1550 results about "Adaptive algorithm" patented technology

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

The invention relates to the technical field of optical detection, in particular to an automatic optical detection method and system based on gasket surface defects. The method comprises the following steps: carrying out parameter extraction on a to-be-detected gasket, obtaining inner and outer diameters, thickness and tolerance range information of the gasket, and obtaining gasket structure parameter data; matching, calibrating and storing the gasket structure parameter data according to a preset element parameter library to obtain a gasket element parameter library; performing surface scanning on the to-be-detected gasket by using a visual sensor to generate original image data; performing multi-channel optical imaging processing on the original image data, and performing illumination unevenness compensation by using a high dynamic range technology to obtain enhanced surface image data; and carrying out image preprocessing on the enhanced surface image data to obtain gasket surface detection image data. According to the method, the multi-channel optical imaging and advanced image processing technologies are adopted, the self-adaptive algorithm is combined, tiny defects can be accurately recognized, and the false detection rate and the omission ratio are remarkably reduced.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention discloses a large-span bridge body structure health monitoring system based on deformation analysis, and relates to the technical field of large-span bridge body structure health monitoring, the system comprises a multi-modal sensor fusion module, a data processing module, an adaptive algorithm module and a deep learning and predictive maintenance module; and the multi-modal sensor fusion module is used for acquiring data of a plurality of sensors and carrying out cooperative processing. According to the large-span bridge body structure health monitoring system based on deformation analysis, through intelligent fusion of multi-modal sensors, the system can provide more accurate angle measurement under the complex conditions of dynamic loads, environment changes, sensor aging and the like, the measurement precision is improved through sensor redundancy design and a self-adaptive algorithm, and the system is suitable for popularization and application. And meanwhile, the influence of environmental interference and sensor faults on a measurement result is reduced.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention relates to the technical field of municipal pipeline detection, and discloses a sewage pipe network full pipe operation early warning analysis method comprising the following steps: S1, arranging sensors at key nodes of a sewage pipe network, and collecting real-time operation data of the sewage pipe network; s2, acquiring real-time meteorological data including rainfall capacity, rainfall intensity and other related meteorological factors; s3, performing data cleaning and standardization processing on the collected sewage pipe network data and meteorological data; s4, on the basis of multivariable regression analysis, establishing a relation model among the flow, the liquid level and the pipe fullness risk; and S5, performing time sequence prediction by using a long short-term memory network, and predicting future flow and liquid level trend. Time sequence prediction is carried out through the long-short-term memory network, the early warning threshold value is dynamically adjusted in combination with the self-adaptive algorithm, the running state change of the sewage pipe network is responded in real time, the future flow and liquid level trend is accurately predicted, and the early warning accuracy and the response speed to the sudden pipe fullness risk are improved.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention provides a self-adaptive impedance matching method and system of a microwave frequency band. The method comprises the following steps: performing initial impedance measurement on a plurality of microwave component ports to obtain a complex impedance parameter as a matching reference; in the operation process, a current impedance state and a reflection coefficient are collected through a reflection monitoring mechanism, and a dynamic impedance state vector is constructed; inputting the state information into an adaptive algorithm module, generating a matching parameter vector and configuring an adjustable device to realize real-time adjustment of a matching network; calculating a matching error according to the current reflection state and a target reference value, and driving parameters to be iteratively updated; after the matching precision requirement is met, the final parameters are loaded to the controller to complete actual configuration; meanwhile, the system periodically collects environmental parameters and matching effects, and dynamically judges whether rematching is needed or not so as to maintain long-term stable operation of the system. The method has the capabilities of automatic sensing, closed-loop optimization and environmental change adaptation, and can improve the matching efficiency and signal transmission quality of a microwave system under multi-band and high-dynamic working conditions.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention relates to a time sequence knowledge graph multi-hop reasoning method and system oriented to the legal field. The method comprises the following steps: establishing a dynamic mapping relationship through a semantic association technology; extracting life cycle states of the legal provisions, and introducing a legal conflict detection algorithm; setting a revocation influence factor; determining time embedding representation, and introducing clause conflict matrix elements to construct a time sequence knowledge graph; constructing a space-time coupled completion model based on the graph attention network and the long short-term memory network to perform path completion; determining three selected agents, performing reinforcement learning, designing a reward function for each agent, constructing a collaborative arbitration mechanism in combination with a dynamic priority strategy and a weight adaptive algorithm, and performing multi-hop reasoning. By collecting multi-source legal data, the authority, the real-time performance and the relevance of the data are ensured; according to the method, the path reasoning capability in a low-coverage scene can be improved, so that a complex legal knowledge multi-hop path reasoning task can be accurately and efficiently completed.

The invention discloses a battery energy storage safety management system based on a BMS, and relates to the technical field of battery energy storage safety management, the system comprises a data acquisition module, a data analysis processing module, an equalization strategy generation module, an equalization execution module and a safety monitoring early warning module; operation parameters of the battery pack and the single batteries are comprehensively acquired through the data acquisition module, the actual operation state of the battery pack is accurately sensed, health factors are calculated by comprehensively considering multiple factors, weight coefficients are dynamically adjusted, the characteristics of the single batteries are comprehensively considered, and the charging and discharging states of the battery pack are calculated according to the health factors, the charging and discharging stages and the environment temperature. The equalization strategy and current are determined through a self-adaptive algorithm, dynamic response to the running state of the battery pack and environment change is achieved, equalization operation is accurately executed through the equalization execution module, accurate equalization control is achieved, and through real-time monitoring of the safety monitoring and early warning module, safe running of the battery in different environments is guaranteed, the performance of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a long-distance communication optical cable fault automatic detection and positioning system and method, and the method comprises the steps: collecting optical signal data in an optical fiber link in real time through the deployment of a multi-optical fiber sensor or a distributed optical fiber sensing technology; denoising processing is carried out on the collected data, abnormal fluctuation is analyzed by applying a self-adaptive algorithm, and the type of a fault which is about to occur is identified; classifying the faults by using a machine learning classification algorithm; time domain reflection and wavelength multiplexing technologies are combined, a composite algorithm is adopted to accurately position a fault point, and path optimization and error correction are carried out through sensing node data comprehensive analysis; an environment compensation algorithm is introduced, influences of environment factors on the optical signals are analyzed in real time, and errors are eliminated; and based on a fault positioning and analysis result, an AI algorithm is adopted to evaluate a fault influence range, a repair scheme is automatically generated, and priorities are scheduled for repair.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention discloses a temperature control system and method for a hot runner of an injection molding machine mold, and the method comprises the steps that the temperature control system collects the temperature data of each region of the hot runner in real time through a temperature sensor, and carries out the adjustment according to the data feedback; a built-in self-adaptive algorithm automatically adjusts a temperature control strategy according to different injection molding materials and production requirements, such as temperature upper limit, temperature lower limit and heating and cooling opportunities, and the production efficiency is optimized; when the system recognizes temperature fluctuation, a heating mode or a cooling mode is automatically switched, and the cooling system is dynamically adjusted for temperature balance; a plurality of temperature sensors are arranged in a key area of the mold for regional monitoring, and temperature control output is adjusted in real time; meanwhile, an embedded learning algorithm gradually optimizes a temperature control strategy according to historical production data, adjusts control parameters, and improves the production precision and efficiency. According to the temperature control system and the temperature control method for the mold hot runner of the injection molding machine, the problems of low temperature control precision, poor energy efficiency and slow response in the prior art are solved, and efficient and intelligent temperature control capability is provided.

The invention relates to the technical field of monitoring and early warning, and discloses an automatic monitoring and early warning method and system for a pump room. The method comprises the following steps: collecting hydraulic pulsation, cavitation acoustic emission and bearing temperature rise data through multiple sensors; a pump efficiency attenuation vector is obtained based on an environment adaptive algorithm optimization feature; predicting the health state of the equipment by using a hydraulic model; carrying out deep learning classification by adopting a fault propagation network; and four-stage grading early warning is realized by combining water inflow-pump efficiency coupling judgment. According to the method and the device, the technical problem that the gradual change fault and the multi-parameter coupling fault mode of the pump room equipment under the underground complex working condition cannot be accurately identified in the prior art is solved, and the accuracy of fault prediction and the timeliness of early warning of the underground drainage pump room are improved.

The invention relates to the technical field of computer science, in particular to a stamping prediction method based on a multi-machine learning model and an automatic compensation device.The stamping prediction method comprises the steps that firstly, a sheet microstructure and material characteristics are obtained through a transmission electron microscope and an atomic probe tomography technology, and then finite element simulation sampling is conducted; a data set is expanded by using an adaptive algorithm, multiple models are constructed and trained to obtain a final springback prediction model, an automatic compensation device is embedded to realize automatic compensation, and the automatic compensation device covers visual parameter input, embedded springback prediction, springback compensation calculation and an automatic control module. Parameters can be visually input, springback can be accurately predicted, a compensation value can be calculated, and the process can be adjusted. The method aims at solving the problems that in the high-strength plate stamping process, due to springback, the size and shape of a part deviate from the design requirement, and a traditional prediction and compensation technology is insufficient in precision and efficiency.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention relates to an image segmentation method for a burn wound area, and the method comprises the steps: collecting burn wound images under different spectrums through a multispectral imaging device, and enabling the burn wound images to comprise a plurality of wavebands, namely visible light, infrared light and ultraviolet light; for each spectral image, analyzing reflection and absorption characteristics of a burn area under different wavebands by using optical characteristics of biological tissues, and marking a potential burn area; designing a self-adaptive algorithm, and dynamically adjusting parameters, including exposure and light intensity, of imaging equipment based on feedback of different spectral characteristics; the feature contrast of the burn area is enhanced; preliminarily delimiting the boundary of the burn region according to the enhanced spectral image through a threshold segmentation or region growing algorithm; inputting the multispectral image into a hierarchical processing model, and decomposing different tissue levels of the burn area including epidermis and dermis based on different wave band or color information; on each layering result, a feature extraction algorithm including texture analysis and edge detection is used for extracting detail features of each layer.

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The invention provides an electric power and magnetic power dynamic joint detection method and system based on multivariable fusion, and relates to the technical field of power electronic equipment detection. According to the method, voltage, current and magnetic field sensors are arranged in a to-be-tested electromagnetic system, original physical quantity data are collected, and a multivariable time sequence data set is constructed; through preprocessing and feature extraction, a dynamic feature matrix related to electric power and magnetic power is generated; constructing a joint estimation model based on the matrix, and comparing the joint estimation model with a reference value to obtain a detection residual error; generating a state label according to the residual size and trend, and evaluating the estimation credibility in combination with a confidence interval; and a feedback mechanism is further constructed, model parameters and feature structures are optimized by adopting an adaptive algorithm, and online iteration and self-learning are realized. The method is high in detection precision, high in state recognition capability, good in adaptability and robustness, and suitable for energy flow monitoring and intelligent diagnosis of a complex electromagnetic system.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention relates to a transformer substation intelligent site selection and line selection planning method, system, equipment and medium, and relates to the field of transformer substation site selection, and the method comprises the steps: carrying out the information collection of a target site selection region through employing the oblique photography of an unmanned plane, generating a three-dimensional model, determining a plurality of environment sensitive regions and the sensitive weights of the environment sensitive regions based on the three-dimensional model, and meanwhile, load demand data and construction cost data of the region are obtained. Establishing a multi-target evaluation model, optimizing the plurality of obtained candidate site selection schemes to obtain an optimal site selection scheme, performing line selection planning on the optimal site selection scheme based on an adaptive algorithm, and adjusting a line selection strategy in real time to obtain an optimal line selection scheme. The technical problem of low planning precision and efficiency caused by lack of intelligent algorithm support in substation site selection and line selection planning in the prior art is solved, and the technical effect of improving the precision and efficiency of substation engineering site selection and line selection planning through collaborative optimization of the intelligent algorithm is achieved.

The invention provides a method and a system for drawing a regional energy consumption map of an electric vehicle. Wherein the acquired driving data and external meteorological information comprise multiple parameters and temperature gradients in the GB32960 standard. A dynamic grid model is constructed based on administrative division and a road network, track points are mapped into space grid units by using an adaptive algorithm, and the units combine information such as gradient, temperature and traffic flow. A trajectory analysis technology is adopted to segment a driving segment crossing a grid boundary, and feature vectors are generated; by establishing a mapping relationship between the feature vector and the energy consumption contribution degree, the original energy consumption is calculated, and multi-factor correction is performed to generate a dynamic energy consumption reference value. And associating the distribution density of the charging piles with the traffic flow weight by using a space-time coupling diffusion model, and generating a three-dimensional dynamic map through a rendering engine. According to the technical scheme provided by the embodiment of the invention, the accuracy of electric vehicle energy consumption estimation and the scientificity of charging facility layout are improved.

The invention provides a monitoring and early warning method and system for a natural protection area of wild animals. The method comprises the following steps: firstly, collecting a multi-modal ecological monitoring data stream containing animal activity track data, a habitat environment parameter sequence and a biological voiceprint signal set; then, dynamically matching the animal activity trajectory data with a reference threat mode to generate a first ecological association network feature; and extracting migration path fragments, combining with an environment parameter change gradient, and deducing second ecological association network features through the model. And then, performing cross-modal fusion on the two features to generate a multi-level threat coupling vector, constructing a map based on the biological voiceprint, performing overlay analysis on the map and the coupling vector, and calibrating and outputting a real-time threat level pedigree through an adaptive algorithm. And finally, according to the mapping relationship between the real-time threat level pedigree and a preset strategy library, activating a corresponding ecological protection intervention instruction set, realizing multi-modal data comprehensive analysis, accurate real-time monitoring and early warning and intelligent response, and effectively protecting the ecology of the natural reserve of wild animals.

The invention discloses a calibration system in an active channel of a waveguide array phased-array antenna, and relates to the technical field of calibration in the active channel. Comprising a calibration judgment module, a waveguide directional coupling module, an amplitude-phase detection module and a digital domain optimization module, the calibration judgment module realizes intelligent triggering calibration through real-time monitoring of a multi-dimensional environment and performance parameters, and ensures that a calibration process is started only when the system state is deteriorated; the waveguide directional coupling module ensures that the calibration signal is accurately injected into each channel, and realizes cross polarization suppression through a polarization grid filter; the amplitude-phase detection module realizes high-precision measurement of amplitude, phase, group delay and polarization parameters of feedback signals of each channel by using superheterodyne and IQ demodulation technologies; and the digital domain optimization module calculates and updates the complex compensation weight of each channel through real-time digital signal processing and a self-adaptive algorithm, and realizes comprehensive and accurate calibration in combination with temperature-frequency interpolation and nonlinear pre-distortion compensation.

The invention discloses an intelligent monitoring method for a parallel DC power supply system based on an edge gateway, and the method comprises the following steps: S1, collecting and processing real-time data, and generating a health baseline; s2, comparing the real-time data with the health baseline, and automatically identifying an abnormal state; s3, generating a fault fingerprint based on a fault fingerprint generation algorithm, and storing the fault fingerprint in a fingerprint database to form a fault file; s4, generating an intention map by using a situation modeling algorithm, dynamically predicting a load demand based on an intention prediction algorithm, and performing adjustment according to a load priority; s5, generating a dynamic power supply parameter adjustment scheme through an evolution rule engine, and performing real-time optimization by adopting a self-evolution algorithm; s6, a multi-time window adaptive memory management module based on an edge gateway performs hierarchical storage and management on real-time data according to time windows; and S7, analyzing the real-time monitoring data based on a closed-loop feedback control system. According to the invention, the edge gateway and the adaptive algorithm are utilized to realize real-time monitoring and optimization of the parallel DC power supply system.