54629results about "Complex mathematical operations" 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 discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention relates to the technical field of digital twinning, in particular to a pile foundation construction whole process digital twinning system which comprises a twinning configuration module, a dynamic mapping module, a coupling analysis module and a twinning synchronization module. According to the method, a time axis driving model sequence is constructed based on space-time interpolation, burial depth coordinates, pouring time and segment numbers are dynamically bound, three-dimensional evolution mapping of a pile body model is achieved, axis coordinate deviation is combined with perpendicularity threshold extraction, virtual model coordinates are corrected in real time through point cloud registration, and a pump pressure value and settlement are fused to generate a pile sinking rate index. A bidirectional coupling relation between parameters and stratum response is established in combination with stratum counter-force and injection rate, the thickness of the model is adjusted according to a settlement rate difference value, the similarity of the injection rate and a counter-force curve is matched, a model parameter reverse updating mechanism is formed, and real-time intervention of construction abnormity, dynamic optimization of parameter coupling and model iteration correction are achieved. And the process controllability and the quality tracing precision are improved.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention relates to the technical field of compression molding of carbon fiber prepregs, in particular to a partitioned pressurization control method and system for compression molding of the carbon fiber prepregs and a molding process. S2, initial pressure calculation; s3, gradient pressurization control; s4, dynamic pressure adjustment; and S5, a global pressure maintaining stage: when the resin curing degree alpha reaches 0.6-0.8, switching to a full-screen pressure maintaining mode, and maintaining the pressure of each region to be 90%-95% of the current maximum pressure value until curing is completed. The invention provides a zoning pressurization control method and system for compression molding of carbon fiber prepreg and a molding process, and realizes the compression molding method and system with accurate zoning pressure regulation and control and directional discharge of volatile components. And by integrating intelligent sensing, multi-channel hydraulic control and a data driving optimization algorithm, the limitation of a traditional process is broken through, and a technical support is provided for efficient forming of the high-performance carbon fiber composite material.

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention provides a flood peak evolution path and dam break risk early warning method and system. According to the method, a time-space coupling water conservancy data set is constructed by fusing multi-dimensional water conservancy monitoring data and regional rainfall prediction information, and a basin topology perception model is established. And further performing mode matching on the parameters and a historical dam break event characteristic spectrum, and generating a dynamic response strategy set including a flood storage and detention area capacity allocation scheme by correcting a space weight of a matching result. And finally, based on a watershed topology perception model, matching the strategy set with historical dam break features, realizing accurate identification of a flood peak evolution path and graded early warning of dam break risks, outputting a water conservancy analysis report containing risk grades, and realizing closed-loop management from data fusion and dynamic prediction to risk decision. According to the technical scheme provided by the invention, the efficiency and accuracy of intelligent analysis of the water conservancy data can be improved.

An available transfer capability evaluation method and apparatus for a multi-region power system, belonging to the technical field of new energy grid connection. The method comprises the steps: in view of multi-dimensional uncertainty of new energy output and a load demand, on the basis of a conditional generative adversarial network method, determining a typical daily source-load scenario set; constructing an initial operation point set on the basis of the typical daily source-load scenario set, and determining a limit operation point of a multi-region power system; on the basis of the initial operation point set and the limit operation point, constructing an ATC evaluation model on the basis of safety indexes of multi-region power grid operation; and, on the basis of the ATC evaluation model and the typical daily source-load scenario set, determining available transfer capability probability distribution of the multi-region power system.

The invention discloses a matrix calculation adaptive optimization method and system based on an ARM architecture. The method comprises the following steps: preprocessing to-be-processed matrix data; performing local activeness calculation and hot spot region identification on the preprocessed matrix data, and determining long-tail distribution characteristics of the matrix; calculating the optimal block size range of the matrix based on the long tail distribution characteristics of the matrix and the multi-level cache capacity parameters in the processor information, and generating an asymmetric block scheme; based on an asymmetric partitioning scheme, establishing a mapping relation between matrix features and optimal partitioning parameters; calculating the calculation density and the memory access mode of each block based on the asymmetric block scheme and the mapping relation, and generating a task scheduling scheme; based on the task scheduling scheme, matrix calculation is executed on the processor, and a final calculation result is output. According to the method, self-adaptive blocking and heterogeneous core scheduling are realized by identifying the long tail distribution characteristics of the matrix, and the performance and energy efficiency of matrix calculation on ARM are improved.

The invention, which relates to the technical field of disaster monitoring and early warning, discloses a landslide and debris flow disaster monitoring and early warning system comprising a multi-source data acquisition module, an intelligent processing module, an analysis decision module and an early warning response module. The space-time reference unified framework is constructed, the high-precision time synchronization and space projection conversion technology is adopted, the space-time matching problem of the satellite-ground heterogeneous data in the prior art is solved, the multi-source parameter fusion proportion is adaptively adjusted according to indexes such as signal quality and environment interference degree through a dynamic weight distribution mechanism based on data reliability, and the space-time matching precision of the satellite-ground heterogeneous data is improved. In addition, a multi-model collaborative decision-making framework is established, the advantages of three models of mechanism driving, probability statistics and intelligent learning are fused, a comprehensive research and judgment result is generated through a confidence quantitative evaluation and conflict resolution algorithm, an early warning response mechanism is matched, a pre-arranged plan library is automatically matched according to a risk level, and an external control system is linked. And a closed-loop management chain of monitoring early warning, analysis decision and emergency disposal is formed.

The invention discloses a multi-sensing and data physical fusion frozen soil hot water force response test system and method, and the system integrates an ultra-weak fiber grating, an active heating fiber, a miniature dielectric constant sensor and an optical frequency domain reflection technology, and constructs a freezing process-oriented temperature, moisture, ice content and strain synchronous monitoring network; the method comprises the steps of constructing a multi-field fusion data set based on thermal disturbance lag correction, temperature-strain decoupling and a moisture-strain residual term compensation mechanism, and extracting key criterion characteristics of an ice lens growth rate, frost heaving force evolution and a shear deformation change rate; and in combination with a physical information neural network (PINN) embedded into a hot water force control equation, risk identification and grade judgment of the freezing abnormal behavior are realized. The method supports indoor heating and water pressure linkage adjustment, realizes a complete closed loop of multi-source sensing-risk identification-response verification under model driving, and breaks through the bottlenecks of low multi-physics field coupling identification precision, large parameter cross interference and insufficient risk identification real-time performance of the existing monitoring technology.

The invention provides a digital twin construction method for metal stamping forming and a computer system, and the method comprises the steps: obtaining a multi-source monitoring data set in a metal stamping forming process, carrying out the collaborative feature extraction processing, and generating a material structure feature set and a process dynamic feature set; a mixed feature space is constructed based on the material structure feature set and the process dynamic feature set, multi-dimensional feature matching processing is executed in the mixed feature space, a feature mapping relation network is generated, a dynamic twin model is constructed according to the feature mapping relation network, and real-time state deduction is conducted on the stamping forming process based on the dynamic twin model. And a forming quality prediction result and a process defect positioning result are generated, and a stamping process parameter adjustment scheme is generated and fed back to a stamping control system to trigger parameter optimization operation. According to the method, the early warning capability of process defects and the precision of process parameter adjustment under complex working conditions are improved, and meanwhile, the real-time performance and engineering feasibility of digital twin construction in the large-scale stamping forming process are guaranteed.

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

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The invention discloses a coastal protection dam settlement monitoring method, and belongs to the technical field of hydraulic engineering safety monitoring. The method comprises the steps that a longitudinal monitoring section is arranged on the slope surface of the back sea side of a dam, a three-measuring-line fiber grating sensor array is arranged, and vertical displacement and horizontal dip angle data are periodically collected through a synchronous triggering unit; establishing a vertical displacement-horizontal dip angle joint analysis model by using a multi-source data fusion module, and eliminating tide level interference through a Kalman filtering algorithm to generate a settlement distribution curve; and triggering third-level to first-level early warning signals based on the grading early warning rule, and transmitting the signals to the terminal equipment. The problems that a traditional monitoring method cannot effectively separate tidal interference, the real-time performance is poor, and multi-dimensional data collaborative analysis is insufficient are solved, high-precision settlement monitoring, complex environment anti-interference and rapid emergency response are achieved through multi-measuring-line sensor deployment, dynamic filtering optimization and a graded early warning mechanism, and the method is suitable for large-scale popularization and application. And the reliability and timeliness of dam safety monitoring are obviously improved.

The present invention relates to the technical field of heat sink structure design, and in particular to a micro-channel heat sink structure design method based on a multi-objective function. The method comprises the following specific steps: S1, constructing a general mathematical description of a topology optimization problem; S2, acquiring input conditions for a specific design scenario, and dividing a topology optimization design domain; S3, establishing a governing equation for a topology optimization model; S4, on the basis of an adjoint method, performing sensitivity analysis of a multi-objective function with respect to design variables; S5, solving the topology optimization model by means of a method of moving asymptotes (MMA); S6, obtaining a two-dimensional flow channel configuration of a heat sink on the basis of contour reconstruction, carrying out finite element analysis of a two-dimensional flow channel structure, and narrowing down the selection range of constraint conditions; and S7, on the basis of a two-dimensional finite element analysis result, constructing a corresponding pseudo-three-dimensional model, carrying out finite element analysis, and determining optimal constraint conditions to obtain a final topology-optimized heat sink configuration. Compared with traditional methods, the present invention can more efficiently solve the problem in respect of heat sink structure optimization under different scenarios.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention relates to the technical field of building design and digitization, and discloses an automatic design method for a special-shaped facing structure based on parametric modeling. The method comprises the following steps: firstly, acquiring a geometrical characteristic parameter set of the special-shaped facing structure, wherein the geometrical characteristic parameter set comprises curved surface curvature distribution, boundary constraint conditions and a material attribute threshold value; thirdly, according to the curved surface curvature distribution, generating an initial geometric topological grid by using a dynamic subdivision algorithm, adjusting node positions according to boundary constraint conditions to obtain an optimized structural framework, and performing layered mapping on material attribute thresholds to generate a material distribution map; then, the input parametric modeling engine generates a three-dimensional parametric model, finally, a self-adaptive adjustment strategy graph is constructed through a multi-objective optimization algorithm, and a final design scheme is output. By means of the method, automation of design of the special-shaped facing structure is achieved, design efficiency and accuracy are improved, material utilization is optimized, scheme adaptability is enhanced, and design process collaboration is improved.

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of obtaining a to-be-simulated 3D convolution model and training data; decomposing the 3D convolution model into cascading of a 3D space convolution model and a 3D time convolution model to obtain a pseudo 3D cascading convolution model; training a pseudo 3D cascade convolution modelby using the training data, and obtaining parameters of a 3D spatial convolution model and a 3D time convolution model; converting the 3D space convolution model and the 3D time convolution model intoa 2D space convolution model and a 2D time convolution model; setting a feature rearrangement rule for the 2D spatial convolution model and the 2D time convolution model; mapping model parameters ofthe 3D spatial convolution model and the 3D time convolution model into parameters of a 2D spatial convolution model and a 2D time convolution model to obtain a 2D cascaded convolution model; and performing convolution operation on the image by using the 2D spatial convolution model and the 2D time convolution model. By means of the mode, image processing conducted through 3D convolution operationcan be achieved through the 2D convolution model.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

The invention discloses a data model dual-drive motor life prediction system and method. The system comprises an information acquisition module, a model fusion module, a model training module and a weight adjustment module. The information acquisition module is used for acquiring state data of different motors; the model fusion module is used for effectively fusing the data driving model and the physical information model; the model training module is used for training and optimizing the fused mechanism-data dual-drive model according to the motor state data provided by the information acquisition module; the weight adjusting module plays a key adjusting role in the whole model training process. The method not only depends on a data-driven model for prediction, but also combines a physical information model to make up for the problem that a pure data-driven method lacks interpretability. Multi-physical-field factors such as environmental stress, vibration impact, thermal management and an electromagnetic field are brought into modeling, so that the performance degradation of the motor is accurately described and evaluated, and the accuracy and physical interpretability of a prediction result are enhanced.

The invention relates to a physical information constraint embedded non-stationary industrial process anomaly detection method, and belongs to the technical field of industrial process time sequence anomaly detection. According to the method, a dynamic coupling relation between key variables is extracted through frequency domain lagging correlation, original time sequence data is decomposed into two subspaces, namely a slowly-varying trend subspace and a quickly-varying disturbance subspace, through slow feature analysis, and long-term stable change and transient disturbance change are modeled respectively. Furthermore, a high-dimensional linear evolution model is constructed in a fast space and a slow space by adopting a Kupman operator, so that the multi-scale dynamic modeling precision is effectively improved. In addition, two types of physical information supervision mechanisms are introduced in the model training process: based on a water pump flow-pressure second-order dynamic equation and a motor electric power conservation law, the physical consistency and engineering interpretability of the prediction process are significantly enhanced. According to the invention, abnormal working condition identification and early warning under variable working conditions in a non-stationary industrial process can be realized.

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.