30 results about "Normalization algorithm" patented technology

The invention discloses a short video content accurate recommendation system based on artificial intelligence image recognition, particularly relates to the technical field of short video personalized recommendation, and is used for solving the problem of matching of user interest and visual bearing capacity. The method comprises the following steps: extracting short video key frame spatial-temporal characteristics through a multi-scale convolutional neural network, constructing a cognitive load threshold curve in combination with a user micro gesture sequence, representing the tolerance range of a user to visual complexity in different time periods, and generating a visual complexity vector; calculating a visual load matching index by using an adaptive dynamic warping algorithm, and adjusting a candidate video sequence through a load penalty factor to generate an initial recommendation probability; modeling a user dynamic interest vector based on a gated loop unit network in combination with an attention mechanism; and finally, user interests and video semantics are fused through multi-dimensional features, a final recommendation list is generated through a multi-objective optimization algorithm under the constraint of visual load, and personalized recommendation of interest matching degree maximization and visual comfort optimization is realized.

The invention discloses an electricity consumption information acquisition terminal transaction semantic monitoring and causal delimiting method, which comprises the following steps: acquiring an application protocol data unit by utilizing an eBPF probe, generating a cross-layer unique transaction fingerprint based on extracted sessions, objects, businesses and calling identifiers, and generating a transaction event record by utilizing a pairing state machine; constructing a transaction observation window containing front and back extensions by taking a transaction observation timestamp as a reference, and screening and aggregating kernel events associated with the transaction fingerprint to obtain a transaction evidence set; and constructing a directed evidence graph based on the set, and calculating the confidence coefficient of each root cause category by using a scoring rule and an index normalization algorithm. According to the invention, non-intrusive deep observation of power acquisition business affairs is realized, and the automation level and fault diagnosis precision of power terminal operation and maintenance are improved.

The invention discloses a three-level inverter open-circuit fault diagnosis method based on fault feature vector prediction, and the method comprises the steps: reconstructing a theoretical stator current through reference current based on a motor steady-state model, and introducing an amplitude per-unit algorithm to calculate a current residual error, thereby recognizing the fault, and locking a fault phase; collecting a system state at the moment of fault triggering, deducing a modulation switch sequence in a future detection period on line by using a reference voltage vector time sequence prediction model, and generating a dynamic reference value of theoretical action times of each switch state; performing real-time statistics on the actually-measured cutoff times of the fault phase in different switch states by using an edge triggering mechanism, and constructing an actually-measured feature vector; and determining a fault device by calculating the minimum distance between the actual measurement vector and each fault modal theoretical feature. The method can effectively eliminate the influence of the rotating speed and load change of the motor on the diagnosis threshold, solves the problem that the fault features of the inner tube and the clamping diode are overlapped and are difficult to distinguish, and improves the robustness and accuracy of fault diagnosis.

The application discloses a kind of internal resistance big data driven safe operation method and system, method includes: the historical and real-time internal resistance data of target equipment is collected, and the internal resistance time series data set after normalization is generated by dynamic time normalization algorithm;The internal resistance time series data set is carried out multi-scale feature extraction and high-dimensional space mapping, and the internal resistance abnormal mode cluster implied in data distribution is identified using adaptive density clustering algorithm;Based on internal resistance abnormal mode cluster, multi-modal fault correlation analysis is carried out, and a fault prediction atlas is generated;According to the fault prediction atlas, a set of differentiated operation and maintenance strategies is generated, and the operation and maintenance management platform is driven to execute corresponding operation and maintenance instructions. Using the embodiment of the application, the timeliness and accuracy of fault early warning can be improved, the operation and maintenance cost is reduced, and the safe and stable operation of equipment is guaranteed.

The invention discloses an urban environment dynamic monitoring system and method based on deep learning, and the method comprises the steps: collecting multi-modal image data, eliminating the modal difference through a self-adaptive normalization algorithm, generating multi-modal features, introducing an attention mechanism to carry out the weighted fusion of the multi-modal features, and obtaining the fusion feature data; an FPN multi-scale feature pyramid network is constructed to carry out pollution source identification on fused feature data, a DSAM dynamic space attention module is embedded to adaptively adjust feature channel weights, and pollution source categories and position coordinates are output; the category and position coordinates of the pollution source are predicted based on an MTL multi-task learning framework, a space-time diagram convolutional network is introduced to evaluate the environmental quality, and an environmental pollution thermodynamic diagram is output; and outputting an urban environment governance strategy through a dynamic decision tree model according to the environmental pollution thermodynamic diagram. The positioning error is reduced, and the accurate traceability requirement is met.

This invention provides an automated evaluation system for vibration monitoring signals of aerospace products, comprising: a raw signal parsing module, a data preprocessing module, a signal parameter configuration module, a signal interpretation and evaluation module, and an evaluation conclusion output module. The raw signal parsing module parses unreadable raw data packets into a program-readable data format; the data preprocessing module eliminates signal start-time errors and performs filtering; the signal parameter configuration module configures parameters for single and related signals, forming a parameter configuration library; the signal interpretation and evaluation module evaluates the waveform similarity of single signals based on a cross-correlation normalization algorithm and interprets the time sequence relationship of related signals; the evaluation conclusion output module displays the evaluation results on a software interface. This invention achieves automated evaluation of vibration monitoring signals of aerospace products, replacing manual interpretation, improving evaluation efficiency and reliability, and possessing good versatility and scalability.

The invention discloses a method for constructing an intelligent colorectal cancer prediction model based on mass spectrum serum proteomics, which comprises the following steps of: screening out candidate micropeptides with diagnostic potential by combining high-precision mass spectrum quantification with AI function prediction, and further constructing the model by adopting an ensemble learning algorithm. The method is rigorous in process, and by introducing an advanced normalization algorithm, integrating a feature selection strategy and targeted sample imbalance processing, the biomarker screening robustness and the prediction model accuracy are remarkably improved. Meanwhile, through model interpretive analysis, theoretical support is provided for clinical application of the marker.

The present invention belongs to the technical field of intelligent power data processing, and provides a normalization algorithm-based concentrator temperature compensation method and device, and a storage medium. A concentrator device is placed in a high-temperature or low-temperature environment for a certain time and energized for a certain time, then values of a phase voltage correction register of the concentrator device are read and subjected to normalization processing, and a temperature compensation algorithm is designed to obtain temperature compensation for a phase voltage channel. The present invention is applicable to current channel compensation correction, active phase compensation correction and reactive phase compensation correction. Different from previous solutions using the same temperature compensation parameter, the present invention can perform point-to-point precise compensation on any phase of three-phase voltages, currents, active power and reactive power. After the temperature compensation, errors of alternating-current sampling analog quantities at different temperatures can be controlled to be 0.1% or less. In addition, temperature compensation calculation is no longer required during device operation, thereby saving calculation resources; and there is no need to configure hardware temperature compensation circuits, thus reducing hardware costs.

% Disclosed is a non-probabilistic model-based intelligent reliability assessment method for corrosion damage to a ship hull structure. The method includes: collecting corrosion data of the ship hull structure, and performing interval processing on the data through an embedded data processing system to obtain a mean and deviation of a residual corrosion thickness; determining a resistance of the ship hull structure through hull structure resistance calculation software; measuring external load data such as wind and wave loads, sailing speed, and cargo's center of gravity, and determining external loads based on geometric characteristics of the ship hull structure; establishing a failure function and simulating a failure mode of the ship hull structure in a failure analysis module; and solving a non-probabilistic reliability index of the ship hull structure through an interval variable normalization algorithm, and displaying an assessment result and giving an early warning through a ship safety management system.

Provided herein are compositions, systems, and methods for ranking cancer drugs for treating a subject's cancer cells, where a plurality of gene signatures (each with a plurality of gene signature genes) with associated cancer drugs are processed with raw mRNA expression levels for genes in the sample. The processing (e.g., by computer) can comprise: i) applying a normalization algorithm to generate normalized mRNA expression values for signature genes, ii) applying a median finding algorithm to the normalized mRNA expression values in each of the plurality of drug gene signatures to generate a plurality of median values, and iii) applying a ranking algorithm such that the median values are ranked from highest value to lowest value (or vice versa), with the highest value being associated with the most effective cancer drug, or most effective combination of two cancer drugs.

The application relates to the technical field of cardiovascular disease diagnosis and treatment and artificial intelligence, and discloses a cardiovascular disease diagnosis and treatment scheme optimization system based on architecture, which comprises an original data preprocessing module, which is used for collecting and standardizing time series monitoring data and static data of medical history texts, adopts an improved and normalized algorithm, utilizes model structured text data, and outputs standardized patient feature data; and an improved logic analysis module, which is used for receiving the standardized patient feature data, optimizing the architecture by introducing a sparse attention mechanism, and performing time series correlation analysis, pathological feature mapping and individual difference modeling. The improved logic analysis module is used for introducing the sparse attention mechanism, deeply mining time series correlation and implicit pathological logic coupling relationships in complex time series monitoring data, generating a high-dimensional patient state feature vector, and solving the problem that traditional methods are shallow in analysis and cannot accurately identify individualized pathological states.

The application discloses a breast cancer subtype classification method and system based on a graph convolutional neural network, which converts breast cancer gene expression data into a graphical representation and captures the correlation between genes using a graph convolution module. The data is preprocessed, including removing duplicate samples and samples without subtype labels, and filling in missing values. A graph representation dataset of breast cancer gene expression is constructed, combined with biological prior knowledge. The local features of the nodes in the graph are captured using the graph convolution method, and the data is normalized using the batch normalization algorithm. The self-attention pooling mechanism is introduced to learn the contribution of the input data to the output data, and the key features are extracted and hierarchical pooled. The local features and hierarchical features are spliced into the classification model to obtain the classification result of the breast cancer subtype. The application can effectively capture the correlation between genes and improve the accuracy of breast cancer subtype classification, and has potential biomedical application value.

The present application relates to big data processing and traffic layering control technical field, especially in a kind of global member whole life cycle operation and traffic layering control method and system, the method, including building global data fusion layer, collection multi-source heterogeneous member data and carry out standardization processing, and generate global unique member identification by identity normalization algorithm;Determine life cycle state and calculate residual life cycle value, obtain member value evaluation result;According to member value evaluation result calculation optimal traffic distribution scheme, generate the operation strategy of adaptation different member using optimal traffic distribution result, implement execution to operation strategy, realize data privacy protection based on differential privacy and federated learning algorithm, the present application realizes the dynamic optimal allocation of operating resources under the multiple constraint conditions of budget, frequency and system load, solves the resource waste and mismatch problem caused by artificial configuration fixed proportion.

The application discloses a cable online defect positioning method and equipment, belongs to the technical field of cable defect positioning, and is used for solving the technical problems that the current cable online detection technology has a positioning blind area, signal attenuation causes end positioning difficulty, and positioning effect still needs to be improved. The cable online defect positioning method comprises the following steps: determining an original positioning curve of a test cable according to an incident signal input into the test cable and a reflected signal collected; performing normalization processing on the original positioning curve to obtain a first positioning curve; performing average energy operator optimization on the first positioning curve to obtain a second positioning curve; and determining a defect position of the test cable according to the second positioning curve. The application utilizes the average energy operator to reduce noise interference caused by the normalization algorithm, improve the blind area of the first end signal oscillation, and greatly improve the positioning amplitude of the original positioning curve.

This invention provides a method, system, device, and medium for compliance risk avoidance in generative artificial intelligence. The method includes: acquiring user input text; preprocessing the input text and calculating its compliance potential value, wherein the compliance potential value is positively correlated with the compliance relevance, discourse empowerment, and group influence of each character in the input text; based on the calculated compliance potential value, using a normalization algorithm to calculate an empirical threshold for the input text, and dividing the input text into three levels according to the empirical threshold; constructing a three-level text database; generating a final question-and-answer result by calling the three-level text database according to the level of the empirical threshold corresponding to the input text; and displaying the final question-and-answer result to the user. This addresses how to ensure that the generated content does not have content compliance issues while providing users with more authoritative, comprehensive, and reliable answers when applying generative artificial intelligence in areas involving compliance expression related to content compliance.

The invention provides a method for detecting the content of ergosterol in lentinus edodes based on near infrared spectroscopy and application. And rapidly detecting the content of the ergosterol in the shiitake mushrooms by adopting a near infrared spectrum technology, and inputting spectral data of a sample to be detected into the optimal model to obtain a predicted value of the ergosterol, so that the prediction of the content of the ergosterol in the shiitake mushrooms is realized. According to the method, the SG smoothing algorithm and the normalization algorithm are combined to preprocess the spectrum, noise generated when the sensor obtains the spectrum data can be effectively removed, and baseline and scattering correction is carried out; the variable combined population analysis-genetic algorithm is used for carrying out characteristic wavelength extraction on an original spectrum, so that required important variables can be effectively extracted, redundant information is efficiently removed, and the operation rate of the model is improved; the content of ergosterol in shiitake mushrooms is predicted by adopting the least square support vector machine model based on the crown porcupine optimization algorithm, and the model is high in adaptive capacity and high in prediction accuracy. And a new technical approach is provided for rapidly detecting the content of ergosterol.

The invention discloses a pulse Jaccard attention and twin network-based electrocardiogram classification method, which comprises the following steps of: acquiring an original electrocardiogram signal, performing cascade filtering on the original electrocardiogram signal, mapping the filtered signal to a dynamic range matched with an LIF neuron membrane potential threshold value by utilizing a self-adaptive threshold value normalization algorithm, obtaining a normalized signal, inputting the normalized signal into a pulse encoder, and outputting the normalized signal into a twin network; a pulse sequence is obtained, and the pulse sequence is input into the twinborn double-branch pulse neural network with the shared weight; based on the output of the twinborn double-branch pulse neural network, calculating the total loss by utilizing a joint loss function, updating network parameters through back propagation, obtaining the trained twinborn double-branch pulse neural network, performing anomaly classification on the real-time electrocardiogram signal, and outputting an anomaly result. According to the method, the event-driven characteristic of the spiking neural network, the high discrimination ability of the Jaccard attention mechanism and the small sample learning ability of the twin network are utilized, so that the robustness of the model in a small sample scene is enhanced.

The invention discloses a security operation and maintenance method and system driven by internal resistance big data, and the method comprises the steps: collecting the historical and real-time internal resistance data of a target device, and generating a structured internal resistance time sequence data set through a dynamic time warping algorithm; performing multi-scale feature extraction and high-dimensional space mapping on the internal resistance time sequence data set, and identifying an internal resistance abnormal mode cluster implied in data distribution by adopting an adaptive density clustering algorithm; performing multi-mode fault correlation analysis based on the internal resistance abnormal mode cluster to generate a fault prediction map; and generating a differentiated operation and maintenance strategy set according to the fault prediction map, and driving the operation and maintenance management platform to execute a corresponding operation and maintenance instruction. According to the embodiment of the invention, the timeliness and accuracy of fault early warning can be improved, the operation and maintenance cost is reduced, and safe and stable operation of equipment is guaranteed.

This invention discloses a method for diagnosing hidden damage in marine bearings using a hybrid attention and Inception graph convolution approach, belonging to the field of marine rotating machinery fault diagnosis technology. It aims to solve the problem of low accuracy in existing bearing diagnosis methods for identifying hidden damage. This invention employs a Min-Max normalization algorithm to process vibration signals, converting one-dimensional vibration signals into two-dimensional images through Gram angle difference fields, Markov transfer fields, and continuous wavelet transforms. A Hatt-GCN-Inception model is constructed, embedding a differential attention module within the model to enhance the sensitivity of damage features. The model's end fuses GCN to capture feature topological correlations. A dual optimizer combined with gradient accumulation and FP16 mixed-precision training techniques are used to achieve efficient model training. Pre-training is conducted using the CWRU dataset, and transfer validation is performed on the MFPT variable load dataset containing four types of noise. This invention offers high diagnostic accuracy and fast detection speed, with a single sample detection time of only 0.005 seconds. The accuracy under variable load and noisy conditions still reaches 99.95%, making it suitable for real-time and accurate fault diagnosis in marine bearings.

The invention discloses an action normalization real-time evaluation method and system based on edge calculation and dynamic weight analysis. According to the method, video streams are collected through an edge computing terminal, human skeleton key points are extracted, and dual modes of teaching input and real-time evaluation are supported. In a teaching mode, the system automatically generates a dynamic weight matrix focused on a key action part by analyzing a joint point displacement variance of a standard action; and in a real-time evaluation mode, performing millisecond judgment on the action by utilizing a multi-stage cascade filtering mechanism and a geometric feature normalization algorithm in combination with the dynamic weight matrix. The problems that a traditional visual monitoring scheme depends on a high-computing-power cloud server, the privacy leakage risk is large, and the fixed rule adaptability is poor are solved, high-precision, low-delay and non-contact type action normative monitoring on low-power-consumption equipment is achieved, and the method is suitable for the fields of industrial safety production, rehabilitation training, physical examination and the like.

The embodiment of the application provides a kind of based on optimal transmission's fingerprint minutia matching method and device.The method includes: obtaining the minutia set of first fingerprint and second fingerprint, and constructs the node feature including position, direction and local structure information;Based on node feature, the matching cost function considering embedding similarity, spatial distance, direction consistency and minutia quality is established, and the minutia matching cost matrix is formed;On the basis of the cost matrix, introduce absorption bucket mechanism to process missing or false minutia, construct extended optimal transport model, and adopt iterative normalization algorithm to solve optimal transport matching matrix;Finally, according to the matching matrix, the fingerprint similarity score is calculated and the matching result is output.The application can effectively overcome the comparison problem of incomplete and deformed fingerprints, improve the accuracy and robustness of on-site fingerprint identification.

The invention relates to the technical field of power grid safety, and particularly discloses a power grid risk quantitative evaluation method, device and system based on a network analysis method and a medium. The method comprises the following steps: integrating multi-source power grid risk data, and constructing a risk database; constructing a risk assessment model comprising a network layer (a mutual influence relationship of a plurality of indexes) and a criterion layer (accident probability, frequency and severity) based on ANP; determining a risk index global weight through an expert questionnaire and a 1-9 scale method; analyzing secondary factors of each index, designing a quantification rule, and calculating a total risk value in combination with a global weight; according to meteorological disaster early warning and historical accident data, weight dynamic adjustment is triggered, and model adaptability is optimized through a normalization algorithm. According to the method, the problems of insufficient multi-source data fusion, lack of risk relevance description and weak dynamic early warning capability are solved, the comprehensiveness and accuracy of power grid risk assessment are remarkably improved, and the method can be widely applied to risk management and control of city power companies.

This invention discloses an automatic method for detecting surface defects in castings. The method simultaneously acquires surface images and morphology data of castings at multiple time points through multiple channels, automatically partitions and extracts texture attributes to achieve accurate spatiotemporal indexing. It employs denoising and brightness normalization algorithms to improve the consistency of basic data. Combining a deep learning segmentation model and feature analysis, it progressively completes temporal spatial defect matching, affine mapping, pseudo-label generation, and self-supervised consistency training, and performs fine-grained optimization of the dynamic changes in segmentation boundaries. This improves the accuracy of defect detection and provides high-quality data support for production process improvement and defect tracing.

The application discloses a kind of water turbine guide vane inertia deviation identification method and system, method includes: the relevant parameters of water turbine guide vane under certain working condition are collected, and the time series data under corresponding index parameter is determined;The relevant parameters include: speed regulator guide vane opening, speed regulator opening given, PID given, primary frequency modulation action, AGC instruction, unit frequency;Dynamic neural normalization network is constructed, and time series data is trained, learned by depth artificial neural network, and feature is extracted;While using dynamic time normalization algorithm to align time series data, and learn its similarity;For real-time data, the similarity of time series is calculated using corresponding similarity measure index, so as to identify water turbine guide vane inertia deviation.The application can adjust network structure and parameter to the specific data characteristics, carry out multiple experiments and optimization, train the most suitable dynamic neural normalization network, so that the guide vane inertia deviation identification calculated has higher reliability.

This invention relates to the field of textile quality inspection technology. It discloses a deep learning-based intelligent defect detection method for textiles, comprising the following steps: S1, acquiring textile images containing various defect types and constructing an image dataset, wherein the dataset includes images with complex textured backgrounds; S2, preprocessing the images in the dataset. This invention achieves a significant performance improvement in textile defect detection through end-to-end optimization. The background normalization algorithm and improved network structure efficiently extract multi-scale features. The dual-branch detection head, coupled with a dedicated training strategy, overcomes the challenges of detecting complex background interference, small targets, and multiple types of defects, significantly improving accuracy and recall. After layer fusion and quantization compression, the model is adapted for embedded deployment, balancing real-time performance and accuracy, providing a standardized intelligent quality control solution for the textile industry and helping enterprises improve quality and efficiency.

The invention discloses an intelligent processing method, system and equipment for digital assets of a data center and a medium. The method comprises the following steps: clustering the digital assets according to asset weights by adopting an artificial fish swarm algorithm; the digital assets are mapped into grids, one grid corresponds to one type of digital assets, if the initially segmented grids comprise sub-grids, the initially segmented grids are subjected to in-grid depth division according to the grid use density and the asset health degree by adopting a grid division algorithm, and subdivision categories of the digital assets are obtained; and generating a corresponding two-dimensional code for each type of digital assets, scoring each type of assets in the grid by adopting a normalization algorithm according to two indexes of asset weight and asset health degree, and allocating asset access authority to the two-dimensional code according to the score. Classification and authority management of the digital assets is facilitated, and the asset safety is improved.

This invention discloses a Transformer-based Generative Adversarial Network (GAN) method for the field of Hyperspectral Image Classification (HIC). This method introduces the Transformer into GANs and proposes a Transformer-based GAN with residual upscale (TRUG) for HIC. TRUG includes a generator G and a discriminator D. In G, we propose a residual upscale (RU) module, which can improve the resolution of the generated image. In D, we employ Transformer blocks with progressively decreasing scales and use a grid self-attention mechanism in the first layer to better extract image features. Furthermore, GANs are prone to training instability; to address this issue, we improve the normalization algorithm and add relative position encoding. TRUG is the first Transformer-based GAN applied to HIC.

The application discloses a short video content accurate recommendation system based on artificial intelligence image recognition, and particularly relates to the technical field of short video personalized recommendation, and is used for solving the matching problem of user interest and visual bearing capacity. Through a multi-scale convolutional neural network, the spatiotemporal features of key frames of a short video are extracted, a cognitive load threshold curve is constructed in combination with a user micro-gesture sequence, the tolerance range of a user to visual complexity in different time periods is represented, and a visual complexity vector is generated; an adaptive dynamic normalization algorithm is used to calculate a visual load matching index, and an initial recommendation probability is generated by adjusting the ranking of candidate videos through a load penalty factor; a user dynamic interest vector is modeled based on a gated recurrent unit network in combination with an attention mechanism; finally, multi-dimensional feature fusion is performed on user interest and video semantics, and a final recommendation list is generated through a multi-objective optimization algorithm under the constraint of visual load, so that personalized recommendation with maximum interest matching degree and optimized visual comfort is realized.

The invention provides an oil exploration and development field-oriented large model extraction knowledge graph construction method and apparatus, and an electronic device, and is completed by applying an intelligent batch processing architecture. The method comprises the following steps of: performing semantic segmentation on a document in the field of oil exploration and development, including a knowledge extraction process of named entity recognition, event extraction, relation reasoning and entity standardization, so as to obtain a standardized sub-graph; the entity standardization is realized by adopting a three-layer progressive relation normalization algorithm; all the extracted and standardized sub-graphs are fused and loaded into a graph database, and a knowledge graph oriented to the field of oil exploration and development is formed; in the whole knowledge graph construction process, input and output Token consumption and knowledge extraction reasoning paths are recorded through a log system. The new knowledge graph intelligent construction normal form is advanced in technology, feasible in economy, controllable in process and credible in result.

The invention discloses a test voltage and insulation tolerance level analysis method, and belongs to the technical field of power transmission and distribution of a power system. The method comprises the following steps: collecting multi-source data such as line voltage, current, insulation parameters and environmental meteorology, and carrying out abnormity elimination and standardization processing through a self-adaptive normalization algorithm; based on the processed data, constructing a line voltage distribution model fused with a dynamic weight factor and an insulation tolerance level dynamic model representing a nonlinear aging rule; calculating a safety margin coefficient of each line segment by adopting an interval decision algorithm and carrying out risk grading, and automatically generating a recommended test voltage interval according to the safety margin coefficient; in a field test, model parameters are dynamically corrected by comparing residual errors of a measured value and a model predicted value, and closed-loop optimization is formed. According to the method, dynamic self-adaption of test voltage setting is achieved, the defects of a traditional static method are effectively overcome, and the accuracy of insulation state evaluation, test safety and operation and maintenance efficiency are remarkably improved.