to reflection coefficients, selecting a wavelet close to the seismic data, convolving it with the reflection coefficients to form a conventional synthetic record, and performing well-seismic calibration; S2, determining the target wavelet for overlay, and convolving it with the reflection coefficients to form a broadband synthetic record; S3, combining well-side seismic data, conventional synthetic records, and broadband synthetic records to construct a sample set, and balancing the number of samples; S4, expanding the sample set through mutation; S5, establishing a nonlinear relationship for overlay based on the sample set; S6, applying the nonlinear relationship to conventional seismic data to obtain high-resolution seismic data. This invention utilizes the advantages of wide bandwidth of well logging data and seismic frequency bands to enhance high-frequency seismic data and broaden the seismic frequency band, thereby improving seismic resolution.

A wind turbine gearbox anomaly detection method considering the similarity of multi-unit operating states, based on the piecewise linear time series similarity evaluation method, evaluates a single state variable; considering the similarity of multiple state variables, the wind turbine operating state spatiotemporal similarity quantification method evaluates the similarity of each wind turbine and the operating state of the unit to be detected; a state estimation LSTM model is constructed, different LSTM models are trained; the accuracy and adaptability of different LSTM model performances are verified; select several LSTM models that perform well in performance verification, construct a combined state estimation model CPEM through weighted combination; estimate the target variable of the unit to be detected, calculate the residual error between the estimated value and the true value, and based on the residual effective value comparison and residual information entropy comparison, identify the anomaly. This method detects the operating state of the gearbox based on the historical operating data of the wind turbine, without the need for maintenance personnel to conduct on-site detection, reducing the operating and maintenance costs of the wind turbine.

This invention relates to the field of chemical production technology and discloses an apparatus and method for the large-scale production of boron nitride nanomaterials. The apparatus includes a vacuum chamber, a cooling water circulation system, a vacuum pumping system, an exhaust system, a gas path, an automatic stirring device, a material reaction container, and a heating device. The automatic stirring device controls the mixing and stirring of the materials. The material reaction container is connected to multiple evenly distributed air inlets. The heating device surrounds the material reaction container and controls the reaction temperature. This invention improves the thermal treatment effect and chemical reaction efficiency of the raw materials in the gas by equipping the material reaction container with an automatic stirring device and multiple densely distributed air inlets, effectively increasing the quality and quantity of materials processed per batch and improving the production efficiency of boron nitride nanomaterials. The apparatus is simple, has low manufacturing cost, and is easy to scale up; the production method is simple, energy-efficient, and pollution-free, meeting the needs of large-scale preparation of boron nitride nanomaterials.

The application discloses a county scale soybean yield prediction method based on reference year time series difference modeling, and relates to the technical field of agricultural remote sensing and intelligent information processing. The method first acquires multi-source crop growth data and administrative level soybean yield data of previous years in a research area, and constructs a spatial scale multi-source time sequence characteristic sequence of a unified space-time scale after preprocessing; then, a reference year is selected to construct a sample pair, and a multi-source time series difference sequence and a yield difference value are calculated; subsequently, a deep learning model is constructed, the difference sequence is taken as input, and the yield difference value is taken as a label to train the model; finally, the difference sequence of a to-be-measured sample is input to obtain a predicted yield difference value, and the yield of the to-be-measured year is obtained by combining the yield of the reference year. The application realizes explicit depiction of interannual differences, effectively expands training samples, reduces extreme value interference, improves prediction stability and precision, adapts to various deep learning models, and is easy to popularize in large-scale areas.

The invention provides a multi-modal data fusion characterization method and a multi-modal data fusion characterization system for commercial vehicle frame performance prediction, which are applied to the technical field of vehicle frame data modeling. Extracting structure topological parameters, section construction parameters, material attribute parameters, connection parameters, P-S-N curve data and field response data, and forming a target domain data set and an independent test set; then establishing a frame full-parameterization finite element model, combining actual measurement load spectrum statistics and derivation to generate a virtual load sample, extracting a two-dimensional load damage characteristic matrix, and constructing a source domain data set; and finally mapping the geometric topology feature, the parameter feature, the connection feature and the load feature to a unified three-dimensional voxel space to generate a three-dimensional multi-channel engineering data tensor. According to the method, the commercial vehicle frame sample construction efficiency, the multi-modal data utilization efficiency and the generalization ability of the performance prediction model can be improved.

The application discloses a data communication method and system, electronic equipment, computer readable storage medium and computer program product, and relates to the technical field of communication. For a data communication system including multiple groups of computing units deployed in different computing nodes and the computing units in the same group being connected to the same switch, when the computing units in different computing nodes are accessed, a target switch is determined according to a routing mapping relationship and resource allocation information carried in an access request; an intermediate forwarding port is determined according to the resource allocation information and switch routing information of a source switch, and the access request is forwarded to a target computing unit through the intermediate forwarding port, a target communication port and target resource allocation information. The application can solve the problems of long delay, large hardware overhead and low communication efficiency in related technologies, and effectively improve the point-to-point communication efficiency in a large-scale data communication system without introducing additional delay and hardware overhead.

The invention discloses a preparation method of a grid electrode of a hundred-bit-level quantum computing chip and the quantum computing chip. The preparation method of the grid electrode of the hundred-bit-level quantum computing chip comprises the following steps: processing and preparing an alignment mark on a substrate processed by a laser direct writing technology on a hydrogen mask of a scanning tunneling microscope; processing a quantum device core structure on the substrate; secondarily processing a top electrode overlay mark on the substrate to obtain a second standard substrate; a plurality of top electrode layers are manufactured on the oxide layer in an overlay mode through the top electrode overlay marks, the top electrode layers are arranged in a staggered mode and partially overlapped in the vertical direction, and the hundred-bit-level quantum computing chip is obtained. According to the method, a plurality of top electrode layers are processed on the oxide layer in a multi-layer stacking mode, different layers do not influence one another, the space utilization rate of the quantum computing chip is greatly increased while the space between the top electrodes is guaranteed, and therefore preparation of the hundred-bit-level quantum computing chip can be achieved in the limited on-chip space.

This invention discloses a method for preparing Yuanjiang asparagus extract and its application. The method uses fresh Yuanjiang asparagus tender stems as raw material. After selection, washing, freeze-drying, pulverizing, and sieving, choline chloride and 1,2-propanediol eutectic solvent are added for constant-temperature extraction. After ultrasonic temperature control, cellulase and pectinase are added for complex enzymatic hydrolysis. After heating to inactivate the enzymes, centrifugation is performed, and the supernatant is collected to obtain the crude extract of Yuanjiang asparagus saponins. This method has high extraction efficiency, and the obtained asparagus saponins have strong antioxidant activity, exhibiting strong scavenging ability against DPPH, ABTS, and hydroxyl radicals, as well as Fe... 2+ The chelation rate was high, and it showed significant in vitro inhibitory effects on the proliferation of human colorectal cancer cells Caco-2 and human hepatocellular carcinoma cells HepG2. The raw material used in this method is fresh, tender stems of Yuanjiang asparagus, which is widely available and of excellent quality. The extraction process is green, environmentally friendly, and simple, not only improving the comprehensive utilization value of Yuanjiang asparagus but also providing a new method for extracting natural saponin active ingredients. The obtained asparagus saponins can be used to develop antioxidant and anti-tumor functional foods, showing potential market prospects.

This invention discloses a robotic arm localization method based on language models and 3D point clouds, comprising the following steps: acquiring images of human-object interaction; expanding the object's 3D point cloud dataset based on Gaussian splash and diffusion models, and adding question-and-answer text information; encoding the object's 3D point cloud to obtain 3D point cloud feature vectors; acquiring text feature vectors; aligning the dimensions of the 3D point cloud feature vectors and text feature vectors; combining them to obtain feature vectors; obtaining the hidden layer of the current feature vector; extracting the hidden layer of the last layer of a set term; mapping the dimensions of the hidden layer back to the dimensions of the 3D point cloud feature vectors to obtain feature vectors used to constrain point cloud decoding; inputting the 3D point cloud feature vectors into a multi-layer point cloud decoder for decoding; converting the feature vectors output by the point cloud decoder into a heatmap of the point cloud to obtain the corresponding action region. This invention can solve the problem of insufficient generalization ability of existing models and improve the adaptability to dynamic environments and tasks.

The application provides a kind of commercial vehicle frame performance prediction multi-modal data fusion representation method and system, applied to vehicle frame data modeling technical field, the method first carries out automatic mining to multi-source heterogeneous engineering data, extracts structure topological parameter, section configuration parameter, material attribute parameter, connection parameter, P-S-N curve data and field response data, and forms target domain data set and independent test set;Then establish the full parameterization finite element model of frame, combined with the virtual load sample generated by statistical derivation of measured load spectrum, extract two-dimensional load damage feature matrix, and construct source domain data set;Finally, the geometric topological feature, parameter feature, connection feature and load feature are mapped to a unified three-dimensional voxel space, generating a three-dimensional multi-channel engineering data tensor.The application can improve the efficiency of commercial vehicle frame sample construction, multi-modal data utilization efficiency and the generalization ability of performance prediction model.

The application discloses a kind of porous alumina microspheres and its preparation method, the method comprises the following steps: 1) configuration preparation porous alumina microspheres precursor sol;2) the sol is carried out spray drying, and porous alumina microspheres precursor is prepared;Porous alumina microspheres precursor is prepared by heat treatment, and porous alumina microspheres are obtained.The porous alumina microspheres provided by the application are prepared by heat treatment of porous alumina microspheres precursor under flow field, and the porous alumina microspheres prepared by the method have the advantages of large scale, low cost, simple operation, short preparation process.

The application discloses a text error correction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a text to be corrected; correcting the text to be corrected based on a trained error correction model to obtain a corrected text; wherein the trained error correction model is obtained by training an initial error correction model using training text; the training text is generated based on a text processed from a first initial text and a text processed from a first modified text; and the first modified text is a text modified from the first initial text. Thus, the trained error correction model can be used to intelligently correct the text to be corrected, thereby improving the accuracy of the text.