2654 results about "Multi parameter" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention discloses a high-precision voltage measurement method and system based on multi-parameter adaptive compensation, and relates to the technical field of intelligent voltage measurement. The high-precision voltage measurement method and system based on multi-parameter adaptive compensation comprises the steps that S1, voltage signal data and disturbance source characteristic data are collected, preprocessing is completed, and an analysis basis is constructed; s2, analyzing voltage excursion in combination with disturbance current characteristics, and dynamically adjusting a compensation path; s3, evaluating a compensation correction effect, and driving a compensation output strategy in real time; and S4, comprehensively analyzing the offset characteristics and the correction result, judging an abnormal section and triggering adaptive adjustment. The problems that disturbance components cannot be comprehensively extracted from multiple parameters such as PWM modulation frequency, wire harness impedance and working condition load change and a compensation function cannot be reversely constructed in an existing method, and a measured value does not have stability are solved.

The invention discloses a lithium battery health state prediction method and system based on multi-parameter fusion, and relates to the technical field of lithium batteries, and the method comprises the following steps: collecting multi-dimensional parameter data in a lithium battery operation process, preprocessing the multi-dimensional parameter data, and constructing a time sequence data set; extracting multi-source degradation characteristics from the time sequence data set, and generating degradation influence factors; building a hybrid model based on an LSTM network and a Transform encoder, inputting the degradation impact factors into the hybrid model for model training until the model converges, and obtaining a lithium battery health state prediction model; and performing health detection on a to-be-detected lithium battery based on the lithium battery health state prediction model, and outputting an SOH prediction value. Multi-source battery parameters can be fused to predict the health state of the lithium battery, and the prediction precision and the real-time performance are improved.

The invention provides a comprehensive and intelligent mariculture net cage management and control system. The system integrates a multi-parameter sensor module, an underwater robot module, an underwater camera module, a central control module, a data storage and analysis module, an early warning module, a remote communication module and an automatic execution module. By monitoring multiple environmental parameters such as water temperature, salinity, dissolved oxygen and the like in real time and utilizing the underwater robot and the underwater camera to carry out autonomous inspection and fish behavior observation, the system can accurately master the culture environment condition. The AI algorithm and the intelligent model are combined, the system dynamically adjusts the bait casting amount, the oxygenation frequency and the net cage position, and the breeding benefits are effectively improved. Meanwhile, the early warning module carries out graded early warning on risk events such as typhoon and diseases, and the breeding safety is ensured. The remote communication and visual human-computer interface further enhances the remote monitoring and management capability of the system.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention relates to the technical field of industrial automatic detection, in particular to an intelligent electromagnetic valve fault detection method and system based on multi-parameter analysis, and the method comprises the following steps: S1, synchronously collecting the dynamic current waveform, vibration spectrum information and environment temperature data; s2, extracting a current characteristic value and vibration energy distribution, and calculating a temperature drift amount; s3, the current characteristic value, the vibration energy distribution and the temperature drift amount are input into a multi-parameter incidence matrix, and a coupling deviation coefficient representing the coupling relation between parameters is generated; s4, calculating a comprehensive health index of the electromagnetic valve based on the coupling deviation coefficient; and S5, matching the comprehensive health index with a preset fault feature library, and outputting a corresponding fault type and a positioning result. According to the method, by fusing the multi-source characteristic parameters and introducing the health index evaluation mechanism, accurate recognition and positioning of the electromagnetic valve faults are achieved, and the detection accuracy and the response efficiency are improved.

According to the multi-parameter monitoring method and device for the material stress corrosion test, the flow rate, temperature and ion concentration of corrosive liquid are adjusted in real time through closed-loop control, and the real service environment of a material is simulated; in combination with a constant strain rate and constant load mixed loading mode, loading parameters are dynamically adjusted according to material failure and environment change, and the behavior of deviating from real failure is avoided. Electrochemical, mechanical and corrosion environment data are synchronously collected, a ridge regression algorithm is utilized to fuse multi-modal features, a Granger causal test and mutual information entropy are combined to analyze a corrosion-mechanical-environment interaction effect, and early-stage tiny damage is captured. And calculating the number of residual cycles, generating a three-dimensional probability cloud picture to quantify a failure risk level, and realizing dynamic regulation and control through a grading strategy instead of traditional fixed threshold judgment. According to the method, the real-time performance, the reliability and the automation level of stress corrosion testing are remarkably improved, and a systematic solution is provided for deep exploration of a material failure mechanism in a complex service environment.

The invention discloses a power transmission line icing real-time monitoring method and system based on multi-parameter fusion, and relates to the field of data processing, and the method comprises the steps: receiving conductor physical parameters at the same moment, and simulating an icing track of a target power transmission line section in combination with current environment data; determining a windward side of the icing track based on the current wind direction, and simulating a windward icing layer of the icing track by taking the windward side as a starting surface, taking the current wind speed as a reference wind speed and adopting an icing layer simulation strategy; based on the topographic data of the target power transmission line section, determining a high-risk icing area in the icing track, and determining icing layer data of the high-risk icing area; determining an icing galloping area in the icing track based on the vibration parameter and the strain parameter; and fitting the windward icing layer, the high-risk icing area and the icing galloping area with the icing track, and optimizing the fitted icing track according to the icing layer data to generate a visual interface. According to the invention, the accuracy of icing monitoring of the power transmission line can be effectively improved.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

The invention relates to the technical field of mine mining, and discloses an underground multi-parameter environment monitoring method, which comprises the following steps of: acquiring equipment pose, vibration spectrum and environment parameters in real time through a multi-source sensor, performing space-time alignment, filtering noise reduction and feature extraction on original data, outputting a standardized state vector, and performing data processing on the standardized state vector; fusing the pose data, the vibration signals and the environmental parameters under a unified space-time reference to construct a multi-dimensional feature matrix; and carrying out dimension reduction and redundant information elimination by adopting principal component analysis, and outputting a fused equipment state vector. According to the method, through real-time fusion of the equipment pose and the airflow dynamic state, the ventilation equipment is cooperatively regulated and controlled, the interference of mechanical vibration on the gas monitoring precision is eliminated and the reliability of gas concentration detection is guaranteed based on coupling modeling of vibration spectrum and pose drift and intelligent compensation of sensor reading errors, and through space correlation analysis and dynamic air volume optimization, the gas concentration detection accuracy is improved. Wind speed sudden drop caused by tramcar passing is actively eliminated, and roadway global continuous monitoring is achieved.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention relates to the technical field of motor control, in particular to a variable frequency motor dynamic adaptive control method and system based on multi-parameter analysis. The method comprises the following steps: collecting a multi-dimensional electrical operation monitoring parameter flow of a variable frequency motor, carrying out multi-parameter nonlinear correlation analysis and parameter topological correlation reconstruction, and constructing a multi-dimensional feature topological correlation network; a variable frequency motor multi-working-condition travel history database is obtained, multi-working-condition disturbance transient response analysis is carried out, multi-modal behavior prediction evolution is carried out based on the multi-dimensional feature topological association network, and a dynamic multi-modal behavior prediction engine is constructed; and acquiring multi-part electromagnetic sensing parameters of the motor, and performing abnormal electromagnetic intensity fluctuation detection and abnormal part power loss calculation to obtain abnormal part power loss characteristics. The self-adaptive real-time parameter motor control is realized, the energy efficiency of the motor is improved, the maintenance frequency and cost are reduced, and the full-life-cycle optimal control of the motor is realized.

The invention discloses a big data-driven hydrogen fluoride purification risk entropy assessment and multi-parameter monitoring system, which relates to the technical field of chemical process monitoring, comprises a multi-mode heterogeneous sensor network, and realizes high-precision detection and three-dimensional monitoring by using a nanowire FET (Field Effect Transistor) and the like. The space-time tensor decomposition module extracts features and identifies a causal relationship; the risk entropy manifold learning module evaluates the risk; the self-adaptive twin network monitors the abnormity and simulates and disposes the abnormity; and a knowledge graph-reinforcement learning system decision making subsystem and a plurality of subsystems such as an optical monitoring subsystem and a multi-scale modeling subsystem are also provided, so that whole-process risk management and control are realized. The hydrogen fluoride purification monitoring level is greatly improved, the detection sensitivity and the anomaly detection accuracy are remarkably improved, risk early warning is more timely, decision response is accelerated, the production efficiency is improved, energy consumption is reduced, the system has self-repairing and self-power-supply capabilities, data are safe and traceable, reliable operation of the system is guaranteed, and economic losses and potential safety hazards are reduced.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention relates to the technical field of air conditioner energy-saving optimization, and discloses an air conditioner energy-saving optimization control system and method based on multi-parameter regulation and control, and the method comprises the following steps: obtaining environment data; the environment data comprises building information model data, indoor environment data, electricity price data and meteorological prediction data; based on the environment data, a three-dimensional game model used for air conditioner energy-saving optimization control is constructed; and solving the three-dimensional game model through a multi-target equalization algorithm, and generating and executing a control instruction of the air conditioning system. Multi-parameter regulation and control of the air conditioning system are achieved, energy can be remarkably saved, the comfort degree of the human body is guaranteed, and meanwhile the electricity utilization cost is reasonably controlled.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses a flowmeter-oriented embedded multi-parameter fusion calibration control method and system, relates to the technical field of industrial measurement and embedded control, and is used for solving the problem of unstable calibration caused by misalignment of multi-link flow signals, compensation lag and fusion oscillation. The method relates to synchronous acquisition, pretreatment and calibration of differential pressure type, ultrasonic wave and thermal type mass measurement links and environment parameters such as temperature, pressure and density. All-channel unified time scales are established through ternary binding, filtering and denoising, zero snapshot and alignment rules, a stability scoring mechanism is constructed in combination with offset behavior recognition, event classification and feature aggregation, and dynamic switching of a compensation path and a fusion strategy is driven. In a double-path fusion structure, table look-up compensation, model estimation, multi-algorithm parallelism and fusion track recording are adopted, version traceability and calibration result closed-loop control are achieved, and the stability and rechecking performance of mass flow output under complex disturbance are effectively improved.

The invention discloses an intelligent electric meter fault early warning method and system based on multi-parameter synchronous measurement. The method comprises the step of synchronously collecting operation videos, reading data and environmental parameters of the electricity meter. Firstly, illumination compensation optimization is performed on a video, image features are extracted through a convolutional layer and a full connection layer, a comprehensive visual feature vector is constructed in combination with a cross attention mechanism and environmental parameters, and the overall shape of an electric meter is extracted to detail features in a layered manner; the change trend and fluctuation characteristics are extracted from the reading data by using time sequence analysis, and the influence of environmental factors is considered. Then, fusing multi-source features to generate a feature map reflecting the state of the electric meter; normal and fault state features are compared through similarity calculation, and a fault risk is identified and early warning is carried out; and evaluating a result by using ID matching and a machine learning algorithm, and optimizing fault early warning in combination with historical data. By implementing the method, multi-source information can be fused, the speed and accuracy of fault diagnosis can be remarkably improved, and continuous and stable operation of a power system is ensured.

The invention belongs to the technical field of deep well engineering safety monitoring, and particularly provides a deep well multi-parameter integrated monitoring method, system and device and a medium, and the method mainly comprises the steps: obtaining deep well parameters, and fusing the deep well parameters to generate a space-time coupling tensor; performing three-dimensional decoupling on the space-time coupling tensor to generate a dynamic risk hologram containing an interference gradient; phase conjugate matching is carried out based on the dynamic risk hologram, and an annular closed co-evolution unit is identified; manifold expansion is carried out on the co-evolution unit along the energy path, and entropy ratio weighted space folding coordinates are generated; and based on the space folding coordinates, catastrophe coordinate early warning is triggered through the index relationship between the phase tearing index and the entropy ratio. According to the method, space-time correlation analysis and risk mode three-dimensional presentation of deep well parameters are realized, catastrophe coordinates can be accurately positioned and early warning can be triggered in advance through energy path manifold expansion and entropy ratio weighted calculation, and the timeliness and accuracy of safety monitoring of deep well engineering are improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention relates to the technical field of mine safety early warning, and particularly provides a mine risk early warning method based on multi-parameter fusion and trend prediction, and the method comprises the steps: collecting multi-dimensional safety parameters including mine environment, equipment operation and production working condition parameters in real time; acquiring mine historical data, and generating a parameter association feature set through time sequence analysis in combination with real-time multi-dimensional parameters; inputting the multi-dimensional parameters and the feature set into a trained risk trend prediction model, and outputting a current risk level and an evolution trend; according to the risk level, the evolution trend and the real-time parameters, early warning information and a response strategy are dynamically generated; and the communication link is adapted based on the risk level, and the early warning information and the strategy are pushed to the target terminal. The method breaks through single parameter monitoring limitation, breaks through traditional alarm hysteresis through multi-parameter fusion and trend pre-judgment, constructs a perception-pre-judgment-response-transmission complete closed loop, solves the problems of early warning and disposal disjunction, transmission failure and the like, and improves the active prevention and control capability and management and control accuracy of mine risks.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention provides a washing composition production regulation and control method and system based on multi-parameter intelligent analysis, and relates to the technical field of production control, and the method comprises the steps: collecting process parameters to form a data flow, inputting the data into a cascade self-coding deep learning network to construct a dynamic association map, grouping parameters based on the map, and calculating collaborative change characteristics, a layered progressive intelligent controller is constructed to generate an optimization regulation and control strategy, adaptive parameter adjustment and closed-loop optimization control are realized, the problem that a traditional regulation and control method is difficult to cope with multi-parameter collaborative change is solved, and the production process stability and the product quality consistency are improved.

The invention relates to a multi-parameter fusion motor performance evaluation method, equipment and a medium. The method comprises the following steps: firstly, acquiring electrical signals, mechanical vibration signals and thermal temperature data in operation of a motor, and performing preprocessing and standardization to form a first data set; secondly, dimension reduction is carried out on multi-dimensional parameters of the first data set, and core parameter combinations are extracted to generate a second feature data set; then, classifying and marking the second feature data set by utilizing a classification algorithm, and determining a key performance index distribution interval to obtain a third classification result set; and finally, constructing a time sequence anomaly detection model based on the third classification result set, analyzing an index change trend, identifying an abnormal point deviating from a preset threshold value, and generating a fourth anomaly detection result. According to the method, the data quality and consistency can be improved, the abnormal state in motor operation can be effectively identified, data support is provided for motor performance evaluation and fault early warning, and the method is suitable for real-time monitoring and operation optimization of a motor system.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.