2773 results about "Real time analysis" patented technology

The invention discloses an experimental calibration method based on multi-sensor signal fusion processing, particularly relates to the field of data analysis, and comprises the steps of a stepped confrontation disturbance protocol, data stream processing and real-time analysis, and multi-stage logic judgment and calibration execution. A stepped confrontation disturbance protocol is created for the first time, potential errors of the sensor are excited in real time and corrected in a closed loop mode under the condition that system operation is not interrupted, and a traditional shutdown calibration mode is thoroughly replaced; a multi-stage disturbance defense system is constructed, and composite interferences such as spectrum aliasing, overload distortion and signal-to-noise ratio attenuation caused by electromechanical thermal multi-physical field strong coupling are effectively suppressed; laser space calibration and dynamic rigidity modulation technologies are fused, and space drift errors caused by deformation of the mounting base and mechanical loosening are accurately diagnosed and compensated. Finally, long-term precision stability and data fusion reliability of the multi-source sensor under complex working conditions are achieved.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention discloses a hydropower station workshop deformation monitoring method, which comprises the following steps of collecting multi-dimensional monitoring data and point cloud data of a hydropower station workshop, performing adaptive filtering and noise reduction on the multi-dimensional monitoring data, performing spatial registration on the multi-dimensional monitoring data and the point cloud data in a unified three-dimensional coordinate system, constructing a multi-channel time sequence deep learning model, and performing multi-channel time sequence deep learning on the multi-channel time sequence deep learning model; a dynamic early warning threshold model is constructed based on material characteristics, equipment operation parameters and historical monitoring data, risk assessment is carried out, a three-level response mechanism is triggered according to a risk assessment result, a sensor reliability evaluation index is established based on deviation analysis of monitoring data and an early warning result, and a data fusion weight is dynamically optimized. Adjusting the decision boundary of the classification model, and carrying out visualization and traceability analysis on the multi-dimensional monitoring data; the invention further discloses a hydropower station plant deformation monitoring system. According to the invention, a multi-dimensional and three-dimensional risk assessment system is established through data monitoring and real-time analysis, and the structural safety of the hydropower house is guaranteed to the maximum extent.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

The invention relates to the technical field of data access security, and discloses a container mirror image security management method and system, and the method comprises the steps: constructing a container mirror image, generating a differential encryption key through a strategy center, carrying out the encryption strategy of a kernel dependence layer, a runtime environment layer, an application code layer and a sensitive configuration layer, and forming a hierarchical protection basis. If an access request is triggered, firstly collecting equipment fingerprints and geofence information and evaluating an environmental risk score, verifying access authority and an access scene matching degree through attribute-based encryption, analyzing operation track characteristics in real time, identifying an abnormal mode, and if the three-layer verification is passed, generating a temporary access token; according to the method, access request authority is verified, a temporary access token is matched, key fragments are synthesized, a master key is only temporarily generated in a memory and encrypted and stored, and through combination of a double-layer encryption channel and inner-layer and outer-layer defense, the anti-attack ability of container mirror image transmission is improved, and man-in-the-middle attack and data tampering are effectively coped with.

The invention discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention discloses a multi-source heterogeneous data fusion method of a sky tower ground well carbon flux observation system, which comprises the following steps of: 1, preprocessing and automatically correcting data to solve the problems of noise, space-time inconsistency, format difference and systematic error in a multi-source heterogeneous data acquisition process; the multi-source heterogeneous data are respectively from satellite remote sensing, unmanned aerial vehicle remote sensing, an eddy covariance flux tower, ground observation and underground observation; step 2, multi-level data fusion: extracting multi-source heterogeneous data from original signals step by step to form unified high-dimensional feature expression, and finally outputting real-time observation data of carbon emission and carbon sink of the coal mining area through a decision model; and step 3, performing comprehensive observation and application output, performing real-time analysis according to the real-time observation data, and outputting a detection result and early warning information. According to the method, an advanced algorithm with adaptive correction and multi-level fusion capability is adopted, and data fusion of five observation modules of the sky tower surface well is realized, so that the key bottleneck in the prior art is broken.

The invention discloses a 5G base station power supply state intelligent monitoring system and method, and belongs to the technical field of intelligent monitoring. According to the system, operation parameters of a 5G base station power supply are acquired in real time through the data acquisition module, and after the operation parameters are processed by the signal conditioning and conversion module, the master control processing unit analyzes digital signals in real time, detects abnormal states and generates alarm information. The communication module is responsible for data transmission and instruction receiving, and the power management and safety protection module provides overvoltage, undervoltage, overcurrent and short circuit protection and triggers emergency measures. The artificial intelligence analysis module performs fault prediction and performance optimization based on historical data and real-time data, and the remote monitoring terminal realizes data visual monitoring and management. According to the invention, the real-time performance and accuracy of monitoring are improved, unified management of multiple base stations is realized, the operation and maintenance cost is reduced, and stable operation of a 5G communication network is guaranteed.

Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of autonomous mobile robots, in particular to a freight robot autonomous navigation obstacle avoidance method based on multi-sensor fusion, which comprises the following steps of: identifying dynamic targets such as pedestrians and vehicles through data fusion of a depth camera and a laser radar, and predicting a future short-term movement track of the dynamic targets by utilizing a filtering algorithm; a risk corridor over time is generated, so that emergency braking or deadlock is avoided, and the operation efficiency and the traffic smoothness are greatly improved; a space-time consistency cross validation mechanism is established by utilizing the characteristics that the laser radar is insensitive to transparent objects and ultrasonic waves are sensitive to the transparent objects, so that false alarm and missing alarm are effectively eliminated, the robot can safely pass through a complex indoor environment, and the application boundary is greatly expanded; a depth camera is used for fitting a ground plane and analyzing point cloud height change in real time, so that obstacles which cannot be expressed by a two-dimensional navigation map can be identified; and an optimal track is generated by minimizing a multi-target cost function, so that the path is ensured to be safe and smooth.

The invention relates to a substation operation risk identification method based on a multi-view video and high-precision positioning. Acquiring video data of a working site through a plurality of cameras with fixed visual angles and mobile video acquisition equipment; a high-precision positioning system is used for obtaining three-dimensional space coordinates of operators and equipment in real time; establishing a three-dimensional digital twinborn model of the substation equipment, and performing dynamic scene reconstruction based on the multi-view video stream to generate a real-time three-dimensional scene of the operation site; fusing the positioning data and the three-dimensional scene by adopting a space-time fusion algorithm to generate a dynamic digital portrait of the operator; and carrying out real-time analysis on behaviors and positions of operators by using a risk assessment algorithm based on a preset risk rule, calculating to obtain a risk assessment value, and setting a feedback mechanism to continuously optimize positioning and scene reconstruction precision. According to the invention, efficient, accurate and real-time identification and early warning of the operation risk of the transformer substation are realized, and the safety management level of an operation site is effectively improved.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention discloses a gradient welding strength control method and system for a welded galvanized steel pipe for fire fighting, particularly relates to the technical field of welding automation control, and aims to solve the problem of abnormal welding seam strength gradient caused by instantaneous unbalance of an electric field and a thermal field during dynamic parameter switching in the prior art. Electromagnetic distortion characteristic quantity is extracted through frequency domain energy analysis, and thermodynamic offset state quantity is deduced in combination with a thermal diffusion trend; on the basis of frequency band correlation mapping of electromagnetic and thermodynamic parameters, thermoelectric cooperative imbalance levels are divided through mutual information entropy evolution, and a self-adaptive harmonic attenuation weight tuning instruction is generated; analyzing a mismatching relation between transient disturbance of an environment magnetic field and arc voltage modulation distortion in real time, dynamically generating a magnetic field compensation factor and reversely superposing the magnetic field compensation factor to a control instruction; and in combination with the matching degree of molten pool oscillation energy and a harmonic frequency spectrum, correcting wire feeding rate calibration, reconstructing a multi-band harmonic energy proportion spectrum, outputting an anti-interference welding control signal, and realizing dynamic balance between arc stability and molten pool heat input.

The invention discloses a construction monitoring system for a municipal asphalt road, relates to the technical field of road construction, and optimizes the road construction process through four modules of whole-process data acquisition, edge analysis and decision, dynamic quality detection and dynamic feedback control. And collecting data of each construction stage in real time. Real-time analysis is carried out through a BP multi-target decision network, the construction process is adjusted, quality detection is carried out on the surface layer and the deep layer of a road through a lightweight detection model, and defect marking and thermodynamic diagram display are carried out through a BIM platform. And on the basis of analysis and detection results, a fuzzy PID algorithm and a near-end strategy optimization algorithm are adopted, construction parameters are adjusted in real time, and construction quality and efficiency optimization are ensured. The problems that an existing sampling detection method is difficult to comprehensively cover a construction area, the quality problems of different levels cannot be visually displayed, and timely processing of constructors is affected are solved, and comprehensive monitoring, real-time analysis and intelligent adjustment of the road construction process can be achieved.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

The invention discloses a substation remote fault early warning system and method based on deep learning. The method comprises the following steps: obtaining a standardized substation operation data set; constructing model data of the improved variational auto-encoder; an optimized improved variational auto-encoder model is obtained; performing multi-round training on the operation data set of the standardized transformer substation by using the optimized improved variational auto-encoder model to form a high-sensitivity and high-accuracy anomaly detection model; acquiring operation data of the transformer substation in real time, inputting the operation data into the anomaly detection model, analyzing the operation data of the transformer substation in real time, and detecting an abnormal mode having significant deviation from the normal operation state of the transformer substation; and when the abnormal detection model detects an abnormal mode, the system automatically triggers remote fault early warning. According to the method, typical modes of temperature rise abnormity, voltage fluctuation abnormity, equipment decline trend abnormity and sudden composite abnormity are further identified according to the deviation degree of the reconstruction error, and the interpretability and guidance of early warning are enhanced.

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

Various aspects disclosed herein are directed to intelligent advantage spin techniques implemented in wager-based games conducted on electronic gaming devices. The intelligent advantage spin techniques may be incorporated into wager-based slot games for offering players various types of re-spin opportunities via the use of Advantage Spin (AVS) credits, loyalty points, or game credits. During gameplay, the system provides real-time analysis of game outcomes, identifying strategic moments such as near-miss scenarios to dynamically present customized advantage spin offers to players. Players can leverage AVS credits to selectively re-spin individual reels and / or symbols. AVS credits may also be employed as promotional incentives, redeemable across different game themes or EGMs, and can persist across gaming sessions, different EGMs, different venues, etc. Integration with systems like TITO and player loyalty accounts ensures seamless credit management, allowing casinos to issue, track, and incentivize credits based on both gaming and non-gaming activities.

The invention provides an underwater ultrasonic Doppler real-time analysis method and device based on a digital transducer, and relates to the technical field of data analys.The method comprises the steps that 1, multiple beams of directional ultrasonic waves are emitted into drilling fluid through a digital transducer array on the outer wall of a marine riser, echo signals reflected by gas-liquid two-phase flow are received, and the digital transducer array is used for receiving the echo signals; performing real-time digital conversion on the echo signal to generate an original digital signal containing phase and amplitude information; 2, Doppler frequency shift feature extraction is conducted on the original digital signals, and the real-time movement speed and direction of bubbles in the drilling fluid are determined by calculating the frequency deviation of reflected ultrasonic waves and transmitted ultrasonic waves. Through the digital transducer array and the ultrasonic Doppler technology, accurate measurement of motion parameters of bubbles in drilling fluid, real-time calculation of local gas content and early warning and grading response of gas cut are achieved, and the safety and the intelligent level of drilling operation are improved.

The invention relates to the technical field of evaporation process monitoring, and discloses an evaporation process monitoring method and system based on data analysis and a medium. The method comprises the following steps: acquiring data of a sensor in an evaporation cavity, and performing filtering standardization treatment to obtain process parameter data; performing time-frequency analysis and correlation calculation on the process parameter data, and extracting key parameter features; setting a dynamic threshold based on the key parameter characteristics, and calculating a deviation degree to obtain an abnormal index; and adjusting the radio frequency power and the substrate temperature in real time according to the abnormal index, and generating a control scheme. According to the method, the complex incidence relation among the multi-source sensing data can be analyzed in real time, the abnormal mode can be recognized in time, potential problems can be predicted before the abnormal condition is obviously expressed, the technological parameters are automatically adjusted through the intelligent control strategy, and therefore the technical defects that in the prior art, response of a monitoring system is lagged, and the technological parameters cannot be adjusted in real time are overcome; and the stability of the evaporation process and the product quality consistency are improved.

The invention discloses a hydraulic engineering intelligent safety monitoring system based on an industrial control system, and belongs to the technical field of industrial control and intelligent monitoring. The system aims at solving the technical problems that existing hydraulic engineering monitoring data are heterogeneous, information is isolated, passive early warning is achieved, and intelligent control is difficult. According to the system, multi-source heterogeneous data are deeply fused, a high-fidelity digital twin model is constructed, an artificial intelligence algorithm is used for real-time analysis, prediction and decision making, and the system is linked with an industrial control system, so that sensing, analysis, decision making and execution closed-loop management of a water conservancy project are realized. The safety margin, the operation efficiency and the intelligent level of the water conservancy project are effectively improved.

The invention relates to the technical field of digital image processing and digital printing control, in particular to a digital printing color calibration method and system based on real-time image processing, and the method comprises the steps: constructing a fabric surface feature point coordinate set through a mixed feature tracking technology; calculating and predicting a target deformation field of the fabric at the printing position by applying a self-adaptive time domain prediction algorithm; and performing dynamic pixel interpolation on the original digital image by combining the target deformation field and the fabric texture feature map analyzed in real time to generate final printing image data. According to the invention, by combining high-robustness tracking, high-precision adaptive prediction and high-fidelity image generation technologies, the geometric precision and visual quality of printed products are significantly improved.

The invention discloses an equipment digital inspection evaluation method and system based on big data, and belongs to the field of artificial intelligence, and the method comprises the steps: S1, collecting multi-source heterogeneous data; s2, unifying timestamps by adopting a time synchronization technology, and realizing accurate alignment of multi-source data; s3, cleaning and structuring data by using a distributed stream processing framework, eliminating invalid redundancy, and completing coding standardization; s4, constructing a graph neural network model based on equipment topology, and extracting a state feature vector; s5, combining with historical fault labels, applying a reinforcement learning training evaluation strategy, and improving prediction accuracy and inspection efficiency; s6, dynamically sorting the inspection tasks according to a strategy, and determining a priority and a path in combination with the risk and the dependency relationship; s7, generating a digital inspection evaluation report fusing real-time analysis and a model result; and S8, performing digital signature and timestamp registration through an intelligent contract uplink evidence storage, and ensuring that a result is verifiable, traceable and non-tampered. The beneficial effects are that inspection efficiency is improved and data safety and reliability are ensured.