109 results about "Preprocessing algorithm" patented technology

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

The invention relates to the field of intelligent operation and maintenance of offshore wind power construction ship equipment, and discloses a remote monitoring and fault diagnosis and repair system for offshore wind power construction ship equipment, and the system comprises a data collection module which is used for collecting the operation data of the ship equipment through a multi-mode sensor, and outputting the data to a federal learning module; the federated learning module is used for receiving the output data of the data acquisition module, performing tensor decomposition on the multi-modal data to obtain a local factor matrix, uploading the factor matrix to a cloud server for global aggregation, and issuing the aggregated global factor matrix to the fault diagnosis module; and the resource allocation module is used for dynamically receiving the communication load and the edge computing requirement of the federated learning module. Through a multi-mode sensor network and an anti-interference preprocessing algorithm, the influence of severe environments such as offshore high salt mist and strong electromagnetic interference is effectively overcome, and continuous and stable acquisition of equipment operation data is ensured.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

This application provides a multi-sensor information fusion method and device. Preprocessed data is obtained by performing a spatiotemporal preprocessing algorithm based on production rules on sensor layer data. The preprocessed data is then subjected to feature-layer fusion using a neural network multi-classifier to obtain feature-fusion data. The feature-fusion data is then subjected to decision-layer fusion using a support-corrected iterative fusion method to obtain decision-layer data. The decision-layer data is then uploaded for decision-making. This application utilizes a multi-level fusion framework to eliminate redundancy and inconsistencies among massive amounts of heterogeneous, multi-source data, resulting in more reliable decision results.

The invention relates to the technical field of water treatment automatic control, in particular to an intelligent PAC dosing system based on alumen ustum visual identification and multilayer fuzzy rules, comprising: a data acquisition unit for acquiring and preprocessing water quality parameters and alumen ustum images in real time; the algorithm processing unit predicts the initial dosage through SVR, identifies alumen ustum through improved YOLOv8, predicts turbidity in combination with XGBoost, and generates an adjustment coefficient through fuzzy control; the control decision unit combines the initial dosing amount with the adjustment coefficient to generate a final dosing instruction, and monitors the system state at the same time; and the execution display unit is used for dosing according to the instruction and visually displaying the data. According to the invention, through multi-algorithm collaboration and precise regulation and control, PAC dosage dynamic optimization is realized, the coagulation effect is effectively improved, and agent waste is avoided; a visual interface facilitates real-time monitoring and decision analysis, the reliability and usability of the system are enhanced, and an efficient and intelligent dosing control scheme is provided for the field of water treatment.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

A method for analyzing datasets for vulnerabilities to adversarial attacks includes storing, by a processing server, a dataset comprising multiple data types in a database; preprocessing the dataset using data preprocessing algorithms based on the data types; performing data featurization to identify dataset features; generating, for each data type, a victim model based on the dataset; determining performance metrics of the victim models; generating attack datasets corresponding to each data type and including the performance metrics and dataset features; executing adversarial attacks, including membership inference, property inference and reconstruction attacks, on the victim models; determining adversarial attack success metrics, including accuracy, precision and F1 score; updating the attack datasets to include the adversarial attack success metrics; and generating, for each updated attack dataset, a vulnerability metric comprising a library of algorithms and models specific to the combination of adversarial attack and data type.

The invention discloses a radiation source data intelligent distillation method and application thereof, and belongs to the field of communication, unmanned aerial vehicles and artificial intelligence. According to the method, an unmanned aerial vehicle data set is collected and established, a time-frequency domain comprehensive anti-interference feature extraction method based on real and virtual part separation and a CV-ResNet classification model are constructed, a data preprocessing algorithm and the classification model are subjected to module integrated distillation, and after the validity of the algorithm is verified, the distillation model is applied to an unmanned aerial vehicle load. Through integrated distillation, soft target output inverse operation, graph feature operation, EGCN graph feature representation and a feature point edge loss function, algorithm embedded application is realized, the recognition efficiency and the attack countermeasure success rate are improved, the load weight of the unmanned aerial vehicle is reduced, and the endurance and comprehensive efficiency are improved.

The invention provides a multi-layer heterogeneous dynamic data fusion adaptive control system. The multi-layer heterogeneous dynamic data fusion adaptive control system comprises an input module, a data acquisition module, a data type analysis module, an adaptive preprocessing module, a multi-layer heterogeneous data fusion module, an adaptive control module, an expansibility support module and an output module, the data type analysis module divides data types into numerical data, text data and image data; the self-adaptive preprocessing module carries out self-adaptive data preprocessing according to data types, introduces a self-adaptive preprocessing algorithm based on deep learning, and automatically adjusts preprocessing parameters according to real-time features of the data. According to the multi-layer heterogeneous dynamic data fusion adaptive control system, through the innovative data fusion method and adaptive control strategy, the fusion efficiency and accuracy of multi-layer heterogeneous dynamic data are greatly improved, the data value can be more comprehensively and deeply mined, and a more reliable basis is provided for a control decision.

The invention provides a transformer abnormity monitoring method and system based on artificial intelligence, relates to the technical field of transformer state monitoring, and solves the technical problems of poor scene adaptability, multi-source data isolation and weak early warning in the prior art. The method comprises the following steps: acquiring real-time data and historical data of a transformer; preprocessing the real-time data through a preprocessing algorithm to obtain standard data; constructing an AI anomaly monitoring model based on historical data; wherein the AI anomaly monitoring model comprises a dynamic baseline module, an anomaly detection module and an anomaly classification module; and inputting the standard data into the AI anomaly monitoring model to generate an anomaly alarm. The method is used in the transformer abnormity monitoring process.

The invention discloses a stand-oriented adaptive spectrum diagnosis method and system, and belongs to the technical field of hyperspectral remote sensing. The method comprises the following steps: firstly, dividing hyperspectral data into a plurality of classified spectrum subsets by adopting unsupervised clustering; then, aiming at each subset, adaptively optimizing a spectrum preprocessing algorithm, and extracting an optimal characteristic wave band by adopting a hybrid strategy of combining a competitive adaptive reweighting algorithm (CARS) and a continuous projection algorithm (SPA); and finally, a dynamic weighted ensemble learning model is constructed, a meta-learner of the dynamic weighted ensemble learning model not only fuses prediction results of a plurality of heterogeneous base learners, but also introduces spectral features and category identifiers of samples to make decisions, so that dynamic weighting of the base learners is realized, and a final diagnosis result is output. According to the method, the technical problems of fixed feature extraction process and poor robustness of a single model in the prior art are solved, and the precision, robustness and generalization ability of the forest stand spectrum diagnosis model are remarkably improved.

The invention discloses a core drilling method automatic recording and defect intelligent identification method based on deep learning, and relates to the technical field of core sample defect intelligent discrimination. Core sample photos shot on site are acquired, and a deep learning model is used for performing a series of processing; comprising the steps of determining boundary points, extracting angular points, preprocessing photos, extracting return information, identifying defects and the like, and finally generating core sample catalog information and a drilling comprehensive histogram. The method is technically characterized in that a corner point recognition model, a return extraction model, a defect extraction model and a defect classification model are combined with algorithms such as a rotary clamping shell and image preprocessing, and automatic analysis and recognition of a core sample photo are achieved; according to the method, various defect types and degrees can be accurately identified, an efficient, accurate and visual catalog result and visual display are provided for engineering detection, and the detection efficiency and reliability of a core drilling method are remarkably improved.

The invention discloses a cognitive function intervention method and system based on multi-mode space-time dynamic decoding biological feedback, and belongs to the technical field of artificial intelligence algorithms and noninvasive nerve regulation. According to the method, brain nerve activity signals and autonomic nerve cooperation signals are captured in real time, the signals are subjected to standardization processing by adopting a multi-modal signal cascade preprocessing algorithm, and cognitive feature extraction of fusion signals and dynamic distribution of multi-modal signal feature weights are realized in combination with a circulating double-flow self-encoding model; and thus, the individual cognitive state is accurately evaluated. And based on the evaluated cognitive state, dynamically identifying and quantifying into self-perceived visual and auditory stimuli, and constructing an iterative closed-loop feedback cognitive nerve plasticity regulation loop to realize individualized targeted regulation of the cognitive function. The method is suitable for non-invasive individualized intervention of the cognitive function of the Parkinson's disease patient, and has remarkable clinical application potential and social value.

The application discloses a kind of qualitative detection method, equipment and medium of the surface micro linear defect of magnetic tile, it is related to the nondestructive testing technical field of magnetic material.The scattering characteristics of different defects are highlighted by dynamic adjustment of light source azimuth, a multi-directional illumination imaging system is constructed, which includes low-angle linear light source and high-resolution polarization camera.In the middle, in addition to the light source, the camera moves along the axial direction through a stepper motor, and dynamic image acquisition is performed.A preprocessing algorithm based on direction gradient histogram enhancement is designed, and a deep convolutional neural network is used to analyze the multi-scale feature fusion of the defect area.A lightweight classification model based on transfer learning is developed to accurately distinguish micro-cracks and pseudo-defects.The supporting equipment integrates a light source control module, an image acquisition unit, and an embedded processing platform, supporting real-time dynamic detection.The application breaks through the detection limit of sub-millimeter linear defects by traditional machine vision, and the miss rate is reduced to below 0.5%.

The application discloses a kind of coal mine goaf monitoring method and system, it is related to coal mine goaf monitoring technical field, including the collection coal mine goaf subsidence characteristic data;Using sound processing technology, sound characteristic data are preprocessed, and subsidence monitoring data are denoising processed;Through weight analysis module, safety factor is calculated based on preprocessed data, and data is transmitted to terminal monitoring system in real time.The coal mine goaf monitoring method provided in the application uses sound processing technology and preprocessing algorithm, effectively preprocesses the sound characteristic data and other monitoring characteristic data collected, improves the accuracy and reliability of data;Sound processing technology includes noise elimination, sound enhancement, echo suppression and fast fourier transform, can effectively extract sound characteristics, provide strong support for subsequent analysis.The application achieves better results in reliability and analysis accuracy.

The invention relates to the technical field of image processing, and discloses an oral squamous cell carcinoma invasion depth measurement method and system based on artificial intelligence. The method comprises the steps of performing standardization processing on oral tissue slices through a three-dimensional preprocessing algorithm to obtain a tumor mask image and a confidence map, then performing multipoint sampling deployment of a virtual measurement agent on the tumor surface, detecting feasibility of an invasion path by using a tissue resistance model, performing multi-branch feature extraction and deep regression calculation through an OSCC-DepthNet network, and finally obtaining the tumor mask image and the confidence map through the OSCC-DepthNet network. And obtaining an invasion path and a depth value of each agent, fusing multi-agent measurement results by adopting a confidence weighting algorithm, and outputting an oral squamous cell carcinoma invasion depth value and an uncertainty estimation value. The technical problems that an existing oral squamous cell carcinoma invasion depth measurement method is insufficient in three-dimensional space measurement capacity, too high in calculation complexity, poor in real-time performance and lack of an effective uncertainty quantification mechanism are solved.

The invention provides a video stream lag intelligent optimization method and device based on AI prediction. The method focuses on the following core links: a data acquisition and feature engineering method covering multilayer network state monitoring, video content deep analysis, user behavior sequence modeling and a real-time data preprocessing algorithm; the AI prediction engine core algorithm comprises an improved Transform architecture, a cross-modal feature fusion mechanism and a multi-scale prediction model; the intelligent optimization decision-making mechanism relates to a reinforcement learning optimization framework, a self-adaptive buffer strategy and an intelligent code rate switching and predictive preloading method; the system architecture and engineering implementation comprises an end-to-end real-time processing architecture, a lightweight model deployment technology, a multi-platform compatible interface and a real-time feedback and tuning mechanism.

The invention discloses a big data analysis data preprocessing algorithm based on an artificial intelligence neural network, and the algorithm comprises the following steps: S1, cleaning original data, and removing invalid data and abnormal values; s2, feature extraction and dimension reduction processing are carried out on the data through a neural network, low-dimensional representation of the data is obtained, the structure of the neural network is a deep neural network, and unsupervised learning is carried out through an auto-encoder; according to the method, by introducing efficient feature selection and dimension reduction technologies, the training time of high-dimensional data is remarkably shortened, meanwhile, the overfitting problem is effectively restrained, by optimizing a data representation mode, key features of the data can be reserved, the calculation complexity is reduced, and the training efficiency of the model is improved; the complexity of the model is controlled by adopting a new regularization method, and overfitting in high-dimensional data is further avoided, so that the generalization ability of the model is improved.

The invention discloses an intelligent prediction method of a human resource system operation management budget system. Comprising an intelligent data collecting and processing module which is connected with each data source through an API interface, automatically collects employee attendance records and salary information, and performs cleaning and standardization processing on data through a data preprocessing algorithm to ensure the accuracy of the data; the multi-dimensional data analysis and modeling module realizes accurate prediction of human resource management budget by combining a random forest algorithm and a near-end strategy optimization PPO algorithm; the random forest algorithm is used for preliminarily analyzing and extracting data features, and the PPO algorithm is used for further optimizing a prediction strategy, so that the accuracy of model prediction in a dynamic environment is improved; the dynamic budget adjustment mechanism automatically adjusts the budget scheme according to market changes and real-time data, the human resource management efficiency and decision scientificity can be effectively improved, the operation cost is reduced, the data safety is guaranteed, and reliable management tools and technical supports are provided for enterprises.

This invention provides an intelligent analysis system for photoelectric detection spectral data based on deep learning, belonging to the fields of photoelectric detection, spectral data analysis, and deep learning application technology. The system includes core units such as a spectral data acquisition unit, a preprocessing unit, a feature extraction unit, and a deep learning analysis unit, as well as auxiliary units for anomaly detection and data storage, forming a closed-loop architecture of acquisition-processing-analysis-output-optimization. It improves data quality through step-by-step preprocessing algorithms, reduces computational load by adaptively selecting key features, deeply mines spectral patterns using a model that integrates convolutional neural networks and attention mechanisms, achieves rapid scenario adaptation through transfer learning, and dynamically optimizes model parameters based on a feedback mechanism. The system solves the problems of low accuracy, poor adaptability, and long training cycles in traditional spectral analysis, achieving accurate, efficient, and intelligent spectral data analysis, and is suitable for various photoelectric detection scenarios such as qualitative and quantitative analysis of substances and environmental monitoring.

Classification and preprocessing algorithms are applied to at least one reference document and a natural language command provided as input by a user to a development environment for computer programming. Approximate matching of keywords in user commands with data in the reference document is performed with a fuzzy hash map and used to develop information metrics that produce a ranking of candidate actions. Selection criteria are then used to select an action from multiple candidate actions to perform the desired operation in the development environment.

The invention provides a spectral data processing method and device, equipment and a storage medium, and belongs to the field of spectral data processing. According to the method, through a series of technical means of performing clustering processing on spectral data, optimizing preprocessing algorithm parameters, determining an optimal preprocessing algorithm combination, performing production process monitoring by applying an optimal spectral data processing model and the like, the problems that spectral data processing needs to spend a large amount of time and resources; the problems of high cost and low efficiency in the prior art are solved, the resource consumption is reduced, the spectral data processing efficiency is improved, the data processing cost is reduced, and the efficient monitoring of the production process is realized.

The present application belongs to the technical field of image processing, and in particular to an improved 3A image preprocessing algorithm based on DM8127. Through the research on the automatic focusing algorithm, the definition evaluation function is analyzed and compared, the search mode for finding the optimal value is improved, the search times are reduced, and better search effect is achieved. Combined with the influence of the aperture on the depth of field blur and the influence of the gain on the noise, a new automatic exposure algorithm is proposed based on the quality evaluation function, and the best comprehensive image quality balance between the depth of field blur and the noise is achieved. In the automatic white balance part, the image is divided, the gray area is found through the YUV component, and the parameters are adjusted on the DM8127 platform to make the result converge, which achieves better effect than the commonly used automatic white balance algorithm.

The invention relates to the technical field of hydrological modeling, in particular to a topographic data priority queue optimization system and method.In the system, a data grouping storage unit is used for obtaining elevation values corresponding to all grid units in depression processing of a digital elevation model, and the grid units are grouped and stored according to the elevation values through a hash table; and mapping the grid units with the same elevation value to the same hash bucket. According to the method and the device, the Hash table and the second data structure are combined to construct the mixed priority queue structure, so that the nodes are grouped and stored according to the priorities through the Hash table, the orderliness among different priorities is maintained through the second data structure, and node memories are pre-allocated in cooperation with a memory pool pre-allocation strategy. According to the method, the performance bottleneck of a priority queue in a traditional depression preprocessing algorithm is effectively broken through, the depression processing efficiency under a large-scale DEM data set is remarkably improved, and a reliable algorithm optimization scheme is provided for processing mass topographic data.

The invention discloses a drain valve fault online monitoring method and system based on voiceprint analysis, and relates to the technical field of industrial equipment state monitoring, and the method comprises the steps: employing a blind source separation algorithm based on REPET to carry out the preprocessing of a voiceprint signal, and removing the interference of environment noise; and voiceprint features are extracted by using an MFCC method, and fault diagnosis is performed by using a GMM model. According to the invention, real-time and accurate monitoring of the fault of the drain valve is realized, the accuracy rate of voice print abnormity recognition of faults such as inner leakage and blockage of the drain valve is high, the signal processing and recognition time is short, the misjudgment rate is low, the efficiency and reliability of fault diagnosis of the drain valve are effectively improved, and a powerful guarantee is provided for safe and stable operation of a steam turbine. For complex field environments such as noisy thermal power plants, narrow space and the like, the method shows excellent adaptability. The advanced voiceprint preprocessing algorithm can effectively filter environmental noise and accurately focus on the voiceprint features of the drain valve.

The application discloses a kind of pre-processing algorithms for remote sensing image coding.The algorithm is mainly to extract image texture feature parameters, extract image texture feature parameters, according to the texture feature parameter bypasses the prediction mode and the division mode with smaller probability.The algorithm mainly includes image texture feature extraction;Fast rate distortion cost estimation;Intra-frame division prediction;Determine candidate prediction mode and division set.The present application, especially for hardware difficult to realize the H.265 encoding algorithm of remote sensing image.The algorithm saves hardware resources, reduces hardware implementation complexity and difficulty under the premise of trying to guarantee image compression quality, effectively utilizes hardware storage resources, and also reduces the time delay of compression and decoding.

This invention discloses an energy-saving motor speed data acquisition method and system based on cloud computing, belonging to the field of data acquisition technology. The method includes: acquiring factory production plans, personnel shift schedules, and historical motor operation data; generating production load level information based on the factory production plans, personnel shift schedules, and historical motor operation data; encoding the production load level information to obtain rhythm commands; and sending the rhythm commands to a local data acquisition terminal, which adjusts data acquisition parameters according to the rhythm commands and performs motor speed data acquisition processing based on the data acquisition parameters to obtain motor speed data. The data acquisition parameters include operating frequency, data sampling rate, and preprocessing algorithm complexity. This invention can combine production load level information with rhythm commands to adjust data acquisition parameters to achieve motor speed data acquisition, reducing the power consumption and cost of acquisition equipment.

The application discloses a near-infrared quantitative model construction method and device and a storage medium, wherein the method comprises the following steps: acquiring near-infrared spectrum data, and constructing a near-infrared quantitative model training sample set; acquiring a plurality of near-infrared spectrum data preprocessing algorithms, variable selection algorithms and modeling algorithms and the value range of parameters thereof, and constructing a particle swarm search space composed of the parameters and the near-infrared spectrum data preprocessing algorithm sequence; based on the near-infrared quantitative model training sample set, the parameters of each algorithm and the preprocessing algorithm sequence are optimized in the particle swarm search space by using a particle swarm optimization algorithm; and the global optimal solution obtained by selection is used as the parameters of each algorithm and the preprocessing algorithm sequence, near-infrared spectrum data is preprocessed, variable selection and modeling are performed, and a near-infrared quantitative model is obtained. The modeling requirement is reduced, and a high-quality quantitative model can be simply and efficiently obtained on the basis of the data modeling experience of researchers.

The invention discloses a Bayer image parallel preprocessing method and a Bayer image parallel preprocessing system, which belong to the technical field of image processing, and adopt a parallel mode to carry out multiple preprocessing operations on a Bayer image so as to reduce the delay of the whole Bayer image preprocessing part. Image fusion is added in the Bayer preprocessing unit, weight parameters of all preprocessing modules are increased, weight adjustment can be carried out on all Bayer preprocessing algorithms through the communication control unit so as to control the algorithm composition of the image preprocessing unit, image fusion is carried out in an HSV color space, the gray scale and the color can be processed separately, and the image fusion efficiency is improved. The system comprises a Bayer image output platform, an image processing algorithm platform and a display.

The invention provides a medical equipment power distribution monitoring method and system based on artificial intelligence, relates to the technical field of medical equipment power management, and solves the technical problems that the detection precision is insufficient and potential risks are lack of intelligent prediction in the existing medical equipment power distribution monitoring process. The method comprises the following steps: acquiring power supply data and resolving the power supply data to obtain power supply parameters; pre-processing the power supply parameters through a pre-processing algorithm to obtain pre-processed data; performing harmonic analysis on the preprocessed data through an FFT algorithm to obtain harmonic parameters; performing anomaly detection on the preprocessed data and the harmonic parameters based on threshold comparison to obtain a real-time anomaly result; inputting the preprocessed data and the harmonic parameters into a pre-trained risk prediction model to obtain a prediction risk result; and performing weighted fusion decision on the real-time abnormal result and the prediction risk result to generate an abnormal alarm signal. The method is used in the medical equipment power management process.