3606 results about "State prediction" patented technology

The invention relates to the technical field of digital twinning, and discloses a pump station working condition monitoring method and system based on digital twinning and a storage medium. The method comprises the following steps: receiving a pump station operation characteristic matrix of pump station equipment through a PLC (Programmable Logic Controller) control cabinet; constructing a hybrid digital twin model based on the pump station operation characteristic matrix and generating operation state prediction data; inputting the operation state prediction data and the actual monitoring data into a Transform network to carry out working condition feature association mode extraction and health index quantitative calculation to obtain an equipment health assessment index; and triggering an early warning response mechanism by using the equipment health assessment index, and outputting a unit start-stop control strategy of the pump station equipment to the scheduling system through the remote management platform. According to the invention, the capability of identifying abnormal working conditions and the accuracy of early warning are improved, the purposes of'few people on duty, remote monitoring and low-consumption operation 'of the pump station are achieved, the operation and maintenance cost is greatly reduced, and the service life of equipment is prolonged.

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention relates to the technical field of intelligent assessment and decision making, in particular to a robot dynamic risk assessment and decision making system and method based on multi-modal perception, and the system comprises a multi-modal sensor module which is used for collecting environment vision, acoustics, mechanics and position data in real time; the edge calculation unit is used for carrying out space-time alignment and feature fusion on the sensor data; the dynamic risk assessment model is used for integrating the environment uncertainty quantification module and the robot state prediction module based on a reinforcement learning framework; the decision execution interface is used for outputting a risk level and obstacle avoidance, speed reduction and shutdown instructions; by integrating visual, acoustic, mechanical and position multi-source sensor data and the like, the system can comprehensively capture various risk factors in a complex dynamic environment, so that the defect that a traditional single sensor system is insufficient in sensing dimension is overcome, and the system is particularly suitable for terrains and weather conditions with variable regions.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention discloses a water turbine state intelligent prediction method which comprises the following steps: collecting multi-source sensor data of a water turbine, including vibration, temperature, pressure, flow and electrical parameters; performing space-time alignment preprocessing on the multi-source sensor data to generate a space-time associated data set; extracting and fusing the features of the space-time associated data through a multi-modal space-time diagram network, and generating a joint feature vector; performing health state prediction on the joint feature vector based on a dynamic digital twinborn model, and outputting a health score, a fault probability and a confidence interval; analyzing a fault propagation path by using a causal reasoning module, positioning a fault root cause and generating an interpretable report; and triggering an early warning or maintenance decision according to the prediction result. According to the technical scheme, the technical problems that multi-source heterogeneous data fusion is difficult, fault coupling and propagation are uncertain, real-time performance and computing resources are contradictory, and interpretability and reliability are insufficient are solved.

The invention relates to the field of frequency converter fault detection, and discloses a frequency converter fault prediction method and system based on machine learning, and the method comprises the steps: obtaining multi-dimensional real-time data in the operation process of a frequency converter; constructing a dynamic mapping relation to obtain a basic feature set; generating a time sequence feature vector capable of reflecting the state change of the equipment based on the basic feature set; comparing, analyzing and judging whether the equipment state deviates from a normal operation interval or not based on the historical operation data and the time sequence feature vector, and outputting a state deviation index; performing abnormal fluctuation judgment on the time sequence feature vector; extracting fluctuation amplitude and frequency characteristics of the key indexes to obtain quantitative description data of abnormal fluctuation; inputting the quantitative description data of the abnormal fluctuation into an abnormal prediction model; and generating a coping strategy and a triggering condition of the coping strategy based on the risk prediction result. The method has the advantages that the abnormal state of the frequency converter is recognized in time, and potential risks are predicted.

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 relates to the technical field of bearing fault detection, and discloses a bearing fault detection method and system based on a health state index. The method comprises the following steps: collecting multi-source sensing signals at least comprising a vibration signal, a temperature signal and an acoustic signal during bearing operation; respectively performing time domain feature extraction and frequency domain feature extraction on the multi-source sensing signals, and performing normalized fusion on the extracted time domain features and frequency domain features to generate a multi-dimensional health state index sequence; constructing a long-short-term memory network model based on an attention mechanism, inputting the multi-dimensional health state index sequence into the model for training, and outputting a bearing health state prediction sequence; and calculating a dynamic early warning threshold according to the historical health state prediction sequence, comparing the current prediction value with the dynamic early warning threshold in real time, and generating a fault early warning signal. The method can improve the accuracy of bearing health state evaluation and fault early warning, and is suitable for complex operation conditions.

The invention discloses a water conservancy reservoir group joint scheduling optimization system based on digital twinning, and the system comprises the following steps: a real-time data collection and preprocessing module which is used for collecting and processing meteorological data; the reservoir group digital twinning modeling module is used for constructing a dynamic digital twinning reservoir group simulation model; the future state prediction module is used for predicting the future state change trend of the reservoir group; the joint scheduling optimization module is used for optimizing a multi-target joint scheduling scheme; the self-adaptive memory mechanism management module is used for maintaining an excellent scheduling strategy memory bank and carrying out intelligent disturbance adjustment; the execution and dynamic adjustment module is used for executing the scheduling scheme and dynamically correcting the scheme; the emergency response module is used for generating an emergency scheduling plan; and the feedback and adaptive optimization module is used for feeding back the real-time execution result to the twin model for dynamic correction. According to the invention, real-time optimization and intelligent emergency response of the reservoir group scheduling system are realized, and the scheduling efficiency and the system stability are obviously improved.

The invention provides a slope quality monitoring and early warning method and system based on seismic oscillation data, and the method comprises the steps: firstly obtaining a seismic oscillation monitoring data set of a target slope region, covering seismic oscillation waveform data and environment correlation parameters of a plurality of monitoring nodes in a preset time period, and then carrying out the dynamic preprocessing of the seismic oscillation monitoring data set, comprising data alignment, noise filtering and dimension unification, a standardized data set is obtained, then a dynamic association feature set containing seismic oscillation energy distribution, slope response frequency and environment coupling fluctuation features is extracted based on the standardized data, and then a slope quality analysis model is called to carry out state prediction on the feature set. And generating a slope quality evaluation result according to the difference degree between the stable state characteristics and the abnormal fluctuation characteristics, and finally determining an early warning strategy according to the slope quality evaluation result and triggering early warning signal output, thereby realizing effective monitoring and early warning of the slope quality.

The invention discloses a high and steep slope dangerous rock falling intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, collecting and preprocessing real-time monitoring data, and generating a standardized input sample set; s2, constructing a long-term prediction network model, and outputting a state prediction sequence of multiple time steps in the future; s3, optimizing structure parameters and training parameters of the prediction network model based on a bald eagle search algorithm; s4, executing multi-step state prediction by using the optimized model, and outputting a crack trend, a displacement trend and an abnormal probability value; s5, constructing an early warning risk scoring function, and fusing multiple prediction indexes to generate a risk scoring value; and S6, setting a multi-level early warning threshold value, outputting an early warning level, and issuing the early warning level in linkage with voice, a terminal and a platform. According to the invention, through constructing the intelligent monitoring and early warning method fusing the long-term prediction network model and the bald eagle search algorithm, accurate prediction and multi-stage linkage early warning of the high and steep slope dangerous rock falling risk are realized.

The invention relates to the technical field of smart park management, in particular to an analysis monitoring method and system based on a smart park operation state, and the method comprises the following steps: obtaining park equipment operation data parameters and associated environment parameters, analyzing the equipment operation difference, recognizing the equipment operation state, and predicting the state change trend. And calculating an abnormal event propagation path and rate, determining a response priority, constructing early warning information, and obtaining a smart park monitoring record. According to the method, the accuracy of park equipment state identification is improved by combining the fluctuation analysis of the equipment operation data and the environmental parameters, early warning of abnormal states is realized by combining the time sequence correlation analysis and equipment state prediction, the influence of equipment faults on park operation is reduced, and the accuracy of park equipment state identification is improved through the topological structure and propagation probability calculation. The method achieves the prediction of an abnormal event propagation path, enhances the accuracy and timeliness of emergency response, improves the real-time monitoring and fault prediction capability, and guarantees the efficient operation and safety of a park.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention discloses a fault diagnosis and detection system for production automation equipment. The fault diagnosis and detection system comprises a data sensing layer which is used for carrying out multi-mode signal acquisition and real-time preprocessing; the feature extraction layer is used for constructing a recursive block convolution module, capturing transient impact features in four time steps by using an L1-layer gating convolution unit, associating a 16-time-step cross-block periodic degradation mode with an L2-layer sparse attention mechanism, aggregating multi-sensor spatial-temporal features by using an L3-layer global context node, and performing multi-scale feature extraction; the causal reasoning layer is used for establishing a physical constraint driven causal graph engine and outputting a fault propagation path with probability weight; the state modeling layer is used for constructing a continuous health evolution model by adopting a Shenchang differential equation, embedding a physical constraint loss function, and performing equipment full life cycle health state prediction and residual service life estimation in combination with a three-stage memory fusion mechanism of LSTM short-term memory, differentiable neural dictionary medium-term memory and knowledge graph long-term memory; and the decision support layer is used for generating a personalized maintenance work order.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The present invention discloses an intelligent measurement and control system for a withdrawable switch cabinet and a control method thereof, which relate to the technical field of automatic control of electric power systems, and solve the problems of slow response speed, inflexible control strategy and difficulty in coping with extreme weather conditions in existing intelligent measurement and control systems when responding to changes in power grids; real-time data acquisition and analysis are realized by integrating an edge processing module, and state prediction and anomaly detection are performed in combination with a long short-term memory network; an adaptive control module is used to optimize the control strategy according to dynamic monitoring results; at the same time, the emergency response module monitors environmental variables through a SCADA system, and an intelligent scheduling algorithm adjusts the switch cabinet operation mode and switches standby equipment; the present invention greatly improves the safety and reliability of the switch cabinet, significantly enhances the rapid response capability and adaptive control capability of the power system to changes in the power grid, and ensures the stability and safety of the power supply.

The invention relates to the technical field of robot anomaly prediction, in particular to a robot anomaly prediction method and system based on multi-dimensional fusion and causal inference. The method comprises the steps of performing multi-scale depth state characterization based on acquired robot multi-joint sensing data, and performing dynamic causal graph fusion based on the multi-scale depth state characterization. Comprising the steps of priori knowledge graph construction based on a kinematics chain, dynamic association attention mechanism construction based on data driving, state fusion of knowledge and attention guidance and global state vector generation. Performing hierarchical space-time dependency prediction based on the fused features, wherein the hierarchical space-time dependency prediction comprises robot joint topological graph construction, spatial dependency dynamic modeling, long-range time evolution prediction and future robot health state prediction; the method shows excellent performance in a plurality of core dimensions such as prediction precision, early warning timeliness and diagnosis interpretability, and has extremely high actual deployment value and engineering popularization potential.

The invention discloses a power grid dynamic modeling method based on a physical information neural network and a related device, and the method comprises the steps: obtaining power grid observation data, inputting the power grid observation data into a neural network for power grid dynamic behavior prediction, and obtaining a power grid state prediction value; calculating data item loss through a power grid state prediction value and a power grid state actual measurement value, and calculating physical residual item loss through power grid observation data; the physical residual item loss comprises current conservation constraint loss, voltage closed-loop constraint loss and generator dynamic response loss; and network parameters of the neural network are updated through the data item loss and the physical residual item loss until the neural network converges, and a power grid state model is obtained. According to the method, the mapping relation between the state variable and the system input is established by using the neural network, and the physical rule of the power grid is introduced into the loss function as a hard constraint term, so that physical consistency control during dynamic behavior modeling of the power system is realized, and the accuracy of dynamic behavior modeling of the power grid is improved.

The invention discloses a power plant equipment fault prediction method based on a time sequence large model, and the method comprises the following steps: S1, collecting and preprocessing the time sequence data of a multi-source sensor of a power plant, and generating a standardized time sequence data set; s2, constructing a time sequence large model, inputting standardized data, and outputting a future operation state predicted value; s3, comparing the running state prediction value with an actual measurement value to generate a prediction residual sequence; s4, constructing a Bayesian neural network model, inputting a prediction residual sequence, and outputting error probability distribution; s5, optimizing a Bayesian neural network structure and hyper-parameters by adopting an ant colony optimization algorithm; s6, confidence interval estimation is executed, and whether the state is a high-risk state or not is judged; and S7, outputting a running state label, and dynamically acquiring a data closed-loop updating model. According to the invention, high-precision prediction and uncertainty evaluation of the operation state of the power plant equipment are realized, so that the accuracy and response time efficiency of fault early warning are improved.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may identify a transmission configuration indicator (TCI) state. The UE may determine that the TCI state is a semi-known TCI-state based at least in part on a known TCI state condition not being satisfied and a TCI state prediction condition being satisfied. The UE may apply a TCI state switching delay timeline that is associated with semi-known TCI states. Numerous other aspects are described.

The invention belongs to the technical field of image analysis, and discloses an old people health state prediction and alarm method based on AI image analysis, and the method comprises the steps: obtaining a depth image, a color image and a pressure image of an activity environment of old people, carrying out the combined analysis of the depth image and the pressure image, recognizing a terrain semantic region in a monitoring region, and carrying out the prediction of the health state of the old people; and carrying out dynamic segmentation on the gait contour of the old people. And the terrain gait coupling degree is calculated by analyzing the depth change characteristics and the motion period characteristics of the gait sub-regions, so that the abnormal gait sub-regions are screened out. And for the abnormal regions, the terrain gait coupling degree, the dynamic texture features of the color image and the pressure distribution features of the pressure image are fused, and a cross-modal health risk vector is constructed. Based on the vector, the health state of the old people is accurately predicted, and a corresponding alarm signal is generated. According to the invention, precise evaluation and timely early warning of the health state of the old people are realized, and intelligent guarantee is provided for the home safety of the old people.

The invention discloses a cooperative control method for multiple AGVs for cooperatively carrying heavy objects, and belongs to the technical field of automatic logistics, and the method comprises the steps: taking a high-dimensional environment description vector as a model condition, inputting the model condition into an improved conditional diffusion model which is introduced into a linkage constraint feature channel in a reverse denoising stage of a conventional conditional diffusion model, and obtaining a preliminary scheme; and then correcting the preliminary scheme by adopting a popular theory and a high-dimensional differential geometric constraint, and then ensuring cooperative control of multiple AGVs by adopting a dynamic environment perception and linkage state prediction mechanism, an online feedback and local path adjustment mechanism and a multi-heavy-load synchronous control strategy in control operation according to the corrected scheme. According to the method, the initial path is obtained based on the improved conditional diffusion model, the feasibility and the collaboration of a generation result are improved, the vehicle speed, the acceleration and the steering angle are controlled and optimized by applying a high-dimensional differential geometry dynamic control method, and the dynamic consistency of synchronous movement of multiple vehicles is ensured.

The invention relates to the technical field of process optimization, and discloses an optimization method and system for a graphite sagger compression molding process. The method comprises the following steps: determining and analyzing particle size distribution, specific surface area, porosity and viscosity-temperature characteristics of graphite powder and a binder to obtain raw material characteristic parameter data; modeling is conducted on the migration behavior of the binder and the deformation characteristic of the graphite powder in the pressing process, a material rheological model is obtained, meanwhile, a multi-point pressure sensor system is arranged on the surface of a pressing mold, and target pressure data are collected; constructing a pressure distribution feature vector and a material flow state prediction result; performing adaptive adjustment on the initial pressing parameters to obtain a segmented pressure curve and a local pressure compensation strategy; and performing multi-objective optimization on the graphite sagger pressing path to obtain an optimal pressing process parameter combination. The method is combined with material flow state prediction, accurate control over the pressing process is achieved, and the intelligent level of the graphite sagger pressing forming technology is improved.

The invention relates to the technical field of abnormal state prediction, provides a multi-dimensional time sequence equipment abnormal state prediction method and system, and solves the problems of high false alarm rate and risk prediction inaccuracy in the prior art. The method comprises the following steps: collecting a multi-node operation state data set and communication transmission layer time sequence offset information of an equipment distributed system; jitter and delay association processing is carried out on the data, and a communication stability feature set is generated by quantizing space-time correlation of a jitter extreme value and delay fluctuation; obtaining physical layer deformation and temperature drift data of the communication cable, analyzing and generating a physical layer disturbance feature sequence based on optical signal feature offset, and converting the physical layer disturbance feature sequence into channel abnormal strength features; and cross-level feature coupling is carried out on the communication stability feature and the channel abnormal strength feature, and a communication node failure or data transmission abnormal risk is predicted based on a coupling result. According to the invention, millisecond-level linkage prediction of equipment-level physical disturbance and system-level communication risks is realized, the fault false alarm rate is reduced, and the cascade failure risk is blocked.

The invention relates to the technical field of offshore data monitoring, and discloses an offshore data monitoring method based on a wireless communication system, and the method comprises the following steps: S1, data collection: obtaining offshore monitoring data of a target; s2, data preprocessing: performing format conversion, duplicate removal, time alignment and data interpolation processing on the data acquired by the data acquisition; s3, data fusion: matching and fusing the processed data based on a time interleaving algorithm and a spatial clustering algorithm; s4, performing target tracking, and performing target trajectory prediction by adopting a Kalman filtering method under the condition of target data missing; and S5, monitoring output, generating a fusion data set, and outputting dynamic information of the target. Through combination of deep learning target identification, LSTM network state prediction and an efficient wireless communication technology, more accurate marine target monitoring, stable real-time data transmission and efficient target state tracking are realized.

The invention discloses an ACM data analysis method and a data monitoring system, and belongs to the technical field of aircraft maintenance, and the method comprises the steps: constructing an ACM data set, setting a feature engineering processing method, carrying out coupling feature extraction through setting a dynamic window and calculating a dynamic correlation coefficient, dividing working conditions, eliminating fluctuation interference, and generating a structured time series data set; the method comprises the steps of obtaining ACM reference data of aircrafts of the same model, setting a reference fault judgment method, judging latest data in a structured time sequence data set, screening abnormal parameters, judging whether the ACM breaks down or not by combining an actual temperature adjusting effect, reducing single-parameter misjudgment, setting a coupling fault positioning method, diagnosing the faulty ACM, and distinguishing true faults and false anomalies. Meanwhile, a prediction scheduling method is set, fault prediction, health state prediction and service life evaluation are carried out on the fault-free ACM, the probability of sudden faults is reduced, and non-planned flight stopping is reduced.

The invention relates to the technical field of energy storage battery health state monitoring and dynamic management and control, and discloses an energy storage battery dynamic management and control method and system.The energy storage battery dynamic management and control method comprises the steps that energy storage battery state data and environment interference data are collected through a multi-modal sensor network, and a multi-modal feature set is obtained; processing the preprocessed multi-modal feature set by using a multi-layer time sequence convolutional network, extracting time sequence features of the health state of the battery, and meanwhile, realizing abnormal noise detection and suppression; establishing a mapping relation between environmental interference and prediction reliability, representing a prediction result as a confidence interval, and calculating a reliability index; dynamically adjusting parameters and a structure of the prediction model, and generating a health state prediction result and reliability evaluation thereof; generating an energy storage battery dynamic management and control strategy with safety margin; through a multi-mode signal acquisition and preprocessing technology, the battery state and environment interference information are effectively captured, and a high-quality data basis is provided for health state prediction.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

The invention relates to the technical field of forestry informatization, in particular to an ecological public welfare forest monitoring and intelligent management system and method. The method comprises the steps of collecting multi-dimensional ecological data in real time and performing data fusion; based on a data fusion result, establishing an ecological behavior evolution graph and a multi-layer state inversion network by adopting a graph attention mechanism, forming a dynamic ecological twinborn body, and realizing ecological system state prediction, historical disturbance inversion and future behavior simulation; a risk tensor map is constructed in combination with state abnormality, trend instability and intervention ineffectiveness in a three-dimensional mode, a dynamic grading thermodynamic diagram is generated, and a personalized regulation and control strategy is recommended through reinforcement learning; and finally, sensing an actual regulation and control effect through a feedback mechanism, dynamically correcting twin model parameters by using deviation analysis, and constructing a knowledge base based on historical strategies and effects to drive strategy self-optimization. According to the method, a sensing-modeling-regulation-feedback closed loop is formed, and the accuracy and adaptability of ecological management are improved.