317 results about "Data expansion" patented technology

The invention belongs to the technical field of high-performance composite materials, and discloses a carbon fiber reinforced thermoplastic composite material performance database, a construction method and application thereof, and the method comprises the following steps: S1, database structure construction; s2, experimental sample collection and data standardization; s3, feature engineering and variable reduction; s4, training a machine learning model; s5, constructing and verifying an adaptive model; and S6, data expansion and feedback optimization. According to the method, material performance prediction and formula parameter reverse design under target performance are realized through systematic acquisition and normalization processing of three types of data of material components, preparation process and performance characterization and building of a nonlinear mapping model among a material structure, a process and performance through a machine learning method. The database can be used for intelligently recommending a high-performance composite material combination scheme, is suitable for rapid screening and customized development of various thermoplastic composite materials, effectively reduces the research and development cost and development cycle, and improves the material design efficiency.

The invention discloses a teaching implementation method based on combination of motion perception and AI driving, and relates to the technical field of education and teaching. The implementation of the teaching method comprises the steps of multi-modal data expansion and acquisition, cross-dimensional dynamic evaluation and cognitive diagnosis, emotion adaptive feedback and intervention, cross-scene learning map construction and capability migration, and whole-process data closed loop and systematic evolution. And the multi-modal data expansion acquisition comprises the steps of dynamic data acquisition, physiological data acquisition, environmental behavior data acquisition and data preprocessing. The method has the advantages that a multi-modal data acquisition scheme of a common mobile phone camera and a built-in sensor is adopted; the system replaces the traditional dance teaching which depends on special three-dimensional modeling equipment and sensor gloves for boxing teaching, reduces the hardware cost, can realize full scene coverage of families, classrooms, outdoors and the like without professional site deployment, and solves the core problems of high hardware cost and limited scenes in the prior art.

The embodiment of the invention relates to the technical field of petroleum and natural gas development, and provides an oil pipe make-up quality evaluation model construction and evaluation method and an intelligent make-up torquemeter, and the method comprises the steps: obtaining an original oil pipe make-up torque curve data set; carrying out expansion processing on the data set by utilizing a pre-trained data expansion model to obtain an expanded oil pipe make-up torque curve data set; constructing a make-up torque curve classification model based on a convolutional neural network; and training the make-up torque curve classification model by using the expanded oil pipe make-up torque curve data set to obtain a trained make-up torque curve classification model. According to the embodiment of the invention, the accuracy and efficiency of oil pipe screwing-on quality evaluation can be improved.

The invention discloses a multi-unmanned aerial vehicle confrontation task execution method and device based on model reinforcement learning, and a medium, and the method comprises the following steps: inputting the current local observation into a reinforcement learning control network for each airframe in an own unmanned aerial vehicle group, and outputting meta-actions (including movement and attack), performing confrontation interaction with the enemy unmanned aerial vehicle group to obtain finite confrontation experience data; the collected historical interaction experience is used for supervised learning of a world model, so that the world model can approach probability distribution of an interaction rule of a real environment; data expansion is carried out on real interaction data by using a world model, an action network and an evaluation network are trained on expanded empirical data, and strategy optimization and parameter updating of a multi-unmanned aerial vehicle system are realized; repeating the process, and sequentially carrying out experience collection, world model training and enhancement strategy updating; and finally, the trained action network is reserved and is used for outputting a specific action strategy in an actual confrontation task.

The invention discloses a vehicle test boundary scene generation method and system based on a pre-boundary scene. The method comprises the following steps: acquiring a first feature data set; obtaining and discriminating a real boundary scene and a real pre-boundary scene based on the first feature data set and a preset risk discrimination criterion; constructing a second feature data set based on the real pre-boundary scene data and the discriminated real boundary scene data; based on the real pre-boundary scene data, utilizing the generation model to obtain generated pre-boundary scene data; training a prediction model based on the real pre-boundary scene data and the discriminated real boundary scene data; and obtaining test prediction boundary scene data based on the generated pre-boundary scene data and the prediction model. According to the method provided by the invention, the test prediction boundary scene data is acquired based on the real pre-boundary scene data, and data expansion is performed on the real boundary scene data by using the generation model and the prediction model, so that the technical problem of sparse generated test scene data caused by limited scene generation data space in the prior art is solved.

The invention discloses a drug combination patent intelligent identification method and device based on a large language model, and the method comprises the steps: obtaining a standard data set built based on drug combination labeling information of patent data, carrying out the data expansion of the standard data set, and generating an initial training set; a parameter efficient fine tuning technology is adopted, the initial training set is utilized to train the base large language model, and an initial recognition model is generated; screening and identifying difficult samples in the unlabeled patent data by using the initial identification model, and adding the difficult samples into the initial training set to form an enhanced training set; performing iterative training on the initial recognition model by using the enhanced training set and adopting a multi-task learning framework in combination with a comparative learning mechanism to obtain a drug combination patent recognition model; the to-be-recognized patent data is input to the drug combination patent recognition model, and a drug combination information recognition result is obtained.The accuracy, data efficiency and model interpretability of drug combination patent recognition can be remarkably improved, and the computing resource requirement and the manual review cost are reduced.

The invention discloses an expressway bridge construction state real-time monitoring method and system, and relates to the technical field of bridge construction monitoring. An expressway bridge construction state real-time monitoring system comprises a data acquisition and processing module, a data expansion and analysis module, a multi-point data fusion module, a state comprehensive evaluation module and a construction scheme adjustment module. According to the method, data interpolation and expansion are carried out on the preprocessed vibration monitoring data through the Kriging interpolation method, the spatial autocorrelation between the vibration monitoring data can be fully utilized, and the monitoring data are expanded, so that the problem of insufficient coverage of monitoring points can be effectively solved, and the integrity and precision of the monitoring data are improved; the change trend and the distribution rule of vibration in space can be reflected more accurately, a more comprehensive and reliable basis can be provided for evaluation of the highway bridge construction state, and the state evaluation accuracy of the highway bridge construction state real-time monitoring method and system is improved.

The invention provides a double-current comparison and DHI combined equipment fault detection method and system, and belongs to the technical field of power equipment state monitoring and fault diagnosis. The method comprises a time sequence perception adversarial enhancement module, a generative adversarial network (GAN) data expansion module, a double-flow contrast attention network (D-CAN) feature extraction module and a dynamic health index (DHI) calculation module. The core lies in that a fault sample with time sequence correlation is supplemented through a physically constrained GAN, robust features are extracted by using a ResNet1D and Transform fused double-flow network, and a health state is quantified in combination with unsupervised clustering and mahalanobis distance. Through simulation and experimental verification, zero-delay detection of the early fault of the transformer can be realized, the output health index and the 3D visualization result can provide an accurate basis for operation and maintenance of the transformer, and the diagnosis accuracy, the data utilization rate and the dynamic adaptability of state evaluation are remarkably improved.

The invention relates to the technical field of data expansion, in particular to a malicious prompt data set expansion method based on Mongolian. According to the method, high-frequency roots and affixes in a Mongolian basic malicious prompt corpus and a universal corpus are extracted, morphological analysis is combined, targeted malicious prompt samples can be constructed, the diversity and authenticity of a Mongolian safety evaluation data set are enhanced, candidate malicious prompt samples are optimized by adopting a genetic algorithm, and the safety evaluation accuracy of the Mongolian safety evaluation data set is improved. According to the method, the expansion sample is enabled to better conform to syntax and semantic rules of the Mongolian, effectiveness of the attack sample in the Mongolian scene is ensured, the expansion data set is enhanced by using the antagonism generation strategy, robustness and antagonism of the data set are improved, attack behaviors possibly occurring in a real scene can be simulated, and the robustness and the antagonism of the data set are improved. Therefore, an effective sample generation tool is provided for safety evaluation in a Mongolian scene, and the anti-attack capability of the Mongolian model is improved.

This application discloses a design method and grouting material for deep coal-bearing strata, comprising the following steps: S1 Data acquisition based on material pre-tests, correlation analysis, and orthogonal experiments; S2 Data expansion based on orthogonal experimental data to establish a dataset; S3 Creating a prediction model based on the RBF algorithm; S4 Optimizing the prediction model based on the PSO-RBF algorithm; S5 Proportion design based on the RBF-PSO-EWM algorithm; S6 Obtaining the proportion of grouting material for deep coal-bearing strata. This application proposes a PSO-RBF-EWM multi-objective optimization grouting material proportion design method and applies this method to design the comprehensive optimal proportion of grouting material for deep coal-bearing strata. The grouting material with the obtained optimal proportion has excellent characteristics such as low water-cement ratio, green environmental protection, and economic efficiency, and is suitable for the reinforcement characteristics of deep coal-bearing strata.

The invention discloses a Kriging agent model-based engineering structure reliability analysis method, which comprises the following steps of: sampling from random variables and probability distribution of an object to be analyzed, and respectively obtaining a candidate sample set and an initial sample set; obtaining an initial experimental design set based on the initial sample set and the corresponding real response value, and constructing an initial Kriging agent model about the to-be-analyzed object; obtaining an optimal sample from the candidate sample set through a random weight learning function, and expanding the optimal sample and a corresponding real response value to an initial experimental design set; before the initial experimental design set enters the next round of data expansion, deleting an optimal sample in the current round and sample data in a preset redundancy distance range of the optimal sample from the candidate sample set; updating and iterating the initial Kriging agent model about the to-be-analyzed object through the experimental design set; and calculating the reliability index of the to-be-analyzed object according to the failure probability calculated by the current Kriging agent model.

The invention provides a data compression method, apparatus and device, and a storage medium. The method comprises the steps of obtaining an original data stream transmitted through a serial port; determining the data type of the original data flow according to the original data flow; determining a corresponding target compression algorithm according to the data type; compressing the original data stream according to the target compression algorithm to obtain compressed data; the defects of low efficiency and even data expansion during data abrupt change in a fixed compression mode are effectively overcome, and the data transmission efficiency and the bandwidth utilization rate of serial port communication in a resource limited environment are remarkably improved.

The application provides a bearing data expansion method and system based on a Gaussian mixture model and a particle swarm optimization, and belongs to the field of data expansion. In order to solve the problem of how to effectively expand the data set, increase the data diversity, alleviate the data scarcity, and improve the generalization ability and detection performance of the model in the existing small sample bearing defect detection, the application adopts a Gaussian mixture model (GMM) to model the original data, optimizes the parameters of the GMM through a particle swarm optimization (PSO) algorithm, generates new data points, and expands the data set. The method can effectively increase the data diversity, alleviate the data scarcity problem, and improve the generalization ability and detection performance of the model.

The invention relates to a locomotive overhaul data standardization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original data set, carrying out the data cleaning and marking of the original data set, and obtaining a first locomotive overhaul data set; executing data expansion, data clustering and data conversion processing on the first locomotive maintenance data set to obtain a second locomotive maintenance data set; performing structured mode prompt and structured extraction on the second locomotive maintenance data set through PadlePadd deep learning to obtain an extraction result; training through an extraction result to obtain a standardized large language model; and performing data standardization processing on the target locomotive maintenance data through the standardized large language model to obtain a data standardization result. The method has the beneficial effects that standardization, normalization and automation of locomotive overhaul data are realized, and the standardization error rate is reduced.

The steam generator state real-time monitoring method based on artificial intelligence belongs to the field of artificial intelligence and comprises the following steps: S1, data acquisition and labeling; S2, sample generation is performed by using a quantum generative adversarial network based on random projection embedding to realize data expansion; S3, the expanded data is input into a feature extraction model to perform training of the feature extraction model, and a five-layer fully connected neural network is used for feature extraction; S4, the feature-extracted data is input into a feature dimension reduction model to perform training of the feature dimension reduction model, and a self-encoding neural network algorithm based on local preserving projection is used to realize feature dimension reduction; S5, the dimension-reduced data is input into a classifier to perform training of the classifier model; and S6, steam generator state recognition and monitoring are performed.The steam generator state real-time monitoring method based on artificial intelligence can solve the problems of insufficient sample quantity and lack of data diversity and enhances the robustness of the model when the model has noise or fuzzy classification boundary data.

The invention provides a small sample channel state information data expansion method based on a generative adversarial network, and the method comprises the steps: generating a vivid time-frequency domain channel sample through a Wasserstein generative adversarial network based on physical constraints under the condition of rare or missing drive test data; and a time-frequency joint discriminator is combined to effectively distinguish real and forged signals. Statistical authenticity of a generated sample is improved through joint time-frequency domain feature discrimination, a physical constraint module is embedded in a generator, and channel physical characteristics are maintained through forced power normalization and a spectrum matching mechanism. A GAN framework with gradient penalty is adopted in the whole training process, and the training stability is ensured. And data sample generation when the nodes are quickly deployed to a new environment is facilitated.

The invention discloses a software defect prediction method based on cGAN and DGRRC, and the method comprises the steps: extracting static code features and defect labels of a software module, and carrying out the preprocessing, and obtaining an original sample set; inputting defect tags in the original sample set into a conditional generative adversarial network (cGAN) for defect sample synthesis and data expansion to obtain a class balanced expansion set; a dynamic greedy correlation-redundancy feature selection DGRRC method is adopted to screen features in the class-balanced expansion set according to correlation and redundancy, and a data set containing core features is obtained; and inputting the data set containing the core features into a Meta-Ensemble model based on an attention mechanism to carry out fusion and software defect prediction. The method combines the advantages of generative data enhancement, feature selection and ensemble learning, and aims to improve the accuracy and robustness of defect prediction from multiple aspects.

The invention discloses a multi-mode joint information source channel coding system for satellite-ground semantic communication. The multi-mode joint information source channel coding system comprises a joint information source channel coder and a decoder, the encoder is used for sequentially performing feature extraction, feature fusion, data cutting and phase modulation operation on input multi-modal data to generate a transmitting signal, and transmitting the transmitting signal to a ground base station through a channel; and the decoder is used for sequentially performing real and virtual part separation, data expansion, feature extraction and modal data recovery operation on the received signal so as to reconstruct multi-modal data. According to the system, public semantic information among multi-modal data can be mined, the time-varying low-signal-to-noise-ratio channel characteristics of a satellite downlink are combined, the information bottleneck theory highly related to data reconstruction quality is used as guidance to optimize a coding space, complex field transmission signals with the high compression ratio are generated in a self-adaptive mode, and the multi-modal multi-domain transmission signals are obtained. The reconstruction quality of the multi-modal data can be ensured, and the bandwidth and the energy consumption overhead of satellite communication can be remarkably reduced.

The invention belongs to the technical field of large model application, and relates to an enterprise technology demand prediction method and system based on large model driving, and the method comprises four parts: enterprise problem deep analysis, problem technology essence identification, technology knowledge base intelligent retrieval and multi-granularity technology demand prediction. In the deep analysis of enterprise problems, the problem description is subjected to structured processing, an entity relationship graph is constructed, and domain knowledge is aligned. The problem technology essence identification utilizes large model semantic understanding to analyze core challenges, and classifies technical problems through a multi-label classification model. And the technical knowledge base intelligently retrieves and calculates the similarity between the problem semantic vector and the knowledge graph, fuses multi-source data expansion, and screens candidate technologies according to the technology maturity. And performing multi-granularity technology demand prediction analysis on the core technology field and the subdivision direction to form a multi-level demand result. According to the invention, through systematic analysis and intelligent prediction, enterprise technology demands are accurately identified, and the method is suitable for scenes such as industry-university-research cooperation, technology transfer and innovative resource configuration.

The invention discloses a battery life prediction method and system based on an adaptive generative adversarial network. The method comprises the steps of collecting battery use data through a sensor of a battery management system; performing manual labeling on the collected data, wherein labeling categories comprise'normal ', 'abnormal' and'performance reduction '; constructing a data set in combination with the data and the annotations; generating additional training data by adopting a generative adversarial network based on adaptive chaos optimization, and adding the training data into the data set to complete data expansion; reading the data set, and training a battery life prediction model by adopting an extreme learning machine algorithm based on a quantum topological phase to obtain a classification result; and inputting a real-time sample into the battery life prediction model for battery life prediction. According to the method, the accuracy and the reliability of a battery life prediction result can be improved, nonlinear characteristics of battery performance degradation can be more effectively captured, and higher efficiency and accuracy are provided when complex data are processed.

The invention discloses a training method and device for a foundation pit supporting scheme generation model, equipment and a medium, and the method comprises the steps: obtaining a foundation pit CAD graph set and a generation graph corresponding to each CAD graph in the foundation pit CAD graph set, carrying out the preprocessing of each CAD graph in the foundation pit CAD graph set, and obtaining training data, the training data comprises a foundation pit outer contour diagram, a punching contour diagram, foundation pit description information and a generative diagram; performing data expansion processing on the training data to obtain an expanded image corresponding to each training data, the expanded image being a noise image; loading a pre-trained deep learning model, and performing fine tuning processing on the deep learning model based on the extended image to obtain a fine-tuned deep learning model; and performing model training on the fine-tuned deep learning model according to the training data and the extended image, and obtaining a trained foundation pit supporting scheme generation model after the training is completed. And the efficiency and the accuracy of generating the integrated support scheme graph are improved.

The invention discloses an R-tree spatial index design method oriented to a DRAM-NVM hybrid storage architecture, relates to the technical field of computer data storage, and provides a write optimization spatial index HR-Tree under the DRAM-NVM hybrid architecture. The core design thought is that internal nodes are deployed on a DRAM, leaf nodes are placed on an NVM, and a targeted data read-write operation process is designed; in the aspect of an index structure and an operation mechanism, a minimum expansion strategy is adopted around a scene of less reading and more writing, a leaf node data structure and an in-situ updating algorithm on the NVM are designed in a targeted manner, and the write request processing efficiency can be improved on the premise of ensuring data consistency. And the multi-character data expansion and efficient fault recovery are provided, so that the actual requirements of the user are better met by allowing more flexible personalized configuration and shortening the service pause time. The method is suitable for the application fields of computer technology, database technology, data storage and the like.

The invention discloses an inverter arc discharge current detection method based on data enhancement. The method comprises the following steps: acquiring a first current signal of an inverter in a normal working state; an analog circuit is constructed with the same circuit topology as the inverter, the inverter is replaced with a load in the analog circuit, and a second current signal is generated in the analog circuit through an arc discharge generator; superposing the first current signal and the second current signal to obtain an approximate arc discharge current signal; and performing data expansion and noise addition processing on the approximate arc discharge current signal to obtain enhanced negative example data. When a new inverter product is researched and developed, current data of normal work of the product only need to be collected again, then arc discharge current data of the product can be obtained according to the steps (data superposition and noise adding processing), and a large amount of arc discharge current data do not need to be collected again.

The application discloses a battery data expansion and energy management closed-loop method for complex traffic flow, and relates to the technical fields of new energy vehicle battery management and artificial intelligence control. The application is aimed at the problems of battery high dynamic operation data scarcity under complex traffic flow conditions, poor generalization of existing energy management strategies, and open-loop data generation being separated from real vehicle control, and first constructs associated data sets of traffic flow dynamic conditions and corresponding battery operation responses; then constructs a conditional generation adversarial network introducing battery physical constraints, directionally generates battery response data and constructs an expanded data set; based on the expanded data set, a deep reinforcement learning energy management strategy is trained offline, and finally the strategy is deployed to a vehicle-mounted control system, and online closed-loop fine-tuning is performed through real vehicle operation data. The application improves the robustness of the energy management strategy under complex conditions and effectively reduces the energy consumption of the whole vehicle.

Embodiments of the present application relate to a speech recognition apparatus, a method, and a program. A speech recognition apparatus, a method, and a program capable of improving speech recognition performance are provided. A speech recognition apparatus according to an embodiment includes a data expansion unit, a sound score calculation unit, an adjustment unit, a sound score merging unit, a lattice generation unit, and a search unit. The data expansion unit generates a plurality of expanded speech data based on input speech data. The sound score calculation unit generates a plurality of sound scores based on each of the plurality of expanded speech data and a sound model. The adjustment unit generates a plurality of adjusted sound scores by performing resampling on the plurality of sound scores, respectively. The sound score merging unit generates a merged sound score by merging the plurality of adjusted sound scores. The lattice generation unit generates a merged lattice based on the merged sound score, a pronunciation dictionary, and a language model. The search unit searches for a speech recognition result having the highest likelihood from the merged lattice.

The invention discloses a heat treatment process parameter deviation intelligent correction method and system and a storage medium, and belongs to the technical field of production optimizing.The method comprises the steps that data expansion is conducted on original production data, a first prediction model is trained locally based on the expansion data, and a second prediction model is obtained, calculating the deviation of the network parameters at the same position of the different second prediction model of each factory, determining an adjustment value based on the deviation, correcting the network parameters based on the adjustment value, defining the corrected second prediction model as a third prediction model, and inputting the real-time production data into the third prediction model. A dynamic adjustment mechanism in the third prediction model adjusts the number of network layers according to the product type, and outputs a process adjustment amount and a corresponding confidence coefficient; and when the confidence coefficient is smaller than a second threshold value, the historical case library is called to correct the process adjustment amount, and real-time dynamic adjustment of the heat treatment process parameters is achieved through a cooperative correction mechanism of the local and cloud models.

The application discloses a powder pressing process parameter optimization method based on digital twinning, which comprises the following steps: constructing a powder pressing process digital twinning model construction and twin data generation module, which constructs a powder pressing process digital twinning model based on filling parameters, material parameters and process parameters, and generates twin data; constructing a quality parameter and energy consumption parameter prediction model construction module, which realizes twin data expansion based on the twin data expansion network, and trains the quality parameter and energy consumption parameter prediction model based on the expanded data; and constructing a process parameter optimization module, which optimizes the process parameters through a particle swarm optimization algorithm based on the quality parameter and energy consumption parameter prediction model. The application can solve the problem that the process parameters are difficult to be accurately optimized under the condition that the powder pressing process data is insufficient, effectively improves the quality of the finished powder column while reducing the energy consumption of the pressing process.

The invention belongs to the technical field of road, bridge and tunnel infrastructure digital design, and particularly relates to a road traffic engineering integrated forward design method. A framework which takes two-dimensional design or three-dimensional design as a starting point for driving and takes a design data set as a transfer is adopted, professional logic is adopted as a functional kernel, dependence of the method on a specific two-dimensional and three-dimensional software platform is eliminated, and two-dimensional and three-dimensional data same-root and same-source integration suitable for linear engineering is achieved. The direct connection between the two-dimensional drawing and the three-dimensional model is relieved in a data + business rule mode; road center line plane design, longitudinal section design, cross section design, structural body characteristic section design, three-dimensional road center lines and three-dimensional structural bodies are comprehensively applied through a design data set, and two-dimensional and three-dimensional integrated forward linkage design is driven. The method is suitable for road traffic engineering two-dimensional and three-dimensional scene integrated design expression output, supports two-dimensional and three-dimensional integrated linkage updating, and has the advantages of being simple and clear in operation, efficient in data expansion and transmission and the like.

The invention discloses a building design planning scheme generation method and device based on artificial intelligence, and a medium. The method comprises the following steps: step 1, obtaining building design historical data from a database; step 2, carrying out data processing on the building design historical data; 3, a second data set is obtained after data expansion; step 4, generating a plurality of expanded first design planning drawing data by using the trained generative adversarial network GAN model; merging the expanded first design planning drawing data to the second data set to obtain a third data set; 5, constructing an architectural design planning drawing generation model; 6, training an architectural design planning drawing generation model by using the third data set; and step 7, generating a plurality of design planning drawing data. According to the method, the problem of insufficient building design training samples is solved, and the building design planning drawing generation model is constructed to ensure the functional layout reasonability of the generated drawing.

This invention relates to the technical field of combining artificial intelligence with traditional Chinese medicine (TCM), and discloses an AI-based intelligent image analysis system for TCM tongue diagnosis. The system includes: an image acquisition and preprocessing module, which receives raw tongue image data from a tongue image acquisition device via a hardware interface and performs multimodal correction processing on the raw tongue image data to generate standardized images; and an environment adaptation module, which is connected to the image acquisition and preprocessing module via a data interface. This invention reduces image errors caused by the shooting environment and equipment through multimodal correction in the image acquisition and preprocessing module, and combines this with a data expansion and enhancement module to generate diverse case images to improve the dataset. This effectively solves the problems of poor image quality and insufficient case coverage, reduces reliance on doctors' experience and subjective judgment, reduces differences in judgment among different diagnostic subjects regarding the same tongue image, and improves the accuracy and stability of diagnostic results.