332 results about "Neural network analysis" patented technology

The invention provides a network attack and defense decision support method and system based on artificial intelligence, and relates to the technical field of network attack and defense, and the method comprises the steps: obtaining network topology information and equipment configuration information, constructing a network environment digital twinborn model, collecting network flow data and security logs, and extracting features through a deep learning model. Generating a network security situation assessment report, analyzing the attack graph by using a graph neural network, identifying a target node and generating an initial defense strategy set, generating a plurality of candidate defense schemes by using a multi-target optimization algorithm based on a validity assessment result of a defense strategy, and performing security and feasibility analysis on the candidate defense schemes, the method comprises the steps of selecting an optimal defense scheme, refining the optimal defense scheme into specific implementation steps, generating alternative schemes for coping with different attack conditions, presenting the optimal defense scheme and the alternative schemes through a visual interface, and providing a decision basis and a risk assessment report.

The invention provides a space field game behavior dynamic evolution and strategy deduction optimization method and system, and relates to the technical field of space field game behavior optimization, and the method comprises the steps: analyzing historical behavior data through a deep neural network, constructing a dynamic relation graph of participants, inputting a multi-agent reinforcement learning model, and outputting a game behavior model. Then, a multi-layer game scene evolution tree is constructed, the instant income and the long-term risk are calculated in combination with a dual neural network, dynamic updating is performed by using a search algorithm of a hierarchical attention mechanism, and candidate evolution paths are generated; and finally, evaluating the candidate paths by using a strategy evaluation model constructed by a space-time diagram neural network, selecting an optimal strategy path, and generating a strategy deployment scheme implemented by stages in combination with a knowledge inference engine. According to the method, the evolution trend of the game behavior in the space field can be effectively predicted, a feasible strategy deployment scheme is provided, and the scientificity and effectiveness of strategy decision are improved.

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

The invention provides an intelligent high-performance collaborative welding robot system, which belongs to the technical field of welding robots and comprises a multi-degree-of-freedom welding robot body, an intelligent control system, a high-performance welding power supply and auxiliary equipment. A welding process control module firstly obtains three-dimensional information of a workpiece based on visual inspection to construct a welding path; then selecting matched process parameters according to material characteristics and welding seam shapes; establishing a welding error matrix to monitor the welding process in real time; constructing a parameter error adjustment matrix for dynamic adjustment; analyzing parameter fluctuation by adopting a neural network to identify molten pool characteristics; the pre-trained neural network is used for evaluating the welding seam quality; and finally, a multi-robot collaborative welding strategy is integrated to achieve efficient operation, and the technical problem that in the prior art, welding process parameters in a welding robot are difficult to adjust accurately in real time so as to adapt to complex working condition changes is solved.

The invention relates to the technical field of power distribution cabinet temperature control, in particular to an intelligent temperature and humidity control system for a power distribution cabinet. Comprising an environmental parameter acquisition module, a data analysis processing module, a temperature and humidity regulation and control module, an energy consumption recovery optimization module and a monitoring alarm module. The environmental parameter acquisition module monitors environmental data such as temperature, humidity and voltage through a sensor, and the data analysis processing module analyzes a trend based on a time convolutional neural network. The temperature and humidity regulation and control module achieves optimal control through an optimization algorithm, and the energy consumption recovery optimization module recovers and optimizes energy through a genetic algorithm. And the monitoring alarm module adopts a Bayesian network to be combined with a self-adaptive sliding window mechanism to monitor environmental fluctuation in real time, and gives an alarm when the environmental fluctuation is abnormal. Efficient operation, safety and stability of the power distribution cabinet in a complex environment are ensured, the safety and reliability of the power distribution cabinet are improved, the operation and maintenance cost can be effectively reduced, and energy conservation and environmental protection are achieved.

The invention relates to a fault diagnosis method for a server. Efficient fault positioning and processing are achieved by constructing a multi-dimensional intelligent diagnosis system. Constructing a server virtual model at each node and establishing a connection relationship, and collecting various data and mapping the data to the virtual model; performing weighted fusion on the data through correlation analysis, and extracting fault features including time and space correlation by using a bidirectional long-short-term memory network; fault probability distribution is obtained by means of a dynamic fault knowledge graph and graph neural network reasoning, and a fault evaluation result is visualized in combination with a virtual model; and finally analyzing the behavior deviation through a neural network and generating a processing strategy. Real-time processing of multi-source data, intelligent extraction of fault features and dynamic deduction of fault propagation are achieved, the accuracy, the real-time performance and the automation level of server fault diagnosis are effectively improved, and predictive maintenance and intelligent decision making of a complex server system are achieved.

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

The invention provides a substation self-adaptive inspection method based on equipment health degree dynamic evaluation. The substation self-adaptive inspection method based on equipment health degree dynamic evaluation comprises the steps of S1, collecting electrical parameters, mechanical vibration parameters and environmental parameters of substation equipment in real time through a multi-source sensor network, and S2, performing data cleaning and time synchronization on the electrical parameters, the mechanical vibration parameters and the environmental parameters, and performing subset dynamic normalization processing. According to the substation self-adaptive inspection method based on equipment health degree dynamic assessment, electrical, mechanical and environmental parameters of the equipment are acquired in real time through the multi-source sensor network, and the real-time performance and accuracy of equipment health degree assessment are remarkably improved by combining dynamic normalization processing and real-time anomaly detection. And the LSTM neural network is used for analyzing the change trend of the equipment health index, so that the residual life of a key component can be predicted, and differentiated inspection and resource optimization allocation can be realized.

Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

The invention relates to the technical field of cross-border e-commerce storage, in particular to a deep learning cross-border e-commerce storage management system, which comprises a data acquisition processing unit, a space-time diagram neural network analysis unit, an alliance management scheduling decision unit and an equipment execution feedback unit, a data acquisition and processing unit acquires and preprocesses storage data, a space-time diagram neural network analysis unit constructs a space-time diagram and predicts task requirements and equipment states, an alliance management scheduling decision-making unit constructs a dynamic alliance and generates a scheduling instruction, and an equipment execution feedback unit receives the instruction and autonomously adjusts a strategy. The problems of unreasonable equipment scheduling, poor collaboration and resource waste in traditional cross-border e-commerce warehouse management are solved, intelligent scheduling and collaborative operation of the warehouse equipment are realized, and the warehouse management efficiency and the resource utilization rate are improved.

The invention discloses a power distribution network line loss abnormity identification method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting and preprocessing historical data, meteorological data and geographic position information, extracting and fusing features to form a comprehensive feature vector, building a power distribution network topological structure diagram based on the comprehensive feature vector, and obtaining a power distribution network line loss abnormity identification result; analyzing by using a graph neural network to obtain a power grid result; a threshold is set in combination with historical line loss data, a line loss prediction model is constructed to output a prediction result, the prediction result is compared with historical data to calculate an actual line loss value, and a monitoring system is triggered to take corresponding measures once the actual line loss value exceeds the threshold. And the fused feature vector is converted into a comprehensive feature vector by using nonlinear conversion, so that the nonlinear fitting capability of the model is enhanced, and the distinction degree of the features and the generalization capability of the model are improved.

The invention relates to the technical field of power system monitoring, in particular to an electric energy meter and concentrator data fusion monitoring system and method. Comprising the steps that data of an electric energy meter are periodically collected, integrity verification is conducted on the data, noise interference in the data is removed, then the data are fused to construct a power network diagram structure, and the power network diagram structure comprises node information and edge weight information; a graph convolutional network is used for modeling, a power network graph structure is used as the input of a graph neural network model, an adaptive learning rate adjustment algorithm is combined with an early stop method to prevent overfitting and deep training of the model, and space-time association between nodes is captured through multi-layer space-time graph convolution operation. According to the method, the power network graph structure is constructed by fusing the data of the electric energy meter through the concentrator, nodes and edges are analyzed by using the graph neural network, key nodes and potential fault points are identified, and the network health state is evaluated in real time, so that comprehensive monitoring and optimal management of the power system are realized, and the overall performance and reliability of the power system are improved.

System (SYS.MS) and related method for facilitating an assessment operation in relation to a trained machine learning model. The system comprises an input interface (IN) for receiving i) parameters of the trained machine learning model (M), and ii) output data producible by the trained model (M), in response to input data processable by the trained model (M). An analyzer component (AZ) computes, based on the output data, a contribution value per model parameter. The contribution value per parameter is configured to measure, given the input data, a respective contribution to the output data of the respective parameter. The contribution values form a contribution map for the trained model given the input data. The analyzer provides the contribution map for the assessment operation in relation to the trained model (M).

The invention discloses an edge calculation optimization and compression algorithm based on distributed optical fiber sensing data, and the algorithm achieves the full-section continuous monitoring of an embankment through the layering burying of a distributed optical fiber network along the axis of the embankment, and eliminates the blind area of a conventional point sensor. Strain data and temperature data are analyzed by using Brillouin scattering and Raman scattering, time sequence characteristics are analyzed through an LSTM neural network, and piping and crack hidden dangers are identified. External environment parameters such as water level and rainfall are fused to construct a multiple regression model, and the seepage risk level is evaluated. Through combination of BIM three-dimensional modeling and a seepage simulation algorithm, hidden dangers are accurately positioned, and spatial distribution of the hidden dangers is visualized. Based on edge calculation optimization, a data compression technology is adopted to reduce transmission requirements, a self-repairing material injection and high-pressure plugging scheme is triggered through a grading early warning system, closed-loop management from monitoring to repairing is formed, the real-time performance and accuracy of hidden danger recognition are improved, and efficient and reliable monitoring support is provided for dike engineering.

Methods and systems for automated electrocardiogram (ECG) analysis using neural networks, enhancing the accuracy of beat-by-beat cardiac monitoring. The system utilizes a Generative Adversarial Network (GAN) and beat classifiers to analyze ECG data and detect conditions various beast properties of an ECG at a discrete level. Additional neural networks may be trained to detect beat based conditions such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). The GAN generates realistic ECG beats, while classifiers detect abnormalities. Additional transformers may be trained to detect rhythm based conditions such as AFib and Aflutter. Methods and Systems support real-time cardiac health insights and integrates with ECG devices for continuous monitoring, offering a robust solution for improving diagnostic accuracy.

The invention provides a thermal imager intelligent temperature measurement method based on a neural network, and relates to the technical field of thermal imaging temperature measurement, and the method comprises the steps: firstly collecting an infrared image and a visible light image of a target object, and environment temperature and humidity data; then, the infrared image is preprocessed; then calculating an initial temperature value through a temperature compensation formula, correcting energy attenuation by utilizing humidity data, and processing an abnormal heating signal in combination with the visible light image and the humidity data; and finally, related data are analyzed by means of a neural network, and a corrected temperature value is output. According to the method, a temperature compensation formula combining heat exchange and atmospheric radiation difference is constructed, correction factors are adjusted according to humidity and atmospheric pressure, a visible light image is used for identifying the dirt accumulation level, infrared image features are analyzed in combination with a neural network, and multi-source data fusion and environmental factor dynamic compensation are achieved. The interference of environmental factors on the temperature measurement of the thermal imager is effectively overcome, and the temperature measurement precision and the equipment defect detection accuracy in a complex environment are improved.

The invention belongs to the technical field of bridge engineering, and particularly discloses a proxy model and finite element fusion method and system for bridge safety early warning. Comprising the following steps: simulating a real load of a bridge by adopting a structural multi-scale finite element model to calculate a bridge structural response, constructing a finite element agent model, and training the finite element agent model by adopting a data set to extract a relationship between weight matrix analysis and output items of a neural network; and in the refined grid division part, considering the damage directly observed by the structure, dividing the model parameters extracted from the monitoring point by the model parameters directly corrected by the damage to obtain a final corrected model. According to the method, sensor signal processing, model correction and finite element calculation are closely combined together through neural network analysis, a method basis is provided for dynamic evolution of a digital object fusion model, and technical support is provided for subsequent bridge structure safety evaluation and health early warning.

Aspects of the present disclosure relate to detecting churn in video games. Gameplay data for a video game is collected. The collected data is then analyzed with a first trained neural network to identify a pattern associated with one or more patterns associated with a player stopping playing the video game. The one or more patterns are analyzed with a second trained neural network to associate the one or more identified patterns with one or more reasons for the player stopping playing the video game. The one or more reasons are presented to a game developer. A system for detecting player churn in video games and methods for training such a system are also disclosed.

The invention discloses a digital station building equipment state intelligent monitoring system and method. The system comprises a data acquisition module, a multi-dimensional data processing module, an LSTM neural network analysis module and a state evaluation module. The data acquisition module acquires multi-modal time series data through a multi-type sensor group; the multi-dimensional data processing module performs space-time alignment and feature fusion on the data to construct a composite feature vector; the LSTM neural network analysis module adopts an attention gating mechanism to fuse dual-scale features, and outputs equipment state probability distribution and a residual life prediction value; and the state evaluation module updates the posterior probability of the health state of the equipment based on the dynamic Bayesian network so as to realize graded early warning and maintenance decision. According to the invention, the accuracy and predictability of equipment state monitoring are improved, the requirements of the digital station building for accurate monitoring and predictive maintenance of the equipment state are met, and the intelligent level of operation and maintenance management is improved.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention provides a device and method for predicting the risk of Pierre Robin syndrome by using audio, and the device comprises an audio collection module, a signal preprocessing module, a feature extraction module, a deep neural network analysis module and a result output module, and forms a complete processing link from signal collection to risk prediction. A continuous mapping function from a high-dimensional acoustic feature space to a low-dimensional risk assessment space is constructed mathematically, the problem of association between nonlinear time-frequency features and pathological states is solved, full-process automation from audio acquisition to risk prediction is realized, the accuracy and accessibility of PRS early diagnosis are improved, and the PRS early diagnosis efficiency is improved. Precious time is won for clinical intervention, the occurrence rate of PRS-related complications is expected to be remarkably reduced, the life quality of sick children is improved, and meanwhile, a new way is opened up for non-invasive acoustic screening of other diseases.

The invention relates to the field of network security, and particularly discloses a network security defense strategy optimization method based on machine learning, and the method comprises the steps: aggregating isolated security events into attack activity clusters through causal association analysis, constructing a dynamically evolved global attack graph, and improving a defense perspective from a discrete event to a full combat view. In order to realize foresight, a graph neural network is utilized to analyze a graph to identify attack tactics and predict the next intention. A hierarchical reinforcement learning framework is innovatively introduced in the decision-making stage; an upper-layer strategic agent formulates a macroscopic defense target based on a global situation; and the lower-layer tactical agent focuses on the related attack sub-graph under the strategic guidance, and selects and executes the specific tactical action which can reach the target most. The strategy and tactical separated decision-making mode ensures that each defense action serves a long-distance target, so that strategic passivity caused by only taking care of previous threats is avoided.

The invention provides an intelligent pod control method and system for an unmanned aerial vehicle. According to the method, multiple frames of thermal radiation images are obtained through an infrared imaging module of an unmanned aerial vehicle pod, a dynamic temperature field atlas is generated through heat source decoupling, and spatial relevance between heat sources is analyzed through an atlas neural network. Damping torque matched with wind disturbance is generated based on the temperature gradient vector, filtering parameters of the magnetofluid holder are optimized, and meanwhile the interference intensity between energy transmission paths is calibrated. A threat propagation path is generated by combining thermal diffusion characteristics, and the pod deflection angle is dynamically adjusted, so that the center of an optical view field is synchronized with the threat centroid, and accurate tracking and anti-interference control are realized. The method is used for intelligent pod control of the unmanned aerial vehicle. Through intelligent heat source analysis and adaptive adjustment, accurate and stable tracking of a threat target by the unmanned aerial vehicle pod is realized.

The invention relates to the technical field of clinical nursing, in particular to a clinical nursing system for interventional operations, which can accurately identify and predict potential complications by performing deep neural network analysis in combination with preoperative medical history, intraoperative real-time physiological data and operation types. It is ensured that a nursing scheme can be finely adjusted based on individual differences of patients, complex physiological data can be processed through a deep neural network, key physiological data are recognized from the complex physiological data, the scheme is dynamically adjusted in the nursing process through a long-short-term memory network, and the nursing accuracy is improved. The change trend of data in the operation is deeply mined, the fluctuation of physiological indexes is tracked, the abnormity is timely identified, the potential risk is predicted, the nursing decision and the physiological state of the patient can be synchronously updated, the working efficiency of nursing personnel is optimized, and the postoperative recovery effect and comfort of the patient are improved.

Near Infrared Spectroscopy is employed to non-invasively detect blood glucose concentrations, in a multi-sensing detection device. A multi-layered artificial neural network is used to assess these relationships of non-linear interference from human tissue, as well as differences among individuals, and accurately estimate blood glucose levels. Diffuse reflectance spectrum from the palm at six different wavelengths analyzed with a neural network, results in a correlation coefficient as high as 0.9216 when compared to a standard electrochemical glucose analysis test.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention discloses a traditional Chinese medicine plant combination anti-hair loss and hair growth formula optimization method based on a neural network algorithm, and the method comprises the following steps: S1, collecting hair health data of a user, and building a personalized health file; s2, based on the health record, analyzing the physiological and psychological states of the user in real time, and establishing an emotion-hair growth feedback mechanism to evaluate the risk of alopecia; s3, analyzing the health archive by using a deep neural network, predicting an alopecia trend and generating a personalized alopecia prevention and hair growth scheme; s4, matching optimal plant components and proportions through a neural network optimization model, and dynamically adjusting a pharmacodynamic model; s5, adaptively adjusting the plant formula proportion through a quantum neural adaptability learning mechanism; s6, simulating different formula effects through the virtual test platform, optimizing the formula and feeding back to the health file; and S7, forming a closed-loop feedback mechanism through continuous user feedback and physiological monitoring results, and regularly optimizing the formula. Through personalized analysis and dynamic adjustment, an accurate anti-hair loss and hair growth solution is provided.

A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify the part of the vehicle that has sustained damage. Damage data on an extent of the damage in the identified part of the vehicle is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified generic part of the vehicle is used to obtain a corresponding Part ID Code by using a parts dictionary. Part Qualifier information obtained using VIN information is then used in conjunction with the Part ID to obtain the OEM-specific part and then generate one or more repair lines for the repair estimate.